NZ617009B2 - Methods of disease activity profiling for personalized therapy management - Google Patents
Methods of disease activity profiling for personalized therapy management Download PDFInfo
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- NZ617009B2 NZ617009B2 NZ617009A NZ61700912A NZ617009B2 NZ 617009 B2 NZ617009 B2 NZ 617009B2 NZ 617009 A NZ617009 A NZ 617009A NZ 61700912 A NZ61700912 A NZ 61700912A NZ 617009 B2 NZ617009 B2 NZ 617009B2
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- A61K39/3955—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum against materials from animals against proteinaceous materials, e.g. enzymes, hormones, lymphokines
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
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- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
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- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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Abstract
method for personalized therapeutic management of a disease in order to optimize therapy and/or monitor therapeutic efficacy is disclosed. An array of one or a plurality of biomarkers is measured at one or more time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment. l healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment.
Description
METHODS OF DISEASE TY PROFILING FOR
PERSONALIZED THERAPY MANAGEMENT
CROSS-REFERENCES TO RELATED ATIONS
This application claims priority to US. Provisional Patent ation No.
61/484,607, filed May 10, 2011, US. Provisional Patent Application No. 61/505,026, filed
July 6, 2011, US. ional Application No. 61/553,909, filed r 31, 2011, US.
Provisional Application No. 61/566,509, filed December 2, 2011, and US. Provisional
Application No. 61/636,575, filed April 20, 2012, the disclosures of which are hereby
incorporated by reference in their entirety for all purposes.
BACKGROUND OF THE ION
Inflammatory bowel disease (IBD) which includes Crohn’s disease (CD) and
ulverative colitis (UC) is a chronic idiopathic inflammatory disorder affecting the
gatrointestine tract. Disease progression of CD and UC es repeated episodes of
inflammation and ulceration of the ine, leading to complications requiring
hospitalization, surgery and escalation of therapy (Peyrin-Biroulet et al., Am. J.
Gastroenterol,. 105: 289-297 (2010); Langholz E., Dan. Med. Bil/1., 46: 400-415 (1999)).
Current treatments such as anti-tumor necrosis factor-alpha (TNF-(x) biologics (e.g.,
infliximab (IFX), cept, adalimumab (ADL) and certolizumab pegol), thiopurine drugs
(e.g., azathioprine (AZA), 6-mercaptopurin (6-MP)), anti-inflammatory drugs (e.g.,
mesalazine), and steroids (e. g., corticosteroids) have been shown to reduce disease activity.
In some clinical trials of CD, mucosal healing which is described as the absence of intestinal
ulcers, was induced in patients on combination therapy of corticosteroids, IFX and ADL.
Furthermore, MH was maintained in patients receiving IFX.
Other s have shown that mucosal healing can be a hallmark of suppression of
bowel inflammation and predict erm disease remission (Froslie et al.,
Gastroenterology, 133: 412-422 (2007); Baert et (1]., Gastroenterology, (2010)). Long-term
mucosal healing has been associated with a decreased risk of colectomy and colorectal cancer
in UC patients, a decreased need for corticosteroid treatment in CD patients, and possibly a
decreased need for alization (Dave et al., Gastroenterology & Hepatology, 8(1): 29-38
(2012)).
The International Organization for the Study of Inflammatory Bowel Disease
ed defining mucosal healing in UC as the absence of friability, blood, erosions an
dulcers in all visualized segments of gut mucosa (D’Haens et al,. Gastroenterology, 132:
763-786 (2007)). MH in CD was proposed to be the absence of ulcers. The gold
standard for measurement of s disease activity is the Crohn’s Disease Endoscopic
Index of Severity (CDEIS). This disease index score is established from several
variables such as superficial and deep ulceration, ulcerated and nonulcerated stenosis,
and surface area of ulcerated and disease segments. A simplified version of the index is
the Simple Endoscopic Score for Crohn’s Disease, which takes into account disease
variables including ulcer size, ulcerated surface, affected surface and presence of
narrowing. Both indices evaluate clinical symptoms of CD, yet fail to measure the
underlying cause of disease (e.g., inflammation) or resolution of disease (e.g., mucosal
healing). A measurement of l healing can be performed to assess disease
induction as well as disease progression and resolution.
[0005] The process of mucosal healing begins with bleeding (e.g., degradation of the
endothelial layers of the blood vessels) and inflammation, then progresses to cell and
tissue proliferation, and finally tissue remodeling. At the inflammation stage,
inflammatory markers and anti-inflammatory s, such as, but not limited to, IL-1,
IL-2, IL-6, IL-14, IL-17, TGF and TNF are expressed. During remodeling, tissue
repair and remodeling growth factors, such as, but not limited to, AREG, EREG, HBEGF
, HGF, NRG1-4, BTC, EGF, IGF, TGF-, VEGFs, FGFs, and TWEAK are
sed. Repair of the intestinal epithelium requires multiple signal transduction
pathways which are necessary for cell survival, proliferation, and migration. We have
identified novel markers of mucosal healing that are predictive of the risk of disease
relapse and disease remission. A measurement of mucosal g can be used to
periodically assess disease status in patients receiving a y regimen.
[0005a] Any discussion of the prior art hout the specification should in no way
be ered as an admission that such prior art is widely known or forms part of
common l knowledge in the field.
[0006] Mucosal g is lly assessed by endoscopy. gh the invasive
procedure is considered to be low-risk, its cost and patient fort and compliance
remain obstacles to nt, regular opies to assess mucosal healing. There is an
unmet need in the art for non-invasive methods of determining mucosal healing in a
patient.
There is a need in the art for methods of therapeutic management of diseases
such as autoimmune disorders using an individualized approach to optimize therapy and
monitor efficacy. The methods need to include ing disease course and clinical
parameters such as phamacokinetics, disease activity s, e burden, and
l status.
[0007a] It is an object of the present invention to overcome or ameliorate at least one of
the disadvantages of the prior art, or to provide a useful alternative.
BRIEF SUMMARY OF THE INVENTION
[0007b] According to a first aspect, the present invention provides a non-invasive
method for measuring mucosal healing in an individual diagnosed with inflammatory
bowel disease (IBD) receiving a therapy regimen, the method comprising:
(a) measuring the levels of an array of mucosal healing markers in a
sample ed from the individual;
(b) comparing the levels of an array of mucosal healing markers in the
individual to that of a control to compute the mucosal healing index of the individual,
wherein the l healing index comprises a representation of the extent of mucosal
healing; and
(c) determining whether the dual undergoing mucosal healing
should maintain the y regimen.
[0007c] According to a second aspect, the present invention provides a method for
monitoring therapeutic efficiency in an individual with inflammatory bowel disease
(IBD) receiving therapy, the method comprising:
(a) measuring the levels of an array of mucosal healing markers in a
sample ed from the individual at a plurality of time points over the course of
y with a therapeutic antibody;
(b) applying a statistical algorithm to the level of the one or more markers
ined in step (a) to generate a mucosal healing index;
(c) comparing the individual’s mucosal healing index to that of a control;
(d) determining whether the therapy is appropriate for the individual to
e l g.
[0007d] According to a third aspect, the present invention provides a method for
selecting a therapy regimen for an individual with inflammatory bowel disease (IBD),
the method comprising:
(a) measuring the levels of an array of mucosal healing markers in a
sample obtained from the individual at a plurality of time points over the course of
therapy, the individual receiving a therapeutic antibody;
(b) applying a tical algorithm to the level of the one or more markers
determined in step (a) to generate a mucosal healing index;
(c) comparing the individual’s l healing index to that of a control;
(d) selecting an appropriate therapy regimen for the individual, wherein
the therapy regimen promotes mucosal healing.
[0007e] According to a fourth aspect, the present ion provides a method for
reducing or zing the risk of surgery in an individual diagnosed with inflammatory
bowel disease (IBD) being administered a therapy regimen, said method comprising:
(a) ing an array of mucosal healing markers at a ity of time
points over the course of therapy with a therapeutic antibody in samples obtained from
an individual;
(b) generating the individual’s l healing index comprising a
representation of the presence and/or concentration levels of each of the markers over
time;
(c) comparing the individual’s mucosal healing index to that of a control;
(d) selecting an riate therapy n to reduce or minimize the
risk of surgery.
[0007f] Unless the context clearly requires otherwise, throughout the description and
the claims, the words “comprise”, “comprising”, and the like are to be construed in an
- 3a -
ive sense as opposed to an exclusive or exhaustive sense; that is to say, in the
sense of “including, but not limited to”.
The present invention provides methods for personalized therapeutic
ment of a disease in order to optimize therapy and/or r therapeutic
efficacy. In ular, the t invention comprises measuring an array of one or a
ity of mucosal healing biomarkers at one or a plurality of time points over the
course of therapy with a therapeutic agent to determine a mucosal healing index for
selecting therapy, optimizing therapy, reducing ty, and/or monitoring the efficacy
of therapeutic treatment. In some embodiments, the therapy is an anti-TNF therapy, an
immunosuppressive agent, a corticosteroid, a drug that targets a different mechanism, a
nutrition therapy and combinations thereof. In certain instances, the anti-TNF therapy is
a TNF inhibitor (e.g., anti-TNF drug, anti-TNFα antibody) for the treatment of a TNFαmediated
e or disorder.
TNFα has been implicated in inflammatory diseases, autoimmune diseases,
viral, ial and parasitic infections, ancies, and/or neurodegenerative diseases
and is a useful target for specific biological therapy in diseases, such as toid
arthritis and Crohn’s disease. TNF inhibitors such as anti-TNFα antibodies are an
important class of therapeutics. In some embodiments, the methods of the present
invention advantageously improve therapeutic management of patients with a TNFα-
mediated disease or disorder by zing therapy and/or monitoring therapeutic
efficacy to anti-TNF drugs such as anti-TNFα therapeutic antibodies.
As such, in a further aspect, the present invention provides a non-invasive
method for measuring mucosal healing in an individual diagnosed with inflammatory
bowel disease (IBD) receiving a therapy regimen, the method comprising:
(a) measuring the levels of an array of mucosal healing markers in a
sample from the individual;
(b) ing the levels of an array of mucosal healing markers in the
individual to that of a control to compute the mucosal healing index of the dual,
wherein the mucosal healing index comprises a representation of the extent of mucosal
healing; and
- 3b -
(c) determining whether the individual oing mucosal healing
should maintain the therapy regimen.
- 3c -
As such, in one , the present invention provides a method for monitoring
therapeutic efficiency in an individual with IBD receiving therapy, the method comprising:
(a) measuring levels of an array of mucosal healing markers in a sample from
the indiVidual at a plurality of time points over the course of therapy with a therapeutic
(b) applying a statistical algorithm to the level of the one or more markers
determined in step (a) to generate a mucosal healing index;
(c) comparing the indiVidual’s mucosal healing index to that of a control; and
(d) determining r the therapy is appropriate for the indiVidual to
e mucosal g.
In another aspect, the present invention es a method for selecting a therapy
regimen in an indiVidual with IBD, the method comprising:
(a) measuring levels of an array of mucosal healing markers in a sample from
the indiVidual at a plurality of time points over the course of therapy, the indiVidual receiVing
a therapeutic dy;
(b) applying a statistical algorithm to the level of the one or more markers
ined in step (a) to generate a mucosal healing index;
(c) comparing the indiVidual’s mucosal healing index to that of a control; and
(d) selecting an appropriate therapy regimen for the indiVidual n the
therapy regimen es mucosal healing
As such, in another aspect, the present invention provides a method for reducing or
minimizing the risk of surgery in an indiVidual diagnosed with IBD being administered a
therapy regimen, the method comprising:
(a) measuring an array of mucosal healing markers at a plurality of time points
over the course of therapy with a eutic antibody;
(b) generating the indiVidual’s mucosal healing index comprising a
representation of the presence and/or concentration levels of each of the s over time;
(c) comparing the indiVidual’s mucosal healing index to that of a control, and
(d) selecting an appropriate therapy regimen for to reduce or minimize the risk
of surgery.
As such, in another aspect, the present invention provides a method for selecting a
therapy n to promote mucosal healing in an indiVidual diagnosed with IBD, the
method comprising:
(a) measuring levels of a panel of mucosal healing markers at time point to to
generate a mucosal healing index at to;
(b) measuring levels of a panel of l g markers at time point t1 to
generate a mucosal healing index at t1;
(c) comparing the change in the mucosal healing index from to to t1; and
(d) selecting the therapy n for the individual to promote mucosal
healing.
As such, in one aspect, the present ion provides a non-invasive method for
measuring mucosal healing in an individual diagnosed with Crohn’s disease ing an
anti-TNF therapy regimen, the method sing:
(a) ing the levels of an array of mucosal healing markers in a sample
from the individual;
(b) comparing the levels of an array of mucosal g s in the
individual to that of a control to compute the mucosal healing index of the individual,
wherein the mucosal healing index comprises a representation of the extent of mucosal
healing; and
(c) determining whether the individual undergoing mucosal healing should
maintain the anti-TNF therapy regimen.
As such, in another aspect, the present invention provides a method for monitoring
therapeutic efficiency in an individual with Crohn’s disease receiving anti-TNF therapy, the
method comprising:
(a) measuring levels of an array of mucosal healing markers in a sample from
the individual at a plurality of time points over the course of y with a therapeutic
antibody;
(b) applying a statistical algorithm to the level of the one or more markers
determined in step (a) to generate a mucosal healing index;
(c) comparing the individual’s mucosal healing index to that of a control; and
(d) ining whether the anti-TNF therapy is appropriate for the individual
to promote mucosal healing.
[0017] As such, in another aspect, the present invention provides a method for selecting an
anti-TNF therapy regimen in an individual with Crohn’s e, the method comprising:
(a) measuring levels of an array of l healing markers in a sample from
the individual at a plurality of time points over the course of therapy, the individual receiving
a therapeutic antibody;
(b) ng a statistical algorithm to the level of the one or more markers
determined in step (a) to generate a mucosal healing index;
(c) comparing the indiVidual’s l healing index to that of a control; and
(d) selecting an appropriate anti-TNF therapy regimen for the individual
n the anti-TNF therapy promotes l healing.
As such, in another aspect, the present invention es a method for reducing or
minimizing the risk of surgery in an individual diagnosed with Crohn’s disease being
administered an anti-TNF antibody therapy n, the method comprising:
(a) measuring an array of l healing markers at a plurality of time points
over the course of therapy with a therapeutic antibody;
(b) generating the indiVidual’s mucosal healing index sing a
representation of the presence and/or concentration levels of each of the markers over time;
(c) comparing the indiVidual’s mucosal healing index to that of a control, and
(d) selecting an appropriate anti-TNF antibody y regimen for to reduce
or minimize the risk of surgery.
As such, in another aspect, the present invention provides a method for selecting an
anti-TNF antibody therapy regimen to promote mucosal healing in an indiVidual diagnosed
with Crohn’s disease, the method comprising:
(a) measuring levels of a panel of mucosal healing markers at time point to to
te a mucosal healing index at to;
(b) measuring levels of a panel of mucosal healing s at time point t1 to
generate a mucosal healing index at t1;
(c) comparing the change in the mucosal healing index from to to t1; and
(d) selecting the anti-TNF antibody therapy regimen for the dual to
promote mucosal healing.
In some embodiments, the disease is a intestinal disease or an autoimmune
disease. In certain ces, the subject has Crohn’s disease (CD) or rheumatoid arthritis
(RA). In other embodiments, the therapeutic antibody is an anti-TNFOL antibody. In some
ments, the anti-TNFOL antibody is a member selected from the group consisting of
REMICADETM (infliximab), ENBRELT'V' (etanercept), HUMIRATM (adalimumab), CIMZIA®
(certolizumab pegol), and combinations thereof. In preferred embodiments, the subject is a
human.
In some embodiments, the array of markers ses a mucosal healing marker. In
some embodiments, the mucosal marker comprises AREG, EREG, HB-EGF, HGF, NRGl,
NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-oc, VEGF-A, VEGF-B, VEGF-C, VEGF-D,
FGFl, FGF2, FGF7, FGF9, TWEAK and combinations thereof.
On other embodiments, the array of markers further comprises a member selected
from the group consisting of an anti-TNFu antibody, an anti-drug antibody (ADA), an
inflammatory marker, an nflammatory marker, a tissue repair marker (e.g., a growth
factor), and combinations thereof. In n instances, the anti-TNFu antibody is a member
selected from the group consisting of REMICADETM (infliximab), ENBRELT'V' (etanercept),
HUMIRATM (adalimumab), CIMZIA® (certolizumab pegol), and combinations thereof In
certain other instances, the anti-drug dy (ADA) is a member selected from the group
consisting of a human himeric antibody (HACA), a human anti-humanized antibody
(HAHA), a human ouse antibody , and combinations thereof. In yet other
instances, the inflammatory marker is a member selected from the group consisting of GM-
CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-u, sTNF R11, and combinations thereof In further
instances, the anti-inflammatory marker is a member selected from the group consisting of
IL-12p70, IL- 1 0, and combinations f.
In n ments, the array comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, ll, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more markers. In some
embodiments, the markers are measured in a biological sample selected from the group
consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells
(PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy (e.g., from a site of
inflammation such as a portion of the gastrointestinal tract or synovial ).
In certain embodiments, the plurality of time points comprises at least 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time points.
In some instances, the first time point in the plurality of time points is prior to the course of
therapy with the therapeutic antibody. In other instances, the first time point in the plurality
of time points is during the course of therapy with the therapeutic antibody. As non-limiting
examples, each of the markers can be measured prior to therapy with a therapeutic antibody
and/or during the course of therapy at one or more (e.g., a ity) of the following weeks:
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36,
38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100, etc.
2012/037375
In some embodiments, selecting an appropriate y comprises maintaining,
increasing, or decreasing a subsequent dose of the course of y for the subject. In other
embodiments, the method further comprises determining a ent course of therapy for the
subject. In certain instances, the different course of therapy comprises treatment with a
different anti-TNFu antibody. In other instances, the different course of therapy comprises
the current course of therapy along with another therapeutic agent, such as, but not limited to
an anti-TNF therapy, an immunosuppressive agent, a corticosteroid, a drug that targets a
different mechanism, a nutrition therapy and other combination treatments.
In some embodiments, selecting an appropriate therapy comprises selecting an
appropriate therapy for initial treatment. In some ces, the therapy comprises an anti-
TNFoc antibody therapy.
In certain embodiments, the methods disclosed herein can be used as confirmation
that a proposed new drug or therapeutic is the same as or is sufficiently similar to an
approved drug product, such that the proposed new drug can be used as a “biosimilar”
therapeutic. For example, if the ed new drug has only a slightly different e
activity profile compared to the branded drug product, this would be apparent using the
methods disclosed herein. If the proposed new drug has a significantly ent disease
activity profile compared to the branded drug product, then the new drug would not be
ilar. Advantageously, the methods disclosed herein can be used in clinical trials of
proposed new drugs in order to assess the effective eutic efficacy or value of the drug.
Accordingly, in some aspects, the methods of the invention provide ation
useful for guiding treatment decisions for patients receiving or about to receive anti-TNF
drug therapy, e.g., by selecting an appropriate anti-TNF therapy for initial treatment, by
determining when or how to adjust or modify (e.g., se or decrease) the subsequent dose
of an anti-TNF drug, by determining when or how to combine an anti-TNF drug (e.g., at an
initial, increased, decreased, or same dose) with one or more immunosuppressive agents such
as methotrexate (MTX) or azathioprine (AZA), and/or by ining when or how to
change the current course of therapy (e.g., switch to a different anti-TNF drug or to a drug
that s a different mechanism such as an IL-6 receptor-inhibiting monoclonal antibody,
anti-integrin molecule (e.g., Tysabri, Vedaluzamab), JAK-2 inhibitor, and tyrosine kinase
inhibitor, or to a nutritition therapy (e.g., special ydrate diet)).
In other embodiments, the s of the present invention can be used to predict
responsiveness to a TNFOL inhibitor, especially to an anti-TNFOL antibody in a subject having
2012/037375
an autoimmune disorder (e.g., rheumatoid arthritis, Crohn’s Disease, ulcerative colitis and the
like). In this method, by assaying the subject for the correct or eutic dose of anti-
TNFOL antibody, z'.e., the therapeutic concentration level, it is possible to predict r the
individual will be responsive to the therapy.
In another embodiment, the present invention provides s for monitoring IBD
(e.g., Crohn’s disease and ulcerative colitis) in a subject having the IBD disorder, wherein the
method comprises assaying the subject for the correct or therapeutic dose of NFu
dy, z'.e., the therapeutic concentration level, over time. In this manner, it is possible to
t whether the individual will be responsive to the therapy over the given time period.
[0031] Other objects, features, and advantages of the present invention will be apparent to
one of skill in the art from the ing detailed description and figures.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 shows a personalized IBD activity profile as described in Example 1.
Figure 2A show various patient infliximab concentrations as a function of treatment
time. Figure 2B shows patient ranks over a course of treatment with events (infliximab
falling below a threshold concentration) noted.
Figure 3A show various patient HACA (ATI) concentrations as a flinction of
treatment time. Figure 3B shows patient ranks over a course of treatment with events
(HACA detection or appearance) noted.
[0035] Figure 4A illustrates an association between the presence ofATI and the level of
IFX in patient samples. Samples with no detectable level of ATI had a significantly higher
IFX median concentration, ed to sample with detectable ATI. Figure 4B illustrates
that the presence of ATI correlates with higher CDAI. Figure 4C shows that rent
immunosuppressant therapy (e.g, MTX) is more likely to suppress the presence of ATI.
[0036] Figure 5A shows that ts with ATI are more likely to develop a poor response
to treatment. Figure 5B illustrates that the inflammatory marker CRP is associated with
sed levels of ATI.
Figure 6 illustrates that the protein levels of an array of one or more inflammatory
and tissue repair s correlate to the formation of antibodies to IFX.
[0038] Figure 7A illustrates that an array of inflammatory markers can be used to establish
an inflammatory index what correlates with the presence ofATI and/or disease progression.
Figure 7B shows the relationship between the P11 and IFX concentrations in samples with
ATI present. Figure 7C illustrates that an ary PRO Inflammatory Index correlates
with levels of IFX (p<0.0001 and R2 = -0. 129) in patient samples of the COMMIT study.
Figure 8A illustrates the correlation between Crohn’s Disease ty Index
(CDAI) score and the concentration of infliximab in serum in a number of patients in clinical
study #1. Figure 8B shows that the presence of IFX in a sample correlated with a higher
CDAI.
Figure 9A illustrates the association between IFX concentration and the presence of
antidrug antibodies to inflixamab in samples analyzed. Figure 9B illustrates that a high
concentration ofATI can lead to neutralizing antibodies and undetectable levels of IFX.
Figure 9C rates that an ATI positive sample ined at an early time point leads to a
higher CDAI at a later time point, compared to the lower CDAI level from an ATI negative
sample. “Vl” = Visit 1; “V3” = Visit 3. Figure 9D rates that in clinical study #1,
patients had lower odds of developing ATI if receiving a combination therapy of infliximab
and an immunosuppressant agent (6.g. MTX and AZA).
Figure 10A shows that correlation between IFX concentration and the presence of
ATI in samples of clinical study #2A. Figure 103 illustrates the relationship between ISA
y and the presence ofATI in the study. Figure 10C illustrates the relationship between
CRP concentrations and the presence ofATI (ATI and/or neutralizing ATI). Figure 10D
illustrates the relationship between loss of responsiveness to IFX therapy and the presence of
ATI in the study.
Figure 11 illustrates that levels of ATI and neutralizing antibodies can be
determined over time in a series of samples from various patients.
Figure 12A illustrates the comparison of CRP levels to the presence of IFX.
Figure IZB illustrates the onship between the presence of ATI and the infusion reaction.
Figure 12C illustrates the relationship n IFX concentration and the presence ofATI in
clinical study #2B. Figure 12D rates the correlation between the presence of ATI and
the awal of ISA therapy at a specific, given date.
Figure 13A illustrates the relationship between ATI and the atory marker
CRP. Our analysis showed that the odds of experiencing a loss of response to IFX was
higher in patients determined to be ATI ve at any time point. Figure 133 illustrates the
correlation between the presence of ATI at any time point and responsiveness to IFX
treatment. Figure 13C shows that loss of response can be related to an increase in CRP.
Figure 13D illustrates the association between the presence of IFX and CRP levels.
Figure 14A shows that lower IFX levels are associated with the presence ofATI in
clinical study #2C. Figure 14B shows that lower IFX levels are associated with the presence
ofATI in clinical study #3. Figure 14C illustrates that the same correlation between IFX
levels and ATI was also present in the study data, follow-up study and in the
pharmacokinetics study.
Figure 15A illustrates the relationship between ATI levels and IFX. It was
determined that samples with high concentration ATI are neutralizing on IFX and thus, IFX
concentration was determined to be 0 . Figure 153 illustrates an association between
ADL tration and the presence ofATA in patient samples.
Figure 16A describes the details of an exemplary PRO Inflammatory Index.
Figure 163 illustrates that there is no obvious relationship between the P11 and the
concentration ofADL in an array of samples with ADL alone or in combination with other
drugs.
Figure 17 shows a plot of the P11 scores for patients receiving Humira and Humira
in ation with other drug such as Remicade, Cimzia, Asathioprine and Methotrexate.
Figure 18 shows details of methods for ed patient ment of CD and/or
[0050] Figure 19 shows the effect of the TNF-oc pathway and related ys on different
cell types, cellular mechanisms and e (e.g., Crohn’s Disease (CD), rheumatoid arthritis
(RA) and Psoriasis (Ps)).
Figure 20 illustrates an exemplary CEER multiplex growth factor array.
Figures 21A-G illustrate multiplexed growth factor profiling of patient samples
using CEER growth factor arrays.
Figure 22 illustrates the association between CRP levels and the growth factor
index score in determining disease remission.
Figure 23 illustrates embodiments of the t invention to assist in developing
alized t treatment for an IBD patient with mild, moderate, or severe disease
activity.
2012/037375
Figure 24 illustrates a treatment paradigm to personalize patient treatment.
Monitoring of disease burden and mucosal healing can assist in determining treatment
selection, dose selection, and initial drug response.
Figure 25 shows the ROC analysis of CRP and IFX trough thresholds.
Figure 26 shows the relationship of CRP, serum IFX concentration and ATI at
sequential time points. Figure 26A shows presence of IFX and ATI in the pair’s first data
point and CRP in the subsequent measurements. Figure 26B shows CRP levels, IFX serum
concentration and ATI status at sequential time points for a sample. In this sample CRP
levels are lowest when the patient is ATI- and has a serum IFX concentration higher than
threshold.
Figure 27 shows that there was no association between IFX levels higher than
threshold and CRP in ATI+ patients. Yet, in ATI- patients CRP levels were significantly
higher in patients with IFX levels less than threshold (3 ug/ml).
DETAILED DESCRIPTION OF THE INVENTION
1. Introduction
The present invention provides methods for ing mucosal healing in patients
with IBD, CD and/or UC. In ular, the present invention es methods of measuring
mucosal healing markers wherein the markers are indicative of intestinal tissue , and
disease resolution or ion.
[0060] The present invention is advantageous because it addresses and overcomes current
limitations associated with monitoring mucosal g in patients with IBD (e.g., Crohn’s
e and ulcerative colitis). The present invention provides non-invasive methods for
monitoring mucosal healing patients receiving anti-TNF therapy. In addition, the present
ion provides methods of predicting therapeutic response, risk of relapse, and risk of
surgery in patients with IBD (e.g., s disease and ulcerative colitis). In particular, the
s of the present ion find utility for selecting an appropriate anti-TNF therapy for
initial treatment, for determining when or how to adjust or modify (e.g., increase or decrease)
the subsequent dose of an anti-TNF drug to optimize therapeutic efficacy and/or to reduce
toxicity, for determining when or how to combine an anti-TNF drug (e.g., at an initial,
increased, sed, or same dose) with one or more immunosuppressive agents such as
methotrexate (MTX) or azathioprine (AZA), and/or for ining when or how to change
the current course of therapy (e.g, switch to a different anti-TNF drug or to a drug that
targets a different mechanism). The present invention also provides methods for selecting an
appropriate therapy for patients diagnosed with CD, n the y promotes mucosal
healing.
11. Definitions
As used herein, the following terms have the meanings ascribed to them unless
specified otherwise.
The phrase “mucosal healing index” includes an empirically derived index that is
based upon an analysis of a plurality of mucosal healing markers. In one aspect, the
concentration of markers or their measured concentration values are transformed into an
index by an algorithm resident on a computer. In certain aspects, the index is a synthetic or
human d output, score, or cut off value(s), which expresses the biological data in
numerical terms. The index can be used to determine or make or aid in making a clinical
decision. A l healing index can be measured multiple times over the course of time.
In one aspect, the algorithm can be trained with known samples and thereafter validated with
samples ofknown identity.
[0063] The phrase al healing index control” es a l healing index
derived from a y individual, or an individual who has progressed from a disease state to
a healthy state. Alternatively, the control can be an index enting a time course of a
more diseased state to a less disease state or to a healthy state.
The phrase “determining the course of therapy” and the like includes the use of an
empirically derived index, score or analysis to select for example, selecting a dose of drug,
selecting an appropriate drug, or a course or length of therapy, a therapy regimen, or
maintenance of an ng drug or dose. In certain aspects, a derived or measured index can
be used to ine the course of y.
The terms “TNF inhibitor”, “TNF-(X inhibitor” and “TNFOL inhibitor” as used herein
are intended to encompass agents including ns, antibodies, antibody fragments, fusion
proteins (6.g. , Ig fiasion proteins or PC fusion proteins), alent binding proteins (e.g.,
DVD Ig), small molecule TNF-0L antagonists and similar naturally- or nonnaturally-occurring
molecules, and/or recombinant and/or engineered forms thereof, that, directly or indirectly,
inhibits TNF or activity, such as by inhibiting interaction of TNF-0L with a cell surface
receptor for TNF-0L, inhibiting TNF-0L protein production, inhibiting TNF-0L gene expression,
inhibiting TNFOL secretion from cells, inhibiting TNF-0L receptor signaling or any other means
resulting in decreased TNF-0L ty in a subject. The term “TNFOL inhibitor” preferably
includes agents which interfere with TNF-0L activity. Examples of TNF-0L inhibitors include
etanercept (ENBRELT'V', Amgen), infliximab (REMICADET'V', Johnson and Johnson), human
anti-TNF monoclonal dy umab (D2E7/HUMIRAT'V', Abbott tories), CDP
571 ech), and CDP 870 (Celltech), as well as other compounds which inhibit TNF-0L
activity, such that when administered to a subject suffering from or at risk of suffering from a
disorder in which TNF-0L activity is detrimental (e.g., RA), the disorder is treated.
The term “predicting responsiveness to a TNFu inhibitor”, as used herein, is
intended to refer to an ability to assess the likelihood that treatment of a subject with a TNF
tor will or will not be effective in (e.g, provide a measurable benefit to) the subject. In
ular, such an y to assess the likelihood that treatment will or will not be effective
typically is exercised after treatment has begun, and an indicator of effectiveness (e.g., an
indicator of able benefit) has been observed in the subject. Particularly preferred
TNFu inhibitors are biologic agents that have been approved by the FDA for use in humans
in the treatment of rheumatoid arthritis, which agents include adalimumab AT'V'),
infliximab (REMICADET'V') and etanercept (ENBRELT'V'), most preferably adalimumab
(HUMIRATM).
The term “course of therapy” includes any therapeutic approach taken to relieve or
prevent one or more symptoms associated with a ediated disease or disorder. The
term encompasses administering any compound, drug, procedure, and/or regimen useful for
improving the health of an individual with a TNFu-mediated disease or disorder and includes
any of the therapeutic agents described herein. One skilled in the art will appreciate that
either the course of therapy or the dose of the current course of y can be changed (e.g.,
increased or decreased) based upon the presence or concentration level of TNF, anti-TNF
drug, and/or anti-drug antibody using the methods of the present invention.
The term “immunosuppressive agent” includes any substance capable of producing
an immunosuppressive , e.g, the prevention or diminution of the immune response, as
by irradiation or by administration of drugs such as anti-metabolites, anti-lymphocyte sera,
antibodies, etc. Examples of suitable immunosuppressive agents include, without tion,
thiopurine drugs such as azathioprine (AZA) and metabolites thereof; etabolites such
as rexate (MTX); sirolimus (rapamycin); temsirolimus; imus; tacrolimus (FK-
506); FK-778; anti-lymphocyte globulin antibodies, anti-thymocyte globulin antibodies, anti-
CD3 antibodies, anti-CD4 antibodies, and antibody-toxin conjugates; cyclosporine;
mycophenolate; mizoribine monophosphate; scoparone; glatiramer acetate; metabolites
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thereof; pharmaceutically acceptable salts thereof; derivatives thereof; prodrugs thereof; and
combinations thereof.
The term “thiopurine drug” includes azathioprine (AZA), 6-mercaptopurine (6-MP),
or any metabolite thereof that has therapeutic cy and includes, t limitation, 6-
thioguanine (6-TG), 6-methylmercaptopurine riboside, 6-thioinosine nucleotides (e.g., 6-
thioinosine monophosphate, 6-thioinosine diphosphate, 6-thioinosine triphosphate), 6-
thioguanine nucleotides (e.g., 6-thioguanosine monophosphate, 6-thioguanosine diphosphate,
6-thioguanosine sphate), 6-thioxanthosine nucleotides (e.g., 6-thioxanthosine
monophosphate, 6-thioxanthosine diphosphate, 6-thioxanthosine triphosphate), derivatives
thereof, analogues thereof, and ations f.
The term “sample” as used herein includes any biological specimen ed from a
t. Samples include, without limitation, whole blood, plasma, serum, red blood cells,
white blood cells (e.g., peripheral blood mononuclear cells (PBMC), polymorphonuclear
(PMN) cells), ductal lavage fluid, nipple aspirate, lymph (e.g., disseminated tumor cells of
the lymph node), bone marrow aspirate, saliva, urine, stool (z'.e., , sputum, bronchial
lavage fluid, tears, fine needle aspirate (e.g, ted by random periareolar fine needle
aspiration), any other bodily fluid, a tissue sample such as a biopsy of a site of inflammation
(e.g., needle biopsy), and cellular extracts thereof. In some embodiments, the sample is
whole blood or a fractional ent thereof such as plasma, serum, or a cell pellet. In
other embodiments, the sample is obtained by isolating PBMCs and/or PMN cells using any
technique known in the art. In yet other embodiments, the sample is a tissue biopsy, e.g.,
from a site of inflammation such as a portion of the gastrointestinal tract or synovial .
The term “Crohn’s Disease Activity Index” or “CDAI” includes a research tool used
to quantify the symptoms of patients with Crohn’s disease (CD). The CDAI is generally used
to define response or remission of CD. The CDAI consists of eight factors, each summed
after adjustment with a weighting factor. The components of the CDAI and ing factors
are the following:
Clinical or laboratory variable Wefzightingactor
Number of liquid or soft stools each day for seven days x 2
Abdominal pain (graded from 0-3 on ty) each day for seven days x 5
General well being, subjectively assessed from 0 (well) to 4 (terrible) each day for
seven days
Presence of complications* x 20
Taking Lomitil or opiates for diarrhea X 30
Presence of an abdominal mass (0 as none, 2 as questionable, 5 as definite) x 10
Hematocrit of <0.47 in men and <0.42 in women x 6
Percentage ion from rd weight x 1
One point each is added for each set of complications:
0 the presence ofjoint pains (arthralgia) or frank arthritis;
0 inflammation of the iris or uveitis;
. presence of erythema nodosum, pyoderma nosum, or aphthous ulcers;
. anal fissures, fistulae or abscesses;
0 other fistulae; and/or
. fever during the previous week.
Remission of Crohn’s disease is typically defined as a fall in the CDAI of less than
150 points. Severe disease is typically defined as a value of greater than 450 points. In
certain aspects, response to a particular medication in a s disease patient is defined as a
fall of the CDAI of r than 70 points.
The terms “mucosal injury” or “mucosal damage” include the formation of
macroscopically visible mucosal lesions in the ines detectable during endoscopy,
granuloma formation and tion of the muscularis layer at the microscopic tissue level,
epithelial apoptosis and infiltration of activated inflammatory and lymphocytic cells at the
cellular level, increased lial permeability at a sub-cellular level, and gap on
tion at a molecular level. In IBD such as s disease, the intestinal epithelium is
damaged by the inflammatory environment, which results in the formation of refractory
ulcers and lesions.
The term “mucosal healing” refers to restoration of normal mucosal appearance of a
previously inflamed region, and complete absence of tion and inflammation at the
endoscopic and microscopic levels. Mucosal healing includes repair and restoration of the
mucosa and muscularis layers. It can also include neuronal and
, submucosa,
lymphangiogenic elements of the intestinal wall.
The term “nutrition-based therapy ” es butyrate, probiotics (e.g., VSL#3, E.
coli Nissle l9 1 7, bacterium bacillus polyfermenticus), vitamins, proteins, macromolecules,
and/or chemicals that promote mucosal healing such as growth and turnover of intestinal
mucosa.
111. Description of the ments
The present invention es methods for personalized therapeutic management
of a disease in order to optimize therapy and/or r therapeutic efficacy. In particular,
the t invention comprises measuring an array of one or a ity of mucosal healing
biomarkers at one or a plurality of time points over the course oftherapy with a therapeutic
agent to determine a mucosal healing index for selecting therapy, optimizing therapy,
reducing toxicity, and/or monitoring the efficacy of therapeutic treatment. In certain
instances, the therapeutic agent is a TNFOL inhibitor for the treatment of a TNFu-mediated
disease or disorder. In some embodiments, the methods of the present invention
advantageously improve therapeutic management of patients with a TNFu-mediated disease
or disorder by optimizing therapy and/or monitoring therapeutic efficacy to anti-TNF drugs
such as anti-TNFOL therapeutic antibodies.
As such, in one aspect, the present invention provides a method for alized
therapeutic management of a disease in order to optimize therapy or monitor therapeutic
efficacy in a subject, the method comprising:
(a) measuring an array of mucosal healing markers at a plurality of time points
over the course of therapy with a therapeutic antibody;
(b) generating the subject’s mucosal healing index comprising a representation
of the ce and/or concentration levels of each of the markers over time;
(c) comparing the subject’s mucosal healing index to that of a l; and
(d) selecting an appropriate therapy for the subject, to thereby achieve
personalized therapeutic management of the disease in the subject.
As such, in r , the present invention provides a method for personalized
therapeutic management of a disease in order to select therapy in a subject, the method
comprising:
(a) ing an array of mucosal healing markers;
(b) generating the t’s mucosal healing index comprising a representation
of the presence and/or concentration levels of each of the markers;
(c) comparing the subject’s mucosal healing index to that of a control; and
(d) selecting an riate y for the subject, to thereby achieve
alized therapeutic management of the disease in the subject.
As such, in one aspect, the present invention provides a method for optimizing
y in a subject, the method comprising:
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(a) measuring an array of mucosal healing markers at a plurality of time points
over the course of therapy with a therapeutic antibody;
(b) applying a statistical algorithm to the level of the one or more s
determined in step (a) to generate a mucosal healing index;
(c) comparing the t’s mucosal healing index to that of a control; and
(d) determining a subsequence dose of the course of therapy for the subject or
r a different course of therapy should be administered to the subject based upon the
mucosal healing index.
As such, in one aspect, the present invention provides a method for ing
therapy in a subject, the method comprising:
(a) measuring an array of mucosal healing markers at a plurality of time points
over the course of therapy with a therapeutic antibody;
(b) applying a statistical algorithm to the level of the one or more markers
determined in step (a) to generate a mucosal healing index;
(c) comparing the subject’s mucosal healing index to that of a control; and
(d) selecting an appropriate course of therapy for the t based upon the
mucosal g index.
As such, in another aspect, the present invention provides a method for reducing the
risk of surgery in a subject diagnosed with IBD (e.g., Crohn’s disease) being administered a
therapy regimen (e.g., an anti-TNF antibody therapy regimen), the method sing:
(a) ing an array of mucosal healing markers at a plurality of time points
over the course of therapy with a therapeutic antibody;
(b) applying a statistical algorithm to the level of the one or more markers
determined in step (a) to generate a mucosal healing index;
(c) comparing the subject’s mucosal healing index to that of a control; and
(d) determining whether the therapy regimen is reducing the t’s risk of
surgery.
As such, in one aspect, the present invention provides a method for monitoring
therapeutic efficiency in a subject receiving y (e.g., anti-TNF therapy), the method
sing:
(a) measuring an array of mucosal g markers at a plurality of time points
over the course of therapy with a therapeutic antibody;
(b) applying a statistical algorithm to the level of the one or more markers
ined in step (a) to generate a mucosal healing index;
(c) comparing the subject’s mucosal healing index to that of a control; and
(d) determining whether the current course of therapy is appropriate for the
subject based upon the mucosal healing index.
In some embodiments, the disease is a gastrointestinal disease or an autoimmune
disease. In certain instances, the subject has inflammatory bowel disease (IBD, e.g., Crohn’s
disease (CD) or ulcerative colitis (UC)). In other instances, the t has rheumatoid
arthritis (RA). In preferred embodiments, the subject is a human.
In some embodiments, the therapy is selected from the group comprising an anti-
TNF therapy, an immunosuppressive agent, a corticosteroid, a drug that targets a ent
mechanism, a nutrition therapy or combinations thereof. In certain instances, the anti-TNF
y is a TNF inhibitor (e.g., anti-TNF drug, anti-TNFu dy).
In other embodiments, the anti-TNF therapy is an anti-TNFu antibody. In some
embodiments, the anti-TNFOL antibody is a member selected from the group consisting of
REMICADETM (infliximab), T'V' rcept), HUMIRAT'V' (adalimumab), CIMZIA®
(certolizumab pegol), and combinations thereof In preferred embodiments, the subject is a
human.
In some embodiments, the therapy is an immunosuppressive agent. Non-limiting
examples of immunosuppressive agents include thiopurine drugs such as azathioprine (AZA),
6-mercaptopurine (6-MP), and/or any metabolite thereof that has therapeutic efficacy and
es, Without limitation, 6-thioguanine (6-TG), 6-methylmercaptopurine de, 6-
thioinosine nucleotides (e.g., 6-thioinosine monophosphate, 6-thioinosine diphosphate, 6-
thioinosine triphosphate), 6-thioguanine tides (e.g, 6-thioguanosine monophosphate,
6-thioguanosine diphosphate, guanosine triphosphate), 6-thioxanthosine nucleotides
(e.g., 6-thioxanthosine osphate, 6-thioxanthosine diphosphate, 6-thioxanthosine
triphosphate), derivatives thereof, analogues thereof, and combinations thereof; anti-
metabolites such as methotrexate (MTX); sirolimus (rapamycin); temsirolimus; everolimus;
imus 6); FK-778; ymphocyte globulin antibodies, anti-thymocyte globulin
antibodies, anti-CD3 antibodies, anti-CD4 dies, and antibody-toxin conjugates;
cyclosporine; mycophenolate; mizoribine monophosphate; one; glatiramer acetate;
lites thereof; pharmaceutically acceptable salts thereof; derivatives thereof; prodrugs
thereof; and combinations thereof.
In other embodiments, the therapy is a corticosteroid. In yet other embodiments, the
therapy is a drug that targets a different mechanism (e.g., a mechanism that is not mediated
by the TNFoc pathway). Non-limiting examples of a drug that targets a different mechanism
include IL-6 receptor inhibiting onal dies, anti-integrin molecules (e.g.,
zumab (Tysabri), vedoluzamab), JAK-2 inhibitors, tyrosine kinase inhibitors, and
combinations thereof.
In other embodiments, the y is a nutrition therapy. In particular embodiments,
the nutrition therapy is a special carbohydrate diet. Special carbohydrate diet (SCD) is a
strict grain-free, lactose-free, and e-free nutritional regimen that was designed to
reduce the symptoms of IBD such as Crohn’s disease and ulcerative colitis. It has been
shown that SCD can promote and/or maintain mucosal healing in patients with IBD (e.g.,
Crohn’s disease or ulcerative colitis). lly, SCD restricts the use of complex
carbohydrates and eliminates refined sugar, grains and starch from the diet. It has been
bed that the microvilli of patients with IBD lack the ability to break down specific types
of complex carbohydrates, resulting in the overgrowth of l bacteria and irritation of
the gut mucosa. It has been recommended that SCD is a therapy for IBD (e.g., s
disease or ulcerative colitis) because it enables the gut to undergo l healing.
In some embodiments, the array of markers comprises a mucosal healing marker. In
some embodiments, the mucosal marker comprises AREG, EREG, HB-EGF, HGF, NRGl,
NRGZ, NRG3, NRG4, BTC, EGF, IGF, TGF-Oc, VEGF-A, VEGF-B, VEGF-C, ,
FGFl, FGF2, FGF7, FGF9, TWEAK and combinations thereof.
[0090] In other embodiments, the array of markers further comprises a member selected
from the group consisting of an anti-TNFu antibody, an rug antibody (ADA), an
inflammatory marker, an anti-inflammatory marker, a tissue repair marker (e.g., a growth
factor), and ations thereof. In certain instances, the anti-TNFu antibody is a member
selected from the group consisting of REMICADETM (infliximab), ENBRELT'V' (etanercept),
HUMIRATM (adalimumab), CIMZIA® (certolizumab pegol), and combinations f In
certain other instances, the anti-drug antibody (ADA) is a member selected from the group
consisting of a human anti-chimeric antibody (HACA), a human anti-humanized antibody
(HAHA), a human anti-mouse antibody , and combinations thereof. In yet other
instances, the inflammatory marker is a member selected from the group consisting of GM-
CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-u, sTNF R11, and combinations thereof In further
instances, the anti-inflammatory marker is a member selected from the group ting of
IL- 12p70, IL- 1 0, and combinations thereof.
In certain embodiments, the array comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more s. In some
embodiments, the markers are ed in a biological sample selected from the group
consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells
(PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy (e.g., from a site of
inflammation such as a portion of the gastrointestinal tract or al ).
In certain embodiments, the plurality of time points comprises at least 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time .
In some instances, the first time point in the plurality of time points is prior to the course of
therapy with the therapeutic antibody. In other instances, the first time point in the plurality
of time points is during the course of therapy with the therapeutic antibody. As non-limiting
examples, each of the markers can be measured prior to therapy with a therapeutic antibody
and/or during the course of y at one or more (e.g., a plurality) of the following weeks:
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36,
38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100, etc.
In fiarther embodiments, the method for assessing or measuring mucosal healing
further comprises comparing the determined level of the mucosal healing marker t in a
sample to an index value or cutoff value or reference value or threshold value, wherein the
level of the mucosal healing marker above or below that value is predictive or indicative of
an increased or higher likelihood of the subject either oing mucosal healing or not
undergoing mucosal healing. One skilled in the art will understand that the index value or
cutoff value or reference value or threshold value is in units such as mg/ml, ug/ml, ng/ml,
pg/ml, fg/ml, EU/ml, or U/ml depending on the marker of interest that is being measured.
In some embodiments, the mucosal healing index includes an empirically derived
index that is based upon an is of a plurality of mucosal healing markers. In one aspect,
the concentration of markers or their measured concentration values are transformed into an
index by an algorithm resident on a computer. In certain aspects, the index is a tic or
human derived output, score, or cut off value(s), which expresses the biological data in
numerical terms. The index can be used to determine or make or aid in making a clinical
decision. A mucosal healing index can be measured multiple times over the course of time.
In one aspect, the algorithm can be trained with known samples and thereafter validated with
s ofknown identity.
In some embodiments, the mucosal healing index control is a mucosal healing index
d from a y individual, or an individual who has progressed from a e state to
a healthy state. Alternatively, the control can be an index representing a time course of a
more diseased state or healthy to disease.
In some embodiments, the methods of ining the course of therapy and the
like include the use of an empirically derived index, score or analysis to select for example,
ing a dose of drug, selecting an appropriate drug, or a course or length of therapy, a
therapy regimen, or maintenance of an existing drug or dose. In certain aspects, a derived or
measured index can be used to determine the course of therapy.
[0097] In some embodiments, mucosal healing can be assessed or monitored by endoscopy.
Non-limiting examples of endoscopy include video capsule endoscopy (capsule endoscopy),
disposable endoscopy, and 3D endoscopy. In other embodiment, the mucosal healing index
is red or confirmed by endoscopy.
In some embodiments, ing an appropriate therapy comprises maintaining,
increasing, or decreasing a subsequent dose of the course of therapy for the subject. In other
embodiments, the method further comprises determining a different course of therapy for the
subject. In certain instances, the different course of therapy comprises treatment with a
different anti-TNFu dy. In other instances, the different course of therapy comprises
the current course of therapy along with another therapeutic agent, such as, but not d to,
an immunosuppressive agent, a corticosteroid, a drug that targets a different mechanism,
nutrition therapy, and combinations thereof).
In some embodiments, selecting an riate therapy comprises selecting an
appropriate y for initial treatment. In some instances, the therapy comprises an anti-
TNFOL dy therapy.
[0100] In certain embodiments, the s disclosed herein can be used as confirmation
that a proposed new drug or therapeutic is the same as or is iently similar to an
approved drug product, such that the proposed new drug can be used as a “biosimilar”
therapeutic. For e, if the proposed new drug has only a slightly different disease
activity profile compared to the branded drug product, this would be apparent using the
methods disclosed herein. If the proposed new drug has a significantly different disease
activity profile ed to the branded drug product, then the new drug would not be
biosimilar. Advantageously, the methods disclosed herein can be used in clinical trials of
proposed new drugs in order to assess the effective therapeutic value of the drug.
Accordingly, in some aspects, the methods of the invention provide information
useful for guiding treatment decisions for ts receiving or about to receive anti-TNF
drug therapy, e.g., by selecting an appropriate anti-TNF therapy for initial treatment, by
determining when or how to adjust or modify (e.g., increase or decrease) the subsequent dose
of an anti-TNF drug, by determining when or how to combine an anti-TNF drug (e.g., at an
initial, sed, sed, or same dose) with one or more immunosuppressive agents such
as rexate (MTX) or azathioprine (AZA), and/or by ining when or how to
change the current course of therapy (e.g., switch to a different NF drug or to a drug
that targets a different mechanism such as an IL-6 or-inhibiting monoclonal antibody,
anti-integrin molecule (e.g., Tysabri, Vedaluzamab), JAK-2 tor, and ne kinase
tor, or to a nutritition therapy (e.g., special carbohydrate diet)).
In other embodiments, the methods of the present invention can be used to predict
responsiveness to a TNFu inhibitor, especially to an anti-TNFu antibody in a subject having
an autoimmune disorder (e.g., rheumatoid arthritis, Crohn’s Disease, ulcerative colitis and the
like). In this method, by assaying the subject for the correct or therapeutic dose of anti-
TNFOL antibody, l'.e., the therapeutic tration level, it is possible to predict whether the
dual will be responsive to the y.
In another embodiment, the present invention provides methods for monitoring IBD
(e.g., Crohn’s disease and ulcerative s) in a subject having the IBD disorder, wherein the
method comprises assaying the subject for the correct or therapeutic dose of anti-TNFu
antibody, z'.e., the therapeutic concentration level, over time. In this manner, it is possible to
predict whether the individual will be responsive to the therapy over the given time period.
In certain embodiments, step (a) comprises determining the presence and/or level of
at least two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, thirty, forty, fifty, or
more markers in the sample.
In other embodiments, the algorithm comprises a ng tical classifier
system. In some instances, the learning statistical classifier system is selected from the group
consisting of a random forest, classification and regression tree, boosted tree, neural network,
support vector machine, general uared automatic interaction detector model, interactive
tree, multiadaptive sion spline, machine learning classifier, and combinations thereof.
In certain instances, the statistical algorithm comprises a single learning statistical classifier
system. In certain other instances, the statistical algorithm comprises a combination of at
least two learning statistical classifier systems. In some instances, the at least two learning
statistical classifier systems are d in tandem. Non-limiting examples of statistical
algorithms and analysis suitable for use in the invention are described in International
Application No. PCT/U8201 1/056777, filed October 18, 2011, the disclosure of which is
hereby incorporated by reference in its entirety for all es.
In other ments, step (b) further comprises applying a statistical algorithm to
the ce and/or level of one or more mucosal g markers determined at an earlier
time during the course of therapy to generate an earlier mucosal healing index. In some
instances, the earlier mucosal healing index is compared to the mucosal healing index
generated in step (b) to determine a subsequent dose of the course of therapy or whether a
different course of therapy should be administered. In certain embodiments, the subsequent
dose of the course of therapy is increased, decreased, or maintained based upon mucosal
healing index generated in step (b). In some instances, the different course of therapy
comprises a different anti-TNFu antibody. In other instances, the different course of therapy
comprises the current course of therapy along with an suppressive agent.
[0107] In some embodiments, step (b) further comprises ng a statistical algorithm to
the presence and/or level of one or more of the mucosal healing markers ined at an
r time to generate an earlier disease activity/severity index. In certain instances, the
mucosal healing index is compared to the mucosal healing index generated in step (b) to
predict the course of the TNF-mediated disease or disorder.
[0108] In some embodiments, the method r comprises sending the results from the
selection or determination of step (d) to a clinician. In other embodiments, step (d) ses
selecting an initial course of therapy for the subject.
Once the diagnosis or prognosis of a subject receiving anti-TNF drug therapy has
been determined or the hood of response to an anti-TNF drug has been predicted in a
subject diagnosed with a disease and disorder in which TNF has been implicated in the
pathophysiology, e.g., but not limited to, shock, sepsis, ions, autoimmune diseases, RA,
Crohn’s disease, transplant rejection and graft-versus-host disease, according to the methods
described herein, the present ion may fiarther comprise recommending a course of
therapy based upon the diagnosis, prognosis, or prediction. In n instances, the present
invention may further comprise administering to a subject a therapeutically effective amount
of an NFOL drug useful for treating one or more symptoms associated with the TNF-
mediated e or disorder. For therapeutic applications, the anti-TNF drug can be
administered alone or co-administered in combination with one or more additional anti-TNF
drugs and/or one or more drugs that reduce the side-effects associated with the anti-TNF drug
(e.g., an immunosuppressive agent). As such, the t ion advantageously enables a
clinician to practice “personalized medicine” by g treatment decisions and informing
therapy selection and optimization for anti-TNFOL drugs such that the right drug is given to the
right patient at the right time.
The present invention is advantageous because it ses and overcomes current
limitations associated with the stration of anti-TNF drugs such as infliximab, in part,
by providing ation useful for guiding treatment decisions for those patients receiving
or about to receive anti-TNF drug therapy. In particular, the methods of the present invention
find utility for selecting an appropriate NF therapy for initial treatment, for determining
when or how to adjust or modify (e.g., increase or decrease) the uent dose of an anti-
TNF drug to optimize therapeutic efficacy and/or to reduce toxicity, for ining when or
how to combine an anti-TNF drug (e.g, at an initial, increased, decreased, or same dose) with
one or more immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA),
and/or for determining when or how to change the current course of therapy (e.g., switch to a
different anti-TNF drug or to a drug that targets a different mechanism).
Accordingly, the present invention is particularly useful in the following methods of
improving patient management by guiding treatment decisions:
1. Crohn’s disease prognostics: Treat patients most likely to benefit from y
2. Anti-therapeutic antibody monitoring (ATM) + Biomarker-based disease activity
profiling
3. ATM sub-stratification
4. ATM with pharmacokinetic modeling
. Monitor response and t risk of relapse:
a. Avoid chronic maintenance therapy in patients with low risk of recurrence
b. Markers of mucosal healing
c. Therapy selection: Whether to e or not to combine anti-TNF drug therapy
with an immunosuppressive agent such as MTX or AZA
6. Patient selection for ics.
[0112] In some embodiments, the present invention es a method for measuring an
inflammatory index for Crohn’s e management for an individual to optimize therapy,
and predict response to the anti-TNF therapeutic, the method comprising:
(a) chromatographically measuring anti-TNF therapeutics and autoantibodies
in a sample from the individual to determine their concentration levels;
(b) chromatographically measuring anti- TNF therapeutics and tibodies
in a sample from the individual to ine their concentration levels;
(c) comparing the measured values to an efficacy scale to optimize therapy,
and predict se to the anti-TNF therapeutic.
In some embodiments, the present invention provides a method for predicting the
likelihood the concentration of an anti-TNF therapeutic during the course of treatment will
fall below a threshold value, the method comprising:
(a) measuring a panel of markers selected from the group consisting of 1) GM-
CSF; 2) IL-2; 3) TNF-0L; 4) sTNFRII; and 5) the disease being ed in the small intestine;
and
(b) predicting the likelihood the concentration of an NFOL therapeutic
will fall below the old based upon the concentration of the markers.
For the purpose of illustration only, Example 5 shows an exemplary embodiment of
the present invention In particular, a method of predicting the likelihood the concentration of
an anti-TNF treatment will fall below a threshold value.
In some embodiments, the present invention provides a method for predicting the
likelihood the concentration of an NF eutic during the course of treatment will
fall below a threshold value, the method comprising:
(a) measuring a panel of markers selected from the group consisting of l)
GM-CSF; 2) IL-2; 3) TNF-0L; 4) sTNFRII; and 5) the e being situated in the small
intestine; and
(b) predicting the hood the concentration of an anti-TNF eutic will
fall below the threshold based upon the concentration of the markers.
In other embodiments, the present invention provides a method for predicting the
likelihood that anti-drug antibodies will occur in an individual on anti-TNF therapy, the
method comprising:
(a) measuring a panel of markers selected from the group consisting of t EGF,
VEGF, IL-8, CRP and VCAM-l; and
(b) predicting the likelihood that anti-drug antibodies will occur in an
individual on anti-TNF y based on the concentration of marker levels.
For the purpose of illustration only, Example 4 is an ary embodiment of the
present invention and demonstrates the detectin of anti-drug antibodies to infliximab (ATI).
WO 54987 2012/037375
In other embodiments, the present invention provides a method for monitoring an
infliximab ent regimen, the method comprising:
(a) measuring infliximab and ug antibodies to infliximab (ATI);
(b) measuring inflammatory markers CRP, SAA, ICAM, VCAM;
(c) measuring tissue repair marker VEGF; and
(d) correlating the measurements to therapeutic efficacy.
For the purpose of illustration only, Example 5 is an exemplary embodiment of the
present invention and shows a method of monitoring an IFX treatment regimen.
In other embodiments, the present invention provides a method for determining
whether an individual is a candidate for combination therapy n said individual is
administered infliximab, the method comprising:
(a) measuring for the presence or absence of ATI in said individual; and
(b) stering an immunosuppressant (e.g., MTX) is the individual has
significant levels of ATI.
[0121] In yet other embodiments, the method also es measuring the concentration
level of CRP which is indicative of the presence of ATI. For the purpose of illustration only,
Examples 6 and 7 show that the presence and absence ofATI are predictive of ders
and non-responders of Remicade therapy. Examples 6 and 7 are exemplary embodiments.
In yet other embodiments, the present invention provides a method for monitoring
Crohn’s disease activity, the method comprising:
(a) determining an inflammatory index comprising the measurement of a panel
of markers sing VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM in ng/ml and
VCAM in ng/ml; and
(b) comparing the index to an efficacy scale to monitor and mange disease.
[0123] For the purpose of illustration only, Example 9 is an ary embodiment and
shows use of the inflammatory index.
In particular embodiments, the present invention provides s for determining
the threshold of an anti-TNF drug such as IFX that can best discriminate disease activity as
measured by C-reactive protein (CRP) levels. For the purpose of illustration only, Example
12 shows that IFX dichotomized at a old of 3 ug/ml can be differentiated by CRP. In
certain instances, random IFX < 3 and IFX Z 3 ug/ml serum samples have higher CRP in IFX
< 3 ug/ml at a 74 % rate (ROC AUC). Example 12 also shows that in ATI+ samples pairs, no
significant difference in CRP between IFX groups (above and below 3 ug/ml) was observed.
In particular, CRP levels were generally higher in ATI+ sample pairs, and CRP levels were
higher in IFX < 3 ug/ml for ATI— samples. Regression confirmed that CRP was positively
related to ATI and negatively related to IFX. As such, the interaction corresponds to a CRP-
IFX relationship that differs between ATI+ and ATI—.
IV. Mucosal Healing Index
The methods of the present invention comprise monitoring therapy response and
predicting risk of relapse. In some embodiments, the methods include detecting, measuring
and/or determining the presence and/or levels of markers of mucosal healing.
[0126] The gut mucosa plays a key role in barrier defense in addition to nutrient digestion,
tion and metabolism. The dynamic ses of intestinal epithelial cell proliferation,
migration, and apoptosis are highly affected by general nutritional status, route of feeding,
and adequacy of specific nutrients in the diet. However, with inflammatory es of the
gut, l cell impairment can result in mucosal injury or , thereby resulting in
enhanced permeability to macromolecules, increased ial translocation from the lumen,
and stimulation of epithelial cell sis.
Mucosal injury is a multi-faceted physiological process ng macroscopic to
molecular levels. l injury includes the formation of macroscopically visible mucosal
lesions detectable during endoscopy, granuloma formation and disruption of the muscularis
layer at the microscopic tissue level, epithelial apoptosis and infiltration of ted
inflammatory and lymphocytic cells at the cellular level, increased epithelial permeability at a
sub-cellular level, and gap junction disruption at a molecular level.
Mucosal injury is likely initiated by a combination of endogenous and
environmental factors. At first stage, it is believed that food-derived compounds, viral and
bacterial-derived factors, as well as host-derived factors, may cause epithelial cell destruction
and activation of innate and adaptive immunity. d mucosa is initially infiltrated by
diverse inflammatory cells consisting of neutrophils, eosinophils, mast cells, atory
monocytes, ted macrophages and dendritic cells. Specific adaptive immune responses
toward the intestinal flora are generated leading to the later recruitment of ted B cells,
CD4+ and CD8+ T cells to the d mucosa. Neutrophils secrete elastase which can
result in extracellular matrix degradation of the epithelium. Likewise, T cells, macrophages
and intestinal fibroblasts express inflammatory factors such as IL-l, IL-2, IL-6, IL-l4, IL-l7,
TGFB and TNFoc that lead to extracellular matrix degradation, epithelial , endothelial
2012/037375
activation, and/or fibrosis stricture formation. Non-limiting examples of markers of mucosal
injury include matrix metalloproteases (MMPs) and markers of oxidative stress (e.g., iNOS,
reactive oxygen metabolites).
A. Array of Mucosal Healing Markers
A y of mucosal markers including growth factors are particularly useful in the
s of the present invention for personalized therapeutic management by selecting
therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic
treatment with one or more therapeutic agents such as biologics (e.g., anti-TNF drugs). In
ular embodiments, the methods described herein utilize the determination of a mucosal
healing index based upon one or more (a plurality of) mucosal healing markers such as
growth factors (e.g., alone or in combination with biomarkers from other ries) to aid or
assist in predicting e course, selecting an appropriate anti-TNF drug therapy,
zing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy,
and/or monitoring the efficacy of therapeutic treatment with an anti-TNF drug.
[0130] As such, in certain embodiments, the determination of the presence and/or level of
one or more growth factors in a sample is useful in the present invention. As used , the
term “growth factor” includes any of a variety of peptides, polypeptides, or proteins that are
capable of ating cellular proliferation and/or cellular differentiation.
In some embodiments, mucosal healing markers include, but are not limited to,
growth factors, inflammatory s, cellular adhesion markers, cytokines, antiinflammatory
markers, matrix metalloproteinases, oxidative stress markers, and/or stress
response markers.
In some embodiments, mucosal healing markers include growth s. Non-
limiting examples of growth s include amphiregulin , epiregulin (EREG),
heparin binding epidermal growth factor (HB-EGF), hepatocye growth factor (HGF),
heregulin-[31 (HRG) and isoforms, neuregulins (NRGl, NRG2, NRG3, NRG4), betacellulin
(BTC), epidermal growth factor (EGF), insulin growth factor-1 (IGF-l), transforming growth
factor (TGF), platelet-derived growth factor (PDGF), vascular endothelial growth s
(VEGF-A, VEGF-B, VEGF-C, VEGF-D), stem cell factor (SCF), platelet derived growth
factor (PDGF), soluble fms-like ne kinase 1 ), placenta growth factor (PIGF,
PLGF or PGF), last growth factors (FGFl, FGF2, FGF7, FGF9), and combinations
thereof. In other embodiments, mucosal g markers also include pigment epithelium-
derived factor (PEDF, also known as SERPINFl), endothelin-1 (ET-1), keratinocyte growth
factor (KGF; also known as FGF7), bone morphogenetic proteins (e.g., MPlS),
platelet-derived growth factor (PDGF), nerve growth factor (NGF), B-nerve growth factor (BNGF
), neurotrophic s (e.g., brain-derived rophic factor (BDNF), rophin 3
(NT3), neurotrophin 4 (NT4), eta), growth differentiation factor-9 (GDP-9), granulocytecolony
stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GMCSF
), myostatin (GDP-8), erythropoietin (EPO), thrombopoietin (TPO), and combinations
thereof.
In other embodiments, mucosal healing markers also include cytokines. Non-
limiting examples of cytokines that can be used to establish a mucosal healing index include
bFGF, TNF-oc, IL-lO, IL-12(p70), IL-l[3, IL-2, IL-6, GM-CSF, IL-l3, IFN—y, TGF-Bl, TGF-
[32, 3, and combinations thereof. Non-limiting examples of cellular adhesion markers
include SAA, CRP, ICAM, VCAM, and combinations thereof. Non-limiting examples of
anti-inflammatory markers include IL- 12p70, IL-10, and combinations thereof
In some embodiments, mucosal g markers include markers specific to the
gastrointestinal tract including inflammatory markers and serology markers as described
herein. miting examples include antibodies to ial antigens such as, e.g., OmpC,
flagellins (cBir—l, Fla-A, Fla-X, etc), 12, and others (pANCA, ASCA, eta); eutrophil
antibodies, anti-Saccharomyces cerevz’sz’ae antibodies, and anti-microbiol antibodies.
The determination of markers of oxidative stress in a sample is also useful in the
present invention. Non-limiting examples of markers of oxidative stress include those that
are protein-based or DNA-based, which can be detected by measuring protein ion and
DNA fragmentation, tively. Other examples of markers of oxidative stress include
organic compounds such as ialdehyde.
Oxidative stress represents an imbalance between the production and manifestation
of reactive oxygen species and a biological system’s ability to readily detoxify the reactive
intermediates or to repair the resulting damage. Disturbances in the normal redox state of
tissues can cause toxic effects through the production of peroxides and free radicals that
damage all components of the cell, including proteins, lipids, and DNA. Some reactive
oxidative species can even act as gers through a phenomenon called redox signaling.
[0137] In n ments, derivatives of ve oxidative metabolites (DROMs),
ratios of oxidized to reduced glutathione (Eh GSH), and/or ratios of oxidized to reduced
cysteine (Eh CySH) can be used to quantify oxidative stress. See, e. g., Neuman et al., Clin.
Chem, 53:1652-1657 (2007). Oxidative modifications of highly reactive cysteine residues in
proteins such as tyrosine phosphatases and thioredoxin-related proteins can also be ed
or measured using a technique such as, e.g., mass spectrometry (MS). See, e.g., Naito et al.,
Anti-Aging Medicine, 7 (5):36-44 (2010). Other markers of ive stress e proteinbound
acrolein as described, e.g., in Uchida et al., PNAS, 95 (9) 4882-4887 (1998), the free
oxygen radical test (FORT), which reflects levels of c hydroperoxides, and the redox
potential of the reduced glutathione/glutathione disulfide couple, (Eh) GSH/GSSG. See, e. g.,
Abramson et al., Atherosclerosis, 178(1):115-21 (2005).
In some embodiments, matrix metalloproteinases (MMPs) include members of a
family of Zn2+-dependent extracellular matrix (ECM) degrading endopeptidases that are able
to degrade all types ofECM proteins. miting examples ofMMPs include MMP-l,
MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, , MMP-13, MTl-MMP-l, and
combinations thereof. It has been shown that MMP-3 and MMP-9 are associated with
mucosal injury and f1stulae in CD patients (Baugh et al., Gastroenterology, 117: 814-822,
(1999); Bailey et al., J. Clin. ., 47: 113-116 (1994)). In some embodiments, stress
response markers include markers of oxidative stress, such as reactive oxygen species (ROS),
superoxide dismutase (SOD), catalase (CAT), and glutathione, and markers of endoplasmic
reticulum (ER) stress. Non-limiting examples of markers of oxidative stress include those
that are n-based or DNA-based, which can be detected by measuring protein oxidation
and DNA fragmentation, tively. In other embodiments, mucosal healing markers
further include markers of oxidative DNA and/or protein damage. Non-limiting examples of
ER stress markers include markers of unfolded protein response (e.g., ATF6, HSPA5,
PDIA4, XBPl, IREl, PERK, EIF2A, GADD34, GRP-78, phosphoylated JNK, caspase-12,
caspase-3, and ations thereof).
The human amphiregulin (AREG) polypeptide sequence is set forth in, e.g.,
k Accession Nos. NP_001648.1 and XP_001 1256841. The human AREG mRNA
(coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_001657.2 and
XM_001125684.3. One skilled in the art will appreciate that AREG is also known as AR,
colorectum cell-derived growth factor, CRDGF, SDGF, and AREGB.
The human epiregulin (EREG) polypeptide sequence is set forth in, e.g., Genbank
ion No. NP_001423. 1. The human EREG mRNA (coding) sequence is set forth in,
e. g., Genbank ion No. NM_001432.2. One skilled in the art will appreciate that
EREG is also known as EPR.
The human heparin—binding EGF-like growth factor (HB-EGF) polypeptide
sequence is set forth in, e.g., Genbank Accession No. NP_001936. 1. The human HB-EGF
mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. 945.2. One
skilled in the art will appreciate that HB-EGF is also known as diphtheria toxin receptor, DT-
R, HBEGF, DTR, DTS, and HEGFL.
The human hepatocyte growth factor (HGF) ptide sequence is set forth in,
e.g., Genbank Accession Nos. NP_000592.3, NP_00101093 1 . 1, 010932. 1,
NP_001010933. 1, and NP_001010934.1. The human HGF mRNA (coding) sequence is set
forth in, e.g., Genbank Accession Nos. NM_000601.4, 010931.1, NM_001010932. 1,
NM_001010933.1 and NM_001010934. 1. One skilled in the art will appreciate that HGF is
also known as r , SF, HPTA and hepatopoietin-A. One of skill will also
appreciate that HGF includes to all m variants.
The human neuregulin-1 (NRG1) polypeptide sequence is set forth in, e.g., Genbank
ion Nos., NP_001153467.1, NP_001153471.1, NP_001153473.1, NP_001153477.1,
NP_039250.2, NP_039251.2, NP_039252.2, NP_039253.1, NP_039254.1, NP_039256.2,
and NP_039258. 1. The human NRGl mRNA (coding) sequence is set forth in, e.g., Genbank
Accession No. NM_001159995.1, NM_001159999.1, NM_001160001.1, NM_001160005.1,
NM_013956.3, NM_013957.3, NM_013958.3, 959.3, NM_013960.3,
NM_013962.2, and NM_013964.3. One skilled in the art will appreciate that NRGl is also
known as GGF, HGL, HRGA, NDF, SMDF, ARIA, acetylcholine receptor-inducing ty,
breast cancer cell differentiation factor p45, glial growth factor, heregulin, HRG, neu
differentiation factor, and y and motor neuron-derived factor. One of skill will also
appreciate that NRGl includes to all isoform variants.
The human neuregulin-2 (NRG2) polypeptide sequence is set forth in, e.g., Genbank
Accession Nos. NP_001171864.1, NP_004874.1, NP_053584.1, NP_053585.1 and
NP_053586.1. The human NRG2 mRNA (coding) sequence is set forth in, e.g., Genbank
Accession Nos. 184935.1, NM_004883.2, NM_013981.3, NM_013982.2 and
NM_013983.2. One skilled in the art will appreciate that NRG2 is also known as NTAK,
neural- and thymus-derived tor for ERBB kinases, DON-1, and divergent of
neuregulin-l. One of skill will also appreciate that NRG2 includes to all isoform variants.
The human neuregulin-3 (NRG3) polypeptide sequence is set forth in, e.g., Genbank
Accession Nos. NP_001010848.2 and NP_001 1594451. The human NRG3 mRNA (coding)
sequence is set forth in, e.g., Genbank Accession Nos. NM_001010848.3 and
NM_001165973.1.. One skilled in the art will appreciate that NRG2 es to all isoform
variants.
The human neuregulin-4 (NRG4) polypeptide sequence is set forth in, e.g., Genbank
Accession No. NP_612640.1. The human NRG4 mRNA g) ce is set forth in,
e. g., Genbank Accession No. NM_138573.3. One skilled in the art will appreciate that
NRG4 includes to all isoform variants.
The human betacellulin (BTC) polypeptide sequence is set forth in, e.g., Genbank
Accession No. NP_001720.1. The human BTC mRNA (coding) sequence is set forth in, e.g.,
Genbank Accession No. NM_001729.2. One skilled in the art will iate that BTC
includes to all isoform ts.
The human epidermal growth factor (EGF) polypeptide sequence is set forth in, e.g.,
Genbank Accession Nos. NP_001954.2 and NP_001 1716021. The human EGF mRNA
(coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_001963.4 and
NM_001178131.1.. One skilled in the art will appreciate that EGF is also known as beta-
urogastrone, urogastrone, URG, and HOMG4.
The human insulin-like growth factor (IGF) polypeptide ce is set forth in,
e. g., Genbank Accession Nos. NP_000609.1 and NP_001104755.1. The human IGF mRNA
(coding) sequence is set forth in, e.g., k Accession No. NM_000618.3 and
NM_001111285.1. One d in the art will appreciate that IGF includes to all isoform
variants. One skilled in the art will also appreciate that IGF is also known as mechano
growth factor, MGF and somatomedin-C.
The human transforming growth factor alpha (TGF-0c) polypeptide ce is set
forth in, e.g., k Accession Nos. NP_003227.1 and NP_001093161.1. The human
TGF-0c mRNA (coding) ce is set forth in, e.g., Genbank Accession Nos.
NM_003236.3 and NM_001099691.2. One skilled in the art will appreciate that TGF-0c
includes to all isoform variants. One d in the art will also appreciate that TGF-0c is also
known as EGF-like TGF, ETGF, and TGF type 1.
[0 1 5 1] The human vascular endothelial growth factor (VEGF-A) polypeptide sequence is
set forth in, e.g. Genbank Accession Nos. NP_001020537, NP_00102053 8, NP_001020539,
NP_001020540, NP_001020541, NP_001028928, and NP_003367. The human VEGF-A
mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_001025366,
NM_001025367, NM_001025368, NM_001025369, NM_001025370, NM_001033756, and
NM_003376. One d in the art will appreciate that VEGF-A is also known as VPF,
VEGFA, VEGF, and MGC70609. . One skilled in the art will appreciate that VEGF-A
includes to all isoform variants.
The human vascular elial growth factor B) polypeptide ce is
set forth in, e.g., k Accession Nos. NP_001230662, and NP_003368. The human
VEGF-B mRNA (coding) sequence is set forth in, e.g., Genbank ion Nos.
NM_001243733 and NM_003377. One skilled in the art will appreciate that VEGF-B is also
known as VEGFB, VEGF-related factor, and VRF. One skilled in the art will appreciate that
VEGF-B includes to all isoform variants.
[0153] The human vascular endothelial growth factor (VEGF-C) polypeptide sequence is
set forth in, e.g., Genbank Accession No. NP_005420. The human VEGF-C mRNA
(coding) sequence is set forth in, e.g., Genbank Accession No. NM_005429. One skilled in
the art will appreciate that VEGF-C is also known as Flt4 ligand, Flt4-L, VRP and vascular
endothelial growth factor-realted protein. One skilled in the art will appreciate that VEGF-C
includes to all isoform variants.
The human fibroblast growth factor 1 (FGF1) ptide sequence is set forth in,
e. g., Genbank Accession Nos. NP_000791, NP_001138364, NP_001138406,
NP_001138407, 138407, NP_149127, and NP_149128. The human FGF1 mRNA
(coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_000800,
NM_001144892, 144934, NM_001144934, NM_001144935, NM_033136 and
NM_033137. One skilled in the art will appreciate that FGF1 is also known as FGFA, FGF-
1, acidic fibroblast growth factor, aFGF, endothelial cell growth factor, ECGF, heparin-
g growth factor 1, and HB-EGFl. One skilled in the art will appreciate that FGF1
includes to all isoform variants.
[0155] The human basic fibroblast growth factor (bFGF) polypeptide sequence is set forth
in, e. g., Genbank Accession No. NP_001997.5. The human bFGF mRNA (coding) sequence
is set forth in, e.g., Genbank Accession No. NM_002006.4. One skilled in the art will
iate that bFGF is also known as FGF2, FGFB, and HBGF-Z.
The human fibroblast growth factor 7 (FGF7) polypeptide sequence is set forth in,
e. g., k Accession No. NP_002000. 1. The human FGF7 mRNA (coding) sequence is
set forth in, e.g., Genbank Accession No. NM_002009.3. One skilled in the art will
appreciate that FGF7 is also known as FGF-7, HBGF-7 and keratinocyte growth factor.
The human fibroblast growth factor 9 (FGF9) polypeptide ce is set forth in,
e. g., k Accession No. NP_002001.l. The human FGF9 mRNA (coding) sequence is
set forth in, e.g., Genbank Accession No. NM_002010.2. One d in the art will
appreciate that FGF9 is also known as FGF-9, GAF, and HBGF-9.
The human TNF-related weak inducer of apoptosis (TWEAK) polypeptide sequence
is set forth in, e.g., Genbank Accession No. NP_003800. l. The human TWEAK mRNA
(coding) sequence is set forth in, e.g., Genbank Accession No. NM_003 809.2. One skilled in
the art will appreciate that TWEAK is also known as TNFl2, APO3 , APO3L, DR3LG,
and UNQlSl/PRO207..
[0159] In certain instances, the presence or level of a particular mucosal healing marker
such as a growth factor is detected at the level ofmRNA expression with an assay such as,
for example, a hybridization assay or an amplification-based assay. In n other
instances, the presence or level of a particular growth factor is detected at the level of protein
expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical
assay. In an ary embodiment, the presence or level of a particular growth factor is
detected using a multiplexed array, such as a Collaborative Enzyme Enhanced
Reactive ImmunoAssay (CEER), also known as the Collaborative Proximity Immunoassay
(COPIA). CEER is described in the following patent nts which are herein
incorporated by reference in their entirety for all purposes: PCT Publication No. WO
2008/036802; PCT Publication No. ; PCT Publication No. WO
2009/108637; PCT Publication No. ; PCT Publication No. WO
2011/008990; and PCT Application No. , filed October 20, 2010.
Suitable ELISA kits for determining the presence or level of a growth factor in a serum,
plasma, saliva, or urine sample are available from, e.g., Antigenix America Inc. (Huntington
Station, NY), Promega (Madison, WI), R&D Systems, Inc. (Minneapolis, MN), Invitrogen
(Camarillo, CA), CHEMICON International, Inc. (Temecula, CA), Neogen Corp. (Lexington,
KY), PeproTech (Rocky Hill, NJ), Alpco Diagnostics (Salem, NH), Pierce Biotechnology,
Inc. ord, IL), and/or Abazyme (Needham, MA).
In ular ments, at least one or a plurality (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10,
ll, l2, l3, l4 ,lS ,l6,l7,18,l9, 20, or 21, such as, e.g., apanel or an array) ofthe
following growth factor markers can be detected (e.g., alone or in combination with
kers from other categories) to aid or assist in predicting disease course, and/or to
improve the accuracy of selecting therapy, zing therapy, reducing toxicity, and/or
monitoring the efficacy of therapeutic treatment to NF drug therapy: AREG, EREG,
HB-EGF, HGF,NRG1,NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-oc, VEGF-A, VEGF-B,
VEGF-C, VEGF-D, FGFl, FGF2, FGF7, FGF9, TWEAK and combinations thereof.
B. Mucosal Healing Index
In certain aspects, the present invention provides an algorithmic-based analysis of
one or a plurality of(e.g., 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16,17,18,19, 20, 21, or
more) l healing markers to e the accuracy of selecting therapy, optimizing
therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment to anti-
TNFOL drug therapy.
A single statistical algorithm or a combination of two or more statistical algorithms
described herein can then be d to the presence or concentration level of the mucosal
healing markers detected, ed, or determined in the sample to thereby select therapy,
optimize therapy, reduce toxicity, or monitor the efficacy of therapeutic treatment with an
anti-TNFOL drug. As such, the s of the invention find utility in determining patient
ment by ining patient immune status.
[0163] In some embodiments, the statistical algorithm comprises a ng statistical
classifier . In some instances, the learning statistical classifier system is ed from
the group consisting of a random forest, classification and regression tree, boosted tree,
neural network, support vector e, general chi-squared automatic interaction detector
model, interactive tree, multiadaptive regression spline, machine learning fier, and
combinations f. In certain instances, the statistical algorithm comprises a single
learning statistical classifier system. In other embodiments, the statistical thm
comprises a combination of at least two learning statistical classifier systems. In some
instances, the at least two ng statistical classifier systems are applied in tandem. Non-
limiting examples of statistical algorithms and analysis le for use in the invention are
described in International Application No. , filed October 18, 2011, the
disclosure of which is hereby incorporated by reference in its entirety for all purposes.
Preferably, mucosal healing index is an empirically derived experimentally ed
index of values. In some instances, the index of values is transformed from an array of
control measurements that were experimentally determined. In one aspect, the concentration
of markers or their measured concentration values are ormed into an index by an
algorithm resident on a computer. In certain aspects, the index is a synthetic or human
derived output, score, or cut off value(s), which expresses the biological data in numerical
terms. The index can be used to determine or make or aid in making a clinical decision. A
mucosal healing index can be measured multiple times over the course of time. In one
, the algorithm can be trained with known samples and thereafter validated with
samples ofknown identity.
In filrther embodiments, the method for ing or measuring mucosal healing
further comprises comparing the determined level of the mucosal healing marker present in a
sample to an index value or cutoff value or reference value or threshold value, wherein the
level of the mucosal healing marker above or below that value is predictive or indicative of
an increased or higher hood of the subject either undergoing mucosal healing or not
undergoing mucosal healing. One skilled in the art will understand that the index value or
cutoff value or reference value or threshold value is in units such as mg/ml, ug/ml, ng/ml,
pg/ml, fg/ml, EU/ml, or U/ml depending on the marker of interest that is being measured.
In some embodiments, the mucosal g index control is a l healing index
derived from a healthy dual, or an individual who has progressed from a disease state to
a healthy state. Alternatively, the control can be an index representing a time course of a
more diseased state or healthy to disease.
In some embodiments, the methods of determining the course of therapy and the
like include the use of an empirically derived index, score or analysis to select for example,
selecting a dose of drug, selecting an appropriate drug, or a course or length of therapy, a
therapy regimen, or nance of an existing drug or dose. In certain aspects, a d or
measured index can be used to determine the course of therapy.
tanding the clinical course of disease will enable physicians to make better
ed treatment decisions for their inflammatory disease patients (e.g., IBD, Crohn’s
disease or ulcerative colitis) and may help to direct new drug development in the future. The
ideal mucosal healing marker(s) for use in the mucosal healing index described herein should
be able to identify individuals at risk for the disease and should be disease-specific.
er, mucosal healing (s) should be able to detect disease activity and monitor
the effect of treatment; and should have a predictive value towards relapse or ence of
the disease. Predicting disease course, however, has now been expanded beyond just disease
recurrence, but perhaps more antly to include predictors of disease complications
including surgery. The present invention is particularly advantageous because it provides
indicators of mucosal healing and enables a prediction of the risk of relapse in those patients
in remission. In addition, the l g markers and mucosal g index of present
invention have enormous implications for patient management as well as therapeutic
decision-making and would aid or assist in directing the appropriate therapy to those patients
who would most likely benefit from it and avoid the expense and ial toxicity of chronic
maintenance y in those who have a low risk of recurrence.
1. Disease Activity Profile
As described herein, the disease activity profile (DAP) of the present invention can
advantageously be used in methods for personalized therapeutic management of a disease in
order to optimize therapy and/or monitor therapeutic efficacy. In certain embodiments, the
methods of the invention can improve the accuracy of ing therapy, optimizing therapy,
reducing toxicity, and/or monitoring the efficacy of therapeutic treatment to anti-TNF drug
therapy. In ular embodiments, the DAP is determined by measuring an array of one or
a plurality of(e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 30, 35, 40, 45, 50, or more) markers at a plurality of time points over the course of
therapy with a therapeutic antibody (e.g., anti-TNF drug) to determine a DAP, wherein the
DAP comprises a representation of the concentration level of each marker over time. In
certain ments, the DAP may comprise a representation of the ce or absence,
concentration (e.g., expression) level, activation (e.g, phosphorylation) level, and/or ty
value (e.g., change in slope of the level of a particular marker) of each marker over time. As
such, the methods of the present invention find utility in ining patient management by
ining patient immune status.
[0170] In certain instances, a single statistical algorithm or a combination of two or more
statistical algorithms can be applied to the concentration level of each marker over the course
of therapy or to the DAP itself.
Understanding the clinical course of disease enables physicians to make better
ed treatment decisions for their atory disease ts (e.g., IBD (e.g., Crohn’s
disease), rheumatoid arthritis (RA), others) and helps to direct new drug development. The
ideal biomarker(s) for use in the disease activity profile described herein is able to fy
individuals at risk for the disease and is disease-specific. Moreover, the biomarker(s) are able
to detect disease activity and monitor the effect of treatment; and have a predictive value
towards relapse or recurrence of the disease. Predicting disease course, however, has now
been expanded beyond just disease recurrence, but more importantly to include predictors of
disease cations including surgery. The present invention is particularly advantageous
e it provides indicators of disease activity and/or severity and enables a prediction of
the risk of relapse in those patients in ion. In addition, the biomarkers and disease
WO 54987
activity profile of the t invention have enormous implications for patient management,
as well as therapeutic decision-making, and aid or assist in directing the riate therapy
to patients who most likely will benefit from it and avoid the expense and potential toxicity of
chronic maintenance therapy in those who have a low risk of recurrence.
As a non-limiting e, the disease actiVity profile (DAP) in one ment
comprises detecting, measuring, or determining the presence, level (concentration (e.g, total)
and/or activation (e.g., phosphorylation)), or genotype of one or more specific biomarkers in
one or more of the following categories of kers:
(1) Drug levels (e.g., NF drug levels);
(2) Anti-drug antibody (ADA) levels (e.g., level of autoantibody to an anti-TNF drug);
(3) Inflammatory s;
(4) Anti-inflammatory markers; and/or
(5) Tissue repair markers.
Non-limiting examples of additional and/or alternative markers in which the
presence, level (concentration (e.g, total) and/or activation (e.g, phosphorylation)), or
genotype can be measured include:
(6) Serology (e.g., immune markers);
(7) Markers of oxidative stress;
(8) Cell e receptors (e.g., CD64, others);
(9) Signaling pathways;
(10) kel, or the elimination rate constant of a drug such as a therapeutic antibody (e.g.,
infliximab); and/or
(1 1) Other markers (e.g., genetic markers such as inflammatory pathway genes).
A. Anti-TNF Drug Levels & Anti-Drug Antibody (ADA) Levels
[0174] In some embodiments, the disease actiVity profile (DAP) comprises determining the
presence and/or level of anti-TNF drug (e.g., level of free anti-TNFu therapeutic antibody
such as infliximab) and/or anti-drug antibody (ADA) (e.g., level of tibody to the anti-
TNF drug such as HACA) in a patient sample (e.g., a serum sample from a patient on anti-
TNF drug therapy) at multiple time points, e.g, before, during, and/or after the course of
therapy.
In particular embodiments, the ce and/or level of anti-TNF drug and/or ADA
is determined with a homogeneous mobility shift assay using size exclusion chromatography.
This , which is described in PCT Application No. PCT/USZO l 0/054125, filed October
26, 2010, the disclosure of which is hereby incorporated by reference in its entirety for all
purposes, is particularly advantageous for measuring the presence or level of TNFu inhibitors
as well as autoantibodies (e.g, HACA, HAHA, etc.) that are generated against them.
In one embodiment, the method for detecting the presence of an anti-TNFu antibody
in a sample comprises:
(a) contacting labeled TNFu with a sample having or suspected of having an anti-
TNFu antibody to form a labeled complex with the anti-TNFOL antibody;
(b) subjecting the d complex to size exclusion chromatography to separate
the labeled complex; and
(c) detecting the labeled complex, thereby detecting the anti-TNFu antibody.
In certain instances, the s are ally useful for the following anti-TNFu
antibodies: REMICADETM (infliximab), ENBRELT'V' (etanercept), HUMIRAT'V' (adalimumab),
and ® lizumab pegol).
Tumor necrosis factor 0L (TNFu) is a cytokine involved in systemic inflammation
and is a member of a group of cytokines that stimulate the acute phase reaction. The primary
role of TNFu is in the regulation of immune cells. TNFu is also able to induce apoptotic cell
death, to induce inflammation, and to inhibit tumorigenesis and viral replication. TNF is
primarily produced as a 212-amino acid-long type II transmembrane protein ed in
stable homotrimers.
[0179] The terms “TNF”, “TNFu,” and L,” as used herein, are intended to include a
human ne that exists as a 17 kDa secreted form and a 26 kDa membrane associated
form, the biologically active form of which is composed of a trimer of noncovalently bound
17 kDa molecules. The structure of TNF-0L is described further in, for example, Jones, et al.
(1989) Nature, 5-228. The term TNF-0L is intended to include human, a recombinant
human TNF-0L (rhTNF-(x), or at least about 80% identity to the human TNFu protein. Human
TNFOL consists of a 35 amino acid (aa) cytoplasmic domain, a 21 aa transmembrane t,
and a 177 aa extracellular domain (ECD) (Pennica, D. et al. (1984) Nature 4). Within
the ECD, human TNFu shares 97% aa sequence identity with rhesus and 71% 92% with
bovine, canine, cotton rat, equine, feline, mouse, porcine, and rat TNFu. TNFu can be
ed by standard inant expression methods or purchased commercially (R & D
Systems, Catalog No. 210-TA, Minneapolis, Minn.).
In certain instances, after the TNF 0L antibody is detected, the TNF 0L antibody is
measured using a standard curve.
In another embodiment, the method for ing an autoantibody to an anti-TNFOL
antibody in a sample comprises:
(a) contacting d anti-TNFOL antibody with the sample to form a labeled
complex with the autoantibody;
(b) subjecting the labeled complex to size exclusion chromatography to separate
the labeled complex; and
(c) detecting the labeled complex, y detecting the tibody.
In n instances, the autoantibodies include human anti-chimeric antibodies
, human anti-humanized antibodies (HAHA), and human anti-mouse antibodies
(HAMA).
Non-limiting es of other methods for determining the presence and/or level
of anti-TNF drug and/or anti-drug antibodies (ADA) include enzyme-linked immunosorbent
assays (ELISAs) such as bridging ELISAs. For example, the Infliximab ELISA from s
Biotek Laboratories detects free infliximab in serum and plasma samples, and the HACA
ELISA from PeaceHealth Laboratories detects HACA in serum samples.
B. Inflammatory Markers
Although disease course of an inflammatory disease is typically measured in terms
of inflammatory activity by noninvasive tests using white blood cell count, this method has a
low specificity and shows limited correlation with disease activity.
[0185] As such, in certain embodiments, a variety of inflammatory markers, including
biochemical markers, serological markers, protein markers, genetic markers, and/or other
clinical or echographic characteristics, are particularly useful in the methods of the present
invention for personalized therapeutic management by selecting therapy, optimizing y,
reducing toxicity, and/or monitoring the efficacy of therapeutic treatment with one or more
eutic agents such as biologics (e.g, NF drugs). In particular embodiments, the
methods described herein utilize the ination of a disease activity profile (DAP) based
upon one or more (a ity of) inflammatory s (e.g., alone or in combination with
biomarkers from other categories) to aid or assist in predicting disease course, selecting an
appropriate anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing toxicity
associated with anti-TNF drug therapy, and/or monitoring the efficacy of therapeutic
treatment with an anti-TNF drug.
Non-limiting examples of atory markers include cytokines, chemokines,
acute phase proteins, cellular adhesion molecules, S100 proteins, and/or other inflammatory
markers. In red embodiments, the inflammatory markers comprise at least 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 15, 20, 25, or more cytokines. In one particular embodiment, the cytokines are
at least 1, 2, 3, 4, 5, 6, 7, or all 8 ofthe following: GM-CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8,
TNF-(x, and sTNF R11.
1. Cytokines and Chemokines
The determination of the presence or level of at least one cytokine or chemokine in
a sample is particularly useful in the t invention. As used herein, the term “cytokine”
includes any of a variety of polypeptides or proteins secreted by immune cells that regulate a
range of immune system fianctions and encompasses small cytokines such as chemokines.
The term “cytokine” also includes adipocytokines, which comprise a group of cytokines
secreted by adipocytes that fianction, for example, in the regulation of body weight,
poiesis, angiogenesis, wound healing, insulin resistance, the immune response, and
the inflammatory response.
In certain embodiments, the presence or level of at least one ne including, but
not limited to, granulocyte-macrophage colony-stimulating factor (GM-CSF), IFN—y, IL-lB,
IL-2, IL-6, IL-8, TNF-(x, soluble tumor necrosis -0L receptor II (sTNF RII), TNF-related
weak inducer of apoptosis (TWEAK), rotegerin (OPG), IFN—u, IFN-B, IL-lu, IL-1
receptor antagonist (IL-lra), IL-4, IL-5, soluble IL-6 receptor R), IL-7, IL-9, IL-l2, IL-
l3, IL-lS, IL-l7, IL-23, and IL-27 is determined in a sample.
[0 1 89] In certain other embodiments, the ce or level of at least one chemokine such
as, for e, CXCLl/GRO l/GROOL, CXCL2/GRO2, CXCL3/GRO3, CXCL4/PF-4,
CXCLS/ENA-78, CXCL6/GCP-2, CXCL7/NAP-2, CXCL9/MIG, CXCL l O/IP- l 0,
CXCLl l/I-TAC, CXCL l2/SDF- l, CXCL l 3/BCA-l, CXCL l4/BRAK, CXCL l 5, CXCL16,
CXCL l 7/DMC, CCLl, CCL2/MCP- l, CCL3/MIP-10L, CCL4/MIP-l B, ANTES,
CCL6/C l 0, CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL l 0, CCLl l/Eotaxin, CCL l 5,
CCL l 3/MCP-4, CCL l4/HCC- l, CCL l S/MIP-S, CCL l 6/LEC, CCL l , CCL18/MIP-
4, CCL l 9/MIP-3 B, CCL2O/MIP-30L, CCL2 l/SLC, CCL22/MDC, CCL23/MPIF1,
CCL24/Eotaxin-2, CCL25/TECK, CCL26/Eotaxin-3, CCL27/CTACK, CCL28/MEC, CLl,
CL2, and CX3CL1 is ined in a sample. In certain further embodiments, the presence
or level of at least one adipocytokine including, but not limited to, leptin, adiponectin,
resistin, active or total plasminogen activator inhibitor-1 (PAL 1 ), visfatin, and retinol binding
protein 4 (RBP4) is determined in a sample. Preferably, the ce or level of GM-CSF,
IFN-y, IL-1 [3, IL-2, IL-6, IL-8, TNF-u, sTNF RII, and/or other cytokines or chemokines is
determined.
In certain instances, the presence or level of a particular ne or chemokine is
detected at the level ofmRNA expression with an assay such as, for example, a hybridization
assay or an amplification-based assay. In certain other instances, the presence or level of a
particular cytokine or chemokine is detected at the level of protein expression using, for
example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable ELISA
kits for determining the presence or level of a cytokine or chemokine of interest in a serum,
plasma, saliva, or urine sample are available from, e.g., R&D Systems, Inc. (Minneapolis,
MN), Neogen Corp. (Lexington, KY), Alpco stics (Salem, NH), Assay Designs, Inc.
(Ann Arbor, MI), BD Biosciences ngen (San Diego, CA), Invitrogen illo, CA),
Calbiochem (San Diego, CA), CHEMICON International, Inc. (Temecula, CA), Antigenix
America Inc. (Huntington Station, NY), QIAGEN Inc. (Valencia, CA), d
Laboratories, Inc. (Hercules, CA), and/or Bender MedSystems Inc. (Burlingame, CA).
[0191] The human IL-6 polypeptide ce is set forth in, e.g., Genbank Accession No.
NP_00059l. The human IL-6 mRNA (coding) sequence is set forth in, e.g., Genbank
Accession No. NM_000600. One d in the art will appreciate that IL-6 is also known as
eron beta 2 (IFNB2), HGF, HSF, and BSF2.
The human IL-lB polypeptide sequence is set forth in, e.g., Genbank Accession No.
NP_000567. The human IL-lB mRNA (coding) sequence is set forth in, e.g., Genbank
Accession No. NM_000576. One skilled in the art will appreciate that IL-lB is also known as
ILlF2 and IL-lbeta.
The human IL-8 ptide sequence is set forth in, e.g., Genbank Accession No.
NP_000575 (SEQ ID NO: 1). The human IL-8 mRNA (coding) sequence is set forth in, e.g.,
Genbank Accession No. NM_0005 84 (SEQ ID NO:2). One skilled in the art will appreciate
that IL-8 is also known as CXCL8, K60, NAF, GCPl, LECT, LUCT, NAPl, 3-lOC, GCP-l,
LYNAP, MDNCF, MONAP, NAP-l, SCYB8, TSG-l, AMCF-I, and b-ENAP.
The human TWEAK ptide ce is set forth in, e.g., k Accession
Nos. NP_003800 and AAC5 1923. The human TWEAK mRNA (coding) sequence is set
forth in, e.g., Genbank Accession Nos. NM_003 809 and BC104420. One skilled in the art
will appreciate that TWEAK is also known as tumor necrosis factor ligand superfamily
member 12 (TNFSF12), APO3 ligand (APO3L), CD255, DR3 ligand, growth factor-
inducible l4 (Fnl4) ligand, and UNQ18 l/PROZO7.
2. Acute Phase Proteins
The determination of the presence or level of one or more acute-phase proteins in a
sample is also useful in the present invention. Acute-phase proteins are a class of proteins
whose plasma concentrations increase ive acute-phase proteins) or decrease (negative
phase proteins) in response to inflammation. This response is called the acute-phase
reaction (also called acute-phase response). Examples of positive acute-phase proteins
include, but are not d to, C-reactive protein (CRP), D-dimer protein, mannose-binding
protein, alpha l-antitrypsin, alpha l-antichymotrypsin, alpha 2-macr0globulin, f1brin0gen,
prothrombin, factor VIII, von Willebrand factor, plasminogen, complement factors, ferritin,
serum amyloid P component, serum amyloid A (SAA), orosomucoid (alpha 1-acid
glycoprotein, AGP), ceruloplasmin, haptoglobin, and combinations thereof. Non-limiting
examples of negative acute-phase proteins include albumin, transferrin, transthyretin,
transcortin, retinol-binding protein, and combinations thereof Preferably, the presence or
level of CRP and/or SAA is determined.
[0196] In certain ces, the presence or level of a particular acute-phase protein is
detected at the level ofmRNA expression with an assay such as, for example, a hybridization
assay or an cation-based assay. In certain other instances, the ce or level of a
particular acute-phase protein is detected at the level of protein expression using, for
example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. For example, a
sandwich colorimetric ELISA assay ble from Alpco Diagnostics (Salem, NH) can be
used to determine the level of CRP in a serum, plasma, urine, or stool sample. rly, an
ELISA kit ble from Biomeda Corporation (Foster City, CA) can be used to detect CRP
levels in a sample. Other methods for determining CRP levels in a sample are described in,
e.g, US. Patent Nos. 6,838,250 and 6,406,862; and US. Patent Publication Nos.
20060024682 and 19410. Additional methods for determining CRP levels include,
e.g., immunoturbidimetry assays, rapid immunodiffusion assays, and visual ination
. Suitable ELISA kits for determining the ce or level of SAA in a sample such
as serum, plasma, saliva, urine, or stool are ble from, e.g., Antigenix America Inc.
(Huntington Station, NY), Abazyme (Needham, MA), USCN Life (Missouri City, TX),
and/or U.S. Biological (Swampscott, MA).
C-reactive protein (CRP) is a protein found in the blood in response to inflammation
(an acute-phase protein). CRP is typically produced by the liver and by fat cells (adipocytes).
It is a member of the pentraxin family of proteins. The human CRP polypeptide sequence is
set forth in, e.g., Genbank Accession No. NP_000558. The human CRP mRNA (coding)
sequence is set forth in, e.g., Genbank Accession No. NM_000567. One skilled in the art will
appreciate that CRP is also known as PTXl, MGC88244, and MGCl49895.
Serum amyloid A (SAA) proteins are a family of apolipoproteins associated with
high-density lipoprotein (HDL) in plasma. Different isoforms of SAA are expressed
constitutively (constitutive SAAs) at different levels or in response to inflammatory stimuli
(acute phase SAAs). These ns are predominantly produced by the liver. The
conservation of these ns hout invertebrates and vertebrates suggests SAAs play a
highly essential role in all animals. Acute phase serum amyloid A proteins (A-SAAs) are
secreted during the acute phase of inflammation. The human SAA polypeptide ce is
set forth in, e.g., Genbank Accession No. NP_000322. The human SAA mRNA (coding)
ce is set forth in, e.g., Genbank Accession No. NM_00033 1. One skilled in the art will
appreciate that SAA is also known as PIG4, , MGCl l 1216, and SAAl.
3. Cellular Adhesion Molecules (IgSF CAMs)
The determination of the presence or level of one or more immunoglobulin
superfamily ar adhesion molecules in a sample is also useful in the t invention.
As used herein, the term “immunoglobulin superfamily cellular adhesion molecule” (IgSF
CAM) includes any of a variety of polypeptides or proteins located on the surface of a cell
that have one or more immunoglobulin-like fold domains, and which on in intercellular
adhesion and/or signal transduction. In many cases, IgSF CAMs are transmembrane proteins.
Non-limiting examples of IgSF CAMs include Neural Cell on les (NCAMs;
e.g., NCAM-lZO, NCAM-lZS, NCAM-l40, 45, 80, NCAM-185, eta),
Intercellular Adhesion Molecules (ICAMs, e.g. and
, ICAM-l, ICAM-2, ICAM-3, ICAM-4,
ICAM-S), Vascular Cell Adhesion Molecule-l (VCAM-l), Platelet-Endothelial Cell
Adhesion Molecule-l (PECAM-l), Ll Cell Adhesion Molecule (LlCAM), cell adhesion
molecule with homology to LlCAM (close homolog of Ll) (CHLl), sialic acid binding Ig-
like lectins (SIGLECs; e.g., -l, SIGLEC-Z, SIGLEC-3, SIGLEC-4, eta), Nectins
(e.g., Nectin-l, Nectin-2, Nectin-3, etc), and Nectin-like molecules (e.g., Necl-l, Neel-2,
Neel-3, Necl-4, and Necl-S). ably, the presence or level of ICAM-l and/or VCAM-l is
determined.
[0200] ICAM-l is a transmembrane cellular adhesion protein that is continuously present in
low concentrations in the membranes of leukocytes and endothelial cells. Upon cytokine
stimulation, the concentrations y increase. ICAM-l can be induced by IL-l and TNFOL
and is expressed by the vascular endothelium, macrophages, and lymphocytes. In IBD,
proinflammatory cytokines cause inflammation by lating expression of on
molecules such as ICAM-l and VCAM-l. The increased expression of adhesion molecules
recruit more lymphocytes to the infected tissue, resulting in tissue inflammation (see, Goke et
al., J., Gastroenterol., 32:480 ; and Rijcken et al., Gut, 51:529 (2002)). ICAM-l is
encoded by the intercellular adhesion molecule 1 gene ; Entrez GeneID:3383;
Genbank Accession No. NM_000201) and is produced after processing of the intercellular
adhesion molecule 1 precursor polypeptide (Genbank Accession No. NP_000192).
VCAM-l is a transmembrane cellular adhesion protein that mediates the adhesion
of lymphocytes, monocytes, eosinophils, and basophils to vascular endothelium.
Upregulation ofVCAM-1 in endothelial cells by cytokines occurs as a result of sed
gene transcription (e.g., in response to Tumor necrosis factor-alpha (TNFu) and Interleukin-l
(IL-1)). VCAM-l is encoded by the vascular cell adhesion molecule 1 gene (VCAMl;
Entrez GeneID:74l2) and is ed after differential splicing of the transcript (Genbank
ion No. NM_001078 (variant 1) or 682 (variant 2)), and processing of the
sor polypeptide splice isoform (Genbank Accession No. NP_001069 (isoform a) or
NP_542413 (isoform b)).
In certain instances, the presence or level of an IgSF CAM is detected at the level of
mRNA sion with an assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other ces, the presence or level of an IgSF CAM
is detected at the level of protein expression using, for example, an immunoassay (e.g.,
ELISA) or an immunohistochemical assay. Suitable antibodies and/or ELISA kits for
determining the presence or level of ICAM-l and/or VCAM-l in a sample such as a tissue
sample, biopsy, serum, , saliva, urine, or stool are available from, e.g., Invitrogen
illo, CA), Santa Cruz Biotechnology, Inc. (Santa Cruz, CA), and/or Abcam Inc.
(Cambridge, MA).
4. S100 Proteins
The determination of the presence or level of at least one S100 protein in a sample
is also useful in the present invention. As used herein, the term “S100 n” includes any
member of a family of low molecular mass acidic proteins characterized by cell-type-speciflc
expression and the presence of 2 EF-hand calcium-binding domains. There are at least 21
different types of S100 proteins in humans. The name is derived from the fact that S100
proteins are 100% soluble in ammonium sulfate at neutral pH. Most S100 proteins are
homodimeric, consisting of two identical polypeptides held together by non-covalent bonds.
gh S100 proteins are structurally similar to calmodulin, they differ in that they are cell-
specif1c, expressed in particular cells at different levels ing on nmental factors.
S-100 proteins are normally present in cells derived from the neural crest (e.g., Schwann
cells, melanocytes, glial cells), chondrocytes, adipocytes, myoepithelial cells, macrophages,
Langerhans cells, dendritic cells, and keratinocytes. S100 proteins have been implicated in a
variety of ellular and extracellular fianctions such as the regulation of protein
phosphorylation, transcription factors, Ca2+ homeostasis, the dynamics of cytoskeleton
constituents, enzyme activities, cell growth and entiation, and the inflammatory
I'GSpOIlSG.
[0204] Calgranulin is an S100 protein that is expressed in le cell types, including
renal epithelial cells and neutrophils, and are abundant in infiltrating monocytes and
ocytes under conditions of chronic inflammation. Examples of calgranulins include,
without limitation, calgranulin A (also known as SlOOA8 or MRP-8), calgranulin B (also
known as SlOOA9 or MRP-l4), and calgranulin C (also known as SlOOA12).
[0205] In certain instances, the ce or level of a particular S100 protein is detected at
the level ofmRNA expression with an assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence or level of a particular
S100 protein is detected at the level of n expression using, for example, an
immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable ELISA kits for
determining the presence or level of an S100 protein such as nulin A (SlOOA8),
calgranulin B (SlOOA9), or calgranulin C (SlOOA12) in a serum, plasma, or urine sample are
available from, e.g., Peninsula Laboratories Inc. (San Carlos, CA) and Hycult biotechnology
b.v. (Uden, The Netherlands).
Calprotectin, the complex of SlOOA8 and SlOOA9, is a calcium- and zinc-binding
protein in the cytosol of neutrophils, tes, and keratinocytes. Calprotectin is a major
protein in neutrophilic granulocytes and macrophages and accounts for as much as 60% of
the total protein in the cytosol fraction in these cells. It is therefore a surrogate marker of
neutrophil turnover. Its tration in stool correlates with the intensity of neutrophil
ration of the intestinal mucosa and with the severity of inflammation. In some instances,
calprotectin can be measured with an ELISA using small (50-100 mg) fecal samples (see,
e.g., Johne et al., ScandJ Gastroenter01., 36:291-296 (2001)).
. Other Inflammatory Markers
The ination of the presence or level of lactoferrin in a sample is also useful in
the present invention. In certain instances, the presence or level of lactoferrin is detected at
the level ofmRNA expression with an assay such as, for example, a hybridization assay or an
amplification-based assay. In certain other instances, the presence or level of lactoferrin is
detected at the level of n expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. A errin ELISA kit available from Calbiochem (San
Diego, CA) can be used to detect human lactoferrin in a plasma, urine, bronchoalveolar
lavage, or cerebrospinal fluid sample. Similarly, an ELISA kit available from US. Biological
(Swampscott, MA) can be used to determine the level of lactoferrin in a plasma sample. US.
Patent Publication No. 20040137536 bes an ELISA assay for determining the presence
of elevated lactoferrin levels in a stool sample. Likewise, US. Patent ation No.
20040033537 describes an ELISA assay for determining the concentration of endogenous
lactoferrin in a stool, mucus, or bile sample. In some embodiments, then presence or level of
actoferrin antibodies can be ed in a sample using, e.g., lactoferrin protein or a
fragment thereof.
The determination of the ce or level of one or more pyruvate kinase isozymes
such as Ml-PK and M2-PK in a sample is also useful in the present invention. In certain
instances, the presence or level of Ml-PK and/or M2-PK is detected at the level ofmRNA
expression with an assay such as, for example, a hybridization assay or an amplification-
based assay. In certain other instances, the presence or level of Ml-PK and/or M2-PK is
detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA)
or an immunohistochemical assay. Pyruvate kinase isozymes Ml/M2 are also known as
pyruvate kinase muscle isozyme (PKM), pyruvate kinase type K, cytosolic thyroid hormone-
binding protein ), thyroid hormone-binding n 1 ), or opa-interacting
n 3 (OIP3).
In fithher embodiments, the determination ofthe presence or level of one or more
growth factors in a sample is also useful in the present invention. Non-limiting examples of
growth factors include transforming growth factors (TGF) such as TGF-(x, TGF-B, TGF-B2,
TGF-B3, etc, which are described in detail below.
6. Exemplary Set of Inflammatory s
In particular embodiments, at least one or a plurality (e.g., two, three, four, five, six,
seven, or all eight, such as, e.g., a panel or an array) of the following inflammatory markers
WO 54987
can be detected (e.g, alone or in combination with biomarkers from other categories) to aid
or assist in predicting disease course, and/or to improve the accuracy of selecting therapy,
optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment
to anti-TNF drug therapy: (1) GM-CSF; (2) IFN-y; (3) IL-lB; (4) IL-2; (5) IL-6; (6) IL-8; (7)
TNF-u; and (8) sTNF RII.
C. Anti-Inflammatory Markers
In certain embodiments, a variety of anti-inflammatory markers are particularly
useful in the methods of the t invention for personalized therapeutic management by
selecting therapy, optimizing therapy, reducing toxicity, and/or ring the efficacy of
therapeutic treatment with one or more therapeutic agents such as biologics (e.g., anti-TNF
drugs). In particular embodiments, the s described herein utilize the determination of
a disease activity profile (DAP) based upon one or more (a ity of) anti-inflammatory
s (e.g., alone or in combination with biomarkers from other categories) to aid or assist
in predicting disease course, selecting an appropriate anti-TNF drug therapy, optimizing anti-
TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, and/or
monitoring the efficacy of therapeutic treatment with an anti-TNF drug.
Non-limiting examples of nflammatory markers include IL-12p70 and IL-10.
In preferred embodiments, the presence and/or concentration levels of both IL-12p70 and IL-
are determined.
[0213] In certain instances, the presence or level of a particular nflammatory marker is
detected at the level ofmRNA sion with an assay such as, for example, a hybridization
assay or an amplification-based assay. In certain other instances, the presence or level of a
particular anti-inflammatory marker is detected at the level of protein expression using, for
example, an immunoassay (e.g., ELISA) or an immunohistochemical assay.
[0214] The human IL-12p70 polypeptide is a heterodimer made up of two subunits of IL-
12 proteins: one is 40kDa (IL-12p40) and one is 35kDa p35). Suitable ELISA kits for
determining the ce or level of IL-12p70 in a serum, plasma, saliva, or urine sample are
available from, e.g., Gen-Probe Diaclone SAS (France), Abazyme (Needham, MA), BD
Biosciences Pharmingen (San Diego, CA), Cell Sciences n, MA), ience (San
Diego, CA), Invitrogen (Camarillo, CA), R&D Systems, Inc. (Minneapolis, MN), and
Thermo Scientific Pierce Protein Research ts (Rockford, IL).
The human IL-lO polypeptide is an anti-inflammatory cytokine that is also known
as human ne synthesis inhibitory factor (CSIF). Suitable ELISA kits for determining
the ce or level of IL-l2p70 in a serum, , saliva, or urine sample are available
from, e.g., Antigenix America Inc. (Huntington Station, NY), BD Biosciences Pharmingen
(San Diego, CA), Cell Sciences (Canton, MA), eBioscience (San Diego, CA), Gen-Probe
Diaclone SAS e), Invitrogen (Camarillo, CA), R&D s, Inc. (Minneapolis, MN),
and Thermo Scientific Pierce Protein Research Products (Rockford, IL).
D. Serology e Markers)
The determination of serological or immune s such as autoantibodies in a
sample (e.g., serum sample) is also useful in the present invention. Antibodies against anti-
inflammatory les such as IL-lO, TGF-B, and others might suppress the body’s ability
to control inflammation and the presence or level of these antibodies in the t indicates
the use of powerful immunosuppressive medications such as anti-TNF drugs. Mucosal
healing might result in a decrease in the antibody titre of antibodies to bacterial antigens such
as, e.g., OmpC, flagellins (cBir—l, Fla-A, Fla-X, etc), 12, and others (pANCA, ASCA, etc.)
As such, in certain aspects, the methods described herein utilize the determination
of a disease activity profile (DAP) based upon one or more (a plurality of) serological or
immune markers (e.g., alone or in combination with biomarkers from other categories) to aid
or assist in predicting disease course, selecting an appropriate anti-TNF drug y,
optimizing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy,
and/or monitoring the efficacy of therapeutic ent with an anti-TNF drug.
[0218] Non-limiting examples of gical immune markers suitable for use in the
present invention include anti-neutrophil antibodies, anti-Saccharomyces cerevisiae
antibodies, and/or other anti-microbial antibodies.
1. Anti-Neutrophil Antibodies
The determination ofANCA levels and/or the presence or absence ofpANCA in a
sample is useful in the methods of the present invention. As used herein, the term “anti-
neutrophil cytoplasmic antibody” or “ANCA” includes dies directed to cytoplasmic
and/or nuclear components of neutrophils. ANCA activity can be divided into several broad
categories based upon the ANCA ng n in neutrophils: (l) cytoplasmic neutrophil
staining without perinuclear highlighting (cANCA); (2) perinuclear staining around the
outside edge of the nucleus (pANCA); (3) perinuclear staining around the inside edge of the
nucleus (NSNA); and (4) diffuse staining with speckling across the entire neutrophil
). In certain ces, pANCA staining is sensitive to DNase ent. The term
ANCA encompasses all varieties of anti-neutrophil reactivity, including, but not limited to,
cANCA, pANCA, NSNA, and SAPPA. Similarly, the term ANCA asses all
immunoglobulin isotypes including, t limitation, immunoglobulin A and G.
ANCA levels in a sample from an dual can be determined, for example, using
an immunoassay such as an enzyme-linked immunosorbent assay (ELISA) with alcohol-fixed
neutrophils. The presence or absence of a particular category ofANCA such as pANCA can
be determined, for example, using an immunohistochemical assay such as an ct
fluorescent antibody (IFA) assay. Preferably, the presence or absence ofpANCA in a sample
is determined using an imrnunofiuorescence assay with DNase-treated, fixed neutrophils. In
addition to fixed neutrophils, antigens specific for ANCA that are suitable for determining
ANCA levels include, without limitation, unpurified or lly purified neutrophil extracts;
purified proteins, protein fragments, or tic es such as histone H1 or ANCA-
reactive fragments thereof (see, e.g., US. Patent No. 6,074,835); histone e antigens,
porin antigens, Bacteroides antigens, or ANCA-reactive fragments thereof (see, e.g., US.
Patent No. 6,033,864); secretory vesicle antigens or ANCA-reactive fragments thereof (see,
e. g., US. Patent Application No. 08/804,106); and anti-ANCA idiotypic antibodies. One
skilled in the art will appreciate that the use of additional ns specific for ANCA is
within the scope of the present invention.
2. Anti-Saccharomyces cerevisiae Antibodies
The determination ofASCA (e.g., ASCA-IgA and/or ASCA-IgG) levels in a sample
is useful in the present invention. As used herein, the term “anti-Saccharomyces ’sz’ae
immunoglobulin A” or “ASCA-IgA” includes antibodies of the immunoglobulin A isotype
that react specifically with S. cerevisiae. Similarly, the term Saccharomyces cerevisiae
immunoglobulin G” or “ASCA-IgG” includes antibodies of the immunoglobulin G isotype
that react specifically with S. cerevisiae.
[0222] The determination of whether a sample is positive for ASCA-IgA or ASCA-IgG is
made using an antigen c for ASCA. Such an antigen can be any antigen or mixture of
antigens that is bound specifically by ASCA-IgA and/or ASCA-IgG. Although ASCA
antibodies were initially characterized by their ability to bind S. cerevisiae, those of skill in
the art will understand that an antigen that is bound specifically by ASCA can be ed
from S. cerevisiae or from a variety of other sources so long as the antigen is capable of
binding specifically to ASCA antibodies. Accordingly, exemplary sources of an antigen
specific for ASCA, which can be used to determine the levels ofASCA-IgA and/or ASCA-
IgG in a , include, t limitation, whole killed yeast cells such as Saccharomyces
or Candida cells; yeast cell wall mannan such as phosphopeptidomannan (PPM);
oligosachharides such as oligomannosides; neoglycolipids; anti-ASCA idiotypic antibodies;
and the like. Different species and strains of yeast, such as S. cerevisiae strain Su1, Su2, CBS
1315, or BM 156, or Candida albicans strain VW32, are suitable for use as an antigen
specific for ASCA-IgA and/or ASCA-IgG. Purified and synthetic antigens specific for
ASCA are also suitable for use in ining the levels ofASCA-IgA and/or ASCA-IgG in
a sample. Examples of purified antigens include, without limitation, purified oligosaccharide
antigens such as oligomannosides. Examples of synthetic antigens e, without
limitation, synthetic oligomannosides such as those described in US. Patent Publication No.
05060, e.g., D-Man [3(1-2) D-Man [3(1-2) D-Man [3(1-2) D-Man-OR, D-Man 0L(1-2)
D-Man 0L(1-2) D-Man 0L(1-2) D-Man-OR, and D-Man 0L(1-3) D-Man 0L(1-2) D-Man 0L(1-2) D-
Man-OR, wherein R is a hydrogen atom, a C1 to C20 alkyl, or an optionally d connector
group .
Preparations of yeast cell wall mannans, e.g., PPM, can be used in determining the
levels ofASCA-IgA and/or gG in a sample. Such water-soluble surface antigens can
be prepared by any riate extraction que known in the art, including, for example,
by autoclaving, or can be obtained commercially (see, e. g., Lindberg et al., Gut, 33:909-913
(1992)). The acid-stable fraction of PPM is also useful in the present invention d et al.,
Clin. Diag. Lab. Imman01., 3:219-226 ). An exemplary PPM that is useful in
determining ASCA levels in a sample is derived from S. avaram strain ATCC #38926.
Purified oligosaccharide ns such as oligomannosides can also be useful in
determining the levels ofASCA-IgA and/or ASCA-IgG in a sample. The purified
oligomannoside antigens are preferably ted into neoglycolipids as described in, for
example, Faille et al., Eur. J. Microbiol. Infect. Dis., 11:438-446 (1992). One skilled in the
art tands that the reactivity of such an oligomannoside antigen with ASCA can be
optimized by varying the mannosyl chain length (Frosh et al. , Proc Natl. Acad. Sci. USA,
82: 1 194-1 198 (1985)); the anomeric ration (Fukazawa et al. In “Immunology of
Fungal Disease,” E. Kurstak (ed.), Marcel Dekker Inc., New York, pp. 37-62 (1989);
awa et al., Microbiol. Immanol., 34:825-840 (1990); Poulain et al., Eur. J. Clin.
Microbiol., 52 (1993); Shibata et al., Arch. Biochem. Biophys, 243:338-348 (1985);
Trinel et al., Infect. Imman, 60:3845-3851 (1992)); or the position of the linkage (Kikuchi et
al., Planta, 190:525-535 (1993)).
le oligomannosides for use in the methods of the present invention e,
without limitation, an oligomannoside having the mannotetraose Man(1-3) Man(1-2) Man(1-
2) Man. Such an oligomannoside can be purified from PPM as described in, e.g., Faille et al.,
supra. An exemplary colipid specific for ASCA can be constructed by ing the
oligomannoside from its respective PPM and subsequently coupling the released
oligomannoside to 4-hexadecylaniline or the like.
3. Anti-Microbial Antibodies
The determination of mpC antibody levels in a sample is also useful in the
present ion. As used herein, the term “anti-outer membrane protein C antibody” or
“anti-OmpC dy” es antibodies ed to a bacterial outer membrane porin as
described in, e. g., PCT Patent ation No. W0 01/89361. The term “outer membrane
protein C” or “OmpC” refers to a bacterial porin that is immunoreactive with an anti-OmpC
The level of anti-OmpC antibody present in a sample from an individual can be
determined using an OmpC protein or a fragment thereof such as an immunoreactive
fragment thereof. le OmpC antigens useful in determining anti-OmpC antibody levels
in a sample include, without tion, an OmpC protein, an OmpC polypeptide having
substantially the same amino acid sequence as the OmpC protein, or a fragment thereof such
as an immunoreactive fragment thereof. As used herein, an OmpC polypeptide generally
describes polypeptides having an amino acid sequence with r than about 50% identity,
preferably greater than about 60% identity, more preferably greater than about 70% identity,
still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99%
amino acid sequence identity with an OmpC protein, with the amino acid identity determined
using a sequence alignment program such as CLUSTALW. Such antigens can be prepared,
for example, by purif1cation from enteric ia such as E. 0011', by recombinant expression
of a nucleic acid such as Genbank Accession No. K0054l, by synthetic means such as
solution or solid phase peptide synthesis, or by using phage display.
The determination of anti-I2 antibody levels in a sample is also useful in the present
invention. As used herein, the term “anti-I2 antibody” includes antibodies directed to a
microbial antigen sharing homology to bacterial transcriptional tors as described in,
e.g., US. Patent No. 6,309,643. The term “12” refers to a microbial antigen that is
immunoreactive with an anti-I2 antibody. The microbial 12 protein is a ptide of 100
amino acids sharing some similarity weak homology with the predicted protein 4 from C.
pasteurianum, Rv3557c from Mycobacterium tuberculosis, and a transcriptional regulator
from ex aeolicus. The nucleic acid and protein sequences for the 12 protein are
described in, e. g., US. Patent No. 6,309,643.
The level of 2 antibody present in a sample from an individual can be
determined using an 12 protein or a fragment thereof such as an immunoreactive fragment
thereof. Suitable 12 antigens useful in determining anti-12 antibody levels in a sample
include, without limitation, an 12 protein, an 12 polypeptide having substantially the same
amino acid ce as the 12 protein, or a fragment thereof such as an immunoreactive
fragment thereof Such 12 polypeptides t greater sequence rity to the 12 protein
than to the C. pasteurianum protein 4 and include isotype variants and homologs f. As
used herein, an 12 polypeptide generally describes polypeptides having an amino acid
sequence with greater than about 50% identity, preferably greater than about 60% identity,
more preferably greater than about 70% identity, still more preferably greater than about
80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a
naturally-occurring 12 protein, with the amino acid identity determined using a sequence
alignment program such as CLUSTALW. Such 12 antigens can be prepared, for example, by
purification from es, by recombinant expression of a nucleic acid encoding an 12
antigen, by synthetic means such as solution or solid phase peptide synthesis, or by using
phage display.
The determination of anti-flagellin antibody levels in a sample is also useful in the
present invention. As used herein, the term “anti-flagellin antibody” es antibodies
directed to a protein component of bacterial flagella as bed in, e.g., PCT Patent
Publication No. WO 03/053220 and US. Patent Publication No. 2004004393 1. The term
“flagellin” refers to a bacterial flagellum protein that is immunoreactive with an anti-flagellin
antibody. Microbial flagellins are proteins found in bacterial flagellum that arrange
themselves in a hollow cylinder to form the filament.
The level of anti-flagellin antibody present in a sample from an individual can be
determined using a flagellin n or a fragment thereof such as an immunoreactive
fragment thereof. Suitable flagellin antigens useful in determining anti-flagellin antibody
levels in a sample e, without limitation, a flagellin protein such as Cbir-l flagellin,
flagellin X, flagellin A, flagellin B, fragments f, and combinations thereof, a flagellin
ptide having ntially the same amino acid sequence as the flagellin protein, or a
fragment thereof such as an immunoreactive fragment thereof. As used herein, a flagellin
polypeptide generally describes polypeptides having an amino acid sequence with greater
than about 50% ty, preferably greater than about 60% ty, more ably greater
than about 70% identity, still more preferably greater than about 80%, 85%, 90%, 95%, 96%,
97%, 98%, or 99% amino acid sequence identity with a naturally-occurring flagellin protein,
with the amino acid ty determined using a sequence alignment program such as
CLUSTALW. Such flagellin antigens can be prepared, e.g., by purification from bacterium
such as Helicobacter Bill's, Helicobacter mustelae, Helicobacter pylori, Butyrz’vz’brz’o
fibrisolvens, and bacterium found in the cecum, by recombinant expression of a nucleic acid
encoding a flagellin antigen, by synthetic means such as solution or solid phase peptide
sis, or by using phage display.
E. Cell Surface Receptors
[0232] The determination of cell surface receptors in a sample is also useful in the present
ion. The half-life of anti-TNF drugs such as de and Humira is significantly
decreased in patients with a high level of ation. CD64, the high-affinity receptor for
immunoglobulin (Ig) G1 and IgG3, is predominantly expressed by mononuclear phagocytes.
Resting polymorphonuclear (PMN) cells scarcely express CD64, but the expression of this
marker is upregulated by interferon and granulocyte-colony-stimulating factor acting on
d precursors in the bone marrow. Crosslinking of CD64 with IgG complexes exerts a
number of cellular responses, including the internalization of immune complexes by
endocytosis, phagocytosis of zed les, degranulation, activation of the oxidative
burst, and the release of cytokines.
[0233] As such, in certain aspects, the methods described herein utilize the determination
of a disease activity profile (DAP) based upon one or more (a plurality of) cell surface
receptors such as CD64 (e.g., alone or in combination with biomarkers from other categories)
to aid or assist in predicting disease course, ing an appropriate anti-TNF drug therapy,
optimizing anti-TNF drug y, reducing toxicity associated with anti-TNF drug therapy,
and/or monitoring the cy of therapeutic treatment with an anti-TNF drug.
F. Signaling Pathways
The determination of ing pathways in a sample is also useful in the present
invention. Polymorphonuclear (PMN) cell activation, ed by infltration into the
intestinal mucosa (synovium for RA) and migration across the crypt epithelium is regarded as
a key feature of IBD. It has been estimated by fecal indium-l l l-labeled leukocyte excretion
that migration ofPMN cells from the ation to the diseased section of the intestine is
increased by 10-fold or more in IBD patients. Thus, measuring activation ofPMN cells from
blood or tissue inflammation by measuring signaling pathways using an assay such as the
gollaborative Enzyme Enhanced Reactive ImmunoAssay (CEER) described herein is an ideal
way to understand inflammatory disease.
As such, in certain aspects, the methods described herein utilize the determination
of a disease activity profile (DAP) based upon one or more (a plurality of) signal transduction
molecules in one or more signaling pathways (e.g., alone or in combination with biomarkers
from other categories) to aid or assist in predicting disease course, selecting an appropriate
anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing toxicity associated with
anti-TNF drug therapy, and/or monitoring the efficacy of therapeutic treatment with an anti-
TNF drug. In preferred embodiments, the total (e.g., expression) level and/or activation (e.g.,
orylation) level of one or more signal transduction molecules in one or more signaling
pathways is measured.
The term “signal uction molecule” or “signal transducer” includes proteins
and other molecules that carry out the process by which a cell converts an extracellular signal
or stimulus into a response, typically ing ordered sequences of biochemical reactions
inside the cell. Examples of signal transduction molecules include, but are not limited to,
receptor tyrosine kinases such as EGFR (e.g, EGFR/HERl/ErbBl, HER2/Neu/ErbB2,
rbB3, HER4/ErbB4), /FLTl, /FLKl/KDR, VEGFR3/FLT4,
FLT3/FLK2, PDGFR (e.g., PDGFRA, PDGFRB), c-KIT/SCFR, INSR (insulin receptor),
IGF-IR, , IRR (insulin receptor-related or), CSF-lR, FGFR l-4, HGFR 1-2,
CCK4, TRK A-C, c-MET, RON, EPHA 1-8, EPHB 1-6, AXL, MER, TYRO3, TIE 1-2,
TEK, RYK, DDR 1-2, RET, c-ROS, erin, LTK (leukocyte tyrosine kinase), ALK
(anaplastic lymphoma kinase), ROR 1-2, MUSK, AATYK 1-3, and RTK 106; truncated
forms of receptor tyrosine s such as truncated HER2 receptors with missing amino-
terminal extracellular s (e.g, p95ErbB2 (p95m), pl 10, p95c, p95n, eta), truncated
cMET receptors with missing amino-terminal ellular domains, and ted HER3
receptors with missing amino-terminal extracellular domains; receptor tyrosine kinase dimers
(e.g., p95HER2/HER3; p95HER2/HER2; truncated HER3 or with HERl , HER2,
HER3, or HER4; HER2/HER2; HER3/HER3; ER3; HERl/HERZ; HERl/HER3;
HER2/HER4; HER3/HER4; eta); non-receptor tyrosine kinases such as BCR—ABL, Src, Frk,
Btk, Csk, Abl, Zap70, Fes/Fps, Fak, Jak, Ack, and LIMK; tyrosine kinase signaling cascade
components such as AKT (e.g, AKTl, AKTZ, AKT3), MEK(MAP2K1), ERK2 (MAPKl),
ERKl (MAPK3), PI3K (e.g., PIK3CA (pl 10), PIK3Rl (p85)), PDKl, PDK2, phosphatase
and tensin homolog (PTEN), SGK3, 4E-BPl, P7OS6K (e.g., p70 S6 kinase splice variant
alpha 1), protein tyrosine atases (e.g., PTPlB, PTPNl3, BDPl, eta), RAF, PLA2,
MEKK, JNKK, JNK, p38, Shc (p66), Ras (e.g., K-Ras, N—Ras, H-Ras), Rho, Racl, Cdc42,
PLC, PKC, p53, cyclin D1, STATl, STAT3, phosphatidylinositol 4,5-bisphosphate (PIP2),
phosphatidylinositol trisphosphate (PIP3), mTOR, BAD, p21, p27, ROCK, 1P3, TSP-l,
NOS, GSK-3B, RSK 1-3, JNK, c-Jun, Rb, CREB, Ki67, paxillin, NF-kB, and IKK; nuclear
hormone receptors such as estrogen receptor (ER), progesterone receptor (PR), en
receptor, glucocorticoid receptor, mineralocorticoid receptor, vitamin A receptor, vitamin D
receptor, retinoid receptor, d hormone receptor, and orphan receptors; nuclear receptor
coactivators and repressors such as ed in breast cancer-l (AIBl) and r receptor
corepressor l (NCOR), respectively; and combinations f.
[0237] The term “activation state” refers to whether a particular signal transduction
molecule is activated. Similarly, the term “activation level” refers to what extent a particular
signal transduction le is activated. The tion state typically corresponds to the
phosphorylation, ubiquitination, and/or complexation status of one or more signal
transduction molecules. miting examples of activation states (listed in parentheses)
e: HERl/EGFR (EGFRvIII, phosphorylated (p-) EGFR, hc, tinated (u-)
EGFR, p-EGFRvIII); ErbB2 (p-ErbB2, p95HER2 (truncated ErbB2), p-p95HER2,
ErbB2:Shc, ErbB2:PI3K, ErbB2:EGFR, ErbB2:ErbB3, ErbB2:ErbB4); ErbB3 (p-ErbB3,
truncated ErbB3, ErbB3:PI3K, p-ErbB3:PI3K, ErbB3:Shc); ErbB4 (p-ErbB4, ErbB4:Shc); c-
MET (p-c-MET, truncated c-MET, c-Met:HGF complex); AKTl (p-AKTl); AKT2 (p-
AKT2); AKT3 (p-AKT3); PTEN (p-PTEN); P7086K (p-P7OS6K); MEK (p-MEK); ERKl
(p-ERKl); ERK2 (p-ERK2); PDKl (p-PDKl); PDK2 2); SGK3 3); 4E-BPl
(p-4E-BPl); PIK3Rl (p-PIK3Rl); c-KIT IT); ER (p-ER); IGF-lR (p-IGF-lR, IGF-
lR:IRS, IRS:PI3K, p-IRS, IGF-lR:PI3K); INSR (p-INSR); FLT3 (p-FLT3); HGFRl (p-
HGFRl); HGFR2 (p-HGFR2); RET (p-RET); PDGFRA (p-PDGFRA); PDGFRB (p-
PDGFRB); VEGFRl (p-VEGFRl, VEGFRl :PLCy, VEGFRl :Src); VEGFR2 (p-VEGFR2,
VEGFR2:PLCy, VEGFR2:Src, VEGFR2:heparin sulphate, VEGFR2:VE-cadherin);
VEGFR3 (p-VEGFR3); FGFRl (p-FGFRl); FGFR2 (p-FGFR2); FGFR3 R3);
FGFR4 (p-FGFR4); TIEl (p-TIEl); TIE2 (p-TIE2); EPHA A); EPHB (p-EPHB);
GSK-3B (p-GSK-3B); NF-kB (p-NF-kB, NF-kB-IkB alpha complex and others), IkB (p-IkB,
p-P65:IkB); IKK (phospho IKK); BAD (p-BAD, BAD:l43); mTOR (p-mTOR); Rsk-l (p-
Rsk-l); Jnk (p-Jnk); P38 (p-P38); STATl (p-STATl); STAT3 (p-STAT3); FAK (p-FAK);
RB (p-RB); Ki67; p53 (p-p53); CREB (p-CREB); c-Jun (p-c-Jun); c-Src (p-c-Src); paxillin
(p-paxillin); GRB2 (p-GRB2), Shc (p-Shc), Ras (p-Ras), GABl (p-GABl), SHP2 (p-SHP2),
WO 54987
GRB2 (p-GRBZ), CRKL (p-CRKL), PLCy y), PKC (e.g., p-PKCOL, p-PKCB, p-
PKCS), adducin (p-adducin), RBl (p-RBl), and PYK2 (p-PYKZ).
The following tables e additional examples of signal transduction molecules
for which total levels and/or activation (e.g., phosphorylation) levels can be determined in a
sample (e.g., alone or in combination with kers from other categories) to aid or assist
in predicting disease course, selecting an appropriate anti-TNF drug therapy, optimizing anti-
TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, or monitoring
the efficacy of therapeutic treatment with an anti-TNF drug.
\\\\\\\\\\\\\\\\\x w\KW\ §\\\\\\\\\\\> \\§§
The Collaborative Enzyme Enhanced Reactive ImmunoAssay , also known
as the Collaborative Proximity Immunoassay (COPIA), is described in the following patent
documents which are herein incorporated by reference in their entirety for all purposes: PCT
ation No. ; PCT Publication No. WO 12140; PCT Publication
No. ; PCT Publication No. WO 32723; PCT Publication No. WO
2011/008990; and PCT Application No. PCT/U820 l 0/053386, filed r 20, 2010.
G. Elimination Rate Constant
In certain embodiments, a marker for the disease activity profile (DAP) is kel, or the
elimination rate constant of an antibody such as an anti-TNF antibody (e.g., infliximab). The
determination of an elimination rate constant such as kel is particularly useful in the methods
of the invention for personalized therapeutic management by selecting therapy, optimizing
y, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment with one or
more therapeutic agents such as biologics (e.g, anti-TNF drugs).
[0241] In certain instances, a differential equation can be used to model drug elimination
from the patient. In certain instances, a two-compartment PK model can be used. In this
instance, the equation for the drug in the central compartment ing intravenous bolus
stration is:
:--‘- ----- 2211.12; 212 . X1 22.; . X2
The kel 0 X1 term describes elimination of the drug from the l compartment,
while the k12 0 X1 and k21 0 X2 terms describe the distribution of drug between the central
and peripheral tments.
H. Genetic Markers
The ination of the presence or absence of allelic variants (e.g., SNPs) in one
or more genetic markers in a sample (e.g., alone or in combination with biomarkers from
other categories) is also useful in the methods of the present invention to aid or assist in
predicting disease , selecting an appropriate anti-TNF drug therapy, optimizing anti-
TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, or monitoring
the efficacy of therapeutic treatment with an anti-TNF drug.
Non-limiting examples of genetic markers include, but are not limited to, any of the
atory pathway genes and corresponding SNPs that can be genotyped as set forth in
Table l (e.g., a NOD2/CARD15 gene, an L23 pathway gene, eta). Preferably, the
ce or absence of at least one allelic variant, e.g, a single nucleotide polymorphism
(SNP), in the NOD2/CARD15 gene and/or one or more genes in the ILl2/IL23 pathway is
determined. See, e. g., Barrett et al., Nat. Genet., 40:955-62 (2008) and Wang et al., Amer. J.
Hum. Genet., 84:399-405 (2009).
Table 1
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Additional SNPs useful in the present invention include, e.g, 62,
rs9286879, rsl 1584383, rs7746082, rsl456893, r5155 1398, rsl7582416, rs3764l47,
rs1736135, rs4807569, rs7758080, and rs8098673. See, e.g., Barrett et al., Nat. ,
40:955-62 (2008).
In particular embodiments, the presence or absence of one or more mutations in one
or more of the ing genetic markers is determined: inflammatory pathway genes, e.g,
the ce or e of t alleles (e.g, SNPs) in one or more inflammatory markers
such as, e. g., ARD15 (e. g., SNP 8, SNP 12, and/or SNP 13 described in US Patent
No. 7,592,437), ATG16L1 (e. g., the rs2241880 (T300A) SNP bed in Lakatos et al.,
Digestive and Liver Disease, 40 (2008) 867-873), IL23R (e.g., the rs11209026 (R381Q) SNP
described in Lakatos et al.), the human leukocyte antigen (HLA) genes and/or ne genes
described in, e. g., Gasche et al. (Eur. J. Gastroenterology & Hepatology, (2003) 15:599-
606), and the DLG5 and/or OCTN genes from the IBD5 locus.
1. NOD2/CARD15
The determination of the presence or absence of allelic variants such as SNPs in the
NOD2/CARD15 gene is particularly useful in the present invention. As used herein, the term
“NOD2/CARD15 variant” or “NOD2 variant” includes a nucleotide sequence of a NOD2
gene containing one or more changes as compared to the wild-type NOD2 gene or an amino
acid sequence of a NOD2 polypeptide containing one or more changes as compared to the
wild-type NOD2 polypeptide sequence. NOD2, also known as CARD15, has been localized
to the IBD1 locus on chromosome 16 and identified by positional-cloning (Hugot et al.,
Nature, 411:599-603 ) as well as a positional candidate gene strategy (Ogura et al.,
Nature, 411:603-606 (2001); Hampe et al., Lancet, 357: 928 (2001)). The IBD1 locus
has a high oint e score (MLS) for inflammatory bowel disease (MLS=5.7 at
marker D16S411 in 16q12). See, e.g., Cho et al., Inflamm. Bowel Dis., 3:186-190 (1997);
Akolkar et al., Am. J. Gastroenterol., 96: 1 127-1 132 (2001); Ohmen et al., Ham. M01. Genet.,
5: 1679-1683 (1996); Parkes et al., Lancet, 348: 1588 (1996); Cavanaugh et al., Ann. Hum.
Genet., 62:291-8 (1998); Brant et al., Gastr0enter010gy, 115 : 1056-1061 (1998); Curran et al.,
Gastr0enter010gy, 115:1066-1071 (1998); Hampe et al. , Am. J. Hum. Genet. , 64:808-816
(1999); and Annese et al., Eur. J. Hum. Genet., 7:567-573 (1999).
The mRNA (coding) and polypeptide sequences of human NOD2 are set forth in,
e. g., Genbank Accession Nos. NM_022162 and NP_071445, respectively. In addition, the
complete ce of human chromosome 16 clone RP11-327F22, which includes NOD2, is
set forth in, e.g., k Accession No. AC007728. Furthermore, the sequence ofNOD2
from other species can be found in the GenBank database.
The NOD2 protein contains terminal caspase recruitment domains (CARDS),
which can activate NF-kappa B (NF-kB), and l carboxy-terminal leucine-rich repeat
domains (Ogura et al., J. Biol. Chem, 276:4812-4818 (2001)). NOD2 has structural
homology with the apoptosis regulator Apaf-1/CED-4 and a class of plant disease resistant
gene products (Ogura et al., supra). Similar to plant disease resistant gene ts, NOD2
has an amino-terminal effector domain, a nucleotide-binding domain and leucine rich repeats
(LRRs). Wild-type NOD2 activates nuclear factor NF-kappa B, making it responsive to
bacterial lipopolysaccharides (LPS; Ogura et al., supra; Inohara et al., J. Biol. Chem,
51-2554 (2001). NOD2 can function as an intercellular receptor for LPS, with the
leucine rich repeats required for responsiveness.
Variations at three single nucleotide polymorphisms in the coding region ofNOD2
have been previously described. These three SNPs, designated R702W (“SNP 8”), G908R
(“SNP 12”), and 1007fs (“SNP 13”), are located in the carboxy-terminal region of the NOD2
gene (Hugot et al., . A fiarther description of SNP 8, SNP 12, and SNP 13, as well as
additional SNPs in the NOD2 gene suitable for use in the invention, can be found in, e.g.,
US. Patent Nos. 6,835,815; 6,858,391; and 7,592,437; and US. Patent Publication Nos.
20030190639, 20050054021, and 72180.
In some embodiments, a NOD2 variant is located in a coding region of the NOD2
locus, for example, within a region encoding l leucine-rich repeats in the carboxy-
terminal portion of the NOD2 polypeptide. Such NOD2 variants located in the e-rich
repeat region ofNOD2 include, t limitation, R702W (“SNP 8”) and G908R (“SNP
12”). A NOD2 variant useful in the invention can also encode a NOD2 polypeptide with
reduced ability to activate NF-kappa B as compared to NF-kappa B activation by a wild-type
NOD2 polypeptide. As a non-limiting example, the NOD2 variant 1007fs (“SNP 13”) results
in a truncated NOD2 polypeptide which has reduced ability to induce pa B in
response to LPS stimulation (Ogura et al., Nature, 411:603-606 (2001)).
A NOD2 variant useful in the invention can be, for example, R702W, G908R, or
1007fs. R702W, G908R, and 1007fs are located within the coding region . In one
embodiment, a method of the invention is practiced with the R702W NOD2 variant. As used
herein, the term “R702W” includes a single nucleotide polymorphism within exon 4 of the
NOD2 gene, which occurs within a triplet encoding amino acid 702 of the NOD2 protein.
The wild-type NOD2 allele contains a cytosine (c) residue at position 138,991 of the
AC007728 sequence, which occurs within a triplet encoding an arginine at amino acid702.
The R702W NOD2 variant ns a thymine (t) e at position 138,991 of the
2012/037375
AC007728 sequence, resulting in an arginine (R) to tryptophan (W) tution at amino
acid 702 of the NOD2 protein. Accordingly, this NOD2 variant is denoted “R702W” or
“702W” and can also be denoted ” based on the earlier ing system of Hugot
et al., supra. In addition, the R702W variant is also known as the “SNP 8” allele or a “2”
allele at SNP 8. The NCBI SNP ID number for R702W or SNP 8 is rs2066844. The
presence of the R702W NOD2 variant and other NOD2 variants can be conveniently
detected, for example, by allelic discrimination assays or sequence analysis.
A method of the invention can also be practiced with the G908R NOD2 variant. As
used herein, the term “G908R” includes a single nucleotide polymorphism within exon 8 of
the NOD2 gene, which occurs within a triplet encoding amino acid 908 of the NOD2 n.
Amino acid 908 is located within the leucine rich repeat region of the NOD2 gene. The wild-
type NOD2 allele ns a guanine (g) residue at position 7 of the AC007728
sequence, which occurs within a triplet encoding glycine at amino acid 908. The G908R
NOD2 variant contains a cytosine (c) residue at position 128,377 of the AC007728 sequence,
resulting in a glycine (G) to arginine (R) substitution at amino acid 908 of the NOD2 protein.
Accordingly, this NOD2 variant is denoted “G908R” or “908R” and can also be denoted
“G88 1R” based on the earlier numbering system of Hugot et al., supra. In addition, the
G908R variant is also known as the “SNP 12” allele or a “2” allele at SNP 12. The NCBI
SNP ID number for G908R SNP 12 is 845.
[0254] A method of the invention can also be practiced with the 1007fs NOD2 variant.
This variant is an insertion of a single nucleotide that results in a frame shift in the tenth
leucine-rich repeat of the NOD2 n and is followed by a premature stop codon. The
resulting truncation of the NOD2 protein appears to prevent activation ofNF-kappaB in
response to bacterial lipopolysaccharides (Ogura et al., supra). As used herein, the term
“1007fs” includes a single nucleotide polymorphism within exon 11 of the NOD2 gene,
which occurs in a triplet encoding amino acid 1007 of the NOD2 protein. The 1007fs t
contains a cytosine which has been added at position 9 of the AC007728 sequence,
resulting in a frame shift mutation at amino acid 1007. Accordingly, this NOD2 variant is
denoted “1007fs” and can also be denoted “3020insC” or ” based on the earlier
numbering system of Hugot et al., supra. In on, the 1007fs NOD2 variant is also
known as the “SNP 13” allele or a “2” allele at SNP 13. The NCBI SNP ID number for
1007fs or SNP 13 is rs2066847.
One skilled in the art recognizes that a particular NOD2 variant allele or other
polymorphic allele can be conveniently defined, for example, in comparison to a Centre
d’Etude du Polymorphisme Humain (CEPH) reference individual such as the dual
designated 1347-02 (Dib et al., Nature, 380: 152-154 (1996)), using cially available
reference DNA obtained, for example, from PE Biosystems (Foster City, CA). In addition,
specific information on SNPs can be ed from the dbSNP of the National Center for
Biotechnology Information (NCBI).
A NOD2 variant can also be located in a non-coding region of the NOD2 locus.
Non-coding regions include, for example, intron sequences as well as 5’ and 3’ untranslated
sequences. A non-limiting example of a NOD2 t allele located in a non-coding region
of the NOD2 gene is the JWl variant, which is bed in Sugimura et al., Am. J. Hum.
Genet., 72:509-518 (2003) and US. Patent Publication No. 20070072180. Examples of
NOD2 variant alleles located in the 3 ’ untranslated region of the NOD2 gene include, without
limitation, the JW15 and JW16 t alleles, which are described in US. Patent ation
No. 20070072180. Examples ofNOD2 variant alleles located in the 5’ slated region
(e.g., promoter region) of the NOD2 gene include, without limitation, the JW17 and JW18
variant alleles, which are described in US. Patent Publication No. 20070072180.
As used herein, the term “JWl variant allele” includes a genetic variation at
nucleotide 158 of ening sequence 8 (intron 8) of the NOD2 gene. In relation to the
AC007728 sequence, the JWl variant allele is located at position 128,143. The genetic
variation at nucleotide 158 of intron 8 can be, but is not limited to, a single nucleotide
substitution, multiple nucleotide substitutions, or a deletion or ion of one or more
nucleotides. The ype sequence of intron 8 has a cytosine at position 158. As non-
limiting examples, a JWl variant allele can have a cytosine (c) to adenine (a), cytosine (c) to
guanine (g), or cytosine (c) to thymine (t) substitution at nucleotide 158 of intron 8. In one
ment, the JWl variant allele is a change from a cytosine (c) to a thymine (t) at
nucleotide 158 ofNOD2 intron 8.
The term “JW15 variant allele” includes a genetic variation in the 3’ untranslated
region ofNOD2 at nucleotide position 118,790 of the AC007728 sequence. The genetic
variation at nucleotide 118,790 can be, but is not limited to, a single nucleotide substitution,
multiple nucleotide substitutions, or a deletion or insertion of one or more nucleotides. The
wild-type sequence has an e (a) at position 118,790. As non-limiting es, a
JW15 variant allele can have an adenine (a) to cytosine (c), adenine (a) to guanine (g), or
e (a) to thymine (t) tution at nucleotide 118,790. In one embodiment, the JW15
variant allele is a change from an adenine (a) to a cytosine (c) at nucleotide 118,790.
As used herein, the term “JW16 t allele” includes a genetic variation in the 3’
untranslated region ofNOD2 at nucleotide position 118,031 of the 28 sequence. The
genetic variation at nucleotide 118,031 can be, but is not limited to, a single nucleotide
tution, multiple nucleotide substitutions, or a on or insertion of one or more
nucleotides. The wild-type sequence has a guanine (g) at position 1. As non-limiting
examples, a JW16 variant allele can have a guanine (g) to cytosine (c), guanine (g) to adenine
(a), or guanine (g) to thymine (t) substitution at nucleotide 1. In one embodiment, the
JW16 variant allele is a change from a guanine (g) to an adenine (a) at nucleotide 118,031.
The term “JW17 variant allele” includes a genetic variation in the 5 ’ untranslated
region ofNOD2 at nucleotide position 154,688 of the 28 sequence. The genetic
variation at nucleotide 154,688 can be, but is not limited to, a single nucleotide substitution,
multiple nucleotide tutions, or a deletion or insertion of one or more nucleotides. The
wild-type sequence has a cytosine (c) at on 154,688. As non-limiting examples, a JW17
variant allele can have a cytosine (c) to guanine (g), cytosine (c) to adenine (a), or ne
(c) to thymine (t) substitution at nucleotide 154,688. In one embodiment, the JW17 variant
allele is a change from a cytosine (c) to a thymine (t) at nucleotide 154,688.
As used herein, the term “JWl 8 variant allele” includes a genetic variation in the 5 ’
untranslated region ofNOD2 at nucleotide position 154,471 of the AC007728 sequence. The
genetic variation at nucleotide 154,471 can be, but is not limited to, a single nucleotide
substitution, multiple nucleotide substitutions, or a on or ion of one or more
tides. The wild-type sequence has a cytosine (c) at position 154,471. As non-limiting
examples, a JW18 variant allele can have a cytosine (c) to guanine (g), cytosine (c) to adenine
(a), or cytosine (c) to thymine (t) substitution at nucleotide 154,471. In one embodiment, the
JW18 variant allele is a change from a cytosine (c) to a thymine (t) at nucleotide 154,471.
[0262] It is understood that the s of the invention can be practiced with these or
other NOD2 variant alleles located in a coding region or non-coding region (e.g., intron or
promoter region) of the NOD2 locus. It is further understood that the methods of the
invention can e determining the ce of one, two, three, four, or more NOD2
variants, including, but not limited to, the SNP 8, SNP 12, and SNP 13 alleles, and other
coding as well as non-coding region variants.
11. tical Analysis
In some aspects, the present invention provides methods for selecting anti-TNF drug
therapy, optimizing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug
y, and/or monitoring the efficacy of anti-TNF drug ent by applying a statistical
thm to one or more (e.g, a combination of two, three, four, five, six, seven, or more)
biochemical markers, serological s, and/or genetic markers to generate a disease
activity profile (DAP). In particular embodiments, quantile analysis is applied to the
presence, level, and/or genotype of one or more markers to guide treatment decisions for
patients receiving anti-TNF drug therapy. In other embodiments, one or a combination of
two ofmore learning statistical classifier systems are applied to the presence, level, and/or
genotype of one or more s to guide treatment decisions for patients receiving anti-TNF
drug therapy. The statistical es of the methods of the present invention ageously
provide improved sensitivity, specificity, negative predictive value, positive predictive value,
and/or overall accuracy for selecting an initial anti-TNF drug therapy and for ining
when or how to adjust or modify (e.g., increase or decrease) the subsequent dose of an anti-
TNF drug, to combine an NF drug (e.g., at an increased, decreased, or same dose) with
one or more immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA),
and/or to change the t course of therapy (e.g., switch to a different anti-TNF drug).
The term “statistical analysis” or “statistical algorithm” or stical process”
es any of a y of statistical methods and models used to determine relationships
between variables. In the present invention, the variables are the presence, level, or genotype
of at least one marker of interest. Any number of markers can be analyzed using a statistical
analysis described herein. For example, the ce or level of l, 2, 3, 4, 5, 6, 7, 8, 9, 10,
ll, 12, l3, 14, 15, l6, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more markers can be
included in a statistical analysis. In one embodiment, logistic sion is used. In another
embodiment, linear regression is used. In yet another embodiment, ordinary least squares
regression or unconditional logistic regression is used. In certain preferred embodiments, the
statistical analyses of the present invention comprise a quantile measurement of one or more
markers, e.g., within a given population, as a variable. Quantiles are a set of “cut points” that
divide a sample of data into groups containing (as far as possible) equal numbers of
observations. For example, quartiles are values that divide a sample of data into four groups
containing (as far as possible) equal numbers of observations. The lower quartile is the data
value a quarter way up through the ordered data set; the upper quartile is the data value a
quarter way down through the ordered data set. les are values that divide a sample of
data into five groups containing (as far as possible) equal numbers of observations. The
present invention can also include the use of tile ranges of marker levels (e.g., tertiles,
quartile, quintiles, etc), or their cumulative indices (e. g., quartile sums of marker levels to
obtain quartile sum scores (QSS), etc.) as variables in the statistical analyses (just as with
continuous variables).
In n embodiments, the present invention involves detecting or determining the
presence, level (e.g., magnitude), and/or genotype of one or more markers of interest using
quartile analysis. In this type of statistical analysis, the level of a marker of interest is defined
as being in the first quartile (<25%), second quartile %), third quartile (5 l%-<75%), or
fourth le (75-100%) in on to a reference database of samples. These quartiles may
be ed a quartile score of l, 2, 3, and 4, respectively. In n instances, a marker that
is not detected in a sample is assigned a quartile score of 0 or 1, while a marker that is
detected (e. g., present) in a sample (e.g., sample is positive for the marker) is assigned a
quartile score of 4. In some embodiments, quartile 1 represents samples with the lowest
marker levels, while quartile 4 represent samples with the highest marker levels. In other
embodiments, quartile 1 represents samples with a particular marker genotype (e.g., wild-
type allele), while quartile 4 ent samples with another particular marker genotype (e.g.,
allelic variant). The reference database of samples can include a large spectrum of patients
with a TNFu-mediated disease or disorder such as, e.g., IBD. From such a database, quartile
cut-offs can be established. A non-limiting example of quartile is suitable for use in
the present invention is described in, e.g., Mow et al., Gastroenterology, l26:4l4-24 (2004).
In some embodiments, the statistical analyses of the present invention comprise one
or more learning statistical classifier s. As used herein, the term “learning statistical
classifier system” includes a machine learning algorithmic technique capable of adapting to
complex data sets (e.g, panel of markers of st) and making decisions based upon such
data sets. In some embodiments, a single learning statistical classifier system such as a
decision/classification tree (e.g., random forest (RF) or classification and regression tree
(C&RT)) is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more
ng statistical classifier systems are used, preferably in tandem. Examples of learning
tical classifier systems e, but are not limited to, those using inductive learning
(e.g., decision/classification trees such as random forests, classification and regression trees
(C&RT), boosted trees, etc), Probably Approximately Correct (PAC) learning, connectionist
learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks
(NFN), k structures, the Cox Proportional-Hazards Model (CPHM), perceptrons such
as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural
networks, an learning in belief networks, eta), reinforcement learning (e.g., passive
learning in a known environment such as na'ive learning, adaptive dynamic learning, and
temporal difference learning, passive learning in an unknown nment, active learning in
an unknown environment, learning action-value fianctions, applications ofreinforcement
learning, etc), and genetic thms and evolutionary programming. Other learning
tical classifier systems include support vector es (e.g., Kernel s),
multivariate ve regression splines (MARS), Levenberg-Marquardt algorithms, Gauss-
Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector
quantization (LVQ).
Random forests are learning statistical classifier systems that are constructed using
an algorithm developed by Leo Breiman and Adele Cutler. Random forests use a large
number of dual decision trees and decide the class by choosing the mode (i.e., most
frequently ing) of the classes as ined by the individual trees. Random forest
analysis can be med, e.g., using the RandomForests software available from Salford
Systems (San Diego, CA). See, e.g., Breiman, Machine Learning, 455-32 (2001); and
http://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm, for a ption
ofrandom forests.
Classification and regression trees ent a computer intensive alternative to
fitting classical regression models and are typically used to determine the best possible model
for a categorical or continuous response of interest based upon one or more predictors.
Classification and regression tree analysis can be performed, e.g., using the C&RT software
available from Salford Systems or the Statistica data analysis software available from
StatSoft, Inc. (Tulsa, OK). A description of classification and regression trees is found, e.g.,
in Breiman et al. “Classification and Regression Trees,” Chapman and Hall, New York
(1984); and Steinberg et al., “CART: Tree-Structured Non-Parametric Data Analysis,”
Salford Systems, San Diego, (1995).
[0269] Neural networks are interconnected groups of artificial neurons that use a
mathematical or computational model for information processing based on a connectionist
approach to computation. Typically, neural networks are adaptive systems that change their
ure based on external or internal information that flows h the network. Specific
examples of neural networks include feed-forward neural networks such as perceptrons,
-layer perceptrons, multi-layer trons, backpropagation networks, E
networks, MADALINE networks, Leammatrix networks, radial basis fianction (RBF)
networks, and self-organizing maps or Kohonen self-organizing networks; recurrent neural
networks such as simple recurrent networks and Hopfield networks; stochastic neural
networks such as Boltzmann machines; modular neural networks such as committee of
machines and associative neural networks; and other types of networks such as
instantaneously trained neural networks, spiking neural networks, dynamic neural networks,
and cascading neural networks. Neural k analysis can be performed, e.g., using the
Statistica data analysis software available from StatSoft, Inc. See, e.g., Freeman et al., In
“Neural Networks: Algorithms, Applications and Programming Techniques,” Addison-
Wesley hing Company ; Zadeh, Information and Control, 8:338-353 (1965);
Zadeh, “IEEE Trans. on Systems, Man and etics,” 3:28-44 ; Gersho et al., In
“Vector Quantization and Signal Compression,” Kluywer Academic Publishers, Boston,
cht, London (1992); and Hassoun, “Fundamentals of Artificial Neural ks,”
MIT Press, Cambridge, Massachusetts, London (1995), for a description of neural networks.
t vector machines are a set of related supervised learning techniques used for
classification and regression and are described, e.g., in Cristianini et al., “An Introduction to
Support Vector Machines and Other Kemel-Based Learning Methods,” Cambridge
University Press (2000). Support vector machine analysis can be performed, e.g., using the
t software developed by Thorsten Joachims (Cornell University) or using the
LIBSVM software developed by Chih-Chung Chang and Chih-Jen Lin (National Taiwan
University).
The various statistical methods and models described herein can be trained and
tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy
individuals and patients with a TNFu-mediated disease or disorder such as, e.g., IBD (e.g.,
CD and/or UC). For example, samples from patients diagnosed by a physician, ably by
a gastroenterologist, as having IBD or a clinical e f using a biopsy, colonoscopy,
or an immunoassay as described in, e.g., US. Patent No. 6,218,129, are suitable for use in
training and testing the statistical methods and models of the t invention. s
from patients diagnosed with IBD can also be stratified into Crohn’s disease or ulcerative
colitis using an assay as bed in, e.g., US. Patent Nos. 5,750,355 and 5,830,675.
Samples from healthy individuals can include those that were not identified as IBD samples.
One skilled in the art will know of additional techniques and diagnostic criteria for obtaining
a cohort of patient samples that can be used in training and testing the statistical methods and
models of the present invention.
As used herein, the term “sensitivity” includes the probability that a method of the
t invention for selecting anti-TNF drug therapy, optimizing anti-TNF drug y,
reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of
anti-TNF drug treatment gives a positive result when the sample is positive, e.g., having the
predicted therapeutic response to NF drug y or toxicity associated with anti-TNF
drug therapy. ivity is calculated as the number of true positive results divided by the
sum of the true positives and false negatives. Sensitivity essentially is a measure ofhow well
the present invention correctly identifies those who have the predicted eutic response to
anti-TNF drug therapy or toxicity associated with anti-TNF drug y from those who do
not have the ted therapeutic response or toxicity. The statistical methods and models
can be selected such that the ivity is at least about 60%, and can be, e.g., at least about
65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,
89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0273] The term “specificity” includes the probability that a method of the present
invention for selecting anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing
toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of anti-TNF
drug treatment gives a negative result when the sample is not positive, e.g., not having the
predicted therapeutic response to anti-TNF drug therapy or toxicity associated with anti-TNF
drug therapy. Specificity is calculated as the number of true ve s divided by the
sum of the true negatives and false positives. Specificity essentially is a measure of how well
the present invention excludes those who do not have the predicted therapeutic response to
anti-TNF drug therapy or toxicity associated with anti-TNF drug therapy from those who do
have the predicted therapeutic response or toxicity. The statistical methods and models can
be selected such that the specificity is at least about 60%, and can be, e.g., at least about 65%,
70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,
90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
The term “negative predictive value” or “NPV” es the probability that an
individual identified as not having the predicted eutic response to anti-TNF drug
therapy or toxicity ated with anti-TNF drug therapy actually does not have the
predicted therapeutic response or toxicity. Negative predictive value can be calculated as the
number of true negatives divided by the sum of the true negatives and false negatives.
ve predictive value is determined by the characteristics of the methods of the present
invention as well as the prevalence of the disease in the population analyzed. The statistical
methods and models can be selected such that the negative predictive value in a population
having a disease ence is in the range of about 70% to about 99% and can be, for
example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,
86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
2012/037375
The term “positive predictive value” or “PPV” includes the probability that an
individual fied as having the predicted therapeutic response to anti-TNF drug therapy or
toxicity ated with anti-TNF drug therapy actually has the predicted therapeutic
response or toxicity. Positive predictive value can be calculated as the number of true
positives divided by the sum of the true positives and false positives. ve predictive
value is determined by the characteristics of the methods of the present ion as well as
the prevalence of the disease in the population analyzed. The tical methods and models
can be selected such that the ve predictive value in a population having a disease
prevalence is in the range of about 70% to about 99% and can be, for example, at least about
70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%,
90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
Predictive values, including ve and positive predictive , are influenced
by the prevalence of the disease in the population analyzed. In the present ion, the
statistical methods and models can be selected to produce a desired clinical parameter for a
clinical population with a particular prevalence for a TNFu-mediated disease or disorder such
as, e.g, IBD. As a non-limiting example, statistical methods and models can be selected for
an IBD prevalence ofup to about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%,
%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, or 70%, which can be seen, e.g., in a
clinician’s office such as a gastroenterologist’s office or a general practitioner’s office.
[0277] As used herein, the term “overall agreement” or “overall accuracy” includes the
accuracy with which a method of the present invention selects anti-TNF drug therapy,
optimizes anti-TNF drug therapy, reduces toxicity associated with anti-TNF drug y,
and/or monitors the y of anti-TNF drug treatment. Overall accuracy is calculated as
the sum of the true positives and true negatives divided by the total number of sample results
and is affected by the prevalence of the disease in the population ed. For example, the
statistical methods and models can be selected such that the overall accuracy in a patient
population having a disease prevalence is at least about 40%, and can be, e.g., at least about
40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%,
56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%,
72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,
88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
III. Examples
The present invention will be described in greater detail by way of specific
examples. The following examples are offered for illustrative purposes, and are not intended
to limit the invention in any manner. Those of skill in the art will readily recognize a variety
of noncritical parameters which can be changed or modified to yield essentially the same
results.
The Examples set forth in US. Provisional Application No. 61/444,097, filed
February 17, 2011, and PCT Application No. l25, flled October 26, 2010,
are hereby incorporated by reference in their entirety for all es.
Example 1. Disease Activity ng for Identifying Responders and Non-Responders
to NFa Biologics.
This example describes methods for personalized therapeutic management of a
TNFd-mediated e in order to optimize therapy or monitor therapeutic efficacy in a
subject using the disease activity ng of the present invention to identify ts as
responders or non-responders to anti-TNF drug therapy.
Figure 1 rates an ary IBD wound response profile in which wound
progression is divided into inflammatory, proliferative, and remodeling phases. As non-
limiting examples, inflammatory response phase markers tested include: anti-TNF drugs
such as Remicade (infliximab); anti-drug antibodies (ADA) such as HACA; inflammatory
markers such as GM-CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-(x, and sTNF RH; and anti-
inflammatory markers such as IL-l2p70 and IL-10. miting examples of eration
response phase markers tested include tissue repair/remodeling factors (also referred to as
mucosal healing markers) such as AREG, EREG, HB-EGF, HGF, NRGl
, NRG2, NRG3,
NRG4, BTC, EGF, IGF, TGF-oc, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGFl, FGF2,
FGF7, FGF9, and TWEAK.
A COMMIT (Combination Of Maintenance Methotrexate-Infliximab Trial) study
was performed to evaluate the safety and efficacy of Remicade (infliximab) in combination
with methotrexate for the long-term treatment of Crohn’s disease (CD). Treatment success
was defined by the proportion of subjects in clinical remission (i.e., complete discontinuation
of prednisone therapy and a s Disease Activity Index (CDAI) score of <150) at week
14, and maintenance of clinical ion between study weeks 14 and 50. In particular,
clinical ment with CDAI was performed at week 0, 46, 50, and 66. Subjects with
CDAI > 150 were identified as sponders. Additional information on the COMMIT
study is provided at htt ://www.clinicaltrials. Iov/th/show/NCTQOl32899, the disclosure of
which is incorporated by reference in its entirety for all purposes.
e activity profiling was performed on a number of subjects in the COMMIT
study. In particular, the following array of markers were measured at various time points
during treatment with Remicade (infliximab) only or a ation of Remicade (infliximab)
with methotrexate: (l) Remicade (infliximab) and HACA; (2) inflammatory markers GM-
CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-(x, and sTNF R11; (3) anti-inflammatory markers
IL-12p70 and IL-lO; and (4) tissue repair markers EGF, bFGF, PIGF, sFltl, and VEGF. The
disease ty profile (DAP) for 7 of these subjects, which provides a comparison between
responder and non-responder profiles, is illustrated . These patient examples show that
markers for ation and tissue repair correlated with infliximab and HACA levels in
select active CD patients, certain markers may predict the disease activity profile, and disease
ty profiling will filrther guide patient therapy and identify mucosal healing markers. In
addition, these t examples show that whenever anti-inflammatory cytokines such as IL-
12p70 and IL-10 are elevated, the patient responds, indicating that they may be markers of
mucosal healing, and that tissue repair markers (TRM) go up in non-responders.
Table of Personalized Disease Activity Profiling: Levels of IFX, HACA, Inflammatory
Markers, Anti-Inflammatory s, and Mucosal Healing Markers
Patient Treatment Clinical Inflammatory Anti- Mucosal
ID No. Regimen Definition Markers inflammatory g
Markers Markers
12209 IFX+ MTX t=O, CDAI was Non- HIGH LOW MEDIUM
202. responder
t=wk 26, CDAI
was 183
t=wk 66,
CDAI=152.
11010 t=O, CDAI was Responder HIGH HIGH
262.
t=wk 46, CDAI
was 85.
10118 t=O, CDAI was Responder MEDIUM HIGH HIGH
251.
t=wk 46, CDAI
was 109.
11602 IFX+ MTX t=O, CDAI was der HIGH HIGH
217.
t=wk 46, CDAI
was 68.
11505 t=O, CDAI was Non- Very Low . MEDIUM LOW HIGH
272. responder at trough
t=wk 46, CDAI (wk 14)
was 145.
t=wk 66,
CDA|=195.
11601 IFX + MTX t=O, CDAI was Responder High at HACA+. HIGH HIGH MEDIUM
207. LOW
t=wk 46, CDAI
was 0.
IFX=infliximab. thotrexate. ND = no detectable level of HACA.
Patient 12209: Infliximab + Methotrexate MTX Treated.
CDAI at time 0 was 202. At week 46, CDAI was 183 (“Delta 19” or 202-19=183).
At week 66, CDAI was 152 (“Delta 50” or 202-50=152). Clinically defined as non-responder.
Disease activity profile (DAP) accurately identified this patient. In particular, DAP showed
that this patient had low infliximab (IFX) levels at trough (“T”; Week 14), the presence of a
able concentration level ofHACA (“HACA +”), high inflammatory marker levels, low
anti-inflammatory marker levels, and medium tissue repair marker (TRM) levels. Suggested
alternative treatment options may include, for example, increasing the dose of IFX, switching
to therapy with adalimumab (HUMIRAT'V'), treating with a different immunosuppressive drug
such as azathioprine (AZA), and/or switching to therapy with a drug that targets a ent
mechanism (e.g., an anti-INFy antibody such as fontolizumab).
Patient 1 1010: Infliximab Treated.
[0285] CDAI at week 0 was 262. At week 46, CDAI was 85 (“Delta 177” or 262-177=85).
Clinical responder. Disease activity profile (DAP) accurately identified this patient. In
ular, DAP showed that this patient had high infliximab (IFX) levels at trough (“T”;
Week 14), no detectable level ofHACA (“HACA --”), low inflammatory marker levels, high
anti-inflammatory marker levels, and high tissue repair marker (TRM) levels. For example,
anti-inflammatory cytokines IL-12p70 and IL-10 were high. As shown with the patients in
this example, whenever anti-inflammatory cytokines were high, the patient responded most
probably with mucosal g. In on, bFGF concentration levels were low at all time
points, although other TRM levels were high, indicating that tissue growth was muted, such
that tissue repair had already occurred.
Patient 101 18: Infliximab Treated.
CDAI at week 0 was 251. At week 46, CDAI was 109 (“Delta 142” or 251-
142=109). al responder. Disease ty profile (DAP) accurately identified this
patient. In ular, DAP showed that this patient had high mab (IFX) levels at
trough (“T”; Week 14), no detectable level ofHACA (“HACA --”), medium inflammatory
marker levels, high anti-inflammatory marker levels, and high tissue repair marker (TRM)
levels. For example, nflammatory cytokines IL-12p70 and IL-10 were high. Again, as
shown with the patients in this example, whenever anti-inflammatory cytokines were high,
the patient ded most probably with mucosal healing. In addition, bFGF concentration
levels were low at all time points and remained fiat over the course of therapy, although other
TRM levels were higher, indicating that tissue growth was muted, such that tissue repair had
already occurred.
Patient 11602: Infiiximab + Methotrexate MTX Treated.
CDAI at week 0 was 217. At week 46, CDAI was 68 (“Delta 149” or 217-149=68).
Clinical responder. Disease activity profile (DAP) accurately identified this patient. In
particular, DAP showed that this patient had high infiiximab (IFX) levels at trough (“T”;
Week 14), no detectable level ofHACA (“HACA --”), low inflammatory marker levels, high
anti-inflammatory marker levels, and high tissue repair marker (TRM) levels. For example,
nflammatory cytokines IL-12p70 and IL-10 were high. Again, as shown with the
patients in this example, whenever anti-inflammatory nes were high, the t
ded most probably with mucosal healing. In addition, bFGF concentration levels were
lower at all time points compared to the other TRM levels, indicating that tissue growth was
muted, such that tissue repair had already occurred.
Patient 11505: Infiiximab Treated.
[0288] CDAI at time 0 was 272. At week 46, CDAI was 145 (“Delta 127” or 7 =
145). At week 66, CDAI was 195. ally defined as non-responder. Disease activity
profile (DAP) accurately identified this patient. In particular, DAP showed that this patient
had very low infiiximab (IFX) levels at trough (“T”; Week 14), a high concentration level of
HACA (“HACA ++”), medium inflammatory marker levels, low anti-inflammatory marker
levels, and high tissue repair marker (TRM) levels. In non-responders, the levels of TRM
such as bFGF go up, while in responders they either go down or do not change. ted
alternative treatment options may include, for e, increasing the dose of IFX, switching
to therapy with adalimumab (HUMIRAT'V'), treating with an immunosuppressive drug such as
MTX or oprine (AZA), and/or switching to y with a drug that targets a different
mechanism (e.g., an anti-INFy antibody such as fontolizumab).
Patient 11601: Infiiximab + Methotrexate MTX Treated.
CDAI at week 0 was 207. At week 46, CDAI was 0 (“Delta 207” or 207-207=0).
The patient was clinically defined as responder. . Disease activity profile (DAP) accurately
identified this t. In particular, DAP showed that this patient had high infliximab (IFX)
levels at trough (“T”; Week 14), low HACA levels (“HACA +”), high inflammatory marker
levels, high anti-inflammatory marker levels, and medium tissue repair marker (TRM) levels.
For example, nflammatory cytokines IL-12p70 and IL-10 were high. Again, as shown
with the patients in this e, whenever anti-inflammatory cytokines were high, the
t responded most probably with l healing, clearly indicating that anti-
inflammatory markers are very important. The presence of high inflammation may be due to
complication.
Patient 101 13: Infliximab d.
[0290] CDAI at time 0 was 150. At week 46, CDAI was 96 (“Delta 54” or 150-54=96). At
visit 10 (“V10”), CDAI was 154, and at visit 11 (“V11”), CDAI was 169. As such, CDAI
started at 150 and stayed around 150. The patient was clinically defined as non-responder.
Disease activity profile (DAP) accurately identified this patient. In particular, DAP showed
that this patient had low infliximab (IFX) levels at trough (“T”; Week 14), a detectable
concentration level of HACA (“HACA +”), medium inflammatory marker levels, low anti-
inflammatory marker levels, and medium tissue repair marker (TRM) . Again, TRM
levels go up in non-responders, while in responders they either go down or do not change.
Suggested alternative treatment options may include, for example, increasing the dose of
IFX, switching to therapy with adalimumab (HUMIRAT'V'), treating with an
immunosuppressive drug such as MTX or oprine, and/or switching to y with a
drug that targets a different mechanism (6.g. , an anti-INFy antibody such as fontolizumab).
Example 2. Disease Activity Profiling ng.
An exemplary 3-dimensional graph rendering of the disease activity profile (DAP)
of the present invention includes each of the different markers t in the array of markers
on the x-axis, ized marker levels on the y-axis, and time on the z-axis (e.g., time points
wherein samples are taken and marker levels measured). An exemplary topographic map of
the DAP of the present invention (also referred to herein as a personalized disease profile)
includes each of the different markers present in the array of markers the y-axis, time on the
x-axis (e.g., time points wherein samples are taken and marker levels measured), and relative
marker levels in grayscale.
The 3D models described herein represent a novel paradigm for treatment because
they are individualized and able such that dose adjustments are made in a personalized
manner. For example, marker panels including markers such as inflammatory, proliferative,
2012/037375
and remodeling markers enable a determination in real-time of the best course of treatment
for a patient on therapy such as anti-TNF drug y, e.g., for treating CD or RA. As a
result, both the time course and the concentration levels of markers in the panel or array of
markers are ant for therapy ment and monitoring to personalize and individualize
therapy and determine optimal doses or dose adjustments. In n instances, the change in
one or more marker levels over time is an important consideration for therapy adjustment and
monitoring. In particular embodiments, the desired therapeutic zone for the set or a subset of
the markers in the array or panel is within a defined range in the 3D graph or topographic
map.
Example 3. Infliximab Non-Detection
This example represents a model for “time-to-event.” In other words, this example
uses the Cox Proportional-Hazards Model (CPHM) to model the time it takes for “an event”
to occur and the risk of such an event happening. The model is a sion analysis with
to-event” on the Y axis, which is a response variable, and “predictor variables” on the
X axis. In this example, the non-detection of infliximab (i.e., the concentration of infliximab
falling below a detection threshold) is the event, with the potential predictors of such an event
being biomarkers: e.g., CRP, IL-2, VEGF, and the like and or clinical ation such as
age, MTX treatment, gender, and the like.
In this e, the “Hazard” is the risk of infliximab not being detected (e.g., non-
ion) by an analytical assay such as a mobility shift assay. For example, Figure 11
shows infliximab concentration levels for various patients during their course of treatment.
An event occurs in this example when the concentration of infliximab falls below a
predetermined detection threshold. In certain instances, the CPHM is being used to predict
the risk of the event occurring (infliximab non-detection). The e also identifies
biomarkers indicative of such a risk occurring.
Using the CPHM, time is modeled until infliximab is not detectable by a mobility
shift assay. In the model, the predetermined threshold is 0.67 ug/mL, which is the lower
bound of the reference range. If the infliximab concentration level is less than the threshold
at time “t,” then the event has occurred at time “t.” In Figure 12, patients were ranked by
their time to the event. The event occurred for various patients at different points during
treatment and is denoted with a bullet point.
In the initial model, there were various markers and clinical information used to
predict the hazard or the risk of infliximab tection by the mobility shift assay. These
markers included the following markers in the Table:
PIGF Months since
dianosis
VEGF TNF-(x Disease @ small
intestine
G—CMM ——
[0297] From the initial marker list, the following list was derived as being the red
s indicative of the event:
sRNFRII Disease @ small
intestine
MM-M ——
The following Table lists the significant predictors of mab non-detection risk
or the hazard:
Predictor coef m_
GM-CSF -1.92E-01
IL-2 1.42E-01
TNF-a 2.33E-02
sTNFRH 3.57E-01
SAA 6.13E-06
Months since -3.20E-03 0.997 1.45E-03 2.68E-02
Disease @ l.lOE+00 2.995 4.46E-01 1.39E-02
small intestine
s 8.84E-Ol 2.421 3.13E-01 4.72E-03
The s in the above Table indicate the following are predictors of the hazard
i.e., risk of the non-detection of infliximab:
GM-CSF: holding all other variables constant, an extra ng/ul of GM-CSF reduces
the weekly hazard of infliximab tection by a factor of 0.826, or 17.4 %.
IL-2: An additional 1 ng/ul of IL-2 increases the hazard by a factor of 1.153, or
.3 %.
TNF-(x: A 1 ng/ul of TNF-(x ses the hazard by a factor of 1.024 / 2.4 %.
sTNFRII: A 1 ng/ul of sTNFRH increases the hazard by a factor of 1.429 / 42.9 %.
SAA: A 1 ng/ul of SAA increases the hazard by a factor of 1.000006/ 0.0006 %,
which is very small, but still a detectable effect (small SE).
Months since diagnosis: Each additional month since diagnosis decreases the hazard
by a factor of 0.997, or 0.3 %.
e site at the small intestine (categorical le): If the disease is located at
the small intestine, the hazard is increased by a factor of 2.995, or nearly 200 %.
[0307] Success (categorical variable): Also a predictive of hazard; in non-successful
patients the hazard is increased by a factor of 2.421 or 142 %.
In summary, the following markers appear to be good predictors of infliximab
“clearance” /or non-detection: 1) GM-CSF; 2) IL-2; 3) TNF-u; 4) sTNFRII; and 5) the
disease being situated in the small intestine.
[0309] As such, in one embodiment, the present invention es:
A method for predicting the likelihood the concentration of an anti-TNF therapeutic
or antibody during the course of treatment will fall below a threshold value, the method
comprising:
measuring a panel of markers selected from the group ting of 1) GM-CSF; 2)
IL-2; 3) TNF-(x; 4) sTNFRH; and 5) the disease being situated in the small intestine; and
ting the likelihood the concentration of an anti-TNF therapeutic or antibody
will fall below the threshold based upon the concentration of the markers.
Example 4. Detection of Antidrug Antibody to Infliximab (“ATP’ or “HACA”)
This example uses the Cox Proportional-Hazards Model (CPHM) to model the time
that it takes for an event to occur. This is a similar analysis to e 3 above, but with the
appearance of the anti-drug antibody also known as ATI or HACA as the event and risk of
ATI formation (detection) as the hazard. Figure 13 shows the concentration ofATI (HACA)
in various patients during the course of treatment. In Figure 14, patients were ranked by their
time to the event. The event occurred for various patients at different points during treatment
and is denoted with a bullet point. The risk of ATI detection is the hazard. Significant
predictors of the hazard include:
tor —-_—_
VEGF
GM-CSF
IL-2
IL-8
TNF-a
VCAM 1.28E-03 1.001 2.01E-04 1.87E-10
The data in the above table indicates that EGF, VEGF, IL-8, CRP and VCAM-l all
have very small, but significant s on the .
GM-CSF: Holding all other variables constant, an extra ng/ul of GM-CSF reduces
the weekly hazard ofATI detection by a factor of 0.762, or 27.4 %.
[0313] IL-2: A 1 ng/ul increase of IL-2 increases the hazard by a factor of 1.85, or 85 %.
TNF-u: A 1 ng/ul increase of TNF-(x increases hazard by a factor of 1.024, or 2.4 %.
In summary, the tors of ATI detection hazard are GM-CSF, IL-2 and TNF-u.
As such, in one embodiment, the present invention provides a method for predicting
the likelihood that anti-drug antibodies will occur in an individual on anti-TNF therapy or
antibodies, said method comprising:
ing a panel of markers selected from the group consisting of EGF, VEGF,
IL-8, CRP and VCAM-l; and
predicting the hood that anti-drug dies will occur in an individual on
anti-TNF therapy based on the concentration of marker levels.
e 5. Disease Activity Profiling for Crohn’s Disease Prognosis Using COMMIT
Study Samples.
This example rates methods for personalized therapeutic management of a
TNFd-mediated disease in order to optimize therapy or monitor therapeutic efficacy in a
subject using the disease activity ng of the present invention. This examples illustrates
disease activity profiling which comprises detecting, measuring, or determining the presence,
level and or activation of one or more specific biomarkers (e.g., drug levels, anti-drug
antibody levels, inflammatory markers, anti-inflammatory markers, and tissue repair
markers).
[0318] This example describes disease activity profiling on a number of samples from the
COMMIT study. As bed in Example 1, the COMMIT nation of Maintenance
rexate-Inflixamab Trial) study was performed to evaluate the safety and efficacy of
de (inflixamab) in combination with methotrexate (MTX) for the long-term treatment
of Crohn’s Disease (CD). In particular, the following array of markers was measured at
various time points during treatment with Remicade (infliximab; IFX) only or a treatment of
Remicade with MTX: (1) Remicade (inflixamab) and antidrug antibodies to infliximab
(ATI); (2) inflammatory s CRP, SAA, ICAM, VCAM; and (3) tissue repair marker
VEGF. This example shows that the markers of inflammation and tissue repair correlated
with IFX and ATI levels in select patients of TNF-oc mediated disease (e.g, Crohn’s Disease
and Ulcerative Colitis). In some instances, arrays of s may predict a disease activity
index (e.g., Crohn’s Disease Activity Index). Analysis of the COMMIT study is rated
herein.
The relationship between the presence ofATI and serum levels of IFX
concentration was investigated. For the evaluation, total ATI levels below the level of
quantitation (BLOQ) were 3.13 U/ml, and were set to 0. IFX concentrations below the level
of detection (BLOD) were set to 0. Per the sample comparison, only trough samples were
used and a total of 219 were used in the evaluation. 24 samples were determined to be ATI
positive (ATI+). It was determined that the median level of IFX was 0 [Lg/ml in ATI+
samples, while the median level of IFX was 8.373 [Lg/ml in ATI negative (ATI-) samples
(p=3.7l x 10'9 by Mann Whitney U test). Figure 4A rates an association between the
presence of ATI and the level of IFX in t s. Patient s with no detectable
level of ATI had a significantly higher IFX median concentration, compared to ATI+
samples.
The relationship between CDAI and the presence ofATI was evaluated. In the
is ATI of 3.13 U/ml was set as the f; only trough s were evaluated and
ATI BLOQ was set as 0. 195 samples were ATI-, while 24 samples from a total of 4 patients
were ATI+. The results showed that the median CDAI for ATI+ samples was 121.5 while
the median CDAI for ATI- samples was 82 (p=0.0132 by Mann Whitney U test). Figure 4B
illustrates that the presence of ATI correlates with higher CDAI. The results show that ATI+
samples have significantly higher CDAI than ATI- s.
The relationship between the presence of ATI and combination therapy of IFX and
immunosuppressant agent (e.g., MTX) was investigated. ATI+ samples at any trough time
point were analyzed. The s showed that there was no significant difference in odds of
having ATI between IFX therapy alone and IFX+MTX combination therapy. The high odds
ratio (e.g., 2.851) indicates that MTX can prevent a patient from developing an immune
se to therapeutic biologics. Figure 4C shows that concurrent suppressant
therapy (e.g, MTX) is more likely to suppress the presence of ATI.
[0322] The relationship between ATI and clinical outcome at follow-up was also
investigated. ATI+ samples at any trough time point were analyzed. Clinical outcome as
described from the al data received from the study was parsed as either “success” or
“non-success”. No significant ence in odds of being ATI+ was seen regardless of
ent n. The low odds ratio (6.g. indicates that ATI+ patients
, 0.1855, p=0. 1459)
tend to have poor clinical outcomes. Figure 5A shows that patients with ATI are more likely
to p a poor response to treatment.
This example also illustrates an association of an exemplary PRO Inflammatory
Index and serum levels of infliximab (IFX) or the presence of antibodies to IFX (ATI) in a
patient sample. Figure 5B illustrates that the inflammatory marker CRP is associated with
increased levels of ATI. The data shows that the median CRP level was 8.11 ug/ml in ATI+
samples and 1.73 [Lg/ml in ATI- samples (p = 2.67 x 10'6 by Mann Whitney U Test). Other
inflammatory and tissue repair markers were evaluated. Figure 6 illustrates that the protein
levels of an array of one or more inflammatory and tissue repair markers correlate to the
formation of antibodies to IFX. The data shows that of a combination of five markers (e.g.,
CRP, SAA, ICAM, VCAM, VEGF and including at least one inflammatory marker) was
expressed in 23 out of 24 ATI positive samples (Figure 7A, grey box). The inflammatory
marker SAA was found to be positive in 19 of the 24 ATI positive samples that were also
clinically described as having “high inflammation”. The results also show that VEGF and
CRP are the most non-overlapping markers in the analysis.
This example further shows an exemplary PRO Inflammatory Index (PII). The
inflammatory index score is created by logarithmic transformation of a combination of values
representing determined sion levels of a plurality of markers (e.g., PII = log(CRP +
SAA + ICAM + VCAM + . Figure 7B illustrates that an exemplary PRO
Inflammatory Index (PII) correlates with levels of IFX (p<0.0001 and R2 = -0.129) in t
samples of the COMMIT study. The results show that ATI positive samples have a
significantly higher inflammatory index score compared to ATI negative samples (P =
6.4x10'8; see Figure 7C).
As such, in one embodiment, the present invention provides a method for
ring an infliximab ent regimen, said method comprising:
a) measuring infliximab and ug antibodies to infliximab (ATI);
b) ing inflammatory markers CRP, SAA, ICAM, VCAM;
c) measuring tissue repair marker VEGF; and
d) correlating the ements to therapeutic efficacy.
Example 6. Disease Activity Profiling For TNF-oc Mediated Disease Prognosis Using
Clinical Study #1 Samples.
This example describes methods for monitoring therapeutic efficacy in a subject
using the disease activity ng of the present invention to identify subjects as responders
or non-responders to anti-TNF drug therapy. This e illustrates the use of disease
actiVity profiling with a number of patient samples from a Crohn’s Disease clinical trial #1.
In particular, an array of s was measured at various time points during
ent with Remicade (infliximab; IFX) only or a treatment of Remicade with MTX:
Remicade (inflixamab), antibodies to infliximab (ATI), and neutralizing antibodies to IFX.
This example shows that a disease actiVity profile can show the relationship among ATI, IFX
and neutralizing antibodies. Analysis of clinical study #1 is illustrated .
Figure 8A-B illustrates the correlation between Crohn’s Disease Activity Index
(CDAI) score and the concentration of mab in serum in a number of patients in clinical
study #1. In brief, 894 samples were analyzed. An IFX concentration 3 0.1 [Lg/ml at the
limit of detection (LOD) was defined to be “present”. The results showed that IFX negative
(IFX-) samples also have significantly higher CDAI (p= 0.0254, calculated by Mann-
Whitney U test), compared to IFX positive samples (IFX+).
r analysis revealed that the presence of ATI correlates with lower IFX
concentrations. It was assumed that total ATI below the level of quantitation (BLOQ) of 3.13
U/ml was set as 0 and IFX concentration below the level of detection (BLOD) was set at 0. It
was determined that 24% of the patients (62/258) in the study were ATI+, as defined as
ve total ATI levels at one of three time . The is of 894 samples showed a
correlation between IFX concentration and ATI levels. In particular, the median IFX was 0
[Lg/ml for ATI+ samples and 7.95 [Lg/ml for ATI- samples (p <2.2 x 10'16 by Mann-Whitney
U test). Figure 9A illustrates the association between IFX concentration and the presence of
antidrug antibodies to mab in samples ed.
Analysis shows that a high concentration ofATI in samples correlates with the
ce of neutralizing antibodies that target TNF-oc biologics. In some embodiments,
assays can be used to detect neutralizing antibodies. Neutralizing antibodies were detected in
t samples with the highest concentrations of ATI. Figure 9B illustrates that a high
concentration ofATI can lead to the presence of neutralizing antibodies and undetectable
levels of IFX.
Longitudinal analysis of the relationship of CDAI and the presence ofATI was
ted in samples collected at clinic visit #1 and #3 from 283 patients. A correlation
n the presence ofATI at visit #1 (V1) was established with CDAI at visit #3 (V3).
The median CDAI was 109 at V1 in ATI+ samples, while the median CDAI was 78 in ATI-
samples (p=0.027 by Mann Whitney U test). The results indicate a causal relationship
between ATI positivity and CDAI. Figure 9C illustrates that ATI+ samples determined at an
early time point were more likely to have a higher CDAI at a later time. The results indicate
that disease activity profiling at an early time point can predict CDAI at a later time point.
Figure 9D illustrates that in Clinical Study #1, patients had lower odds of developing ATI if
receiving a combination therapy of infiiximab (IFX) and an immunosuppressant agent (e.g.,
MTX and AZA). The odds ratio was 0.320 (p = 0.0009 by Fisher’s Exact test). In this
analysis, ATI positivity (ATI+) was defined as total ATI 3 3.13U/ml.
2012/037375
Example 7. Disease Activity Profiling For TNF-oc ed Disease Prognosis Using
Clinical Study #2 Samples.
A. Clinical Study #2A
This example illustrates the use of a method for monitoring therapeutic y in
patients ing Remicade (inflixamab) alone or in combination with an
immunosuppressant agent (e.g., methotrexate, azathioprine and/0r corticosteroids). This
example describes using methods of the prevent invention to determine the disease activity
profiles of samples from a series of clinical .
In the analysis, we investigated the relationship between antidrug antibodies to
inflixamab (ATI) and IFX concentrations in the cohort. It was determined that 90.6% of the
patients were ATI+ (5 8/64), when ATI+ samples were defined to be those with total ATI >
3.13 U/ml at at least one time point. The median concentration of IFX in ATI positive
samples was 0 [Lg/ml and 3.74 [Lg/ml in ATI negative samples (P<2.2 10-16 by Mann Whitney
U Test). The concentration of neutralizing antibodies was 0 in ATI+ samples. The results
suggest that the presence of ATI reduces IFX concentration in a patient on IFX therapy. The
range of IFX concentration for ATI- samples was 0.0-67.28 [Lg/ml. In ATI+ s the IFX
concentration was 00-26. 15 [Lg/ml. In ATI+ samples with neutralizing antibodies (Nab) the
IFX concentration ranged from 0-1.07 [Lg/ml. Figure 10A shows that correlation between
IFX concentration and the presence ofATI in samples of clinical study #2A. The results also
demonstrated that the odds ofbeing ATI positive versus ATI negative are significantly less
for samples treated with an immunosuppressant agent (ISA, e.g., methotrexate, azathioprine,
corticosteroids, and combinations thereof). In this analysis 814 samples were evaluated. The
odds of being ATI+ was significantly less for ISA-treated samples than of being ATI- (odd
ratio = 0.564; p < 0.00001 by Fisher’s Exact Test). In addition, fewer ISA treated samples
expressed lizing ATIs. Of the 34 ATI+ samples with neutralizing antibodies analyzed,
9 0f the 34 s were ISA-treated and 25 samples were non-ISA d samples. This
tes that ISA therapy can reduce the progression to ATI, and even neutralizing
antibodies to IFX. Figure 10B illustrates the relationship between ISA therapy and the
presence of ATI in the study.
[0334] Next, we igated the relationship between ATI and atory markers. As
described herein, total ATI BLOQ was set at 0. CRP concentration was determined by
methods such as a CEER assay. The s show that the median tration of CRP was
lowest (5.0 [Lg/ml) in ATI- samples and higher (10.0 [Lg/ml) in ATI+ samples. Sample
2012/037375
expressing neutralizing ATI had a yet higher median concentration of CRP (10.0 [Lg/ml). All
pair-wise comparisons between CRP concentrations and ATI status should that the values
were significantly different (p < 0.0001 by Mann Whitney U tests). Figure 10C illustrates the
relationship between CRP concentrations and the presence ofATI (ATI and/or neutralizing
ATI).
We also igated the relationship between ATI and loss of response to y.
In the cohort, samples were marked as having a nse”, “loss of se” and “no
information” regarding IFX therapy. The s were fiarther categorized as being “True” if
having a loss of response or “False” if not having a loss of response. In total 777 samples
were analyzed. The results showed that in samples marked as “True”, there was a
significantly higher odds ratio of also being ATI positive (odds ratio = 2.254, p<0.0001 by
Fisher’s Exact Test). Surprisingly, more samples that were positive for neutralizing
antibodies to IFX were determined to be responsive to IFX, as compared to being no longer
responsive. Of 34 neutralizing ATI+ samples, 21 were marked as “response” and 8 were
marked as “loss of response”. Figure 10D illustrates the relationship between loss of
responsiveness to IFX therapy and the presence ofATI in the study. Figure 11 illustrates that
levels of ATI and lizing dies can be determined over time in a series of samples
from various patients
We compared the concentration of IFX to the presence of the inflammatory marker
CRP. We defined “IFX presence” per sample as “True” if IFX was >= 0.1 ug/ml which is
the LCD of the assay. The results suggest that the median CRP concentration was not
different between samples with IFX present or without IFX present. The median CRP level
was 7.40 [Lg/ml in samples with IFX, while median CRP = 7.55 [Lg/ml in samples with IFX
absent (p = 0.591 by Mann Whitney U Test). Figure 12A illustrates the comparison of CRP
levels to the presence of IFX.
We also compared the relationship between infusion reaction to the ce of
ATI. The analysis included a total of 797 samples; 30 samples were categorized as having
infusion reaction (“Yes”) and 767 samples were categorized as having no infusion reaction
(“No”). 29 s that had an infusion reaction were also ATI+ (odds ratio = 35.54,
p<0.0001 by Fisher’s Exact Test). Figure 12B illustrates the relationship n the
presence of ATI and the on reaction. Patients expressing ATI were more likely to have
had an infusion reaction. Yet, for the 27 samples with neutralizing ATI, no infilsion reaction
was observed in 22 samples. The remaining 5 samples with neutralizing ATI had infusion
reaction.
B. Clinical Study #2B
In this analysis of clinical study #2B, we investigated the relationships n the
presence of ATI, IFX concentration, administration of ISA, the expression of inflammatory
markers (e.g., CRP), and loss of se to IFX treatment. We determined that the median
IFX concentration was higher in samples expressing ATI compared to those not sing
the antidrug antibodies. 15.2% of the patients (16 out of 105) were ATI+ with a total ATI
>3.13 U/ml at at least one time point. Of the 489 samples analyzed, the median IFX
concentrations were 0.59 ug/ml in ATI+ samples and 7.78 ug/ml in ATI- samples (p <2.2 x
'16 by Mann Whitney U Test). Figure 12C illustrates the relationship between IFX
concentration and the presence ofATI in the . The analysis showed that there are high
odds of developing antibodies to IFX when immunosuppressants have been awn (odds
ratio = 0.412, p = 0.0367 by Fisher’s Exact Test). Figure 12D illustrates the correlation
between the presence ofATI and the withdrawal of ISA therapy at a specific, given date. We
determined that ATI positive samples have a higher median concentration of CRP (9.6 ug/ml,
p = 1.25 x 10'12 by Mann Whitney U Test), compared to ATI negative samples (median CRP
= 1.5 ug/ml). Figure 13A illustrates the relationship between ATI and the inflammatory
marker CRP. Our is showed that the odds of experiencing a loss of response to IFX
was higher in patients determined to be ATI positive at any time point. (odds ratio = 3.967, p
= 0.0374 for Fisher’s Exact Test). Figure 13B illustrates the correlation between the presence
ofATI at any time point and responsiveness to IFX ent. Loss of response to IFX was
also correlated to a higher median concentration of the inflammatory marker CRP. In the
analysis there were 14 samples with loss of response at follow-up and 91 samples from
responders. The median CRP levels were 11.767 ug/ml for those with loss of response and
2.585 ug/ml for those with se. Patients who had lost response to IFX had a
cantly higher mean CRP (p = 7.45 x 10'5 by Mann Whitney U Test). Figure 13C
shows that loss of response can be related to an se in CRP. CRP was also significantly
higher in samples lacking detectable IFX 2. Samples were determined to have IFX (“IFX
present”) if the level of IFX was >= to 0.1 ug/ml per sample (e.g., LOD of the assay). The
median CRP was 1.6 ug/ml in IFX t s and 13 ug/ml in IFX absent samples (p =
3.69x10'5 by Mann Whitney U Test). Figure 13D illustrates the association between the
presence of IFX and CRP levels. In this study “ATI+” was defined as a sample with total
ATI >3.13 U/ml at at least one time point.
C. Clinical Study #2C
In this is of al study #2C, we investigated the relationship between IFX
levels and the presence of ATI. It was determined that ATI+ have a significantly lower
median IFX of 0.43 [Lg/ml as compared to ATI- samples which have a median IFX of 3.28
[Lg/ml (p = 1.95x10'4 by Mann Whitney U test). Figure 14A shows that lower IFX levels are
associated with the presence of ATI.
As such, in one embodiment, the present ion provides a method for
determining r an individual is a candidate for combination therapy wherein said
individual is administered infliximab, the method comprising:measuring for the presence or
e ofATI in said individual; and administering an suppressant (e. g., MTX) is
the individual has significant levels of ATI. In certain aspects, the concentration level of
CRP is indicative of the presence of ATI.
Example 8. e Activity ng For TNF-oc ed Disease Prognosis Using
Patient Samples from Clinical Study #3.
[0341] This example illustrates using methods of the present invention to monitor the
therapeutic efficacy of anti-TNF drug therapy. In particular, pooled data including study
data, pharmacokinetics data, follow-up study data of clinical study #3 were analyzed. The
results showed that the median IFX tration of 0.0 [Lg/ml was lower in ATI positive
samples compared to an IFX concentration of 12.21 [Lg/ml ATI negative samples (P < 2.2 x
10-16 by Mann Whitney U test). Figure 14B shows that lower IFX levels are associated with
the presence ofATI in these al samples. Figure 14C illustrates that the same correlation
between IFX levels and ATI was also present in the study data, follow-up study and in the
pharmacokinetics study (p< 0.05 by Mann Whitney U tests). We also used methods of the
present invention to determine that a high concentration of ATI in a sample have a
neutralizing effect on IFX. In particular, high concentrations of ATI act as neutralizing
antibodies to infiixamab. Samples with a high concentration ofATI had an IFX level of 0
[Lg/ml. Figure 15A illustrates the relationship between ATI levels including neutralizing
ATI and IFX.
Example 9. Methods of Disease Activity Profiling Including the PRO Inflammatory
Index in Patients Receiving Humira.
This example illustrates methods of the present invention including determining the
level of TNF-Oc biologic (e.g., adalimumab a); ADL) and the presence of anti-drug
antibodies to the TNF-oc biologic (e.g., ATA) in a patient sample. In this analysis, one
sample represents one patient and a total of 98 CD s were evaluated. 2.04% (2 out of
98 CD patients) of the samples were positive for ATA., when ATA vity was set as total
ATA> 0. singly, the two ATA positive samples also had the highest concentrations of
ADL. Figure 15B illustrates an association between ADL concentration and the presence of
ATA in patient samples.
This e describes an exemplary PRO Inflammatory Index (PII). The example
also illustrates the use of the P11 in patient samples receiving Humira (adalimumab) and
different drug combinations. Figure 16A describes the details of an exemplary PRO
Inflammatory Index. The PII can represent a single per-sample score describing
inflammation levels based on five biomarkers. The score is ed from the logarithmic
ormation of the sum of the five biomarkers. In some embodiments, the biomarkers
include VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM in ng/ml and VCAM in ng/ml.
Figure 16B illustrates that there is no obvious relationship between the P11 and the
concentration ofADL in an array of samples with ADL alone or in combination with other
drugs. This could be due to the appearance of high ADL trough serum concentration in the
sample cohort. These is a significant negative correlation between PII and ADL
concentration (p=l .66x10'5 and Spearman’s Rho =-0.459). A similar negative correlation
relationship was found between IFX and P11.
[0344] We also compared the relationship between the P11 and the presence of eutic
agents used to treat TNF-oc mediated diseases. ADL positive samples were defined as
samples with an ADL concentration of greater than 0 [Lg/ml. The results showed that a
higher PII was detected in ts on Humira compared to ts on Remicade and
Humira. Figure 17 shows a plot of the P11 scores for patients receiving Humira and Humira
in combination with other drug such as Remicade, Cimzia, Asathioprine and Methotrexate.
As such, in one embodiment, the present invention provides a method for
monitoring Crohn’s disease activity, the method sing:
determining an inflammatory index sing the measurement of a panel of
markers comprising VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM in ng/ml and
VCAM in ng/ml;
comparing the index to an efflcacy scale or index to monitor and manage the disease.
2012/037375
Example 10. Methods for Improved Patient Management.
This example describes methods for ed patient management to assist in
developing personalized patient treatment.
In some embodiments, patients with active CD and UC can be analyzed using a
ty shift assay (see, e.g, PCT Publication No. , the disclosure of
which is hereby incorporated by reference in its ty for all purposes) in conjunction with
disease activity profiling. Figure 18 shows details of the methods of the present invention for
improving the management of patients with CD and/or UC. In some embodiments, the
methods of disease ty profiling comprise pharmacokinetics, and determining the
presence and/or levels of disease activity profile s and/or mucosal healing markers.
In some embodiments, disease activity profiling comprises methods of detecting,
measuring, and determining the presence and/or levels of biomarkers, nes, and/or
growth factors. Non-limiting examples of cytokines that can be used in disease activity
profiling include bFGF, TNF-oc, IL-lO, IL-l2p70, IL- 1 [3, IL-2, IL-6, GM-CSF, IL- 1 3, IFN—y,
TGF-Bl, TGF-BZ, TGF-[33, and combinations thereof. Non-limiting examples of
inflammatory markers include SAA, CRP, ICAM, VCAM, and combinations thereof. Non-
limiting examples of anti-inflammatory markers include TGF-B, IL-10, and combinations
thereof. Non-limiting es of growth factors include amphiregulin (AREG), epiregulin
(EREG), heparin binding epidermal growth factor (HB-EGF), hepatocye growth factor
(HGF), lin-Bl (HRG) and isoforms, neuregulins (NRGl, NRG2, NRG3, NRG4),
betacellulin (BTC), epidermal growth factor (EGF), insulin growth factor -1 (IGF-l),
transforming growth factor (TGF), platelet-derived growth factor (PDGF), vascular
endothelial growth factor (VEGF), stem cell factor (SCF), et d growth factor
(PDGF), soluble fms-like tyrosine kinase 1 (sFltl), placenta growth factor (PIGF), fibroblast
growth s , and combinations thereof.
In other embodiments, disease activity ng comprises detecting, ing and
determining pharmacokinetics and mucosal healing. In some aspects, mucosal healing can be
ed by the presence and/or level of selected biomarkers and/or endoscopy. In some
instances, mucosal healing can be defined as the absence of friability, blood, erosions and
ulcers in all visualized segments of gut . In some embodiments, biomarkers of
mucosal healing, include, but are not limited to, AREG, EREG, HG-EGF, HGF, NRGl,
NRG2, NRG3, NRG4, BTC, EGF, IGF-l, HRG, FGFl, FGF2 (bFGF), FGF7, FGF9, SCF,
PDGF, TWEAK, GM-CSF, TNF-oc, IL-12p70, IL-1[3, 11—2, IL-6, IL-10, IL-13, IFN-y, TGF-oc,
1, TGF-[32, TGF-[33, SAA, CRP, ICAM, VCAM, and combinations thereof. In some
embodiments, a growth factor index can be established using tical analyses of the
detected levels of biomarkers of mucosal healing. In some instances, the growth factor index
can be associated with other markers of disease activity, and utilized in methods of the
present invention to personalize patient treatment.
Figure 19 shows the effect of the TNF-oc pathway and related pathways on different
cell types, cellular mechanisms and disease (e.g., Crohn’s Disease (CD), rheumatoid arthritis
(RA) and Psoriasis (Ps)). Figure 20 illustrates a schematic of an exemplary CEER lex
growth factor array. In particular embodiments, the methods of the present invention can
employ this array. As non-limiting examples, Figure 21A-F illustrate lexed growth
factor profiling of patient samples using this array. In particular, longitudinal analysis of
growth factors, such as AREG, EREG, HB-EGF, HGF, HRG. BTC, EGF, IGF, TGFoc, and
VEGF, was performed on a collection of patient samples. Figures 21B and E illustrate the
determination of the level of serological and immune s, such as ASCA-a, ASCA-g,
Cbirl and OmpC, in samples from Patient 10109, Patient 10118 and Patient 10308. Figure
21G shows the exemplary growth factor arrays performed on samples from healthy controls,
patients with IBS-C, and patients with IBS-D.
A series of multiplexed CEER growth factor and CRP arrays was performed on
patient samples. Tables A-D (below) highlight longitudinal analysis of mucosal healing in
patient samples. The ing Table (A) shows that CRP and growth factors can be
predictive of mucosal healing:
Subject Collection TGF TGF
ID Date CRP VEGF Tweak beta1 beta2
10101 tion 1 IIIIIIIIIIIII
10101 tion 2 IIIIIIIIIIIIII!
10103 colloollon l MIIIIIIIIIIII
10103 oolloollon 2 IIIIIIIIIIIIIIflIl
10109 colloollon l IIIIIII
10109 oolloollon 2 IIIIlllIIIIIIII
10118 oolloollon l IIIIIIIIIIIII
10118 collocllon 2 IIIIIIIIIIIIIIIIIII
10308 oolloollon l IIIIIIIIIIIII
10308 oolloollon 2 IIIIIIIIIIII!
10503 oollocllon l IIIIIIIIIIIII
10503 llon 2 IIIIIIIIIIIIIIIII
11003 oolloollon l IIIIIIIIIIIII
11003 colloollon 2 IIIIIIIIIIIIII
11601 collocllon l IIIIIIIIIIIII
11601 oolloollon 2 IIIIIflIIIIIEIIIIIlIII
WO 54987
11602 Collection 1 1.. 71 327. 92 15.31 562.30 12.82 12.13 58.15 1120.06
11602 Collection 2 IIIIIIIII!
12121 Collection 1 IIIIIIIIIIII
12121 Collection 2 IIIflIIIflIIIII
12121 Collection 3 IIIIIIIIIIIIIII
190 Collection 1 IIIIIIIIIIIII
190 Collection 2 IIIIIIIIIIIIIIII
492 Collection 1 MIIIIIIIIIIII
492 Collection 2 IIIIIIIIIIIIIII
2546 Collection 1 IIIIIIIIIIIII
2546 Collection 2 IIIIIIIIIIIIII
“N" and “P" denote a negative or positive relationship between pairs of ations for each marker, respectively
per subject. Underlined data are number pairs above upper limit of quantitation and are d to have a positive
relationship.
[0352] The following Table B lists CRP and growth factors predictive of mucosal healing:
Subject Collection TGF
ID Date CRP BTC alpha
IIIIIIIIIIEIIIEIII
IEIIIIIIIIIIIIIII
IEIIIIIIIIIIIIEIEI
IIIIIIIIIIIIIIIIIEII@II
IIIIIIIIIIIIIEIII
IIIIIIIIIIIIIEIEII
IIIIIIIIIIIIMIII
IIIIIIIIIIIIIIIIIIIII
IIIIIIEIIIIEIIIIEIIIEI
IIIIIEIIIIIIIEIIIIIEIIIEI
IIIIIIIIIEIIIIEIIIEII
IIIIIIIIIIIIIIIIIEIIIII
IIIIIIIEIII
IEIIIIIIIIIIIIIII
IIIIIIIEIIIIEIIII
IflmIIIIflflflflflfl
IIIIIIIIEIIIIEIIIII
IIIIIEIIIIIIIIIEIIIII
MEI-IIIIIIIIIEIIII
IIIIIEIIIIIIIIEIIIIII
IIIIIIIEIIIIEIIIIEIII
IIIIIEIIIIIM
IEIIIIIIIIIIIIEIII
IIIIIIIIIIIIIIIWI
IIIIIIIIIIIIIEIII
IIIIIIIIIIIIIIIIIEIIIII
“N" and “P" denote a negative or positive relationship between pairs of observations for each marker, respectively
per subject. Underlined data are number pairs above upper limit of quantitation and are assumed to have a
positive relationship.
[0353] The following Table C shows that CRP and growth s can be predictive of
mucosal healing:
Subject Collection TGF
ID Date CRP VEGF Tweak beta 1
2834 Collection 1 6.88 604.22 624.03 2.00 68.05
2834 Collection 2 24.33 P 631.31 P 509.73 3.72 P 44.79 N
3570 Collection 1 105.46 1046.04 191.49 5.51 33.61
3570 tion 2 1.31 Z 487.25 N 237.91 P 6.33 P 41.29
3713 Collection 1 7.76 1117.85 1267.74 3.94 45.08
3713 Collection 2 107.22 P 633.56 N 957.18 Z 5.44 P 39.59 2
5301 Collection 1 7.62 32.19
5301 Collection 2 36.61 P 217.02 389.33 2.88 30.89 2
7757 Collection 1 838.39 11.24 7.90 43.35
7757 Collection 2 138.56 P 705.18 N 5.33 N
7966 Collection 1 3.03 120.82 326.72 5.59 38.67
7966 Collection 2 31.04 P 1089.52 P 691.29 P 6.81 P 48.68
8075 Collection 1 6.81 968.26 840.06 8.10 58.65
8075 Collection 2 34.62 P 620.97 N 876.55 P 6.27 N 51.36
8127 tion 1 34.41 323.51 310.67 5.54 41.13
8127 Collection 2 2.78 2 318.02 N 284.46 Z 6.87 P 51.87
8431 tion 1 4.53 1829.91 214.78 2.18 52.82
8431 Collection 2 30.51 P 816.10 N 301.14 P 3.47 P 58.41
3831 Collection 1 32.95 804.87 491.46 6.83 36.16
3831 Collection 2 0.29 Z 491.17 N 912.29 P 7.31 P 23.62 N
3852 Collection 1 68.59 494.06 252.18 6.10 32.76
3852 Collection 2 1.00 Z 291.49 N 122.66 Z 6.56 P 39.22
3852 Collection 3 0.60 Z 375.97 N 100.53 1.34 N 22.83
5477 Collection 1 23.17 550.58 485.76 7.51 36.73
5477 Collection 2 2.12 N 1101.83 P 575.69 P 7.55 P 34.98 N
7456 Collection 1 35.21 51.23 452.45 6.13 22.05
7456 Collection 2 0.89 N 496.87 P 366.73 N 14.19
“N" and “P" denote a negative or positive relationship between pairs of ations for each marker,
tively per subject. Underlined data are number pairs above upper limit of quantitation and are
assumed to have a positive relationship.
[0354] The following Table D shows that CRP and growth factors can be predictive of
mucosal healing:
Collection
Date CRP
__----_-—
__-Il--_-—
_—----_-—
_—----_-—
mum—“Im-
_-_-_—IIIIIIN
_—--_—----
Collection 1
_-_-------
ImI—Im-m-m-mm
—I-II-m-lm-m-
——m---—-—-------
_—--__-----Im-Immumm-n
———-—---m---lm--
_—-_--__MIIIm--
“N" and “P" denote a negative or positive relationship between pairs of observations for each marker, respectively
per subject. Underlined data are number pairs above upper limit of quantitation and are assumed to have a
positive relationship.
Tables A, B, C and D Show marker values and relationships between pairs of
observations in CRP and growth factor data. Using a ion of or = 0.1, we identified an
ation between three growth factors and CRP. The ing Table (E) shows a -
two gency table that highlights that an increase or decrease in AREG, HRG and TGF
was found to be significantly associated with an increase or decrease of CRP:
AREG* TGF-alha***
* denotes ** denotes *** denotes
p= 0.034. p= 0.026. p= 0.07.
Figure 22 illustrates the association between CRP levels and the growth factor index
score in determining disease remission.
Further studies for identifying predictive markers of mucosal healing may e
samples from several clinical s. As one non-limiting example, Clinical Study A may
include 413 samples (paired samples with 1-5 samples per patient). Clinical data may detail
patient age, sex, weight, date of diagnosis, disease location, sample collection dates, dose,
colonoscopy, improvement of mucosa, presence of mucosal healing, and/or concomitant
medication useage. In al Study A, colonoscopy may be performed prior to first drug
infusion. As another non-limiting example, in Clinical Study B, 212 UC samples may be
analyzed (110 samples were diagnosed for CD at follow-up and 102 samples were diagnosed
for UC based on mucosal g). Clinical data may detail patient age, sex, weight, date of
diagnosis, disease location, sample collection dates, IFX dose, colonoscopy s
copic activity score), albumin level, CRP level, and/or Mayo score. In Clinical Studies
A and B, three infusions may occur at week 0, 2 and 6 during ion. 6 additional drug
infusions may be performed during the maintenance phase at week 14, 22, 30, 38, 46 and 52.
A second colonoscopy may be performed during the maintenance phase. A third colonscopy
may be performed during follow-up and patients may continue treatment if responsive to
drug.
The methods of the present invention can be used to create personalized therapeutic
ment of a TNFoc-mediated disease. A personalized therapeutic regimen for a patient
diagnosed with IBD can be selected based on predictors of e status and/or long-term
outcome as described herein, including, but not limited to, a Crohn’s prognostic test (see,
e. g., PCT Publication No. 14, the disclosure of which is hereby incorporated
by reference in its entirety for all purposes), a disease activity profile (e.g., disease burden), a
mucosal status index, and/or a PRO Inflammatory Index as described in Example 5. Using
the methods of the present invention, it can be determined that a patient has mild disease
activity and the clinician can recommend, prescribe, and/or administer a nutrition-based
therapy (Figure 23A). Yet, if it is determined that a patient has mild disease activity with an
aggressive ype, a ion-based therapy in addition to thiopurines can be
ended, prescribed, and/or administered. A r therapy can be ended,
prescribed, and/or administered if it is determined that the patient has moderate disease
activity (Figure 23B). If it is determined that a patient has moderate e activity with an
aggressive phenotype, either a combination of rines and nutrition therapy (Nx) or an
appropriate anti-TNF drug can be recommended, prescribed, and/or stered. In some
instances, an anti-TNF monitoring test (see, e.g., PCT Publication No. WO 56590, the
disclosure of which is hereby incorporated by reference in its entirety for all purposes) can be
used to determine if the patient is likely to d to the therapy. In the case when severe
disease activity is determined, an appropriate anti-TNF drug administered at an optimized
dose can be recommended and/or prescribed (Figure 23C). In such instances, an NF
monitoring test (see, e. g., PCT Publication No. , the disclosure of which is
hereby incorporated by reference in its entirety for all purposes) can be used to predict if the
patient is likely to be responsive to drug. In other instances, it can be recommended and/or
prescribed that a patient having severe e ty also receive nutrition-based therapy.
In some embodiments, the methods of the present invention can be used in a
ent gm to personalize patient treatment (Figure 24). First, treatment can be
selected based on the expression of mucosal status markers. Next, drug dose can be selected
based on disease burden (e.g., disease activity index). After the therapeutic drug is
administered, the initial response can be ined from the expression of markers of
mucosal healing. ATM monitioring can be used to identify patient who are responsive or
non-responsive to therapy. Non-responsive patients can then be prescribed an appropriate
anti-TNF drug.
Example 11. Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI) Assays are
Predictive of Disease ty in Patients with Crohn’s disease (CD).
Previous studies te that patients with CD who have a higher trough
concentration of IFX during maintenance dosing are more likely to benefit from treatment.
However, development ofATIs can result in increased drug clearance and loss of response.
Therapeutic drug monitoring may allow clinicians to maintain effective drug concentrations.
gh previous ATI assays have been limited by the inability to measure ATIs in the
presence of drug, fluid-phase IFX and ATI assays have overcome this problem (see, e. g.,
PCT Publication No. WO 56590, the disclosure of which is hereby incorporated by
reference in its entirety for all purposes). We used these assays to evaluate the relationship
between serum IFX concentration, ATIs and disease activity.
Methods: 2021 serum samples from 532 participants in 4 prospective CD RCTs or
cohort studies (COMMIT, Leuven dose optimization study, Canadian Multicenter and
IMEDEXl) that evaluated the maintenance phase of IFX treatment were used, and data were
combined for analysis. IFX and ATI serum levels were ed using a HPLC-based fluid
phase assay. CRP, measured by ELISA, was used to assess disease activity. ROC analysis
determined the IFX threshold that best minated disease activity, as measured by CRP.
We examined pairs of samples taken over sequential time points and evaluated the
onship between IFX and ATI presence in the pair’s first data point and CRP in the
subsequent measurement. There were 1205 such observations. We identified four ct
t groups, namely IFX 2 threshold and ATI-, IFX < threshold and ATI-, IFX 2 threshold
and ATI+, and IFX < threshold and ATI+. Regression es assessed the ial
interaction between IFX and ATI as predictors of CRP.
Results: CRP can best differentiate IFX status with an IFX concentration threshold
of 3 ug/ml (ROC AUC = 74 %). Using paired sequential samples both ATI and IFX were
associated with median CRP (Table 2). Although ATI+ patients had higher CRP levels
overall, within this group there was no association n IFX higher than threshold and
subsequent CRP. In ATI- ts, CRP was significantly higher in patients with IFX levels
<3 ug/ml. In the regression analysis ATI positivity, IFX Z 3 ug/ml and the interaction term
were all significant predictors of CRP. CRP was 31 % higher in ATI positive patients than
2012/037375
those who were ATI negative and 62 % lower in patients with IFX levels 2 3 ug/ml compared
to those with IFX < 3 ug/ml.
sions: We have shown that ATI positivity is predictive of increased disease
activity, while an IFX tration above the threshold value of 3 ug/ml is predictive of
significantly lower disease activity. In ATI+ patients, IFX trations above 3ug/ml had
no effect on CRP, indicating that the benefits of IFX are diminished in the presence of ATI
despite the presence of optimal drug concentration. These findings t the concept that
therapeutic drug monitoring is an important tool in optimizing IFX therapy. Using paired
sequential samples and regression analysis, both ATI and IFX were associated with median
CRP as shown in the following table:
Median CRP Concentration (ng/ml;
interquartile range) Significance
In ATI— Patients In ATI+ ts
IFX < 3 ug/ml 5.65 (1.68, 16.1) 8.40 (3.10, 20.1) m
IFX 2 3 ug/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2)
Median CRP concentrations and interquartile ranges (in parentheses) in ng/ml. Asterisks
denote significance levels of two-sample Mann-Whitney U tests (***, p < 0.001; **, p <
0.01; *, p < 0.05; NS, not significant).
Example 12. Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI) Assays are
Predictive of Disease Activity in Patients with Crohn’s disease (CD).
This example illustrates the use of infiiximab (IFX) and antibody-to-infiiximab
(ATI) assay in predicting disease ty in ts with Crohn’s disease (CD). This
e also illustrates a method of determining the threshold of IFX that can best
discriminate disease activity as measured by C-reactive protein (CRP) levels. This example
also illustrates the association of both ATI and IFX to CD and CRP levels, which can serve as
a measure of disease activity.
Previous studies have indicated that patients with CD who have a higher trough
concentration of IFX during maintenance dosing are more likely to benefit from treatment.
However, pment ofATIs can result in increased drug clearance and loss of response.
Therapeutic drug monitoring may allow clinicians to in effective drug concentrations.
Although previous ATI assays have been limited by the inability to measure ATIs in the
presence of drug, the fluid-phase IFX and ATI assays described in PCT Publication No. WO
201 1/056590 (the disclosure of which is hereby incorporated by nce in its entirety for
all purposes) have overcome this problem.
2012/037375
In this study we used fluid-phase IFX and ATI assays to evaluate the relationship
between serum IFX concentration, ATIs and disease activity, as measured by CRP. We
analyzed 2,021 serum samples from 532 participants in 4 ctive CD randomized
controlled trials (RCTs) or cohort studies, including COMMIT, Leuven dose optimization
study, Canadian enter and IMEDEXl. The combined analysis was restricted to
samples during maintenance of IFX treatment. There was evidence of non-heterogeneity
among pooled CRP.
IFX and ATI serum levels were measured using a HPLC-based fluid phase assay.
CRP was measured by ELISA and used to assess disease activity. Receiver-operator curve
(ROC) is was performed to determine the IFX trough threshold (e.g., amount or
concentration) that can best discriminate disease activity (e.g., between high and low CRP
values). Figure 25 shows the ROC analysis. CRP and nine IFX trough thresholds were
analyzed and the ROC area under receiver-operator characteristic curve (AUC) are as
follows:
"III-IN.“
ROC AUC 0.682 0.727 0.733 0.743 0.727 0.717 0.699 0.689 0.678
The ROC is showed that CRP can best differentiate IFX status with an IFX
concentration threshold of 3 ug/ml (ROC AUC = 74 %). For example, at an IFX through
concentration threshold of 3.0 ug/ml, a randomly chosen sample with a “low” IFX serum
concentration will have a higher CRP level than a randomly chosen sample with a “high” IFX
serum concentration 74.3% of the time. In the IFX, ATI and CRP association analysis, a
serum IFX trough threshold of 3.0 ug/ml was used.
To determine the association of serum IFX concentration, ATI, and CRP levels over
time, we examined pairs of samples taken over sequential time points. A 100-day time gap
limit was d for the time . We evaluated the relationship between the presence of
IFX and ATI in the pair’s first data point and CRP in the subsequent measurements (Figure
26A). Figure 26B shows CRP levels, IFX serum concentration and ATI status at tial
time points for a sample. In total, 1,205 observations were examined.
Regression analysis (e.g, ordinary least squares regression) was performed to assess
the potential ction n prior IFX and prior ATI as predictors of disease (i.e., CRP
levels). In particular, CRP was log transformed at the second time point observation. Prior
WO 54987 2012/037375
IFX is the first time point with IFX concentration above or below the calculated trough
threshold of 3 ug/ml. Prior ATI is the first time point ATI is above or below 3.13 U/ml which
is the limit of detection (LOD). Using paired sequential samples and regression analysis,
both ATI and IFX were associated with median CRP as shown in the following table:
Median CRP Concentration ;
interquartile range) Significance
In ATI— Patients In ATI+ Patients
IFX < 3 [Lg/ml 5.65 (1.68, 16.1) 8.40 (3.10, 20.1) 14*
IFX 2 3 pg/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2)
Median CRP concentrations and interquartile ranges (in parentheses) in ng/ml. Asterisks
denote significance levels of two-sample hitney U tests (***, p < 0.001; **, p <
0.01; *, p < 0.05; NS, not significant).
The results shows that the factors and interactions between the factors are
significant. The regression coefficients were ated to be 0.272 for ATI+ samples and
-0.979 for IFX 3 3 ug/ml.
We identified four distinct patient groups: (1) IFX 2 threshold and ATI-, (2) IFX <
threshold and ATI-, (3) IFX 2 threshold and ATI+, and (4) IFX < threshold and ATI+. Of the
1,205 observations used in the analysis, 605 were IFX 2 threshold and ATI-; 196 were IFX <
threshold and ATI-; 41 were IFX 2 threshold and ATI+; and 363 were IFX < threshold and
ATI+.
Although ATI+ patients had higher CRP levels overall, within this group there was
no association between IFX levels higher than threshold and CRP (Figure 27). In ATI-
patients, CRP levels were significantly higher in patients with IFX levels less than threshold
(Figure 27).
[0374] In the regression analysis, ATI positivity, IFX Z 3 ug/ml and their interaction were
all significant predictors of CRP . CRP was 31 % higher in ATI + patients than those
who were ATI-, and 62 % lower in patients with IFX levels 2 3 ug/ml compared to those with
IFX < 3 ug/ml. The onship n IFX concentration and CRP levels differs n
ATI+ and ATI- patient groups.
[0375] In this study we showed that ATI positivity is predictive of increased disease
actiVity, as measured by CRP. We also showed that IFX concentration above the threshold
value of 3 ug/ml is predictive of significantly lower disease activity. In ATI+ patients, IFX
concentrations above 3 ug/ml had no effect on CRP levels, suggesting that the benefits of
IFX are diminished in the presence ofATI even despite the presence of optimal drug
concentration.
We showed that disease activity, as measured by CRP, is strongly linked to both
IFX and ATI in a large combined dataset. Thus, ts with active Crohn’s disease can
benefit from knowledge of both IFX and ATI levels at . Based on the experimental
derivation of these onships, the following ent paradigms were created. For
instance, a symptomatic t with Crohn’s disease with IFX< threshold at trough and ATI-
can benefit from an increased dose of IFX therapy. A patient with IFX 3 threshold and ATI-
can benefit from receiving endoscopy or switching therapy. A symptomatic patient with
IFX< threshold at trough and ATI+ can benefit from switching therapy ifATI is high or
optimizing therapy dose if ATI is low. A patient with IFX 3 threshold and ATI+ can benefit
from switching y if disease activity (e.g., CRP level) is high. Alternatively, if disease
activity (e.g., CRP level) is low in that patient, further monitoring is recommended. The
treatment paradigms are described in the ing table:
Switch therapy (high ATI)
IFX < threshold se dose
Optimize dose (low ATI)
Check egldoscopy Sw1tch therapyr(h1gh act1v1ty)
IFX 2 threshold
Switch therapy Monitor (low ty)
These s demonstrate that therapeutic drug monitoring using methods of the
present invention are important tools in optimizing IFX therapy.
Although the foregoing invention has been described in some detail by way of
illustration and example for purposes of clarity of understanding, one of skill in the art will
appreciate that certain changes and modifications may be practiced within the scope of the
appended claims. In addition, each reference ed herein is incorporated by reference in
its entirety to the same extent as if each reference was individually incorporated by reference.
Claims (33)
1. A non-invasive method for ing mucosal g in an individual diagnosed with inflammatory bowel disease (IBD) receiving a therapy regimen, the method comprising: 5 (a) measuring the levels of an array of mucosal healing markers in a sample obtained from the individual; (b) comparing the levels of an array of l healing markers in the individual to that of a control to compute the mucosal healing index of the individual, n the mucosal healing index ses a representation of the extent of mucosal healing; and 10 (c) determining whether the individual undergoing mucosal healing should maintain the therapy regimen.
2. A method for monitoring therapeutic efficiency in an individual with inflammatory bowel disease (IBD) receiving therapy, the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample 15 obtained from the dual at a ity of time points over the course of therapy with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the individual’s mucosal healing index to that of a control; and 20 (d) determining whether the therapy is appropriate for the individual to promote mucosal healing.
3. A method for selecting a y regimen for an individual with inflammatory bowel e (IBD), the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample 25 obtained from the individual at a plurality of time points over the course of therapy, the individual ing a therapeutic antibody; (b) ng a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the individual’s mucosal healing index to that of a control; and 30 (d) selecting an appropriate therapy regimen for the individual, wherein the therapy regimen promotes mucosal healing.
4. The method of any one of claims 1 to 3, wherein the IBD comprises Crohn’s disease or ulcerative colitis.
5. The method of any one of claims 1 to 3, wherein the IBD comprises Crohn’s disease. 5
6. The method of any one claims 1 to 5, wherein the mucosal healing marker is a member selected from the group consisting of AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-, VEGF-A, VEGF-B, VEGF-C, VEGFD , FGF1, FGF2, FGF7, FGF9, TWEAK and combinations thereof.
7. The method of any one of claims 1 to 6, wherein the markers are ed in a 10 sample ed from the group consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy.
8. The method of any one of claims 1 to 7, wherein the therapy is selected from the group consisting of TNFα inhibitor therapy, an immunosuppressive agent, a 15 corticosteroid, a drug that targets a different mechanism, nutrition therapy, and combinations thereof.
9. The method of claim 8, wherein the TNFα inhibitor y comprises an anti- TNFα antibody.
10. The method of claim 9, wherein the anti-TNFα antibody is a member selected 20 from the group ting of infliximab, etanercept, adalimumab, izumab pegol, and combinations thereof.
11. The method of claim 8, n the suppressive agent is a member ed from the group consisting of azathioprine, 6-mercaptopurine, methotrexate, and combinations thereof. 25
12. The method of claim 8, wherein the drug that targets a different mechanism is a member selected from the group consisting of an IL-6 receptor inhibiting antibody, an anti-integrin molecule, a JAK-2 inhibitor, a tyrosine kinase inhibitor, and combinations thereof.
13. The method of claim 8, wherein the nutrition therapy comprises a special ydrate diet.
14. The method of any one of claims 1 to 13, wherein the array of mucosal healing markers further ses at least one member ed from the group consisting of an 5 anti-TNFα antibody, an anti-drug antibody (ADA), an inflammatory marker, an antiinflammatory marker, a mucosal healing marker, and combinations thereof.
15. The method of claim 14, wherein the anti-TNFα antibody is a member selected from the group ting of infliximab, etanercept, umab, certolizumab pegol, and combinations thereof. 10
16. The method of claim 14, wherein the anti-drug antibody (ADA) is a member selected from the group consisting of a human anti-chimeric antibody (HACA), a human anti-humanized antibody (HAHA), a human anti-mouse dy (HAMA), and combinations thereof.
17. The method of claim 14, wherein the mucosal healing marker is a member 15 selected from the group consisting of AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, TWEAK and combinations thereof.
18. The method of claim 14, n the inflammatory marker is a member selected from the group consisting of GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, sTNF 20 RII, and combinations thereof.
19. The method of claim 14, wherein the anti-inflammatory marker is a member selected from the group consisting of IL-12p70, IL-10, and ations thereof.
20. A method for reducing or minimizing the risk of surgery in an dual diagnosed with inflammatory bowel disease (IBD) being administered a therapy 25 regimen, said method comprising: (a) measuring an array of l healing markers at a plurality of time points over the course of therapy with a therapeutic antibody in samples obtained from an individual; (b) generating the individual’s mucosal healing index comprising a representation of the presence and/or concentration levels of each of the markers over time; (c) comparing the individual’s l healing index to that of a l; and 5 (d) selecting an appropriate therapy regimen to reduce or ze the risk of surgery.
21. The method of claim 20, wherein the mucosal healing marker is a member selected from the group ting of AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, 10 FGF2, FGF7, FGF9, TWEAK and ations thereof.
22. The method of claim 20 or claim 21, wherein the control is a healthy control.
23. The method of any one of claims 20 to 22, wherein the therapeutic antibody comprises an anti-TNF antibody.
24. The method of claim 23, wherein the anti-TNF antibody is a member selected 15 from the group consisting of infliximab, etanercept, adalimumab, certolizumab pegol, and combinations thereof.
25. The method of any one of claims 20 to 24, wherein the markers are measured in a sample selected from the group consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN) cells, and a 20 tissue biopsy.
26. The method of any one of claims 20 to 25, wherein the plurality of time points comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time points.
27. The method of any one of claims 20 to 26, wherein the IBD comprises Crohn’s 25 e or ulcerative colitis.
28. The method of any one of claims 20 to 27, wherein the first time point in the plurality of time points is prior to the course of therapy with the therapeutic antibody.
29. The method of any one of claims 20 to 27, n the first time point in the plurality of time points is during the course of therapy with the therapeutic antibody.
30. A non-invasive method for measuring mucosal g in an individual diagnosed with inflammatory bowel disease (IBD) receiving a therapy regimen 5 according to claim 1, ntially as herein described with reference to any one or more of the examples but excluding comparative examples.
31. A method for monitoring therapeutic efficiency in an individual with inflammatory bowel disease (IBD) receiving therapy according to claim 2, substantially as herein described with reference to any one or more of the es but excluding 10 comparative examples.
32. A method for selecting a therapy regimen for an individual with inflammatory bowel disease (IBD) according to claim 3, substantially as herein described with reference to any one or more of the examples but excluding comparative examples.
33. A method for reducing or minimizing the risk of surgery in an individual 15 diagnosed with matory bowel disease (IBD) being administered a therapy regimen ing to claim 20, substantially as herein described with reference to any one or more of the examples but excluding comparative examples.
Priority Applications (1)
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NZ711144A NZ711144B2 (en) | 2011-05-10 | 2012-05-10 | Methods of disease activity profiling for personalized therapy management |
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US201161484607P | 2011-05-10 | 2011-05-10 | |
US61/484,607 | 2011-05-10 | ||
US201161505026P | 2011-07-06 | 2011-07-06 | |
US61/505,026 | 2011-07-06 | ||
US201161553909P | 2011-10-31 | 2011-10-31 | |
US61/553,909 | 2011-10-31 | ||
US201161566509P | 2011-12-02 | 2011-12-02 | |
US61/566,509 | 2011-12-02 | ||
US201261636575P | 2012-04-20 | 2012-04-20 | |
US61/636,575 | 2012-04-20 | ||
PCT/US2012/037375 WO2012154987A1 (en) | 2011-05-10 | 2012-05-10 | Methods of disease activity profiling for personalized therapy management |
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NZ617009A NZ617009A (en) | 2015-09-25 |
NZ617009B2 true NZ617009B2 (en) | 2016-01-06 |
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