EP4347891A1 - Biomarqueurs et méthodes de classification de sujets suite à une exposition virale - Google Patents

Biomarqueurs et méthodes de classification de sujets suite à une exposition virale

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Publication number
EP4347891A1
EP4347891A1 EP22731779.9A EP22731779A EP4347891A1 EP 4347891 A1 EP4347891 A1 EP 4347891A1 EP 22731779 A EP22731779 A EP 22731779A EP 4347891 A1 EP4347891 A1 EP 4347891A1
Authority
EP
European Patent Office
Prior art keywords
disease
biomarker
panel
gene
genes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22731779.9A
Other languages
German (de)
English (en)
Inventor
Alexander James MANN
Gareth GUENIGAULT
Aruna BASAL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Poolbeg Pharma UK Ltd
Original Assignee
Poolbeg Pharma UK Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Poolbeg Pharma UK Ltd filed Critical Poolbeg Pharma UK Ltd
Publication of EP4347891A1 publication Critical patent/EP4347891A1/fr
Pending legal-status Critical Current

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Classifications

    • 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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56983—Viruses
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present invention relates to biomarkers for predicting whether a subject will develop acute symptoms or signs of disease following exposine, or possible exposure, to a respiratory virus such, for example, as an influenza virus.
  • a respiratory virus such, for example, as an influenza virus.
  • the present invention provides methods for predicting whether a subject will develop a severe or complicated form of disease.
  • the invention includes methods of conducting clinical trials or field studies comprising analysing the biomarkers, but more generally, the biomarkers of the invention may be used in any healthcare or non-healthcare setting; for example to triage patients infected with a respiratory virus to identify those who are susceptible to developing acute signs or symptoms and may therefore require medical intervention.
  • Subjects may have been administered a medicinal product for treatment or prevention of respiratory disease, and the biomarkers of the invention may therefore be used as a companion analytical product to predict the likely efficacy of the medicinal product.
  • the present invention further provides computer programmes, computer readable media, computer implemented-methods and classification algorithms that generate or utilise the biomarkers of the invention.
  • RNA viruses such as respiratory syncytial virus (RSV), influenza virus, parainfluenza virus, metapneumovirus, rhinovirus (HRV) and coronavirus (Hodinka, “Respiratory RNA Viruses”, Microbiol Spectr., 2016 Aug; 4(4)).
  • Influenza infects all age groups and causes a range of outcomes from asymptomatic infection and mild respiratory disease through to severe respiratory disease and even death.
  • different subjects exposed to the same influenza virus may be asymptomatic, mildly symptomatic, subclinical, exhibit acute symptoms, or require medical attention, or even urgent hospitalization (Cox et al 1999).
  • the proportion of infections that are asymptomatic or subclinical, and the degree to which these are contagious, as well as the proportion of shedding which occurs prior to onset of symptoms affect the potential impact of control measures and decisions regarding treatment and the administration of medicaments (Lau et al., 2010).
  • Woods et al., “A Host Transcriptional Signature for Presymptomatic Detection of Infection in Humans Exposed to Influenza H1N1 or H3N2”, PLOS ONE, January 2013; 8(1): e52198 describe the generation of a viral gene signature (or factor) for symptomatic influenza that is capable of detecting 94% of infected cases.
  • Woods et al. discloses methods for identifying a subject infected with a respiratory virus prior to presentation of symptoms, such methods do not predict whether or not an individual will develop acute symptoms of an influenza-like disease.
  • the present invention provides a biomarker for predicting whether a subject will develop acute symptoms of disease after exposure, or possible exposine, to a respiratory virus, wherein the biomarker comprises or is derived from expression levels of one or more genes selected from a gene panel comprising PHF20, ABCA1, APBA2, M0RC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, H0MER3, NSUN6, EPHA4 and BMP2K measured in a biological sample obtained from the subject after exposure, or possible exposure, to a respiratory virus.
  • a gene panel comprising PHF20, ABCA1, APBA2, M0RC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, H0MER3, NSUN6, EPHA4 and BMP2K measured in a biological sample obtained from the subject after exposure, or possible exposure, to a respiratory virus.
  • a biomarker in the context of the present invention is a measurable indicator of biological state or condition, in particular, an output to predict whether a subject will develop acute symptoms of disease.
  • the output may be a numerical output.
  • the biomarker of the invention may be a composite biomarker comprising expression levels of two or more genes of the gene panel or the expression level of at least one gene of the gene panel in combination with at least one other factor as described herein.
  • the subject may be a human or non-human mammal.
  • the acute symptoms of disease may consist of symptoms of an influenza-like or other respiratory disease, as disclosed herein.
  • Acute symptoms of an influenza-like or other respiratory disease means that the subject experiences four or more of the following symptoms and these symptoms, either individually or combined, interfere with daily activities.
  • the symptoms include runny nose, stuffy nose, sore throat, sneezing, earache, cough, shortness of breath, wheezing, chest tightness, headache, malaise, myalgia, muscle and/or joint aches, elevated temperature, chilliness, and feverishness.
  • the elevated temperature may be a temperature of 38 °C or more, optionally experienced together with a cough, optionally with onset within the last 8-12 (e.g. 10) days.
  • a subset of subjects will have acute symptoms, these subject’s symptoms will score in the 85 th percentile, for example the subject’s total VAS or CAT score will be in the 85 th percentile.
  • a total VAS score of greater than or equal to 25 units, or a CAT score greater than or equal to 10 units equates to acute symptoms.
  • a subject may be identified as susceptible to progression to a complicated form of an influenza-like or other respiratory disease.
  • a complicated respiratory disease such as influenza is defined as disease requiring hospital admission and/or with symptoms and signs of lower respiratory tract infection (hypoxaemia, dyspnoea, lung infiltrate), central nervous system involvement and/or a significant exacerbation of an underlying medical condition.
  • a subject is predicted to develop acute symptoms of an influenza-like or other respiratory disease, such as complicated flu or other respiratory disease, then in accordance with the present invention, it is predicted that the subject will go on to exhibit acute symptoms as defined above. It may be predicted that a subject will develop acute symptoms of an influenza-like or other respiratory disease, but the subject may self-resolve, or action may be taken to prevent the subject developing acute symptoms; for example a medicament may be administered. Thus, a subject predicted to develop acute symptoms of an influenza-like or other respiratory disease does not inevitably develop acute symptoms of an influenza- like or other respiratory disease.
  • Respiratory virus includes all viral infections of the respiratory tract including respiratory syncytial virus (RSV), parainfluenza virus (HPIV), metapneumovirus (HMPV), rhinovirus (HRV), coronavirus, adenovirus (HAdV), enterovirus (EV), bocavirus (HBoV), parechovirus (HPeV), influenza including influenza A and influenza B.
  • RSV respiratory syncytial virus
  • HPIV parainfluenza virus
  • HPIV metapneumovirus
  • HRV coronavirus
  • HdV adenovirus
  • EV enterovirus
  • bocavirus bocavirus
  • parechovirus HPeV
  • influenza including influenza A and influenza B.
  • a gene panel in the context of the present invention is a set of genes the expression levels of which can be analysed and used to predict the progression and/or outcome of an influenza-like or other respiratory disease in a subject.
  • a gene sub-panel is a set of genes selected from a gene panel which may be used to predict at a certain stage in the progression of the disease, for example early, mid or late stage progression towards possibly developing acute symptoms of an influenza like or other respiratory disease, or at a certain time point following inoculation with, or exposine to, a respiratory virus, for example up to 25 hours (for example 13-25 hours), or 37-49 hours, or 49-61 hours, in some embodiments these time frames may be referred to as early, mid or late stage respectively.
  • it may not be possible to determine when a subject was exposed to a respiratory virus in addition some subjects develop symptoms quicker than other subjects, and so disease progression and stage of disease progression may be estimated based on evaluated symptoms.
  • the gene panel of the present invention may include one or more genes (including two genes, three genes, four genes, five genes, six genes, etc.) selected from PHF20, ABCA1, APBA2, M0RC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
  • the gene panel may consist of up to 16 genes (typically up to 10 genes, and more typically up to 6 genes) including one or more genes (including two genes, three genes, four genes, five genes, six genes, etc.) selected from PHF20, ABCA1, APBA2, M0RC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
  • genes including two genes, three genes, four genes, five genes, six genes, etc.
  • the gene panel may comprise PHF20. In some embodiments, the gene panel may comprise NOL9. In some embodiments, the gene panel may comprise both PHF20 and NOL9.
  • the gene panel of the present invention may consist of one, two, three, four, five or six genes selected from PHF20, ABCA1, APBA2, M0RC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
  • the gene panel of the present invention may consist of one, two, three, four, five or six genes, including PHF20 and optionally NOL9.
  • a first gene sub-panel may comprise PHF20, ABCA1, APBA2, M0RC2, SNU13 and DCUN1D2.
  • the first gene sub-panel may comprise the expression level of PHF20.
  • the first gene sub-panel may comprise the expression level of one or both of APBA2 and ABCA1.
  • the first gene sub-panel may additionally comprise the expression level of one, two or three of M0RC2, SNU13 and DCUN1D2.
  • the first gene sub-panel may consist of one, two, three, four, five, or six of PHF20, ABCA1, APBA2, M0RC2, SNU13 and DCUN1D2.
  • the first gene sub-panel may consist of one gene, which is PHF20.
  • the first gene sub-panel may consist of two genes, one of which is PHF20.
  • the first gene sub-panel may consist of three genes, one of which is PHF20.
  • the first gene sub-panel may consist of four genes, one of which is PHF20.
  • the first gene sub-panel may consist of five genes, one of which is PHF20.
  • the first gene sub-panel may consist of six genes, one of which is PHF20.
  • the first gene sub-panel may consist of two genes, one of which is PHF20, and the other of which is APBA2 or ABCA1.
  • the first gene sub-panel may consist of three genes, including PHF20, accompanied by one or both of APBA2 and ABCA1.
  • the first gene sub-panel may consist of four genes, including PHF20, accompanied by one or both of APBA2 and ABCA1.
  • the first gene sub-panel may consist of five genes, including PHF20, accompanied by one or both of APBA2 and ABCA1.
  • the first gene sub-panel may consist of six genes, including PHF20, accompanied by one or both of APBA2 and ABCA1.
  • a second gene sub-panel may comprise MAX, NOL9, MPRIP, HP, BST1 and TM9SF2.
  • the second gene sub-panel may comprise the expression level of one or more of NOL9, HP and MAX (particularly NOL9).
  • the second gene sub-panel may comprise the expression level of one or both of BST1 and MPRIP.
  • the second gene sub-panel may additionally comprise the expression level of TM9SF2.
  • the second gene sub-panel may consist of one, two, three, four, five, or six of MAX, NOL9, MPRIP, HP, BST1 and TM9SF2.
  • the second gene sub-panel may consist of one gene, which is NOL9, HP or MAX (particularly NOL9).
  • the second gene sub-panel may consist of two genes, one or both of which are selected from NOL9, HP and MAX (particularly NOL9).
  • the second gene sub-panel may consist of three genes, one, two or all of which are selected from NOL9, HP and MAX (particularly NOL9).
  • the second gene sub-panel may consist of four genes, one, two or three of which are selected from NOL9, HP and MAX (particularly NOL9).
  • the second gene sub-panel may consist of five genes, one, two or three of which are selected from NOL9, HP and MAX (particularly NOL9).
  • the second gene sub-panel may consist of six genes, one, two or three of which are selected from NOL9, HP and MAX (particularly NOL9).
  • the second gene sub-panel may consist of two genes, one of which is NOL9, HP or MAX (particularly NOL9) and the other of which is BST1 or MPRIP.
  • the second gene sub-panel may consist of three genes, including one or more of NOL9, HP or MAX (particularly NOL9), accompanied by one or both of BST1 and MPRIP.
  • the second gene sub-panel may consist of four genes, including one or more of NOL9, HP or MAX (particularly NOL9), accompanied by one or both of BST1 and MPRIP.
  • the second gene sub-panel may consist of five genes, including one or more of NOL9, HP or MAX (particularly NOL9), accompanied by one or both of BST1 and MPRIP.
  • the second gene sub-panel may consist of six genes, including one or more of NOL9, HP or MAX (particularly NOL9), accompanied by one or both of BST1 and MPRIP.
  • a third gene sub-panel may comprise HOMER3, NSUN6, HP, EPHA4 and BMP2K.
  • the third gene sub-panel may comprise the expression level of one or both of HP and HOMER3.
  • the third gene sub-panel may comprise the expression level of one or both of EPHA4 and BMP2K.
  • the third gene sub-panel may additionally comprise the expression level ofNSUN6.
  • the third gene sub-panel may consist of one, two, three, four or five of H0MER3, NSUN6, HP, EPHA4 and BMP2K.
  • the third gene sub-panel may consist of one gene, which is HP or H0MER3.
  • the third gene sub-panel may consist of two genes, one or both of which are selected from HP and HOMER3.
  • the third gene sub-panel may consist of three genes, one or two of which are selected from HP and HOMER3.
  • the third gene sub-panel may consist of four genes, one or two of which are selected from HP and H0MER3.
  • the third gene sub-panel may consist of five genes, one or two of which are selected from HP and HOMER3.
  • the third gene sub-panel may consist of two genes, one of which is HP or H0MER3; and the other of which is EPHA4 or BMP2K.
  • the third gene sub-panel may consist of three genes, including one or both of HP and H0MER3, accompanied by one or both of EPHA4 and BMP2K.
  • the third gene sub- panel may consist of four genes, including one or both of HP and H0MER3, accompanied by one or both of EPHA4 and BMP2K.
  • the third gene sub-panel may consist of five genes, including one or both of HP and H0MER3, accompanied by one or both of EPHA4 and BMP2K.
  • any of the aforementioned gene panels may further comprise 1 to 2 genes in addition to those listed above, without departing from the essential character of the panels of the present disclosure.
  • the genes that have been identified as being predictive of a subject developing acute symptoms of an influenza like or other respiratory disease exhibit altered expression levels following inoculation with a virus in subjects who then go on to exhibit acute symptoms relative to those who do not develop acute symptoms, as defined above. This indicates the potential of the genes to identify subjects who are more likely to develop acute symptoms of an influenza-like or other respiratory disease. Since symptoms of viral infection develop sooner in some subjects than in others, altered expression of the one or more genes according to the present invention may be predictive of acute symptoms, before a subject shows any symptoms of infection, or an early diagnostic indicator of acute symptoms at about the same time as the subject starts to show the first symptoms of infection.
  • the expression levels of the one or more genes in the biological sample may be measured using any suitable method known in the art for quantifying the expression level of a gene, particularly a mammahan gene.
  • the expression level of the one or more genes may be measured by quantifying mRNA transcripts of the one or more genes according to the invention in the biological sample.
  • a PCR-based method may be used such, for example, as RT-qPCR.
  • RT-qPCR-based methods are disclosed by United States patent no. 7,101,663, the contents of which are incorporated herein by reference.
  • An advantage of real-time PCR is its relative ease and convenience of use.
  • a gene expression microarray may be used of the kind disclosed in, for example, United States patent no. 6,040,138, the contents of which are incorporated herein by reference, in which a pool of labelled target cRNA molecules, which are obtained by transcribing double-stranded cDNA derived from the mRNA transcripts that are isolated from the biological sample and fragmenting the resulting cRNA transcripts, are hybridised to oligonucleotide probes having specific sequences that are immobilised at specific addresses on a solid support. After incubating the cRNA targets with the surface- bound probes, the arrays are washed and the labels on the targets may be used to quantify how much target is bound to any given feature on the array. The amount of a given surface-bound target cRNA is proportional to the expression level of the corresponding gene.
  • RNA-seq may be used to quantify, discover and profile RNAs. This uses next- generation sequencing on cDNA converted from RNA (Wang et al 2009).
  • the biological sample may be a blood or a respiratory sample.
  • the sample may be a sample containing immune cells.
  • the expression level of each of the one or more genes may be compared with a respective reference level.
  • the reference level may be a threshold expression level that indicates acute symptoms of an influenza -like or other respiratory disease or a prediction of acute symptoms developing.
  • the reference level may be a baseline level of expression which indicates that the subject is unlikely to develop acute symptoms of an influenza-like or other respiratory disease.
  • the method may involve an individual reference level for each gene. Altered expression of at least one of the genes, preferably two or more of the genes, relative to their respective reference levels may indicate acute symptoms of an influenza like or other respiratory disease or predict developing acute symptoms of an influenza like or other respiratory disease in accordance with the present invention.
  • the reference level for the, or each, gene may be a previously measured expression level for the gene in the same subject.
  • the reference level for the, or each, gene may comprise a baseline expression level of the gene for the subject which is measured at a time when the subject is known not to be infected with a respiratory virus such, for example, as influenza.
  • a respiratory virus such, for example, as influenza.
  • the reference level for each gene may comprise an average of multiple previous levels.
  • a subject may be tested once to obtain baseline levels for the one or more genes, which form reference levels that may be used subsequently in case of suspected viral infection or a routine check, for comparison with contemporaneous expression levels to predict whether or not the subject is likely to develop acute symptoms of an influenza like or other respiratory disease.
  • Exposure to a respiratory virus includes any contact or possible contact with a respiratory virus including, exposure to a community acquired respiratory viral infection, exposure to respiratory virus at home, within a care home, hospital or military setting. Exposure also includes inoculation of subjects during a human challenge model and/or clinical trial.
  • a single gene panel containing one or more genes, may be analysed using a first algorithm.
  • a combination of gene panels and gene-sub panels may be analysed.
  • the different gene panels and gene sub-panels may be analysed simultaneously, for example using the same biological sample or samples obtained within a similar, or the same, time fame.
  • the different gene panels and gene sub-panels may be analysed sequentially with a first gene panel or gene sub-panel analysed in a sample taken at a first time point using a first algorithm, and a second gene panel or gene sub-panel analysed in a sample taken at a second time point using a second algorithm, and a third gene panel or gene sub-panel analysed in a sample taken at a third time point using a third algorithm etc.
  • the biomarker of the invention may be based on a number of input variables or factors, including gene expression levels, for example the age of the subject, or other underlying conditions that a subject may suffer e.g. asthma, may be included in the variables used to calculate the biomarker.
  • the biomarker may therefore be a composite biomarker.
  • the output of the biomarker may be a numerical value.
  • the numerical value may be determined using a threshold, reference level, or baseline level, for example a numerical value above a certain reference level predicts that a subject will develop acute symptoms of an influenza-like or other respiratory disease.
  • the biomarker may be computer-generated and comprises an output variable of a classification algorithm that uses as input variables the expression levels of one or more genes in the gene panel; or one or more genes in the first gene sub-panel; or one or more genes in the second gene sub-panel; or one or more genes in the third gene sub-panel.
  • the classification algorithm may be configured to prioritise accuracy such that the algorithm produces the greatest number of correct predictions.
  • the classification algorithm may be configured to prioritise Negative Predictive Value (NPV), the proportion of negative test results that are true negatives, the aim being to minimise the number of subjects predicted not to develop acute symptoms of influenza-like disease who in fact go on to develop acute symptoms of an influenza-like or other respiratory disease.
  • NPV Prioritise Negative Predictive Value
  • the classification algorithm may be derived by machine-learning fiom a training data-set that uses as input variables expression levels of one or more genes fiom the gene panel measured fiom a biological sample obtained fiom a group of subjects at a predetermined time after exposure to the respiratory virus, wherein the group of subjects is divided into two classes according to whether or not they developed acute symptoms of an influenza-like or other respiratory disease after exposure to the respiratory virus, and wherein the classification algorithm operates on the expression levels to produce an output variable that differentiates between the classes.
  • classification algorithms are available to those skilled in the art for classifying subjects into two or more classes based on their symptoms scores.
  • numerous machine learning techniques are available for using a training dataset comprising the two or more classes and their respective expression levels for the one or more genes to derive a classification algorithm that is able to classify a new subject based on their expression levels of the one or more genes.
  • the performance of a classification algorithm built using a machine learning process may be validated using one or more known validation methods, e.g. cross-validation, and calculating statistical parameters (e.g. accuracy, sensitivity, specificity) so that the person skilled in the art can obtain a classification algorithm that is best suited for classifying subjects based on their expression levels of the one or more genes.
  • the acute symptoms of an influenza-like or other respiratory disease in the subjects of the group in the training data set may be assessed by evaluating one or more symptoms of influenza-like or other respiratory disease at a series of pre-set times after exposine to the respiratory virus.
  • the one or more symptoms are evaluated by the subjects using diary cards, optionally visual analogue scare symptom diary cards (VAS), or optionally categorical symptoms (CAT) are recorded using a modified standardized symptom score for example the modified Jackson Score.
  • the symptoms evaluated may include runny nose, stuffy nose, sore throat, sneezing, earache, cough, shortness of breath, headache, malaise, myalgia, muscle and/or joint aches, chilliness, and feverishness.
  • the first class of subjects may record a total VAS of greater than or equal to 25 units and/or a total CAT score of 10 units or greater, or may show one or more of: greatest variance in total VAS or CAT up to the peak of symptoms; greatest variance in total VAS or CAT over the duration of quarantine; or steepest gradient (slope of regression line) of total VAS or CAT up to the peak of symptoms.
  • machine learning processes and the resulting classification algorithms may be carried out using a computer.
  • the gene panels and gene sub-panels may be selected by i) analysing expression levels in biological samples obtained from the group of subjects in the data training set across the whole series of pre-set times after exposure to the virus; and ii) identifying genes that show a nominal association with acute symptoms of an influenza-like or other respiratory disease, and iii) using a variable selection process to select panels of the identified genes whose expression levels at a predetermined time after exposure to the virus exhibit maximal predictive value for developing acute symptoms of an influenza- like or other respiratory disease.
  • the variable selection process may comprise subjecting the expression levels of the identified genes at the predetermined time after exposure to the respiratory virus to a repeated gradient boosting process and selecting a set of 1, 2, 3, 4, 5 or 6 genes that are selected most frequently by the gradient boosting process.
  • a variable selection process is illustrated in FIG. 6 of the accompanying drawings and is performed by a gradient boosting machine (GBM ; Friedman 2001; Friedman 2002).
  • GBM gradient boosting machine
  • differential expression analysis of genes between subjects that developed acute symptoms of influenza-like or other respiratory disease and subjects that did not develop acute symptoms of influenza-like or other respiratory disease was performed by application of a cubic p-spline model.
  • Nominal associations arising from the cubic spline analysis were input into a variable selection process comprising gradient boosting machine, and iterative searches were conducted using fifty starting point (seeds), to determine the best gene predictors of developing acute symptoms of influenza-like or other respiratory disease.
  • the biomarker may be used to allocate subjects to groups in a clinical trial or to make treatment decisions. Subjects allocated to one subgroup are administered a medicament while those allocated to another subgroup are not administered a medicament or do not receive a medicament until later in the trial or study.
  • the biomarker may also be used to monitor the efficacy of a medicament by assessing whether a subject to whom the medicament has been administered is likely to develop acute symptoms of disease after exposure, or possible exposure, to a respiratory virus.
  • the medicament may comprise a therapeutic agent or a preventative agent such, for example, as a vaccine.
  • the present invention provides a method of predicting whether a subject will develop acute or complicated symptoms of disease after exposure, or possible exposure, to a respiratory virus which comprises analysing a biomarker according to the invention and comparing the biomarker to a reference for the biomarker.
  • the present invention also provides a method of conducting a clinical trial or field study in which a group of subjects are exposed to a respiratory virus, the method comprising analysing a biomarker according to the invention for each subject and comparing the biomarker to a reference for the biomarker to predict whether the subject is likely to develop acute symptoms of disease, and including subjects who are predicted to develop acute symptoms of disease in a first subgroup of the clinical trial or field study and including subjects who are predicted not to develop acute symptoms of disease in a second subgroup.
  • the methods of the invention may include comparing the biomarker to a reference for the biomarker or to a baseline for the biomarker.
  • the baseline for the biomarker may be determined at a time when the subject is known not to be infected with a respiratory virus.
  • the disease may be an influenza-like or other respiratory disease.
  • a medicament includes all substances used for medical treatment and includes vaccines, drugs, placebos and investigational medicaments, for example investigational medicaments that are the subject of a clinical trial. Therefore medicament includes licensed, unlicensed and investigational medicaments. Medicament also includes products that already have a marketing authorisation but that are being tested for a different use, or for efficacy when assembled in a different way, or tested to gain further information about the authorised use.
  • the invention also provides a computer program for predicting whether a subject will develop acute symptoms of disease such, for example, as an influenza-like disease after exposure, or possible exposure, to a respiratory virus, which comprises instractions which, when the program is executed by a computer, cause the computer to generate a biomarker according to the invention.
  • the invention further provides a classification algorithm for predicting whether a subject will develop acute symptoms of disease such for example as an influenza-like or other respiratory disease after exposure, or possible exposure, to a respiratory virus, wherein the classification algorithm is derived by analysing expression levels of one or more genes in subjects who have developed acute symptoms of disease and comparing with the expression levels in subjects who do not develop acute symptoms of disease, wherein the one or more genes are PHF20, AB CAI, APBA2, M0RC2, SNU13 DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
  • the classification algorithm is derived by analysing expression levels of one or more genes in subjects who have developed acute symptoms of disease and comparing with the expression levels in subjects who do not develop acute symptoms of disease, wherein the one or more genes are PHF20, AB CAI, APBA2, M0RC2, SNU13 DCUN1D2, MAX, NOL9, MPRIP, HP, BST1,
  • the invention further provides a computer readable medium and/or computer program comprising instructions which, when executed by a computer, cause the computer to carry out the classification algorithm according to the invention.
  • the invention also provides a computer-implemented method for predicting whether a subject will develop acute symptoms of disease such for example as an influenza-like disease, wherein a biomarker is generated by analysing expression levels of one or more genes in subjects who have developed acute symptoms of disease following inoculation with a respiratory virus and comparing with the expression levels in subjects who do not develop acute symptoms of disease following inoculation with a respiratory virus, wherein the one or more genes are PHF20, ABCA1, APBA2, MORC2, SNU13 DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
  • a biomarker is generated by analysing expression levels of one or more genes in subjects who have developed acute symptoms of disease following inoculation with a respiratory virus and comparing with the expression levels in subjects who do not develop acute symptoms of disease following inoculation with a respiratory virus, wherein the one or more genes are PHF20, ABCA1, APBA2, MORC
  • the invention also provides a computer-implemented method, wherein the method comprises a graphical user interface which displays the biomarker to the user. It is also contemplated that the computer-implemented aspects of the invention may be carried out by more than one computer e.g. two or more computer operating in different locations. Two or more computers may communication via a data channel, for example the internet.
  • the invention also provides a method of predicting whether a subject will develop acute symptoms of disease after exposure, or possible exposure, to a respiratory virus, which comprises estimating time elapsed after the exposure, or possible exposure, to the respiratory virus by analysing expression levels of one or more genes selected from PHF20, AB CAI, APBA2, M0RC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, H0MER3, NSUN6, EPHA4, in a biological sample obtained from the subject; selecting a biomarker described herein, which at said time exhibits maximal predictive value for developing acute symptoms of disease; and comparing the biomarker to a reference for the biomarker.
  • the time elapsed may be estimated to be about a day (i.e. about 23-26 hours, e.g. 25 hours) after exposine, or possible exposure, to the respiratory virus.
  • the selected biomarker may comprise expression levels of one or more genes from the first gene sub-panel described herein.
  • the time elapsed may be estimated to be about 1.5-2 days (i.e. about 37-49 hours) after exposure, or possible exposure, to the respiratory virus. If so, the selected biomarker may comprise expression levels of one or more genes from the second gene sub-panel described herein.
  • the time elapsed may be estimated to be he about 2-2.5 days (i.e. about 49-61 hours) after exposure, or possible exposure, to the respiratory virus. If so, the selected biomarker may comprise expression levels of one or more genes from the third gene sub-panel described herein.
  • the invention also provides a kit for use in a method according to the invention.
  • the kit may comprise one or more reagents that allow detection, optionally quantitation, of one or more nucleotides, or one or more peptides, corresponding to one or more genes from the gene panel or the first, second or third gene sub-panel described herein.
  • the kit may be for detection of one or more analytes in, or extracted from, a biological sample, such as, but not limited to, a blood, serum, plasma, mine, saliva, tissue biopsy, stool, sputum, skin, nose or throat sample.
  • the kit may comprise a device for conducting an assay, such as a lateral flow assay.
  • the device may be configured for autonomous use by a patient (without assistance from a physician), for example in the home (as opposed to a hospital or other medical facility).
  • the device may comprise a strip of porous material, which is capable of supporting capillary flow, wherein there is a zone for receiving a sample; a zone comprising a reagent for detection of an analyte; a detection zone; and a control zone.
  • a reaction between the reagent and the analyte may be detected in the detection zone, for example by a change in colour of material in the detection zone.
  • the control zone may serve as a reference against which to benchmark the reaction detected in the detection zone.
  • the device may comprise more than one test strip, for example the device may comprise two, three, four, five or six test strips, in communication with the same or separate receiving zones.
  • FIG. 1 is a chart showing how a subject is processed through a typical clinical study as described in Example 1 below.
  • FIG. 2 is a graph showing peak level of Total VAS for infected (left) and non-infected (right) subjects.
  • FIG. 3 is a graph showing change in maximum variance of VAS scores demonstrating that the four individuals with acute symptoms of influenza-like disease at peak all experienced a change in variance of Total VAS greater than 30 units.
  • FIG. 4 is a graph showing peak level of peak categorical scores for infected (left) and non- infected (right) subjects across three studies (hVIVO-l, Duke-1, Duke-2).
  • FIG. 5 is a principal component analysis to show greater homogeneity after imposing adjustment for study.
  • FIG. 6 is a flow chart to demonstrate the variable selection process performed by gradient boosting machines.
  • FIG. 7 is a flow diagram to demonstrate scenarios in which 1 to 3 of the algorithms are run in parallel to assign subjects to groups for actioning (e.g. dosing with a medicament, additional clinical assessments).
  • the scenarios include: within the human viral challenge model, field study, or in the community, wherein subjects are exposed to a respiratory virus.
  • FIG. 8 is a flow diagram to demonstrate scenarios in which each gene algorithm is used sequentially to assign subjects to groups for actioning (e.g. dosing with a medicament, additional clinical assessments) within the human viral challenge model.
  • AfiymetrixTM HG-U133 Plus 2.0 microarray across the whole quarantine post inoculation were used to perform transcriptomics analysis.
  • Differential expression analysis between subjects developing acute symptoms of an influenza-like disease and subjects that did not develop acute symptoms of an influenza-like disease was performed by application of a cubic p-spline model. Nominal associations arising from the cubic spline analysis were input into a variable selection process to determine the best six gene predictors of acute symptoms of influenza-like disease at Day 1 morning, Day 2 morning, and Day 2 evening after inoculation. These genes could be used to distinguish between subjects that developed acute symptoms of an influenza-like disease and subjects that did not develop acute symptoms of an influenza- like disease within the model at different times after exposine to virus.
  • influenza A was instilled into bilateral nares of subjects using standard pipetting methods.
  • the volunteers had clinical measurements and samples taken until discharged from quarantine and then at each follow up visit.
  • hVIVO-1 33 of the 60 subjects became infected after inoculation (evidenced by confirmed viral shedding), 25 were identified as not infected and 2 inconclusive. An interim analysis was performed after the first 27 were inoculated and all samples for each subject were sent for gene microarray assays. One of the 27 subjects did not complete the quarantine and so was excluded from analysis. Of the 26 subjects with viable microarray data, 13 were identified as confirmed as infected and 11 as not infected, 2 were inconclusive.
  • Subjects self-assessed their symptoms three times daily throughout quarantine on both categorical and continuous (Visual Analogue Scale, VAS) symptom diary cards.
  • Categorical symptoms were recorded using a modified standardized symptom score.
  • the modified Jackson Score requires subjects to rank 10 symptoms consisting of: upper respiratory tract symptoms (runny nose, stuffy nose, sore throat, sneezing, and earache), lower respiratory symptoms (cough and shortness of breath) and systemic symptoms (headache, myalgia, and muscle and/or joint aches) on a scale of 0-3 of “no symptoms”, “just noticeable”, “bothersome but can still do activities” and “bothersome and cannot do daily activities”.
  • RNA PAXGeneTM collection tubes were collected into RNA PAXGeneTM collection tubes. This occurred once on Day -1, in the morning on the day of inoculation (approximately 5 hours before inoculation) followed by every 12 hours for hVIVO-1 and Duke-2 and every 8 horns for Duke-1 for the remainder of the quarantine.
  • PCA Principal Components Analysis
  • FIG. 6 shows the process that was followed.
  • Table 3 shows the variables selected by gradient boosting
  • Table 4 shows the signature arising from logistic regression and lastly
  • Table 5 shows the test performance characteristics at all time- points considered. It can be seen that the signature, or gene sub-panel, performs well (AUG > 0.80) on Day 1 data.
  • This signature includes the genes PHF20, ABCA1, APBA2, M0RC2, SNU13, and DCUN1D2.
  • Table 6 shows the variables selected by gradient boosting
  • Table 7 shows the signature arising from logistic regression and lastly
  • Table 8 shows the test performance characteristics at all time- points considered. The signature performs well at Day 2 (AM).
  • This signature, or gene sub-panel includes the genes MAX, N0L9, MPRIP, HP, BST1,
  • Table 9 shows the variables selected by gradient boosting
  • Table 10 shows the signature arising from logistic regression and lastly
  • Table 11 shows the test performance characteristics at all time- points considered.
  • This signature includes the genes H0MER3, NSUN6, HP, EPHA4,
  • a group of individuals are recruited into a human viral challenge model and inoculated with a respiratory virus. It is beneficial to identify, in advance, subjects who will progress to develop acute symptoms of an influenza like disease, allowing selective dosing of these subjects with an investigational or licensed medicament (drug/vaccine/ placebo) at the earliest opportunity. This will improve the ability to detect a clinically relevant reduction in disease in response to the medicament by only evaluating the medicament effects in individuals that will/would have developed acute symptoms of an influenza-like disease. This will also reduce unnecessary exposure of subjects to an investigational medicament. This will also reduce the amount of medicament required.
  • Volunteers are screened for eligibility for the evaluation of efficacy of an investigational medicament in a human challenge study with a respiratory virus, in particular with influenza.
  • Eligible volunteers arrive at the clinic and baseline samples and clinical measures are taken, before they are exposed to virus (e.g. inoculation). Baseline values are obtained pre-inoculation using one or more blood samples over varying time-points.
  • Blood samples are taken regularly before and after virus exposure (e.g. paxgene RNA samples twice, three times a day, or more) alongside clinical measures of their disease.
  • virus exposure e.g. paxgene RNA samples twice, three times a day, or more
  • Expression levels of specific gene panels and sub-panels are measured in real-time from the blood paxgenes.
  • blood is assessed for gene expression utilising Affymetrix HG- U133 Plus 2.0 microarray chips, which were used to measure the transcripts’ expression.
  • Microarray data was pre-processed using RMA background correction and quantiles normalization.
  • Three separate gene subpanels can be used (i.e. 3 algorithms) to identify which individuals will develop acute symptoms of an influenza-like disease a.
  • the 3 gene sub panels are: i.
  • Subpanel A PHF20, ABCA1, APBA2, M0RC2, SNU13 and DCUN1D2 ii.
  • Subpanel B MAX, NOL9, MPRIP, HP, BST1 and TM9SF2 iii.
  • Subpanel C H0MER3, NSUN6, HP, EPHA4 and BMP2K
  • the different gene subpanels are used at different time points (FIG. 8, sub panel A, followed by sub panel B, followed by subpanel C).
  • subpanels sequentially 1, 2, or 3 gene sub panels are used at the same time (FIG. 7), and may be repeated at several points following inoculation.
  • a positive result for any test immediately triggers dosing of the subject with the investigational medicament (drug/vaccine/placebo).
  • two positive results, or three positive results, are required to trigger dosing.
  • the stringency can be varied by modifying the threshold at which a positive result is obtained.
  • the threshold for gene sub-panel 1 could be set to be more stringent avoiding false positives.
  • Gene sub-panel 2 is then set with lower stringency and gene sub- panel 3 with even lower stringency, thus increasing the chances of identifying and dosing all subjects who will develop acute symptoms of influenza-like disease as early as possible.
  • results may be combined with a diagnostic test that confirms the subject has the respiratory viral infection relevant to the trial (e.g. a viral test).
  • a diagnostic test that confirms the subject has the respiratory viral infection relevant to the trial (e.g. a viral test).
  • results may be combined with measurements of the change in variance/gradient of symptoms.
  • Other actions that can be triggered alongside dosing with a medicament include increasing the observations/samples/measurements in those predicted to develop acute symptoms of an influenza-like disease or reducing observations/samples/measurements in those who are predicted not to develop acute symptoms of an influenza-like disease.
  • the subjects may be dosed at a predetermined time point post exposine or inoculation (e.g. Day 4). These subjects form a further subgroup for analysis.
  • a group of individuals are recruited into an efficacy field study and become infected in the community with a respiratory virus, in particular influenza. Following exposure, it would be beneficial to identify, in advance, subjects who will develop acute symptoms of an influenza like disease, allowing selective dosing of these subjects with an investigational or licensed medicament (drug/vaccine/ placebo) at the earliest opportunity. This would improve ability to detect clinically relevant reduction in disease by only evaluating the medicament effects in individuals that would have gone on to present with acute symptoms of an influenza-like disease. This will reduce unnecessary exposine of subjects to an investigational medicament as well as reducing the amount of medicament required.
  • Eligible volunteers arrive at the clinic and baseline samples and clinical measures are taken as they are enrolled in the study. Baseline values would be obtained using one or more blood samples over varying time-points post enrolment and prior to contracting a respiratory virus infection in the community.
  • Blood samples e.g. paxgene RNA samples twice, three times a day, or more
  • blood samples are taken after virus exposure from a household contact that has a respiratory infection, or after showing initial symptoms of respiratory disease (the trial subjects may or may not be using a study questionnaire/symptom diary card that captures these symptoms).
  • Specific gene sub-panels are measured real-time in the blood paxgenes using the methods described in Examples 1 and 2.
  • Three separate gene sub-panels can be used (i.e. 3 algorithms) to identify which individuals will progress to have acute symptoms of an influenza-like disease a.
  • the 3 subpanels of genes are: i.
  • Subpanel A PHF20, ABCA1, APBA2, M0RC2, SNU13 and DCUN1D2 ii.
  • Subpanel B MAX, NOL9, MPRIP, HP, BST1 and TM9SF2 iii.
  • Subpanel C HOMER3, NSUN6, HP, EPHA4 and BMP2K
  • the stringency can vary by modifying the threshold at which a positive result is obtained.
  • the threshold for gene sub-panel 1 could be set to be more stringent avoiding false positives.
  • Gene sub-panel 2 is then set with lower stringency and gene sub-panel 3 with even lower stringency, thus increasing the chances of identifying and dosing all subjects who will develop acute symptoms of an influenza-like disease as early as possible.
  • results may be combined with a diagnostic test that confirms the subject has the respiratory viral infection relevant to the trial (e.g. viral test).
  • results may be combined with measurements of the change in variance/gradient of symptoms.
  • Other actions that can be triggered alongside dosing with a medicament include increasing the observations/samples/measurements in those predicted to go on develop acute symptoms of influenza-like disease or reducing observations/samples/measurements in those predicted to not develop acute symptoms of an influenza-like disease.
  • the subjects may be dosed at a predetermined time points post exposine (e.g. Day 4, Day 5), with these subjects forming a secondary subgroup analysis.
  • exposine e.g. Day 4, Day 5
  • Subjects may become infected with a respiratory virus in the community or exposed to an infected person for an extended period in the community.
  • Community settings can include at home (family member, household contact), at work, in transit (e.g. on a train, coach, plane, ship), within a care home (fellow resident, family visitor, carer), as an inpatient in hospital (fellow inpatient, healthcare worker, visitor), within military setting (fellow personnel). Following exposure, it would be beneficial to identify, in advance, people who will progress to have acute symptoms of an influenza-like disease, allowing an intervention at the earliest opportunity.
  • Interventions include assisting referral to healthcare professionals, enabling earlier treatment with an antiviral than would otherwise be possible (for example Tamiflu), administration of an immunomodulator drug or combination of antiviral and immunomodulator, separation fiom others (quarantine), inclusion in a study (IMP trial, transmission study), initiate sampling for disease/biomarker monitoring.
  • a subject becomes infected with a respiratory virus in the community or is exposed to an infected person for a prolonged period in the community.
  • Post viral exposure blood samples may be collected and gene levels quantified following one or more triggers: a. a positive diagnostic test (e.g. based on viral replication, diagnostic biomarker) b. initial symptoms of respiratory viral disease c. prolonged exposine to an infected contact
  • Specific gene panels or gene sub-panels are measured real-time in the blood sample (e.g. using a Point of Care test).
  • the absolute value of each gene at a given time-point can be used or alternatively where a baseline gene level was obtained or is available, the gene levels post exposure to virus can be baseline normalised for each subject (i.e. compared to baseline).
  • three separate gene sub-panels are used (i.e. three algorithms) to identify which individuals are likely to develop acute symptoms of an influenza-like disease a.
  • the three subpanels of genes are: i.
  • Subpanel A PHF20, ABCA1, APBA2, M0RC2, SNU13 and DCUN1D2 ii.
  • Subpanel B MAX, NOL9, MPRIP, HP, BST1 and TM9SF2 iii.
  • Subpanel C HOMER3, NSUN6, HP, EPHA4 and BMP2K
  • a positive result for any test may trigger one or more of the following: i. assisting referral to healthcare professionals, ii. enabling earlier treatment with an antiviral than would otherwise be possible (e.g.Tamiflu), iii. administration of an immunomodulator drug or combination of antiviral and immunomodulator, iv. separation from others (quarantine, using barriers to transmission e.g. masks), v. inclusion in a study (e.g. IMP trial, disease study, transmission study), vi. initiate sampling for disease/biomarker monitoring b.
  • two positive results or three positive results are required to trigger the actions, as listed above.
  • the stringency can vary by modifying the threshold at which a positive result is obtained.
  • the threshold for gene sub-panel 1 could be set to be more stringent avoiding false positives.
  • Gene sub-panel 2 is then set with lower stringency and gene sub- panel 3 with even lower stringency, thus increasing the chances of identifying and intervening as early as possible.
  • results may be combined with a diagnostic test that confirms the subject has the respiratory viral infection (if not already performed).
  • RNA-Seq a revolutionary tool for transcriptomics. Nature Reviews. Genetics, 10(1), 57-63.

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Abstract

L'invention concerne des méthodes permettant de prédire si un sujet est susceptible de développer des symptômes aigus de maladie après une exposition, ou une éventuelle exposition, à un virus respiratoire, qui comprennent l'analyse d'un échantillon biologique obtenu à partir du sujet à la recherche d'un biomarqueur et la comparaison du biomarqueur à une référence pour le biomarqueur, le biomarqueur comprenant ou étant dérivé de niveaux d'expression d'un ou plusieurs gènes choisis parmi un panel de gènes comprenant PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 et BMP2K. L'invention concerne également des procédés prédictifs associés, des procédés de réalisation d'une étude clinique ou d'un essai sur le terrain, des programmes informatiques, des algorithmes de classification, des supports lisibles par ordinateur et des procédés mis en œuvre par ordinateur.
EP22731779.9A 2021-06-02 2022-06-01 Biomarqueurs et méthodes de classification de sujets suite à une exposition virale Pending EP4347891A1 (fr)

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