WO2020175633A1

WO2020175633A1 - METHOD FOR PREDICTING AND DETERMINING EFFICACY OF TREATMENT OF RHEUMATOID ARTHRITIS USING ANTI-TNF–α AGENT

Info

Publication number: WO2020175633A1
Application number: PCT/JP2020/008079
Authority: WO
Inventors: 吉崎　和幸; 宇野　賀津子; 仁藤宮
Original assignee: 吉崎　和幸
Priority date: 2019-02-28
Filing date: 2020-02-27
Publication date: 2020-09-03
Also published as: JPWO2020175633A1

Abstract

The present disclosure provides a method for easily, inexpensively, and accurately determining treatment efficacy (especially whether complete remission will be achieved), prior to the administration of a biological pharmaceutical such as an anti-TNF–α agent to a rheumatoid arthritis patient. Said method includes a step in which at least two types of clinical data or biological parameters are obtained from a rheumatoid arthritis patient prior to the administration of a biological pharmaceutical, wherein the treatment efficacy (specifically, whether complete remission will be achieved) of a biological pharmaceutical such as an anti-TNF–α agent is determined on the basis of the biological parameters. The biological parameters used can include the concentrations of cytokine, chemokine, and soluble receptors therefor in a body fluid sample such as serum. The treatment efficacy can also be determined using only clinical data.

Description

\¥0 2020/175633 1 ?<:17 2020/008079 Clarification

Title of invention:

A method for predicting and judging the therapeutic effect of rheumatoid arthritis by anti-F_a agent

Technical field

[0001] The present disclosure relates to a method for predicting and determining the therapeutic effect of a biologic on rheumatoid arthritis patients. More specifically, the present disclosure relates to a method for predicting and determining the therapeutic effect of rheumatoid arthritis by a biological preparation containing an anti-drug. More specifically, the present disclosure relates to a method for predicting and determining a therapeutic effect such as a degree of improvement of a symptom and a possibility of remission, before administering a biologic to a patient with rheumatoid arthritis. Furthermore, the present disclosure relates to a diagnostic agent for predicting the therapeutic effect of a biologic on rheumatoid arthritis patients. More specifically, the present disclosure relates to techniques for pre-determining treatment with a biological agent containing an anti-drug and providing an effective therapeutic agent to a patient.

Background technology

[0002] Rheumatoid arthritis is a systemic inflammatory disease centering on the synovial membrane of the joint, and the number of patients is said to be about 700,000 in Japan. Many biologics targeting inflammatory cytokines have been developed for the treatment of rheumatoid arthritis. For example, in recent years, anti-drug agents that inhibit the action of Ding have been clinically put to practical use. ..

[0003] — As an example, etanercept is a complete human soluble soluble / / _ that consists of the human ◯ 9 ◦ 1 region and the human antibody domain of the human tumor necrosis factor 丨 type receptor. Ding is a receptor formulation,

It is a drug that calms rheumatoid arthritis by binding to both of these and inhibiting signal transduction to the receptor. Adalimumab and infliximab are human and chimeric anti-antibodies that produce overproduced Dingamu.

It is a drug that calms rheumatoid arthritis by specifically binding to _ and inhibiting binding to _ _ and _ receptors. Like this 〇 2020/175633 2 (:171? 2020/008079

Drugs targeting gnal have multiple types of targets, but the mechanism of action after binding to the target is similar.Therefore, the mechanism of inhibition is the same regardless of the type of drug. , Is generally called an anti-drug. For example, etanercept, adalimumab, infliximab, golimumab, and certolizumab are classified as anti-drug agents.

[0004] Such biologics have been confirmed to have a certain effectiveness in the treatment of rheumatoid arthritis, and the types thereof are increasing, and are becoming popular. On the other hand, in addition to the drawbacks such as high drug price and time-consuming determination of therapeutic effect, there were also a certain proportion of ineffective patients, and side effects such as infectious diseases and interstitial pneumonia were also observed. May be As a result, there are limited cases where biologics can be used. Therefore, if the efficacy of a biologic targeting inflammatory cytokine can be estimated in advance for each rheumatoid arthritis patient, it will bring the gospel to rheumatoid arthritis patients and contribute to the medical economy. In other words, it is a reasonably good tool for the government authorities responsible for social security costs, such as the Ministry of Health, Labor and Welfare, to reduce social security costs, and even for patients, the drug is not effective after it deteriorates. It is possible to avoid quality concerns and receive high-quality treatment. Also, for pharmaceutical manufacturers, the increased accuracy of the biologics or the degree of establishment of the therapeutic effect will significantly increase the possibility that the biologics will be applied to patients who have not previously selected.

Summary of the invention

Means for solving the problem

[0005] The present disclosure predicts therapeutic effect (improvement of symptoms and possible remission) with high accuracy before administering a biological agent such as an anti-drug to a patient with rheumatoid arthritis. Provide a way. The present disclosure also provides a diagnostic agent for carrying out the above method, and a therapeutic agent including a biological agent such as an anti-drug, which is characterized by carrying out the above method.

[0006] The present inventors have investigated the prognosis state of a rheumatoid arthritis patient administered with a biological agent and the parameters of the patient before the administration of the biological agent (for example, body fluids such as serum). 〇 2020/175633 3 boxes (：171? 2020 /008079

(Including the concentrations of cytokines, chemokines and their soluble receptors in the sample) and a retrospective analysis to determine the remission of rheumatoid arthritis with biologics such as anti-drugs. It was found that the possibility, the degree of improvement in symptoms, and the disease activity index can be predicted and determined. As a result of this analysis, the inventors of the present invention used the clinical data as a marker for such a determination, including the clinical data, 0 weeks 3 [¾, 881 1\/1 3 1 3 1 3 , 0 0 4 4 ,0 Week 〇㊀门

We have found that parameters such as 1 1 0, Ding [¾ 2, 0 0 3 0, 1 1--1 3, 1 !_-9, and 3 Ding can be used. By utilizing the values of these specific markers, the present inventors have investigated the therapeutic effect on rheumatoid arthritis patients before administration of biologics targeting inflammatory cytokines such as G. For example, they have found that the possibility of remission, the degree of improvement of symptoms, and the disease activity index) can be determined easily and inexpensively at any facility and can be determined in advance (prediction determination) with high accuracy.

[0007] Examples of embodiments of the present disclosure are provided in the following items.

(Item 1)

A method for predicting and determining the therapeutic effect of a biologic agent on rheumatoid arthritis patients, comprising:

Obtaining at least two clinical data or biological parameters in a rheumatoid arthritis patient prior to administration of said biologic, and

Using at least one of the clinical data or the biological parameter as a control, analyzing the biological parameter, and using it as an index for predicting and determining the therapeutic effect

Including the method.

(Item 2)

The method according to the preceding item, wherein the biological agent comprises an anti-drug. 〇 2020/175633 4 boxes (：171? 2020/008079

(Item 3)

The method according to any of the preceding items, wherein the therapeutic effect is remission of the patient.

(Item 4)

Obtaining at least two clinical data or biological parameters in a rheumatoid arthritis patient prior to administration of the biological agent

The biological agent is an anti-drug, the therapeutic effect is amelioration of the patient,

The at least two types of clinical data or biological parameters are

1\/1〇1, Dinghachi, Week 0 [¾,

Osteopontin,

2, 〇 0 3 0, 1 !_-1 3,

and

A method comprising a parameter selected from the group consisting of:

(Item 48)

The method of any of the preceding items, further comprising the features of any one or more of the above items.

(Item 5)

The method according to any of the preceding items, characterized in that at least one of the at least two clinical data or biological parameters is used as an endogenous control.

(Item 6)

The method according to any of the preceding items, characterized by using a ratio of the at least two types of clinical data or biological parameters that is not the endogenous control to the endogenous control. The method according to any of the preceding items, wherein the anti-TN Fa agent is selected from the group consisting of infliximab, etanercept, adalimumab, golimumab, and certolizumab.

(Item 8)

The method of any of the preceding items, wherein the anti-TNF agent is infliximab.

(Item 9)

The at least two clinical data or biological parameters are s TN FR 丨, Week 0 ESR, I L- 12 p 70, MC P 1, T RA IL, TNF a, A DAMTS 13 3, CD44, The method according to any of the preceding items, comprising a parameter selected from the group consisting of GM-CS F, BMP 2, MMP 2, SM, IL-1/3, TWEAK, and BMP2.

(Item 10)

The at least two clinical data or biological parameters are as follows:

BLogTNF.a, DAS. ESR.0w, ESR— 0w, Genera l. VAS.0W, RlogADAMTS13, RlogCD 44, and RlogTRAIL;

BLogChitinase.3, BlogBAFF, BlogMCP. U BlogRANTES, BlogTWEAK, DAS. ES R.0w, ESR— 0w, General. VAS.0W, RlogADAMTS13, RlogBMP.2, RlogCD44 and logRF0w;

BLoglL.lb, DAS. ESR.0w, ESR— 0w, Genera l. VAS.0W, RlogADAMTS13, RlogBM P.2 and RlogCD44;

B logOsteopont in _% B logMCP.1 and B logRANTES;

DAS. ESR.0w, ESR— 0w, RlogADAMTS13, RlogBMP.2, R logCD44 and logRF0w

MLogsTNFRI, BlogIL.12p70 _% BlogMCP. U DAS. ESR.0w and ESR — 0w;

RlogTRAIU BlogMCP. U DAS. ESR.0w, ESR— 0w, Genera L VAS.0W and LogRF

0w; 020/175633 6 卩 (: 171? 2020 /008079

44 and [^ 〇 9 〇 311/1;

And 109 (^01/\/;

.0!«, 1^乂. ₁₁₁₉

, 1^ 〇 98 808 1^313, 1^0981^.2 and 1^ 〇 9 〇 044 ;and

81_Rei ₉ 11.9, 81_Rei ₉ 110 ³ .1, _DAS.ESR.0w, Snake 0! «And (^ 〇 ₉ eight hundred and eight 1 ^ 313

The method according to any of the preceding items, comprising a combination of parameters selected from the group consisting of:

(Item 11)

The at least two types of clinical data or biological parameters are as follows:

neral.VAS.0W, 1^ 〇 9 88 808 1^313, (^ 〇 9 〇 44, and 1^ 〇 9 Ding [^11_;

81 〇〇 |1; 丨 36.3, and 81 〇 988 Dodo, 81 〇 91^1, 81 〇9 (^ town £3, 81 〇9 die 1^8 1<, DAS.ESR as an endogenous control .0w, Min 0!«, General.VAS.0W, [^ 〇988 1^313, 1^0981^.2, 1^ 〇 9〇 044 and 10 〇 9 (^01«;

〇911_.11) as intrinsic control, and

! neral.VAS.0W, (^ 〇 ₉ eight hundred and eight 1 ^ 313, (¾ [098 ^ · 2 and 1_Rei ₉ 0044;

81 〇 9 〇〇〇〇 ^1 as an intrinsic control and

1^0 9 8 0 8 1^313 20/175633 7 卩 (: 171? 2020 /008079

, [¾10 981^.2, (¾1 090 444 and 1 090 (^01/\/;

1^09 as an internal control [^1

DAS.ESR.0w

, Min — 〇, 〇 6 “31. \ZAS_ 0111/ and 1 〇9 (^01/\/;

As an intrinsic control

And

£3 01/\/, 1^1 〇988 1^313, 1^0981^.2, [¾1 〇〇 044, 〇9 (½11/1 and 1^ 〇9 Ding [^11_;

(Item 1 2)

The at least two clinical data or biological parameters are as follows:

〇 2020/175633 8 卩 (：171? 2020 /008079

(Item 13)

The at least two types of clinical data or biological parameters are as follows: 1 ^ 〇 93 as an endogenous control, and 81 〇 91!_.12 70,

〇卩.1 and £3 01 «;

! 1 ^ Rei_9 Ding of as an endogenous control [^ 1 tooth, as well as, 81_Rei ₉ 1/0 ^3.1 and £ 3 (^ 0;

As an intrinsic control

As well as £3 01/\/ and 1^0 9 8 08 1^3

13 ;

As an endogenous control (^ Rei_9_rei_044, as well as, as a 81_Rei_91! / 0 ^3.1 and £ 3 01 / \ / endogenous control

And £3 01/\/ and [^ 〇988

1^13;

1^098 881^3 13 as an endogenous control, and 81〇 ₉ 11_.12 70,

〇91^?.1 as intrinsic control, and £3 01/\/, 1^〇 988 8 1^313,

Any of the preceding items, including a combination of parameters selected from the group consisting of 20/175633 9 卩 (: 171? 2020 /008079

The method described in.

(Item 14)

A method of using at least two kinds of clinical data or biological parameters as an index for predicting the therapeutic effect of a biological agent on rheumatoid arthritis patients, wherein the at least two kinds of clinical data or biological parameters are 《, 0th week 38, 8th 08 1\/1cho 31 3, 0044, 0th week 〇 6 门 6 “3 3rd 8th, 0th week 0th 8 3 — simi 3 [¾, tho [¾8 _! Chitinase 3,

Minhachi, 1\/1 〇

0030, 丨! _— 1 3, 丨! _— 9, and

A method comprising a parameter selected from the group consisting of:

(Item 14-1)

The method according to the preceding item, characterized in that at least one of the at least two types of clinical data or biological parameters is used as an endogenous control.

(Item 1 4-2)

The method according to any of the preceding items, characterized by using a ratio of the at least two types of clinical data or biological parameters that is not the endogenous control to the endogenous control.

(Item 1 4-3)

The method according to any of the preceding items, wherein the anti-drug agent is selected from the group consisting of infliximab, etanercept, adalimumab, golimumab, and certolizumab.

(Item 1 4-4)

The method according to any of the preceding items, wherein the antidote is infliximab.

(Item 1 4-5)

The at least two types of clinical data or biological parameters are , Week 0 ESR, I L- 12 p 70, MC P 1, T RA IL, TNF a, A DAMTS 13 3, CD44, GM-CS F, BMP 2, MMP 2, ○ SM, IL— The method according to any of the preceding items, comprising a parameter selected from the group consisting of 1/3, TWEAK, and BMP2.

(Item 1 4-6)

The at least two clinical data or biological parameters are as follows:

BlogIL.1b, DAS. ESR.0w, ESR—0w, Genera l. VAS.0W, RlogADAMTS13, RlogBM P.2 and RlogCD44;

B logOsteopont in _% B logMCP.1 and B logRANTES;

DAS. ESR.0w, ESR— 0w, RlogADAMTS13, RlogBMP.2, R logCD44 and logRF0w

MLogsTNFRI, BlogIL.12p70 _% BlogMCP. U DAS. ESR.0w and ESR — 0w;

RlogTRAIU BlogMCP. U DAS. ESR.0w, ESR— 0w, Genera L VAS.0W and LogRF 0w;

BlogMCP. U DAS. ESR.0w, ESR 0w, RlogADAMTS13, RLogBMP.2, RlogCD44 _% R LogOSM and RlogTRAIL;

RLogADAMTS13, BlogGM.CSF, BlogIL.12p70 _% BlogMCP. U BlogTNF.a, DAS. E SR.0w and ESR — 0w;

BLogGM.CSF, DAS. ESR.0w, ESR— 0w, Genera 1. VAS.0W _% RLogADAMTS13, RlogC D44 and RlogOSM;

BLogTNF.R2, BlogMCP. U DAS. ESR.0w, ESR 0w, RlogADAMTS13, RlogBMP.2, RlogCD44 and logRF0w;

BlogCD30, BlogMCP.1, DAS. ESR.0w, ESR 0w, RlogADAMTS13, RlogCD44 and

BlogIL.13, BlogIL.12p70 _% BlogMCP. U DAS. ESR. 0w, ESR 0w, General. VAS 0W, MTX.mg, RlogADAMTS13, R logBMP.2 and R logCD44; and

BlogIL.9, BlogMCP. U DAS. ESR.0w, ESR 0w and RLogADAMTS13

(Item 1 4-7)

The at least two clinical data or biological parameters are: BLogTNF.a as an endogenous control, and DAS. ESR.0w, ESR_0w, General.VAS.0W _% RlogADAMTS13, RlogCD44 _% and RLogTRAIL;

BlogChitinase.3 as an endogenous control, as well as B logBAFF, B logMC P.1, BlogRANTES, BlogTWEAK, DAS. ESR.0w, ESR—0w, Genera 1. VAS.0W _% RlogA DAMTS13, RLogBMP.2, RLogCD44 and LogRF0w. ;

BlogIL.1b as an endogenous control, and DAS.ESR.0w, ESR_0w, General.VAS.0W _% RlogADAMTS13, R LogBMP.2 and R LogCD44;

BLogOsteopontin as an endogenous control, and BlogMCP.1 and B LogRANTES;

DAS. ESR.0w as an endogenous control, and ESR_0w, RlogADAMTS13, RlogBMP.2, RlogCD44 and logRF0w;

MLogsTNFRI as an endogenous control, as well as B LogIL.12p70, BlogMCP .1, DAS. ESR.0w and ESR — 0w;

RLogTRAIL as an endogenous control, and B LogMCP.1, DAS. ESR.0w, ESR—0w, Genera l. VAS.0W and logRF0w;

BlogMCP.1 as an endogenous control, and DAS. ESR.0w, ESR_0w, RL ogADAMTS13, RlogBMP.2, RlogCD44, RlogOSM and RlogTRAIL;

RL 〇 gADAMTS13 as an endogenous control, and BlogGM.CSF, Blogl L.12p70, BlogMCP. U BlogTNF.a, DAS. ESR.0w and ESR— 0w;

BLogGM.CSF as an endogenous control, as well as DAS.ESR.0w, ESR_0w 20/175633 12 卩 (: 171? 2020 /008079

, General.VAS.0W, (^ 〇 98 808 1^313, 1^ 〇 ₉ 〇 044 and [^ 〇 ₉ 〇 311/1;

(Item 1 4-9)

RLogTRAIL as an endogenous control, and B LogMCP.1 and ESR_0 w;

BLogTNF.a as an endogenous control, and ESR_0w and R LogADAMTS 13;

RL 〇 gCD44 as an endogenous control and B LogMCP.1 and ESR_0w BLogGM.CSF as an endogenous control and ESR_0w and R LogADA MTS13;

RL o gADAMTS13 as an endogenous control, and B logIL.12p70 _% Bio gMCP.1 and ESR_0w;

RLogBMP.2 as an endogenous control, and BLogMCP.1 and ESR_0w BLogMCP.1 as an endogenous control, and ESR_0w, RlogADAMTS13 _% RLogCD44 and RlogOSM; and

BLogIL.1b as an endogenous control, and BlogTWEAK, ESR_0w and RlogADAMTS13;

(Item 15)

A method of treating a patient with rheumatoid arthritis, comprising:

(i) A step of predicting and determining the therapeutic effect of a biological agent on rheumatoid arthritis patients,

Obtaining at least two clinical data or biological parameters in a patient with rheumatoid arthritis prior to administration of the biologic

The biological agent is an anti-TN F-a agent, the therapeutic effect is amelioration of the patient,

The at least two types of clinical data or biological parameters are 20/175633 14 卩 (: 171? 2020 /008079

1\/1〇1, Dinghae, Week 0 [¾, 1 1_- 1/3, Osteopontin,

1\1 Chome 3, 丨 1_- 1 2 70, 〇 31\/1, 〇 1\/1- 〇 3, 丨 10, Ding [¾ 2, 〇 030, 1 !_- 1 3, I! _-9, and

A process comprising a parameter selected from the group consisting of:

(II) administering the biological agent to the rheumatoid arthritis patient when it is determined that the rheumatoid arthritis patient achieves remission with the biological agent.

(Item 1558)

(Item 15-1)

The process of predicting and determining the therapeutic effect of the biological agent on rheumatoid arthritis patients includes determining the therapeutic effect of the biological agent and the measured value of the clinical data or biological parameter from the at least two clinical data or biological parameters. The method according to any of the preceding items, further comprising the step of applying a regression equation to

(Item 1 5-2)

The regression equation is used to calculate the probability that the rheumatoid arthritis patient will achieve remission with the biological agent, and if the probability is greater than or equal to a predetermined criterion, the rheumatoid arthritis patient achieves remission with the biological agent. The method according to any of the preceding items, further including a step of determining.

(Item 1 5-3)

The regression equation is used to calculate the probability that the rheumatoid arthritis patient will achieve remission with the biological agent, and if the probability is greater than or equal to a predetermined criterion, the rheumatoid arthritis patient achieves remission with the biological agent. If so, the method according to any one of the above items or the method for treating or preventing a rheumatoid arthritis patient, further comprising the step of administering an effective amount of the biological agent to the rheumatoid arthritis patient. Way for.

(Item 16)

An in vitro method for predicting the therapeutic effect of a biologic agent on rheumatoid arthritis patients, comprising:

The at least two clinical data or biological parameters are TN F a, 0 week ESR, ADAMTS 13 3, CD44, 0 week Ge ne r a l .VAS,

〇 Week DAS-ES R, T RA IL, Chitinase 3, BMP 2, BA FF, MC P 1, TWEAK, Week 0 RF, IL-1/S, Osteopontin, RA NT ES, IL-1 2 p 70, o SM, GM-CSF, IP 10, TN FR 2, CD 30, IL-13, IL-9, and s A method comprising a parameter selected from the group consisting of TNFRI.

(Item 16 A)

(Item 17)

An in vitro method, wherein at least two types of clinical data or biological parameters are used as an index for predicting the therapeutic effect of a biologic on rheumatoid arthritis patients, wherein the at least two types of clinical data or biological parameters are , TNF (X, Week 0 ESR, ADAMTS 13, CD44, Week 0 General .VAS, Week 0 DAS-ES R, T RA IL, Chitinase 3, BMP 2, BAFF, MC P 1, TWEAK, Week 0 RF, IL-1/S, Osteopontin, RANT ES, IL-1 2p 70 _% 〇 SM, GM-CS F, 丨P10, TN FR2, CD30, IL- A method comprising a parameter selected from the group consisting of 13, IL-9, and s TNFRI. 20/175633 16 卩 (: 171? 2020 /008079

(Item 1 78)

(Item 18)

A method for identifying an index for predicting and determining a therapeutic effect of a biologic in a patient with rheumatoid arthritis, comprising:

(8) A step of performing a brute force multivariate analysis for the parameters of the biomarker candidates, wherein in the analysis, at least one of the parameters is used as a control and the relative value is brute force. Steps, including multivariate analysis,

(B) Step of selecting a combination of parameters with high prediction ability based on the analysis

Including and.

(Item 1 88)

(Item 19)

The method according to any one of the preceding items, wherein the biological agent is an anti-drug.

(Item 20)

Obtaining at least two clinical data in a patient with rheumatoid arthritis prior to administration of the biologic, and

A method comprising: analyzing the clinical data and using it as an index for predicting and determining the therapeutic effect.

(Item 208)

Further comprising the feature of any one or more of the preceding items 20/175633 17 卩 (: 171? 2020 /008079

The method described in the eye.

(Item 21)

The method according to any of the preceding items, wherein the biologic comprises an anti-drug.

(Item 22)

(Item 23)

(Item 24)

(Item 25)

The clinical data of at least two types are 0 8 3 _ 〇 [¾, 0 8 3 _ _ 3 [¾, Rheumatoid arthritis dysfunction classification, gender, tender joint count (D) 〇, expanded joint count

, (Erythrocyte sedimentation rate), 30 1, 〇 0 1, 6 I 〇Starting age, age, disease duration (years), presence or absence of combination of antibody therapy and methotrexate (!\/! Ding), IV! Ding X dose, with or without prednisolone (3 !_), [¾,

With or without 1//1

The method according to any of the preceding items, comprising parameters selected from the group consisting of with or without drug concomitant and with or without 3!_ concomitant drug.

(Item 26)

The at least two clinical data are

Gender, Patient 8 3, Tom 3 [¾ (erythrocyte sedimentation rate), 〇〇丨, IV!

And 跳丨〇 Any of the preceding items, including parameters selected from the group consisting of age at onset The method described in.

(Item 27)

The at least two clinical data are

ESR_0W and logRF0W;

ESR 0w, DAS. ESR. 0w and logRF0w;

Genera l. VAS. 0W _% ESR 0w, DAS. ESR. 0w, and logRF0w;

Genera l. VAS. 0W _% ESR 0w, DAS. ESR. 0w, MTX. mg and logRF0w; and sex, Genera l. VAS. 0W _% ESR 0w, DAS. ESR. 0w, CDAI. 0W, BioStartAge, MTX The method of any of the preceding items comprising a combination of parameters selected from the group consisting of .mg and LogRF0w.

(Item A 1)

A program for causing a computer to execute a method for predicting and determining the therapeutic effect of a biological agent on a rheumatoid arthritis patient, the method comprising:

A step of analyzing biological parameters and predicting and determining the therapeutic effect by using at least one of the clinical data or biological parameters as a control

Including the program.

(Item A 1 A)

The program according to any one of the above items, further comprising the features described in any one or more of the above items.

(Item A 2)

A method for causing a computer to execute a method for predicting and determining a therapeutic effect of a biological agent on a rheumatoid arthritis patient, the method comprising:

The biological agent is an anti-TNF-a agent, the therapeutic effect is amelioration of the patient, 20/175633 19 卩 (: 171? 2020 /008079

The at least two types of clinical data or biological parameters are

1\/1〇1, Dinghae, Week 0 [¾, 1 1_- 1/3, Osteopontin,

1\1 Chome 3, 丨 1_- 1 2 70, 〇 31\/1, 〇 1\/1- 〇 3, 丨 10, Ding [¾ 2, 〇 030, 1 !_- 1 3,

and

A program containing parameters selected from the group consisting of.

(Item 8 28)

(Item 8 3)

A program that causes a computer to execute a method for identifying an index for predicting and determining a therapeutic effect of a biological agent in a patient with rheumatoid arthritis, the method comprising:

(Mimi) Step of selecting a combination of parameters with high predictive ability based on the analysis

Programs, including and.

(Item 8 3 8)

(Item 8 4)

A recording medium storing the program according to any one of the above items.

(Item 8 5)

A system for performing the method according to any of the preceding items, comprising: 〇 2020/175633 20 boxes (: 171-1?2020/008079

A system comprising the program or recording medium according to any one of the items, and a computing device for executing an instruction instructed by the program.

Effect of the invention

[0008] According to the present disclosure, rheumatoid arthritis prior to administration of a biologic targeting such inflammatory cytokine (hereinafter referred to as "biologic" unless otherwise specified) according to the present disclosure. It is possible to accurately estimate the therapeutic effect on the patient, and to judge with high accuracy whether or not the biological agent brings rheumatoid arthritis into a complete remission state. Further, according to the present disclosure, it is possible to accurately determine the degree of improvement of symptoms in a patient with rheumatoid arthritis before administration of the biological agent, and to perform appropriate treatment in anticipation of the therapeutic effect of the biological agent. It becomes possible to make a policy. In addition, according to the present disclosure, it is possible to predict which biological agent is most effective for pre-treatment rheumatoid arthritis patients by increasing the number of drugs to be treated, and thus, it is possible to predict whether or not the treatment is performed for each patient. It is also possible to select the most suitable biologic for each patient and formulate the most effective treatment policy for each patient.

[0009] As described above, by utilizing the present disclosure, it is possible to identify a rheumatoid arthritis patient for whom the administration of a biological agent is effective. Therefore, the treatment cost can be suppressed for the patient, and the therapeutic effect can be predicted. There are merits such as giving a sense of security, and it will be possible for doctors to establish an appropriate treatment policy based on accurate prediction of the effectiveness of biological agents.

[0010] By utilizing the present disclosure, it is possible to reduce the number of patients with ineffective drugs as much as possible.

MODE FOR CARRYING OUT THE INVENTION

[001 1] Hereinafter, the present disclosure will be described with reference to the best mode. It is to be understood that throughout this specification, the use of the singular includes the concept of the plural unless specifically stated otherwise. Therefore, terms referred to in the singular (eg, in English, “3”, “311”, “116”, etc.) may also include the plural unless otherwise stated. Should be understood. In this description It should be understood that the terms used in the same manner are also used as commonly used in the art unless otherwise specified. Accordingly, all technical and scientific terms used herein are to be commonly understood by one of skill in the art to which this disclosure belongs, unless defined otherwise as to their commonly used meaning. Have the same meaning. In case of any discrepancy between the description in the present specification and the description in the relevant field, the present specification (including the definition) takes precedence.

[0012] 1. Judgment method

The present disclosure determines the efficacy of treatment of rheumatoid arthritis patients with a biologic targeting inflammatory cytokines (eg, remission potential, post-treatment symptom improvement, disease activity index, etc.). Provide a way to do.

[0013] In one embodiment, the methods of the present disclosure include a method of proactively determining a patient's remission with a biologic by obtaining parameters of the patient with rheumatoid arthritis. In another embodiment, the method of the present disclosure includes a method of obtaining a parameter of a rheumatoid arthritis patient to proactively determine the degree of improvement in symptoms after treatment of the patient with a biologic. In another embodiment, the disclosure includes a method of proactively determining a disease activity index of a rheumatoid arthritis patient after treatment of the patient with a particular biologic.

[0014] As used herein, the "body sample" refers to any sample collected from the body of a patient, and includes, but is not limited to, serum, blood, urine, saliva and the like. Serum is preferably used in the present disclosure. Therefore, in a preferred embodiment of the present disclosure, the method of the present disclosure is carried out using the parameters obtained from the measurement in the serum collected from the rheumatoid arthritis patient before the administration of the biological agent.

[0015] In the present disclosure, the parameters that are used to determine the effectiveness of treatment of biological products, TN F a _s 0 Week ES R, ADAMTS 1 3, CD 44, 0 week Ge neral .VAS, Week 0 DAS-ES R, T RA IL, Chitinase 3, BMP 2, BA FF, MC P 1, TWEAK, Week 0 RF, IL _ 1/S, Osteopontin, RANT ES, IL 1 2 p 70, ○ S 〇 2020/175633 22 卩 (：171? 2020 /008079

1\/1, 〇1\/1-〇3, 丨? 1 0, Ding [¾ 2, 0 0 0 3, 0 1_-1 3, 0! _-9, and

One or more selected from the group consisting of If necessary, parameters of the patient's pre-treatment status (eg pre-treatment mouth score 328, 088, 308, etc.) may be used as parameters. Therefore, the parameters in the present disclosure also include an index of the pretreatment state of the patient. In the present disclosure, there is provided a method of using at least two types of clinical data or biological parameters as an index for predicting the therapeutic effect of a biologic agent on rheumatoid arthritis patients, comprising at least two types of clinical data or biological parameters. As a method including any of the parameters (or combinations thereof) specifically referred to herein. In the present disclosure, it is possible to use only clinical data or only biological parameters.

[0016] In one embodiment of the determination method of the present disclosure, the determination is performed using a parameter including the concentration of the marker of the present disclosure in the body sample, and thus the method of the present disclosure includes , In serum) may be included. Hereinafter, the determination method of the present disclosure will be described in detail.

[0017] In the determination method of the present disclosure, at least one of at least two types of clinical data or biological parameters is used as a control, biological parameters are analyzed, and it can be used as an index for predictive determination of a therapeutic effect. In one embodiment, the method may include the step of obtaining at least two clinical data or biological parameters in the rheumatoid arthritis patient prior to administration of the biologic. Biological products

Agents may be included. Also, the therapeutic effect may be patient remission.

[0018] (Biological product to be determined)

The determination method of the present disclosure is a method for predicting and determining the therapeutic effect of a biologic targeting inflammatory cytokine on patients with rheumatoid arthritis. In the determination method of the present disclosure, the biologic targeting inflammatory cytokine is not limited to the biologic used for the treatment of rheumatoid arthritis, and the biologic used The therapeutic effect can be predicted and determined according to the type of 〇 2020/175633 23 卩 (：171? 2020 /008079

.. Biological agents targeting inflammatory cytokines include, for example, anti-drugs.

[0019] As used herein, the "anti-_" agent refers to a drug that can treat rheumatoid arthritis by suppressing the D__ signal transduction pathway. Specifically, as an anti-drug, a human soluble D// consisting of the human I 9 ◦ 1 ◯ region and the subunit dimer of the extracellular domain of the human tumor necrosis factor-type receptor /! _ Ding <<Receptor, anti-drug antibody, etc. Specific examples of the human soluble FXLTa receptor include etanercept, and specific examples of the anti-antibody include adalimumab, infliximab, golimumab, certolizumab and the like. Infliximab may be referred to herein as IX. In the present specification, the term “infliximab” and the like also include biosimilar of the biological drug.

[0020] The anti-drug agent is a human soluble type consisting of its type (for example, the ◯ region of human cancer 9 ◦ 1 and the subunit dimer of the extracellular domain of human tumor necrosis factor type receptor). Ding /!_Ding <<Receptor, anti-Ding _« antibody) Regardless of whether it is a binding, the binding effect between Ding _« and Ding _« receptors is suppressed in some way by the same mechanism of action. Is to demonstrate. Ding

It is understood that the response after suppression of signal transduction is the same regardless of the mode of suppression, so the response in vivo regarding the treatment process of rheumatoid arthritis is the same regardless of the type of anti-adhesive agent. Therefore,

Findings regarding the in vivo response of a therapeutic agent belonging to a drug are related to other therapeutic agents belonging to the same category (for example, if a therapeutic agent is a human soluble soluble/! It is understood that it can also be applied to human soluble FXT a receptor or anti-antibodies, fragments, derivatives, etc.).

[0021] (Patient to be judged)

According to the determination method of the present disclosure, it is determined whether or not the administration of the biological agent is effective in the rheumatoid arthritis patient before the administration of the biological agent. [0022] In the determination method of the present disclosure, the subject rheumatoid arthritis patient is not particularly limited as long as it is before administration of the above-mentioned biological agent, and is a drug for administration of DMA RD s such as methotrexate. It does not matter whether or not the patient has been treated, and the history of past anti-cysteine caine therapy (administration of infliximab, etanercept, adalimumam, tocilizumab, etc.). If necessary, in the determination method of the present disclosure, past drug administration history can be incorporated as a parameter for prediction to predict and determine the therapeutic effect of a biological agent.

[0023] (Marker)

According to the determination method of the present disclosure, D AS _C RP, D AS-E SR of rheumatoid arthritis patients, dysfunction classification of rheumatoid arthritis, sex, tender joint number (TJC), inflated joint number (SJC), doctor VAS, patient VAS, cyclic citrullinated peptide (CCP), CRP (C-reactive protein value), ESR (erythrocyte sedimentation rate), SDA I (S implifieddiseaseactivity index), CDA I (C linicaliseaseactivit yindex), B i 〇 Start Age (age at the time of disclosure of biologics), age, disease duration (years), with or without antibody therapy and methotrexate (MTX), MTX dose, with or without prednisolone (PSL), RF (R heumatoid F) actor), MM P-3 level, with or without MTX, with or without DMARD s drug, with or without PSL, and APRIL, BA FF, CD 1 63, CD 30, chitinase 3, eotaxin, basic FG F, G -CS F, GM— CS F, IFN— a 2, IFN— /S b, IFN—a, 丨! _— 1/3, I L-1 R a, 丨 L-2, I L-4, I L-5, I L-6, I L-7, IL -8, 丨 L-9, I L- 1 0, I L- 11, I L- 12 p 70, IL-1 3, I L- 17, I L- 19, I L-20, I L-22, I L-28A, IL -35, I P- 10, MC P-1, M l P-1 a, M l P-1 b, MM P-1, MMP-2, MMP-3, osteocalcin, osteobontin, PDGF- BB , Pentorakishin 3, RANT ES, TN F a s TS LP, TWEAK, VEG F, I L-6 R a, sl L-6 R, TN FR 1, TN FR 2, VEG FR 1, VEG FR 2, sgp 1 30, AD AMT S 1 3, angiopoietin 2, BMP-2, CD40 ligand, CX3CL 1 fractalkine, HG F, IFN r RU L-selectin, LIF, T RA IL, VEG FR 2/KD R (where the slash “/” is another name for the same substance) Aggrecan, B 7 H 1 /PDL— 1, CD40, CD4 4, E-selectin, I One or more parameters selected from the group consisting of CAM-1, IL-18, lebutin, SM and VCAM-1 or one or more types of cytokine levels. (Also referred to herein as “judgment parameter”) may be used.

[0024] In the present disclosure, "clinical data" refers to parameters clinically obtained from a patient and excluding blood cytokine concentration. Examples of clinical data include DAS—CRP, DAS—ESR of rheumatoid arthritis patients, dysfunction classification of rheumatoid arthritis, gender, number of tender joints (TJC), number of swollen joints (SJC), physician VAS, patient VAS, CC P, CR P (C-reactive protein level)

, ESR (erythrocyte sedimentation rate), SDA I (S implified diseaseaseactivity index), CDA I (Clinical diseaseseactivit y index), B io age at onset, age, disease duration (years), combined with antibody therapy and methotrexate (MTX) Presence/absence, MTX dose, presence/absence of prednisolone (PSL) combination, RF (R heumatoid F actor), MMP-3 level, presence/absence of MTX combination, presence/absence of DM ARDs drug combination, presence/absence of PS L combination. Clinical data can be discrete rather than continuous. For example, the presence/absence of the combined use can be calculated by setting “Yes” to “1” and “No” to “O”. Although it was thought that the diagnosis based on the clinical data alone would be inaccurate, the present disclosure has shown that the combination of the clinical data can predict and evaluate the therapeutic effect of the biological agent. In particular, the combination of specific clinical parameters identified in the present disclosure and the regression equation using them may be useful for predicting and determining the therapeutic effect of a biological agent. [0025] Clinical data include, for example, at least a parameter selected from the group consisting of DAS—ESR, sex, patient VAS, ESR (erythrocyte sedimentation rate), CDA I, MTX dose, RF and B i 〇 starting age. Two types of clinical data can be used.

[0026] In the determination method of the present disclosure, one of the above-mentioned parameters may be used alone as a determination parameter, but from the viewpoint of predicting the therapeutic effect of a biological agent with higher accuracy, the It is preferable to use two or more of the above in combination. Further, it is more preferable to use the set of parameters with high determination accuracy shown in this specification. The parameters can be appropriately selected and used according to the content of the therapeutic effect to be predicted and determined. Hereinafter, preferred examples of the determination parameters will be specifically shown.

[0027] For example, as a preferable combination of the determination parameters, the following is given:

B logOsteopont in _% B logMCP.1 and B logRANTES;

DAS. ESR.0w, ESR— 0w, RlogADAMTS13, RlogBMP.2, R logCD44 and logRF0w

MLogsTNFRI, BlogIL.12p70 _% BlogMCP. U DAS. ESR.0w and ESR — 0w;

RlogTRAIU BlogMCP. U DAS. ESR.0w, ESR— 0w, Genera L VAS.0W and LogRF 0w;

RLogADAMTS13, BlogGM.CSF, BlogIL.12p70 _% BlogMCP. U BlogTNF.a, DAS . ESR.0w and ESR — 0w;

BLogGM.CSF, DAS. ESR.0w, ESR— 0w, Genera 1. VAS.0W _% RLogADAMTS13, RLog gCD44 and RlogOSM;

BlogCD30, BlogMCP.1, DAS. ESR.0w, ESR 0w, RlogADAMTS13, RlogCD44 and logRF0w;

BlogIL.9, BlogMCP. U DAS. ESR.0w, ESR 0w and RLogADAMTS13 are included, which include a combination of parameters selected from the group.

[0028] For example, as a preferable combination of the determination parameters, the following is given:

BLogTNF.a as an endogenous control, and DAS. ESR.0w, ESR_0w, General.VAS.0W, RlogADAMTS13, RlogCD44, and RlogTRAIL;

BlogIL.1b as an endogenous control, and DAS.ESR.0w, ESR_0w, General.VAS.0W, RlogADAMTS13, RlogBMP.2 and RlogCD44;

BLogOsteopontin as an endogenous control, and BlogMCP.1 and B LogRANTES;

BlogMCP.1 as an internal control, and DAS. ESR.0w, ESR_0w, RL 〇 2020/175633 28 卩 (：171? 2020 /008079

〇 9 88 08 1^313, 1^0981^. 2, 1^ 〇 9 〇 044, 〇 9 (½11/1 and 1^ 〇 9 end [^11_;

Those containing a combination of parameters selected from the group consisting of [0029] For example, as another example of a preferable combination of the determination parameters, the following:

Those containing a combination of parameters selected from the group consisting of [0030] For example, as another example of a preferable combination of the determination parameters, the following are given: 1^0 93 as internal control, and 8 1 91!_. 12 70, CP.1 and ESR_0W;

RLogTRAIL as an endogenous control, and B LogMCP.1 and ESR_0 w;

BLogTNF.a as an endogenous control, and ESR_0w and R LogADAMTS 13;

BLogIL.1b as an endogenous control, and BlogTWEAK, ESR_0w and RlogADAMTS13;

Those containing a combination of parameters selected from the group consisting of

[0031] For example, as an example of a preferable combination of only clinical data,

ESR_0W and logRF0W;

ESR—0w, DAS. ESR.0w and logRF0w;

General. VAS.0W _% ESR— 0w, DAS. ESR.0w, and logRF0w;

_{.. General VAS.0W% ESR- 0w,} DAS ESR.0w, MTX mg and logRF0w;... And _{sex, General VAS.0W% ESR- 0w,} DAS ESR.0w, CDAI.0 W, BioStartAge, MTX. mg and logRF0w

[0032] In the parameter combinations of the present disclosure, at least one of the combinations can be used as an endogenous control. In this specification 〇 2020/175633 30 卩 (：171? 2020 /008079

, "Using a certain parameter as an intrinsic control" means using another parameter in a combination of parameters as a relative value to the parameter used as an intrinsic control (the difference is used in the logarithmic form). .. One example of a relative value is the ratio of parameters. By using the endogenous control, it is possible to cancel the difference between patients as well as the error of each biochemical assay lot, such as an artificial shake in the measurement. Such accuracy is very important when testing a single sample rather than a population. If there is lot variability, it is not possible to know the lot variability when testing each one individually, so it is possible to use two or more endogenous controls to correct the value of the variable. If the lot correction is effective, there will be relatively few variables selected and one intrinsic control may be sufficient.

[0033] It is known that the concentrations of cytokine, chemokine, and soluble receptor in serum that can be used as parameters can be measured by a measurement system utilizing an antigen-antibody reaction such as M! Measurement kits are also commercially available. Therefore, in the determination method of the present disclosure, cytokines, chemokines, and soluble receptors can be measured by a known method and a known measurement kit. The reagents used in the assay system utilizing such an antigen-antibody reaction can be provided individually or as a set as a diagnostic agent for determination of each biological preparation.

[0034] (Judgment of predicting therapeutic effect of biological agent)

Based on the above parameters, the therapeutic effect of a specific biological agent such as an anti-drug can be predicted and determined. For example, in patients who were in complete remission or in non-remission by treatment with a specific biological agent, the specific marker was measured in advance, and the therapeutic effect of the biological agent (target variable) and For example, a regression formula of the measured value (explanatory variable) of a specific marker is obtained, and the measured value of the specific marker of the rheumatoid arthritis patient to be judged is applied to the regression formula. 〇 2020/175633 31 卩(: 171? 2020/008079

[0035] Examples of regression equations include, but are not limited to,

.1) + ((^ 〇 ₉ 〇 44 - 81_Rei _{9 11_ · 11)) X (} ! 0.1 ± 0.1) + ( 1_Rei ₉ furlongs (^ 1 _- 81_Rei ₉ 11_ · 1) X (Sat 0.01) +1 0 _{9 (} ^0 _1/\ ^ (Sat 0.01));

#0.7 ± 0.1 + (1 0 ₉ 1 ^ 卩· 1-81 〇 ₉ 〇 ₃ gen _{6 6}卩〇门 1; 丨门) X (-0.2 ± 0.1) + (BlogRAN Ding-81 〇 ₉ 〇 ₃ ₆ 〇卩〇门^1;. ₁门) X (0.2 ± 0.1) + 〇 eight _{3. £ 3 (¾.0 1 / \} / \ ( Sat 0.01) Jumi -〇 X (-0.01 ± 0.01) ten ( 1¾1_Rei ₉ eight hundred and eight 11/1 chome 313-81_Rei ₉ 〇 ₃ mechanic ₆ 〇卩〇门^1;. ₁门) X (0.4 ± 0.1) + (

0.1) + ○ 8 3 3 [^ 0 _{1 «\ (} Sat 0.01) Tomi 0 _{1 «\ (} Sat 0.01) + General.VAS.0WX (

±0.01));

# 0.9 ± 0.1 + ((81_Rei ₉ 110 ^3.1 - [^ 〇 ₉ furlongs [^ 11_) X (-0.3 ± 0.1) + 〇 eight 3 3 (^ 0 _{1 «\ (-} 0.03 ± 0.1) +ESR 0wX (-0.01 ±0.1) ± Genera l. VAS.0WX (±0.01) + logR F0wX (-0.1 ±0.1) );

• 1.0 ± 0.1 ± (DAS.ESR.0wX (-0.07 ±0.01) ±ESR_0wX (-0.01 ±0.01) ±General.VAS.0WX (±0.01) ± (RlogADAMTS13-BlogMCP.1) X (0.3 ±0.1) ±

(RlogBMP.2-BlogMCP.1) X (0.02±0.01) ± (RlogCD44 -BLogMCP.1) X (

O.2 ± 0.1) ± (RlogOSM- BlogMCP.1) X (0.1 ± 0.1) ± (RlogTRAIL- BlogMC

P.1) X (0.2 ± 0.1) );

0.7 ± 0.1 + ((B logChiti nase.3-R logADAMTSI 3) X (±0.01) ± (BlogGM.CSF- RlogADAMTS13) X (-0.02 ±0.1) ± (BlogIL.12p70-R logADAMTSI 3) X ( -0.4 ± 0.1) ± (BlogMCP.1 -R logADAMTSI 3) X (-0.2 ± 0.1) ± (BlogTNF.aR logADAMTSI 3) X (-0.2 ± 0.1) ±DAS.ESR.0wX (-0.01 ±0.01) ± ESR_0w X (-0.01 ±0.01 )± Genera l. VAS.0WX (±0.01) ±logRF0wX (±0.01))

• 1.0 ± 0.1 ± (DAS.ESR.0wX (-0.07 ±0.01) ±ESR_0wX (-0.01 ±0.01) ±General.VAS.0WX (±0.01) ± (R logADAMTSI 3-BlogGM.CSF) X (0.6 ± 0.1)

± (RlogBMP.2-BlogGM.CSF) X (±0.01) ± (RlogCD44-BlogGM.CSF) X (0.1 ±0.1) ± (RloglL.18-BlogGM.CSF) X (±0.01) ± (RlogOSM- BlogGM. CSF) X (0.04 ± 0.1) ± (RlogTRAIL-BlogGM.CSF) X (± 0.01) );

• 0.9 ± 0.1 ± ((BlogMCP.1-B logTNF. R2) X (-0.3 ± 0.1) ± (B logRANTES- B logTNF. R2) X (0.03 ± 0.01) ± DAS.ESR.0wX (-0.02 ± 0.01) ) ±ESR_0wX (-0.01 ±0.01) ±MTX.mg X (±0.01) ± (R logADAMTSI 3-B logTNF. R2) X (0.3 ±0.1) ± (RlogBMP.2-BlogTNF.R2) X (0.1 ±0.1 ) ± (RlogCD44-BlogTN F.R2) X (0.2 ± 0.1) ± (RlogTRAIL-B logTNF. R2) X (0.01 ± 0.01) ± logRF 0wX (-0.05 ± 0.01 ));

• 1.0 ±0.1 ± ((BlogMCP.1-B logCD30) X (-0.3 ±0.1 )±DAS. ESR.0wX (-0 .05 ±0.01 )±ESR 0wX (-0.01 ±0.01 )±Genera l. VAS. 0WX (±0.01) ± (R logADAMTSI 3-B logCD30) X (0.4 ±0.1) ± (RlogCD44- BlogCD30) X (0.2 ± 0.1) ± (RlogTRAIL-BlogCD30) X (±0.01) ±logRF0wX (-0.03 ±0.01) ) );

0.8 ±0.1 + ((BLogIL.12p70-B Log IL.13) X (-0.3 ±0.1) ± (BlogIL.1b- BLoglL.13) X (±0.01) + (BlogMCP.1-BlogIL.13) X (-0.3±0.1) +D AS.ESR.0wX (-0.03±0.1) +ESR 0wX (-0.01 ±0.1) ± Genera l. VAS.0WX (±0.01) ±MTX.mg X (±0.01) ± ( RlogADAMTS13-BlogIL.13) X (0.4±0.1) ± (RlogBMP.2-BlogIL.13) X (±0.01) ± (RlogCD44-BlogIL.13) X (0.1 ±0.1) ± (RlogTRAIL-BloglL.13) X (±0.01) ±logRF0wX (±0.01 ));

#1.1 ±0.1 + ((B logMCP.1-B logIL.9) X (-0.2 ±0.1) ±DAS.ESR.0wX (-0.06 ±0.01) ±ESR 0wX (-0.01 ±0.01) ± Genera l .VAS.0WX (±0.01) ±MTX .mg X (±0.01) ± (RlogADAMTS13-BlogIL.9) X (0.6 ±0.1) ± (RlogCD44- BlogIL.9) X (0.01 ±0.1) );

• 0.3 ± 0.1 ± ((BlogIL.12p70-MlogsTNFRI) X (-0.3 ±0.1) ± (BlogMCP.1-MlogsTNFRI) X (-0.1 ±0.1) ±ESR_0wX (-0.01 ±0.01 ));

• 0.5 ± 0.1 ± ((B logMCP.1-RlogTRAIL) X (-0.2 ±0.1) ±ESR_0wX (-0.01

±0.01));

• 0.5 ± 0.1 ± (ESR 0wX (-0.01 ±0.01) ± (R logADAMTSI 3-B logTNF.a) X (0.3 ±0.1) ± (RlogCD44- B logTNF.a) X (±0.1) );

• 0.3 ± 0.1 ± ((B logMCP.1-RlogCD44) X (-0.2 ±0.1) ±ESR_0wX (-0.01

±0.01));

• 0.4 ± 0.1 ± (ESR 0wX (-0.01 ±0.01) ± (R logADAMTSI 3-B logGM.CSF) X (0.2 ±0.1) ± (RlogCD44- B logGM.CSF) X (-0.01 ±0.01) );

0.3 ± 0.1 ± ((BlogIL.12p70-R logADAMTSI 3) X (-0.3 ± 0.1) ± (BlogMCP .1- R logADAMTSI 3) X (-0.1 ± 0.1) ± (B logTNF. aR logADAMTSI 3) X ( ±0.01) ±ESR 0wX (-0.01 ±0.01) );

0.4 ± 0.1 + ((B logChitinase.3-R logBMP.2) X (±0.01) ± (BlogMCP.1-RlogBMP.2) X (-0.2 ±0.1) ±ESR_0wX (-0.01 ±0.01 )) ;

• 0.4 ± 0.1 + ((B logMMP.2-B logMCP.1) X (±0.01) ±ESR_0wX (-0.01 ±0 〇 2020/175633 34 卩 (：171? 2020 /008079

gIL.1t)) X (Sat 0.01) Ten (〇 ₉ 〇 44-81 〇 ₉ 11_・ 11)) X (Sat 0.01))

And so on.

[0036] Examples of regression equations using combinations of clinical data include, but are not limited to,

(-0.01 ±0.01) +1 〇 ₉ (^01« \ (-0.04 ±0.01);

(-0.01 ±0.01) + ○ 8 3 3 (^01 «\ (-0 007 ± 0 001) + I ○ 9 (^01/\^ (-0 1 ±0 1)

1.1 ± 0 1 + 〇 6 ``3 shar 3.01 «\ (-7.1 ± 0 16-05) Tomi 01/\^ (-1.3 ± 0.16-0 2) + 〇 s 8. £3 .01«\ (-2.6±0·16-02) +1 〇 ₉ (^01«\ (-1 7±0·16-01 );

#1.4 ± 0·1 + 〇 6 ``3 SHIBA 3.01 «\ (-0.002 ± 0.001) JUMI 01/\ ^ (-0.01 ± 0.01) + 〇 8 3.£3 [¾.01/\/\ ( -0.03±0.01) 11/11/1. 1119\ (0 006±0·001 )+1 0 9 (^01/\^ (-0.3±0.1); and

#1.6 ± 0 1 +36 (2.7 ± 0 166-01) + General.VAS.0WX (-5.0 ± 1 06-03) 10 [01 «\ (-1.6 ± 0 16-02) + ○ 8 3.£3 [¾.01«\ (-2.3±0·16-02) 10 〇 08 1.01/^ (-6.8 ± 0·16-05) +8_1 〇 3 fighter 3 ``Shihachi 96 \ (- 1 0 ± 0 16-04) 10/11/1. 1119 \ (3.5 ± 0.1 6-02) +1 〇 9 (^01/\^ (-4.2 ± 0·16-01 ))

And so on.

[0037] Alternatively, the probability of remission may be determined in advance or based on experience.

Predetermine the criteria for individual adoption of biological agents such as drugs, and determine that the specific biological agent should be administered if the actually calculated probability of remission is greater than or equal to the prescribed criteria. Can also Specifically, the predicted remission probability is

30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, 85% or more, 90% or more or 95% or more of the specific biological It can be determined that the formulation is administered. The specific value of such a standard can be finely adjusted based on experience, etc., in addition to the specific numerical value described in the present specification, and the specific value described in the present specification. It can be adopted even if it is other than the value.

[0038] 2. Diagnostic agent

The present disclosure further provides a diagnostic agent for carrying out the detection method. Specifically, the diagnostic agent of the present disclosure is a diagnostic agent for determining the effectiveness of treatment of a rheumatoid arthritis patient with a biologic targeting inflammatory cytokine, and is a TN F a _s Week 0 ESR, ADAMTS 1 3, CD44, Week 0 General .VAS, Week 0 DAS-ES R, T RA IL, Chitinase 3, BM P 2, BA FF, MC P 1, TWEAK, Week 0 Eyes RF, IL-1/S, Osteopontin, RANT ES, IL-12 p 70, SM, GM-CSF, IP10, TN FR2, CD30, IL-13, IL -9, and at least one marker selected from the group consisting of s TNFRI and a reagent capable of detecting at least one marker.

[0039] In one embodiment, the diagnostic agent of the present disclosure provides a method for measuring the concentration of a marker in a body sample of a patient with rheumatoid arthritis to proactively determine the remission of the patient by a specific biological agent. A diagnostic agent used, which comprises a reagent for detecting the marker. Once the remission has been determined in advance, it can be used to determine the administration of the biologic by presetting a predetermined level for the probability of remission. As a specific method of each marker used in these embodiments, a method specifically described in other places in the present specification including the section “1. Determination method” can be used.

[0040] In a specific embodiment, the reagent for the required label can be selected and used based on the information provided herein. If there are multiple such reagents, they may be provided separately, together as a set, or as a kit with other required reagents (eg, color former). Good. 〇 2020/175633 36 卩 (：171? 2020 /008079

[0041] The marker can be measured by a measurement system utilizing an antigen-antibody reaction such as Mi!__38, and the reagent that can detect the marker is, specifically, specifically for the marker. Antibodies capable of binding, and fragments thereof. Further, the antibody capable of specifically binding to the marker may be bound on a suitable support and provided as an antibody array.

[0042] Further, the diagnostic agent of the present disclosure may include a reagent (secondary antibody, chromogenic substance, etc.) necessary for detecting the marker by an antigen-antibody reaction.

[0043] 3. Therapeutic agent

The present disclosure further provides a therapeutic agent (also referred to as a precision medicine, a personalized medicine or a companion therapeutic agent) selected or determined to be appropriate by the detection method, diagnostic method and selection method. More specifically, the therapeutic agent of the present disclosure is a therapeutic agent for treating rheumatoid arthritis patients, which comprises an anti-inflammatory drug, and the efficacy of the anti-active drug is determined based on the parameters of the patient. However, it is characterized in that an anti-pharmaceutical agent that is selected or determined to be appropriate based on the determined matters is administered. To determine whether an anti-drug should be administered, a patient parameter (eg, which can include the concentration of the marker in a body sample (eg, serum)) is measured to determine the efficacy of the anti-drug. However, a package insert or the like explaining that the drug is selected based on the determined items or that it is administered when it is determined to be appropriate may be attached. The package insert may be provided in a paper medium, but may be provided in an electronic medium, an internet or the like. Anti-therapeutic agents that can be used as the therapeutic agent or set of therapeutic agents of the present disclosure include, but are not limited to, etanercept, adalimumab, infliximab, golimumab, certolizumab, and the like.

[0044] In one embodiment, the method for treating a patient with rheumatoid arthritis according to the present disclosure includes () a step of obtaining a parameter of a patient with rheumatoid arthritis in advance and determining a remission of the patient with an anti-drug. (Mimi) (Hachi) Anti-pitch by the process

If the agent determines that the patient is in remission, administering an anti-drug to the patient. 〇 2020/175633 37 卩(：171? 2020/008079

Include. The determination of remission can be made when the probability of remission is greater than or equal to a certain criterion in the regression equation that predicts remission. Such a judgment can be made using the method specifically described elsewhere in this specification including the sections of “1. Judgment method” and “2. Diagnostic agent”.

[0045] Here, it is understood that any kind of biological agent may be used as the anti-drug agent as long as it belongs to the category. As described elsewhere in this specification, it is understood that the specific marker of the present disclosure can be applied as long as the categories of biological agents (eg, anti-drugs etc.) are the same. .. For other specific selection and judgment methods, the methods specifically described elsewhere in this specification including the above-mentioned "1. Judgment method" and "2. Diagnostic agent" are used. be able to.

[0046] In the present disclosure, the probability that the rheumatoid arthritis patient achieves remission with the biological agent is calculated by the regression equation, and when the probability is equal to or higher than a predetermined criterion, the rheumatoid arthritis patient has the biological property. Treatment of a rheumatoid arthritis patient, further comprising the step of administering to the rheumatoid arthritis patient an effective amount of the biological preparation when it is determined that a remission is achieved by a specific preparation. Or methods for prevention are provided. In addition, specific selection and determination methods will be specifically described elsewhere in this specification including the above-mentioned "1. determination method", "2. diagnostic agent" and "3. therapeutic agent". Can be done using the same method.

[0047] 4. Analytical method

The disclosure may provide, in another aspect, a method of identifying a biological parameter as a predictive marker of a disease or a suitable medicament for the disease. In particular, a large number of parameters can provide a method of identifying suitable combinations.

[0048] In one embodiment, the method is a step of performing a brute force multivariate analysis for the parameters of (8) biomarker candidates, wherein: This includes multivariate analysis of relative values (the difference between the logarithmized ones) using at least one as a control. And (B) selecting a combination of highly predictive parameters based on the analysis.

[0049] In a further embodiment, the method comprises: (A) performing a brute force multivariate analysis on the parameters of the n biomarker candidates, and (B) based on the analysis, a prediction capability. Selecting a combination of high parameters, wherein selecting the combination of candidate parameters within a certain standard deviation from s min in the selection.

[0050] In the analysis method of the present disclosure, the multivariate analysis may use LASSO (Least Absolute Shrinkage and Selection Operator) (Trevor et. al.: Statistical Learning with Sparsity: The Lasso and Generalizations, CRC Press, 2015). This is a method of simultaneously performing parameter estimation and variable selection (a technology that combines regression estimation and optimization theory of system engineering) and can be calculated even if the explanatory variables are larger than the number of samples. The background lies in the assumption that there are few variables that are really valid. For example, in genetic information analysis, it is possible to apply 20,000 explanatory variables to several hundred samples. The regression coefficient can be obtained while selecting the explanatory variable. Since the representative explanatory variable is automatically selected, the problem of multicollinearity does not occur. Therefore, it is possible to determine the explanatory variable while numerically evaluating the degree of overfitting as much as possible in the limited data.

[0051] The performance of the model is the predicted value obtained for the remaining 20% of the data by performing regression model construction using the LASS 〇 using 80% of the data of the dataset that was previously divided into 8/2. Can be evaluated using Co he n'sd (J. Co hen: Statistical Power A nalysistorthe B ehavioral S ciences

, secondedition, P sycology Pres s.). Co he n'sd is a value that does not depend on the sample size. Cohen'sd is the difference between the mean values of the two groups divided by the combined (pooled) standard deviation of both groups. The performance of the prediction model is the same as that of the verification group and the learning group. Co He n'sd between can be used for evaluation, and in the field of medical-related fields, a model with a C o he n'sd of 1.2 or more is preferable.

[0052] 5. Other Embodiments

The determination method and the analysis method according to one or more aspects of the present disclosure have been described based on the embodiment, but the present disclosure is not limited to this embodiment. As long as it does not depart from the gist of the present disclosure, various modifications that can be conceived by those skilled in the art may be applied to the present embodiment, or a configuration in which components of different embodiments are combined may be used. May be included within the scope of the embodiment.

[0053] The determination method or the analysis method can be executed by a program. That is, a program that causes a computer to execute a method for predicting and determining the therapeutic effect of a biological agent on a rheumatoid arthritis patient, the method comprising at least 2 A program is provided which includes a step of obtaining clinical data or a biological parameter of a species, and a step of analyzing a biological parameter and predicting and determining the therapeutic effect by using at least one of the clinical data or the biological parameter as a control. obtain. Alternatively, it is a program for causing a computer to execute a method for predicting and determining the therapeutic effect of a biologic agent on a rheumatoid arthritis patient, the method comprising at least 2 in the rheumatoid arthritis patient before administration of the biologic agent. Obtaining clinical data or biological parameters of the species, the biological agent is an anti-TN Fa agent, the therapeutic effect is amelioration of the patient, and the at least two clinical data or biological parameters, TN F a _s 0 week ES R, ADAMTS 1 3, CD44 , 0 week Ge neral. VAS, 0 week DAS-ES R, T RA IL , chitinase 3, BMP 2, BA FF, MC P 1, TWEAK, Week 0 RF, 丨 L _ 1/S, Osteopontin, RANT ES, I L_ 1 2 p 70,

A program may be provided that includes parameters selected from the group consisting of SM, GM-CSF, IP 10, TN FR 2, CD30, IL-13, IL-9, and s TN FR. Or biological in rheumatoid arthritis patients A program for causing a computer to execute a method for identifying an index for predicting and determining a therapeutic effect of a drug product, the method comprising: (A) performing a brute force multivariate analysis for parameters of n biomarker candidates. A step of performing a multivariate analysis of relative values by brute force in which at least one of the n parameters is used as a control in the analysis, and (B) based on the analysis. And a step of selecting a combination of parameters with high predictive ability. The program may further include a display step for displaying a result of the prediction determination of the therapeutic effect or an index for predicting the therapeutic effect. A recording medium storing such a program can also be provided. Recording medium may be a ^non-_ synchronic recording medium.

[0054] The system may include a program for causing a computer to execute the method described herein, and may include, for example, a recording medium storing such a program. Further, it may include a computing device (eg, computer) for executing the instructions instructed by the program. The computing device may be physically integrated or may be composed of multiple physically separate components. The functions corresponding to the learning unit, the calculation unit, the acquisition unit, and the like may be provided inside these computing devices as needed.

The system of the present disclosure can be realized as an ultra-multifunctional LS device manufactured by integrating a plurality of constituent parts on one chip, and specifically, a microprocessor, a ROM ( The computer system may include a read only memory (RAM) and a RAM (random memory memory). The ROM stores the computer program. The system LS achieves its function by the microprocessor operating according to the computer program.

[0056] Note that the system LS is used here, but due to the difference in the degree of integration,

It is also called LSI, Super LSI, Ultra LS. Also, the method of circuit integration is not limited to LSI, and it may be realized by a dedicated circuit or a general-purpose processor. Programmable FP after LSI manufacture A GA (Field Programmable Gate Array) or a reconfigurable processor that can reconfigure the connection and settings of the circuit cells inside the LSI may be used. Furthermore, if an integrated circuit technology that replaces LSI appears as a result of the advancement of semiconductor technology or a derivative other technology, it is naturally also possible to carry out function block integration using that technology. It is possible that biotechnology will be applied.

[0057] Further, one aspect of the present disclosure is not only a system for specifying an index for such a therapeutic effect prediction determination or a therapeutic effect prediction determination, but also a therapeutic effect prediction determination or a therapeutic effect prediction. It may be a method for predictive determination of therapeutic effect or a method for predictive determination of therapeutic effect, which comprises a characteristic component part included in the system for determining index for determination. .. Further, one aspect of the present disclosure is a computer program that causes a computer to execute each characteristic step included in a method for predicting determination of therapeutic effect or specifying an index for predicting therapeutic effect. Good. Further, one aspect of the present disclosure may be a computer-readable non-transitory recording medium in which such a computer program is recorded.

[0058] In each of the above-described embodiments, each component may be configured by dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded in a recording medium such as a hard disk or a semiconductor memory.

[0059] In the present specification, the "program" is used in the ordinary meaning used in the field, describes the processing to be performed by a computer in order, and is legally treated as "a thing". is there. All computers are operating according to the program. In modern computers, programs are represented as data and stored in recording media or storage devices.

[0060] In the present specification, the "recording medium" stores a program for executing the present disclosure. 〇 2020/175633 42 卩 (：171? 2020 /008079

The recording medium may be any recording medium as long as the program can be recorded. For example, it can be stored internally

It may be an external storage device such as a magnetic disk or a flash memory such as a II 3 memory, but is not limited thereto.

[0061] In the present specification, the "system" means a configuration for executing the method or program of the present invention, and originally means a system or organization for carrying out the purpose, and a plurality of elements is a system. In the field of computing, it refers to the overall configuration of hardware, software, network, and so on.

[0062] The present disclosure has been described using various embodiments. The patents, patent applications and literatures cited for explaining the present disclosure in the present specification are incorporated by reference in the same manner as the contents themselves are specifically described in the present specification. To be done.

Example

[0063] Hereinafter, the present disclosure will be specifically described with reference to Examples for ease of understanding. However, the provided examples are provided for purposes of illustration only and not for the purpose of limiting the present disclosure. Accordingly, the scope of the present disclosure is not limited to the embodiments and examples specifically described herein, but only by the claims.

[0064] (Abbreviation)

In this specification including this example, each parameter may be abbreviated as follows. [table 1 ]

(Example 1: Model for predicting therapeutic effect in patients treated with infliximab) 〇 2020/175633 44 卩 (：171? 2020 /008079

Patient and efficacy assessment

The diagnosis of rheumatism (8) in this example was carried out by the American College of Rheumatology (80).

It was done based on the classification criteria. In this example, it was refractory to 1 ^ (3 6111 ₉ >661 〇, and between May 2007 and January 2017, at Keio University Hospital and Higashihiroshima Memorial Hospital. We enrolled 85 patients who received at least one of the first biologics (infliximab), which was administered according to the Japan Rheumatology Society guidelines ( 111^ :/ _/ ½ä. Achievement of remission based on VIII I) ([¾mi 1\/1; Clinical Disease Activity Index!; 08 (defined as ¾43 £2.6)) or no remission

Was defined as

[0066] Research procedure

All patients with rheumatism provided serum samples prior to being treated. This preserved serum was used to measure levels of cytokines, chemokines, and soluble receptors in a cohort of eighteen patients who were naive to biologics.

[0067] Patients had IV! Ding X, and 31_/01\/180 with or without IV by injecting 31^/1^ once every two weeks and every four weeks. Was administered to. A retrospective assessment of the clinical efficacy of the treatment and a determination of whether the patient achieved remission or was non-remission was performed by the patient.

This was done using the score (grading method validated by 1613). 28-

Based on the severity of the symptoms reflected in the score, they were classified into three groups: low (2.6-3.1), medium (3.2-5.1) and high 05.1). In this example, in the statistical analysis, complete remission

And non-remission (0-83-28-〇^³2.6).

[0068] Clinical parameters 〇 2020/175633 45 卩 (：171? 2020 /008079

Table 2 below summarizes the baseline clinical characteristics of patients. For the purposes of this observational study, patients were selected who underwent treatment for at least 22 weeks, had serum and clinical data available, and did not develop an adverse response to treatment.

[Table 2-1]

\¥0 2020/175633 47 卩(: 17 2020/008079

[Table 2-2]

\¥0 2020/175633 48 卩(: 17 2020/008079

[Table 2-3]

[Table 2-4]

[0069] Cytokine/chemokine/soluble receptor assay

Cytokines and bimarkers were quantified using the multiplex cytokine array system Bio-Plex 200 (Bio-Rad Laboratories, CA, USA) according to the manufacturer's instructions. Serum from RA patients was collected and centrifuged at 1600Xg for 10 minutes. Serum samples were frozen at -80 ^° C until analysis. Simultaneous quantification of cytokine/chemokine/soluble receptor in RA patients was performed. The following kits or devices: TheBio-PlexHumanCytokine 27-Plex Pane I (IL-1 _/ S, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8 , IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, basicFGF, eotaxin, G-CSF, GM-CSF, IFN-y, IP-10, MC

MIP-1 _/ S, PDGF-bb, RANTES, TNF-a, VEGF) and Inf Lammation 1 kit (APRIL, BAFF, sCD30, sCD163, sgp130, IFN-a2, IFN- _/ S, sIL-6Ra, IL- 11, IL-12(p40), IL-19, IL-20, IL-22, IL-26, IL-27, IL-28A, IL-29, IL-32, IL-34, IL-35, LIGHT , MMP-1, MMP-2, MMP-3, Osteocalcin, Osteo pontin, Pent rax i n-3, sTNF-R1, sTNF-R2, TSLP, TWEAK:Bi○-Rad Laborator ie s, CA, USA), HumanSo lub le Cytokine Receptor Pane l (sIL-6R, sgp130, sT NFR-I, sTNFR-II: Mi Uiplex ^R MAP, :Mi Ui pore Co., MA, USA) and R & D Lu minixScreening Assay 21plex (ADAMTS13, Aggrecan , Ang i opo i et i n-2, PD-L1, BMP-2, CD40, CD40Ligand, CD44, CX3XL1, E-Selectin, HGF, ICAM-1, IFN -yRU L-Selectin, Leptin, LIF, OncostatinM, TRAIL, VCAM-1, VEGFR2, IL-18:R&D Systems, Inc. Minneapolis _^ MN 55413 Toll Free USA, Canada). Quantitation of tocaine/chemokine/soluble receptor was performed. These Myplex Cytokine arrays were measured according to the manufacturer's instructions. Data acquisition and analysis were performed using Bio-Plex Manager software version 5.0.

[0070] (Statistical analysis)

An overview of the Tenth Ten Analysis is summarized in Table 3 below.

[Table 3]

V start

h Loading clinical data and cytokine data

h Calibration of cytokine data with calibration curve

loglOO correction of h cytokine data

h Missing value complement

h Reagent lot variation correction

h Variable selection by LASSO for all variables

◎ ₁ 50 times Repeated Cross Validation

h Random sampling: 80% of the data is used as training data,

Randomly divided into data Evaluation by intrinsic control

Model building with LASSO using training data; I min,

Determine the lse position variable

|- Imin, min+lse model to calculate the predicted value of the training data

Prediction value of verification data is calculated by h x min, min+lse model

h Calculate Cohen’ s d from 50 times

Draw the result in dot plot

△ End

[0071] All parameters were log-transformed because the distribution was more normalized. Cytokine/chemokine/soluble receptor values are expressed as pg/mL. In this example, the physician will observe whether the patient experiences a remission (DA S28-ESR less than 2.6) or no remission before selecting a treatment protocol. Performed an analysis to determine if the patient was in remission or non-remission.

[0072] The Lasso approach was used to select the markers to include in the model. In the process of constructing a prognostic model including multiple functions, leave-some-randomly-selected-samples-out cross-validation (LSRSSO) was used to evaluate performance while avoiding overfitting. Here, Some is set to 3. Within each LSRSSO cycle, the least absolute contraction/selection operator (Lasso) approach implemented in the glmn et package was used to select the most beneficial combination markers for remission or non-remission. The Lasso model was selected and included in the remission or non-remission model and then used to generate the risk score for the excluded model.

[0073] Using receiver operating characteristic (ROC) analysis, REM vs. N at 22 to 24 weeks.

The ability of the model to predict the N-REM category was evaluated. When performing true cross validation within each LSRSSO cycle, the markers selected were slightly different due to the variability of the data. The model was applied to cytokine/chemokine/soluble receptor values in the same sample groups used to build the model. N0N-REM was defined as "positive". Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were determined by the optimum cutoff value (threshold) from the R0C curve. The analysis was performed using R software version 3.4.1.

[0074] (1) Reading clinical data and cytokine data

(1-1) Explanatory variable

The data obtained from the above patients and samples were read as explanatory variables.

[0075] The clinical data include the following: \¥0 2020/175633 52 卩(: 17 2020/008079

[Table 4]

Read the parameters.

[0076] The sitekine data are as follows:

[Table 5-1]

〇 2020/175 633 54 卩 (: 171? 2020 /008079

[Table 5-2]

Read the parameters.

[0077] (1 -2) Objective variable

After treatment

When the threshold value is 2.6 and below 2.6, the objective variable is remission = 1, and when the threshold is over 2.6, the objective variable is non-remission = 0. Among patients, the number of remissions = 46 and the number of non-remissions = 39.

[0078] (2) Correction of variations between

(2-1) I ○ Totaled by 1

Since the data for the reagents used were different, we grouped the patient data by lot and calculated the median value of the data for each reagent.

[0079] (2-2)

Corrected by 8

The median value for each reagent obtained in the previous section was used and subtracted from the measurement data of the subject in the corresponding reagent lot. Therefore, apparently all measured values have moved to small values. In this example, in order to confirm the variation of the reagent lot, the variation is corrected by using the median value of a large number of people.However, in the actual test, the standard sample was used for the specified marker. Since it can be corrected by measuring, there is no problem.

[0080] (3) Evaluation of judgment performance using intrinsic control

Subtraction (because it was logarithmic) was carried out for each of the 78 sitekine data, and then, 80 was carried out. Using 80% of the training data, a model was constructed in 80 and the variables at position 111_1 and 111_ +136 were determined. In the case of 800, the samples are randomly shuffled, and every 3 samples are set to be excluded in order.

Went _10

(This time 0 1 (^ = equivalent to about 20) A min,

Calculate the predicted value of the training data with n + 1 3 ㊀ model

C. m P PV. m N PV. m ACC. m A U C. t P PV. t

Calculate N PV. t ACC. t. (M: learning data, t: verification data)

Calculate the predicted value of the verification data with the model Am i n, m i n + 1 s e A U C. m P PV. m N PV. m ACC. m A U C. t P PV. t

Calculate N PV. t ACC. t. (M: learning data, t: verification data)

[0081] (4) R e p e a t e d C r o s s V a l i d a t i o n

Using 0.8 of the data as the data for the model, LASS 〇 regression is performed, and the remaining 0.2 is given to the prediction model as validation data for evaluation.While randomly sampling these 50 times, Repeated processing, Repeated Cross Validation was executed. Using the distribution of predicted values of the remission group and the distribution of predicted values of the non-remission group obtained from the prediction results of the 50 verification data, Co he n'sd (J. Co hen :S tatistica IP owe r A nalysistorthe B ehaviora IS ciences, second edition, P sycology Pres s.) was calculated.

[0082] (Result)

In models of Am in, BlogTNF.a, B logChiti nase.3, B logIL.1 b _% B logo Osteopont in, MlogsTNFRI, RlogTRAIL, BlogMCP.1, RlogADAMTS13, B logGM CSF, BlogTNF.R2 _% BlogCD30, B LogIL Cohen' d of 1.35 or more was obtained when .13 and B LogIL.9 were used as the endogenous control.

[0083] For each of the endogenous controls, out of 50 trials, the following parameters were included in the prediction model at the frequency indicated. \¥0 2020/175633 56 卩 (: 17 2020 /008079

[Table 6-1]

\¥02020/175633 57 卩(: 17 2020/008079

[Table 6-2]

[0084] In the model of X 111 I 1^ + 1 36, ¾/11 93 93 [¾ 1, [¾ 109 9 [^ 1 then 81 0 9 ^.8, [^ 〇 044, 81 〇 9_Rei_1103 de, ^ 〇 ₉ 0 1 ^ 313, when used (^ 098! ^ - 2, 81_Rei ₉ 11/0 ^5.1, and 〇 911_.11) as endogenous control, 1.25 or more (: 〇116 001 was obtained.

[0085] For each of the endogenous controls, out of 50 trials, the following parameters were included in the predictive model at the frequency indicated.

[Table 7]

[0086] (Discussion)

From the above results, TN Fa, Week 0 ESR, ADAMTS 13, CD44, Week 0 Genera and V AS, Week 0 DAS-ES R, T RA IL, Chitinase 3, BMP 2, BA FF, MC P 1, TWEAK, Week 0 RF, IL— 1/S, Osteopontin, RANT ES, I L_ 12 p 70, 〇 SM, GM-CS F, IP 10, TN FR 2, CD30, I It is considered that the combination of the parameters of L- 13, IL-9, and s TNFRI can be used to construct a model that predicts patient responsiveness to biologics with high predictive power. Among them, s TN FRI, Week 0 ESR, I L_ 12 p 70, MC P 1, T RA IL, TN Fa _s ADAMTS 13 3, CD44, GM-CS F, BMP 2, MMP 2 , ○ SM, I L- 1/S, TWEAK, and BMP 2 by using a combination of parameters will be more robust model 〇2020/175633 59 卩(: 171-1?2020/008079

Conceivable.

(Example 1 — 2: Model using clinical data)

Of the above data, we verified whether remission could be predicted by a model that combined only clinical data. No intrinsic control was used because clinical data had different dimensions for each parameter. Using the clinical data of the data read in Example 1, a model was created in the same manner as in Example 1 and trials were performed 50 times.

[0088] Of the 50 trials, the parameters shown below were included in the predictive model at the frequencies shown.

[Table 8]

[0089] As a model, the following regression equation model:

[Table 9] Position: 1 ,!¾|> !To 41

When using, the remission could be predicted with 8110 = 0.78. this 〇 2020/175633 60 卩(：171? 2020/008079

In the regression equation, 〇〇 11611 ‘3 〇１ was ···. 036.

(Example)

3 〇〇

According to the method described in Example 1,

The performance of the predictive model of remission was calculated when only was used as the sole explanatory variable. In this case, the value of 0,0,6,3 in the prediction model was 0,9558004 (95% confidence interval, 0,4996557 -1.41 1 945 1).

(Example 3: Actual clinical application)

1. Collect pretreatment serum of rheumatism patients scheduled to undergo X treatment

2. Measure and quantify the identified biomarkers _ (multiple) that can predict remission/non-remission in advance

3. Introduce the specified clinical data value and the specified biomarker amount into a certain mathematical formula and make a judgment by calculation. In other words, a remission of 10 is expected to be remission, and a remission of 1 is not remission.

4. Patients who are predicted to be in remission receive medical treatment

5. Patients predicted to be in non-remission consider alternative therapies

[0092] (Note)

While the present disclosure has been illustrated using the preferred embodiments of the present disclosure as described above, it is understood that the scope of the present disclosure should be construed only by the claims. The patents, patent applications, and other references cited herein should be incorporated by reference in their content as if their contents were specifically described in this specification. It is understood that This application has priority over Japanese Patent Application No. 201 9-036782 (filed on February 28, 2010) and No. 2019-1 03 186 (filed on May 31, 1919). The entire contents of which are incorporated herein by reference.

Industrial availability

[0093] The present disclosure provides a technique for appropriately selecting a therapeutic agent for rheumatism and can be used in the pharmaceutical industry.

Claims

The scope of the claims

[Claim 1] A method for predicting and determining the therapeutic effect of a biologic on rheumatoid arthritis patients, the method comprising:

Obtaining at least two clinical data or biological parameters in a rheumatoid arthritis patient prior to administration of the biologic, and

Using at least one of the clinical data or the biological parameter as a control, analyzing the biological parameter, and using it as an index for predicting and determining the therapeutic effect.

Including the method.

[Claim 2] The method according to claim 1, wherein the biologic comprises an anti-TNF_a agent.

[Claim 3] The method according to claim 1, wherein the therapeutic effect is remission of the patient.

[Claim 4] A method for predicting and determining the therapeutic effect of a biologic on rheumatoid arthritis patients, comprising:

Obtaining at least two clinical data or biological parameters in a rheumatoid arthritis patient prior to administration of the biologic

The biological agent is an anti-TN F-a agent, the therapeutic effect is remission of the patient,

The at least two types of clinical data or biological parameters are TNFa, 0 week ESR, ADAMTS13, CD44, 0 week Genera I.V AS, 0 week DAS-ESR, T RA IL, chitinase 3, B MP 2, BA FF, MC P 1, TWEAK, Week 0 RF, IL-1/S, Osteopontin, RANT ES, IL— 12 p 70, 〇 SM, G M-CS F, IP 10 , TN FR 2, CD30, IL-13, IL-9, and s TN FR, including a parameter selected from the group consisting of:

[Claim 5] At least one of the at least two types of clinical data or biological parameters is used as an endogenous control, The method of claim 4.

[Claim 6] A ratio of a parameter of the at least two types of clinical data or biological parameters that is not the endogenous control to the endogenous control is used. Either method described in item 1.

7. The method according to any one of claims 1 to 6, wherein the anti-TNF_a agent is selected from the group consisting of infliximab, etanercept, adalimumab, golimumab, and certolizumab.

8. The method according to claim 7, wherein the anti-TNF_a agent is infliximab.

[Claim 9] The at least two types of clinical data or biological parameters are s TN

FRI, 0 week ES R, I L- 1 2 p 70, MC P 1, T RA IL, TN F a s ADAMTS 1 3, CD44, GM-CS F, BMP 2, MMP 2, 〇 SM, I L -The method according to any one of claims 1 to 8, comprising a parameter selected from the group consisting of 1/S, TWEAK, and BMP 2.

[Claim 10] The at least two types of clinical data or biological parameters are as follows:

BLogTNF.a, DAS. ESR.0w, ESR— 0w, Genera l. VAS.0W, RlogADAMTS13, RlogCD44, and RlogTRAIL;

BLogChitinase.3, BlogBAFF, BlogMCP. U BlogRANTES, BlogTWEAK, DAS. ESR.0w, ESR— 0w, Genera l VAS.0W, RlogADAMTS13, RlogBMP.2, RlogCD44 and logRF0w;

BLoglL.lb, DAS. ESR.0w, ESR— 0w, Genera l. VAS.0W, RlogADAMTS13, RlogBMP.2 and RlogCD44;

B logOsteopont in _% B logMCP.1 and B logRANTES;

DAS. ESR.0w, ESR 0w, RlogADAMTS13, RlogBMP.2, RlogCD44 and LogRF0w;

〇 2020/175633 63 卩 (：171? 2020 /008079

—

, 1^ 〇 9 〇 044 and [^ 〇 9 〇 311/1;

81_Rei_9 chome. (¾2, 81_Rei_911 / 0 ³ .1, DAS.ESR.0w, Snake 0! «, 1 ^ Rei_9 eight hundred and eight 1 ^ 313, 〇 9已1 ^ .2, (¾1_Rei 9044 and 109 (^01/\/;

81_Rei_9_rei_rei_30, 81_Rei_911 / 0 ³ .1, DAS.ESR.0w, Snake 0! «, 1 ^ Rei_9 eight hundred and eight 1 ^ 313, [^ Rei_9_rei 44 and 1_Rei_9 (^ 01/\/;

81_Rei ₉ 11.13, 81_Rei ₉ 1! _, 12卩70, 81_Rei ₉ 110 ³ .1, _DAS.ESR.0w, snake 0, Rei_㊀ ral.VAS.0W, 1 ^ say yes-1 ^, (^ 〇 ₉ 808 1^313, (¾ 1 〇 981^·2 and 1 (^0044;

81_Rei ₉ 11.9, 81_Rei ₉ 110 ^3.1, including DAS.ESR.0W, a combination of Snake 0! «And (^ 〇 ₉ 80 parameter selected from the group consisting of eight 1 ^ 313, claim 1 ~ The method according to any one of 9 to 9.

[Claim 11] The at least two types of clinical data or biological parameters are as follows: 8109 .8 as an endogenous control, and

[0 | «, General.VAS.0W, ( ^ 〇 ₉ eight hundred and eight 1 ^ 313, 1 ^ Rei_9_rei_044, and (^ 〇 ₉ chome [^ 11_; as endogenous control Rei_9 (: 114丨36.3, as well as Rei_98 eight Dodo,已1_Rei_911 / 0 ^3.1, 81_Rei_9 (^ town £ 3, 81_Rei_9 Ding 1 ^ eight 1 <, DAS.ESR.0w, Snake 0! «, Rei_6 "3 1 1 8 3.01«, 1^0 9 8 8 1^313, 1^0981^.2, 1 (^0044 and 1 0 9 (^01«; 〇911_.11 as an endogenous control), and

[_0 | «, General.VAS.0W, ( ^ 〇 ₉ eight hundred and eight 1 ^ 313, (¾ 1_Rei_981 ^ · 2 and 1 (^ 0044; 81_Rei_9_rei_316_rei as endogenous control 〇 ^ 1 And 〇 2020/175633 64 卩 (：171? 2020 /008079

[0!«, Genera l. VAS. 0W, (^ 〇 98 808 1^313, 1^ 〇 ₉ 〇 044 and [^ 〇 ₉ 〇 311/1;

The method according to any one of claims 1 to 10, comprising a combination of parameters selected from the group consisting of:

[Claim 12] The at least two types of clinical data or biological parameters are as follows:

RlogTRAIU B LogMCP.1 and ESR — 0w;

BlogTNF.a, ESR 0w and RlogADAMTS13;

RlogCD44 _% B logMCP.1 and ESR — 0w;

BlogGM.CSF, ESR 0w and RlogADAMTS13;

RLogADAMTS13, B loglL.12p70, B logMCP.1 and ESR 0w;

RLogBMP.2, B LogMCP.1 and ESR — 0w;

BlogMCP. U ESR 0w, RlogADAMTS13, RlogCD44 and RlogOSM; and

BLogIL.1b, BlogTWEAK, ESR 0w and RlogADAMTS13;

13. The method of any one of claims 1-11, comprising a combination of parameters selected from the group consisting of:

[Claim 13] The at least two clinical data or biological parameters are as follows: MLogsTNFRI as an endogenous control, and BlogIL.12p70, BlogMCP.1 and ESR — 0w;

RLogTRAIL as an endogenous control, and BlogMCP.1 and ESR_0w;

BLogTNF.a as an endogenous control, and ESR_0w and R Log ADAMTS13:

RL 〇 gCD44 as an endogenous control, and BlogMCP.1 and ESR_0w;

BLogGM.CSF as an endogenous control, and ESR_0w and RlogADAMTS13:

RL 〇 gADAMTS13 as an endogenous control, and BlogIL.12p 70, BlogMCP.1 and ESR — 0w;

RLogBMP.2 as an endogenous control, and B LogMCP.1 and ESR— 0w;

BlogMCP.1 as an endogenous control, as well as ESR 0w, R log ADA

and BLogIL.1b as an endogenous control, and BlogTWEAK, ES R_0w and RlogADAMTS13;

A method according to any one of claims 1 to 12, comprising a combination of parameters selected from the group consisting of:

[Claim 14] A method of using at least two types of clinical data or biological parameters as an index for predicting and determining the therapeutic effect of a biological agent on a patient with rheumatoid arthritis, wherein the at least two types of clinical data or biological parameters are used. parameters, TN F a _s 0 week ES R, ADAMTS 1 3, CD44 , 0 week Ge neral. VAS, 0 week DAS-ES R, T RA IL , Kichina Ze 3, BMP 2, BA FF, MC P 1, TWEAK, Week 0 RF, IL— 1/S, Osteopontin, RANT ES, IL— 1 2 p 70, 〇 SM, GM-CS F, IP 10 TN FR 2, CD30, IL-1 3 , A L-9, and s TNFR 丨, including a parameter selected from the group consisting of:

[Claim 15] A method for treating a patient with rheumatoid arthritis, comprising:

Comprising the step of obtaining at least two clinical data or biological parameters in a rheumatoid arthritis patient prior to administration of the biological agent, wherein the biological agent is an anti-TN F_a agent, and the therapeutic effect is The remission of the patient,

The at least two types of clinical data or biological parameters are TNFa, 0 week ESR, ADAMTS13, CD44, 0 week Genera I.V AS, 0 week DAS-ESR, T RA IL, chitinase 3, B MP 2, BA FF, MC P 1, TWEAK, Week 0 RF, IL -1/S, Osteopontin, RANT ES, IL— 12 p 70, 〇 SM, G M-CS F, IP 10 , TN FR 2, CD30, IL-13, IL-9, and s TN FR, including parameters selected from the group consisting of , Process;

(ii) administering the biologic to the rheumatoid arthritis patient when it is determined that the rheumatoid arthritis patient achieves remission with the biologic.

Including the method.

[Claim 16] An in vitro method for predicting and determining the therapeutic effect of a biologic on rheumatoid arthritis patients, comprising:

[Claim 17] An in vitro method, wherein at least two types of clinical data or biological parameters are used as an index for predicting the therapeutic effect of a biological agent on rheumatoid arthritis patients. data or biometric parameter is, TN F a _s 0 week ES R, ADAMTS 1 3, CD44 , 0 week Ge neral. VAS, 0 week DAS-ES R, T RA IL , chitinase 3, BMP 2, BA FF, MC P 1, TWEAK, Week 0 RF, IL -1/S, Osteopontin, RANT ES, IL-12 p 70, 〇 SM, GM-CS F, 丨 P 10, TN FR 2, CD30, Selected from the group consisting of IL-1 3, L-9, and s TN FR. 〇 2020/175633 68 卩 (：171? 2020 /008079

Including the parameters to be included.

[Claim 18] A method for identifying an index for predicting and determining the therapeutic effect of a biological agent in a patient with rheumatoid arthritis, comprising:

(8) A step of performing a brute force multivariate analysis for the parameters of the biomarker candidates, wherein in the analysis, at least one of the parameters is used as a control and the relative value is brute force. A step including a random analysis,

(B) A step of selecting a combination of parameters with high prediction ability based on the analysis

Including and.

[Claim 19] The method according to claim 18, wherein the biological agent is an anti-drug.

[Claim 20] A method for predicting and determining the therapeutic effect of a biologic on rheumatoid arthritis patients, the method comprising:

Obtaining at least two clinical data in a rheumatoid arthritis patient prior to administration of the biologic, and

A step of analyzing the clinical data and using it as an index for predicting and judging the therapeutic effect

Including the method.

[Claim 21] The method according to claim 20, wherein the biological agent comprises an anti-drug.

22. The method of claim 20, wherein the therapeutic effect is remission of the patient.

[Claim 23] The method according to any one of claims 20 to 22, wherein the anti-drug agent is selected from the group consisting of infliximab, etanercept, adalimumab, golimumab, and certolizumab.

24. The method of claim 23, wherein the antidote is infliximab. 〇 2020/175633 69 卩(: 171-1? 2020/008079

[Claim 25] The at least two types of clinical data are 0 8 3_ 〇 [¾, 08 3_ _

Rheumatoid arthritis dysfunction classification, sex, tender joint number (Ding) 〇, inflated joint number (3” 〇), doctor 8 3, patient 8 3, 〇〇, 〇 [¾ (〇 reactive protein level),

(Erythrocyte sedimentation rate),

I, Mimi 〇Starting age, age, disease duration (years), presence or absence of concomitant use of antibody therapy and methotrexate (IV! D), IV! D dose, prednisolone (3 levels, IV! D combined) Existence, 0 IV!

The method according to any one of claims 20 to 24, which comprises a parameter selected from the group consisting of the presence or absence of concomitant use.

[Claim 26] The at least two types of clinical data are:

Sex, patient eight 3,

(Erythrocyte sedimentation rate), 〇〇丨, IV!

26. The method of any one of claims 20-25, comprising a parameter selected from the group consisting of:

[Claim 27] The at least two types of clinical data are:

£3 01« and 10 ₉ (^01«;

Min — 01/\/, eight ·Min ·01/\/ and 10 9 (^01/\/;

〇 6 ``31. \ZAS_ 0111/, £3 (^— 01/\/, DAS.ESR.0w, and 1 0 9 (^0¾\/;

〇 6 ”31.

㊀, 〇 6 “31. \/8 8 0111/, M — 01/\/, DAS.ESR.0w,. 〇 8 1.0¾\/, 8.1 〇 8 8 8 ”

27. The method of any one of claims 20-26, comprising a combination of parameters selected from the group consisting of: