Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data

Abstract

Introduction: In rheumatoid arthritis, time spent using ineffective medications may lead to irreversible disease progression. Despite availability of targeted treatments, only a minority of patients achieve sustained remission, and little evidence exists to direct the choice of biologic disease-modifying antirheumatic drugs in individual patients. Machine learning was used to identify a rule to predict the response to sarilumab and discriminate between responses to sarilumab versus adalimumab, with a focus on clinically feasible blood biomarkers.

Methods: The decision tree model GUIDE was trained using a data subset from the sarilumab trial with the most biomarker data, MOBILITY, to identify a rule to predict disease activity after sarilumab 200 mg. The training set comprised 18 categorical and 24 continuous baseline variables; some data were omitted from training and used for validation by the algorithm (cross-validation). The rule was tested using full datasets from four trials (MOBILITY, MONARCH, TARGET, and ASCERTAIN), focusing on the recommended sarilumab dose of 200 mg.

Results: In the training set, the presence of anti-cyclic citrullinated peptide antibodies, combined with C-reactive protein > 12.3 mg/l, was identified as the "rule" that predicts American College of Rheumatology 20% response (ACR20) to sarilumab. In testing, the rule reliably predicted response to sarilumab in MOBILITY, MONARCH, and ASCERTAIN for many efficacy parameters (e.g., ACR70 and the 28-joint disease activity score using CRP [DAS28-CRP] remission). The rule applied less to TARGET, which recruited individuals refractory to tumor necrosis factor inhibitors. The potential clinical benefit of the rule was highlighted in a clinical scenario based on MONARCH data, which found that increased ACR70 rates could be achieved by treating either rule-positive patients with sarilumab or rule-negative patients with adalimumab.

Conclusions: Well-established and clinically feasible blood biomarkers can guide individual treatment choice. Real-world validation of the rule identified in this post hoc analysis is merited.

Clinical trial registration: NCT01061736, NCT02332590, NCT01709578, NCT01768572.

Keywords: Adalimumab; Clinical trial; Machine learning; Precision medicine; Rheumatoid arthritis; Sarilumab.

Fig. 1

Schematic of the resulting GUIDE decision tree classification approach model (A) and the reduced final model (B). Anti-CCP anti-cyclic citrullinated peptide, C1M metabolite of type I collagen, CRP C-reactive protein, sgp130 soluble glycoprotein 130

Fig. 2

Response rates in rule-positive and rule-negative sarilumab-treated patients. The patient stratification rule was the combined presence of anti-CCP and CRP > 12.3 mg/l. ACR20 ACR 20%, ACR50 ACR 50%, ACR70 ACR 70%, CDAI Clinical Disease Activity Index, DAS28-CRP 28-joint Disease Activity Score using C-reactive protein, DAS28-ESR DAS28 using erythrocyte sedimentation rate, HAQ-DI Health Assessment Questionnaire-Disability Index, LDA low disease activity, MCID minimal clinically important difference, REM remission

Fig. 3

Odds ratios of achieving clinical response at week 24 in placebo- (MOBILITY, TARGET) or active-controlled studies (ASCERTAIN): rule-positive versus rule-negative patients. The patient stratification rule was the combined presence of anti-CCP and CRP > 12.3 mg/l. Data presented for MOBILITY and TARGET are placebo-adjusted. ACR20 ACR 20%, ACR50 ACR 50%, ACR70 ACR 70%, DAS28-CRP 28-joint Disease Activity Score using C-reactive protein, HAQ-DI Health Assessment Questionnaire-Disability Index, LDA low disease activity, MCID minimal clinically important difference