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

Markus Rehberg, Clemens Giegerich, Amy Praestgaard, Hubert Van Hoogstraten, Melitza Iglesias-Rodriguez, Jeffrey R Curtis, Jacques-Eric Gottenberg, Andreas Schwarting, Santos Castañeda, Andrea Rubbert-Roth, Ernest H S Choy & MOBILITY, MONARCH, TARGET, And ASCERTAIN Investigators

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.
   
citation Rehberg M, Giegerich C, Praestgaard A, Van Hoogstraten H, Iglesias-Rodriguez M, Curtis J R, Gottenberg J E, Schwarting A, Castañeda S, Rubbert-Roth A, Choy E H S, MOBILITY, MONARCH, TARGET, And ASCERTAIN Investigators . Identification of a Rule to Predict Response to Sarilumab in Patients with Rheumatoid Arthritis Using Machine Learning and Clinical Trial Data. Rheumatol Ther 2021; 8:1661-1675.
   
type journal paper/review (English)
date of publishing 14-09-2021
journal title Rheumatol Ther (8/4)
ISSN print 2198-6576
pages 1661-1675
PubMed 34519964
DOI 10.1007/s40744-021-00361-5