Improvement in clinical disease activity index when treatment selection is informed by the tumor necrosis factor-ɑ inhibitor molecular signature response classifier: analysis from the study to accelerate information of molecular signatures in rheumatoid arthritis

ABSTRACT

Background

A blood-based molecular signature response classifier (MSRC) predicts non-response to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA).

Research design and methods

This is an interim analysis of data collected in the Study to Accelerate Information of Molecular Signatures (AIMS) in RA from patients who received the MSRC test between September 2020 and November 2021. Absolute changes in clinical disease activity index (CDAI) scores from baseline were evaluated at 12 weeks (n = 470) and 24 weeks (n = 274).

Results

Predicted TNFi non-responders who received a biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) with an alternative mechanism of action (altMOA) experienced up to 1.8-fold greater improvements in CDAI scores than those treated with a TNFi (12 weeks: 12.2 vs 8.0; p-value = 0.083; 24 weeks: 14.2 vs 7.8 p-value = 0.009). In patients with a molecular signature of non-response to TNFi in high disease activity at baseline, this corresponded to 43.2% relative improvement in achieving a lower CDAI disease activity level when likely TNFi non-responders were treated with a non-TNFi therapy (38.9% vs 55.7%). Commensurate improvements in efficiency of spend are expected when TNFi are avoided in favor of altMOA.

Conclusions

RA treatment selection informed by MSRC test results improves clinical outcomes in real-world care and offers improvements in efficiency of healthcare spending.

KEYWORDS:

• Clinical utility
• molecular signature
• precision medicine
• real-world evidence
• response prediction
• rheumatoid arthritis
• TNFi therapy
• tumor necrosis factor-ɑ inhibitors
• b/tsDMARD
• MSRC

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Acknowledgments

The authors thank the healthcare providers and participants who made this study possible.

Declaration of interest

L Zhang, A Arnaud, E Connolly-Strong, S Asgarian and J Withers are full-time employees of Scipher Medicine Corporation. V Strand serves as a consultant to Scipher Medicine. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

Conceptualization: all authors; Methodology: Lixia Zhang, Alix Arnaud; Formal analysis and investigation: Lixia Zhang, Alix Arnaud, Erin Connolly-Strong, Vibeke Strand; Writing - original draft preparation: Lixia Zhang, Alix Arnaud, Johanna Withers; Writing - review and editing: all authors.

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Compliance with ethics guidelines

This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and are consistent with the International Committee on Harmonization of Good Clinical Practice, as well as other applicable local and federal laws, regulations, and guidelines. All participants gave informed consent.

Data availability

The algorithm underlying the MSRC is proprietary to Scipher Medicine Corporation.

Additional information

Funding

This study was funded by Scipher Medicine Corporation.