The importance of predicting patient responses to monoclonal antibodies for Crohn’s disease

ABSTRACT

Introduction

Crohn’s disease (CD) is a chronic immune-mediated inflammatory bowel disease that results in relapsing and remitting symptoms but progressive transmural bowel damage leading to significant morbidity. CD results from dysregulation of the immune system related to genetic and environmental factors. While the use of monoclonal antibodies targeting cytokines and adhesion molecules has been shown to improve outcomes in CD patients, their widespread use has been limited due to high costs as well as variable access. Here, we summarize the factors that have been shown to correlate with responsiveness to biologic agents for use in practice.

Areas covered

We summarize the current literature regarding factors that have been shown to influence patient response to various biologic agents including: patient-related factors (e.g. age, gender, weight smoking history); disease-specific factors (e.g. disease duration, location/extension, behavior/phenotype, severity); genetic markers; transcription factors, and the gut microbiome. Finally, we review the utility of prediction models and present data supporting the use of recently developed decision support tools.

Expert opinion

Clinical decision support tools developed by machine learning are currently available for the selection of biologic agents in CD patients. We expect these models to become an integral tool for clinicians in the treatment of CD in the coming years.

KEYWORDS:

• Artificial intelligence
• biologic agent
• clinical decision support tool
• Crohn’s disease
• machine learning
• monoclonal antibody
• predictive models

Article highlights

• Factors that influence patient response to biologic agents include patient-related (age, gender, weight smoking history), disease-related (disease duration, location/extension, behavior/phenotype, severity), genetic, transcription factors, and gut microbiome.

• Higher body weight is associated with reduced response to biologic agents.

• Isolated colonic disease has been shown to ‘respond’ more favorably to biologic agents than ileal disease.

• Increased gut microbial diversity improves response to anti-TNF and ustekinumab therapy.

• Machine learning models utilize large data sets to predict response to therapy, allowing for risk stratification and prognostication.

• Clinical decision support tools, to date, have been developed using machine learning models to predict response to infliximab, vedolizumab, and ustekinumab.

Declaration of interest

S Hanauer: AbbVie Consultant, Clinical Research (Institution), Speaker; Amgen Consultant, Clinical Research (Institution); Boehringer-Ingelheim Consultant, DSMB; BMS Consultant, DSMB; Celltrion Consultant; Fresenius-Kabi, Consultant: Genentech Consultant, Clinical Research, (Institution); Gilead Consultant, Clinical Research (Institution); Gossamer DSMB, GSK Consultant, Clinical Research (Institution); Immunic, Consultant; Intercept Pharmaceuticals; Janssen Consultant, Clinical Research (Institution), Speaker; Lilly Consultant, Clinical Research (Institution); Merck Consultant; Novartis Consultant, Clinical Research (Institution); Organon Consultant; Pfizer Consultant, Clinical Research (Institution), Speaker; Progenity Consultant; Prometheus Consultant, Clinical Research (Institution); Protagonist Consultant, DSMB; Receptos Consultant, Clinical Research (Institution), Salix (Consultant); Samsung Bioepis Consultant; Seres Therapeutics Consultant, Clinical Research (Institution); Takeda Consultant, Clinical Research (Institution), Speaker; UCB Consultant, Clinical Research (Institution); Ventyx DSMB VHsquared Consultant

P Dulai: Consulting: Abvvie, Abviax, Adiso, Bristol Meyers Squibb, GSK, Janssen, Lilly, Pfizer, Roivant, Takeda.

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.

Additional information

Funding

This paper was not funded.