A Clinical Prediction Model to Determine Probability of Response to Certolizumab Pegol for Crohn's Disease

ECCO Topical Review on Predictive Models on Inflammatory Bowel Disease Disease Course and Treatment Response

BACKGROUND AND AIMS

Inflammatory bowel disease (IBD) poses a clinical challenge due to its variable progression and treatment response. Despite the development of predictive models, their clinical application remains limited due to validation and methodological inconsistencies. The current topical review examines existing predictive models, assesses their relevance, and discusses the barriers to their clinical implementation.

METHODS

An expert panel formed by European Crohn's and Colitis Organisation, including gastroenterologists, surgeons, and clinical epidemiologists, reviewed predictive models on IBD disease course and treatment response. Delphi methodology was applied to develop practice position statements. A practice position was set when at least 80% of participants reached agreement on a recommendation.

RESULTS

Fourteen practice positions and 2 perspective points were developed, highlighting factors included in models predicting IBD disease course and treatment response identified in the literature and barriers to clinical implementation. The appropriate methodological approaches for model development and validation have been defined, while methodological barriers to tackle have been identified. Perspectives on the inclusion of relevant biomarkers, and flexible study design have been outlined.

CONCLUSIONS

This topical review offers practice recommendations and guidance for future predictive models on IBD disease course and treatment response including their implementation in clinical practice.

Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion

INTRODUCTION

Artificial intelligence (AI) is expected to impact all facets of inflammatory bowel disease (IBD) management, including disease assessment, treatment decisions, discovery and development of new biomarkers and therapeutics, as well as clinician-patient communication.

AREAS COVERED

This perspective paper provides an overview of the application of AI in the clinical management of IBD through a review of the currently available AI models that could be potential tools for prognosis, shared decision-making, and precision medicine. This overview covers models that measure treatment response based on statistical or machine-learning methods, or a combination of the two. We briefly discuss a computational model that allows integration of immune/biological system knowledge with mathematical modeling and also involves a 'digital twin', which allows measurement of temporal trends in mucosal inflammatory activity for predicting treatment response. A viewpoint on AI-enabled wearables and nearables and their use to improve IBD management is also included.

EXPERT OPINION

Although challenges regarding data quality, privacy, and security; ethical concerns; technical limitations; and regulatory barriers remain to be fully addressed, a growing body of evidence suggests a tremendous potential for integration of AI into daily clinical practice to enable precision medicine and shared decision-making.

Drug Clearance in Patients with Inflammatory Bowel Disease Treated with Biologics

Biological therapy is very effective for treating patients with moderate to severe inflammatory bowel disease (IBD). However, up to 40% can have primary non-response, and up to 50% of the patients can experience a loss of response to anti-tumor necrosis factor therapy. These undesirable outcomes can be attributed to either a mechanistic failure or pharmacokinetic (PK) issues characterized by an inadequate drug exposure and a high drug clearance. There are several factors associated with accelerated clearance of biologics including increased body weight, low serum albumin and immunogenicity. Drug clearance has gained a lot of attention recently as cumulative data suggest that there is an association between drug clearance and therapeutic outcomes in patients with IBD. Moreover, clearance is used by model informed precision dosing (MIDP) tools, or PK dashboards, to adjust the dosing for reaching a target drug concentration threshold towards a more personalized application of TDM. However, the role of drug clearance in clinical practice is yet to be determined. This comprehensive review aims to present data regarding the variables affecting the clearance of specific biologics, the association of clearance with therapeutic outcomes and the role of clearance monitoring and MIPD in patients with IBD.