Abstract
Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn’s disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from complex host and environmental interactions. Investigating drug efficacy for IBD can improve our understanding of why treatment response can vary between patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. Using explanation of our models, we interpret the ML models’ predictions to infer unique combinations of important features associated with pharmacological responses obtained during preclinical testing of drug candidates in ex vivo patient-derived fresh tissues. Our inferred multi-modal features that are predictive of drug efficacy include multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data. Our aim is to understand variation in patient responses before a drug candidate moves forward to clinical trials. As a pharmacological measure of drug efficacy, we measured the reduction in the release of the inflammatory cytokine TNFα from the fresh IBD tissues in the presence/absence of test drugs. We initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor; however, we later showed our approach can be applied to other targets, test drugs or mechanisms of interest. Our best model predicted TNFα levels from demographic, medicinal and genomic features with an error of only 4.98% on unseen patients. We incorporated transcriptomic data to validate insights from genomic features. Our results showed variations in drug effectiveness (measured by ex vivo assays) between patients that differed in gender, age or condition and linked new genetic polymorphisms to patient response variation to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models IBD drug response while also identifying its most predictive features as part of a transparent ML precision medicine strategy.
Highlights
Precision medicine has become a widely recognised and desirable medical model for its ability to stratify patients into different groups based on their susceptibility to a particular disease or their response to a specific drug [1]
Here, we developed an explainable machine learning (ML) workflow that combines multi-omic data, demographic data, medicinal data and pharmacology data, all derived from a preclinical human fresh tissue assay, to predict patient-specific drug responses and to inform clinical trial precision medicine strategies (Fig 1)
All features were found to be correlated with response to one or more of the compounds at a correlation |rs| > 0.3 except for smoking history. This could be a result of the inconsistent and incomplete information provided for smoking history, as such, for the purposes of the present study, this feature was removed, it is noted that smoking history can be a factor in Inflammatory bowel diseases (IBDs) and further exploration could be merited in future studies
Summary
Precision medicine has become a widely recognised and desirable medical model for its ability to stratify patients into different groups based on their susceptibility to a particular disease or their response to a specific drug [1]. If patient stratification is found to be necessary to achieve the required efficacy, this high cost/late-stage approach could have a major impact on the commercial viability of a drug due to the smaller than expected target patient population [4]. An earlier understanding of the target patient population during the preclinical stage of drug development would help address this problem by allowing the creation of earlier/more accurate economic models and would allow a more targeted and streamlined approach to be taken during the demographic phase of development. A key challenge lies in finding suitable preclinical test systems and models that can help inform patient selection for clinical trials
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