Mechanistic-enriched models: integrating transcription factor networks and metabolic deregulation in cancer

A Computational Platform Integrating a Mechanistic Model of Crohn's Disease for Predicting Temporal Progression of Mucosal Damage and Healing

INTRODUCTION

Physicians are often required to make treatment decisions for patients with Crohn's disease on the basis of limited objective information about the state of the patient's gastrointestinal tissue while aiming to achieve mucosal healing. Tools to predict changes in mucosal health with treatment are needed. We evaluated a computational approach integrating a mechanistic model of Crohn's disease with a responder classifier to predict temporal changes in mucosal health.

METHODS

A hybrid mechanistic-statistical platform was developed to predict biomarker and tissue health time courses in patients with Crohn's disease. Eligible patients from the VERSIFY study (n = 69) were classified into archetypical response cohorts using a decision tree based on early treatment data and baseline characteristics. A virtual patient matching algorithm assigned a digital twin to each patient from their corresponding response cohort. The digital twin was used to forecast response to treatment using the mechanistic model.

RESULTS

The responder classifier predicted endoscopic remission and mucosal healing for treatment with vedolizumab over 26 weeks, with overall sensitivities of 80% and 75% and overall specificities of 69% and 70%, respectively. Predictions for changes in tissue damage over time in the validation set (n = 31), a measure of the overall performance of the platform, were considered good (at least 70% of data points matched), fair (at least 50%), and poor (less than 50%) for 71%, 23%, and 6% of patients, respectively.

CONCLUSION

Hybrid computational tools including mechanistic components represent a promising form of decision support that can predict outcomes and patient progress in Crohn's disease.

The oxidative stress theory of disease: levels of evidence and epistemological aspects

The theory that oxidative stress (OS) is at the root of several diseases is extremely popular. However, so far, no antioxidant has been recommended or offered by healthcare systems neither has any been approved as therapy by regulatory agencies that base their decisions on evidence-based medicine. This is simply because, so far, despite many preclinical and clinical studies indicating a beneficial effect of antioxidants in many disease conditions, randomised clinical trials have failed to provide the evidence of efficacy required for drug approval. In this review, we discuss the levels of evidence required to claim causality in preclinical research on OS, the weakness of the oversimplification associated with OS theory of disease and the importance of the narrative in its popularity. Finally, from a more translational perspective, we discuss the reasons why antioxidants acting by scavenging ROS might not only prevent their detrimental effects but also interfere with essential signalling roles. We propose that ROS have a complex metabolism and are generated by different enzymes at diverse sites and at different times. Aggregating this plurality of systems into a single theory of disease may not be the best way to develop new drugs, and future research may need to focus on specific oxygen-toxifying pathways rather than on non-specific ROS scavengers. Finally, similarly to what is nowadays required for clinical trials, we recommend making unpublished data available in repositories (open data), as this will allow big data approaches or meta-analyses, without the drawbacks of publication bias.

LINKED ARTICLES

This article is part of a themed section on Redox Biology and Oxidative Stress in Health and Disease. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.12/issuetoc.