McCoubrey, Laura Elizabeth; (2023) Predicting and exploiting interactions between medicines and the microbiome. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is clear that the gut microbiota has the metabolic power to alter the bioavailability of drugs to clinically significant extents. At the same time, medicines, composed of drugs and excipients, have the propensity to alter gut microbiome functioning, potentially affecting health and response to other drugs. This thesis aimed to predict interactions between microbiota and medicines and exploit them for therapeutic benefit. In the first two experimental chapters, machine learning models were developed to 1.) predict the effect of drugs and excipients on gut bacteria, and 2.) predict the susceptibility of drugs to transformation or accumulation by the gut microbiota. The best models achieved validation accuracies of ≥ 83 %, revealed new medicine-microbiome interactions, and uncovered key structure-activity relationships. The third experimental chapter characterised, predicted, and prevented the inactivation of an anti-colon cancer drug, trifluridine, by gut microbiota. Linear regression predicted the rate at which the microbiota sourced from 6 healthy humans would inactivate the drug ex vivo. In addition, trifluridine inactivation was prevented using uridine, a competitive inhibitor of the inactivating bacterial enzyme. In the final chapter, poly(lactic-co-glycolic acid) (PLGA) was developed as a novel microbiome therapeutic. PLGA’s interactions with the microbiota were examined using an advanced in vitro model of the human colon. Lactate release, microbial metabolite synthesis, gas production, and effects on microbiome composition and the colonic epithelium were analysed. Low molecular weight PLGA was metabolised by the microbiota to lactate, leading to increased production of butyrate, a short chain fatty acid associated with a myriad of health benefits. This research showcases that medicine-microbiome interactions can be accurately predicted and leveraged for therapeutic benefit.

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