Towards the prediction of the effect of food on orally administered medicines using preclinical in vivo models and machine learning technologies - Doctor of Philosophy thesis

Aug 5, 2026

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Oral intake of food and drinks with medicines can alter therapeutic efficacy and adverse side effects, creating barriers in patient populations. This research develops in vivo and in silico approaches for early drug development to improve food-effect prediction. The work examines how food changes intestinal efflux transporter expression in rodent models and applies machine learning to predict food effects. Results show fed-state differences by prandial state, sex, and strain, including fibre-induced upregulation of key transporters, and demonstrate ML classification/regression performance on datasets exceeding 300 drugs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages312

Reading Time13 h 6 min

AccessFree to download

Abstract

Food effect problem and unmet needs

In vivo transporter characterization

Machine learning prediction on drug datasets

Impact Statement

Clinical and regulatory drivers

Preclinical tools and limitations

Animal model rationale (fasted vs fed, sex considerations)

Towards the prediction of the effect of food on orally administered medicines using preclinical in vivo models and machine learning technologies Francesca Gavins Thesis Submitted for the Degree of Doctor of Philosophy Declaration I, Francesca Katherine Hilary Gavins confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Francesca K. H. Gavins Signed: Research funding This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) UK, grant number EP/L01646X/1 . Acknowledgements A big thank you to the Central for Doctoral Training (CDT) in Advanced Therapeutics and Nanomedicines for providing training opportunities and funding (Engineering and Physical Sciences Research Council [EP/L01646X/1]) . Special mentions to Prof Steve Brocchini and Prof Gareth Williams for their support and encouragement. Thank you to my supervisors Prof Mine Orlu and Prof Abdul Basit for giving me continuous opportunities, challenging my abilities, and pushing me to become the pharmaceutical scientist and pharmacist I am today. Big highlights of my PhD were in collaboration by them: writing a news piece for Nature Biomedical Engineering and speaking at the Controlled Release Society conference. MPharm then PhD, I have spent eight years at the UCL School of Pharmacy and believe it to be a special place, full of inspiring people. Special thanks to the Basit Research Group and my CDT cohort 2018 for the fun times, too many names to mention. Thank you especially Christine Madla for your friendship, a kind and incredible person and friend. From a research perspective, I have met the most brilliant minds. Collaborations with the UCL School of Electrical and Electronic Engineering and Sun Yat-sen University allowed me to work with Miya, Moe, Zihao, Youssef, and Fanying, producing novel findings in biopharmaceutics and machine learning. I am forever grateful for their expertise. Finally, and most importantly, I would like to thank my family and friends who have been there for me, throughout the challenges. Alex, forever grateful for all your support and guidance, encouraging me to work hard, and most importantly, play hard. Also, my family Joanna, Philip, Gaby, Vicky, Lily, and Charlie, this would not have been possible without you. Having spent eight years at UCL School of Pharmacy, it is the end of era. I feel very privileged to be part of a strong community of pharmacists and pharmaceutical scientists. Abstract The intake of food and drinks with orally administered medicines can significantly impact the therapeutic efficacy or adverse side effects of a drug, posing barriers to effective therapeutic treatment in patient populations. There are unmet pharmaceutical and clinical needs to improve the prediction of the food effect in drug product development. This research has focused on in vivo and in silico tools that can be used in early drug development to predict the food effect. The overall aims of this research were to: explore the food-mediated changes to intestinal efflux transporter expression in rodent animal models, and leverage machine learning tools to predict the food effect. Our understanding of the effects of the fed state on clinically relevant transporters in preclinical rodent animal models has been enhanced. P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), and multidrug resistance-associated protein 2 (MRP2) expression were altered to different extents between the prandial states, sexes, and strains. A non-nutritive fibre meal increased the acute expression of intestinal P-gp, BCRP, and MRP2 . Significant changes were seen in male rats, when comparing the fibre meal and the standard housing meal, but not in female rats. The repertoire of computational tools to predict the food effect was expanded. Here, classification and regression machine learning tech

FAQs

What is the food effect and why does it matter for oral medicines?

Co-administration of a drug with food can change bioavailability compared with the fasted state, affecting efficacy and adverse side effects. This can complicate treatment decisions in patient populations.

Which intestinal efflux transporters were investigated in rodent models?

The study assessed P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), and multidrug resistance-associated protein 2 (MRP2). Their expression changed across prandial states, sexes, and strains.

How were machine learning methods used in this thesis?

Classification and regression machine learning technologies were tested to predict the food effect using large datasets of more than 300 drugs, based on key drug physicochemical properties.

Aug 5, 2026

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