Gavins, Francesca;
(2023)
Towards the prediction of the effect of food on orally administered medicines using preclinical in vivo models and machine learning technologies.
Doctoral thesis (Ph.D), UCL (University College London).
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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 technologies were tested to predict the food effect on large datasets of >300 drugs using key drug physicochemical properties. In summary, this work has uncovered that the rodent animal model shows food, sex, and strain differences for the expression of key intestinal efflux transporters. Furthermore, machine learning technologies were harnessed to predict the food effect from the drug structure. While more work is needed to further understand the mechanisms of the food effect and to build more accurate machine learning tools, these findings offer insights to guide early drug development.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Towards the prediction of the effect of food on orally administered medicines using preclinical in vivo models and machine learning technologies |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10166514 |
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