PREreviews of “Simulation and Machine Learning Assessment of P-Glycoprotein Pharmacology in the Blood-Brain Barrier: Inhibition and Substrate Transport”

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Simulation and Machine Learning Assessment of P-Glycoprotein Pharmacology in the Blood-Brain Barrier: Inhibition and Substrate Transport

by Christian Jorgensen, Elizabeth Oliphant, Milly Barker, Eduardo López Martínez, Saaihasamreen Thulasi, Holly Prior, Ben William Franey, Charley Gregory, Jerry Oluwasegun, Anjalee Ajay, and Roger R. Draheim

Posted: August 27, 2025
Server: Preprints.org
DOI: 10.20944/preprints202508.1917.v1

Abstract

(1) Background: We explore the pharmacology of the P-glycoprotein (P-gp) efflux pump and its role in multidrug resistance; (2) Methods: We use Protein Data Bank (PDB) database mining and an artificial intelligence (AI) model Boltz-2.1.1 developed for simultaneous structure and affinity prediction to explore the multimeric nature of recent P-gp inhibitors. We construct a MARTINI coarse-grain (CG) force field description of P-gp embedded in a model of the endothelial blood-brain barrier; (3) Results: We found that recent P-gp inhibitors have been captured in either monomeric, dimeric or trimeric states. Our CG model demonstrates the ability of P-gp substrates to permeate and transition across the BBB bilayer; (4) Conclusions: We report a multimodal binding model of P-gp inhibition, in which later generations of inhibitors are found in dimeric and trimeric states. We report analysis of P-gp substrates which point to an extended binding surface that explains how P-gp can bind over 300 substrates non-selectively. Our coarse-grain model of substrate permeation into P-gp shows benchmarking similarities to prior atomistic models and provide new insights at far longer timescales.

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PREreviews of Simulation and Machine Learning Assessment of P-Glycoprotein Pharmacology in the Blood-Brain Barrier: Inhibition and Substrate Transport

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