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

(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.