<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Jorgensen C</submitter><funding>European Commission</funding><pagination>9050</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12469845</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>26(18)</volume><pubmed_abstract>We explored the pharmacology of the P-glycoprotein (P-gp) efflux pump and its role in multidrug resistance. We used Protein Data Bank (PDB) database mining and the 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-grained (CG) force field description of P-gp embedded in a model of the endothelial blood-brain barrier. 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. 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 analyses of P-gp substrates that point to an extended binding surface that explains how P-gp can bind over 300 substrates non-selectively. Our coarse-grained model of substrate permeation into membranes expressing P-gp shows benchmarking similarities to prior atomistic models and provide new insights on far longer timescales.</pubmed_abstract><journal>International journal of molecular sciences</journal><pubmed_title>Simulation and Machine Learning Assessment of P-Glycoprotein Pharmacology in the Blood-Brain Barrier: Inhibition and Substrate Transport.</pubmed_title><pmcid>PMC12469845</pmcid><funding_grant_id>101023783</funding_grant_id><pubmed_authors>Thulasi S</pubmed_authors><pubmed_authors>Lopez Martinez E</pubmed_authors><pubmed_authors>Prior H</pubmed_authors><pubmed_authors>Draheim RR</pubmed_authors><pubmed_authors>Barker M</pubmed_authors><pubmed_authors>Gregory C</pubmed_authors><pubmed_authors>Jorgensen C</pubmed_authors><pubmed_authors>Oliphant E</pubmed_authors><pubmed_authors>Franey BW</pubmed_authors><pubmed_authors>Ajay A</pubmed_authors><pubmed_authors>Oluwasegun J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Simulation and Machine Learning Assessment of P-Glycoprotein Pharmacology in the Blood-Brain Barrier: Inhibition and Substrate Transport.</name><description>We explored the pharmacology of the P-glycoprotein (P-gp) efflux pump and its role in multidrug resistance. We used Protein Data Bank (PDB) database mining and the 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-grained (CG) force field description of P-gp embedded in a model of the endothelial blood-brain barrier. 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. 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 analyses of P-gp substrates that point to an extended binding surface that explains how P-gp can bind over 300 substrates non-selectively. Our coarse-grained model of substrate permeation into membranes expressing P-gp shows benchmarking similarities to prior atomistic models and provide new insights on far longer timescales.</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Sep</publication><modification>2026-05-02T03:17:11.962Z</modification><creation>2026-05-02T03:11:42.894Z</creation></dates><accession>S-EPMC12469845</accession><cross_references><pubmed>41009615</pubmed><doi>10.3390/ijms26189050</doi></cross_references></HashMap>