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Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research Laboratory animalsC57Bl/6 male mice, aged (19 months from NIA rodent colony), young (3 months from Charles River or Jackson Labs) Wild animalsThis study did not involve wild animals.Field-collected samples This study did not involve field-collected samples. Ethics oversightInstitutional Animal Care and Use Committee at Stanford University Note that full information on the approval of the study protocol must also be provided in the manuscript. Human research participantsPolicy information about studies involving human research participants Population characteristicsMale and female aged (58-93 years old) cognitively normal and clinically-diagnosed Alzheimer's disease patients. Cognitively normal patients do not display atypical vascular pathologies. Patients are mixed in APOE genotype. RecruitmentSubjects were not recruited specifically for this study. Samples are derived from a brain bank maintained by the Stanford/ VA/ NIA Aging Clinical Research Center (ACRC) from patients that provide consent for broad, de-identified data sharing under Institutional Review Board (IRB) approval. Ethics oversightStanford/ VA/ NIA Aging Clinical Research Center (ACRC) Note that full information on the approval of the study protocol must also be provided in the manuscript.

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.