Predicting a Drug's Membrane Permeability: A Computational Model Validated with in Vitro Permeability Assay Data

Brian J. Bennion, Nicholas A. Be, M. Windy McNerney, Victoria Lao, Emma M. Carlson, Carlos A. Valdez, Michael A. Malfatti, Heather A. Enright, Tuan H. Nguyen, Felice C. Lightstone, Timothy S. Carpenter*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

184 Scopus citations


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.

Original languageEnglish
Pages (from-to)5228-5237
Number of pages10
JournalJournal of Physical Chemistry B
Issue number20
StatePublished - 25 May 2017
Externally publishedYes


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