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Title: Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data

Abstract

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.

Authors:
 [1];  [1];  [2];  [1];  [3];  [4];  [1];  [1];  [5];  [1]; ORCiD logo [1]
+ Show Author Affiliations
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Biosciences and Biotechnology Div.
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Biosciences and Biotechnology Div.; War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto, CA (United States)
  3. U.S. Naval Academy, Annapolis, MD (United States)
  4. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Nuclear and Chemical Sciences Div.
  5. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Global Security Directorate
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1414358
Report Number(s):
LLNL-JRNL-725792
Journal ID: ISSN 1520-6106
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry
Additional Journal Information:
Journal Volume: 121; Journal Issue: 20; Journal ID: ISSN 1520-6106
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Bennion, Brian J., Be, Nicholas A., McNerney, M. Windy, Lao, Victoria, Carlson, Emma M., Valdez, Carlos A., Malfatti, Michael A., Enright, Heather A., Nguyen, Tuan H., Lightstone, Felice C., and Carpenter, Timothy S. Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data. United States: N. p., 2017. Web. doi:10.1021/acs.jpcb.7b02914.
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Bennion, Brian J., Be, Nicholas A., McNerney, M. Windy, Lao, Victoria, Carlson, Emma M., Valdez, Carlos A., Malfatti, Michael A., Enright, Heather A., Nguyen, Tuan H., Lightstone, Felice C., & Carpenter, Timothy S. Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data. United States. https://doi.org/10.1021/acs.jpcb.7b02914
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Bennion, Brian J., Be, Nicholas A., McNerney, M. Windy, Lao, Victoria, Carlson, Emma M., Valdez, Carlos A., Malfatti, Michael A., Enright, Heather A., Nguyen, Tuan H., Lightstone, Felice C., and Carpenter, Timothy S. Fri . "Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data". United States. https://doi.org/10.1021/acs.jpcb.7b02914. https://www.osti.gov/servlets/purl/1414358.
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@article{osti_1414358,
title = {Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data},
author = {Bennion, Brian J. and Be, Nicholas A. and McNerney, M. Windy and Lao, Victoria and Carlson, Emma M. and Valdez, Carlos A. and Malfatti, Michael A. and Enright, Heather A. and Nguyen, Tuan H. and Lightstone, Felice C. and Carpenter, Timothy S.},
abstractNote = {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.},
doi = {10.1021/acs.jpcb.7b02914},
journal = {Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry},
number = 20,
volume = 121,
place = {United States},
year = {Fri Apr 28 00:00:00 EDT 2017},
month = {Fri Apr 28 00:00:00 EDT 2017}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1021/acs.jpcb.7b02914
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Cited by: 151 works
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Figures / Tables:

Figure 1 Figure 1: The control compounds used in the calibration data set.
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