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Title: Predicting A Drug'S Membrane Permeability: Evolution of a Computational Model Validated with in Vitro Permeability Assay Data

Membrane permeability is a key property to consider in drug design, especially when the drugs in question need to cross the blood-brain barrier (BBB). A comprehensive in vivo assessment of the BBB permeability of a drug takes considerable time and financial resources. A current, simplified in vitro model to investigate drug permeability is a Parallel Artificial Membrane Permeability Assay (PAMPA) that generally provides higher throughput and initial quantification of a drug's passive permeability. Computational methods can also be used to predict drug permeability. Our methods are highly advantageous as they do not require the synthesis of the desired drug, and can be implemented rapidly using high-performance computing. In this study, we have used umbrella sampling Molecular Dynamics (MD) methods to assess the passive permeability of a range of compounds through a lipid bilayer. Furthermore, the permeability of these compounds was comprehensively quantified using the PAMPA assay to calibrate and validate the MD methodology. And after demonstrating a firm correlation between the two approaches, we then implemented our MD method to quantitatively predict the most permeable potential drug from a series of potential scaffolds. This permeability was then confirmed by the in vitro PAMPA methodology. Therefore, in this work we havemore » illustrated the potential that these computational methods hold as useful tools to help predict a drug's permeability in a faster and more cost-effective manner. Release number: LLNL-ABS-677757.« less
Authors:
 [1] ;  [2] ;  [1] ;  [1] ;  [3] ;  [1] ;  [1] ;  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Biosciences and Biotechnology (BBTD)
  2. Veterans Affairs, Palo Alto, CA (United States). War Related Illness and Injury Study Center
  3. U.S. Naval Academy, Annapolis, MD (United States)
Publication Date:
Report Number(s):
LLNL-JRNL-725792
Journal ID: ISSN 0006-3495
Grant/Contract Number:
AC52-07NA27344
Type:
Accepted Manuscript
Journal Name:
Biophysical Journal
Additional Journal Information:
Journal Volume: 110; Journal Issue: S1; Journal ID: ISSN 0006-3495
Publisher:
Elsevier
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES
OSTI Identifier:
1414358