Literature DB >> 17989930

An atomistic model of passive membrane permeability: application to a series of FDA approved drugs.

Chakrapani Kalyanaraman1, Matthew P Jacobson.   

Abstract

We apply an atomistic model of passive membrane permeability to a series of weakly basic drugs. The computational model uses conformational sampling in combination with an all-atom force field and implicit solvent model to estimate relative passive membrane permeabilities. The model does not require the use of training data for rank-ordering compounds, and as such represents a different approach from the more commonly employed QSPR models. We compare the computational results to previously published experimental PAMPA and Caco-2 permeabilities.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17989930     DOI: 10.1007/s10822-007-9141-z

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  25 in total

1.  Three-dimensional quantitative structure-permeability relationship analysis for a series of inhibitors of rhinovirus replication.

Authors:  S Ekins; G L Durst; R E Stratford; D A Thorner; R Lewis; R J Loncharich; J H Wikel
Journal:  J Chem Inf Comput Sci       Date:  2001 Nov-Dec

2.  In silico ADME/Tox: the state of the art.

Authors:  Sean Ekins; John Rose
Journal:  J Mol Graph Model       Date:  2002-01       Impact factor: 2.518

Review 3.  Progress in predicting human ADME parameters in silico.

Authors:  S Ekins; C L Waller; P W Swaan; G Cruciani; S A Wrighton; J H Wikel
Journal:  J Pharmacol Toxicol Methods       Date:  2000 Jul-Aug       Impact factor: 1.950

Review 4.  In silico ADME/Tox: why models fail.

Authors:  Terry R Stouch; James R Kenyon; Stephen R Johnson; Xue-Qing Chen; Arthur Doweyko; Yi Li
Journal:  J Comput Aided Mol Des       Date:  2003 Feb-Apr       Impact factor: 3.686

5.  Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method.

Authors:  Fumiyoshi Yamashita; Suchada Wanchana; Mitsuru Hashida
Journal:  J Pharm Sci       Date:  2002-10       Impact factor: 3.534

6.  Caco-2 permeability of weakly basic drugs predicted with the double-sink PAMPA pKa(flux) method.

Authors:  Alex Avdeef; Per Artursson; Sibylle Neuhoff; Lucia Lazorova; Johan Gråsjö; Staffan Tavelin
Journal:  Eur J Pharm Sci       Date:  2005-01-20       Impact factor: 4.384

7.  In silico prediction of membrane permeability from calculated molecular parameters.

Authors:  Hanne H F Refsgaard; Berith F Jensen; Per B Brockhoff; Søren B Padkjaer; Mette Guldbrandt; Michael S Christensen
Journal:  J Med Chem       Date:  2005-02-10       Impact factor: 7.446

8.  Behaviour of small solutes and large drugs in a lipid bilayer from computer simulations.

Authors:  D Bemporad; C Luttmann; J W Essex
Journal:  Biochim Biophys Acta       Date:  2005-08-09

9.  Conformational flexibility, internal hydrogen bonding, and passive membrane permeability: successful in silico prediction of the relative permeabilities of cyclic peptides.

Authors:  Taha Rezai; Jonathan E Bock; Mai V Zhou; Chakrapani Kalyanaraman; R Scott Lokey; Matthew P Jacobson
Journal:  J Am Chem Soc       Date:  2006-11-01       Impact factor: 15.419

10.  Predicting drug absorption from molecular surface properties based on molecular dynamics simulations.

Authors:  L H Krarup; I T Christensen; L Hovgaard; S Frokjaer
Journal:  Pharm Res       Date:  1998-07       Impact factor: 4.200

View more
  9 in total

1.  Optimizing PK properties of cyclic peptides: the effect of side chain substitutions on permeability and clearance().

Authors:  Arthur C Rand; Siegfried S F Leung; Heather Eng; Charles J Rotter; Raman Sharma; Amit S Kalgutkar; Yizhong Zhang; Manthena V Varma; Kathleen A Farley; Bhagyashree Khunte; Chris Limberakis; David A Price; Spiros Liras; Alan M Mathiowetz; Matthew P Jacobson; R Scott Lokey
Journal:  Medchemcomm       Date:  2012-10       Impact factor: 3.597

2.  Predicting efflux ratios and blood-brain barrier penetration from chemical structure: combining passive permeability with active efflux by P-glycoprotein.

Authors:  Elena Dolghih; Matthew P Jacobson
Journal:  ACS Chem Neurosci       Date:  2012-12-11       Impact factor: 4.418

3.  Testing physical models of passive membrane permeation.

Authors:  Siegfried S F Leung; Jona Mijalkovic; Kenneth Borrelli; Matthew P Jacobson
Journal:  J Chem Inf Model       Date:  2012-05-24       Impact factor: 4.956

4.  Predicting and improving the membrane permeability of peptidic small molecules.

Authors:  Salma B Rafi; Brian R Hearn; Punitha Vedantham; Matthew P Jacobson; Adam R Renslo
Journal:  J Med Chem       Date:  2012-03-20       Impact factor: 7.446

5.  Modeling the pharmacodynamics of passive membrane permeability.

Authors:  Robert V Swift; Rommie E Amaro
Journal:  J Comput Aided Mol Des       Date:  2011-11-01       Impact factor: 3.686

6.  Physics-Based Method for Modeling Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules.

Authors:  Andrei L Lomize; Irina D Pogozheva
Journal:  J Chem Inf Model       Date:  2019-07-01       Impact factor: 4.956

7.  Computational study of peptide permeation through membrane: Searching for hidden slow variables.

Authors:  Alfredo E Cardenas; Ron Elber
Journal:  Mol Phys       Date:  2013-11-25       Impact factor: 1.962

Review 8.  Back to the future: can physical models of passive membrane permeability help reduce drug candidate attrition and move us beyond QSPR?

Authors:  Robert V Swift; Rommie E Amaro
Journal:  Chem Biol Drug Des       Date:  2013-01       Impact factor: 2.817

9.  Predicting binding to p-glycoprotein by flexible receptor docking.

Authors:  Elena Dolghih; Clifford Bryant; Adam R Renslo; Matthew P Jacobson
Journal:  PLoS Comput Biol       Date:  2011-06-23       Impact factor: 4.475

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.