Literature DB >> 13677477

In silico ADME/Tox: why models fail.

Terry R Stouch1, James R Kenyon, Stephen R Johnson, Xue-Qing Chen, Arthur Doweyko, Yi Li.   

Abstract

By way of example, we discuss the apparent 'failure' of in silico ADME/Tox models and attempt to understand the causes. Often, the interpretation of the success of models lies in their use and the expectations of the user. Other times, models are, in fact, of little value. Disappointing results can be linked to the key aspects of the model and modeling procedure, many of these related to the original data and its interpretation. We make recommendations to providers of models regarding the development, description, and use of models as well as the data and information that are important to understanding a model's quality and scope of use.

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Year:  2003        PMID: 13677477     DOI: 10.1023/a:1025358319677

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


  2 in total

Review 1.  Drug-like properties and the causes of poor solubility and poor permeability.

Authors:  C A Lipinski
Journal:  J Pharmacol Toxicol Methods       Date:  2000 Jul-Aug       Impact factor: 1.950

2.  Chance factors in studies of quantitative structure-activity relationships.

Authors:  J G Topliss; R P Edwards
Journal:  J Med Chem       Date:  1979-10       Impact factor: 7.446

  2 in total
  35 in total

1.  Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Authors:  Ivan Rusyn; Alexander Sedykh; Yen Low; Kathryn Z Guyton; Alexander Tropsha
Journal:  Toxicol Sci       Date:  2012-03-02       Impact factor: 4.849

Review 2.  Recent progress in the computational prediction of aqueous solubility and absorption.

Authors:  Stephen R Johnson; Weifan Zheng
Journal:  AAPS J       Date:  2006-02-03       Impact factor: 4.009

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

Authors:  Chakrapani Kalyanaraman; Matthew P Jacobson
Journal:  J Comput Aided Mol Des       Date:  2007-11-08       Impact factor: 3.686

Review 4.  Chemical predictive modelling to improve compound quality.

Authors:  John G Cumming; Andrew M Davis; Sorel Muresan; Markus Haeberlein; Hongming Chen
Journal:  Nat Rev Drug Discov       Date:  2013-12       Impact factor: 84.694

5.  Naïve Bayesian Models for Vero Cell Cytotoxicity.

Authors:  Alexander L Perryman; Jimmy S Patel; Riccardo Russo; Eric Singleton; Nancy Connell; Sean Ekins; Joel S Freundlich
Journal:  Pharm Res       Date:  2018-06-29       Impact factor: 4.200

Review 6.  At the biological modeling and simulation frontier.

Authors:  C Anthony Hunt; Glen E P Ropella; Tai Ning Lam; Jonathan Tang; Sean H J Kim; Jesse A Engelberg; Shahab Sheikh-Bahaei
Journal:  Pharm Res       Date:  2009-09-09       Impact factor: 4.200

7.  Interpreting physicochemical experimental data sets.

Authors:  Nicola Colclough; Mark C Wenlock
Journal:  J Comput Aided Mol Des       Date:  2015-06-09       Impact factor: 3.686

Review 8.  Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.

Authors:  Yen Sia Low; Alexander Yeugenyevich Sedykh; Ivan Rusyn; Alexander Tropsha
Journal:  Curr Top Med Chem       Date:  2014       Impact factor: 3.295

Review 9.  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

10.  A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

Authors:  Hao Zhu; Lin Ye; Ann Richard; Alexander Golbraikh; Fred A Wright; Ivan Rusyn; Alexander Tropsha
Journal:  Environ Health Perspect       Date:  2009-04-03       Impact factor: 9.031

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