Literature DB >> 14529504

In silico ADME prediction: data, models, facts and myths.

Franco Lombardo1, Eric Gifford, Marina Y Shalaeva.   

Abstract

A critical review of a very recent work in the field of in silico ADME prediction is presented with emphasis on the work published during the period 2000-2002, and several other review articles are mentioned in order to offer a broader view of the field. We find that not much progress has been made in developing robust and predictive models, and that the lack of accurate data, together with the use of questionable modeling end-points, has greatly hindered the real progress in defining generally applicable models. Due to the largely empirical nature of QSAR/QSPR approaches, general and truly predictive models for complex phenomena, such as absorption and clearance, may still be chimeric. The development of local models for use within focused chemical series may be the most appropriate way of utilizing in silico ADME predictions, once experience and data have been gained on a given project and/or structural class.

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Year:  2003        PMID: 14529504     DOI: 10.2174/1389557033487629

Source DB:  PubMed          Journal:  Mini Rev Med Chem        ISSN: 1389-5575            Impact factor:   3.862


  19 in total

1.  Pharmacophore modeling, molecular docking, QSAR, and in silico ADMET studies of gallic acid derivatives for immunomodulatory activity.

Authors:  Dharmendra Kumar Yadav; Feroz Khan; Arvind Singh Negi
Journal:  J Mol Model       Date:  2011-10-27       Impact factor: 1.810

2.  Pharmacophore, QSAR, and ADME based semisynthesis and in vitro evaluation of ursolic acid analogs for anticancer activity.

Authors:  Komal Kalani; Dharmendra Kumar Yadav; Feroz Khan; Santosh K Srivastava; Nitasha Suri
Journal:  J Mol Model       Date:  2012-01-21       Impact factor: 1.810

Review 3.  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

Review 4.  Fragment-based QSAR: perspectives in drug design.

Authors:  Lívia B Salum; Adriano D Andricopulo
Journal:  Mol Divers       Date:  2009-01-31       Impact factor: 2.943

Review 5.  Modeling kinetics of subcellular disposition of chemicals.

Authors:  Stefan Balaz
Journal:  Chem Rev       Date:  2009-05       Impact factor: 60.622

Review 6.  Drug absorption modeling as a tool to define the strategy in clinical formulation development.

Authors:  Martin Kuentz
Journal:  AAPS J       Date:  2008-08-27       Impact factor: 4.009

Review 7.  From QSAR to QSIIR: searching for enhanced computational toxicology models.

Authors:  Hao Zhu
Journal:  Methods Mol Biol       Date:  2013

8.  FAF-Drugs: free ADME/tox filtering of compound collections.

Authors:  Maria A Miteva; Stephanie Violas; Matthieu Montes; David Gomez; Pierre Tuffery; Bruno O Villoutreix
Journal:  Nucleic Acids Res       Date:  2006-07-01       Impact factor: 16.971

9.  Inferences from the ADMET analysis of predicted inhibitors to Follicle Stimulating Hormone in the context of infertility.

Authors:  Narasimharao Bhogireddy; Ganesh Kumar Veeramachaneni; Naga Vamsi Krishna Ambatipudi; Pardhasaradhi Mathi; Jayasri Ippaguntla; Uma Ramani Ganta; Sivaji Ganesh Adusumalli; Venkata Raman Bokka
Journal:  Bioinformation       Date:  2013-08-28

10.  Assessing the pharmacokinetic profile of the CamMedNP natural products database: an in silico approach.

Authors:  Fidele Ntie-Kang; James A Mbah; Lydia L Lifongo; Luc C Owono Owono; Eugene Megnassan; Luc Meva'a Mbaze; Philip N Judson; Wolfgang Sippl; Simon Mn Efange
Journal:  Org Med Chem Lett       Date:  2013-08-30
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