Literature DB >> 12489686

Towards a new age of virtual ADME/TOX and multidimensional drug discovery.

Sean Ekins1, Bruno Boulanger, Peter W Swaan, Maggie A Z Hupcey.   

Abstract

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.

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Year:  2002        PMID: 12489686     DOI: 10.1023/a:1020816005910

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


  98 in total

1.  Quantitative structure-retention and retention-activity relationships of beta-blocking agents by micellar liquid chromatography.

Authors:  A Detroyer; Y Vander Heyden; S Carda-Broch; M C García-Alvarez-Coque; D L Massart
Journal:  J Chromatogr A       Date:  2001-04-06       Impact factor: 4.759

2.  An approach to QSAR of 16-substituted pregnenolones as microsomal enzyme inducers.

Authors:  E A Rekka; P N Kourounakis
Journal:  Eur J Drug Metab Pharmacokinet       Date:  1996 Jan-Mar       Impact factor: 2.441

3.  QSAR models for discriminating between mutagenic and nonmutagenic aromatic and heteroaromatic amines.

Authors:  R Benigni; L Passerini; G Gallo; F Giorgi; M Cotta-Ramusino
Journal:  Environ Mol Mutagen       Date:  1998       Impact factor: 3.216

4.  Representing metabolic pathway information: an object-oriented approach.

Authors:  L B Ellis; S M Speedie; R McLeish
Journal:  Bioinformatics       Date:  1998       Impact factor: 6.937

5.  Identification of common functional configurations among molecules.

Authors:  D Barnum; J Greene; A Smellie; P Sprague
Journal:  J Chem Inf Comput Sci       Date:  1996 May-Jun

Review 6.  Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites.

Authors:  S Ekins; M J de Groot; J P Jones
Journal:  Drug Metab Dispos       Date:  2001-07       Impact factor: 3.922

7.  Structural features of the uniporter/symporter/antiporter superfamily.

Authors:  V C Goswitz; R J Brooker
Journal:  Protein Sci       Date:  1995-03       Impact factor: 6.725

8.  A QSAR model for the eye irritation of cationic surfactants.

Authors:  G Y Patlewicz; R A Rodford; G Ellis; M D Barratt
Journal:  Toxicol In Vitro       Date:  2000-02       Impact factor: 3.500

9.  Integration of QSAR and in vitro toxicology.

Authors:  M D Barratt
Journal:  Environ Health Perspect       Date:  1998-04       Impact factor: 9.031

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  8 in total

Review 1.  A ligand-based approach to understanding selectivity of nuclear hormone receptors PXR, CAR, FXR, LXRalpha, and LXRbeta.

Authors:  Sean Ekins; Leonid Mirny; Erin G Schuetz
Journal:  Pharm Res       Date:  2002-12       Impact factor: 4.200

2.  Can we really do computer-aided drug design?

Authors:  Matthew Segall
Journal:  J Comput Aided Mol Des       Date:  2011-12-11       Impact factor: 3.686

3.  Making priors a priority.

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Journal:  J Comput Aided Mol Des       Date:  2010-10-16       Impact factor: 3.686

Review 4.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

5.  ALOHA: a novel probability fusion approach for scoring multi-parameter drug-likeness during the lead optimization stage of drug discovery.

Authors:  Derek A Debe; Ravindra B Mamidipaka; Robert J Gregg; James T Metz; Rishi R Gupta; Steven W Muchmore
Journal:  J Comput Aided Mol Des       Date:  2013-10-11       Impact factor: 3.686

6.  Computer-Aided Drug Design Methods.

Authors:  Wenbo Yu; Alexander D MacKerell
Journal:  Methods Mol Biol       Date:  2017

7.  Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds.

Authors:  Humberto Gonzáles-Díaz; Ornella Gia; Eugenio Uriarte; Ivan Hernádez; Ronal Ramos; Mayrelis Chaviano; Santiago Seijo; Juan A Castillo; Lázaro Morales; Lourdes Santana; Delali Akpaloo; Enrique Molina; Maikel Cruz; Luis A Torres; Miguel A Cabrera
Journal:  J Mol Model       Date:  2003-09-16       Impact factor: 1.810

8.  Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.

Authors:  Alex M Clark; Krishna Dole; Anna Coulon-Spektor; Andrew McNutt; George Grass; Joel S Freundlich; Robert C Reynolds; Sean Ekins
Journal:  J Chem Inf Model       Date:  2015-06-03       Impact factor: 4.956

  8 in total

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