Literature DB >> 12549676

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: 12549676     DOI: 10.1023/a:1021376212320

Source DB:  PubMed          Journal:  Mol Divers        ISSN: 1381-1991            Impact factor:   2.943


  98 in total

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

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Journal:  J Chromatogr A       Date:  2001-04-06       Impact factor: 4.759

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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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  6 in total

1.  Computational ligand-based rational design: Role of conformational sampling and force fields in model development.

Authors:  Jihyun Shim; Alexander D Mackerell
Journal:  Medchemcomm       Date:  2011-05       Impact factor: 3.597

2.  Data Mining and Computational Modeling of High-Throughput Screening Datasets.

Authors:  Sean Ekins; Alex M Clark; Krishna Dole; Kellan Gregory; Andrew M Mcnutt; Anna Coulon Spektor; Charlie Weatherall; Nadia K Litterman; Barry A Bunin
Journal:  Methods Mol Biol       Date:  2018

3.  Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Authors:  Alexandru Korotcov; Valery Tkachenko; Daniel P Russo; Sean Ekins
Journal:  Mol Pharm       Date:  2017-11-13       Impact factor: 4.939

Review 4.  Big Data and Artificial Intelligence Modeling for Drug Discovery.

Authors:  Hao Zhu
Journal:  Annu Rev Pharmacol Toxicol       Date:  2019-09-13       Impact factor: 13.820

5.  Evaluation of Quantitative Structure Property Relationship Algorithms for Predicting Plasma Protein Binding in Humans.

Authors:  Yejin Esther Yun; Rogelio Tornero-Velez; S Thomas Purucker; Daniel T Chang; Andrea N Edginton
Journal:  Comput Toxicol       Date:  2021-02-01

6.  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

  6 in total

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