Literature DB >> 35188629

In Silico Tools and Software to Predict ADMET of New Drug Candidates.

Supratik Kar1, Kunal Roy2, Jerzy Leszczynski1.   

Abstract

Implication of computational techniques and in silico tools promote not only reduction of animal experimentations but also save time and money followed by rational designing of drugs as well as controlled synthesis of those "Hits" which show drug-likeness and possess suitable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile. With globalization of diseases, resistance of drugs over the time and modification of viruses and microorganisms, computational tools, and artificial intelligence are the future of drug design and one of the important areas where the principles of sustainability and green chemistry (GC) perfectly fit. Most of the new drug entities fail in the clinical trials over the issue of drug-associated human toxicity. Although ecotoxicity related to new drugs is rarely considered, but this is the high time when ecotoxicity prediction should get equal importance along with human-associated drug toxicity. Thus, the present book chapter discusses the available in silico tools and software for the fast and preliminary prediction of a series of human-associated toxicity and ecotoxicity of new drug entities to screen possibly safer drugs before going into preclinical and clinical trials.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  ADME; Endpoint; In silico; Software; Toxicity; Web server

Mesh:

Substances:

Year:  2022        PMID: 35188629     DOI: 10.1007/978-1-0716-1960-5_4

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  33 in total

1.  Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6.

Authors:  Virginie Y Martiny; Pablo Carbonell; Florent Chevillard; Gautier Moroy; Arnaud B Nicot; Philippe Vayer; Bruno O Villoutreix; Maria A Miteva
Journal:  Bioinformatics       Date:  2015-08-26       Impact factor: 6.937

Review 2.  An analysis of the attrition of drug candidates from four major pharmaceutical companies.

Authors:  Michael J Waring; John Arrowsmith; Andrew R Leach; Paul D Leeson; Sam Mandrell; Robert M Owen; Garry Pairaudeau; William D Pennie; Stephen D Pickett; Jibo Wang; Owen Wallace; Alex Weir
Journal:  Nat Rev Drug Discov       Date:  2015-06-19       Impact factor: 84.694

Review 3.  Recent Advances of Computational Modeling for Predicting Drug Metabolism: A Perspective.

Authors:  Supratik Kar; Jerzy Leszczynski
Journal:  Curr Drug Metab       Date:  2017       Impact factor: 3.731

4.  How artificial intelligence is changing drug discovery.

Authors:  Nic Fleming
Journal:  Nature       Date:  2018-05       Impact factor: 49.962

Review 5.  Open access in silico tools to predict the ADMET profiling of drug candidates.

Authors:  Supratik Kar; Jerzy Leszczynski
Journal:  Expert Opin Drug Discov       Date:  2020-07-31       Impact factor: 6.098

Review 6.  A drug-likeness toolbox facilitates ADMET study in drug discovery.

Authors:  Chen-Yang Jia; Jing-Yi Li; Ge-Fei Hao; Guang-Fu Yang
Journal:  Drug Discov Today       Date:  2019-11-06       Impact factor: 7.851

7.  EPA plan to end animal testing splits scientists.

Authors:  David Grimm
Journal:  Science       Date:  2019-09-20       Impact factor: 47.728

Review 8.  Estimating human ADME properties, pharmacokinetic parameters and likely clinical dose in drug discovery.

Authors:  Adam J Lucas; Joanne L Sproston; Patrick Barton; Robert J Riley
Journal:  Expert Opin Drug Discov       Date:  2019-09-20       Impact factor: 6.098

9.  admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties.

Authors:  Feixiong Cheng; Weihua Li; Yadi Zhou; Jie Shen; Zengrui Wu; Guixia Liu; Philip W Lee; Yun Tang
Journal:  J Chem Inf Model       Date:  2012-11-01       Impact factor: 4.956

10.  ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database.

Authors:  Jie Dong; Ning-Ning Wang; Zhi-Jiang Yao; Lin Zhang; Yan Cheng; Defang Ouyang; Ai-Ping Lu; Dong-Sheng Cao
Journal:  J Cheminform       Date:  2018-06-26       Impact factor: 5.514

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  2 in total

1.  In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

Authors:  Mustapha Abdullahi; Adamu Uzairu; Gideon Adamu Shallangwa; Paul Andrew Mamza; Muhammad Tukur Ibrahim
Journal:  Heliyon       Date:  2022-08-08

2.  Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

Authors:  Mustapha Abdullahi; Adamu Uzairu; Gideon Adamu Shallangwa; Paul Andrew Mamza; Muhammad Tukur Ibrahim
Journal:  Beni Suef Univ J Basic Appl Sci       Date:  2022-08-19
  2 in total

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