Literature DB >> 24451294

Addressing toxicity risk when designing and selecting compounds in early drug discovery.

Matthew D Segall1, Chris Barber2.   

Abstract

Prioritising compounds with a lower chance of causing toxicity, early in the drug discovery process, would help to address the high attrition rate in pharmaceutical R&D. Expert knowledge-based prediction of toxicity can alert chemists if their proposed compounds are likely to have an increased likelihood of causing toxicity. We will discuss how multiparameter optimisation approaches can be used to balance the potential for toxicity with other properties required in a high-quality candidate drug, giving appropriate weight to the alert in the selection of compounds. Furthermore, we will describe how information about the region of a compound that triggers a toxicity alert can be interactively visualised to guide the modification of a compound to reduce the likelihood of toxicity.
Copyright © 2014 Elsevier Ltd. All rights reserved.

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Year:  2014        PMID: 24451294     DOI: 10.1016/j.drudis.2014.01.006

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  21 in total

1.  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

2.  MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints.

Authors:  Shimeng Li; Li Zhang; Huawei Feng; Jinhui Meng; Di Xie; Liwei Yi; Isaiah T Arkin; Hongsheng Liu
Journal:  Interdiscip Sci       Date:  2021-01-27       Impact factor: 2.233

Review 3.  Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications.

Authors:  Hsin-Pei Shih; Xiaodan Zhang; Alex M Aronov
Journal:  Nat Rev Drug Discov       Date:  2017-10-27       Impact factor: 84.694

4.  Screening cyclooxygenase-2 inhibitors from Allium sativum L. compounds: in silico approach.

Authors:  Morteza Sadeghi; Mehran Miroliaei; Fatemeh Fateminasab; Mohammad Moradi
Journal:  J Mol Model       Date:  2021-12-30       Impact factor: 1.810

5.  Potential Therapeutic Candidates against Chlamydia pneumonia Discovered and Developed In Silico Using Core Proteomics and Molecular Docking and Simulation-Based Approaches.

Authors:  Roqayah H Kadi; Khadijah A Altammar; Mohamed M Hassan; Abdullah F Shater; Fayez M Saleh; Hattan Gattan; Bassam M Al-Ahmadi; Qwait AlGabbani; Zuhair M Mohammedsaleh
Journal:  Int J Environ Res Public Health       Date:  2022-06-15       Impact factor: 4.614

6.  Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.

Authors:  Curran Landry; Marlene T Kim; Naomi L Kruhlak; Kevin P Cross; Roustem Saiakhov; Suman Chakravarti; Lidiya Stavitskaya
Journal:  Regul Toxicol Pharmacol       Date:  2019-10-03       Impact factor: 3.271

Review 7.  Artificial Intelligence for Drug Toxicity and Safety.

Authors:  Anna O Basile; Alexandre Yahi; Nicholas P Tatonetti
Journal:  Trends Pharmacol Sci       Date:  2019-08-02       Impact factor: 14.819

Review 8.  Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation.

Authors:  Kirk E Hevener
Journal:  Methods Mol Biol       Date:  2018

9.  ProTox: a web server for the in silico prediction of rodent oral toxicity.

Authors:  Malgorzata N Drwal; Priyanka Banerjee; Mathias Dunkel; Martin R Wettig; Robert Preissner
Journal:  Nucleic Acids Res       Date:  2014-05-16       Impact factor: 16.971

Review 10.  Stimulated Raman scattering microscopy: an emerging tool for drug discovery.

Authors:  W J Tipping; M Lee; A Serrels; V G Brunton; A N Hulme
Journal:  Chem Soc Rev       Date:  2016-02-03       Impact factor: 54.564

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