Literature DB >> 26686752

Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity.

Grace Patlewicz1, Jeremy M Fitzpatrick1.   

Abstract

Exploiting non-testing approaches to predict toxicity early in the drug discovery development cycle is a helpful component in minimizing expensive drug failures due to toxicity being identified in late development or even during clinical trials. Changes in regulations in the industrial chemicals and cosmetics sectors in recent years have prompted a significant number of advances in the development, application, and assessment of non-testing approaches, such as (Q)SARs. Many efforts have also been undertaken to establish guiding principles for performing read-across within category and analogue approaches. This review offers a perspective, as taken from these sectors, of the current status of non-testing approaches, their evolution in light of the advances in high-throughput approaches and constructs such as adverse outcome pathways, and their potential relevance for drug discovery. It also proposes a workflow for how non-testing approaches could be practically integrated within testing and assessment strategies.

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Year:  2016        PMID: 26686752     DOI: 10.1021/acs.chemrestox.5b00388

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  19 in total

1.  The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.

Authors:  Russell S Thomas; Tina Bahadori; Timothy J Buckley; John Cowden; Chad Deisenroth; Kathie L Dionisio; Jeffrey B Frithsen; Christopher M Grulke; Maureen R Gwinn; Joshua A Harrill; Mark Higuchi; Keith A Houck; Michael F Hughes; E Sidney Hunter; Kristin K Isaacs; Richard S Judson; Thomas B Knudsen; Jason C Lambert; Monica Linnenbrink; Todd M Martin; Seth R Newton; Stephanie Padilla; Grace Patlewicz; Katie Paul-Friedman; Katherine A Phillips; Ann M Richard; Reeder Sams; Timothy J Shafer; R Woodrow Setzer; Imran Shah; Jane E Simmons; Steven O Simmons; Amar Singh; Jon R Sobus; Mark Strynar; Adam Swank; Rogelio Tornero-Valez; Elin M Ulrich; Daniel L Villeneuve; John F Wambaugh; Barbara A Wetmore; Antony J Williams
Journal:  Toxicol Sci       Date:  2019-06-01       Impact factor: 4.849

2.  An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

Authors:  J M Fitzpatrick; D W Roberts; G Patlewicz
Journal:  SAR QSAR Environ Res       Date:  2018-04-20       Impact factor: 3.000

3.  Extending the Generalised Read-Across approach (GenRA): A systematic analysis of the impact of physicochemical property information on read-across performance.

Authors:  George Helman; Imran Shah; Grace Patlewicz
Journal:  Comput Toxicol       Date:  2018

4.  A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials.

Authors:  Kaitlyn M Gayvert; Neel S Madhukar; Olivier Elemento
Journal:  Cell Chem Biol       Date:  2016-09-15       Impact factor: 8.116

5.  Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity.

Authors:  Grace Patlewicz; Jeffry L Dean; Catherine F Gibbons; Richard S Judson; Nagalakshmi Keshava; Leora Vegosen; Todd M Martin; Prachi Pradeep; Anita Simha; Sarah H Warren; Maureen R Gwinn; David M DeMarini
Journal:  Comput Toxicol       Date:  2021-11-01

6.  Commentary: cumulative risk assessment of perfluoroalkyl carboxylic acids and perfluoralkyl sulfonic acids: what is the scientific support for deriving tolerable exposures by assembling 27 PFAS into 1 common assessment group?

Authors:  Thomas Colnot; Wolfgang Dekant
Journal:  Arch Toxicol       Date:  2022-08-17       Impact factor: 6.168

Review 7.  Gas sensors based on mass-sensitive transducers. Part 2: Improving the sensors towards practical application.

Authors:  Alexandru Oprea; Udo Weimar
Journal:  Anal Bioanal Chem       Date:  2020-07-31       Impact factor: 4.142

Review 8.  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

9.  Use of big data in drug development for precision medicine: an update.

Authors:  Tongqi Qian; Shijia Zhu; Yujin Hoshida
Journal:  Expert Rev Precis Med Drug Dev       Date:  2019-05-20

10.  Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways.

Authors:  Paul S Price; Annie M Jarabek; Lyle D Burgoon
Journal:  Environ Int       Date:  2020-03-24       Impact factor: 9.621

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