Literature DB >> 26375963

Development of an in Silico Profiler for Mitochondrial Toxicity.

Mark D Nelms1, Claire L Mellor1, Mark T D Cronin1, Judith C Madden1, Steven J Enoch1.   

Abstract

This study outlines the analysis of mitochondrial toxicity for a variety of pharmaceutical drugs extracted from Zhang et al. ((2009) Toxicol. In Vitro, 23, 134-140). These chemicals were grouped into categories based upon structural similarity. Subsequently, mechanistic analysis was undertaken for each category to identify the molecular initiating event driving mitochondrial toxicity. The mechanistic information elucidated during the analysis enabled mechanism-based structural alerts to be developed and combined together to form an in silico profiler. This profiler is envisaged to be used to develop chemical categories based upon similar mechanisms as part of the adverse outcome pathway paradigm. Additionally, the profiler could be utilized in screening large data sets in order to identify chemicals with the potential to induce mitochondrial toxicity.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 26375963     DOI: 10.1021/acs.chemrestox.5b00275

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


  8 in total

1.  A computational approach for predicting off-target toxicity of antiviral ribonucleoside analogues to mitochondrial RNA polymerase.

Authors:  Holly Freedman; Philip Winter; Jack Tuszynski; D Lorne Tyrrell; Michael Houghton
Journal:  J Biol Chem       Date:  2018-05-08       Impact factor: 5.157

2.  Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches.

Authors:  Kevin M Crofton; Arianna Bassan; Mamta Behl; Yaroslav G Chushak; Ellen Fritsche; Jeffery M Gearhart; Mary Sue Marty; Moiz Mumtaz; Manuela Pavan; Patricia Ruiz; Magdalini Sachana; Rajamani Selvam; Timothy J Shafer; Lidiya Stavitskaya; David T Szabo; Steven T Szabo; Raymond R Tice; Dan Wilson; David Woolley; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2022-03-17

3.  In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.

Authors:  Arianna Bassan; Vinicius M Alves; Alexander Amberg; Lennart T Anger; Lisa Beilke; Andreas Bender; Autumn Bernal; Mark T D Cronin; Jui-Hua Hsieh; Candice Johnson; Raymond Kemper; Moiz Mumtaz; Louise Neilson; Manuela Pavan; Amy Pointon; Julia Pletz; Patricia Ruiz; Daniel P Russo; Yogesh Sabnis; Reena Sandhu; Markus Schaefer; Lidiya Stavitskaya; David T Szabo; Jean-Pierre Valentin; David Woolley; Craig Zwickl; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2021-09-13

Review 4.  In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects.

Authors:  Mark T D Cronin; Steven J Enoch; Claire L Mellor; Katarzyna R Przybylak; Andrea-Nicole Richarz; Judith C Madden
Journal:  Toxicol Res       Date:  2017-07-15

5.  SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

Authors:  Yuqing Hua; Xueyan Cui; Bo Liu; Yinping Shi; Huizhu Guo; Ruiqiu Zhang; Xiao Li
Journal:  Front Chem       Date:  2022-07-13       Impact factor: 5.545

6.  Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods.

Authors:  Elisabet Berggren; Andrew White; Gladys Ouedraogo; Alicia Paini; Andrea-Nicole Richarz; Frederic Y Bois; Thomas Exner; Sofia Leite; Leo A van Grunsven; Andrew Worth; Catherine Mahony
Journal:  Comput Toxicol       Date:  2017-11

7.  Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity.

Authors:  Jennifer Hemmerich; Florentina Troger; Barbara Füzi; Gerhard F Ecker
Journal:  Mol Inform       Date:  2020-03-23       Impact factor: 4.050

8.  MitoTox: a comprehensive mitochondrial toxicity database.

Authors:  Yu-Te Lin; Ko-Hong Lin; Chi-Jung Huang; An-Chi Wei
Journal:  BMC Bioinformatics       Date:  2021-07-15       Impact factor: 3.169

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.