Literature DB >> 17040094

Future of toxicology--predictive toxicology: An expanded view of "chemical toxicity".

Ann M Richard1.   

Abstract

A chemistry approach to predictive toxicology relies on structure-activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. These approaches are less well-suited, however, to the challenges of global toxicity prediction, i.e., to predicting the potential toxicity of structurally diverse chemicals across a wide range of end points of regulatory and pharmaceutical concern. New approaches that have the potential to significantly improve capabilities in predictive toxicology are elaborating the "activity" portion of the SAR paradigm. Recent advances in two areas of endeavor are particularly promising. Toxicity data informatics relies on standardized data schema, developed for particular areas of toxicological study, to facilitate data integration and enable relational exploration and mining of data across both historical and new areas of toxicological investigation. Bioassay profiling refers to large-scale high-throughput screening approaches that use chemicals as probes to broadly characterize biological response space, extending the concept of chemical "properties" to the biological activity domain. The effective capture and representation of legacy and new toxicity data into mineable form and the large-scale generation of new bioassay data in relation to chemical toxicity, both employing chemical structure information to inform and integrate diverse biological data, are opening exciting new horizons in predictive toxicology.

Entities:  

Mesh:

Year:  2006        PMID: 17040094     DOI: 10.1021/tx060116u

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


  12 in total

1.  Shaping the future of safer innovative drugs in Europe.

Authors:  Jordi Mestres; Sharon D Bryant; Ismael Zamora; Johann Gasteiger
Journal:  Nat Biotechnol       Date:  2011-09-08       Impact factor: 54.908

2.  Toxicokinetic Triage for Environmental Chemicals.

Authors:  John F Wambaugh; Barbara A Wetmore; Robert Pearce; Cory Strope; Rocky Goldsmith; James P Sluka; Alexander Sedykh; Alex Tropsha; Sieto Bosgra; Imran Shah; Richard Judson; Russell S Thomas; R Woodrow Setzer
Journal:  Toxicol Sci       Date:  2015-06-16       Impact factor: 4.849

3.  Many InChIs and quite some feat.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2015-06-17       Impact factor: 3.686

4.  THE INTERACTIVE DECISION COMMITTEE FOR CHEMICAL TOXICITY ANALYSIS.

Authors:  Chaeryon Kang; Hao Zhu; Fred A Wright; Fei Zou; Michael R Kosorok
Journal:  J Stat Res       Date:  2012

5.  Mixed learning algorithms and features ensemble in hepatotoxicity prediction.

Authors:  Chin Yee Liew; Yen Ching Lim; Chun Wei Yap
Journal:  J Comput Aided Mol Des       Date:  2011-09-06       Impact factor: 3.686

6.  Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

Authors:  Hao Zhu; Todd M Martin; Lin Ye; Alexander Sedykh; Douglas M Young; Alexander Tropsha
Journal:  Chem Res Toxicol       Date:  2009-12       Impact factor: 3.739

Review 7.  Adaptation of high-throughput screening in drug discovery-toxicological screening tests.

Authors:  Paweł Szymański; Magdalena Markowicz; Elżbieta Mikiciuk-Olasik
Journal:  Int J Mol Sci       Date:  2011-12-29       Impact factor: 5.923

8.  The impact of composite AUC estimates on the prediction of systemic exposure in toxicology experiments.

Authors:  Tarjinder Sahota; Meindert Danhof; Oscar Della Pasqua
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-04-14       Impact factor: 2.745

9.  Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity.

Authors:  Hao Zhu; Ivan Rusyn; Ann Richard; Alexander Tropsha
Journal:  Environ Health Perspect       Date:  2008-04       Impact factor: 9.031

10.  Predictive toxicology of cobalt ferrite nanoparticles: comparative in-vitro study of different cellular models using methods of knowledge discovery from data.

Authors:  Limor Horev-Azaria; Giovanni Baldi; Delila Beno; Daniel Bonacchi; Ute Golla-Schindler; James C Kirkpatrick; Susanne Kolle; Robert Landsiedel; Oded Maimon; Patrice N Marche; Jessica Ponti; Roni Romano; François Rossi; Dieter Sommer; Chiara Uboldi; Ronald E Unger; Christian Villiers; Rafi Korenstein
Journal:  Part Fibre Toxicol       Date:  2013-07-29       Impact factor: 9.400

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.