Literature DB >> 16472241

Assessment of the health effects of chemicals in humans: II. Construction of an adverse effects database for QSAR modeling.

Edwin J Matthews1, Naomi L Kruhlak, James L Weaver, R Daniel Benz, Joseph F Contrera.   

Abstract

The FDA's Spontaneous Reporting System (SRS) database contains over 1.5 million adverse drug reaction (ADR) reports for 8620 drugs/biologics that are listed for 1191 Coding Symbols for Thesaurus of Adverse Reaction (COSTAR) terms of adverse effects. We have linked the trade names of the drugs to 1861 generic names and retrieved molecular structures for each chemical to obtain a set of 1515 organic chemicals that are suitable for modeling with commercially available QSAR software packages. ADR report data for 631 of these compounds were extracted and pooled for the first five years that each drug was marketed. Patient exposure was estimated during this period using pharmaceutical shipping units obtained from IMS Health. Significant drug effects were identified using a Reporting Index (RI), where RI = (# ADR reports / # shipping units) x 1,000,000. MCASE/MC4PC software was used to identify the optimal conditions for defining a significant adverse effect finding. Results suggest that a significant effect in our database is characterized by > or = 4 ADR reports and > or = 20,000 shipping units during five years of marketing, and an RI > or = 4.0. Furthermore, for a test chemical to be evaluated as active it must contain a statistically significant molecular structural alert, called a decision alert, in two or more toxicologically related endpoints. We also report the use of a composite module, which pools observations from two or more toxicologically related COSTAR term endpoints to provide signal enhancement for detecting adverse effects.

Entities:  

Mesh:

Year:  2004        PMID: 16472241     DOI: 10.2174/1570163043334794

Source DB:  PubMed          Journal:  Curr Drug Discov Technol        ISSN: 1570-1638


  5 in total

1.  Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

Authors:  Amie D Rodgers; Hao Zhu; Denis Fourches; Ivan Rusyn; Alexander Tropsha
Journal:  Chem Res Toxicol       Date:  2010-04-19       Impact factor: 3.739

2.  A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts.

Authors:  Fabiola Pizzo; Anna Lombardo; Alberto Manganaro; Emilio Benfenati
Journal:  Front Pharmacol       Date:  2016-11-22       Impact factor: 5.810

3.  Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency.

Authors:  T Liu; T Oprea; O Ursu; C Hasselgren; R B Altman
Journal:  Clin Transl Sci       Date:  2016-10-13       Impact factor: 4.689

4.  Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.

Authors:  Yen S Low; Ola Caster; Tomas Bergvall; Denis Fourches; Xiaoling Zang; G Niklas Norén; Ivan Rusyn; Ralph Edwards; Alexander Tropsha
Journal:  J Am Med Inform Assoc       Date:  2015-10-24       Impact factor: 4.497

5.  Predicting the potential toxicity of 26 components in Cassiae semen using in silico and in vitro approaches.

Authors:  Jinlan Yang; Shuo Wang; Tao Zhang; Yuqing Sun; Lifeng Han; Prince Osei Banahene; Qi Wang
Journal:  Curr Res Toxicol       Date:  2021-07-05
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.