Literature DB >> 19422096

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans. Part A: use of FDA post-market reports to create a database of hepatobiliary and urinary tract toxicities.

Carling J Ursem1, Naomi L Kruhlak, Joseph F Contrera, Philip M MacLaughlin, R Daniel Benz, Edwin J Matthews.   

Abstract

The Informatics and Computational Safety Analysis Staff at the US FDA's Center for Drug Evaluation and Research has created a database of pharmaceutical adverse effects (AEs) linked to pharmaceutical chemical structures and estimated population exposures. The database is being used to develop quantitative structure-activity relationship (QSAR) models for the prediction of drug-induced liver and renal injury, as well as to identify relationships among AEs. The post-market observations contained in the database were obtained from FDA's Spontaneous Reporting System (SRS) and the Adverse Event Reporting System (AERS) accessed through Elsevier PharmaPendium software. The database contains approximately 3100 unique pharmaceutical compounds and 9685 AE endpoints. To account for variations in AE reports due to different patient populations and exposures for each drug, a proportional reporting ratio (PRR) was used. The PRR was applied to all AEs to identify chemicals that could be scored as positive in the training datasets of QSAR models. Additionally, toxicologically similar AEs were grouped into clusters based upon both biological effects and statistical correlation. This clustering created a weight of evidence paradigm for the identification of compounds most likely to cause human harm based upon findings in multiple related AE endpoints.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19422096     DOI: 10.1016/j.yrtph.2008.12.009

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  10 in total

1.  Data Mining FAERS to Analyze Molecular Targets of Drugs Highly Associated with Stevens-Johnson Syndrome.

Authors:  Keith K Burkhart; Darrell Abernethy; David Jackson
Journal:  J Med Toxicol       Date:  2015-06

2.  Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

Authors:  Hui Zhang; Lan Ding; Yi Zou; Shui-Qing Hu; Hai-Guo Huang; Wei-Bao Kong; Ji Zhang
Journal:  J Comput Aided Mol Des       Date:  2016-09-17       Impact factor: 3.686

3.  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

4.  MultiCASE Platform for In Silico Toxicology.

Authors:  Suman K Chakravarti; Roustem D Saiakhov
Journal:  Methods Mol Biol       Date:  2022

Review 5.  In Silico Models for Hepatotoxicity.

Authors:  Claire Ellison; Mark Hewitt; Katarzyna Przybylak
Journal:  Methods Mol Biol       Date:  2022

6.  In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity.

Authors:  Arianna Bassan; Vinicius M Alves; Alexander Amberg; Lennart T Anger; Scott Auerbach; Lisa Beilke; Andreas Bender; Mark T D Cronin; Kevin P Cross; Jui-Hua Hsieh; Nigel Greene; Raymond Kemper; Marlene T Kim; Moiz Mumtaz; Tobias Noeske; Manuela Pavan; Julia Pletz; Daniel P Russo; Yogesh Sabnis; Markus Schaefer; David T Szabo; Jean-Pierre Valentin; Joerg Wichard; Dominic Williams; David Woolley; Craig Zwickl; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2021-09-09

7.  Relationships Between Pharmacovigilance, Molecular, Structural, and Pathway Data: Revealing Mechanisms for Immune-Mediated Drug-Induced Liver Injury.

Authors:  S S Ho; A J McLachlan; T F Chen; D E Hibbs; R A Fois
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2015-06-18

8.  Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints.

Authors:  Xiaobin Liu; Danhua Zheng; Yi Zhong; Zhaofan Xia; Heng Luo; Zuquan Weng
Journal:  Biomed Res Int       Date:  2020-05-19       Impact factor: 3.411

9.  Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions.

Authors:  Sergey Ivanov; Alexey Lagunin; Dmitry Filimonov; Vladimir Poroikov
Journal:  PLoS Comput Biol       Date:  2019-07-19       Impact factor: 4.475

Review 10.  Key Challenges and Opportunities Associated with the Use of In Vitro Models to Detect Human DILI: Integrated Risk Assessment and Mitigation Plans.

Authors:  Franck A Atienzar; Eric A Blomme; Minjun Chen; Philip Hewitt; J Gerry Kenna; Gilles Labbe; Frederic Moulin; Francois Pognan; Adrian B Roth; Laura Suter-Dick; Okechukwu Ukairo; Richard J Weaver; Yvonne Will; Donna M Dambach
Journal:  Biomed Res Int       Date:  2016-09-05       Impact factor: 3.411

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.