Comput Biol Chem - Pharmacoepidemiological characterization of drug-induced adverse reaction clusters towards understanding of their mechanisms.

Tópicos

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Resumo

A big challenge in pharmacology is the understanding of the underlying mechanisms that cause drug-induced adverse reactions (ADRs), which are in some cases similar to each other regardless of different drug indications, and are in other cases different regardless of same drug indications. The FDA Adverse Event Reporting System (FAERS) provides a valuable resource for pharmacoepidemiology, the study of the uses and the effects of drugs in large human population. However, FAERS is a spontaneous reporting system that inevitably contains noise that deviates the application of conventional clustering approaches. By performing a biclustering analysis on the FAERS data we identified 163 biclusters of drug-induced adverse reactions, counting for 691 ADRs and 240 drugs in total, where the number of ADR occurrences are consistently high across the associated drugs. Medically similar ADRs are derived from several distinct indications for use in the majority (145/163=88%) of the biclusters, which enabled us to interpret the underlying mechanisms that lead to similar ADRs. Furthermore, we compared the biclusters that contain same drugs but different ADRs, finding the cases where the populations of the patients were different in terms of age, sex, and body weight. We applied a biclustering approach to catalogue the relationship between drugs and adverse reactions from a large FAERS data set, and demonstrated a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients' conditions.

Resumo Limpo

big challeng pharmacolog understand under mechan caus druginduc advers reaction adr case similar regardless differ drug indic case differ regardless drug indic fda advers event report system faer provid valuabl resourc pharmacoepidemiolog studi use effect drug larg human popul howev faer spontan report system inevit contain nois deviat applic convent cluster approach perform biclust analysi faer data identifi biclust druginduc advers reaction count adr drug total number adr occurr consist high across associ drug medic similar adr deriv sever distinct indic use major biclust enabl us interpret under mechan lead similar adr furthermor compar biclust contain drug differ adr find case popul patient differ term age sex bodi weight appli biclust approach catalogu relationship drug advers reaction larg faer data set demonstr systemat way uncov case differ drug administr result similar advers reaction drug can caus differ reaction depend patient condit

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