title: [Analysis of drug safety information using large-scale adverse drug reactions database].
abstract: The worldwide situations of drug safety have changed dramatically. Drugs are used based on the evaluation of safety data collected in clinical practice worldwide. US Food Drug Administration collects spontaneous reports and requires manufacturers to report adverse drug reactions (ADRs) of US marketed drugs occurring worldwide. These worldwide data are available through the Adverse Event Reporting System (AERS) (about 4.1 million reports on about 3,073,340 patients, for 13 years: 1997.4th qr-2010.4th qr.). The current issues are how to analyze and utilize such large-scale safety data. Potential biases should always be kept in mind, because AERS is based on spontaneous reports. However, its huge volumes and exhaustiveness allow for sufficient scientific evaluation with the aid of current IT technology. Therefore, analysis of large-scale ADR database becomes a new research area not only from the medical science but also from the statistical viewpoint. In this report, I introduce some case studies in which we analyzed the AERS data on psychotropics including antipsychotics, antiepileptics, and antidepressants. Antipsychotics caused ADRs specific to each drug, and, in combination therapy, increased the incidences of diabetes mellitus, pancreatitis, and neuroleptic malignant syndrome; antiepileptics caused AEs (adverse events) including serious skin reactions such as Stevens-Johnson syndrome (SJS), congenital anomaly, and closed-angle glaucoma; and antidepressants caused AEs including serotonin syndrome, suicidal events, and congenital anomaly, and AEs occurring at a higher incidence for other indications, drugs often used in the elderly and AEs in combination therapy. We have analyzed ADRs associated with concomitant drug therapies using Bayesian approach. In the analysis we faced difficulties of overdispersion and we have to estimate a number of parameters, given a large number of target drugs as well as ADRs. In addition, ADR reports are not collected from uniform populations, we also have to consider the variations in the target populations. So, we use Bayesian statistics. Bayesian analysis has become feasible with advances in computer technologies and the Markov chain Monte Carlo (MCMC) methods. It allows us to analyze ADRs associated with concomitant drug therapies and estimate the ADR signals for each drug. Therefore, the analysis and evaluation of large-scale ADR database can provide important safety information in clinical practice and the studies on ADR database are the most important issues in ensuring the postmark safety of pharmaceutical products.
authors.affiliation: Division of Safety Information on Drug, Food and Chemicals, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158-8501, Japan. morikawa@nihs.go.jp
authors.forename: Kaoru
authors.initials: K
authors.lastname: Morikawa
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000927:Anticonvulsants / Q000009:adverse effects; D000928:Antidepressive Agents / Q000009:adverse effects; D014150:Antipsychotic Agents / Q000009:adverse effects; D016208:Databases, Factual; D004346:Drug Information Services; D064420:Drug-Related Side Effects and Adverse Reactions; D023381:Endpoint Determination; D006801:Humans
chemical_list: D000927:Anticonvulsants; D000928:Antidepressive Agents; D014150:Antipsychotic Agents