CAUSAL ANALYSIS OF AES--PATTERN RECOGNITION SOFTWARE

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DESCRIPTION (Adapted from applicant's abstract): Determining if an adverse event is an adverse drug reaction is a difficult process made more complicated in clinical trials that address complex chronic diseases such as immune system compromise. Patients on study typically experience many adverse events, some serious, over the course of drug treatment and after treatment ceases. Previous attempts at facilitating safety analysis have concentrated on simple scoring mechanisms, probability algorithms including a Bayesian approach, recently automated, and global clinical impressions. These analytic methods work best on simple infections and skin diseases, but cannot address complex chronic diseases. In Phase I, software will be developed to analyze clinical trials safety data for chronic diseases. A prototype will be built for multiple sclerosis using actual Phase III trial data. The software will 1) facilitate linkages or pattern recognition among events, between events and medication administration, and between events and lab test results 2) cluster events temporally and by body system and 3) facilitate comparisons between placebo and study drug safety profiles. At the end of Phase I, a panel of clinicians, including medical monitors who prepare safety profiles for clinical trials results, will evaluate the software for ease of use, robustness, and regulatory compliance. In Phase II, the software will be refined and a comparative trial of its analytic power will be conducted on a different chronic disease data set. The goal of the project is to produce software that can be used by clinical researchers, drug and device manufacturers, the FDA, and regulatory agencies in the European Union in determining the relative safety of a new drug.