Assessment of the Health Effects of Chemicals in Humans: II. Construction of an Adverse Effects Database for QSAR Modeling

Edwin   J.   Matthews; Naomi   L.   Kruhlak; James   L.   Weaver; R.   Daniel   Benz; Joseph   F.   Contrera

Abstract

The FDAs 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) 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 ≥4 ADR reports and ≥20,000 shipping units during five years of marketing, and an RI ≥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.

Keywords: adr, adverse effect, fda, computational toxicology, predictive modeling, qsar, srs database, human clinical data