Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis

The US Food and Drug Administration's (FDA) adverse event reporting system (AERS)1 is currently used in the USA as the main source for drug safety surveillance. The FDA receives reports of suspected adverse drug events (ADE),2 which are then entered into the AERS database and used to discover new ADE. Despite its relative success, AERS has some limitations: the system captures only approximately 10% of events,3 the number of patients taking any drug is uncertain, and therefore the number of patients actually at risk is unknown. In addition, there are data quality issues preventing its effective use.4 The limitations imply that many serious ADE go undetected. It is now widely recognized that only through the integration of several information sources can a more effective surveillance system be achieved.5 6 Medicinal chemistry has exploited the idea that similar molecules from the point of view of chemical structure have similar biological properties.7–9 Molecular fingerprints can be used to evaluate molecular similarity and identify molecules structurally similar to those with a certain property.10 From the ADE perspective, computer-aided methods based on molecular structures are useful for pharmaceutical companies working in drug design to predict toxicological effects leading to the selection of new candidates without possible undesirable effects.11 However, computer-aided structural studies intended to predict preclinical ADE have important limitations due to the complexity of modeling poly-pharmacology systems with multiple mechanisms of action.11 Nevertheless, the chemical aspect for ADE discovery provided by structure–activity relationship (SAR) models could be very useful in combination with different types of sources, such as AERS, in the detection of post-marketed ADE. The potential of SAR models to detect post-marketed ADE was studied by the FDA (informatics and computational safety analysis),12 which used AERS as a basis to construct SAR models. We hypothesize that computer-aided structural studies, and in particular molecular fingerprint-based modeling (MFBM), can be used to analyze novel ADE for which evidential support is not yet conclusive, to enrich subsets of suspected drugs generated by other sources likely to be interesting for further study, and to explain the molecular mechanisms responsible for ADE. In this article, we present a new ADE discovery paradigm that uses chemical information via MFBM to enhance initial ADE signals generated from another source, such as AERS, and provide a decision support mechanism to facilitate the identification of novel ADE. The basic idea is to assess the likelihood of an ADE for a candidate drug based on its structural similarity to a set of drugs known to cause the event in which the candidate drug has been determined by another source. Our study is applied to the identification of drugs reported in AERS that were found to be associated with the serious ADE rhabdomyolysis, an important ADE characterized by skeletal muscle breakdown and the release of intracellular constituents into the bloodstream. Different studies have been published analyzing adverse events from the point of view of AERS analysis or computer-aided structural studies.11 13 14 However, to the best of our knowledge, no publication has combined the two methods. In this paper we describe the combination of AERS analysis and MFBM, and show that it reduces the number of false positives and yields improved results to identify adverse events in drugs better than either method by itself.