Description:
<jats:title>Abstract</jats:title>
<jats:p>Adverse events (AE's) are a common and unavoidable consequence of therapeutic intervention. Nevertheless, available tomes of such data now provide us with an invaluable opportunity to study the relationship between human phenotype and drug-induced protein perturbations within a patient system. Deciphering the molecular basis of such adverse responses is not only paramount to the development of safer drugs, but it also presents a unique opportunity to dissect disease systems in search of novel response biomarkers, drug targets and efficacious combination therapies. Inspired by the potential application of this approach in clinical oncology, we have developed an in silico platform dedicated to the Molecular Analysis of Side Effect information (MASE). Combining data from the FDA's Adverse Event Reporting System (AERS) with highly curated knowledge about drug, target and pathway associations, MASE provides a biosystem level view on adverse event reports. In terms of data integration process, the free-text drug names provided by the AERS system are first mapped to standard drug synonyms. By then associating these small-molecule/biological drugs with known and predicted protein partners, we transform adverse events information from a purely drug-centric resource, to one that emphasizes the functional mediators of drug activity within the patient system.</jats:p>
<jats:p>Employing this strategy, AE cases associated with a total of 97 marketed cancer drugs were curated and contextualized. A total of 208,364 adverse event cases (i.e. 14% of all) were reported as either involving a cancer drug or cancer indication, with 1243 other drugs (79%) reported as co-medicated throughout these cases. Using this cancer-focused subset, case-specific molecular views were generated highlighting all elements of the proteome perturbed through multi-component therapies – totaling 1663 direct targets, 49 metabolizing enzymes and 407 pathways. The system may be queried to analyze side effects and outcomes associated with specific combinations of drugs, targets, metabolizing enzymes, pathways or cancer indications. Importantly, we have developed a set of analytical approaches that mine MASE for evidence of 1) efficacious target combinations 2) target combinations with increased side effects, 3) target combinations that appear to attenuate certain drug side effects. In addition, we also report a strategy to predict novel targets of established drugs, based on side effect dissimilarities between otherwise structurally comparable agents.</jats:p>
<jats:p>In summary, by permitting direct assessment relationships between the human proteome and drug-induced phenotypes, MASE provides a novel approach to the analysis and molecular dissection of AE information in oncology. Current developments are focused on the integration of patient specific clinico-molecular data and the combined application to treatment decision support.</jats:p>
<jats:p>Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 56. doi:10.1158/1538-7445.AM2011-56</jats:p>