Drug-drug interactions (DDIs) represent an increasing threat to public health, causing an estimated 195,000 annual hospitalizations and 74,000 emergency room visits. Current DDI research investigates different aspects of drug interactions, both computationally and experimentally. Although these approaches are complementary, they are usually conducted independently and without coordination. In vitro pharmacology experiments use intact cells, microsomal protein fractions, or recombinant systems to investigate drug interaction mechanisms. Pharmaco-epidemiology (in populo) uses a population-based approach and large electronic medical record (EMR) databases to investigate the contribution of a DDI to drug efficacy and adverse drug reactions (ADRs). In this grant proposal, novel bioinformatics data mining approaches will be developed to mine DDIs from EMR, and they will be further validated in vitro. The following are specific aims.
In Aim 1, a nove dynamic nested case-control design is proposed to detect of either single drug or DDI effects on the ADR. A new empirical Bayes method is developed to test the drug and DDI-induced ADRs, and it will estimate false discovery rates.
In Aim 2, a novel generalized propensity score method is proposed to analyze high dimensional medication data. This method possesses more power in identifying ADR effects from highly correlated drugs, than the conventional propensity score method.
Aim 3, using the univariate and multivariate data mining methods developed in aims 1 and 2, we will detect novel drugs and DDIs that increase the risk of one well-defined ADR, myopathy, using the EMR database and high-throughput enzymatic screening assays. In our preliminary work, using our proposed methodology and a 2.2 million record EMR database, six myopathy risk DDIs were identified (p < 5?10-6), including a newly discovered interaction between quetiapine and chloroquine. If taken together, they increase myopathy risk 2.17-fold higher than their added individual risks due to quetiapine inhibition of chloroquine metabolism by the CYP3A4 pathway and blockage of the OATP1B1/1B3 transmembrane transporter. Thus, the successful execution of this work will demonstrate a complete translational scope, starting with EMR-based DDI discovery, and ending with the elucidation of molecular DDI mechanisms through pharmacological experiments. Together, these preliminary data demonstrate that our translational approach is a highly feasible and extremely powerful method for clinical DDI research, likely to yield valuable insight into this emerging public health peril.
In this grant proposal, drug interaction pairs will be mined from electronic medical record databases; and their interaction mechanisms will be investigated using cell based experiments. Our proposed bioinformatics drug interaction research represents an ideal model for the bi-directional translational research between basic science and clinical research. It will provide significant clinical and biological evidence for better drug interaction management in the patient health care system.
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