Clinical Practice Data to Aid Narrow Therapeutic Index Drug Classification

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PROJECT SUMMARY/ABSTRACT Drugs with a narrow therapeutic index (NTI) can cause mortality. For these drugs, small differences in concentrations may lead to serious toxicities or therapeutic failures. Consequently, in the setting of generic drug development, it is critical that NTI generic drugs achieve exposures similar to reference products. To enhance the therapeutic equivalence of drugs with NTI, the Food and Drug Administration has tightened their bioequivalence standards. However, broad implementation of these new standards is challenging because some drugs do not have an established NTI classification. This can be due to a lack in characterization of the drugs' therapeutic range or dose/concentration-response relationship. Therefore, there is an urgent and unmet need for a systematic approach to identify and classify drugs with a NTI. This proposal will use a unique infrastructure to integrate clinical practice data with statistical tools to characterize the drug dose/concentration- response relationship and classify drugs with NTI. The central hypothesis of this proposal is that the proposed systematic approach will result in successful classification of drugs with a NTI. Dr. Cohen-Wolkowiez will evaluate this hypothesis by performing a comprehensive search of publicly available data (literature and databases) to extract information and allow classification of drugs with NTI. Because the amount of detail included in publicly available databases is variable, Dr. Cohen-Wolkowiez will supplement this data with information extracted from medical records at the Duke University Medical Center. If sufficient data is available from these two sources, he will then characterize the concentration-response relationship of candidate drugs with NTI using population pharmacokinetics (PK)-pharmacodynamic (PD) methodologies. This project evaluates a novel, systematic approach to classify drugs with NTI. If the combination of publicly available data, supplemental data from medical records, and population PK-PD methodologies can classify drugs with NTI, this will substantially affect public health and streamline applicability of new FDA standards for bioequivalence of generic drugs with NTI.