Improving prediction of drug interactions mediated by time-dependent inhibitors

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 DESCRIPTION (provided by applicant) The overriding goal of this work is to understand the factors that are important in time dependent inhibition (TDI), and to use this knowledge to predict when TDI will have the potential for clinical adverse reactions. At present, in vitro TDI analyses lack the resolution for quantitative prediction of clinical outcomes. The possible origins of poor predictions include inadequate in vitro analyses, unreliable in vivo parameters, and complex biochemical mechanisms. Developing models that will better predict TDI in vivo will allow for the development of drugs that show TDI in vitro but have only minor effects in vivo. If even one new drug can be developed for a life-threatening illness the impact of this work will be highly significant. The overriding goal of this work is to understand the factors that are importan in time dependent inhibition (TDI), and to use this knowledge to predict when TDIs have the potential for adverse reactions. At present most TDIs are excluded from further consideration as drugs during the development process, even though they may not present significant problems in vivo. With the goal of improving predictions of drug interactions in the clinic when a TDI is involved, we propose three specific aims. 1) Understanding the mechanisms by which metabolite intermediate complexes (MICs) and their precursors form, are released, and breakdown, can result in better IVIVEs. Specifically, the fate of MICs and their precursors in different environments will be explored, and kinetic rates will be determined with new biophysical methods. 2) In order to investigate the impact of complex kinetics on TDI parameter estimation, new kinetic schemes will be developed, and in vitro data collected and analyzed with a novel numerical method. By using our numerical method and appropriate kinetic schemes for the analysis of time- dependent inhibition data in vitro, we can provide relevant kinetic parameters that accurately predict human drug-drug interaction (DDI). Specifically, we will develop models for metabolite intermediate complex (MIC) formation, sequential metabolism, and enzyme activation by TDIs. Mechanistic rate constants determined in Aim 1 will be used to develop models under Aim 2. 3) We hypothesize that improved in vitro TDI models, parameters, and mechanistic insights resulting from Aims 1 and 2 will allow us to predict human DDI. The mechanistic equations that are developed in Aims 1 and 2 will be incorporated into in vivo models using standard IVIVE approaches, and novel compartmental and physiologically based pharmacokinetic (PBPK) approaches. Rat and human in vitro data will be used to correlate with in vivo TDI data. Rat in vivo data will be generated within this aim, and literature human data wil be used, and TDI models and parameters from Aims 1 and 2 will be incorporated into standard IVIVE equations, and novel compartmental and PBPK models.