Improving prediction of drug interactions mediated by time-dependent inhibitors

改进对时间依赖性抑制剂介导的药物相互作用的预测

• 批准号: 9199095
• 负责人: Jeffrey P Jones
• 金额: $ 41.06万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9199095
• 关键词: Adverse reactions; Affect; Binding; Biochemical; Biophysics; Cells; Clinic; Clinical; Clinical Data; Complex; Computer Simulation; Cytochrome P450; Data; Development; Dissociation; Drug Interactions; Drug Kinetics; Environment; Enzymatic Biochemistry; Enzyme Activation; Equation; Goals; Human; Illness impact; In Vitro; Kinetics; Knowledge; Lead; Life; Literature; Mediating; Membrane; Metabolism; Methods; Minor; Modeling; Molecular; Pharmaceutical Preparations; Physiological; Process; Publishing; Rattus; Research; Resolution; Rodent Model; Scheme; Time; Work; base; biophysical analysis; biophysical techniques; clinically significant; drug development; human data; improved; in vivo; in vivo Model; inhibitor/antagonist; insight; mathematical methods; novel; novel therapeutics; pharmacokinetic model; predict clinical outcome; public health relevance

 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.

 描述（由申请人提供）这项工作的首要目标是了解时间依赖性抑制（TDI）中的重要因素，并利用这些知识预测TDI何时可能发生临床不良反应。目前，体外TDI分析缺乏定量预测临床结局的分辨率。可能的起源 预测不佳的原因包括体外分析不充分、体内参数不可靠和生化机制复杂。开发能够更好地预测体内TDI的模型将允许开发在体外显示TDI但在体内仅具有微小作用的药物。如果能为一种危及生命的疾病开发出一种新药，这项工作的影响将是非常重大的。这项工作的首要目标是了解时间依赖性抑制（TDI）的重要因素，并利用这些知识来预测TDI何时可能发生不良反应。目前，大多数TDI在开发过程中被排除在进一步考虑药物之外，即使它们在体内可能不会出现重大问题。当涉及TDI时，为了改善临床药物相互作用的预测，我们提出了三个具体目标。1)了解代谢物中间体复合物（MIC）及其前体形成、释放和分解的机制可以产生更好的IVIVIE。具体而言，将探索MIC及其前体在不同环境中的命运，并将用新的生物物理方法确定动力学速率。2)为了研究复杂动力学对TDI参数估计的影响，将开发新的动力学方案，并使用新的数值方法收集和分析体外数据。通过使用我们的数值方法和适当的动力学方案分析体外时间依赖性抑制数据，我们可以提供相关的动力学参数，准确预测人类药物-药物相互作用（DDI）。具体来说，我们将开发代谢物中间体复合物（MIC）的形成，顺序代谢和酶激活TDI的模型。目标1中确定的机械速率常数将用于目标2下的模型开发。3)我们假设，改进的体外TDI模型，参数和机制的见解，从目标1和2将使我们能够预测人类DDI。在目标1和2中开发的机理方程将使用标准IVIVE方法和新的房室和生理药代动力学（PBPK）方法纳入体内模型。将使用大鼠和人体外数据与体内TDI数据相关联。将在此目标范围内生成大鼠体内数据，并将使用文献人类数据，将目标1和2的TDI模型和参数纳入标准IVIVE方程以及新型房室模型和PBPK模型。