PREDICTION OF DRUG INTERACTIONS IN VIVO AND IN VITRO

最近成功的体内-体外代谢相关性 基于药物相互作用的研究表明，酶的行为主要是 保守的然而，目前预测抑制的方法 基础互动基本上仍是定性的。为众多 抑制剂，体外测定的抑制常数不足以 预测体内相互作用的程度。这些抑制剂似乎 体内比体外更有效。一个基本的问题，在该领域的 体外-体内相关性是缺乏一种通用方法， 获得酶位点处抑制剂的浓度， 抑制常数，实际上占主导地位的网站。本 应用程序，我们将测试“免费药物假说”，即概念 该抑制剂效应由实际位点上未结合的抑制剂控制 的酶。在前两个具体目标中，我们将证明， 游离药物假说对于体外竞争性抑制剂是有效的 系统，只要使用广泛的 具有不同蛋白质结合和亲脂性的抑制剂列表 特色具体目标2（a）和2（B）将检验 体内“游离药物假说”。这将通过使用新的 参数，“抑制常数比”（R/kI）。理论认为 该比率（定义为在纯化的重组体中测定的K/i/（pre 系统除以体内测定的K/i/iv）将等于1， 它将不依赖于酶。因此，这项建议将提供一个有用的 从数据导出的实现定量体内预测的框架 体外

The recent successes of in vivo-in vitro correlations for metabolically based drug interactions suggests that enzyme behavior is largely conserved. However, current approaches to the prediction of inhibition based interactions have remained essentially qualitative. For numerous inhibitors, inhibition constants determined in vitro do not adequately predict the extent of interaction in vivo. These inhibitors appear to be more potent in vivo than in vitro. A fundamental problem in the field of in vitro-in vivo correlations is the absence of a general methodology for obtaining the concentration of inhibitor at the enzyme site and the inhibition constant that actually prevails at the site. In the present application, we will test the "free drug hypothesis," i.e. the concept that inhibitor effect is governed by unbound inhibitor at the actual site of the enzyme. In the first two specific aims, we will demonstrate that the free drug hypothesis is valid for competitive inhibitors in in vitro systems so long as unbound concentrations are measured using an extensive list of inhibitors with different protein binding and lipophilicity characteristics. Specific Aims 2(a) and 2(b) will test the validity of the "free drug hypothesis" in vivo. This will be accomplished using a new parameter, the "inhibition constants ratio" (R/kI). Theory suggests that this ratio (defined as K/i/(pre) determined in a purified recombinant system divided by the K/i/iv determined in vivo) will equal one and that it will be independent of enzyme. This proposal will thus provide a useful framework to effectuate quantitative in vivo predictions from data derived in vitro.