OPTIMIZED CANCER CHEMOTHERAPY VIA PHARMACODYNAMIC MODELS

描述：(申请人摘要)抗癌药物的测定 剂量通常基于#年获得的最大耐受剂量(MTD)。 第一阶段试验。由于该药物随后在第二阶段和第二阶段使用 III试验，所有患者接受相同的剂量，归一化为身体 体重或身体表面积，由MTD决定，因此，防止 使用针对个人量身定做的给药方案。在这种情况下， 许多患者要么服药不足，要么服药过量。此应用程序 旨在通过使用人口来纠正这些不足 药动学(PK)和药效学(PD)模型。这样的模型可以 在个体中表征药物的PK和PD属性 总体和个人的协变量中的这些属性，或 患者特定的因素(例如，年龄、性别、肾功能) 会影响药物的PK和PD。基于人口的模型可以 为设计个性化患者剂量提供量化平台 治疗方案以达到所需的PK或PD终点。其中一部小说 此应用程序的几个方面是对PD的时间依赖性质进行建模 可随后应用于优化的设计的响应 给药方案。将使用不同的PD建模技术来 描述药物引起的骨髓抑制(Myls)，这是一个关键的剂量限制 对许多抗癌药物都有毒性。两个大型临床数据库，(1， 现有的约翰·霍普金斯肿瘤学中心数据库，以及2，正在进行的 福克斯大通癌症中心数据库)将提供一套广泛的PK 以及包括患者协变量的PD(即Myls)数据以评估PD Topotecan(TPT‘s)Myls的模型，既作为单一试剂，也作为 组合。每种类型的PD模型将考虑到时间- 测定的TPT血浆浓度与Myls、As之间的解离 以及学科内和学科间的可变性。预测性的 每种PD建模策略的性能将从 从两个大型数据库构建的索引数据集，并进一步 经过严格的自举验证分析。这些程序将 指出聚乳酸的最佳建模技术，以及定量方法 设计可提高药物质量的个体化给药方案 心理治疗。

DESCRIPTION: (Applicant's Abstract) Determination of anticancer drug doses is normally based on the maximum tolerated dose (MTD) obtained in Phase I trials. As the drug is subsequently used in Phase II and Phase III trials, all patients receive the same dose, normalized to body weight or body surface area, dictated by the MTD, thus, preventing the use of a dosing regimen tailored to an individual. In this scenario, many patients will be either underdosed or overdosed. This application is aimed at rectifying these deficiencies through the use of population pharmacokinetic (PK) and pharmacodynamic (PD) models. Such models can characterize a drug's PK and PD properties in an individual based upon these properties in the population and the individual's covariates, or patient specific factors (for example, age, sex, renal function) that can influence the drug's PK and PD. The population-based models can provide a quantitative platform to design individual patient dosing regimens to achieve a desired PK or PD endpoint. One of the novel aspects of this application is to model the time-dependent nature of PD responses that can be subsequently applied to the design of optimized drug dosing regimens. Different PD modeling techniques will be used to characterize drug-induced myelosuppression (MYLS), a key dose-limiting toxicity for many anticancer drugs. Two large clinical databases, (1, an existing Johns Hopkins Oncology Center database, and 2, an ongoing Fox Chase Cancer Center database) will provide an extensive set of PK and PD (i.e. MYLS) data including patient covariates to evaluate PD models for topotecan's (TPT's) MYLS, both as a single agent and in combination. Each type of PD model will account for the time- dissociation between measured TPT's plasma concentrations and MYLS, as well as intrasubject and intersubject variability. The predictive performance of each PD modeling strategy will be compared from numerous index datasets constructed from the two large databases, and further undergo rigorous bootstrap validation analyses. These procedures will indicate optimal modeling techniques for MYLS, and quantitative methods to design individual patient dosing regimens that may improve drug therapy.