Pharmacokinetic and Pharmacodynamic Modeling of Anticancer Agents

抗癌药物的药代动力学和药效学模型

• 批准号: 9154287
• 负责人: William Douglas Figg
• 金额: $ 47.96万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9154287
• 关键词: Adult; Antibodies; Antineoplastic Agents; BAY 54-9085; Biological Assay; CCR; CYP17A1 gene; Carboplatin; Clinical Pharmacology; Clinical Trials; Clinical Trials Design; Computer Simulation; Concentration measurement; Data; Depsipeptides; Dose; Drug Kinetics; ENG gene; Endothelial Cells; Equation; Excretory function; Frequencies; Genotype; Human; IgG1; Kinetics; Laboratories; Malignant neoplasm of prostate; Mediating; Metabolism; Modeling; Modification; Monoclonal Antibodies; Mus; P-Glycoprotein; Pharmaceutical Preparations; Pharmacodynamics; Physiological Processes; Plicamycin; Population; Resistance; Sampling; Scheme; Solid Neoplasm; Systemic Therapy; Tariquidar; Testing; Time; Toxic effect; absorption; angiogenesis; base; cell growth; chemotherapy; data modeling; docetaxel; inhibitor/antagonist; novel; pharmacodynamic model; pharmacokinetic model; phase 1 study; simulation; tumor growth

In order to optimize therapy, a full understanding of the pharmacokinetics of any systemic therapy is desired. We routinely model the pharmacokinetic (PK) data of agents being tested for antitumor activity and correlate that with activity and/or toxicity (pharmacodynamics modeling). The laboratory is currently collaborating on 80 clinical trials to characterize the clinical pharmacology of novel chemotherapy agents. We utilize compartmental and noncompartmental approaches to define the disposition of agents. Analysis of PK data (using concentration measurements provided by sample analysis using validated assays) allows for assessment of drug disposition, including the absorption, distribution, metabolism and excretion of a drug. Modeling this data, essentially describing these physiological processes as a mathematical equation, allows for optimization of drug administration (including dose and frequency of dosing,) in silico. Over the years, we have conducted population pharmacokinetic modeling of the following compounds: depsipeptide, romidepsin, sorafenib, olaparib, docetaxel in combination with the p-glycoprotein antagonist tariquidar, TRC105, and TRC102. Studies are ongoing for population PK modeling of mithramycin, VT464 and belinostat. A population PK analysis of a phase I study of TRC105 in adults with solid tumors was conducted. TRC105 is a human/murine chimeric IgG1 anti-CD105 monoclonal antibody that inhibits angiogenesis and tumor growth via endothelial cell growth inhibition. The analysis characterized dose-specific clearance and target-mediated disposition of the antibody. Finally, recent efforts have focused on building a population PK model to understand the disposition kinetics of mithramycin in the body to best optimize dose. In addition, we are developing a PK/PD model to understand the disposition kinetics of belinostat in the body and correlations with pharmacological effect to best optimize dose based on certain covariates such as genotype status. To further understand the mechanistic relationship between carboplatin and olaparib clearance, a population PK model was developed and validated by the CPP. The CYP17 inhibitor, VT464, is being developed for metastatic castrate-resistant prostate cancer. An initial noncompartmental analysis revealed a significantly slower clearance during steady-state compared to first dose, that while not explicitly dose-dependent, could indicated time-dependent autoinhibition. Population PK model is being developed to better understand the drug's PK profile and assess the model's ability to predict time-dependent, and potentially dose-dependent, autoinhibition.

为了优化治疗，需要充分了解任何全身治疗的药代动力学。我们对正在测试抗肿瘤活性的药物的药代动力学（PK）数据进行常规建模，并将其与活性和/或毒性相关联（药效学建模）。该实验室目前正在合作进行80项临床试验，以表征新型化疗药物的临床药理学。我们利用区隔和非区隔的方法来定义代理的处置。PK数据的分析（使用样品分析提供的浓度测量，使用经过验证的分析）允许评估药物处置，包括药物的吸收、分布、代谢和排泄。对这些数据进行建模，基本上将这些生理过程描述为数学方程，从而可以在计算机上优化药物给药（包括剂量和给药频率）。多年来，我们对以下化合物进行了群体药代动力学建模：抑郁肽、罗米地辛、索拉非尼、奥拉帕尼、多西他赛与p糖蛋白拮抗剂tariquar、TRC105和TRC102联合使用。米霉素、VT464和belinostat的群体PK模型研究正在进行中。对TRC105在成人实体瘤患者中的I期研究进行了人群PK分析。TRC105是一种人/鼠嵌合IgG1抗cd105单克隆抗体，通过抑制内皮细胞生长抑制血管生成和肿瘤生长。该分析的特点是剂量特异性清除和靶向介导的抗体处置。最后，最近的研究重点是建立种群PK模型，以了解米霉素在体内的配置动力学，以优化剂量。此外，我们正在开发一个PK/PD模型，以了解belinostat在体内的配置动力学及其与药理作用的相关性，从而基于某些协变量（如基因型状态）优化剂量。为了进一步了解卡铂和奥拉帕尼清除率之间的机制关系，CPP建立了一个种群PK模型并进行了验证。CYP17抑制剂VT464正在开发用于转移性去势抵抗性前列腺癌。初步的非区室分析显示，与首次给药相比，在稳态期间清除率明显较慢，虽然不是明确的剂量依赖，但可能表明时间依赖的自抑制。正在开发群体PK模型，以更好地了解药物的PK谱，并评估模型预测时间依赖性和潜在剂量依赖性自抑制的能力。