Pharmacokinetics, Dynamics and Metabolism, La Jolla Laboratories, Pfizer Worldwide Research & Development, 10777 Science Center Drive, San Diego, CA 92121, USA.
AAPS J. 2013 Apr;15(2):354-66. doi: 10.1208/s12248-012-9436-4. Epub 2012 Dec 19.
Attrition risk related to efficacy is still a major reason why new chemical entities fail in clinical trials despite recently increased understanding of translational pharmacology. Pharmacokinetic-pharmacodynamic (PKPD) analysis is key to translating in vivo drug potency from nonclinical models to patients by providing a quantitative assessment of in vivo drug potency with mechanistic insight of drug action. The pharmaceutical industry is clearly moving toward more mechanistic and quantitative PKPD modeling to have a deeper understanding of translational pharmacology. This paper summarizes an anticancer drug case study describing the translational PKPD modeling of crizotinib, an orally available, potent small molecule inhibitor of multiple tyrosine kinases including anaplastic lymphoma kinase (ALK) and mesenchymal-epithelial transition factor (MET), from nonclinical to clinical development. Overall, the PKPD relationships among crizotinib systemic exposure, ALK or MET inhibition, and tumor growth inhibition (TGI) in human tumor xenograft models were well characterized in a quantitative manner using mathematical modeling: the results suggest that 50% ALK inhibition is required for >50% TGI whereas >90% MET inhibition is required for >50% TGI. Furthermore, >75% ALK inhibition and >95% MET inhibition in patient tumors were projected by PKPD modeling during the clinically recommended dosing regimen, twice daily doses of crizotinib 250 mg (500 mg/day). These simulation results of crizotinib-mediated ALK and MET inhibition appeared consistent with the currently reported clinical responses. In summary, the present paper presents an anticancer drug example to demonstrate that quantitative PKPD modeling can be used for predictive translational pharmacology from nonclinical to clinical development.
尽管最近对转化药理学的理解有所增加,但与疗效相关的淘汰风险仍然是新化学实体在临床试验中失败的主要原因。药代动力学-药效学(PKPD)分析是将非临床模型中的体内药物效力转化为患者的关键,通过提供对体内药物效力的定量评估和对药物作用的机制理解。制药行业显然正在朝着更具机制性和定量性的 PKPD 建模方向发展,以更深入地了解转化药理学。本文总结了一个抗癌药物案例研究,描述了口服、强效小分子多酪氨酸激酶抑制剂克唑替尼(包括间变性淋巴瘤激酶(ALK)和间质-上皮转化因子(MET))从非临床到临床开发的转化 PKPD 建模。总体而言,使用数学建模以定量方式很好地描述了克唑替尼系统暴露、ALK 或 MET 抑制与人体肿瘤异种移植模型中肿瘤生长抑制(TGI)之间的 PKPD 关系:结果表明,需要 50%的 ALK 抑制才能实现 >50%的 TGI,而 >90%的 MET 抑制才能实现 >50%的 TGI。此外,在临床推荐的给药方案期间,即每天两次剂量为 250 mg 克唑替尼(500 mg/天),PKPD 模型预测患者肿瘤中的 ALK 和 MET 抑制率 >75%和 >95%。克唑替尼介导的 ALK 和 MET 抑制的这些模拟结果与目前报道的临床反应一致。总之,本文提供了一个抗癌药物的例子,证明了定量 PKPD 建模可用于从非临床到临床开发的预测性转化药理学。