Narayanan Rangaraj, Hoffmann Matthew, Kumar Gondi, Surapaneni Sekhar
Department of Drug Metabolism and Pharmacokinetics, Celgene Corporation, Summit, NJ 07901, USA.
Drug Metab Lett. 2016;10(3):172-179. doi: 10.2174/1872312810666160729124745.
Oncology therapy typically involves drug combinations since monotherapy seldom provides the desired outcome. But combination therapy presents the potential for drug-drug interactions (DDIs). Due to the narrow window between therapeutic concentrations and onset of toxicity often observed with oncology therapeutics, managing DDIs with combination therapy in cancer is critical. Physiologically based pharmacokinetic (PBPK) modeling can be effectively used for predicting DDIs and guiding dose-selection, but requires development of PBPK models of cancer drugs. Among various types of cancer, metastatic prostate cancer is an area of high unmet medical need with minimal therapeutic options. Recently, enzalutamide was approved for treatment of metastatic prostate cancer and is often dosed as a combination in clinical practice. Enzalutamide is a potent CYP3A inducer and a model-based approach to guide dose-selection for enzalutamide combinations that are CYP3A substrates is needed.
A "fit for purpose" PBPK model of enzalutamide was developed to illustrate the CYP3A4 induction potential, understand the kinetics of de-induction of CYP3A4 following cessation of enzalutamide dosing and guide dose-selection of a co-administered CYP3A substrate.
The population-based simulator, Simcyp, was used for model building purposes. Model input parameters were obtained from public information, primarily from the FDA summaries.
The simulated concentration time profiles of enzalutamide in healthy male subjects were comparable to observed profiles in male patients. Model predicted enzalutamide pharmacokinetic (PK) parameters, i.e. AUC, Cmax and half-life were within 1.5-fold of observed results obtained from two reported studies, supporting verification of the PBPK model. Model application was demonstrated by simulating a drug-drug interaction between enzalutamide and midazolam, a sensitive CYP3A4 substrate. Based on simulations, the midazolam AUC ratio ranged from 0.06 to 0.16 and was comparable to the observed ratio of 0.14. Based on modeling, upon cessation of enzalutamide dosing, it is predicted that at least 8 weeks are needed to re-attain baseline CYP3A4 activity. Based on PBPK modeling, dose adjustment of up to 3-fold for a co-administered CYP3A substrate was shown to re-attain baseline exposure.
A "fit for purpose" PBPK model of enzalutamide was successfully developed using public information that recapitulated it's observed pharmacokinetics, CYP3A4 induction potential and the potential need for dose-adjustment of co-administered CYP3A substrates.
肿瘤治疗通常涉及联合用药,因为单一疗法很少能达到预期效果。但联合治疗存在药物相互作用(DDIs)的可能性。由于肿瘤治疗药物的治疗浓度与毒性发作之间的窗口较窄,因此在癌症治疗中管理联合治疗的药物相互作用至关重要。基于生理的药代动力学(PBPK)模型可有效用于预测药物相互作用并指导剂量选择,但需要开发癌症药物的PBPK模型。在各种癌症类型中,转移性前列腺癌是医疗需求未得到满足的领域,治疗选择极少。最近,恩杂鲁胺被批准用于治疗转移性前列腺癌,在临床实践中常作为联合用药给药。恩杂鲁胺是一种强效的CYP3A诱导剂,需要一种基于模型的方法来指导作为CYP3A底物的恩杂鲁胺联合用药的剂量选择。
开发一种“适用”的恩杂鲁胺PBPK模型,以说明CYP3A4诱导潜力,了解恩杂鲁胺停药后CYP3A4去诱导的动力学,并指导联合使用的CYP3A底物的剂量选择。
基于群体的模拟器Simcyp用于模型构建。模型输入参数从公开信息中获取,主要来自FDA总结。
健康男性受试者中恩杂鲁胺的模拟浓度-时间曲线与男性患者的观察曲线相当。模型预测的恩杂鲁胺药代动力学(PK)参数,即AUC、Cmax和半衰期在两项报道研究所得观察结果的1.5倍以内,支持了PBPK模型的验证。通过模拟恩杂鲁胺与咪达唑仑(一种敏感的CYP3A4底物)之间的药物相互作用,展示了模型的应用。基于模拟,咪达唑仑的AUC比值在0.06至0.16之间,与观察到的0.14的比值相当。基于建模,预计在恩杂鲁胺停药后,至少需要8周才能重新达到基线CYP3A4活性。基于PBPK建模,联合使用的CYP3A底物剂量调整高达3倍可重新达到基线暴露水平。
利用公开信息成功开发了一种“适用”的恩杂鲁胺PBPK模型,该模型概括了其观察到的药代动力学、CYP3A4诱导潜力以及联合使用的CYP3A底物剂量调整的潜在需求。