Pharmacometrics/Modeling & Simulation, Research & Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany.
Clinical Pharmacy, Saarland University, Saarbrücken, Germany.
CPT Pharmacometrics Syst Pharmacol. 2021 Jun;10(6):633-644. doi: 10.1002/psp4.12636. Epub 2021 May 24.
The success of applications of physiologically-based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re-)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re-)qualification of PK-Sim embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK-Sim for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)-mediated drug-drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism-based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration-time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK-Sim can be applied to quantitatively assess CYP3A4-mediated DDI in clinically untested scenarios.
生理药代动力学（PBPK）模型在药物开发和药物标签中的应用的成功促使监管机构要求严格证明特定 PBPK 平台对于特定预期应用目的的预测能力。满足这些资格要求所需的努力超过了任何单个 PBPK 应用的成本。由于 PBPK 平台的更改或更新需要（重新）资格认证，因此需要可靠且高效的通用资格认证框架。我们描述了在开放系统药理学的开源和开放科学 GitHub 环境中嵌入的 PK-Sim 的自动 PBPK 平台（重新）资格认证的敏捷和可持续技术框架的开发和实施。该资格认证方法能够有效地评估与特定目的资格相关的所有方面，并为所有利益相关者提供透明度和可追溯性。作为资格认证框架的强大功能和多功能性的展示示例，我们展示了 PK-Sim 用于预测细胞色素 P450 3A4（CYP3A4）介导的药物相互作用（DDI）的预期目的的资格认证。几个具有不同程度 CYP3A4 调节作用和不同类型机制（竞争性抑制、基于机制的失活和诱导）的肇事者 PBPK 模型与一组敏感 CYP3A4 受者药物的 PBPK 模型相结合。模拟结果与来自已发表的临床 DDI 研究的 135 个观察结果的综合数据集进行了比较。该平台的整体预测性能显示出合理的准确性和精密度（无肇事者时，曲线下血浆浓度-时间曲线比和峰血浆浓度比的几何平均倍误差均为 1.4），表明 PK-Sim 可用于定量评估临床上未测试情况下的 CYP3A4 介导的 DDI。
