Clinical Pharmacy, Saarland University, Saarbrücken, Germany.
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.
Clin Pharmacol Ther. 2021 Jan;109(1):201-211. doi: 10.1002/cpt.2111. Epub 2020 Dec 6.
Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug-drug-gene interaction (DDGI) scenarios. Here, a whole-body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs.
药物-药物相互作用(DDIs)和药物-基因相互作用(DGIs)是已知的药物不良反应(ADRs)的主要介导因素,在许多国家,ADRs 是导致死亡的主要原因之一。由于基于生理的药代动力学(PBPK)模型已被证明是改善受 DDIs 或 DGIs 影响的药物治疗的有价值的工具,因此它也可能对广泛相互作用网络情况下的精准剂量调整有用。本文提出了一种新方法,旨在扩展 PBPK 模型的预测能力,以应对复杂的药物-药物-基因相互作用(DDGI)情况。在此,建立了辛伐他汀的全身 PBPK 网络,其中包括三个多态性(SLCO1B1(rs4149056)、ABCG2(rs2231142)和 CYP3A5(rs776746))和四种致剂药物(克拉霉素、吉非贝齐、伊曲康唑和利福平)。进行了详尽的网络模拟并进行了排序,根据暴露标志物成本函数对 10368 种 DDGI 情况进行了优化。所得的剂量建议被转换为数字决策支持系统,可在 simvastatin.precisiondosing.de 上获得。尽管该网络仅涵盖了辛伐他汀可能的 DDGIs 的一小部分,但它为 PBPK 模型如何在未来用于个体化药物治疗提供了指导。此外,该网络模型易于扩展以涵盖其他 DDGIs。总的来说,本文的工作是朝着在日常临床实践中基于 PBPK 模型实现全面精准剂量调整的愿景迈出的第一步,这将极大地降低 ADRs 的风险。