Chu Xiaoyan, Chan Grace Hoyee, Houle Robert, Lin Meihong, Yabut Jocelyn, Fandozzi Christine
Department of Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co., Inc., RY800-D211, 126 East Lincoln Avenue, Rahway, New Jersey, 07065, USA.
AAPS J. 2022 Mar 21;24(3):45. doi: 10.1208/s12248-021-00677-8.
Inhibitory effects of asunaprevir, daclatasvir, grazoprevir, paritaprevir, simeprevir, and voxilaprevir, direct-acting antiviral (DAA) drugs for the treatment of chronic hepatitis C virus (HCV) infection, were evaluated in vitro against a range of clinically important drug transporters. In vitro inhibition studies were conducted using transporter transfected cells and membrane vesicles. The risk of clinical drug-drug interactions (DDIs) was assessed using simplified static models recommended by regulatory agencies. Furthermore, we refined and developed static models to predict complex DDIs with several statins (pitavastatin, rosuvastatin, atorvastatin, and pravastatin) by mechanistically assessing differential inhibitory effects of perpetrator drugs on multiple transporters, such as organic anion transporting polypeptides (OATP1B), breast cancer resistance protein (BCRP), multidrug resistance protein 2 (MRP2), organic anion transporter 3 (OAT3), and cytochrome P450 CYP3A enzyme, as they are known to contribute to absorption, distribution, metabolism and excretion (ADME) of above statins. These models successfully predicted a total of 46 statin DDIs, including above DAA drugs and their fix-dose combination regimens. Predicted plasma area under curve ratio (AUCR) with and without perpetrator drugs was within ~ 2-fold of observed values. In contrast, simplified static R-value model resulted in increased false negative and false positive predictions when different prediction cut-off values were applied. Our studies suggest that mechanistic static model is a promising and useful tool to provide more accurate prediction of the risk and magnitude of DDIs with statins in early drug development and may help to improve the management of clinical DDIs for HCV drugs to ensure effective and safe HCV therapy. GRAPHICAL ABSTRACT.
asunaprevir、daclatasvir、grazoprevir、paritaprevir、simeprevir和voxilaprevir是用于治疗慢性丙型肝炎病毒(HCV)感染的直接作用抗病毒(DAA)药物,其体外对一系列临床重要药物转运体的抑制作用进行了评估。使用转运体转染细胞和膜囊泡进行体外抑制研究。使用监管机构推荐的简化静态模型评估临床药物相互作用(DDI)的风险。此外,我们通过机械评估肇事药物对多种转运体(如有机阴离子转运多肽(OATP1B)、乳腺癌耐药蛋白(BCRP)、多药耐药蛋白2(MRP2)、有机阴离子转运体3(OAT3)和细胞色素P450 CYP3A酶)的差异抑制作用,完善并开发了静态模型,以预测与几种他汀类药物(匹伐他汀、瑞舒伐他汀、阿托伐他汀和普伐他汀)的复杂DDI,因为已知这些转运体有助于上述他汀类药物的吸收、分布、代谢和排泄(ADME)。这些模型成功预测了总共46种他汀类药物DDI,包括上述DAA药物及其固定剂量联合用药方案。有和没有肇事药物时预测的血浆曲线下面积比(AUCR)在观察值的约2倍范围内。相比之下,当应用不同的预测截止值时,简化的静态R值模型导致假阴性和假阳性预测增加。我们的研究表明,机械静态模型是一种有前景且有用的工具,可在药物研发早期更准确地预测与他汀类药物发生DDI的风险和程度,并可能有助于改善HCV药物临床DDI的管理,以确保有效的HCV治疗和安全。图形摘要。