Application of Physiologically Based Pharmacokinetic Modeling to Inform Dose Selection of Mezigdomide in a Phase I Drug–Drug Interaction Study

基于生理学的药代动力学模型 药理学 药代动力学 CYP3A型 药代动力学相互作用 药物相互作用 加药 计算生物学 临床药理学 P-糖蛋白 医学 化学 临床试验 CYP3A5 药品 Abcg2型 运输机 流出 非金属 临床研究阶段 选择(遗传算法) 血浆浓度 细胞色素P450 EC50型 口服剂量
作者
Joseph W. Burnett,Caroline Sychterz,Jessica Katz,Faisal Shakeel,J. Fernando Silva,Wen‐Cong Chen,Aditi Shahane,Xiaomin Wang,Manisha Lamba,Allison Gaudy
出处
期刊:Clinical Pharmacology & Therapeutics [Wiley]
卷期号:119 (3): 641-650 被引量:1
标识
DOI:10.1002/cpt.70082
摘要

Mezigdomide (MEZI) is an oral, highly potent CELMoD™ agent with promising antitumor and immune-stimulatory activity, optimized for Aiolos and Ikaros degradation. Preclinical evidence suggests MEZI is primarily metabolized by cytochrome P450 (CYP) 3A4/5 and has the potential to inhibit efflux transporters P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) in vitro. To predict the magnitude of enzyme- and transporter-mediated drug-drug interactions (DDI) and inform clinical study design, a physiologically based pharmacokinetic (PBPK) model was developed. A PBPK-informed Phase I clinical DDI study was conducted that evaluated MEZI as an object of CYP3A induction (rifampin) and inhibition (itraconazole) and as a precipitant of transporter-mediated interactions (digoxin and rosuvastatin). PBPK modeling predicted substantial interactions with strong and moderate CYP3A modulators, which informed a unique dose selection strategy, PK sampling time, and washout period. Clinical results confirmed reductions in MEZI exposure with rifampin (AUC reduced 93-95%) and increases with itraconazole (~14-fold for dose normalized AUC). MEZI was well-tolerated despite these changes in exposure. Additionally, coadministration of MEZI with P-gp and BCRP substrates, digoxin and rosuvastatin, showed no clinically meaningful changes in substrate plasma PK, indicating a low likelihood of significant transporter-mediated DDIs. The prospective PBPK model was refined with clinical data, improving predictions and supporting simulations for moderate/weak CYP3A modulators. This iterative "learn-confirm" approach underscores the utility of PBPK modeling in optimizing clinical trial design, ensuring participant safety, and anticipating DDI risks. The findings support MEZI's clinical development with informed dosing strategies, particularly for coadministration with CYP3A modulators.
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