Physiologically Based Pharmacokinetic Modeling of Nilotinib for Drug‐Drug Interactions, Pediatric Patients, and Pregnancy and Lactation

尼罗替尼 基于生理学的药代动力学模型 药代动力学 药理学 医学 药品 人口 酪氨酸激酶抑制剂 肿瘤科 内科学 髓系白血病 伊马替尼 癌症 环境卫生
作者
Xiaomei I. Liu,Rupert W. Leong,Gilbert J. Burckart,André Dallmann
出处
期刊:The Journal of Clinical Pharmacology [Wiley]
卷期号:64 (3): 323-333 被引量:1
标识
DOI:10.1002/jcph.2379
摘要

Abstract Nilotinib is a second‐generation BCR‐ABL tyrosine kinase inhibitor for the treatment of Philadelphia chromosome‐positive chronic myeloid leukemia in both adult and pediatric patients. The pharmacokinetics (PK) of nilotinib in specific populations such as pregnant and lactating people remain poorly understood. Therefore, the objectives of the current study were to develop a physiologically based pharmacokinetic (PBPK) model to predict nilotinib PK in virtual drug‐drug interaction (DDI) studies, as well as in pediatric, pregnant, and lactating populations. The nilotinib PBPK model was built in PK‐Sim, which is part of the free and open‐source software Open Systems Pharmacology. The observed clinical data for the validation of the nilotinib models were obtained from the literature. The model reasonably predicted nilotinib concentrations in the adult population; the DDIs between nilotinib and rifampin or ketoconazole in the adult population; and the PK in the pediatric, pregnant, and lactating populations, although in the latter 2 populations plasma concentrations were slightly underestimated. The ratio of predicted versus observed PK parameters for the adult model ranged from 0.71 to 1.11 for area under the concentration‐time curve and 0.55 to 0.95 for maximum concentration. For the DDI, the predicted area under the concentration‐time curve ratio and maximum concentration ratio fell within the Guest criterion. The current study demonstrated the utility of using PBPK modeling to understand the mechanistic basis of PK differences between adults and specific populations, such as pediatrics, and pregnant and lactating individuals, indicating that this technology can potentially inform or optimize dosing conditions in specific populations.
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