Changes in Individual Drug-Independent System Parameters during Virtual Paediatric Pharmacokinetic Trials: Introducing Time-Varying Physiology into a Paediatric PBPK Model
Changes in Individual Drug-Independent System Parameters during Virtual Paediatric Pharmacokinetic Trials: Introducing Time-Varying Physiology into a Paediatric PBPK Model
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DOI：
10.1208/s12248-014-9592-9
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发表时间：
2014-05-01
期刊：
影响因子：
4.5
通讯作者：
Johnson, Trevor N.
中科院分区：
文献类型：
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作者：
Abduljalil, Khaled;Jamei, Masoud;Johnson, Trevor N.
虽然POPPK和基于生理的药代动力学(PBPK)模型都可以解释儿科人群中的年龄和其他协变量，但它们通常不能通过药物暴露的时间进程来解释个体的实时生长和成熟；这在长期的新生儿研究中可能是重要的。这项研究的主要目的是将年龄增长引入儿科PBPK模型，以允许随着时间的推移不断更新每个个体受试者的解剖、生理和生物过程。对Simcyp儿科PBPK模型模拟器的系统参数进行了重新分析，以评估在研究期间重新定义个体的影响。在延长的研究期间，制定了在Simcyp儿科模拟器内为每个受试者重新定义参数的时间表，以允许无缝预测药代动力学(PK)。该模型被应用于预测新生儿对西地那非和苯妥英钠进行的多天研究的浓度-时间数据。在PBPK系统参数中，CYP3A4丰度是变化最快的协变量之一，对于3.5天以下的婴儿，需要1小时的重新采样计划，以便随着受试者的成熟无缝预测PK(丰度变化5%)。重采样频率随着年龄的增加而减少，到6月龄时达到两周一次。在延长的研究期结束时，使用时变和固定的PBPK模型可以更好地预测西地那非和苯妥英的PK。儿科PBPK模型在长期研究中考虑了随时间变化的系统参数，可能会为新生儿和婴儿提供更机械性的PK预测。
Although both POPPK and physiologically based pharmacokinetic (PBPK) models can account for age and other covariates within a paediatric population, they generally do not account for real-time growth and maturation of the individuals through the time course of drug exposure; this may be significant in prolonged neonatal studies. The major objective of this study was to introduce age progression into a paediatric PBPK model, to allow for continuous updating of anatomical, physiological and biological processes in each individual subject over time. The Simcyp paediatric PBPK model simulator system parameters were reanalysed to assess the impact of re-defining the individual over the study period. A schedule for re-defining parameters within the Simcyp paediatric simulator, for each subject, over a prolonged study period, was devised to allow seamless prediction of pharmacokinetics (PK). The model was applied to predict concentration-time data from multiday studies on sildenafil and phenytoin performed in neonates. Among PBPK system parameters, CYP3A4 abundance was one of the fastest changing covariates and a 1-h re-sampling schedule was needed for babies below age 3.5 days in order to seamlessly predict PK (< 5% change in abundance) with subject maturation. The re-sampling frequency decreased as age increased, reaching biweekly by 6 months of age. The PK of both sildenafil and phenytoin were predicted better at the end of a prolonged study period using the time varying vs fixed PBPK models. Paediatric PBPK models which account for time-varying system parameters during prolonged studies may provide more mechanistic PK predictions in neonates and infants.