Malmborg Jonas, Ploeger Bart A
DMPK Department, AstraZeneca CNS and Pain R&D, S-151 85 Sodertalje, Sweden.
J Pharmacol Toxicol Methods. 2013 May-Jun;67(3):203-13. doi: 10.1016/j.vascn.2012.12.002. Epub 2012 Dec 29.
Predicting the pharmacokinetics (PK) of prodrugs and their corresponding active drugs is challenging, as there are many variables to consider. Prodrug conversion characteristics in different tissues are generally measured, but integrating these variables to a PK profile is not a common practice. In this paper, a joined in vitro/in silico-in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetic (PBPK) modeling approach is presented to predict active drug exposure in human after oral prodrug administration.
Physico-chemical and in vitro assays as well as in silico predictions were proposed to characterize key pharmacokinetic properties (e.g. clearance, volume of distribution, conversion rates) of three marketed prodrugs. These data were used to parameterize a PBPK model for simulating human PK profiles of the active drugs after prodrug administration, which were compared to literature data by evaluating the accuracy and uncertainty of the predictions.
For mycophenate mofetil and midodrine the PK of their active moieties could be adequately predicted. The assumptions of the PBPK-IVIVE approach were valid, i.e. being hepatically cleared, converted in the gut lumen, blood and liver and not metabolized in the gut wall. However, the observed profiles after oral bambuterol administration clearly fell outside the prediction interval as the PBPK model failed to predict the observed bioavailability.
Adding quantitative information about prodrug conversion in the gut, liver and blood to a PBPK model for the absorption, distribution, metabolism and excretion (ADME) properties of prodrugs and their active moieties resulted, retrospectively, in reasonable predictions of the human PK when the ADME properties are well understood. Also in a prospective compound selection process, this integrative approach can improve decision making on prodrug candidates by putting relative differences in prodrug conversion of a large number of candidates into the perspective of their human PK profile, before conducting any in vivo experiments.
预测前药及其相应活性药物的药代动力学（PK）具有挑战性，因为需要考虑许多变量。通常会测量前药在不同组织中的转化特性，但将这些变量整合到PK曲线中并非常见做法。本文提出了一种联合体外/计算机模拟-体内外推（IVIVE）和基于生理学的药代动力学（PBPK）建模方法，以预测口服前药后人体中活性药物的暴露情况。
提出了物理化学和体外试验以及计算机模拟预测，以表征三种上市前药的关键药代动力学特性（例如清除率、分布容积、转化率）。这些数据用于参数化一个PBPK模型，以模拟前药给药后活性药物的人体PK曲线，并通过评估预测的准确性和不确定性与文献数据进行比较。
对于霉酚酸酯和米多君，其活性部分的PK可以得到充分预测。PBPK-IVIVE方法的假设是有效的，即通过肝脏清除，在肠腔、血液和肝脏中转化，且不在肠壁中代谢。然而，口服班布特罗后的观察曲线明显超出预测区间，因为PBPK模型未能预测观察到的生物利用度。
将前药在肠道、肝脏和血液中转化的定量信息添加到一个用于前药及其活性部分的吸收、分布、代谢和排泄（ADME）特性的PBPK模型中，在回顾性分析中，当ADME特性得到充分理解时，可对人体PK做出合理预测。同样，在一个前瞻性的化合物筛选过程中，这种综合方法可以在进行任何体内实验之前，通过将大量候选前药转化的相对差异纳入其人体PK曲线的视角，改善对前药候选物的决策。
