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A comparative evaluation of models to predict human intestinal metabolism from nonclinical data

机译:从非界限数据预测人肠道代谢的模型的比较评价

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摘要

Abstract Extensive gut metabolism is often associated with the risk of low and variable bioavailability. The prediction of the fraction of drug escaping gut wall metabolism as well as transporter‐mediated secretion ( F g ) has been challenged by the lack of appropriate preclinical models. The purpose of this study is to compare the performance of models that are widely employed in the pharmaceutical industry today to estimate F g and, based on the outcome, to provide recommendations for the prediction of human F g during drug discovery and early drug development. The use of in vitro intrinsic clearance from human liver microsomes (HLM) in three mechanistic models – the ADAM, Q gut and Competing Rates – was evaluated for drugs whose metabolism is dominated by CYP450s, assuming that the effect of transporters is negligible. The utility of rat as a model for human F g was also explored. The ADAM, Q gut and Competing Rates models had comparable prediction success (70%, 74%, 69%, respectively) and bias (AFE?=?1.26, 0.74 and 0.81, respectively). However, the ADAM model showed better accuracy compared with the Q gut and Competing Rates models (RMSE =0.20 vs 0.30 and 0.25, respectively). Rat is not a good model (prediction success =32%, RMSE =0.48 and AFE?=?0.44) as it seems systematically to under‐predict human F g . Hence, we would recommend the use of rat to identify the need for F g assessment, followed by the use of HLM in simple models to predict human F g . ? 2017 Merck KGaA. Biopharmaceutics & Drug Disposition Published by John Wiley & Sons, Ltd.
机译:摘要广泛的肠道代谢通常与低和可变生物利用度的风险有关。通过缺乏适当的临床前模型,对脱毛肠道壁代谢和转运蛋白介导的分泌(F G)的预测是缺乏肠壁代谢和转运蛋白的分泌(F G)。本研究的目的是比较当今制药行业广泛采用的模型的性能来估计F G,并根据结果,为在药物发现和早期药物开发期间提供人体F G预测的建议。在三种机制模型中使用来自人肝微粒体(HLM)的体外间隙 - ADAM,Q GUT和竞争率 - 对于由CYP450S支配的药物评估了药物,假设运输车的效果可以忽略不计。还探讨了大鼠作为人体F G模型的效用。 ADAM,Q GUT和竞争率模型具有可比的预测成功(分别为70%,74%,69%)和偏置(AFE?=?1.26,0.74和0.81)。然而,与Q肠道和竞争率模型相比,ADAM模型显示出更好的准确性(分别为RMSE = 0.20 Vs 0.30和0.25)。大鼠不是一个好的模型(预测成功= 32%,RMSE = 0.48和AFE?=?0.44),因为它似乎系统地到预测人体f g。因此,我们建议使用大鼠来确定F G评估的需要,然后在简单模型中使用HLM来预测人类的F G。还2017年默克kgaa。 Biopharmaceutics& John Wiley&amp出版的药物处理; SONS,LTD.

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