Bayer AG, Pharmaceuticals R&D, Translational Sciences, Research Pharmacokinetics, Berlin, Germany (M.V.S., A.R., P.L.); School of Life Sciences, Tsinghua University, Beijing, China (X.L.); and Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany (M.V.S., G.F.).
Bayer AG, Pharmaceuticals R&D, Translational Sciences, Research Pharmacokinetics, Berlin, Germany (M.V.S., A.R., P.L.); School of Life Sciences, Tsinghua University, Beijing, China (X.L.); and Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany (M.V.S., G.F.)
Drug Metab Dispos. 2021 Jan;49(1):53-61. doi: 10.1124/dmd.120.000161. Epub 2020 Nov 4.
Physiologically based pharmacokinetic modeling has become a standard tool to predict drug distribution in early stages of drug discovery; however, this does not currently encompass lysosomal trapping. For basic lipophilic compounds, lysosomal sequestration is known to potentially influence intracellular as well as tissue distribution. The aim of our research was to reliably predict the lysosomal drug content and ultimately integrate this mechanism into pharmacokinetic prediction models. First, we further validated our previously presented method to predict the lysosomal drug content (Schmitt et al., 2019) for a larger set of compounds ( = 41) showing a very good predictivity. Using the lysosomal marker lipid bis(monoacylglycero)phosphate, we estimated the lysosomal volume fraction for all major tissues in the rat, ranging from 0.03% for adipose up to 5.3% for spleen. The pH-driven lysosomal trapping was then estimated and fully integrated into the mechanistic distribution model published by Rodgers et al. (2005) Predictions of Kpu improved for all lysosome-rich tissues. For instance, Kpu increased for nicotine 4-fold (spleen) and 2-fold (lung and kidney) and for quinidine 1.8-fold (brain), although for most other drugs the effects were much less (≤7%). Overall, the effect was strongest for basic compounds with a lower lipophilicity, such as nicotine, for which the unbound volume of distribution at steady-state prediction changed from 1.34 to 1.58 l/kg. For more lipophilic (basic) compounds or those that already show strong interactions with acidic phospholipids, the additional contribution of lysosomal trapping was less pronounced. Nevertheless, lysosomal trapping will also affect intracellular distribution of such compounds. SIGNIFICANCE STATEMENT: The estimation of the lysosomal content in all body tissues facilitated the incorporation of lysosomal sequestration into a general physiologically based pharmacokinetic model, leading to improved predictions as well as elucidating its influence on tissue and subcellular distribution in the rat.
基于生理学的药代动力学模型已成为预测药物在药物发现早期分布的标准工具；然而，目前它还不能涵盖溶酶体捕获。对于基本的亲脂性化合物，已知溶酶体隔离有可能影响细胞内和组织分布。我们研究的目的是可靠地预测溶酶体药物含量，并最终将该机制纳入药代动力学预测模型。首先，我们使用更大的化合物数据集（= 41）进一步验证了我们之前提出的预测溶酶体药物含量的方法（Schmitt 等人，2019 年），结果表明该方法具有很好的预测性。我们使用溶酶体标记脂质双（单酰基甘油）磷酸酯，估计了大鼠所有主要组织中的溶酶体体积分数，范围从脂肪的 0.03%到脾脏的 5.3%。然后，我们估计了 pH 驱动的溶酶体捕获，并将其完全整合到 Rodgers 等人（2005 年）发布的机制分布模型中。对于所有富含溶酶体的组织，Kpu 的预测得到了改善。例如，尼古丁的 Kpu 增加了 4 倍（脾脏）和 2 倍（肺和肾脏），而奎尼丁增加了 1.8 倍（大脑），尽管对于大多数其他药物，影响要小得多（≤7%）。总体而言，这种效应在具有较低脂溶性的碱性化合物（如尼古丁）中最强，其稳态预测下的未结合分布容积从 1.34 变为 1.58 l/kg。对于更亲脂（碱性）的化合物或那些已经与酸性磷脂强烈相互作用的化合物，溶酶体捕获的额外贡献则不太明显。然而，溶酶体捕获也会影响此类化合物的细胞内分布。意义陈述：所有体组织中溶酶体含量的估算促进了溶酶体隔离纳入一般生理学基于药代动力学模型，从而提高了预测能力，并阐明了其对大鼠组织和亚细胞分布的影响。
