Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, M13 9PT Manchester, United Kingdom.
Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, M13 9PT Manchester, United Kingdom.
Eur J Pharm Sci. 2017 Nov 15;109:419-430. doi: 10.1016/j.ejps.2017.08.014. Epub 2017 Aug 18.
The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among cell types. Despite this extensive lysosomal sequestration in the individual cells types, the maximal change in the overall predicted tissue Kpu was <3-fold for lysosome-rich tissues investigated here. Accounting for the variability in cellular physiological model input parameters, in particular lysosomal pH and fraction of the cellular volume occupied by the lysosomes, only partially explained discrepancies between observed and predicted Kpu data in the lung. Improved understanding of the system properties, e.g., cell/organelle composition is required to support further development of mechanistic equations for the prediction of drug tissue distribution. Application of this revised mechanistic model is recommended for prediction of Kpu in lysosome-rich tissue to facilitate the advancement of physiologically-based prediction of volume of distribution and drug exposure in the tissues.
从体外和计算数据预测组织-血浆水分配系数(Kpu),使用基于组织成分的模型(Rodgers 和 Rowland,J Pharm Sci. 2005,94(6):1237-48.)已经得到很好的确立。然而,这种方法往往低估了碱性药物,特别是进入富含溶酶体的肺组织的分布。本研究的目的是开发一种扩展的机制模型,用于预测 Kpu,该模型考虑了溶酶体隔离以及感兴趣组织中不同细胞类型的贡献。扩展模型基于化合物特异性物理化学性质和组织组成数据,描述药物的离子化、分布到组织水中以及药物与组织中的中性脂质、中性磷脂和酸性磷脂结合,包括溶酶体。从文献中收集了与肺、肾和肝有关的细胞类型、溶酶体含量和溶酶体 pH 的生理数据。使用 28 种碱性药物(pK≥7.8、17β-阻滞剂、11 种结构不同的药物)的数据集评估了扩展机制模型的预测能力,这些药物在大鼠组织中的实验测定的 Kpu 数据已经报道。与原始 Rodgers 模型相比,在扩展的机制模型中考虑到溶酶体隔离可以提高 Kpu 在肺中的预测准确性(56%的药物在观察值的 2 倍或 88%的 3 倍以内)。在肝和肾中,也可以明显减少 Kpu 预测不足的程度。然而,考虑到溶酶体隔离,会导致过度预测的情况增加,从而使肾和肝的模型性能相当,分别有 68%和 54%的 Kpu 值在 2 倍误差范围内。研究的药物在溶酶体中相对于细胞质的浓度比值很高(>1000 倍);这种程度取决于细胞类型中的溶酶体 pH 和酸性磷脂的浓度。尽管个别细胞类型中的溶酶体隔离非常严重,但在此处研究的富含溶酶体的组织中,整体预测组织 Kpu 的最大变化<3 倍。考虑到细胞生理模型输入参数的变异性,特别是溶酶体 pH 和溶酶体占据细胞体积的分数,只能部分解释肺中观察到的和预测的 Kpu 数据之间的差异。需要进一步了解系统特性,例如细胞/细胞器组成,以支持用于预测药物组织分布的机制方程的进一步发展。建议在富含溶酶体的组织中应用这种修订后的机制模型,以促进基于生理学的分布容积和药物暴露预测在组织中的发展。