Faculty of Pharmaceutical Sciences, The University of British Columbia, 2405 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada.
Genentech Inc, 1 DNA Way, South San Francisco, California, 94080, USA.
Eur J Drug Metab Pharmacokinet. 2021 Mar;46(2):173-183. doi: 10.1007/s13318-020-00666-w. Epub 2020 Dec 19.
Mathematical models that can predict the kinetics of compounds have been increasingly adopted for drug development and risk assessment. Data for these models may be generated from in vitro experimental systems containing enzymes contributing to metabolic clearance, such as subcellular tissue fractions including microsomes and cytosol. Extrapolation from these systems is facilitated by common scaling factors, known as microsomal protein per gram (MPPG) and cytosolic protein per gram (CPPG). Historically, parameterization of MPPG and CPPG has employed the use of recovery factors, commonly benchmarked to cytochromes P450 which work well in some contexts, but could be problematic for other enzymes. Here, we propose absolute quantification of protein content and supplementary assays to evaluate microsomal/cytosolic purity that should be employed. Examples include calculation of microsomal latency by mannose-6-phosphatase activity and immunoblotting of subcellular fractions with fraction-specific markers. Further considerations include tissue source, as disease states can affect enzyme expression and activity, and the methodology used for scalar parameterization. Regional- and organ-specific expression of enzymes, in addition to differences in organ physiology, is another important consideration. Because most efforts have focused on the liver that is, for the most part, homogeneous, derived scalars may not capture the heterogeneity of other major tissues contributing to xenobiotic metabolism including the kidneys and small intestine. Better understanding of these scalars, and how to appropriately derive them from extrahepatic tissues can provide support to the inferences made with physiologically based pharmacokinetic modeling, increase its accuracy in characterizing in vivo drug pharmacokinetics, and improve confidence in go-no-go decisions for clinical trials.
用于预测化合物动力学的数学模型已越来越多地被用于药物开发和风险评估。这些模型的数据可以从包含参与代谢清除的酶的体外实验系统中生成,例如包含微粒体和胞质溶胶的亚细胞组织部分。通过常见的缩放因子(称为每克微粒体蛋白(MPPG)和每克胞质蛋白(CPPG)),可以从这些系统中进行外推。从历史上看,MPPG 和 CPPG 的参数化采用了回收因子的使用,通常以细胞色素 P450 为基准,在某些情况下效果很好,但对于其他酶可能会有问题。在这里,我们提出了应该采用的绝对蛋白质含量定量和补充评估微粒体/胞质纯度的方法。示例包括通过甘露糖-6-磷酸酶活性计算微粒体潜伏期和用亚细胞部分的特异性标志物进行免疫印迹。其他需要考虑的因素包括组织来源,因为疾病状态会影响酶的表达和活性,以及用于标度参数化的方法。除了器官生理学的差异外,酶的区域和器官特异性表达也是另一个重要的考虑因素。由于大多数研究都集中在肝脏上,而肝脏在很大程度上是同质的,因此衍生的标度可能无法捕捉到其他主要组织(包括肾脏和小肠)对外源物质代谢的异质性。更好地理解这些标度以及如何从肝外组织中适当地推导它们,可以为基于生理的药代动力学模型的推断提供支持,提高其在体内药物药代动力学特征描述中的准确性,并提高临床试验中进行或不进行的决策的信心。