Certara UK Ltd., Sheffield, United Kingdom.
Pharmaron Beijing Co. Ltd., Beijing, China.
Xenobiotica. 2022 Aug;52(8):943-956. doi: 10.1080/00498254.2022.2132426.
Non-specific binding in metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore binding needs to be accounted for when extrapolating data to predict the metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the system.Experimental fraction unbound data ( 60) were generated in liver microsomes () from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (=0.86) or mouse (=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes ( of >0.89, 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human using four different models for a set of 53-60 compounds. The correlation () and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (<0.5) using any of the models. While the lipophilicity-based models can be used, the binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.
非特异性结合会导致代谢系统中化合物的真实内在代谢清除率被低估。因此,在将数据外推以预测化合物的代谢清除率时,需要考虑结合情况。虽然存在用于实验确定代谢系统中化合物未结合部分的技术,但在药物发现计划的早期阶段,通常使用计算方法来估计系统中的结合情况。
实验得到的 60 种化合物在五种常用的临床前物种(鼠、兔、犬、小型猪、猴)和人类肝微粒体中的未结合分数数据。还确定了在与小鼠、大鼠或人肝细胞孵育时的未结合分数。对这些数据进行了分析,以确定不同基质中实验确定的结合情况和不同物种之间的关系。在肝细胞中,人源和鼠源(=0.86)或大鼠源(=0.82)肝细胞中的未结合分数之间存在良好的相关性。在人类肝微粒体与大鼠、小鼠、犬、豚鼠或猴肝微粒体之间观察到类似的相关性(>0.89,51-52 种不同物种的测量值)。对所有评估的化合物进行了物理化学参数(logP、pKa 和 logD)预测。此外,还对一部分化合物进行了 logP 和/或 logD 的测量。
使用 5 种不同的方法预测人源肝细胞中的结合情况,并与一组 59 种化合物的实测数据进行比较。评价的最佳方法是使用人类肝微粒体中的实测微粒体结合来预测肝细胞结合。整理后的物理化学数据用于预测一组 53-60 种化合物的人类。对于 Turner-Simcyp、Austin、Hallifax-Houston 和 Poulin 模型,预测与观察到的微粒体结合之间的相关性()和均方根误差分别为 0.69 和 0.20、0.47 和 0.23、0.56 和 0.21 和 0.54 和 0.26。这些分析扩展到包括一组更大的化合物(697 种)在人肝微粒体中结合的文献实测值。对于更大的化合物数据集,使用 Poulin、Austin 或 Turner-Simcyp 方法可以很好地预测中性化合物(=0.67-0.70)的微粒体结合,但使用任何模型都不能很好地预测酸性或碱性化合物(<0.5)的微粒体结合。虽然可以使用基于脂溶性的模型,但对于需要更高确定性的化合物,应测量结合情况,使用适当校准的测定法,并可能使用弱、中、强结合物作为参考化合物,以便在不同数据库之间进行比较。