Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fumic) 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 (r2=0.86) or mouse (r2=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 (r2 of >0.89, n = 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 fumic using four different in silico models for a set of 53–60 compounds. The correlation (r2) 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 (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r2=0.67 − 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r2<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro 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. Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system. Experimental fraction unbound data (n = 60) were generated in liver microsomes (fumic) 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 (r2=0.86) or mouse (r2=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 (r2 of >0.89, n = 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 fumic using four different in silico models for a set of 53–60 compounds. The correlation (r2) 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 (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r2=0.67 − 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r2<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro 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.体外代谢系统中的非特异性结合会导致低估所研究化合物的真实内在代谢清除率。因此，在通过体外数据外推以预测化合物的体内代谢清除率时，需要考虑体外结合效应。尽管已有实验测定体外代谢系统中化合物游离分数的技术，但在药物发现项目的早期阶段，通常会采用计算方法来估算体外系统中的结合情况。 本数据集包含60个化合物（n=60）在5种常用临床前物种（大鼠、小鼠、犬、小型猪、猴）及人源肝微粒体（liver microsomes）中的实验游离分数（fumic）数据。同时，研究人员测定了同一60个化合物在小鼠、大鼠或人源肝细胞（hepatocytes）孵育体系中的游离分数。通过对这些数据的分析，明确了不同实验基质间、不同物种间实验测定结合情况的相关性。 在肝细胞体系中，人源与大鼠（r²=0.86）或小鼠（r²=0.82）肝细胞的游离分数呈现良好相关性。人源肝微粒体与大鼠、小鼠、犬、哥廷根小型猪（Göttingen minipig）或猴源肝微粒体的结合情况也存在类似相关性（r²>0.89，不同物种的检测样本量为n=51−52）。 研究人员为所有评估化合物预测了理化参数（logP、pKa及logD），此外还对部分化合物实测了logP和/或logD。 采用5种不同方法预测的人源肝细胞结合情况，与59个化合物的实测数据进行了对比。评估结果显示，最优方法是利用人源肝微粒体的实测微粒体结合数据来预测肝细胞结合情况。 通过4种不同的计算机（in silico）模型，对53−60个化合物的人源fumic进行预测。其中，Turner-Simcyp、Austin、Hallifax-Houston及Poulin模型的预测值与实测微粒体结合值的决定系数（r²）和均方根误差分别为0.69 & 0.20、0.47 & 0.23、0.56 & 0.21及0.54 & 0.26。 本分析进一步纳入了更大规模化合物集合（n=697）的人源肝微粒体结合文献实测值。针对该更大规模数据集，采用Poulin、Austin或Turner-Simcyp方法可较好地预测中性化合物的微粒体结合情况（r²=0.67−0.70），但对于酸性或碱性化合物，所有模型的预测效果均不佳（r²<0.5）。 尽管可使用基于亲脂性的模型，但对于需要更高准确性的化合物，应采用经过适当校准的实验检测方法测定其体外结合情况，可使用已确立的弱、中、强结合化合物作为参照物质，以实现不同数据库间的比对。