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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 ( fu mic) 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 &gt;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  fu micusing 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 &amp; 0.20, 0.47 &amp; 0.23, 0.56 &amp; 0.21 and 0.54 &amp; 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 &lt;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 ( fu mic) 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 &gt;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  fu micusing 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 &amp; 0.20, 0.47 &amp; 0.23, 0.56 &amp; 0.21 and 0.54 &amp; 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 &lt;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.

xenobiotica

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