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.

SimRFlow: An R-based workflow for automated high-throughput PBPK simulation with the Simcyp® simulator.

SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara's Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasma-protein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations.