Application of Empirical Scalars To Enable Early Prediction of Human Hepatic Clearance Using In Vitro-In Vivo Extrapolation in Drug Discovery: An Evaluation of 173 Drugs

The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for dec-ades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Pre-dicted drug clearance (CL) from experimental data (i.e., intrinsic clear-ance: CLint; fraction unbound in plasma: fu,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to cor-rect for CL mispredictions. Drugs (N = 28) were used to generate nu-merical scalars on CLint (a) and fu,p (13) to minimize the absolute average fold error (AAFE) for CL predictions. These scalars were vali-dated using an additional dataset (N = 28 drugs) and applied to a non -redundant AstraZeneca (AZ) dataset available in the literature (N = 117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an a value of 3.66 (-64% < 2-fold) compared with no scaling (-46% < 2-fold). Similarly, using a b value of 0.55 or combination of a and 13 scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (-64% < 2-fold and-65% < 2-fold, respectively). For highly bound compounds (fu,p # 0.01), AAFE was substantially re-duced across all scaling methods. Using the 13 scalar alone or a com-bination of a and 13 appeared optimal and produced larger magnitude corrections for highly bound compounds. Some drugs are still dis-proportionally mispredicted; however, the improvements in predic-tion error and simplicity of applying these scalars suggest its utility for early-stage CL predictions. SIGNIFICANCE STATEMENT In early drug discovery, prediction of human clearance using in vitro experimental data plays an essential role in triaging compounds prior to in vivo studies. These predictions have been systematically under-estimated. Here we introduce empirical scalars calibrated on the ex-tent of plasma protein binding that appear to improve clearance predictions across multiple datasets. This approach can be used in early phases of drug discovery prior to the availability of preclinical data for early quantitative predictions of human clearance.