LAUSR

The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e., intrinsic clearance: 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 correct for CL mispredictions. Drugs (N = 28) were used to generate numerical scalars on CLint (α) and fu,p (β) to minimize the absolute average fold error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N = 28 drugs) and applied to a nonredundant 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 α value of 3.66 (∼64% < 2-fold) compared with no scaling (∼46% < 2-fold). Similarly, using a β value of 0.55 or combination of α and β 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 reduced across all scaling methods. Using the β scalar alone or a combination of α and β appeared optimal and produced larger magnitude corrections for highly bound compounds. Some drugs are still disproportionally mispredicted; however, the improvements in prediction 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 underestimated. Here we introduce empirical scalars calibrated on the extent 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.