Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work

A dataset consisting of measured values for LogD, solubility, metabolic stability in human liver microsomes (HLMs), and Caco-2 permeability was used to evaluate the prediction models for lipophilicity (S+LogD), water solubility (S+Sw_pH), metabolic stability in HLM (CYP_HLM_Clint), intestinal permeability (S+Peff), and P-glycoprotein (P-gp) substrate identification (P-gp substrate) in the software ADMET Predictor (AP) from Simulations Plus. The dataset consisted of a total of 4,794 compounds, with at least data from metabolic stability determinations in HLM, from multiple discovery projects at Medivir. Our evaluation shows that the global AP models can be used for categorization of high and low values based on predicted results for metabolic stability in HLM and intestinal permeability, and to give good predictions of LogD (R2= 0.79), guiding the synthesis of new compounds and for prioritizing in vitro ADME experiments. The model seems to overpredict solubility for the Medivir compounds, however. We also used the in-house datasets to build local models for LogD, solubility, metabolic stability, and permeability by using artificial neural network (ANN) models in the optional Modeler module of AP. Predictions of the test sets were performed with both the global and the local models, and the R2 values for linear regression for predicted versus measured HLM in vitro intrinsic clearance (CLint) based on logarithmic data were 0.72 for the in-house model and 0.53 for the AP model. The improved predictions with the local models are likely explained both by the specific chemical space of the Medivir dataset and laboratory-specific assay conditions for parameters that require biologic assay systems. SIGNIFICANCE STATEMENT: AP is useful early in projects for predicting and categorizing LogD, metabolic stability, and permeability, to guide the synthesis of new compounds, and for prioritizing in vitro ADME experiments. The building of local in-house prediction models with the optional AP Modeler Module can yield improved prediction success since these models are built on data from the same experimental setup and can also be based on compounds with similar structures.