An artificial neural network−pharmacokinetic model and its interpretation using Shapley additive explanations

We developed a method to apply artificial neural networks (ANNs) for predicting time‐series pharmacokinetics (PKs), and an interpretable the ANN‐PK model, which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). A previous population PK (PopPK) model of cyclosporin A was used as the comparison model. The patients’ data were used for the ANN‐PK model input, and the output by ANN was the clearance (CL). The estimated CL value from the ANN were substituted into the one‐compartment with one‐order absorption model, the concentrations were calculated, and the parameters of ANN were updated by the back‐propagation method. Kernel SHAP was applied to the trained model and the SHAP value of each input was calculated. The root mean squared error for the PopPK model and the ANN‐PK model were 41.1 and 31.0 ng/ml, respectively. The goodness of fit plots for the ANN‐PK model represented more convergence to y = x compared with that for the PopPK model, with good model performance for the ANN‐PK model. The most influential factors on CL output were age and body weight from the evaluation using Kernel SHAP, and these factors were incorporated into the PopPK model as the significant covariates of CL. The ANN‐PK model could handle time‐series data and showed higher prediction accuracy then the conventional PopPK model, and the scientific validity for the model could be evaluated by applying SHAP. STUDY HIGHLIGHTS: WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? WHAT QUESTION DID THIS STUDY ADDRESS? WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? HOW MIGHT THIS CHANGE DRUG DISCOVERY, DEVELOPMENT, AND/OR THERAPEUTICS? We expect that our study will contribute to develop the interpretable ANN model, which can predict the time‐series PKs, drug efficacies, and side effects with high prediction performance.