Predictive performance of parent-metabolite population pharmacokinetic models of (S)-ketamine in healthy volunteers

Abstract

Purpose The recent repurposing of ketamine as treatment for pain and depression has increased the need for accurate population pharmacokinetic (PK) models to inform the design of new clinical trials. Therefore, the objectives of this study were to externally validate available PK models on (S)-(nor)ketamine concentrations with in-house data and to improve the best performing model when necessary. Methods Based on predefined criteria, five models were selected from literature. Data of two previously performed clinical trials on (S)-ketamine administration in healthy volunteers were available for validation. The predictive performances of the selected models were compared through visual predictive checks (VPCs) and calculation of the (root) mean (square) prediction errors (ME and RMSE). The available data was used to adapt the best performing model through alterations to the model structure and re-estimation of inter-individual variability (IIV). Results The model developed by Fanta et al. (Eur J Clin Pharmacol 71:441–447, 2015) performed best at predicting the (S)-ketamine concentration over time, but failed to capture the (S)-norketamine Cmax correctly. Other models with similar population demographics and study designs had estimated relatively small distribution volumes of (S)-ketamine and thus overpredicted concentrations after start of infusion, most likely due to the influence of circulatory dynamics and sampling methodology. Model predictions were improved through a reduction in complexity of the (S)-(nor)ketamine model and re-estimation of IIV. Conclusion The modified model resulted in accurate predictions of both (S)-ketamine and (S)-norketamine and thereby provides a solid foundation for future simulation studies of (S)-(nor)ketamine PK in healthy volunteers after (S)-ketamine infusion.

nlmeVPC: Visual Model Diagnosis for the Nonlinear Mixed Effect Model

“…Karlsson and Holford 2008) in the original data space. Currently, it is a widely used method for diagnosing population pharmacometric models: Heus et al (2022) used the VPC method to evaluate their population pharmacokinetic models of vancomycin, Mudde et al (2022) checked their final population PK model for each regimen of antituberculosis drug using the VPC method, and Otto et al (2021) compares the predictive performance of parent-metabolite models of (S)-ketamine with the VPC method. This VPC method can be used for general nonlinear mixed effect models, including hierarchical models.…”