Towards model-informed precision dosing of cyclosporine in adult renal transplantation: Assessing population pharmacokinetic models and multi-model strategies.

Background: Cyclosporine's narrow therapeutic index and pharmacokinetic variability challenge its optimal dosing in renal transplantation. Model-informed precision dosing (MIPD), utilizing population pharmacokinetic (popPK) models and Bayesian forecasting, can enhance dosing optimization and improve clinical outcomes. This study evaluated the predictive performance of published popPK models and multi-model strategies for cyclosporine, using therapeutic drug monitoring (TDM) data and full pharmacokinetic (PK) profiles.

Methods: We evaluated 15 published popPK models and 2 multi-model strategies (averaging/selection) using TDM data (1,856 concentrations from 114 patients) and full-PK profiles (259 concentrations from 24 patients). Bayesian forecasting with objective function value (OFV)- and Akaike information criterion (AIC)-based weighting was applied to predict cyclosporine peak concentration (C₂) and area under the curve (AUC). Predictive accuracy and precision were assessed using relative bias (rBias) and relative root mean square error (rRMSE).

Results: The two-compartment model with first-order absorption and transit compartments (Press et al., 2010) provided the best prediction for C₂ (rBias = -1.40%, rRMSE = 5.38%), while the model with lag time and covariates postoperative days, age, and weight (Baek et al., 2014) excelled in AUC prediction (rBias = 4.82%, rRMSE = 1.92%). Multi-model averaging/selection performed comparably to top-performing single popPK models for C₂ (rBias <10% with OFV-based weighting) but underperformed for AUC prediction (rBias >20%).

Conclusion: While single popPK models provide reliable predictions for specific PK endpoints, multi-model strategies do not consistently outperform individual models. The optimal model selection for MIPD should be fit-for-purpose to ensure optimal cyclosporine dosing and improved clinical outcomes in renal transplantation.