Incorporating Nonlinear Kinetics to Improve Predictive Performance of Population Pharmacokinetic Models for Ciclosporin in Adult Renal Transplant Recipients: A Comparison of Modelling Strategies.

Abstract Background : Ciclosporin has been shown to follow nonlinear pharmacokinetics (PK) in renal transplant recipients who received ciclosporin (NeoralⓇ, Novartis)-based triple immunosuppressive therapy. Some of these nonlinear properties have not been fully considered in population PK (popPK) analysis. Therefore, the aim of this study was to determine the potential influence of nonlinearity and the functional forms of covariates on model predictability as well as to analyze multiple nonlinear factors in the in vivo process. Methods : A total of 2969 ciclosporin whole-blood measurements, including 1328 pre-dose and 1641 2-h post-dose concentrations, were collected from 173 patients who underwent their first renal transplantation. Four popPK models based on different modelling strategies were developed to investigate the discrepancy between empirical and theory-based, linear and nonlinear compartmental kinetic models and empirical formulae on model predictability. Prediction and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine the stability and predictive performance of these four models. Results : Model predictability improved when nonlinearity was considered. The theory-based nonlinear model which incorporated nonlinear property based on known theoretical relationships performed better than the other two compartmental models. The nonlinear Michaelis-Menten model showed a remarkable improvement in predictive performance compared to the other three compartmental models. The saturated binding of ciclosporin to erythrocytes, auto-inhibition induced by the inhibitory effects of ciclosporin on cytochrome P450 3A4/P-glycoprotein may have contributed to the nonlinearity. Ciclosporin-prednisolone drug interaction should be given serious consideration in clinical settings. Conclusions : Incorporation of nonlinear properties is likely to be a promising approach for improving ciclosporin model predictability. Theory-based modelling is helpful to improve model predictability. However, ciclosporin nonlinear kinetics resources need further investigation.