Limited sampling strategies for estimation of cyclosporine exposure in pediatric hematopoietic stem cell transplant recipients : methodological improvement and introduction of sampling time deviation analysis
Therapeutic drug monitoring is recommended for dose adjustment of potent immunosuppressive agents. Accumulating evidence supports the usefulness of the area under the curve (AUC) for monitoring cyclosporine (CsA) in hematopoietic stem cell transplantation (HSCT). However, the use of AUC in clinical settings is restricted for practical reasons. Limited sampling strategies using regression (R-LSS) and Bayesian (B-LSS) approaches have been used to estimate AUC. However, for adequate application in clinical settings, these LSS approaches should be convenient in terms of the number of required concentration-time points as well as the duration of sampling. Furthermore, a particular attention should be given to ensure their adequate development and validation. Moreover, irregularity in the time of blood sample collection may have a non-negligible impact on the prediction performance of R-LSS; this impact has not yet been studied. The investigation of these issues is the main focus of the current study in order to ultimately achieve convenient and reliable estimation of AUC using LSS. Pediatric HSCT patients receiving intravenous (IV) and oral (PO) CsA were investigated. Thorough regression and population-pharmacokinetic (Pop-PK) analyses were carried out in order to adequately develop and validate LSS. Several Pop-PK models were evaluated while bearing in mind their intended use for AUC estimation. The performance of B-LSS for targeting different versions of AUC was also investigated. Moreover, the impact of the deviations of actual sampling times from the planned nominal times on R-LSS prediction performance, in diverse sampling time deviation (STD) scenarios, was examined using a simulation approach. These investigations led to the identification of LSS that have clinically acceptable predictive performance. These LSS are suitable for clinical application since they require 4 or fewer sampling points drawn within 4 hours post-dose. Besides, following the Pop-PK analysis, a two-compartment structural model with a lag time and a combined additive and proportional error was retained. However, the final covariate model did not improve B-LSS performance. It turned out that the structural models (without covariates) had a better performance. Moreover, B-LSS performed better for the estimation of the ‘underlying’ AUC derived from the Pop-PK simulated concentrations that exclude the residual errors, compared to their predictions of the observed AUC directly calculated using measured concentrations. Finally, our results showed that time deviation in blood sample collection have a significant impact on R-LSS prediction performance. This impact depends on the number of the involved samples, but more importantly on the duration of the sampling process. Furthermore, sampling errors at time points at which the concentration changes rapidly are critical for AUC prediction. Moreover, we have shown that R-LSS may have similar performance in terms of nominal times, but their tolerance to STD can be quite different. Hence, adequate consideration of the impact of STD can lead to a more reliable selection and use of R-LSS. This thesis provided a thorough investigation of different issues related to LSS. Methodological improvement and proposition of new avenues have been realized while giving a careful consideration to ensure a convenient use in real-life clinical practice.
Pharmacokinetics Pharmacometrics Therapeutic Drug Monitoring Population-pharmacokinetics Pop-PK Limited Sampling Strategies Area Under the Curve Sampling Time Deviation Bayesian approach Cyclosporine Pediatrics Hematopoietic Stem Cell Transplantation