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As an alternative, limited sampling strategies (LSS) have been proposed to predict AUC with an adequate precision, using a reduced number of sampling points drawn within a short time interval. This method can be cumbersome for patients and their care providers since it requires a frequent sampling over a time interval long enough to fully represent the drug disposition. When estimating AUC, we generally refer to the observed AUC, usually denoted AUC obs, which is obtained using the trapezoidal method. While its use as an optimal marker for immunosuppressant agents monitoring remains controversial its correlation with clinical outcomes is increasingly being investigated. These risks call for the implication of other PK based surrogates, such as the area under the concentration-time curve (AUC) which is generally known as the best indicator of drug systemic exposure. Nonetheless, treatment failure, adverse effects, and toxicity can still arise even in situations where C 0 is within the recognized therapeutic range. In clinical practice, the pre-dose concentration (C 0) is widely used as a PK marker for the therapeutic drug monitoring due to its accessibility. Currently, therapeutic drug monitoring approach, which involves the measurement of drug concentrations and their interpretation, has become a standard of care in immunosuppressant therapy for dose optimization, with the aim of maximizing therapeutic benefits and minimizing adverse effects. A non-monitored dosing can increase the risk for therapeutic failure or induce serious undesirable effects. Therapeutic drug monitoring is a common practice for the use of immunosuppressant drugs, which generally exhibit considerable inter- or intra- pharmacokinetic (PK) variability and narrow therapeutic window. Therefore, for B-LSS application, Pop-PK model diagnostic criteria should additionally account for AUC prediction errors.
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However, B-LSS performance is not perfectly in line with the standard Pop-PK model selection criteria hence the final model might not be ideal for AUC prediction purpose. Conclusionsī-LSS can adequately estimate cyclosporine AUC. Moreover, B-LSS perform better for the prediction of the ‘underlying’ AUC derived from the Pop-PK model estimated concentrations that exclude the residual errors, in comparison to their prediction of the observed AUC directly calculated using measured concentrations. Twelve B-LSS, consisting of 4 or less sampling points obtained within 4 hours post-dose, predict AUC with 95 th percentile of the absolute values of relative prediction errors of 20% or less.
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The best performing models for intravenous and oral cyclosporine are the structure ones with combined and additive error, respectively. The final covariate model does not improve the B-LSS prediction performance. ResultsĪ two-compartment structure model with a lag time and a combined additive and proportional error is retained. The performance of B-LSS when targeting different versions of AUC was also discussed. Pop-PK analyses were carried out and the predictive performance of B-LSS was evaluated using the final Pop-PK model and several related ones. Twenty five pediatric hematopoietic stem cell transplantation patients receiving intravenous and oral cyclosporine were investigated. In this paper, we develop Bayesian limited sampling strategies (B-LSS) for cyclosporine AUC estimation using population pharmacokinetic (Pop-PK) models and investigate related issues, with the aim to improve B-LSS prediction performance. However, there is a growing interest in the use of the area under the concentration-time curve (AUC), particularly for cyclosporine dose adjustment in pediatric hematopoietic stem cell transplantation. The optimal marker for cyclosporine (CsA) monitoring in transplantation patients remains controversial.