دورية أكاديمية
A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation
| العنوان: | A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation |
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| المؤلفون: | Janet Kim, Sam Wilson, Nasrullah A. Undre, Fei Shi, Rita M. Kristy, Jason J. Schwartz |
| المصدر: | Drugs in R&D, Vol 19, Iss 2, Pp 201-212 (2019) |
| بيانات النشر: | Adis, Springer Healthcare, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Therapeutics. Pharmacology |
| مصطلحات موضوعية: | Therapeutics. Pharmacology, RM1-950 |
| الوصف: | Abstract Background and Objective Given that a high intrapatient variability (IPV) of tacrolimus whole blood concentration increases the risk for a poor kidney transplant outcome, some experts advocate routine IPV monitoring for detection of high-risk patients. However, attempts to estimate the variance of tacrolimus trough concentrations (TTC) are limited by the need for patients to receive a fixed dose over time and/or the use of linear statistical models. A goal of this study is to overcome the current limitations through the novel application of statistical methodology generalizing the relationship between TTC and dose through the use of nonparametric functional regression modeling. Methods With TTC as a response and dose as a covariate, the model employs an unknown bivariate function, allowing for the potentially complex, nonlinear relationship between the two parameters. A dose-adjusted variance of TTC is then derived based on standard functional principal component analysis (FPCA). To assess the model, it was compared against an FPCA-based model and linear mixed-effects models using prediction error, bias, and coverage probabilities for simulated data as well as phase III data from the Astellas new drug application studies for extended-release tacrolimus. Results Our numerical investigation indicates that the new model better predicts dose-adjusted TTCs compared with the prediction of linear mixed effects models. Estimated coverage probabilities also indicate that the new model accurately accounts for the variance of TTC during the periods of large fluctuation in dose, whereas the linear mixed effects model consistently underestimates the coverage probabilities because of the inaccurate characterization of TTC fluctuation. Conclusion This is the first known application of a functional regression model to assess complex relationships between TTC and dose in a real clinical setting. This new method has applicability in future clinical trials including real-world data sets due to flexibility of the nonparametric modeling approach. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1174-5886 1179-6901 |
| Relation: | http://link.springer.com/article/10.1007/s40268-019-0271-2; https://doaj.org/toc/1174-5886; https://doaj.org/toc/1179-6901 |
| DOI: | 10.1007/s40268-019-0271-2 |
| URL الوصول: | https://doaj.org/article/fb3baa81bce74d509af0e9d63bfdc90d |
| رقم الأكسشن: | edsdoj.fb3baa81bce74d509af0e9d63bfdc90d |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 11745886 11796901 |
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| DOI: | 10.1007/s40268-019-0271-2 |