Optimising time samples for determining area under the curve of pharmacokinetic data using non-compartmental analysis
| Autor: | Mitch A. Phelps, Jim H. Hughes, David J. R. Foster, Richard N. Upton, Stephanie E. Reuter |
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| Přispěvatelé: | Hughes, Jim H, Upton, Richard N, Reuter, Stephanie E, Phelps, Mitch A, Foster, David JR |
| Rok vydání: | 2019 |
| Předmět: |
area under the curve
040301 veterinary sciences Pharmaceutical Science Sample (statistics) 0403 veterinary science 03 medical and health sciences 0302 clinical medicine Pharmacokinetics Genetic algorithm Statistics Humans non-compartmentalanalysis Selection (genetic algorithm) Mathematics Pharmacology Optimal sampling Area under the curve optimal sampling Sampling (statistics) 04 agricultural and veterinary sciences simulation studies Confidence interval Pharmaceutical Preparations 030220 oncology & carcinogenesis Area Under Curve pharmacokinetics Algorithms |
| Zdroj: | The Journal of pharmacy and pharmacologyReferences. 71(11) |
| ISSN: | 2042-7158 |
| Popis: | Objectives The selection of sample times for a pharmacokinetic study is important when trapezoidal integration (e.g. non-compartmental analysis) is used to determine the area under the concentration–time curve (AUC). The aim of this study was to develop an algorithm that determines optimal times that provide the most accurate AUC by minimising trapezoidal integration error. Methods The algorithm required initial single individual or mean pooled concentration data but did not specifically require a prior pharmacokinetic model. Optimal sample intervals were determined by minimising trapezoidal error using a genetic algorithm followed by a quasi-Newton method. The method was evaluated against simulated and clinical datasets to determine the method's ability to estimate the AUC. Key findings The sample times produced by the algorithm were able to accurately estimate the AUC of pharmacokinetic profiles, with the relative AUC having 90% confidence intervals of 0.919–1.05 for profiles with two-compartment kinetics. When comparing the algorithm with rich sampling (e.g. phase I trial), the algorithm provided equivalent or superior sample times with fewer observations. Conclusions The creation of the algorithm and its companion web application allows users with limited pharmacometric or programming training can obtain optimal sampling times for pharmacokinetic studies. |
| Databáze: | OpenAIRE |
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