Purpose: The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 ∞ and any AUC 0 ∞ -based NCA parameter or derivation. Methods: In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 ∞ s and the tissue-to-plasma AUC 0 ∞ ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Results: Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 ∞ and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. Conclusions: This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 ∞ -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.
Bibliographical noteFunding Information:
This work was supported by National Institutes of Health grants CA 138437, and NS 077921.
- Bayesian approach
- drug targeting index
- partition coefficient
- variance estimation