Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design

Tianli Wang, Kyle Baron, Wei Zhong, Richard Brundage, William Elmquist

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)649-659
Number of pages11
JournalPharmaceutical research
Volume31
Issue number3
DOIs
StatePublished - Mar 2014

Keywords

  • Bayesian approach
  • NCA
  • drug targeting index
  • partition coefficient
  • variance estimation

Fingerprint Dive into the research topics of 'Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design'. Together they form a unique fingerprint.

Cite this