QUANTIFYING UNCERTAINTY IN PREDICTIONS OF HEPATIC CLEARANCE

CHRISTOPHER P STEINER1,  PAUL BERNHARDT2,  JAYE L BUPP3,  NATHAN LANGHOLZ4,  MORGAN GIESEKE5,  JAYSON WILBUR*6

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN1, ECONOMICS, URBANA, IL 61801
MESSIAH COLLEGE2, MATHEMATICAL SCIENCES, GRANTHAM, PA 17027
ALMA COLLEGE3, MATHEMATICS, ALMA, MI 48801
ST. OLAF COLLEGE4, MATHEMATICS, NORTHFIELD, MN 55057
WINONA STATE UNIVERSITY5, MATHEMATICS AND STATISTICS, WINONA, MN 55987
WORCESTER POLYTECHNIC INSTITUTE6, MATHEMATICAL SCIENCES, WORCESTER, MA 01609

jwilbur@wpi.edu


Abstract

In the process of drug development, it is important to understand the pharmacokinetics of candidate compounds. Among the pharmacokinetic parameters of interest is hepatic clearance, which is the rate at which blood is cleared of the drug by the liver. Generally, point estimates for hepatic clearance are obtained from preclinical data and used in clinical models without accounting for the uncertainty in these estimates. The goal of this project is to construct a Bayesian model for hepatic clearance which can be used to quantify the uncertainty in terms of a posterior distribution and evaluate the sensitivity of the distribution to various model assumptions.

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