The objective of the current article is to formulate and test a simple
Bayesian MBDoE approach that is readily usable by model developers. The
effectiveness of the proposed Bayesian approach is compared to that of
the LO approach for designing A-optimal experiments when theFIM is noninvertible. We use the pharmaceutical case study of
Domagalski et al., (2015), which is of interest to our industrial
sponsor.6,21 The associated dynamic model uses
Michaelis−Menten kinetics and enzyme-catalyzed reactions to describe the
production of a pharmaceutical agent.52 The remainder
of this article is organized as follows. First, background on theFIM and sequential A-optimal design is presented. Next, details
of the Bayesian and LO approaches for parameter estimation and
experimental design are presented. A simple Bayesian approach is
proposed and a pharmaceutical case study is presented. Results obtained
using Monte Carlo (MC) simulations are provided, revealing that the
proposed Bayesian approach is superior to the LO approach for this case
study.