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.