Pharmacodynamic (PD) models are mathematical models of cellular reaction
networks that include drug mechanisms of action. These models are useful for
studying predictive therapeutic outcomes of novel drug therapies in silico.
However, PD models are known to possess significant uncertainty with respect to
constituent parameter data, leading to uncertainty in the model predictions.
Furthermore, experimental data to calibrate these models is often limited or
unavailable for novel pathways. In this study, we present a Bayesian optimal
experimental design approach for improving PD model prediction accuracy. We
then apply our method using simulated experimental data to account for
uncertainty in hypothetical laboratory measurements. This leads to a
probabilistic prediction of drug performance and a quantitative measure of
which prospective laboratory experiment will optimally reduce prediction
uncertainty in the PD model. The methods proposed here provide a way forward
for uncertainty quantification and guided experimental design for models of
novel biological pathways