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.

翻译：药效学（PD）模型是包含药物作用机制的细胞反应网络数学模型。这类模型可用于在计算机中研究新药疗法的预测性治疗效果。然而，药效学模型在组成性参数数据方面存在显著不确定性，导致模型预测结果具有不确定性。此外，用于校准这些模型的实验数据在新通路中往往有限或不可得。本研究提出一种贝叶斯最优实验设计方法，用于提升药效学模型预测精度。我们随后利用模拟实验数据应用该方法，以考量假设性实验室测量中的不确定性。由此得到了药物性能的概率性预测，以及定量评估何种前瞻性实验室实验能够最优降低药效学模型预测不确定性的指标。本文提出的方法为新型生物通路模型的不确定性量化与指导性实验设计提供了可行的方案。