Mechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA.
Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Sci Rep. 2019 Feb 5;9(1):1428. doi: 10.1038/s41598-019-38763-9.
The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy. Therapeutic targeting of autophagy is currently being evaluated in diverse clinical trials but without the benefit of a control engineering perspective. Using a nonlinear ordinary differential equation (ODE) model that accounts for activating and inhibiting influences among protein and lipid kinases that regulate autophagy (MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal control strategies, we find optimal drug dosing schedules (open-loop controllers) for each of six classes of drugs and drug pairs. Our approach is generalizable to designing monotherapy and multi therapy drug schedules that affect different cell signaling networks of interest.
由于细胞调控网络的复杂行为，仅凭借直觉很难预测靶向药物干扰对细胞活动和命运的影响。克服这个问题的一种方法是开发用于预测药物效果的数学模型。这种方法需要共同开发用于提取有助于指导治疗选择和优化药物调度的见解的计算方法。在这里，我们提出并评估了一种可普遍应用的策略，用于确定药物剂量方案，以最大程度地减少达到持续抑制或升高重要细胞活动/过程（通过（巨）自噬回收细胞质内容物）所需的药物量。自噬的治疗靶向目前正在各种临床试验中进行评估，但没有控制工程学的观点。我们使用了一个非线性常微分方程 (ODE) 模型，该模型考虑了调节自噬的蛋白质和脂质激酶（MTORC1、ULK1、AMPK 和 VPS34）之间的激活和抑制影响，以及保证找到局部最优控制策略的方法，为六类药物和药物对中的每一种找到了最佳的药物剂量方案（开环控制器）。我们的方法可推广用于设计影响不同感兴趣的细胞信号转导网络的单药和多药治疗方案。
