Prediction of Optimal Drug Schedules for Controlling Autophagy.
Prediction of Optimal Drug Schedules for Controlling Autophagy.
复制标题
控制自噬的最佳药物方案的预测。
DOI：
10.1038/s41598-019-38763-9
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发表时间：
2019
影响因子：
4.6
通讯作者：
Sorrentino,Francesco
中科院分区：
文献类型：
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作者：
Shirin,Afroza;Klickstein,IsaacS;Feng,Song;Lin,YenTing;Hlavacek,WilliamS;Sorrentino,Francesco
由于细胞调控网络的复杂行为，单凭直觉很难预测分子靶向药物扰动对细胞活动和命运的影响。解决这一问题的一种方法是开发预测药物效果的数学模型。这种方法需要共同开发计算方法，以提取有助于指导治疗选择和优化药物安排的见解。在这里，我们提出并评估了一种确定给药时间表的通用策略，该策略最大限度地减少了实现重要细胞活动/过程的持续抑制或升高所需的药量，即通过(宏观)自噬回收细胞质内容物。自噬的治疗靶向目前正在不同的临床试验中进行评估，但没有从控制工程学的角度受益。利用一个非线性常微分方程(ODE)模型和保证找到局部最优控制策略的方法，我们找到了六类药物和药物对中每一类的最优给药方案(开环控制器)。我们的方法可推广到设计影响感兴趣的不同细胞信号网络的单一疗法和多疗法药物方案。
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.