预测控制自噬的最优药物方案
Prediction of Optimal Drug Schedules for Controlling Autophagy
AI总结：
本文提出了一种通用策略，用于确定最小化药物剂量以实现持续抑制或提升自噬过程的药物给药方案，通过非线性常微分方程模型和优化方法来指导治疗选择和优化药物方案。
摘要：
分子靶向药物扰动对细胞活动和命运的影响难以仅凭直觉预测，因为细胞调控网络的行为复杂。为解决这一问题，开发数学模型来预测药物效应是一种方法。本文提出并评估了一种通用策略，用于确定药物给药方案，以最小化所需药物剂量，以实现持续抑制或提升重要的细胞活动过程，即通过（宏）自噬作用回收胞质内容物。目前，自噬治疗靶向在多种临床试验中被评估，但缺乏控制工程视角。使用考虑蛋白质和脂质激酶（MTORC1、ULK1、AMPK和VPS34）激活和抑制影响的非线性常微分方程（ODE）模型以及保证找到局部最优控制策略的方法，我们为六类药物和药物对的每个类别找到了最优药物给药方案（开环控制器）。我们的方法可推广到设计影响不同细胞信号网络的单药治疗和多药治疗方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
