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通过电路设计实现药物鸡尾酒配方

Drug Cocktail Formulation via Circuit Design.

作者信息

Beahm Douglas Raymond, Deng Yijie, DeAngelo Thomas M, Sarpeshkar Rahul

机构信息

Thayer School or Engineering, Dartmouth College, Hanover, NH 03755 USA.

Departments of Engineering, Physics, Microbiology & Immunobiology, and Molecular & Systems Biology, Dartmouth College, Hanover, NH 03755 USA.

出版信息

IEEE Trans Mol Biol Multiscale Commun. 2023 Mar;9(1):28-48. doi: 10.1109/tmbmc.2023.3246928. Epub 2023 Feb 22.

Abstract

Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Drug cocktail therapies are a powerful tool against diseases that exhibit such dynamics. We show that just six key states, which are represented in a feedback circuit, enable drug-cocktail formulation: 1) healthy cell number; 2) infected cell number; 3) extracellular pathogen number; 4) intracellular pathogenic molecule number; 5) innate immune system strength; and 6) adaptive immune system strength. To enable drug cocktail formulation, the model represents the effects of the drugs in the circuit. For example, a nonlinear feedback circuit model fits measured clinical data, represents cytokine storm and adaptive autoimmune behavior, and accounts for age, sex, and variant effects for SARS-CoV-2 with few free parameters. The latter circuit model provided three quantitative insights on the optimal timing and dosage of drug components in a cocktail: 1) antipathogenic drugs should be given early in the infection, but immunosuppressant timing involves a tradeoff between controlling pathogen load and mitigating inflammation; 2) both within and across-class combinations of drugs have synergistic effects; 3) if they are administered sufficiently early in the infection, anti-pathogenic drugs are more effective at mitigating autoimmune behavior than immunosuppressant drugs.

摘要

电子电路通过具有复杂动力学的非线性微分方程直观地可视化并定量模拟生物系统。药物鸡尾酒疗法是对抗具有此类动力学疾病的有力工具。我们表明,反馈回路中所代表的仅六个关键状态就能实现药物鸡尾酒配方:1)健康细胞数量;2)受感染细胞数量;3)细胞外病原体数量;4)细胞内致病分子数量;5)先天免疫系统强度;以及6)适应性免疫系统强度。为了实现药物鸡尾酒配方,该模型表示了回路中药物的作用。例如,一个非线性反馈回路模型拟合了实测临床数据,呈现了细胞因子风暴和适应性自身免疫行为,并以很少的自由参数说明了年龄、性别和SARS-CoV-2变异的影响。后一个回路模型对鸡尾酒中药物成分的最佳给药时间和剂量提供了三个定量见解:1)抗病原体药物应在感染早期给药,但免疫抑制剂的给药时间涉及控制病原体负荷和减轻炎症之间的权衡;2)药物的同类和跨类组合都具有协同作用;3)如果在感染足够早的阶段给药,抗病原体药物在减轻自身免疫行为方面比免疫抑制剂药物更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8540/10312325/46f3bef56392/nihms-1884629-f0005.jpg

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