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吸引子——药物发现的新转折点。

Attractor - a new turning point in drug discovery.

作者信息

Hou Xucan, Li Meng, Jia Congmin, Zhang Xianbao, Wang Yun

机构信息

Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China.

出版信息

Drug Des Devel Ther. 2019 Aug 22;13:2957-2968. doi: 10.2147/DDDT.S216397. eCollection 2019.

DOI:10.2147/DDDT.S216397
PMID:31686779
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6709805/
Abstract

Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.

摘要

复杂疾病的药物发现可被视为一个具有挑战性的问题,其中应考虑化合物对疾病系统动态特征的影响,特别是逃离疾病吸引子的策略。此外,逃离与疾病相关的吸引子已被证明是治疗复杂疾病的一个线索。基于吸引子理论的药物发现方法为靶点识别、药物发现和药物联合设计指明了新的解决方案。该方法基于生物体的整体水平和系统动力学特征,因此在复杂疾病分类和药物发现方面具有优势。目前,该方法的研究成果不断增加,拓宽了药物发现的视野范围。本文介绍了药学发展史上主要的药物发现方法及其特点,以阐明吸引子方法出现的原因和必然性、其在药学发展史上的地位以及其在药物发现和设计方面的优势,从而证明吸引子方法确实能够成为下一个主要的药物开发方法。此外,它还对吸引子的概念、吸引子分析流程、各环节常用方法及其研究进展进行了全面描述,为后续研究人员提供一个宏观框架以及可供选择的方法和工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eda3/6709805/ed6721cfb767/DDDT-13-2957-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eda3/6709805/c88f2c8f21cc/DDDT-13-2957-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eda3/6709805/38323358cc6c/DDDT-13-2957-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eda3/6709805/ed6721cfb767/DDDT-13-2957-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eda3/6709805/c88f2c8f21cc/DDDT-13-2957-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eda3/6709805/38323358cc6c/DDDT-13-2957-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eda3/6709805/ed6721cfb767/DDDT-13-2957-g0003.jpg

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本文引用的文献

1
ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming.ATLANTIS - 细胞命运发现和重编程的吸引景观分析工具箱。
Sci Rep. 2018 Feb 23;8(1):3554. doi: 10.1038/s41598-018-22031-3.
2
PharmGKB: A worldwide resource for pharmacogenomic information.PharmGKB:一个全球性的药物基因组学信息资源。
Wiley Interdiscip Rev Syst Biol Med. 2018 Jul;10(4):e1417. doi: 10.1002/wsbm.1417. Epub 2018 Feb 23.
3
Network-based technologies for early drug discovery.基于网络的早期药物发现技术。
Drug Discov Today. 2018 Mar;23(3):626-635. doi: 10.1016/j.drudis.2017.12.001. Epub 2017 Dec 30.
4
Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response.基于网络动力学的癌症panel 分层用于系统预测抗癌药物反应。
Nat Commun. 2017 Dec 5;8(1):1940. doi: 10.1038/s41467-017-02160-5.
5
Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer.结直肠癌细胞内调控网络吸引子景观的定量评估与回归分析
BMC Syst Biol. 2017 Apr 5;11(1):45. doi: 10.1186/s12918-017-0424-2.
6
Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms.使用最小描述长度原则降低最佳拟合算法的误报率。
EURASIP J Bioinform Syst Biol. 2014 Jul 3;2014:13. doi: 10.1186/s13637-014-0013-2. eCollection 2014 Dec.
7
Attractor landscape analysis of colorectal tumorigenesis and its reversion.结直肠癌发生及其逆转的吸引子景观分析
BMC Syst Biol. 2016 Oct 20;10(1):96. doi: 10.1186/s12918-016-0341-9.
8
Multi-target pharmacology: possibilities and limitations of the "skeleton key approach" from a medicinal chemist perspective.多靶点药理学:从药物化学家角度看“万能钥匙方法”的可能性与局限性
Front Pharmacol. 2015 Sep 22;6:205. doi: 10.3389/fphar.2015.00205. eCollection 2015.
9
Cell fate reprogramming by control of intracellular network dynamics.通过控制细胞内网络动力学实现细胞命运重编程。
PLoS Comput Biol. 2015 Apr 7;11(4):e1004193. doi: 10.1371/journal.pcbi.1004193. eCollection 2015 Apr.
10
How to escape the cancer attractor: rationale and limitations of multi-target drugs.如何逃离癌症吸引子:多靶点药物的原理和局限性。
Semin Cancer Biol. 2013 Aug;23(4):270-8. doi: 10.1016/j.semcancer.2013.06.003. Epub 2013 Jun 20.