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策略:开发针对神经退行性疾病的多靶点药物的一种有前景的手段。

Strategy: A Promising Implement in the Development of Multitarget Drugs against Neurodegenerative Diseases.

作者信息

Banjare Purusottam, Wamanrao Matore Balaji, Murmu Anjali, Kumar Vikash, Singh Jagadish, Roy Partha Pratim

机构信息

Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India.

Department of Pharmaceutical Chemistry, Apollo College of Pharmacy, Durg, 491001, India.

出版信息

Curr Top Med Chem. 2023;23(29):2765-2791. doi: 10.2174/1568026623666230811113231.

DOI:10.2174/1568026623666230811113231
PMID:37723952
Abstract

Multi-target drug development (MTDD) is the demand of the recent era, especially in the case of multi-factorial conditions such as cancer, depression, neurodegenerative diseases (NDs), etc. The MTDD approaches have many advantages; avoidance of drug-drug interactions, predictable pharmacokinetic profile, and less drug resistance. The wet lab practice in MTDD is very challenging for the researchers, and the chances of late-stage failure are obvious. Identification of an appropriate target (Target fishing) is another challenging task in the development of multi-target drugs. The tools will be one of the promising tools in the MTDD for the NDs. Therefore the outlook of the review comprises a short description of NDs, target associated with different NDs, studies so far done for MTDD for various NDs. The main thrust of this review is to explore the present and future aspects of tools used in MTDD for different NDs in combating the challenge of drug development and the application of various tools to solve the problem of target fishing.

摘要

多靶点药物开发(MTDD)是当代的需求,尤其是在癌症、抑郁症、神经退行性疾病(NDs)等多因素疾病的情况下。MTDD方法有许多优点:避免药物相互作用、可预测的药代动力学特征以及较低的耐药性。MTDD中的湿实验室实践对研究人员来说非常具有挑战性,后期失败的可能性很明显。识别合适的靶点(靶点筛选)是多靶点药物开发中的另一项具有挑战性的任务。[此处原文“tools”有误,可能影响准确理解,推测可能是某种特定工具]将是用于NDs的MTDD中有前景的工具之一。因此,本综述的内容包括对NDs的简要描述、与不同NDs相关的靶点、迄今为止针对各种NDs进行的MTDD研究。本综述的主要重点是探讨用于不同NDs的MTDD中所使用工具的当前和未来方面,以应对药物开发的挑战,以及应用各种[此处原文“tools”有误,可能影响准确理解,推测可能是某种特定工具]工具来解决靶点筛选问题。

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First report on exploration of structural features of natural compounds (NPACT database) for anti-breast cancer activity (MCF-7): QSAR-based virtual screening, molecular docking, ADMET, MD simulation, and DFT studies.关于天然化合物结构特征探索(NPACT数据库)用于抗乳腺癌活性(MCF-7)的首次报告:基于定量构效关系的虚拟筛选、分子对接、药物代谢动力学/药物毒性预测、分子动力学模拟和密度泛函理论研究。
In Silico Pharmacol. 2024 Oct 19;12(2):92. doi: 10.1007/s40203-024-00266-5. eCollection 2024.