Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy.
Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy.
Biochem Pharmacol. 2024 Oct;228:116078. doi: 10.1016/j.bcp.2024.116078. Epub 2024 Feb 23.
A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.
药物作用机制(MoA)是描述生物活性化合物如何产生药理作用的复杂生物学现象。完全了解 MoA 是充分理解药物活性的基础。多年来,已经开发出许多实验方法,并产生了大量数据。如今,考虑到组学数据可用性的增加和可访问计算资源的改善,药物 MoA 的研究通过整合实验和生物信息学方法来进行。针对此类分析的新型计算解决方案正在不断开发中;本文对这类生物信息学方法进行了更新综述。所引用的方法基于生化网络和机器学习(ML)中的多组学数据集成。分析了每种方法可用的多种类型输入数据以及它们的优缺点,并重点介绍了它们的应用。选择了三个特定的研究领域(即癌症药物开发、抗生素发现和药物再利用),因为它们在药物发现领域中非常重要,通过新型生物信息学方法对药物 MoA 的研究特别有成效。