Bioinformatic Approaches in the Understanding of Mechanism of Action ( MoA )
Maria‐Anna Trapotsi, Ian P. Barrett, Ola Engkvist, Andreas Bender · Methods and principles in medicinal chemistry · 2019
Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high throughput data in public databases have significantly contributed in the better understanding of compounds' Mechanism of Action (MoA), which is a challenging task in drug discovery process. There are different types of information which can be used to better understand MoA, but it is difficult to know which can help us to better understand MoA and therefore existing studies use or integrate different types of information. In this chapter, we focus on three different high-throughput data and their application in MoA studies. These are the transcriptomic, pathway, and image-based data. For each type of information, we provide an overview of databases that can be used to extract this data. Moreover, we highlight methodologies and cases, where each type of data or data combinations have been successfully used and the MoA of compounds was better understood. Firstly, we focus on transcriptomics data and studies that used gene expression signatures to repurpose compounds and/or identify potential targets. Secondly, we describe how pathway information can be used in drug networks or how the link between drug targets and pathways' activation can help us to better understand MoA. Finally, we describe how image data can contribute in target identification, compound repurposing and how associations between transcriptomic alterations and changes in cell morphology can help in MoA understanding.