Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents

Abstract

Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.

Keywords: AD, Alzheimer’s Disease; AEs, autoencoders; ASCVD, atherosclerotic cardiovascular disease; Antidiabetic drugs; CNNs, convolutional neural networks; CV, cardiovascular; CVD, cardiovascular diseases; DBNs, deep brief networks; DDA, drug-disease association; DDI, drug-drug interaction; DL, deep learning; DM, diabetes mellitus; DNNs, deep neural networks; DPP-4, dipeptidyl peptidase 4; DTI, drug-target interaction; Deep learning; Drug repositioning; Drug repurposing; GLP-1, glucagon-like peptide 1; GNNs, graph neural networks; ML, machine learning; Machine learning; PD, Parkinson’s Disease; PI3K/AKT, phosphatidylinositol 3-kinase/AKT; RNNs, recurrent neural networks; SGLT-2, sodium-glucose cotransporter 2; T2DM; T2DM, type 2 diabetes mellitus; TZD, thiazolidinedione; cAMP/PKA, cyclic adenosine monophosphate/protein kinase A.

Fig. 1

Schematic diagram of T2DM. It shows the high-risk factors, complications, and management of T2DM.

Fig. 2

The machine learning methods for drug-disease pairs prediction in drug repositioning. DTI: drug-target interaction, DDI: drug-drug interaction, DDA: drug-disease association, DBN: deep belief network, CNN: convolutional neural network, DNN: deep neural network, RNN: recurrent neural network, FNN: feedforward neural network, GNN: graph neural network, AE: autoencoder.