Application of machine learning methods for the development of antidiabetic drugs

Diabetes is a chronic non-communicable disease caused by several different routes, which has attracted increasing attention. In order to speed up the development of new selective drugs, machine learning (ML) technology has been applied in the process of diabetes drug development, which opens up a new blueprint for drug design. This review provides a comprehensive portrayal of the application of ML in antidiabetic drug use.

Comprehensive structure–activity relationship (SAR) investigation of C-aryl glycoside derivatives for the development of SGLT1/SGLT2 dual inhibitors

Multi-combined computational approaches were used to explore the SAR and design novel potential SGLT1/SGLT2 dual inhibitors.

SGLT2 inhibitors, an accomplished development in field of medicinal chemistry: an extensive review

Diabetes is a chronic progressive metabolic disease caused by insulin deficiency or insulin resistance. In spite of the availability of several antihyperglycaemics, there is a need for the development of safer antidiabetic drugs due to their undesirable effects. Sodium-glucose cotransporter-2 inhibitors are a class of antidiabetics, which hinder the reabsorption of glucose in the kidneys, causing excretion of glucose via urine. Sodium-glucose cotransporter-2 inhibitors are a well-tolerated class with no significant adverse effects and are found to be favorable in certain conditions, which may be rudimentary to cardiovascular and renal diseases. The current advancements in their design and development, their mechanism of action, structure–activity relationship, synthesis and in silico development along with their auxiliary roles have been extensively reviewed.

An exhaustive perspective on structural insights of SGLT2 inhibitors: A novel class of antidiabetic agent

Diabetes mellitus is the global health issue and become an alarming threat in the modern era where human lifestyle gets compromised with modernization. According to the latest statistical report 2020, USA has 9.47% (31 million among 32.72 cr), China has 8.3% (116.4 million among 139.27 cr) and India has 5.6% (77 million among 135.26 cr) of the diabetic people, indicating that diabetes is more prevailing in developed countries as compared to the developing countries. The number of diabetic patients is rising day by day at a tremendous rate and soon it may affect each and every person in a family. So, there is an urgent need to develop novel entities that can meet the scarcity of present antidiabetic agents. In the last few decades, the sodium-glucose co-transporter 2 (SGLT2) has emerged as a prominent target for the treatment of Type 2 diabetes mellitus due to its novel mechanism of action & no involvement in insulin signaling pathway. Most of the inhibitors that target SGLT2 contain three basic moieties: glucose, two benzene rings (one is connected with glucose and the other with methylene), and the methylene bridge which are similar to dapagliflozin. Several SGLT2 inhibitors and their derivatives such as remogliflozin etabonate (phase-II), sotagliflozin (phase-III) and bexagliflozin (phase-III) are under different phases of clinical trial studies and some have been patented. The present review is focused on SGLT2 inhibitors, structure activity relationships (SARs) of dapagliflozin and its several analogues for their binding affinity with SGLT2. We have also presented and summarized the efforts made by various researchers in terms of the synthesis of various dapagliflozin derivatives till date.

In silico modeling and in vitro activity of vitexin and isovitexin against SGLT2

The homology model of hSGLT2 (human sodium dependent glucose co-transporter 2) was used as a target for diabetes mellitus. Molecular docking and dynamics simulations were carried out on vitexin- and isovitexin-SGLT2 complexes with dapagliflozin as positive control. The results show that both vitexin and isovitexin have weaker binding energies compared to dapagliflozin, indicating that both ligands may exhibit weak anti-diabetic effects through inhibiting SGLT2. The poor binding mode of vitexin and isovitexin may be responsible for their weak anti-diabetic effect. These results are in accordance with the inhibitory activity against hSGLT2 in vitro test with the inhibitory rate 26.3% of vitexin and 11.2% of isovitexin at the dose of 10[Formula: see text][Formula: see text]mol[Formula: see text][Formula: see text][Formula: see text]L[Formula: see text]. The results of calculation and in vitro test may explain the possible inhibiting mechanism of vitexin and isovitexin against SGLT2, and therefore enhance our understanding of the structure-activity relationships of SGLT2 inhibitors.

Development of sodium glucose co-transporter 2 (SGLT2) inhibitors with novel structure by molecular docking and dynamics simulation

In this study, molecular docking studies were carried out to explore the binding interactions of sodium glucose co-transporter 2 (SGLT2) with its inhibitors. A correlation between the docking scores and the experimental bioactivity was observed (R² = 0.8368, N = 24). The new inhibitors were designed using the 3D quantitative structure activity relationship (3D-QSAR) method, and the activities were predicted by the docking method. In order to understand the structure-activity correlation of compound 1 m (the highest score of docking) and compound 1 t (the lowest score), we carried out a combined molecular dynamics simulation and MM-GBSA method. It was found that, in the system of SGLT2-1 m, the interaction between Gln271 and Val272 exhibited significant effects, which were absent in the SGLT2-1 t system. This study is expected to shed light on the mechanism of action of compound 1 m, leading to development of active drug candidates targeting SGLT2.

In silico modeling of aspalathin and nothofagin against SGLT2

Aspalathin and nothofagin are the major dihydrochalcones found in rooibos (Aspalathus linearis), which display anti-diabetic activities, but the mechanism is still unclear. In this paper, hSGLT2 (human sodium dependent glucose co-transporter 2), a target for diabetes mellitus, was built using homology modeling method. Molecular docking and dynamics simulations were carried out on aspalathin, nothofagin and SGLT2 complexes with dapagliflozin as positive control. The results show that both the binding energies and binding modes of aspalathin and nothofagin are similar to dapagliflozin, indicating that either component of rooibos may exhibit anti-diabetic effects through inhibiting SGLT2 receptor. However, the predicted permeability value of aspalathin and nothofagin is low, which may cause poor absorption, resulting in weak SGLT2 inhibition. Calculation results elucidate the possible inhibiting mechanism of aspalathin and nothofagin against SGLT2, and therefore enhance our understanding of anti-diabetic activities of rooibos.

QSAR studies in the discovery of novel type-II diabetic therapies

INTRODUCTION

Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity.

AREAS COVERED

This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds.

EXPERT OPINION

Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.