DiaNat-DB: a molecular database of antidiabetic compounds from medicinal plants

Exploring the rare variants associated with Type 2 Diabetes Mellitus in Indian population and its disease-drug association studies: an in-silico approach

The diversified eating habits and religious culture of Indian population may be one of the reasons they largely contribute to the global diabetes burden. In the present investigation, an in-silico approach was carried out to explore hub genes in the Indian population with Type 2 Diabetes Mellitus (T2DM) that are scantily reported in the GWAS catalogue and probable potential anti-diabetic drugs from plants. This computational approach unwrapped LEP (leptin) as the hub gene among 170 genes analyzed with 14 non-synonymous single nucleotide polymorphisms (nsSNPs) with MAF < 0.01. The mutation of the LEP gene leads to a decrease in leptin concentration, which increases the risk of obesity and T2DM. According to the DUET webserver, 11 of 14 mutations examined were found to destabilize the LEP protein. Among 14, four barely reported LEP variants rs781301976 (I45N), rs776443424 (S52F), rs200915360 (D76Y), and rs1191666811 (D162N) were unzipped to be associated with T2DM, which may be the probable potential drug targets. The virtual screening revealed Vescalagin as having the highest binding energy among 336 natural compounds. Molecular docking of Vescalagin depicted higher binding energy (-9.0 kcal/mol) against mutated LEP [rs200915360 (D76Y)] compared to wild (-8.9 kcal/mol) and LEP-Metformin complexes. The trajectory analysis of MD simulations revealed that Vescalagin was more effective than Metformin in stabilizing the system. The present study suggests that the associations of the investigated nsSNPs in LEP [rs200915360 (D76Y)] and others can be key factors in the predominant role of T2DM morbidity in the Indian population that can be used as potential markers and drug targets for T2DM therapeutics.Communicated by Ramaswamy H. Sarma.

Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = -8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = -13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = -25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

Novel molecular hybrids of EGCG and quinoxaline: Potent multi-targeting antidiabetic agents that inhibit α-glucosidase, α-amylase, and oxidative stress

Diabetes mellitus is a multifactorial disease and its effective therapy often demands several drugs with different modes of action. Herein, we report a rational design and synthesis of multi-targeting novel molecular hybrids comprised of EGCG and quinoxaline derivatives that can effectively inhibit α-glucosidase, α-amylase as well as control oxidative stress by scavenging ROS. The hybrids showed superior inhibition of α-glucosidase along with similar α-amylase inhibition as compared to standard drug, acarbose. Most potent compound, 15c showed an IC50 of 0.50 μM (IC50 of acarbose 190 μM) against α-glucosidase. Kinetics studies with 15c revealed a competitive inhibition against α-glucosidase. Binding affinity of 15c (-9.5 kcal/mol) towards α-glucosidase was significantly higher than acarbose (-7.7 kcal/mol). 15c exhibited remarkably high antioxidant activity (IC50 = 18.84 μM), much better than vitamin C (IC50 = 33.04 μM). Of note, acarbose shows no antioxidant activity. Furthermore, α-amylase activity was effectively inhibited by 15c with an IC50 value of 16.35 μM. No cytotoxicity was observed for 15c (up to 40 μM) in MCF-7 cells. Taken together, we report a series of multi-targeting molecular hybrids capable of inhibiting carbohydrate hydrolysing enzymes as well as reducing oxidative stress, thus representing an advancement towards effective and novel therapeutic approaches for diabetes.

Design of a multi-target focused library for antidiabetic targets using a comprehensive set of chemical transformation rules

Virtual small molecule libraries are valuable resources for identifying bioactive compounds in virtual screening campaigns and improving the quality of libraries in terms of physicochemical properties, complexity, and structural diversity. In this context, the computational-aided design of libraries focused against antidiabetic targets can provide novel alternatives for treating type II diabetes mellitus (T2DM). In this work, we integrated the information generated to date on compounds with antidiabetic activity, advances in computational methods, and knowledge of chemical transformations available in the literature to design multi-target compound libraries focused on T2DM. We evaluated the novelty and diversity of the newly generated library by comparing it with antidiabetic compounds approved for clinical use, natural products, and multi-target compounds tested in vivo in experimental antidiabetic models. The designed libraries are freely available and are a valuable starting point for drug design, chemical synthesis, and biological evaluation or further computational filtering. Also, the compendium of 280 transformation rules identified in a medicinal chemistry context is made available in the linear notation SMIRKS for use in other chemical library enumeration or hit optimization approaches.

Potential use of the Asteraceae family as a cure for diabetes: A review of ethnopharmacology to modern day drug and nutraceuticals developments

The diabetes-associated mortality rate is increasing annually, along with the severity of its accompanying disorders that impair human health. Worldwide, several medicinal plants are frequently urged for the management of diabetes. Reports are available on the use of medicinal plants by traditional healers for their blood-sugar-lowering effects, along with scientific evidence to support such claims. The Asteraceae family is one of the most diverse flowering plants, with about 1,690 genera and 32,000 species. Since ancient times, people have consumed various herbs of the Asteraceae family as food and employed them as medicine. Despite the wide variety of members within the family, most of them are rich in naturally occurring polysaccharides that possess potent prebiotic effects, which trigger their use as potential nutraceuticals. This review provides detailed information on the reported Asteraceae plants traditionally used as antidiabetic agents, with a major focus on the plants of this family that are known to exert antioxidant, hepatoprotective, vasodilation, and wound healing effects, which further action for the prevention of major diseases like cardiovascular disease (CVD), liver cirrhosis, and diabetes mellitus (DM). Moreover, this review highlights the potential of Asteraceae plants to counteract diabetic conditions when used as food and nutraceuticals. The information documented in this review article can serve as a pioneer for developing research initiatives directed at the exploration of Asteraceae and, at the forefront, the development of a botanical drug for the treatment of DM.

Natural products targeting inflammation-related metabolic disorders: A comprehensive review

Currently, the incidence of metabolic disorders is increasing, setting a challenge to global health. With major advancement in the diagnostic tools and clinical procedures, much has been known in the etiology of metabolic disorders and their corresponding pathophysiologies. In addition, the use of in vitro and in vivo experimental models prior to clinical studies has promoted numerous biomedical breakthroughs, including in the discovery and development of drug candidates to treat metabolic disorders. Indeed, chemicals isolated from natural products have been extensively studied as prospective drug candidates to manage diabetes, obesity, heart-related diseases, and cancer, partly due to their antioxidant and anti-inflammatory properties. Continuous efforts have been made in parallel to improve their bioactivity and bioavailability using selected drug delivery approaches. Here, we provide insights on recent progress in the role of inflammatory-mediated responses on the initiation of metabolic disorders, with particular reference to diabetes mellitus, obesity, heart-related diseases, and cancer. In addition, we discussed the prospective role of natural products in the management of diabetes, obesity, heart-related diseases, and cancers and provide lists of potential biological targets for high throughput screening in drug discovery and development. Lastly, we discussed findings observed in the preclinical and clinical studies prior to identifying suitable approaches on the phytochemical drug delivery systems that are potential to be used in the treatment of metabolic disorders.

Chemo-structural diversity of anti-obesity compound database

Nature plays a major role in the development of new drugs which helps in preventing and treating human diseases. Anti-obesity compound database (AOCD) contains comprehensive information on all published small molecules from natural sources with anti-obesity potential targeting pancreatic lipase (PL), appetite suppressant (AS) and adipogenesis (AD). Presently the database contains 349 compounds isolated from 307 plants, 26 marine and 16 microbial sources. Users can query the AOCD database (https://aocd.swmd.co.in/) in several ways. The database was divided into three datasets (PL, AS and AD) to perform chemoinformatic analysis using Platform for Unified Molecular Analysis (PUMA), which were analyzed based on molecular descriptors, scaffold diversity and structural fingerprint diversity. Chemoinformatics study inferred the PL dataset has the highest diversity of compounds based on the Euclidean distance on molecular properties, scaffold diversity and pairwise similarity on fingerprint diversity. This study would hasten the process of anti-obesity drug discovery.

Traditional Medicinal Plants as a Source of Inspiration for Osteosarcoma Therapy

Osteosarcoma is one of the most common types of bone cancers among paediatric patients. Despite the advances made in surgery, chemo-, and radiotherapy, the mortality rate of metastatic osteosarcoma remains unchangeably high. The standard drug combination used to treat this bone cancer has remained the same for the last 20 years, and it produces many dangerous side effects. Through history, from ancient to modern times, nature has been a remarkable source of chemical diversity, used to alleviate human disease. The application of modern scientific technology to the study of natural products has identified many specific molecules with anti-cancer properties. This review describes the latest discovered anti-cancer compounds extracted from traditional medicinal plants, with a focus on osteosarcoma research, and on their cellular and molecular mechanisms of action. The presented compounds have proven to kill osteosarcoma cells by interfering with different pathways: apoptosis induction, stimulation of autophagy, generation of reactive oxygen species, etc. This wide variety of cellular targets confer natural products the potential to be used as chemotherapeutic drugs, and also the ability to act as sensitizers in drug combination treatments. The major hindrance for these molecules is low bioavailability. A problem that may be solved by chemical modification or nano-encapsulation.

Approaches for enhancing the analysis of chemical space for drug discovery

INTRODUCTION

Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods.

AREAS COVERED

The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods.

EXPERT OPINION

Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.

The LOTUS initiative for open knowledge management in natural products research

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

In Vitro Antioxidant and Alpha-Glucosidase Inhibition Activity of Polygonatum verticillatum of Karnali, Nepal

Background:

Diabetes mellitus is a metabolic disorder that has become a major health issue in modern era due to the long-term health consequences. α-amylase and α-glucosidase are the key enzymes involved in the digestion of starchy foods, and the inhibition of these enzymes is regarded as a postprandial hyperglycemia control strategy.

Objective:

The primary goal of this work is to examine the antioxidant activity as well as α-glucosidase inhibitory activity of Polygonatum verticillatum rhizomes via in vitro test.

Method:

The in vitro α-glucosidase inhibition activity was performed using p-nitrophenyl α-D¬-glucopyranoside (PNPG) substrate. 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay was performed to estimate the antioxidant activity.

Results:

The in vitro -glucosidase inhibitory activity of Polygonatum verticillatum was investigated for the very first time. Of the three fractions and a crude extract, the ethyl acetate (EA) fraction disclosed potent inhibition activity against α-glucosidase enzyme with an IC50 value of 22.3 ± 0.1 μg/mL. Likewise, the IC50 values for dichloromethane (DCM) fraction and the crude extract against α-glucosidase were reported 34 ± 0.1 μg/mL, and 402.2 ± 0.2 μg/mL, respectively. Similarly, the EA fraction, crude extract, and DCM fraction disclosed promising antioxidant activity with IC50 55 ± 0.3 μg/mL, 171.5 ± 0.6 μg/mL, and 164.1 ± 3.4 μg/mL, respectively.

Conclusion:

These findings concluded that among the crude extract and fractions of Polygonatum verticillatum of Nepalese origin, the EA fraction constituted a potent α-glucosidase inhibiting and antioxidant agent. Further research is required to expose the inhibiting compounds.

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic β-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure–activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms. The best model (M2) meets the Tropsha’s test requirements and has the statistics parameters R² = 0.843, Q²_CV = 0.785, and Q²_ext = 0.855. Also, coverage of 100% of the test set based on the applicability domain analysis was obtained. Furthermore, a deep analysis based on the ADME predictions, molecular docking, and molecular dynamics simulations was performed. The lipophilicity and the residue interactions were used as relevant criteria for selecting a candidate from the screening of the DiaNat and DrugBank databases. Finally, the FDA-approved drugs bilastine, bromfenac, and fenofibric acid are suggested as potential and lead FFA1 agonists.

Chemoinformatic Characterization of Synthetic Screening Libraries Focused on Epigenetic Targets

The importance of epigenetic drug and probe discovery is on the rise. This is not only paramount to identify and develop therapeutic treatments associated with epigenetic processes but also to understand the underlying epigenetic mechanisms involved in biological processes. To this end, chemical vendors have been developing synthetic compound libraries focused on epigenetic targets to increase the probabilities of identifying promising starting points for drug or probe candidates. However, the chemical contents of these data sets, the distribution of their physicochemical properties, and diversity remain unknown. To fill this gap and make this information available to the scientific community, we report a comprehensive analysis of eleven libraries focused on epigenetic targets containing more than 50,000 compounds. We used well-validated chemoinformatics approaches to characterize these sets, including novel methods such as automated detection of analog series and visual representations of the chemical space based on Constellation Plots and Chemical Library Networks. This work will guide the efforts of experimental groups working on high-throughput and medium-throughput screening of epigenetic-focused libraries. The outcome of this work can also be used as a reference to design and describe novel focused epigenetic libraries.

Advanced Bioinformatics Tools in the Pharmacokinetic Profiles of Natural and Synthetic Compounds with Anti-Diabetic Activity

Diabetes represents a major health problem, involving a severe imbalance of blood sugar levels, which can disturb the nerves, eyes, kidneys, and other organs. Diabes management involves several synthetic drugs focused on improving insulin sensitivity, increasing insulin production, and decreasing blood glucose levels, but with unclear molecular mechanisms and severe side effects. Natural chemicals extracted from several plants such as Gymnema sylvestre, Momordica charantia or Ophiopogon planiscapus Niger have aroused great interest for their anti-diabetes activity, but also their hypolipidemic and anti-obesity activity. Here, we focused on the anti-diabetic activity of a few natural and synthetic compounds, in correlation with their pharmacokinetic/pharmacodynamic profiles, especially with their blood-brain barrier (BBB) permeability. We reviewed studies that used bioinformatics methods such as predicted BBB, molecular docking, molecular dynamics and quantitative structure-activity relationship (QSAR) to elucidate the proper action mechanisms of antidiabetic compounds. Currently, it is evident that BBB damage plays a significant role in diabetes disorders, but the molecular mechanisms are not clear. Here, we presented the efficacy of natural (gymnemic acids, quercetin, resveratrol) and synthetic (TAK-242, propofol, or APX3330) compounds in reducing diabetes symptoms and improving BBB dysfunctions. Bioinformatics tools can be helpful in the quest for chemical compounds with effective anti-diabetic activity that can enhance the druggability of molecular targets and provide a deeper understanding of diabetes mechanisms.

Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.

Computational Approaches for the Discovery and Development of Pharmacologically Active Natural Products

Natural products continue to be a significant source of active compounds [...].