Thiazolidinedione–triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential
Abstract
Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione–triazole conjugates (7a–7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione–triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.
Graphical abstract
Papers of special note have been highlighted as: • of interest; •• of considerable interest
References
- 1. . The burden and risks of emerging complications of diabetes mellitus. Nat. Rev. Endocrinol. 18(9), 525–539 (2022).
- 2. . Nutritional strategies to attenuate postprandial glycemic response. Obes. Rev. 23(9), e13486 (2022).
- 3. . The role of obesity and diabetes in dementia. Int. J. Mol. Sci. 23(16), 9267 (2022).
- 4. . Combination therapy of bioactive compounds with acarbose: a proposal to control hyperglycemia in Type 2 diabetes. J. Food Biochem. 46(10), e14268 (2022).
- 5. . Thiazolidinediones and PPAR orchestra as antidiabetic agents: from past to present. Eur. J. Med. Chem. 126, 879–893 (2017).
- 6. . Design and development of new thiazolidinone-based drug-like molecules. Biopolym. Cell 35(3), 222–222 (2019).
- 7. . Rhodanine scaffold: a review of antidiabetic potential and structure–activity relationships (SAR). Med. Drug Discov. 15,
doi: 10.1016/j.medidd.2022.100131 (2022) (Epub ahead of print). - 8. Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: a combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies. Comput. Biol. Med. 157,
doi: 10.1016/j.compbiomed.2023.106776 (2023) (Epub ahead of print). •• This study reveals the application of in vitro and in silico methods for the identification of structural fragments necessary for inhibitory activity. - 9. . Thiazolidine-4-one clubbed pyrazoles hybrids: potent α-amylase and α-glucosidase inhibitors with NLO properties. J. Heterocycl. Chem. 57(4), 1573–1587 (2020).
- 10. Thiazolidine-2,4-dione framework containing spiropyrrolidine-oxindole and 1,2,3-triazole scaffold: synthesis, in vitro α-amylase inhibition and in silico studies. New J. Chem. 47(11), 5399–5412 (2023).
- 11. Synthesis, crystal structures, α-glucosidase and α-amylase inhibition, DFT and molecular docking investigations of two thiazolidine-2,4-dione derivatives. J. Mol. Struct. 1261,
doi: 10.1016/j.molstruc.2022.132960 (2022) (Epub ahead of print). - 12. Thiazolidinedione derivatives: in silico, in vitro, in vivo, antioxidant and anti-diabetic evaluation. Molecules 27(3), 830 (2022).
- 13. . Discovery potent of thiazolidinedione derivatives as antioxidant, α-amylase inhibitor, and antidiabetic agent. Biomedicines 10(1), 24 (2022).
- 14. . Synthesis, α-amylase inhibitory activity and molecular docking studies of 2, 4-thiazolidinedione derivatives. Open Chem. J. 5(1), 134–144 (2018).
- 15. Cinnamaldehyde improves metabolic functions in streptozotocin-induced diabetic mice by regulating gut microbiota. Drug Des. Dev. Ther. 15, 2339–2355 (2021).
- 16. Cinnamaldehyde in diabetes: a review of pharmacology, pharmacokinetics and safety. Pharmacol. Res. 122, 78–89 (2017). • This article highlights the role of cinnamaldehyde in diabetes management.
- 17. . Reversal of diabetes-induced behavioral and neurochemical deficits by cinnamaldehyde. Phytomedicine 23(9), 923–930 (2016).
- 18. . Cinnamaldehyde – a potential antidiabetic agent. Phytomedicine 14(1), 15–22 (2007).
- 19. . 1,2,3-Triazole-containing hybrids as leads in medicinal chemistry: a recent overview. Bioorg. Med. Chem. 27(16), 3511–3531 (2019). • This article presents the utility of 1,2,3-triazoles in medicinal chemistry.
- 20. Development of coumarin tagged 1,2,3-triazole derivatives targeting α-glucosidase inhibition: synthetic modification, biological evaluation, kinetic and in silico studies. J. Mol. Struct. 1282,
doi: 10.1016/j.molstruc.2023.135194 (2023) (Epub ahead of print). - 21. . Synthesis of novel inhibitors of α-amylase based on the thiazolidine-4-one skeleton containing a pyrazole moiety and their configurational studies. MedChemComm 8(7), 1468–1476 (2017). • This study demonstrates the applications of thiazolidinones in inhibiting α-amylase.
- 22. Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation. J. Biomol. Struct. Dyn. 40(11), 4933–4953 (2022).
- 23. Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor. J. Biomol. Struct. Dyn. 39(1), 91–107 (2021).
- 24. Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies. Comput. Biol. Med. 138,
doi: 10.1016/j.compbiomed.2021.104876 (2021) (Epub ahead of print). - 25. Thiazolidin-2,4-dione framework containing spiropyrrolidine-oxindole and 1,2,3-triazole scaffold: synthesis, in vitro α-amylase inhibition and in silico studies. New J. Chem. 47, 5399–5412 (2023).
- 26. Molecular hybridization as a tool in the design of multi-target directed drug candidates for neurodegenerative diseases. Curr. Neuropharmacol. 18(5), 348–407 (2020).
- 27. . Comprehension of drug toxicity: software and databases. Comput. Biol. Med. 45, 20–25 (2014).
- 28. . A structure-based drug discovery paradigm. Int. J. Mol. Sci. 20(11), 2783 (2019).
- 29. An in silico target fishing approach to identify novel ochratoxin A hydrolyzing enzyme. Toxins 12(4), 258 (2020).
- 30. Drug repositioning and repurposing for Alzheimer disease. Nat. Rev. Neurol. 16(12), 661–673 (2020).
- 31. . Polypharmacology in drug development: a minireview of current technologies. ChemMedChem 11(12), 1211–1218 (2016).
- 32. . Recent advances in in silico target fishing. Molecules 26(17), 5124 (2021).
- 33. Computational drug repurposing study elucidating simultaneous inhibition of entry and replication of novel corona virus by grazoprevir. Sci. Rep. 11(1), 7307 (2021).
- 34. . Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin. Drug Discov. 15(12), 1473–1487 (2020). • This study demonstrates the various tools available for the prediction of pharmacokinetic properties and drug likeliness behavior.
- 35. Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis. SAR QSAR Environ. Res. 33(9), 677–700 (2022).
- 36. . Prediction reliability of QSAR models: an overview of various validation tools. Arch. Toxicol. 96(5), 1279–1295 (2022).
- 37. QSAR modeling: where have you been? Where are you going to? J. Med. Chem. 57(12), 4977–5010 (2014).
- 38. . The relevance of goodness-of-fit, robustness and prediction validation categories of OECD-QSAR principles with respect to sample size and model type. Mol. Inf. 41(11),
doi: 10.1002/minf.202200072 (2022) (Epub ahead of print). - 39. . Quantitative structure–property relationship approach in formulation development: an overview. AAPS PharmSciTech 20(7), 268 (2019).
- 40. . Molecular descriptors. In: Handbook of Computational Chemistry. Leszczynski JKaczmarek-Kedziera APuzyn TPapadopoulos MGReis HShukla MK (Eds). Springer International Publishing, NY, USA, 2065–2093 (2017). • This study describes the role of descriptors in quantitative structure–activity relationship modeling and the mechanistic interpretation from the descriptors.
- 41. . In silico toxicology: computational methods for the prediction of chemical toxicity. WIREs Comput. Mol. Sci. 6(2), 147–172 (2016).
- 42. . PaDEL-Descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32(7), 1466–1474 (2011).
- 43. Use of biomimetic chromatography and in vitro assay to develop predictive GA-MLR model for use in drug-property prediction among anti-depressant drug candidates. Microchem. J. 175,
doi: 10.1016/j.microc.2022.107183 (2022) (Epub ahead of print). - 44. . Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches. Environ. Sci. Pollut. Res. 29(58), 87628–87644 (2022).
- 45. Identification of anti-SARS-CoV-2 compounds from food using QSAR-based virtual screening, molecular docking, and molecular dynamics simulation analysis. Pharmaceuticals 14(4), 357 (2021).
- 46. . Docking and QSAR analysis of tetracyclic oxindole derivatives as α-glucosidase inhibitors. Comput. Biol. Chem. 76, 283–292 (2018).
- 47. A statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds. Sci. Rep. 6(1),
doi: 10.1038/srep34256 (2016) (Epub ahead of print). - 48. . QSAR study of tetrahydropteridin derivatives as polo-like kinase 1 (PLK1) Inhibitors with molecular docking and dynamics study. SAR QSAR Environ. Res. 34(2), 91–116 (2023).
- 49. . In silico drug discovery of acetylcholinesterase and butyrylcholinesterase enzymes inhibitors based on quantitative structure-activity relationship (QSAR) and drug-likeness evaluation. J. Mol. Struct. 1229,
doi: 10.1016/j.molstruc.2020.129845 (2021) (Epub ahead of print). - 50. . Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors. Chemosphere 67(2), 351–358 (2007).
- 51. . On the development and validation of QSAR models. In: Computational Toxicology: Volume II. Reisfeld BMayeno AN (Eds). Humana Press, NJ, USA, 499–526 (2013). •• This study demonstrates the method for quantitative structure–activity relationship model building and validation.
- 52. . On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst. 145, 22–29 (2015).
- 53. . Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies. J. Biomol. Struct. Dyn. 37(1), 75–94 (2019).
- 54. . Ecotoxicological QSAR modeling of endocrine disruptor chemicals. J. Hazard. Mater. 369, 707–718 (2019).
- 55. . QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors. SAR QSAR Environ. Res. 16(1–2), 93–102 (2005).
- 56. Use of multi-criteria ranking method for environmental risk assessment of antineoplastic agents and their transformation products. J. Environ. Chem. Eng. 11(2),
doi: 10.1016/j.jece.2023.109588 (2023) (Epub ahead of print). - 57. Ecotoxicological QSAR modelling of the acute toxicity of fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) against two aquatic organisms: consensus modelling and comparison with ECOSAR. Aquat. Toxicol. 255,
doi: 10.1016/j.aquatox.2022.106393 (2023) (Epub ahead of print). - 58. . Predicting the gas-liquid critical temperature of binary mixtures based on the quantitative structure property relationship. Chemom. Intell. Lab. Syst. 167, 190–195 (2017).
- 59. . Enlarging applicability domain of quantitative structure–activity relationship models through uncertainty-based active learning. ACS EST Engg. 2(7), 1211–1220 (2022).
- 60. . Quantitative structure-activity relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta. Comput. Toxicol. 21,
doi: 10.1016/j.comtox.2021.100211 (2022) (Epub ahead of print). - 61. . An approach to identify new antihypertensive agents using thermolysin as model: in silico study based on QSARINS and docking. Arab. J. Chem. 12(8), 4861–4877 (2019).
- 62. . 3D-QSAR, docking and ADMET properties of aurone analogues as antimalarial agents. Heliyon 6(4), e03580 (2020).
- 63. . Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity–activity modelling. J. Hazard. Mater. 258–259, 50–60 (2013).
- 64. . Principles of QSAR modeling: comments and suggestions from personal experience. Int. J. Quant. Struct. Prop. Relatsh. 5(3), 61–97 (2020). •• This study presents the key steps and principles in quantitative structure–activity relationship modeling.
- 65. Development of a quantitative structure-activity relationship model for mechanistic interpretation and quantum yield prediction of singlet oxygen generation from dissolved organic matter. Sci. Total Environ. 712,
doi:10.1016/j.scitotenv.2019.136450 (2020) (Epub ahead of print). - 66. Consensus QSAR models: do the benefits outweigh the complexity? J. Chem. Inf. Model. 47(4), 1460–1468 (2007).
- 67. . Application of QSAR for the identification of key molecular fragments and reliable predictions of effects of textile dyes on growth rate and biomass values of Raphidocelis subcapitata. Aquat. Toxicol. 238,
doi: 10.1016/j.aquatox.2021.105925 (2021) (Epub ahead of print). - 68. Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity. J. Chem. Inf. Model 60(1), 204–211 (2020).
- 69. . Plectrabarbene, a new abietane diterpene from Plectranthus barbatus aerial parts. Molecules 25(10), 2365 (2020).
- 70. . Antiproliferative activity of antibiotics through DNA binding mechanism: evaluation and molecular docking studies. Int. J. Mol. Sci. 24(3), 1–14 (2023).
- 71. . Mangosteen metabolites as promising alpha-amylase inhibitor candidates: in silico and in vitro evaluations. Metabolites 12(12), 1229 (2022).
- 72. . Sampling the bulk viscosity of water with molecular dynamics simulation in the canonical ensemble. J. Phys. Chem. B 126(48), 10172–10184 (2022).
- 73. Design, synthesis, docking, ADMET studies, and anticancer evaluation of new 3-methylquinoxaline derivatives as VEGFR-2 inhibitors and apoptosis inducers. J. Enzyme Inhib. Med. Chem. 36(1), 1760–1782 (2021).
- 74. . In silico molecular docking, DFT analysis and ADMET studies of carbazole alkaloid and coumarins from roots of Clausena anisata: a potent inhibitor for quorum sensing. Adv. Appl. Bioinform. Chem. 14, 13–24 (2021).
- 75. An overview on applications of SwissADME web tool in the design and development of anticancer, antitubercular and antimicrobial agents: a medicinal chemist's perspective. J. Mol. Struct. 1259,
doi: 10.1016/j.molstruc.2022.132712 (2022) (Epub ahead of print). - 76. . Combinative ex vivo studies and in silico models ProTox-II for investigating the toxicity of chemicals used mainly in cosmetic products. Toxicol. Mech. Methods 32(7), 542–548 (2022).
- 77. . Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information. J. Chem. Inf. Comput. Sci. 35(6), 1039–1045 (1995).
- 78. . Molecular similarity based on novel atom-type electrotopological state indices. J. Chem. Inf. Comput. Sci. 35(6), 1074–1080 (1995).
- 79. . A GMDH-type neural network with multi-filter feature selection for the prediction of transition temperatures of bent-core liquid crystals. RSC Adv. 6(102), 99676–99684 (2016).
- 80. . In silico modelling of quantitative structure–activity relationship of multi-target anticancer compounds on k-562 cell line. Netw. Model. Anal. Health Inform. Bioinform. 7(1), 11 (2018).
- 81. . vHTS, 3-D pharmacophore, QSAR and molecular docking studies for the identification of phyto-derived ATP-competitive inhibitors of the BCR-ABL kinase domain. Curr. Drug Discov. Technol. 19(2), 53–61 (2022).