Synthesis and Antidiabetic Evaluation of  N ’-Benzylidenebenzohydrazide Derivatives by  In Silico  Studies

Yusuf Syaril Alam; Pratiwi Pudjiastuti; Saipul Maulana; Nur Rahmayanti Afifah; Fahimah Martak; Arif Fadlan; Tutik Sri Wahyuni; Syukri Arief

doi:10.22146/ijc.82073

Synthesis and Antidiabetic Evaluation of N’-Benzylidenebenzohydrazide Derivatives by In Silico Studies

https://doi.org/10.22146/ijc.82073

Yusuf Syaril Alam⁽¹⁾, Pratiwi Pudjiastuti^(2*), Saipul Maulana⁽³⁾, Nur Rahmayanti Afifah⁽⁴⁾, Fahimah Martak⁽⁵⁾, Arif Fadlan⁽⁶⁾, Tutik Sri Wahyuni⁽⁷⁾, Syukri Arief⁽⁸⁾

(1) Department of Chemistry, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Jl. Arif Rahman Hakim, Surabaya 60115, Indonesia
(2) Department of Chemistry, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia
(3) Department of Pharmaceutical Science, Faculty of Pharmacy, Airlangga University, Surabaya 60115, Indonesia
(4) Department of Chemistry, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Jl. Arif Rahman Hakim, Surabaya 60115, Indonesia
(5) Department of Chemistry, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Jl. Arif Rahman Hakim, Surabaya 60115, Indonesia
(6) Department of Chemistry, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Jl. Arif Rahman Hakim, Surabaya 60115, Indonesia
(7) Department of Pharmaceutical Science, Faculty of Pharmacy, Airlangga University, Surabaya 60115, Indonesia
(8) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Andalas University, Limau Manis Campus, Padang 25163, Indonesia
(*) Corresponding Author

Abstract

Two new of N’-benzylidenebenzohydrazide (NBB) derivatives were successfully synthesized and yielded 50–58%. FTIR, ESI-MS, ¹H-NMR and ¹³C-NMR were used to investigate the characteristic of NBB derivates. The structure and relationship of NBB derivatives into α-glucosidase and α-amylase as good targets for diabetes treatment were evaluated using in silico screening. Molecular Mechanics-Poisson Boltzmann/Generalized Born Surface Area (MM-PB/GBSA) was used to calculate the free binding energy (ΔG_{bind (MM-GBSA)}) of NBB to α-glucosidase and α-amylase receptors showed that the results of −0.45 and −20.79 kcal/mol respectively. In the ortho position, NBB derivatives exhibited electron donating groups (EDG like -OCH₃, -OH and -Cl with binding free energies of −21.94, −6.71 and 21.94, respectively, and acarbose, a native ligand energy of 32.62 kcal/mol. In addition, the binding free energy of N-2-(-OCH₃, -OH and -Cl)-NBB to the α-amylase receptor showed the number of −39.33, −43.96, −42.81, respectively and −46.51 kcal/mol in comparing with a native ligand. As a result, it was found that all the NBB derivatives were able to interact with several amino acids in the α-glucosidase cavity as well as the native ones, including Ala281, Asp282, and Asp616. NBB and native ligand showed similar interaction between α-amylase with Gly110 amino acid residue.

Keywords

N’-Benzylidenebenzohydrazide; α-amylase; derivatives; antidiabetic; In silico

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References

[1] Ramadhan, R., and Phuwapraisirisan, P., 2015, Arylalkanones from Horsfieldia macrobotrys are effective antidiabetic agents achieved by α-glucosidase inhibition and radical scavenging, Nat. Prod. Commun., 10 (2), 325–328.

[2] Poovitha, S., and Parani, M., 2016, In vitro and in vivo α-amylase and α-glucosidase inhibiting activities of the protein extracts from two varieties of bitter gourd (Momordica charantia L.), BMC Complementary Altern. Med., 16 (1), 185.

[3] Dirir, A.M., Daou, M., Yousef, A.F., and Yousef, L.F., 2022, A review of alpha-glucosidase inhibitors from plants as potential candidates for the treatment of type-2 diabetes, Phytochem. Rev., 21 (4), 1049–1079.

[4] Tan, K., Tesar, C., Wilton, R., Jedrzejczak, R.P., and Joachimiak, A., 2018, Interaction of antidiabetic a-glucosidase inhibitors and gut bacteria α-glucosidase, Protein Sci., 27 (8), 1498–1508.

[5] Bashary, R., Vyas, M., Nayak, S.K., Suttee, A., Verma, S., Narang, R., and Khatik, G.L., 2020, An Insight of alpha-amylase inhibitors as a valuable tool in the management of type 2 diabetes mellitus, Curr. Diabetes Rev., 16 (2), 117–136.

[6] Kaur, N., Kumar, V., Nayak, S.K., Wadhwa, P., Kaur, P., and Sahu, S.K., 2021, Alpha-amylase as molecular target for treatment of diabetes mellitus: A comprehensive review, Chem. Biol. Drug Des., 98 (4), 539–560.

[7] Taha, M., Ismail, N.H., Lalani, S., Fatmi, M.Q., tul-Wahab, A., Siddiqui, S., Khan, K.M., Imran, S., and Choudary, M.I., 2015, Synthesis of novel inhibitors of α-glucosidase based on the benzothiazole skeleton containing benzohydrazide moiety and their molecular docking studies, Eur. J. Med. Chem., 92, 387–400.

[8] Ullah, H., Uddin, I, Rahim, F., Khan, F., Sobia, S., Taha, S.M., Khan, M.U., Hayat, S., Ullah, M., Gul, Z., Ullah, S., Zada, H., and Hussain, J., 2022, In vitro α-glucosidase and α-amylase inhibitory potential and molecular docking studies of benzohydrazide based imines and thiazolidine-4-one derivatives, J. Mol. Struct., 1251, 132058.

[9] Fan, M., Yang, W., Peng, Z., He, Y., and Wang, G., 2023, Chromone-based benzohydrazide derivatives as potential α-glucosidase inhibitor: Synthesis, biological evaluation and molecular docking study, Bioorg. Chem., 131, 106276.

[10] Jubie, S., Ashish, W., Sabaritha, K., Nishanthini, P., Thomas, A., and Antony, J., 2016, Synthesis and in-vitro anti-cancer screening of N¹ [(substituted phenyl)benzylidene]benzohydrazides, J. Pharm. Sci. Res., 8 (7), 582–585.

[11] Lanka, G., Bathula, R., Bhargavi, M., and Potlapally, S.R., 2019, Homology modeling and molecular docking studies for the identification of novel potential therapeutics against human PHD3 as a drug target for type 2 diabetes mellitus, J. Drug Delivery Ther., 9 (4), 265–273.

[12] Johnston, R.C., Yao, K., Kaplan, Z., Chelliah, M., Leswing, K., Seekins, S., Watts, S., Calkins, D., Chief Elk, J., Jerome, S.V., Repasky, M.P., and Shelley, J.C., 2023, Epik: pK_a and protonation state prediction through machine learning, J. Chem. Theory Comput., 19 (8), 2380–2388.

[13] Lu, C., Wu, C., Ghoreishi, D., Chen, W., Wang, L., Damm, W., Ross, G.A., Dahlgren, M.K., Russell, E., Von Bargen, C.D., Abel, R., Friesner, R.A., and Harder, E.D., 2021, OPLS4: Improving force field accuracy on challenging regimes of chemical space, J. Chem. Theory Comput., 17 (7), 4291–4300.

[14] Choudhary, M.I., Shaikh, M., tul-Wahab, A., and ur-Rahman, A., 2020, In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation, PLoS One, 15 (7), e0235030.

[15] Khairul Ikram, N.K., Durrant, J.D., Muchtaridi, M., Zalaludin, A.S., Purwitasari, N., Mohamed, N., Abdul Rahim, A.S., Lam, C.K., Normi, Y.M., Abd Rahman, N., Amaro, R.E., and Wahab, H.A., 2015, A virtual screening approach for identifying plants with anti H5N1 neuraminidase activity, J. Chem. Inf. Model., 55 (2), 308–316.

[16] Schrödinger Release 2022-1, 2021, Desmond Molecular Dynamics System, D.E. Shaw Research, New York.

[17] Fusani, L., Palmer, D.S., Somers, D.O., and Wall, I.D., 2020, Exploring ligand stability in protein crystal structures using binding pose metadynamics, J. Chem. Inf. Model., 60 (3), 1528–1539.

[18] Rostkowski, M., Olsson, M.H.M., Søndergaard, C.R., and Jensen, J.H., 2011, Graphical analysis of pH-dependent properties of proteins predicted using PROPKA, BMC Struct. Biol., 11 (1), 6.

[19] Schrödinger Release 2022-1, 2022, Protein Preparation Wizard, Epik, Schrödinger, LLC, New York.

[20] Jokinen, E.M., Niemeläinen, M., Kurkinen, S.T., Lehtonen, J.V., Lätti, S., Postila, P.A., Pentikäinen, O.T., and Niinivehmas, S.P., 2023, Virtual screening strategy to identify retinoic acid-related orphan receptor γt modulators, Molecules, 28 (8), 3420.

[21] Friesner, R.A., Banks, J.L., Murphy, R.B., Halgren, T.A., Klicic, J.J., Mainz, D.T., Repasky, M.P., Knoll, E.H., Shelley, M., Perry, J.K., Shaw, D.E., Francis, P., and Shenkin, P.S., 2004, Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy, J. Med. Chem., 47 (7), 1739–1749.

[22] Hevener, K.E., Zhao, W., Ball, D.M., Babaoglu, K., Qi, J., White, S., and Lee, R.E., 2009, Validation of molecular docking programs for virtual screening against dihydropteroate synthase, J. Chem. Inf. Model., 49 (2), 444–460.

[23] Schrödinger Release 2022-1, 2022, Glide, Schrödinger, LLC, New York.

[24] Genheden, S., and Ryde, U., 2015, The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities, Expert Opin. Drug Discovery, 10 (5), 449–461.

[25] Friesner, R.A., Murphy, R.B., Repasky, M.P., Frye, L.L., Greenwood, J.R., Halgren, T.A., Sanschagrin, P.C., and Mainz, D.T., 2006, Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes, J. Med. Chem., 49 (21), 6177–6196.

[26] Schrödinger Release 2022-1, 2022, Prime, Schrödinger, LLC, New York.

[27] Al-Karmalawy, A.A., Dahab, M.A., Metwaly, A.M., Elhady, S.S., Elkaeed, E.B., Eissa, I.H., and Darwish, K.M., 2021, Molecular docking and dynamics simulation revealed the potential inhibitory activity of ACEIs against SARS-CoV-2 targeting the hACE2 receptor, Front. Chem., 9, 661230.

[28] Kumar, S., Narwal, S., Kumar, V., and Prakash, O., 2011, α-Glucosidase inhibitors from plants: A natural approach to treat diabetes, Pharmacogn. Rev., 5 (9), 19–29.

[29] Kazmi, M., Zaib, S., Ibrar, A., Amjad, S.T., Shafique, Z., Mehsud, S., Saeed, A., Iqbal, J., and Khan, I., 2018, A new entry into the portfolio of α-glucosidase inhibitors as potent therapeutics for type 2 diabetes: Design, bioevaluation and one-pot multi-component synthesis of diamine-bridged coumarinyl oxadiazole conjugates, Bioorg. Chem., 77, 190–202.

[30] Feng, Y., Nan, H., Zhou, H., Xi, P., and Li, B., 2022, Mechanism of inhibition of α-glucosidase activity by bavachalcone, Food Sci. Technol., 42, e123421.

[31] Hakim, R.W., Fadilah, F., Tarigan, T.J.E., Jusman, S.W.A., and Purwaningsih, E.H., 2021, Molecular study of Acalypha indica to leptin, alpha glucosidase, and its antihyperglycemic effect on alpha alucosidase, Pharmacogn. J., 13 (6s), 1639–1647.

[32] Li, D.Q., Qian, Z.M., and Li, S.P., 2010, Inhibition of three selected beverage extracts on α-glucosidase and rapid identification of their active compounds using HPLC-DAD-MS/MS and biochemical detection, J. Agric. Food Chem., 58 (11), 6608–6613.

[33] Kim, K.T., Rioux, L.E., and Turgeon, S.L., 2014, Alpha-amylase and alpha-glucosidase inhibition is differentially modulated by fucoidan obtained from Fucus vesiculosus and Ascophyllum nodosum, Phytochemistry, 98, 27–33.

[34] Papoutsis, K., Zhang, J., Bowyer, M.C., Brunton, N., Gibney, E.R., and Lyng, J., 2021, Fruit, vegetables, and mushrooms for the preparation of extracts with α-amylase and α-glucosidase inhibition properties: A review, Food Chem., 338, 128119.

[35] Lahlou, M., 2007, Screening of natural products for drug discovery, Expert Opin. Drug Discovery, 2 (5), 697–705.

[36] Xue, Q., Liu, X., Russell, P., Li, J., Pan, W., Fu, J., and Zhang, A., 2022, Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock, Ecotoxicol. Environ. Saf., 233, 113323.

[37] Peng, W., Shen, H., Lin, B., Han, P., Li, C., Zhang, Q., Ye, B., Rahman, K., Xin, H., Qin, L., and Han, T., 2018, Docking study and antiosteoporosis effects of a dibenzybutane ligan isolated from Litsea cubeba targeting Capthepsin K and MEK1, Med. Chem. Res., 27 (9), 2062–2070.

[38] Roig-Zamboni, V., Cobucci-Ponzano, B., Iacono, R., Ferrara, M.C., Germany, S., Bourne, Y., Parenti, G., Morraci, M., and Sulzenbacher, G., 2017, Structure of human lysosomal acid α-glucosidase–a guide for the treatment of Pompe disease, Nat. Commun., 8 (1), 1111.

DOI: https://doi.org/10.22146/ijc.82073