Advances in computer-aided drug design for type 2 diabetes

References

• Ogurtsova K, Rfj D, Huang Y, et al. IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40–50.
   PubMed Web of Science ®Google Scholar
• Alejandro EU, Mamerto TP, and Chung G, et al. Gestational diabetes mellitus: a harbinger of the vicious cycle of diabetes. Int J Mol Sci. 2020;21(14).
   PubMed Web of Science ®Google Scholar
• Gaal Z, Balogh I. Monogenic forms of diabetes mellitus. Exp Suppl. 2019;111:385–416.
   PubMedGoogle Scholar
• Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93(1):137–188.
   PubMed Web of Science ®Google Scholar
• Zimmermann LM, Baptista MS, Tardivo JP, et al. Type II diabetes patients under sildenafil citrate: case series showing benefits and a side effect. Case Rep Med. 2020;2020:4065452.
   PubMed Web of Science ®Google Scholar
• Tramontana M, Hansel K, Bianchi L, et al. Allergic contact dermatitis caused by a glucose monitoring system: an emerging side-effect of diabetes medical devices. J Eur Acad Dermatol Venereol. 2020;34(5):e223–e225.
   PubMed Web of Science ®Google Scholar
• Kuo C, Foon D, Waters K, et al. Central diabetes insipidus: a rare unreported side effect of temozolomide in pediatrics. Pediatr Blood Cancer. 2020;67(12):e28516.
   PubMed Web of Science ®Google Scholar
• Braunwald E. Diabetes, heart failure, and renal dysfunction: the vicious circles. Prog Cardiovasc Dis. 2019;62(4):298–302.
   PubMed Web of Science ®Google Scholar
• Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.
   PubMed Web of Science ®Google Scholar
• Bodmer M, Meier C, Krahenbuhl S, et al. Metformin, sulfonylureas, or other antidiabetes drugs and the risk of lactic acidosis or hypoglycemia: a nested case-control analysis. Diabetes Care. 2008;31(11):2086–2091.
   PubMed Web of Science ®Google Scholar
• Fuchtenbusch M, Standl E, Schatz H. Clinical efficacy of new thiazolidinediones and glinides in the treatment of type 2 diabetes mellitus. Exp Clin Endocrinol Diabetes. 2000;108(3):151–163.
   PubMed Web of Science ®Google Scholar
• Pfeiffer AF, Klein HH. The treatment of type 2 diabetes. Dtsch Arztebl Int. 2014;111(5):69–81, 82.
   PubMed Web of Science ®Google Scholar
• Padhi S, Nayak AK, Behera A. Type II diabetes mellitus: a review on recent drug based therapeutics. Biomed Pharmacother. 2020;131:110708. *A classic review of diabetes.
   PubMed Web of Science ®Google Scholar
• Lu S, Jiang Y, Lv J, et al. Molecular docking and molecular dynamics simulation studies of GPR40 receptor–agonist interactions. J Mol Graphics Modell. 2010;28(8):766–774.
   Google Scholar
• Yuan S, Yu B, Liu HM. New drug approvals for 2019: synthesis and clinical applications. Eur J Med Chem. 2020;205:112667.
   PubMed Web of Science ®Google Scholar
• Fox S, Farr-Jones S, Sopchak L, et al. High-throughput screening: update on practices and success. J Biomol Screen. 2006;11(7):864–869.
   PubMed Web of Science ®Google Scholar
• Basak SC. Recent developments and future directions at current computer aided drug design. Curr Comput Aided Drug Des. 2013;9(1):1.
   PubMed Web of Science ®Google Scholar
• Hurle MR, Yang L, Xie Q, et al. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 2013;93(4):335–341.
   PubMed Web of Science ®Google Scholar
• Macalino SJ, Gosu V, Hong S, et al. Role of computer-aided drug design in modern drug discovery. Arch Pharm Res. 2015;38(9):1686–1701.
   PubMed Web of Science ®Google Scholar
• Yu W, MacKerell AJ. Computer-aided drug design methods. Methods Mol Biol. 2017;1520:85–106.
   PubMedGoogle Scholar
• Edwards G, Winstanley PA, Ward SA. Clinical pharmacokinetics in the treatment of tropical diseases. Some applications and limitations. Clin Pharmacokinet. 1994;27(2):150–165.
   PubMed Web of Science ®Google Scholar
• Scarpino A, Ferenczy GG, Keseru GM. Covalent docking in drug discovery: scope and limitations. Curr Pharm Des. 2020;26(44):5684–5699.
   PubMed Web of Science ®Google Scholar
• Sacan A, Ekins S, Kortagere S. Applications and limitations of in silico models in drug discovery. Methods Mol Biol. 2012;910:87–124.
   PubMedGoogle Scholar
• Imai M, Jobo T, Sato R, et al. Medroxyprogesterone acetate therapy for patients with adenocarcinoma of the endometrium who wish to preserve the uterus-usefulness and limitations. Eur J Gynaecol Oncol. 2001;22(3):217–220.
   PubMed Web of Science ®Google Scholar
• Baig MH, Ahmad K, Roy S, et al. Computer aided drug design: success and limitations. Curr Pharm Des. 2016;22(5):572–581.
   PubMed Web of Science ®Google Scholar
• Fukunishi Y. Structure-based drug screening and ligand-based drug screening with machine learning. Comb Chem High Throughput Screen. 2009;12(4):397–408.
   PubMed Web of Science ®Google Scholar
• Aparoy P, Reddy KK, Reddanna P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem. 2012;19(22):3763–3778.
   PubMed Web of Science ®Google Scholar
• Lionta E, Spyrou G, Vassilatis DK, et al. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem. 2014;14(16):1923–1938.
   PubMed Web of Science ®Google Scholar
• Majekova M. Ligand-based drug design of novel aldose reductase inhibitors. Future Med Chem. 2018;10(21):2493–2496.
   PubMed Web of Science ®Google Scholar
• Nandi S, Saxena M, Saxena AK. Identification of therapeutically active molecules against anthrax through structure and ligand based drug design. Curr Top Med Chem. 2018;18(27):2294–2312.
   PubMed Web of Science ®Google Scholar
• Wang X, Song K, Li L, et al. Structure-based drug design strategies and challenges. Curr Top Med Chem. 2018;18(12):998–1006.
   PubMed Web of Science ®Google Scholar
• Ferreira LG, Dos SR, Oliva G, et al. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384–13421.
   PubMed Web of Science ®Google Scholar
• van Montfort R, Workman P. Structure-based drug design: aiming for a perfect fit. Essays Biochem. 2017;61(5):431–437.
   PubMed Web of Science ®Google Scholar
• Yang GF. Structure-based drug design: strategies and challenges. Curr Pharm Des. 2014;20(5):685–686.
   PubMed Web of Science ®Google Scholar
• Matsumoto K, Miyao T, Funatsu K. Ranking-oriented quantitative structure-activity relationship modeling combined with assay-wise data integration. ACS Omega. 2021;6(18):11964–11973.
   PubMed Web of Science ®Google Scholar
• Booth RG, Selassie CD, Hansch C, et al. Quantitative structure-activity relationship of triazine-antifolate inhibition of Leishmania dihydrofolate reductase and cell growth. J Med Chem. 1987;30(7):1218–1224.
   PubMed Web of Science ®Google Scholar
• Hayashi M, Nakamura Y, Higashi K, et al. A quantitative structure-activity relationship study of the skin irritation potential of phenols. Toxicol in Vitro. 1999;13(6):915–922.
   PubMed Web of Science ®Google Scholar
• Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design–a review. Curr Top Med Chem. 2010;10(1):95–115.
   PubMed Web of Science ®Google Scholar
• Hadni H, Elhallaoui M. 3D-QSAR, docking and ADMET properties of aurone analogues as antimalarial agents. Heliyon. 2020;6(4):e3580.
   PubMed Web of Science ®Google Scholar
• Garro MJ, Vega-Hissi EG, Andrada MF, et al. QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity. Expert Opin Drug Discov. 2015;10(1):37–51.
   PubMed Web of Science ®Google Scholar
• Kitchen DB, Decornez H, Furr JR, et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3(11):935–949.
   PubMed Web of Science ®Google Scholar
• Kontoyianni M. Docking and virtual screening in drug discovery. Methods Mol Biol. 2017;1647:255–266.
   PubMedGoogle Scholar
• Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci. 2019;20(18):4331.
   PubMed Web of Science ®Google Scholar
• Lin X, Li X, Lin X. A review on applications of computational methods in drug screening and design. Molecules. 2020;25(6):1375.
   PubMed Web of Science ®Google Scholar
• Sivakumar KC, Haixiao J, Naman CB, et al. Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein–ligand recognition process. Drug Develop Res. 2020;81(6):685–699.
   PubMed Web of Science ®Google Scholar
• Hildebrand PW, Rose AS, Tiemann J. Bringing molecular dynamics simulation data into view. Trends Biochem Sci. 2019;44(11):902–913.
   PubMed Web of Science ®Google Scholar
• Do PC, Lee EH, Le L. Steered molecular dynamics simulation in rational drug design. J Chem Inf Model. 2018;58(8):1473–1482.
   PubMed Web of Science ®Google Scholar
• Cheng F, Li W, Liu G, et al. In silico admet prediction: recent advances, current challenges and future trends. Curr Top Med Chem. 2013;13(11):1273–1289.
   PubMed Web of Science ®Google Scholar
• Merlot C. Computational toxicology-a tool for early safety evaluation. Drug Discov Today. 2010;15(1–2):16–22.
   PubMed Web of Science ®Google Scholar
• Bharatam PV, Patel DS, Adane L, et al. Modeling and informatics in designing anti-diabetic agents. Curr Pharm Des. 2007;13(34):3518–3530.
   PubMed Web of Science ®Google Scholar
• Desvergne B, Wahli W. Peroxisome proliferator-activated receptors: nuclear control of metabolism. Endocr Rev. 1999;20(5):649–688.
   PubMed Web of Science ®Google Scholar
• Sundriyal S, Viswanad B, Bharathy E, et al. New PPARgamma ligands based on 2-hydroxy-1,4-naphthoquinone: computer-aided design, synthesis, and receptor-binding studies. Bioorg Med Chem Lett. 2008;18(11):3192–3195.
   PubMed Web of Science ®Google Scholar
• Maltarollo VG, Togashi M, Nascimento AS, et al. Structure-based virtual screening and discovery of new PPARdelta/gamma dual agonist and PPARdelta and gamma agonists. Plos One. 2015;10(3):e118790.
   PubMed Web of Science ®Google Scholar
• Prabitha P, Shanmugarajan D, and Kumar T, et al. Multi-conformational frame from molecular dynamics as a structure-based pharmacophore model for mapping, screening and identifying ligands against PPAR-gamma: a new protocol to develop promising candidates. J Biomol Struct Dyn. 2020;1–11.
   PubMed Web of Science ®Google Scholar
• Feng XY, Jia WQ, Liu X, et al. Identification of novel PPARalpha/gamma dual agonists by pharmacophore screening, docking analysis, ADMET prediction and molecular dynamics simulations. Comput Biol Chem. 2019;78:178–189.
   PubMed Web of Science ®Google Scholar
• Al-Najjar BO, Wahab HA, Tengku MT, et al. Discovery of new nanomolar peroxisome proliferator-activated receptor gamma activators via elaborate ligand-based modeling. Eur J Med Chem. 2011;46(6):2513–2529.
   PubMed Web of Science ®Google Scholar
• Covington DK, Briscoe CA, Brown AJ, et al. The G-protein-coupled receptor 40 family (GPR40-GPR43) and its role in nutrient sensing. Biochem Soc Trans. 2006;34(Pt 5):770–773.
   PubMedGoogle Scholar
• Li Z, Qiu Q, Geng X, et al. Free fatty acid receptor agonists for the treatment of type 2 diabetes: drugs in preclinical to phase II clinical development. Expert Opin Inv Drug. 2016;25(8):871–890.
   PubMed Web of Science ®Google Scholar
• Li Z, Xu X, Huang W, et al. Free fatty acid receptor 1 (FFAR1) as an emerging therapeutic target for type 2 diabetes mellitus: recent progress and prevailing challenges. Med Res Rev. 2018;38(2):381–425.
   PubMed Web of Science ®Google Scholar
• Li Z, Zhou Z, Zhang L. Current status of GPR40/FFAR1 modulators in medicinal chemistry (2016-2019): a patent review. Expert Opin Ther Pat. 2020;30(1):27–38.
   PubMed Web of Science ®Google Scholar
• Nath V, Ahuja R, Kumar V. Identification of novel G-protein-coupled receptor 40 (GPR40) agonists by hybrid in silico-screening techniques and molecular dynamics simulations thereof. J Biomol Struct Dyn. 2019;37(14):3764–3787.
   PubMed Web of Science ®Google Scholar
• Takano R, Yoshida M, Inoue M, et al. Discovery of 3-aryl-3-ethoxypropanoic acids as orally active GPR40 agonists. Bioorg Med Chem Lett. 2014;24(13):2949–2953.
   PubMed Web of Science ®Google Scholar
• Negoro N, Sasaki S, Mikami S, et al. Discovery of TAK-875: a potent, selective, and orally bioavailable gpr40 agonist. Acs Med Chem Lett. 2010;1(6):290–294.
   PubMed Web of Science ®Google Scholar
• Gajjar KA, Gajjar AK. Combiphore (structure and ligand based pharmacophore) - approach for the design of gpr40 modulators in the management of diabetes. Curr Drug Discov Technol. 2020;17(2):233–247.
   PubMedGoogle Scholar
• Chagas CM, Moss S, Alisaraie L. Drug metabolites and their effects on the development of adverse reactions: revisiting Lipinski’s rule of five. Int J Pharm. 2018;549(1–2):133–149.
   PubMed Web of Science ®Google Scholar
• Chen X, Li H, Tian L, et al. Analysis of the physicochemical properties of acaricides based on Lipinski’s rule of five. J Comput Biol. 2020;27(9):1397–1406.
   PubMed Web of Science ®Google Scholar
• Li Z, Hu L, Wang X, et al. Design, synthesis, and biological evaluation of novel dual FFA1 (GPR40)/PPARdelta agonists as potential anti-diabetic agents. Bioorg Chem. 2019;92:103254.
   PubMed Web of Science ®Google Scholar
• Li Z, Ren Q, Wang X, et al. Discovery of HWL-088: a highly potent FFA1/GPR40 agonist bearing a phenoxyacetic acid scaffold. Bioorg Chem. 2019;92:103209.
   PubMed Web of Science ®Google Scholar
• Li Z, Zhou Z, Deng F, et al. Design, synthesis, and biological evaluation of novel pan agonists of FFA1, PPARgamma and PPARdelta. Eur J Med Chem. 2018;159:267–276.
   PubMed Web of Science ®Google Scholar
• Nongonierma AB, Cadamuro C, Le Gouic A, et al. Dipeptidyl peptidase IV (DPP-IV) inhibitory properties of a camel whey protein enriched hydrolysate preparation. Food Chem. 2019;279:70–79.
   PubMed Web of Science ®Google Scholar
• Cai J, Li C, Liu Z, et al. Predicting DPP-IV inhibitors with machine learning approaches. J Comput Aided Mol Des. 2017;31(4):393–402.
   PubMed Web of Science ®Google Scholar
• Tanwar O, Tanwar L, Shaquiquzzaman M, et al. Structure based virtual screening of MDPI database: discovery of structurally diverse and novel DPP-IV inhibitors. Bioorg Med Chem Lett. 2014;24(15):3447–3451.
   PubMed Web of Science ®Google Scholar
• Xing J, Li Q, Zhang S, et al. Identification of dipeptidyl peptidase IV inhibitors: virtual screening, synthesis and biological evaluation. Chem Biol Drug Des. 2014;84(3):364–377.
   PubMed Web of Science ®Google Scholar
• Shen J, Deng X, and Sun R, et al. Structural optimization of pyrazolo[1,5-a]pyrimidine derivatives as potent and highly selective DPP-4 inhibitors. Eur J Med Chem. 2020 208 15;112850.
   PubMedGoogle Scholar
• Grimshaw CE, Jennings A, Kamran R, et al. Trelagliptin (SYR-472, Zafatek), novel once-weekly treatment for type 2 diabetes, inhibits dipeptidyl peptidase-4 (dpp-4) via a non-covalent mechanism. Plos One. 2016;11(6):e157509.
   PubMed Web of Science ®Google Scholar
• Li S, Qin C, Cui S, et al. Discovery of a natural-product-derived preclinical candidate for once-weekly treatment of type 2 diabetes. J Med Chem. 2019;62(9):4779. (vol 62, pg 2348, 2019).
   PubMed Web of Science ®Google Scholar
• Eleftheriou P, Geronikaki A, Petrou A. PTP1b inhibition, a promising approach for the treatment of diabetes type II. Curr Top Med Chem. 2019;19(4):246–263.
   PubMed Web of Science ®Google Scholar
• Wu J, Ma Y, Zhou H, et al. Identification of protein tyrosine phosphatase 1B (PTP1B) inhibitors through De Novo evoluton, synthesis, biological evaluation and molecular dynamics simulation. Biochem Biophys Res Commun. 2020;526(1):273–280.
   PubMed Web of Science ®Google Scholar
• Gimeno A, Ardid-Ruiz A, Jose Ojeda-Montes M, et al. combined ligand- and receptor-based virtual screening methodology to identify structurally diverse protein tyrosine phosphatase 1b inhibitors. Chemmedchem. 2018;13(18):1939–1948.
   PubMed Web of Science ®Google Scholar
• Manoj A, Das S, Kunnath RA, et al. SGLT-2 inhibitors, an accomplished development in field of medicinal chemistry: an extensive review. Future Med Chem. 2020;12(21):1961–1990.
   PubMed Web of Science ®Google Scholar
• Verma S, McMurray J. SGLT-2 inhibitors and mechanisms of cardiovascular benefit: a state-of-the-art review. Diabetologia. 2018;61(10):2108–2117.
   PubMed Web of Science ®Google Scholar
• Zou CY, Liu XK, Sang YQ, et al. Effects of SGLT-2 inhibitors on cardiovascular outcomes and mortality in type 2 diabetes: a meta-analysis. Medicine (Baltimore). 2019;98(49):e18245.
   PubMed Web of Science ®Google Scholar
• McGuire DK, Shih WJ, Cosentino F, et al. Association of SGLT-2 inhibitors with cardiovascular and kidney outcomes in patients with type 2 diabetes: a meta-analysis. Jama Cardiol. 2021;6(2):148–158.
   PubMed Web of Science ®Google Scholar
• Liu W, Wang H, and Meng F. In silico modeling of aspalathin and nothofagin against SGLT-2. J Theor Comput Chem. 2015;14(8).
   Web of Science ®Google Scholar
• Kumar S, Khatik GL, Mittal A. In silico molecular docking study to search new sglt-2 inhibitor based on dioxabicyclo[3.2.1] octane scaffold. Curr Comput Aided Drug Des. 2020;16(2):145–154.
   PubMed Web of Science ®Google Scholar
• Wu JS, Peng YH, Wu JM, et al. Discovery of non-glycoside sodium-dependent glucose co-transporter 2 (SGLT-2) inhibitors by ligand-based virtual screening. J Med Chem. 2010;53(24):8770–8774.
   PubMed Web of Science ®Google Scholar
• Sato S, Takeo J, Aoyama C, et al. Na+-glucose cotransporter (SGLT) inhibitory flavonoids from the roots of sophora flavescens. Bioorg Med Chem. 2007;15(10):3445–3449.
   PubMed Web of Science ®Google Scholar
• Thilagavathi R, Hosseini Zare MS, Malini M, et al. A comprehensive review on glucokinase activators: promising agents for the treatment of type 2 diabetes. Chem Biol Drug Des. 2021;99(2):247–263.
   PubMed Web of Science ®Google Scholar
• Min Q, Cai X, and Sun W, et al. Identification of mangiferin as a potential glucokinase activator by structure-based virtual ligand screening. Sci Rep-Uk. 2017;7(1 44681).
   PubMedGoogle Scholar
• Ashton KS, Andrews KL, Bryan MC, et al. Small molecule disruptors of the glucokinase–glucokinase regulatory protein interaction: 1. discovery of a novel tool compound for in vivo proof-of-concept. J Med Chem. 2014;57(2):309–324.
   PubMed Web of Science ®Google Scholar
• Tamayo NA, Norman MH, Bartberger MD, et al. Small molecule disruptors of the glucokinase–glucokinase regulatory protein interaction: 5. a novel aryl sulfone series, optimization through conformational analysis. J Med Chem. 2015;58(11):4462–4482.
   PubMed Web of Science ®Google Scholar
• Villhauer EB, Brinkman JA, Naderi GB, et al. 1-[[(3-hydroxy-1-adamantyl)amino]acetyl]-2-cyano-(S)-pyrrolidine: a potent, selective, and orally bioavailable dipeptidyl peptidase IV inhibitor with antihyperglycemic properties. J Med Chem. 2003;46(13):2774–2789.
   PubMed Web of Science ®Google Scholar
• Grinter SZ, Zou X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules. 2014;19(7):10150–10176.
   PubMed Web of Science ®Google Scholar
• Lee Y, Basith S, Choi S. Recent advances in structure-based drug design targeting class a g protein-coupled receptors utilizing crystal structures and computational simulations. J Med Chem. 2018;61(1):1–46.
   PubMed Web of Science ®Google Scholar
• Senior AW, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706–710.
   PubMed Web of Science ®Google Scholar
• Zhong F, Xing J, Li X, et al., Artificial intelligence in drug design. Sci China Life Sci. 2018;61(10): 1191–1204.
   PubMed Web of Science ®Google Scholar
• Hessler G, Baringhaus KH. Artificial intelligence in drug design. Molecules. 2018;23(10):2520.
   PubMed Web of Science ®Google Scholar
• Rampton V. Artificial intelligence versus clinicians. BMJ. 2020;369:m1326.
   PubMedGoogle Scholar
• Richards BA, Lillicrap TP, Beaudoin P, et al. A deep learning framework for neuroscience. Nat Neurosci. 2019;22(11):1761–1770.
   PubMed Web of Science ®Google Scholar
• Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688–702.
   PubMed Web of Science ®Google Scholar
• Jing Y, Bian Y, Hu Z, et al. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. Aaps J. 2018;20(3):58.
   PubMed Web of Science ®Google Scholar
• Langner S, Beller E, Streckenbach F. Artificial intelligence and big data. Klin Monbl Augenheilkd. 2020;237(12):1438–1441.
   PubMed Web of Science ®Google Scholar
• Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038.
   PubMed Web of Science ®Google Scholar