Gliptins vs. Milk-derived Dipeptidyl-Peptidase IV Inhibiting Biopeptides: Physicochemical Characterization and Pharmacokinetic Profiling

Authors

  • Jorge Andrés Barrero Universidad Nacional de Colombia
  • Fabio Cabrera Department of Science & Technology Studies, Cornell University.
  • Claudia Marcela Cruz Gimnasio Vermont, Head of the Natural Science Department, IB Chemistry Division

DOI:

https://doi.org/10.17533/udea.vitae.v28n3a346531

Keywords:

Bioactive peptides, Dipeptidyl-Peptidase IV inhibitors, Type 2 Diabetes Mellitus, Pharmacokinetics

Abstract

Background: Milk-derived biopeptides have reported in vitro dipeptidyl-peptidase IV (DPP-IV) inhibition, suggesting a glycemic-regulatory effect in Type 2 Diabetes Mellitus (T2DM). Nonetheless, the therapeutic application of these nutraceuticals is limited by the scarcity of knowledge regarding their pharmacokinetic profile. Objective: This study aimed to characterize and assess the pharmacokinetics of milk-derived biopeptides. Through an in silico comparative analysis with gliptins, we expected to identify enhanced properties in food-hydrolysates and suitable DPP-IV inhibiting peptides as candidates for T2DM therapy. Methods: A comparison between gliptins and biopeptides was conducted based on in silico evaluation of drug-likeness, physicochemical properties, pharmacokinetics, and synthetic accessibility. Suitable target proteins for gastrointestinal-absorbable biopeptides were determined as well. Data collection was performed on SwissADME, ADMETlab, DrugBank, SwissTargetPrediction, ChemDes, and BIOPEP-UWM platforms. Statistical analysis was carried out using a one-way ANOVA test. Results: Drug-likeness compliance showed no significant difference between gliptins and biopeptides (p>0.05) in three out of nine assessed rules, though gastrointestinal-absorbable biopeptides exhibited no significant difference with gliptins in five drug-likeness guidelines. The physicochemical evaluation revealed a significant difference (p<0.05) between both groups, with peptides exhibiting enhanced solubility, flexibility, and polarity. Nine out of thirty-six assessed biopeptides reported being likely gastrointestinal-absorbable molecules, from which six displayed ≥30% predicted bioavailability, two reported CYP450 interactions, and all were determined to be blood confined. Biopeptides showed a slightly lower clearance than gliptins yet counteracted by a significantly lower half-life. Moreover, synthetic accessibility scores indicated higher synthetic ease for biopeptides. In addition, absorbable bioactive peptides reported a considerable binding affinity to DPP-IV and Calpain-I. Conclusions: Compared to gliptins, gastrointestinal-absorbable biopeptides exhibit superior physicochemical properties (higher solubility, flexibility, and polarity), lesser CYP450 interactions, higher synthetic ease, and some reported an important affinity for DPP-IV and Calpain-I. Only a small fraction of milk-derived biopeptides are suitable drug-like compounds and feasible candidates for T2DM therapy; yet, testing their therapeutic potency remains subject to further studies.

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References

Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16(7):377–90. DOI: https://doi.org/10.1038/s41581-020-0278-5

Rieddle MC, Ahmann AJ. Therapeutics of Type 2 Diabetes Mellitus. In: Williams Textbook of Endocrinology. 14th ed. Elsevier; 2020. p. 1386.

Doumas M, Imprialos K, Stavropoulos K, Athyros VG. Pharmacological Management of Type 2 Diabetes Complications. Curr Vasc Pharmacol. 2020;18(2):101–3. DOI: https://doi.org/10.2174/157016111802200101155519

Mockus I, Trujillo M. Obesidad y Enfermedades Asociadas, 1st ed., Ismena M, Martha T, editors. Universidad Nacional de Colombia; 2013. 230 p.

Paschou SA, Siasos G, Bletsa E, Stampouloglou PK, Oikonomou E, Antonopoulos AS, et al. The Effect of DPP-4i on Endothelial Function and Arterial Stiffness in Patients with Type 2 Diabetes: A Systematic Review of Randomized Placebo-controlled Trials. Curr Pharm Des. 2020;26(46):5980–7. DOI: https://doi.org/10.2174/1381612826666200417153241

Sachdeva V, Roy A, Bharadvaja N. Current Prospects of Nutraceuticals: A Review. Curr Pharm Biotechnol. 2020;21(10):884–96. DOI: https://doi.org/10.2174/1389201021666200130113441

Premi M, Bansal V. Nutraceuticals for Management of Metabolic Disorders. In: Treating endocrine and metabolic disorders with herbal medicines. 2021. p. 298–320.

Jao C-L, Hung C-C, Tung Y-S, Lin P-Y, Chen M-C, Hsu K-C. The development of bioactive peptides from dietary proteins as a dipeptidyl peptidase IV inhibitor for the management of type 2 diabetes. BioMedicine. 2015;5(3):14. DOI: https://doi.org/10.7603/s40681-015-0014-9

Patil P, Mandal S, Tomar SK, Anand S. Food protein-derived bioactive peptides in management of type 2 diabetes. Eur J Nutr. 2015;54(6):863–80. DOI: https://doi.org/10.1007/s00394-015-0974-2

Acquah C, Dzuvor CK, Tosh S, Agyei D. Anti-diabetic effects of bioactive peptides: recent advances and clinical implications. Crit Rev Food Sci Nutr. 2020;1–14. DOI: https://doi.org/10.1080/10408398.2020.1851168

Iwaniak A, Minkiewicz P, Darewicz M, Hrynkiewicz M. Food protein-originating peptides as tastants - Physiological, technological, sensory, and bioinformatic approaches. Food Res Int. 2016;89:27–38. DOI: https://doi.org/10.1016/j.foodres.2016.08.010

Dong J, Wang N-N, Yao Z-J, Zhang L, Cheng Y, Ouyang D, et al. ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminformatics. 2018;10(1):29. DOI: https://doi.org/10.1186/s13321-018-0283-x

Barrero JA, Cruz CM, Casallas J, Vásquez JS. Evaluación in silico de péptidos bioactivos derivados de la digestión de proteínas presentes en la leche de bovino (B.taurus), oveja (O.aries), cabra (C.hircus) y búfalo (B.bubalis). TecnoLógicas. 2020;50(24). DOI: https://doi.org/10.22430/22565337.1731

Nongonierma AB, Mooney C, Shields DC, FitzGerald RJ. Inhibition of dipeptidyl peptidase IV and xanthine oxidase by amino acids and dipeptides. Food Chem. 2013;141(1):644–53. DOI: https://doi.org/10.1016/j.foodchem.2013.02.115

Nongonierma AB, FitzGerald RJ. An in silico model to predict the potential of dietary proteins as sources of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. Food Chem. 2014;165:489–98. DOI: https://doi.org/10.1016/j.foodchem.2014.05.090

Cheung HS, Wang FL, Ondetti MA, Sabo EF, Cushman DW. Binding of peptide substrates and inhibitors of angiotensin-converting enzyme. Importance of the COOH-terminal dipeptide sequence. J Biol Chem. 1980;255(2):401–7. https://pubmed.ncbi.nlm.nih.gov/6243277/

Lan VTT, Ito K, Ohno M, Motoyama T, Ito S, Kawarasaki Y. Analyzing a dipeptide library to identify human dipeptidyl peptidase IV inhibitor. Food Chem. 2015;175:66–73. DOI: https://doi.org/10.1016/j.foodchem.2014.11.131

Nongonierma AB, FitzGerald RJ. Susceptibility of milk protein-derived peptides to dipeptidyl peptidase IV (DPP-IV) hydrolysis. Food Chem. 2014;145:845–52. DOI: https://doi.org/10.1016/j.foodchem.2013.08.097

Nongonierma AB, FitzGerald RJ. Inhibition of dipeptidyl peptidase IV (DPP-IV) by proline containing casein-derived peptides. J Funct Foods. 2013;5(4):1909–17. DOI: https://doi.org/10.1016/j.jff.2013.09.012

Silveira ST, Martínez D, Recio I, Hernández B. Dipeptidyl peptidase-IV inhibitory peptides generated by tryptic hydrolysis of a whey protein concentrate rich in β-lactoglobulin. Food Chem. 2013;141(2):1072–7. DOI: https://doi.org/10.1016/j.foodchem.2013.03.056

Gallego M, Aristoy M-C, Toldrá F. Dipeptidyl peptidase IV inhibitory peptides generated in Spanish dry-cured ham. Meat Sci. 2014;96(2):757–61. DOI: https://doi.org/10.1016/j.meatsci.2013.09.014

Dong J, Cao D-S, Miao H-Y, Liu S, Deng B-C, Yun Y-H, et al. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminformatics. 2015;7(1):60. DOI: https://doi.org/10.1186/s13321-015-0109-z

Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7(1):42717. DOI: https://doi.org/10.1038/srep42717

Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357–64. DOI: https://doi.org/10.1093/nar/gkz382

Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–82. DOI: https://doi.org/10.1093/nar/gkx1037

Minkiewicz, Iwaniak, Darewicz. BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities. Int J Mol Sci. 2019;20(23):5978. DOI: https://doi.org/10.3390/ijms20235978

Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods. 2000;44(1):235–49. DOI: https://doi.org/10.1016/s1056-8719(00)00107-6

Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. J Med Chem. 2002;45(12):2615–23. DOI: https://doi.org/10.1021/jm020017n

Ghose AK, Viswanadhan VN, Wendoloski JJ. A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases. J Comb Chem. 1999;1(1):55–68. DOI: https://doi.org/10.1021/cc9800071

Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol. 2004;1(4):337–41. DOI: https://doi.org/10.1016/j.ddtec.2004.11.007

Oprea TI. Property distribution of drug-related chemical databases. J Comput Aided Mol Des. 2000;14(3):251–64. DOI: https://doi.org/10.1023/A:1008130001697

Gupta M, Lee HJ, Barden CJ, Weaver DF. The Blood–Brain Barrier (BBB) Score. J Med Chem. 2019;62(21):9824–36. DOI: https://doi.org/10.1021/acs.jmedchem.9b01220

Egan WJ, Merz, KM, Baldwin JJ. Prediction of Drug Absorption Using Multivariate Statistics. J Med Chem. 2000;43(21):3867–77. DOI: https://doi.org/10.1021/jm000292e

Muegge I, Heald SL, Brittelli D. Simple Selection Criteria for Drug-like Chemical Matter. J Med Chem. 2001;44(12):1841–6. DOI: https://doi.org/10.1021/jm015507e

Varma MV, Obach RS, Rotter C, Miller HR, Chang G, Steyn SJ, et al. Physicochemical Space for Optimum Oral Bioavailability: Contribution of Human Intestinal Absorption and First-Pass Elimination. J Med Chem. 2010;53(3):1098–108. DOI: https://doi.org/10.1021/jm901371v

Daina A, Zoete V. A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem. 2016;11(11):1117-21. DOI: https://doi.org/10.1002/cmdc.201600182

Tian S, Li Y, Wang J, Zhang J, Hou T. ADME Evaluation in Drug Discovery. 9. Prediction of Oral Bioavailability in Humans Based on Molecular Properties and Structural Fingerprints. Mol Pharm. 2011;8(3):841–51. DOI: https://doi.org/10.1021/mp100444g

Tian S, Wang J, Li Y, Li D, Xu L, Hou T. The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev. 2015;86:2–10. DOI: https://doi.org/ 10.1016/j.addr.2015.01.009

Ou-Yang S, Lu J, Kong X, Liang Z, Luo C, Jiang H. Computational drug discovery. Acta Pharmacol Sin. 2012;33(9):1131–40. DOI: https://doi.org/10.1038/aps.2012.109

Loureiro DRP, Soares JX, Costa JC, Magalhães ÁF, Azevedo CMG, Pinto MMM, et al. Structures, Activities and Drug-Likeness of Anti-Infective Xanthone Derivatives Isolated from the Marine Environment: A Review. Molecules. 2019;24(2):243. DOI: https://doi.org/10.3390/molecules24020243

Hitchcock SA. Blood–brain barrier permeability considerations for CNS-targeted compound library design. Curr Opin Chem Biol. 2008;12(3):318–23. DOI: https://doi.org/10.1016/j.cbpa.2008.03.019

Jia C-Y, Li J-Y, Hao G-F, Yang G-F. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discov Today. 2020;25(1):248–58. DOI: https://doi.org/10.1016/j.drudis.2019.10.014

J Ji D, Xu M, Udenigwe CC, Agyei D. Physicochemical characterisation, molecular docking, and drug-likeness evaluation of hypotensive peptides encrypted in flaxseed proteome. Curr Res Food Sci. 2020;3:41–50. DOI: https://doi.org/10.1016/j.crfs.2020.03.001

Meanwell NA. Improving Drug Candidates by Design: A Focus on Physicochemical Properties As a Means of Improving Compound Disposition and Safety. Chem Res Toxicol. 2011 Sep 19;24(9):1420–56. DOI: https://doi.org/10.1021/tx200211v

Das T, Mehta CH, Nayak UY. Multiple approaches for achieving drug solubility: an in silico perspective. Drug Discov Today. 2020;25(7):1206–12. DOI: https://doi.org/10.1016/j.drudis.2020.04.016

Caron G, Digiesi V, Solaro S, Ermondi G. Flexibility in early drug discovery: focus on the beyond-Rule-of-5 chemical space. Drug Discov Today. 2020;25(4):621–7. DOI: https://doi.org/10.1016/j.drudis.2020.01.012

Testa B, van de Waterbeemd H, Folkers G, Guy R. Pharmacokinetic Optimization in Drug Research: Biological, Physicochemical, and Computational Strategies [Internet]. 1st ed. Wiley; 2001 [cited 2021 May 22]. Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/9783906390437

Acquah C, Stefano ED, Udenigwe CC. Role of hydrophobicity in food peptide functionality and bioactivity. J Food Bioact. 2018;4(1):88–98. DOI: https://doi.org/10.31665/JFB.2018.4164

Shultz MD. Two Decades under the Influence of the Rule of Five and the Changing Properties of Approved Oral Drugs. J Med Chem. 2019;62(4):1701–14. DOI: https://doi.org/10.1021/acs.jmedchem.8b00686

Alam A, Kowal J, Broude E, Roninson I, Locher KP. Structural insight into substrate and inhibitor discrimination by human P-glycoprotein. Science. 2019;363(6428):753–6. DOI: https://doi.org/10.1126/science.aav7102

Di L, Kerns EH. Drug-Like Properties - Concepts, Structure Design and Methods from ADME to Toxicity Optimization [Internet]. 2nd ed. Elsevier; 2016 [cited 2021 May 2]. 580 p. Available from: https://linkinghub.elsevier.com/retrieve/pii/C2013018378X

Mulvihill EE, Varin EM, Gladanac B, Campbell JE, Ussher JR, Baggio LL, et al. Cellular Sites and Mechanisms Linking Reduction of Dipeptidyl Peptidase-4 Activity to Control of Incretin Hormone Action and Glucose Homeostasis. Cell Metab. 2017;25(1):152–65. DOI: https://doi.org/10.1016/j.cmet.2016.10.007

Kim MT, Sedykh A, Chakravarti SK, Saiakhov RD, Zhu H. Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches. Pharm Res. 2014;31(4):1002–14. DOI: https://doi.org/10.1007/s11095-013-1222-1

Isvoran A, Louet M, Vladoiu DL, Craciun D, Loriot M-A, Villoutreix BO, et al. Pharmacogenomics of the cytochrome P450 2C family: impacts of amino acid variations on drug metabolism. Drug Discov Today. 2017;22(2):366–76. DOI: https://doi.org/10.1016/j.drudis.2016.09.015

Deodhar M, Rihani SBA, Darakjian L, Turgeon J, Michaud V. Assessing the Mechanism of Fluoxetine-Mediated CYP2D6 Inhibition. Pharmaceutics. 2021;13(2):148. DOI: https://doi.org/10.3390/pharmaceutics13020148

Smith DA, Beaumont K, Maurer TS, Di L. Relevance of Half-Life in Drug Design. J Med Chem. 2018;61(10):4273–82. DOI: https://doi.org/10.1021/acs.jmedchem.7b00969

Mignani S, Rodrigues J, Tomas H, Jalal R, Singh PP, Majoral J-P, et al. Present drug-likeness filters in medicinal chemistry during the hit and lead optimization process: how far can they be simplified? Drug Discov Today. 2018;23(3):605–15. DOI: https://doi.org/10.1016/j.drudis.2018.01.010

Teague SJ, Davis AM, Leeson PD, Oprea T. The Design of Leadlike Combinatorial Libraries. Angew Chem Int Ed. 1999;38(24):3743–8. DOI: https://doi.org/10.1002/(SICI)1521-3773(19991216)38:24<3743::AID-ANIE3743>3.0.CO;2-U

Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminformatics. 2009;1(1):8. DOI: https://doi.org/10.1186/1758-2946-1-8

Azad I, Nasibullah M, Khan T, Hassan F, Akhter Y. Exploring the novel heterocyclic derivatives as lead molecules for design and development of potent anticancer agents. J Mol Graph Model. 2018;81(1):211–28. DOI: https://doi.org/10.1016/j.jmgm.2018.02.013

Iwaniak A, Minkiewicz P, Pliszka M, Mogut D, Darewicz M. Characteristics of Biopeptides Released In Silico from Collagens Using Quantitative Parameters. Foods. 2020;9(7):965. DOI: https://doi.org/10.3390/foods9070965

Kyselova A, Elgheznawy A, Wittig I, Heidler J, Mann AW, Ruf W, et al. Platelet-derived calpain cleaves the endothelial protease-activated receptor 1 to induce vascular inflammation in diabetes. Basic Res Cardiol. 2020;115(6):75. DOI: https://doi.org/10.1007/s00395-020-00833-9

Dókus LE, Yousef M, Bánóczi Z. Modulators of calpain activity: inhibitors and activators as potential drugs. Expert Opin Drug Discov. 2020;15(4):471–86. DOI: https://doi.org/10.1080/17460441.2020.1722638

Donkor IO. An update on the therapeutic potential of calpain inhibitors: a patent review. Expert Opin Ther Pat. 2020;30(9):659–75. DOI: https://doi.org/10.1080/13543776.2020.1797678

Andrade EL, Bento AF, Cavalli J, Oliveira SK, Freitas CS, Marcon R, et al. Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies. Braz J Med Biol Res. 2016;49(11):e5644. DOI: https://doi.org/10.1590/1414-431X20165644

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04-10-2021

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Barrero, J. A., Cabrera, F., & Cruz, C. M. (2021). Gliptins vs. Milk-derived Dipeptidyl-Peptidase IV Inhibiting Biopeptides: Physicochemical Characterization and Pharmacokinetic Profiling. Vitae, 28(3). https://doi.org/10.17533/udea.vitae.v28n3a346531

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Pharmacology and Toxicology

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