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Optimization of structures, biochemical properties of ketorolac and its degradation products based on computational studies

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Abstract

Background

Ketorolac (KTR) is used as an analgesic drug with an efficacy close to that of the opioid family. It is mainly used for the short term treatment of post-operative pain. It can inhibit the prostaglandin synthesis by blocking cyclooxygenase (COX).

Methods

In this investigation, the inherent stability and biochemical interaction of Ketorolac (KTR) and its degradation products have been studiedon the basis of quantum mechanical approaches. Density functional theory (DFT) with B3LYP/ 6-31G (d) has been employed to optimize the structures. Thermodynamic properties, frontier molecular orbital features, dipole moment, electrostatic potential, equilibrium geometry, vibrational frequencies and atomic partial charges of these optimized structureswere investigated. Molecular docking has been performed against prostaglandin H2 (PGH2) synthase protein 5F19 to search the binding affinity and mode(s). ADMET prediction has performed to evaluate the absorption, metabolism and carcinogenic properties.

Results

The equilibrium geometry calculations support the optimized structures. Thermodynamic results disclosed the thermal stability of all structures. From molecular orbital data, all the degradents are chemically more reactive than parent drug (except K3). However, the substitution of carboxymethyl radicalin K4 improved the physicochemical properties and binding affinity. ADMET calculations predict the improved pharmacokinetic and non-carcinogenic properties of all degradents.

Conclusion

Based on physicochemical, molecular docking, and ADMET calculation, this study can be helpful to understand the biochemical activities of Ketorolac and its degradents and to design a potent analgesic drug.

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Abbreviations

KTR:

Ketorolac

DFT:

Density functional theory

HOMO:

Highest occupied molecular orbital

LUMO:

Lowest unoccupied molecular orbital

MEP:

Molecular electrostatic potential

ADMET:

Absorption, distribution, metabolism, excretion, toxicity

References

  1. Mallinson TE. A review of ketorolac as a prehospital analgesic. J Paramed Pract. 2017;9:522–6.

    Article  Google Scholar 

  2. Litvak KM, McEvoy GK. Ketorolac, an injectable nonnarcotic analgesic. Clin Pharm. 1990;9:921–35.

    CAS  PubMed  Google Scholar 

  3. H.P. Rang, Rang and Dale’s pharmacology, Churchill Livingstone, (2007).

  4. Myers KG, Trotman IF. Use of ketorolac by continuous subcutaneous infusion for the control of cancer-related pain. Postgrad Med J. 1994;70:359–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Forget P, Bentin C, Machiels J-P, Berlière M, Coulie PG, De Kock M. Intraoperative use of ketorolac or diclofenac is associated with improved disease-free survival and overall survival in conservative breast cancer surgery. Br J Anaesth. 2014;113:i82–7.

    Article  CAS  PubMed  Google Scholar 

  6. R. Reddy, S.J. Kim, Critical appraisal of ophthalmic ketorolac in treatment of pain and inflammation following cataract surgery, clinical ophthalmology (Auckland, NZ) 5 (2011) 751.

  7. Reinhart DJ. Minimising the adverse effects of ketorolac. Drug Saf. 2000;22:487–97.

    Article  CAS  PubMed  Google Scholar 

  8. Galán-Herrera JF, Poo JL, Maya-Barrios JA, de Lago A, Oliva I, González-de la Parra M, et al. Bioavailability of two sublingual formulations of ketorolac tromethamine 30 mg: a randomized, open-label, single-dose, two-period crossover comparison in healthy Mexican adult volunteers. Clin Ther. 2008;30:1667–74.

    Article  CAS  PubMed  Google Scholar 

  9. Warner TD, Mitchell JA. Cyclooxygenases: new forms, new inhibitors, and lessons from the clinic. FASEB J. 2004;18:790–804.

    Article  CAS  PubMed  Google Scholar 

  10. Strom BL, Berlin JA, Kinman JL, Spitz PW, Hennessy S, Feldman H, et al. Parenteral ketorolac and risk of gastrointestinal and operative site bleeding: a postmarketing surveillance study. JAMA. 1996;275:376–82.

    Article  CAS  PubMed  Google Scholar 

  11. C. on S. of Medicines. Ketorolac: new restrictions on dose and duration of treatment. Current Problems in Pharmacovigilance. 1993;19:5–6.

    Google Scholar 

  12. Uddin MN. A novel validated uplc method for the estimation of ketorolac tromethamine in pharmaceutical formulation. Research. 2014.

  13. Salaris M, Nieddu M, Rubattu N, Testa C, Luongo E, Rimoli MG, et al. Acid and base degraded products of ketorolac. J Pharm Biomed Anal. 2010;52:320–2.

    Article  CAS  PubMed  Google Scholar 

  14. Leo G, Hi-Shi C, Johnson D. Light degradation of ketorolac tromethamine. Int J Pharm. 1988;41:105–13.

    Article  Google Scholar 

  15. Kalariya PD, Raju B, Borkar RM, Namdev D, Gananadhamu S, Nandekar PP, et al. Characterization of forced degradation products of ketorolac tromethamine using LC/ESI/Q/TOF/MS/MS and in silico toxicity prediction. J Mass Spectrom. 2014;49:380–91.

    Article  CAS  PubMed  Google Scholar 

  16. Samal SK, Routray S, Veeramachaneni GK, Dash R, Botlagunta M. Ketorolac salt is a newly discovered DDX3 inhibitor to treat oral cancer. Sci Rep. 2015;5:9982. https://doi.org/10.1038/srep09982.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Uzzaman TM, Mohammed JH, Mohammad NU. Amrin a, quantum chemical, molecular Docking, and ADMET predictions of ketorolac and its modified analogues. Biomedical Journal of Scientific & Technical Resesarch. 2018;11:1–7.

    Google Scholar 

  18. Gleeson MP, Gleeson D. QM/MM calculations in drug discovery: a useful method for studying binding phenomena? J Chem Inf Model. 2009;49:670–7. https://doi.org/10.1021/ci800419j.

    Article  CAS  PubMed  Google Scholar 

  19. Pence HE, Williams A. ChemSpider: an online chemical information resource. J Chem Educ. 2010;87:1123–4. https://doi.org/10.1021/ed100697w.

    Article  CAS  Google Scholar 

  20. R.A. Gaussian09, MJ Frisch, GW Trucks, HB Schlegel, GE Scuseria, MA Robb, JR Cheeseman, G. Scalmani, V. Barone, B. Mennucci, GA Petersson et al., Gaussian, Inc, Wallingford CT. (2009).

  21. Becke AD. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A. 1988;38:3098–100. https://doi.org/10.1103/PhysRevA.38.3098.

    Article  CAS  Google Scholar 

  22. Lee C, Yang W, Parr RG. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B. 1988;37:785–9. https://doi.org/10.1103/PhysRevB.37.785.

    Article  CAS  Google Scholar 

  23. Kruse H, Goerigk L, Grimme S. Why the standard B3LYP/6-31G* model chemistry should not be used in DFT calculations of molecular thermochemistry: understanding and correcting the problem. J Organic Chem. 2012;77:10824–34. https://doi.org/10.1021/jo302156p.

    Article  CAS  Google Scholar 

  24. Tomasi J, Mennucci B, Cammi R. Quantum mechanical continuum solvation models. Chem Rev. 2005;105:2999–3094.

    Article  CAS  PubMed  Google Scholar 

  25. Onawole AT, Popoola SA, Saleh TA, Al-Saadi AA. Silver-loaded graphene as an effective SERS substrate for clotrimazole detection: DFT and spectroscopic studies. Spectrochim Acta A Mol Biomol Spectrosc. 2018;201:354–61.

    Article  CAS  PubMed  Google Scholar 

  26. Halim MA, Shaw DM, Poirier RA. Medium effect on the equilibrium geometries, vibrational frequencies and solvation energies of sulfanilamide. J Mol Struct THEOCHEM. 2010;960:63–72.

    Article  CAS  Google Scholar 

  27. Azam F, Alabdullah NH, Ehmedat HM, Abulifa AR, Taban I, Upadhyayula S. NSAIDs as potential treatment option for preventing amyloid β toxicity in Alzheimer’s disease: an investigation by docking, molecular dynamics, and DFT studies. J Biomol Struct Dyn. 2017:1–19.

  28. Calais J-L. Density-functional theory of atoms and molecules. R.G. Parr and W. Yang, Oxford University press, New York, Oxford, 1989. IX + 333 pp. Price £45.00. Int J Quantum Chem. 1993;47:101. https://doi.org/10.1002/qua.560470107.

    Article  Google Scholar 

  29. Pearson RG. The HSAB principle — more quantitative aspects. Inorg Chim Acta. 1995;240:93–8. https://doi.org/10.1016/0020-1693(95)04648-8.

    Article  CAS  Google Scholar 

  30. Pearson RG. Absolute electronegativity and hardness correlated with molecular orbital theory. Proc Natl Acad Sci. 1986;83:8440–1 http://www.pnas.org/content/83/22/8440.abstract.

    Article  CAS  PubMed  Google Scholar 

  31. Lucido MJ, Orlando BJ, Vecchio AJ, Malkowski MG. Crystal structure of aspirin-acetylated human Cyclooxygenase-2: insight into the formation of products with reversed stereochemistry. Biochemistry. 2016;55:1226–38. https://doi.org/10.1021/acs.biochem.5b01378.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. W.L. DELANO, The PyMOL molecular graphics system. De-Lano scientific, San Carlos, CA, USA, http://www.pymolorg. (2002). http://ci.nii.ac.jp/naid/10025409089/en/.

  33. Guex N, Peitsch MC. SWISS-MODEL and the Swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis. 1997;18:2714–23. https://doi.org/10.1002/elps.1150181505.

    Article  CAS  PubMed  Google Scholar 

  34. Seeliger D, De Groot BL. Conformational transitions upon ligand binding: holo-structure prediction from apo conformations. PLoS Comput Biol. 2010;6:e1000634.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. G.M. Morris, M. Lim-Wilby, Molecular Docking, in: A. Kukol (Ed.), Molecular modeling of proteins, Humana press, Totowa, NJ, 2008: pp. 365–382. https://doi.org/10.1007/978-1-59745-177-2_19., Molecular Docking

  36. Dallakyan S, Olson AJ. Small-molecule library screening by Docking with PyRx. In: Hempel JE, Williams CH, Hong CC, editors. Chemical biology: methods and protocols, springer New York, New York, NY; 2015. p. 243–50. https://doi.org/10.1007/978-1-4939-2269-7_19.

    Chapter  Google Scholar 

  37. A.D.S. Version, 4.0, Accelrys, San Diego, USA, (2017).

  38. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model. 2012;52:3099–105. https://doi.org/10.1021/ci300367a.

    Article  CAS  PubMed  Google Scholar 

  39. Cohen N, Benson SW. Estimation of heats of formation of organic compounds by additivity methods. Chem Rev. 1993;93:2419–38.

    Article  CAS  Google Scholar 

  40. Lien EJ, Guo Z-R, Li R-L, Su C-T. Use of dipole moment as a parameter in drug-receptor interaction and quantitative structure-activity relationship studies. J Pharm Sci. 1982;71:641–55. https://doi.org/10.1002/jps.2600710611.

    Article  CAS  PubMed  Google Scholar 

  41. Saravanan S, Balachandran V. Quantum chemical studies, natural bond orbital analysis and thermodynamic function of 2, 5-dichlorophenylisocyanate. Spectrochim Acta A Mol Biomol Spectrosc. 2014;120:351–64.

    Article  CAS  PubMed  Google Scholar 

  42. Parr RG, Zhou Z. Absolute hardness: unifying concept for identifying shells and subshells in nuclei, atoms, molecules, and metallic clusters. Acc Chem Res. 1993;26:256–8. https://doi.org/10.1021/ar00029a005.

    Article  CAS  Google Scholar 

  43. Politzer P, Murray JS. Molecular electrostatic potentials and chemical reactivity. Rev Comput Chem. 1991:273–312.

  44. Pappalardo RR, Sanchez Marcos E, Ruiz-Lopez MF, Rinaldi D, Rivail JL. Solvent effects on molecular geometries and isomerization processes: a study of push-pull ethylenes in solution. J Am Chem Soc. 1993;115:3722–30.

    Article  CAS  Google Scholar 

  45. JASINSKI JP, Butcher RJ, Narayana B, SWAMY MT, Yathirajan HS. Crystal structure of (±)-5-Benzoyl-2, 3-dihydro-1H-pyrrolidine-1-carboxylic acid, ketorolac. Analytical Sciences: X-Ray Structure Analysis Online. 2008;24:x205–6.

    CAS  Google Scholar 

  46. Gross KC, Seybold PG, Hadad CM. Comparison of different atomic charge schemes for predicting pKa variations in substituted anilines and phenols. Int J Quantum Chem. 2002;90:445–58. https://doi.org/10.1002/qua.10108.

    Article  CAS  Google Scholar 

  47. Mulliken RS. Electronic population analysis on LCAO–MO molecular wave functions. I. J Chem Phys. 1955;23:1833–40.

    Article  CAS  Google Scholar 

  48. Wade RC, Goodford PJ. The role of hydrogen-bonds in drug binding. Prog Clin Biol Res. 1989;289:433–44 http://europepmc.org/abstract/MED/2726808.

    CAS  PubMed  Google Scholar 

  49. Viegas A, Manso J, Corvo MC, Marques MMB, Cabrita EJ. Binding of ibuprofen, ketorolac, and diclofenac to COX-1 and COX-2 studied by saturation transfer difference NMR. J Med Chem. 2011;54:8555–62. https://doi.org/10.1021/jm201090k.

    Article  CAS  PubMed  Google Scholar 

  50. Amin ML. P-glycoprotein inhibition for optimal drug delivery. Drug Target Insights. 2013;2013:27–34. https://doi.org/10.4137/DTI.S12519.

    Article  CAS  Google Scholar 

  51. Sanguinetti MC, Tristani-Firouzi M. hERG potassium channels and cardiac arrhythmia. Nature. 2006;440:463–9.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Authors are thankful to Dr. Mohammad A. Halim (COE, The Red –Green Research Centre, Dhaka, Bangladesh) for his valuable suggestion.

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Authors and Affiliations

  1. Department of Chemistry, University of Chittagong, Chittagong, 4331, Bangladesh

    Monir Uzzaman & Mohammad Nasir Uddin

  2. Department of Applied Chemistry and Biochemical Engineering, Shizuoka University, 3-5-1, Johoku, Hamamatsu, 432-8011, Japan

    Monir Uzzaman

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Contributions

Mohammad Nasir Uddin (MNU) and Moniruzzaman (M) conceived the idea and prepared the manuscript. M performed all quantum chemical calculations and data collection. All authors read and approved the manuscript.

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Correspondence to Mohammad Nasir Uddin.

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Uzzaman, M., Uddin, M.N. Optimization of structures, biochemical properties of ketorolac and its degradation products based on computational studies. DARU J Pharm Sci 27, 71–82 (2019). https://doi.org/10.1007/s40199-019-00243-w

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