In silico modeling of the effects of SV-1010 candidate molecule interaction with opioid receptors

Background. The search for promising nonsteroidal anti-inflammatory drugs (NSAIDs) is aimed, in particular, at identifying molecules with multitargeted anti-inflammatory and analgesic effects (including through central mechanisms).

Objective: To study the interactions of a candidate NSAID molecule (SV-1010) with opioid receptors and compare them with the effects of known agonist molecules (butorphanol and U-50488) using chemoreactomic analysis and docking.

Material and methods. Chemoreactomic analysis of NSAID mechanisms of action was conducted in three stages: data sampling, establishment of lists of molecules with known properties, and calculation of Kd binding constants and EC50 activation constants. Docking of kappa opioid receptors was performed using MarvinSketch, MOPAC2012, and AutoDock Vina. A comparison of the results of chemoreactomic modeling and docking was performed.

Results. Chemoreactomic analysis of the interactions of the studied molecules with opioid receptors showed that the median and average values ​​of the binding constants Kd of the SV-1010 compound are comparable with the estimates of the constants obtained for butorphanol and U-50488 (75–98 nM for delta receptors, 62–81 nM for kappa receptors, 198–244 nM for mu receptors). Among the studied opioid receptor subtypes, the lowest Kd values ​​were established for SV-1010 for kappa receptors (64.8±46.3 nM; delta and mu receptors: 79.9±77.6 and 243.8±246.9 nM, respectively). No significant difference in the binding of SV-1010 molecules to kappa-1 and kappa-2 opioid receptors was detected (Kd in the range of 23.7–54.5 nM). Docking of the studied molecules into the structure of the human kappa receptor allowed us to obtain Kd values ​​and formulate the mechanism of binding of SV-1010 to the kappa-opioid receptor site (potentially, the key binding amino acids of the kappa-opioid receptor site are ILE730, VAL667, MET579, ILE726, TRP723, ILE460 and TYR464). A comparison of the results of chemoreactomic modeling and docking made it possible to find a correlation expressed by the equation “35.8x – 4790” with a correlation coefficient close to unity. The results of chemoreactome modeling of EC50 constants confirmed the results of the Kd binding constant analysis, including the finding that SV-1010 exhibits greater affinity for kappa receptors than for mu receptors.

Conclusion. Chemoreactomic and docking modeling of the SV-1010 molecule's effects support the hypothesis that this compound may be a kappa-opioid receptor agonist, indicating the potential for experimental and other studies of SV-1010 with a focus on kappa-opioid receptors.

1. Gromova O.A., Torshin I.Iu., Putilina M.V., et al. The chemoreactomic analysis of the central mechanisms of action of non-steroidal anti-inflammatory drugs. S.S. Korsakov Journal of Neurology and Psychiatry. 2020; 120 (1): 70–7 (in Russ.). https://doi.org/10.17116/jnevro202012001170

2. Galenko-Yaroshevsky P.A., Torshin I.Y., Gromov A.N., et al. Chemoproteomic analysis of the promising candidate molecule of the indole derivative with lab code SV-1010 and other non-steroidal anti-inflammatory drugs. Research Results in Pharmacology. 2024; 10 (3): 1–9. https://doi.org/10.18413/rrpharmacology.10.497.

3. Karkhanis A., Holleran K.M., Jones S.R. Dynorphin/kappa opioid receptor signaling in preclinical models of alcohol, drug, and food addiction. Int Rev Neurobiol. 2017; 136: 53–88. https://doi.org/10.1016/bs.irn.2017.08.001.

4. Hasebe K., Kawai K., Suzuki T., et al. Possible pharmacotherapy of the opioid kappa receptor agonist for drug dependence. Ann N Y Acad Sci. 2004; 1025: 404–13. https://doi.org/10.1196/annals.1316.050.

5. Frankel P.S., Alburges M.E., Bush L., et al. Striatal and ventral pallidum dynorphin concentrations are markedly increased in human chronic cocaine users. Neuropharmacology. 2008; 55 (1): 41–6. https://doi.org/10.1016/j.neuropharm.2008.04.019.

6. Urbano M., Guerrero M., Rosen H., Roberts E. Antagonists of the kappa opioid receptor. Bioorg Med Chem Lett. 2014; 24 (9): 2021–32. https://doi.org/10.1016/j.bmcl.2014.03.040.

7. Land B.B., Bruchas M.R., Lemos J.C., et al. The dysphoric component of stress is encoded by activation of the dynorphin kappa-opioid system. J Neurosci. 2008; 28 (2): 407–14. https://doi.org/10.1523/JNEUROSCI.4458-07.2008.

8. de Costa B.R., Rothman R.B., Bykov V., et al. Selective and enantiospecific acylation of kappa opioid receptors by (1S,2S)-trans-2-isothiocyanato-N-methyl-N-[2-(1-pyrrolidinyl) cyclohexy l] benzeneacetamide. Demonstration of kappa receptor heterogeneity. J Med Chem. 1989; 32 (2): 281–3. https://doi.org/10.1021/jm00122a001.

9. Mansson E., Bare L., Yang D. Isolation of a human kappa opioid receptor cDNA from placenta. Biochem Biophys Res Commun. 1994; 202 (3): 1431–7. https://doi.org/10.1006/bbrc.1994.2091 PMID 8060324.

10. Jordan B.A., Devi L.A. G-protein-coupled receptor heterodimerization modulates receptor function. Nature. 1999; 399 (6737): 697–700. https://doi.org/10.1038/21441.

11. Szczepaniak A., Machelak W., Fichna J., Zielińska M. The role of kappa opioid receptors in immune system – an overview. Eur J Pharmacol. 2022; 933: 175214. https://doi.org/10.1016/j.ejphar.2022.175214.

12. Mansour A., Fox C.A., Akil H., Watson S.J. Opioid-receptor mRNA expression in the rat CNS: anatomical and functional implications. Trends Neurosci. 1995; 18 (1): 22–9. https://doi.org/10.1016/0166-2236(95)93946-U.

13. Torshin I.Yu. Sensing the change: from molecular genetics to personalized medicine. Nova Biomedical Pub. Inc.; 2012: 366 pp.

14. Torshin I.Yu. On the application of a topological approach to analysis of poorly formalized problems for constructing algorithms for virtual screening of quantum-mechanical properties of organic molecules I: the basics of the problem-oriented theory. Informatics and Applications. 2022; 16 (1): 39–45 (in Russ.). https://doi.org/10.14357/19922264220106.

15. Torshin I.Y., Rudakov K.V. On the application of the combinatorial theory of solvability to the analysis of chemographs: part 2. Local completeness of invariants of chemographs in view of the combinatorial theory of solvability. Pattern Recognit Image Anal. 2014; 24: 196–208. https://link.springer.com/article/10.1134/S1054661814020151.

16. Torshin I.Yu. On solvability, regularity, and locality of the problem of genome annotation. Pattern Recognit Image Anal. 2010; 20: 386–95. https://doi.org/10.1134/S1054661810030156

17. Torshin I.Yu., Rudakov K.V. On the procedures of generation of numerical features over partitions of sets of objects in the problem of predicting numerical target variables. Pattern Recognit Image Anal. 2019; 29 (4): 654–67. https://doi.org/10.1134/S1054661819040175.

18. Wu H., Wacker D., Mileni M., et al. Structure of the human κ-opioid receptor in complex with JDTic. Nature. 2012; 485 (7398): 327–32. https://doi.org/10.1038/nature10939.

19. Trott O., Olson A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010; 31 (2): 455–61. https://doi.org/10.1002/jcc.21334.

20. Vasiliev P.M., Spasov A.A., Kochetkov A.N., et al. Consensus approach to in silico search for antidiabetic compounds. In: Spasov A.A., Petrov V.I. (Eds) Target-oriented search for antidiabetic agents. Volgograd: Volgograd State Medical University; 2016: 126–81 (in Russ.).

21. Gear R.W., Miaskowski C., Gordon N.C., et al. The kappa opioid nalbuphine produces gender- and dose-dependent analgesia and antianalgesia in patients with postoperative pain. Pain. 1999; 83 (2): 339–45. https://doi.org/10.1016/S0304-3959(99)00119-0.