Preview

Neurology, Neuropsychiatry, Psychosomatics

Advanced search

Differential chemoreactome analysis of synergistic combinations of tolperisone and nonsteroidal anti-inflammatory drugs

https://doi.org/10.14412/2074-2711-2019-2-78-85

Full Text:

Abstract

The concurrent use of muscle relaxants and nonsteroidal anti-inflammatory drugs (NSAIDs) is a promising treatment for painful muscle hypertonia and convulsive states.

Objective: to identify the most effective and safe synergist combinations of tolperisone and NSAIDs.

Material and methods. A differential chemoreactome analysis was employed to evaluate the effects of the muscle relaxant tolperisone and five NSAIDs (dexketoprofen, etoricoxib, meloxicam, naproxen, and diclofenac). The biological activities of the molecules under study were assessed in five sections: 1) inhibition of the proteins of prostaglandin and leukotriene metabolism; 2) inhibition of the effects of the transcription factor nuclear factor kappa, tumor necrosis factor-, and other anti-inflammatory mechanisms; 3) inhibition of excessive blood coagulation and platelet aggregation; 4) vasodynamic effects; 5) antitumor properties on cell lines in culture.

Results and discussion. Based on the differences in the pharmacological activity profiles of tolperisone and NSAIDs under study, the investigators identified the most promising synergistic combinations, in which both active ingredients complemented each other as effectively and safely as possible. The obtained estimates of the degree of synergism of various combinations of tolperisone and NSAIDs hold that the most promising antithrombotic, and antitumor effects.

Conclusion. The results of this study will help adequately choose combinations of muscle relaxants and NSAIDs in patients with muscle hypertonia, which will be able to improve the efficiency and safety of treatment.

About the Authors

I. Yu. Torshin
Federal Research Center «Informatics and Management», Russian Academy of Sciences; Big Data Storage and Analysis Center, M.V. Lomonosov Moscow State University
Russian Federation

44, Vavilov St., Build. 2, Moscow 119333, 

1, Leninskie Gory, Moscow 119234



O. A. Gromova
Federal Research Center «Informatics and Management», Russian Academy of Sciences; Big Data Storage and Analysis Center, M.V. Lomonosov Moscow State University
Russian Federation

Olga Alekseevna Gromova

44, Vavilov St., Build. 2, Moscow 119333, 

1, Leninskie Gory, Moscow 119234



L. V. Stakhovskaya
Research Institute of Cerebrovascular Pathology and Stroke, N.I. Pirogov Russian National Research Medical University, Ministry of Health of Russia
Russian Federation

1, Ostrovityanov St., Moscow 117997



V. A. Semenov
Federal Research Center «Informatics and Management», Russian Academy of Sciences; Kemerovo State Medical University, Ministry of Health of Russia
Russian Federation

44, Vavilov St., Build. 2, Moscow 119333, 

22a, Voroshilov St., Kemerovo 650056



A. N. Gromov
Federal Research Center «Informatics and Management», Russian Academy of Sciences; Big Data Storage and Analysis Center, M.V. Lomonosov Moscow State University
Russian Federation

44, Vavilov St., Build. 2, Moscow 119333, 

1, Leninskie Gory, Moscow 119234



References

1. Mashkovskii MD. Lekarstvennye sredstva [Medicines]. 17th ed. Moscow: Novaya Volna; 2014. P. 98—9.

2. Torshin IYu, Gromova OA, Stakhovskaya LV, Semenov VA. Chemoreactome analysis of tolperisone, tizanidine, and baclofen molecules: anticholinergic, antispasmodic, and analgesic mechanisms of action. Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2018;10(4):72-80. (In Russ.). doi: 10.14412/2074-2711-2018-4-72-80

3. Torshin IYu. Bioinformatics in the postgenomic era: physiology and medicine. New York: NovaBiomedicalBooks; 2007.

4. Torshin IY, Rudakov KV. On the application of the combinatorial theory of solvability to the analysis of chemographs. Part 1: fundamentals of modern chemical bonding theory and the concept of the chemograph. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2014;24(1):11-23.

5. Torshin IY, Rudakov KV. 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 Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2014;24(2):196-208.

6. Torshin IY, Rudakov KV. On the theoretical basis of the metric analysis of poorly formalized problems of recognition and classification. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2015;25(4):577–87.

7. Torshin IY, Rudakov KV. Combinatorial analysis of the solvability properties of the problems of recognition and completeness of algorithmic models. Part 1: factorization approach. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2017;27(1):16-28.

8. Torshin IY, Rudakov KV. Combinatorial analysis of the solvability properties of the problems of recognition and completeness of algorithmic models. Part 2: metric approach within the framework of the theory of classification of feature values. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2017;27(2):184-99.

9. Torshin IY. Optimal dictionaries of the final information on the basis of the solvability criterion and their applications in bioinformatics. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2013;23(2):319-27.

10. Torshin IY, Rudakov KV. On metric spaces arising during formalization of recognition and classification problems. Part 1: Properties of compactness. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2016;26(2):274–284.

11. Torshin IY, Rudakov KV. On metric spaces arising during formalization of problems of recognition and classification. Part 2: density properties. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications). 2016;26(3):483-96.

12. Galluzzi L, Lopez-Soto A, Kumar S, Kroemer G. Caspases Connect Cell-Death Signaling to Organismal Homeostasis. Immunity. 2016 Feb 16;44(2):221-31. doi: 10.1016/j.immuni.2016.01.020.

13. Sollberger G, Strittmatter GE, Garstkiewicz M, et al. Caspase-1: the inflammasome and beyond. Innate Immun. 2014 Feb;20(2):115-25. doi: 10.1177/1753425913484374. Epub 2013 May 15.

14. Ladha S, Qiu X, Casal L, et al. Constitutive ablation of caspase-6 reduces the inflammatory response and behavioural changes caused by peripheral pro-inflammatory stimuli. Cell Death Discov. 2018 Mar 12;4:40. doi: 10.1038/s41420018-0043-8. eCollection 2018.

15. Monie TP, Bryant CE. Caspase-8 functions as a key mediator of inflammation and pro-IL-1processing via both canonical and non-canonical pathways. Immunol Rev. 2015 May;265(1):181-93. doi: 10.1111/imr.12284.

16. Parfenov VA, Yakhno NN, Kukushkin ML, et al. Acute nonspecific (musculoskeletal) low back pain Guidelines of the Russian Society for the Study of Pain (RSSP). Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2018;10(2):4–11. (In Russ.). doi: 10.14412/2074-2711-2018-2-4-11

17. Pareek A, Chandurkar N, Chandanwale A, et al. Aceclofenac-tizanidine in the treatment of acute low back pain: a double-blind, doubledummy, randomized, multicentric, comparative study against aceclofenac alone. Eur Spine J. 2009;18(12):1836-42. doi: 10.1007/s00586-0091019-4

18. Andreev AV, Gromova OA, Skoromets AA. The use of midocalm blockades in the treatment of spondylogenic lumbar pain syndromes. Results of the double-blind study. Russkii meditsinskii zhurnal. 2002;10(21):968-72. (In Russ.).

19. Fischoff D, Spivakovsky S. Are pharmacological treatments for oro-facial pain effective? Evid Based Dent. 2018 Mar 23;19(1):28-29. doi: 10.1038/sj.ebd.6401294.

20. Gerasimova ON, Parfenov VA, Kalimeeva EYu. Treatment of patients with acute and subacute dorsalgia. Nevrologiya, neiropsikhiatriya, psikhosomatika = Neurology, Neuropsychiatry, Psychosomatics. 2018;10(3):36–41. (In Russ.). doi: 10.14412/2074-2711-2018-3-36-41


For citation:


Torshin I.Y., Gromova O.A., Stakhovskaya L.V., Semenov V.A., Gromov A.N. Differential chemoreactome analysis of synergistic combinations of tolperisone and nonsteroidal anti-inflammatory drugs. Neurology, Neuropsychiatry, Psychosomatics. 2019;11(2):78-85. (In Russ.) https://doi.org/10.14412/2074-2711-2019-2-78-85

Views: 31


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2074-2711 (Print)
ISSN 2310-1342 (Online)