Download PDF Quantitative prediction of counter attack profiles for (Z)-N, N-dimethyl-2-(perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide using the GUSAR program

Quantitative prediction of counter attack profiles for (Z)-N, N-dimethyl-2-(perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide using the GUSAR program

U.F. Askerova

UNEC Journal of Engineering and Applied Sciences Article number: (2022) Cite this article,  73

Abstract

There are a sufficient number of theories about the nature of  physiological effect, such us receptor, neural, biochemical, physicochemical to quantum. All of them  are true, because nature is a much more complex system than we can imagine. In this article we try to open the curtain on the nature physiological activity in the framework of synthesized by us - (Z) -N, N-dimethyl-2- (perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide, using the modern capabilities of computer programs.

Introduction

What is physiological activity by the chemist's point? It is the ability to interact with a biological target. What is a biological target? A polymolecular system that has a certain structure and composition, depending on which they can enter certain interactions (reactions) with chemical compounds. Thus, the assessment of possible interactions between chemical compounds and proteins is an important task in the process of studying the physiological activity of substances. One of the possible ways to solve this problem is using QSAR models to predict the endpoints of counteraction. GUSAR software was developed to create QSAR/QSPR models on the basis of the appropriate training sets represented as SD file contained data about chemical structures and endpoint in quantitative terms. Three nearest neighbours from the training set are calculated for each test chemical compound using a similarity value. The average similarity of three nearest neighbours is used for assessment of the applicability domain (AD) of the model. 

Methods Quantitative Prediction of counter attack profiles for Chemical Compounds

Data on the chemical structure and quantitative endpoints (IC50, or concentration of half-maximal inhibition is an indicator of the effectiveness of a ligand in inhibiting biochemical or biological interactions. IC50 is a quantitative indicator that shows how much ligand-inhibitor is needed to inhibit a biological process by 50%. Ki is a constant inhibition, a coefficient characterizing the affinity of a ligand for a cellular receptor or another protein or DNA and an activation constant- Kact) for approximately 4000 chemical compounds interacting with 18 antibodies to proteins (13 receptors, 2 enzymes and 3 carriers) were collected from various literature sources [1]. Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test suites were used for external validation of the QSAR models generated from the training suites. The prediction coverage for all test sets exceeded 95%, and for half of the test sets it was 100%. The prediction accuracy for the 32 endpoints, based on external test sets, was typically in the range of R2test = 0.6–0.9; three sets of tests had lower test R 2 values, namely 0.55-0.6. The proposed approach showed reasonable prediction accuracy for 91% of antibody endpoints and high coverage for all external test sets [2]. Based on the created models, a freely available online service was developed for in silico prediction of 32 endpoints of counteraction: http://www.pharmaexpert.ru/GUSAR/antitargets.html.      

Table1Quantitative prediction of antitarget interaction profiles for chemical compounds

Discussion Quantitative prediction of counter attack profiles for (Z)-N,N-dimethyl-2-(perfluorophenyl)-2-(2-phenylhydrazinylidene)acetamide.

For the first time, by means of a tandem reaction, under the conditions of a catalytic olefination reaction, we have synthesized  (Z)-N,N-dimethyl-2-(perfluorophenyl)-2-(2- phenyldiazenyl)acetamide.

a) (Z)-N,N-dimethyl-2-(perfluorophenyl)-2-(2- phenyldiazenyl)acetamide

The structural features of this compound [3] have been studied.
In order to investigate possible interactions between (Z)-N,N-dimethyl-2-(perfluorophenyl)-2-(2- phenyldiazenyl)acetamide and antibody proteins, we used QSAR models to predict counteraction endpoints. Table 2 shows the quantitative prediction data of the counter attack profiles for (Z) -N, N-dimethyl-2- (perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide.
In medicine, have developed and used compounds that change the activity of enzymes in order to regulate the rate of metabolic reactions and reduce the synthesis of certain substances in the body are actively developed and used (antagonists, inhibitors, activators and inactivators, etc.).
An antagonist (receptor antagonist) in biochemistry and pharmacology is a subtype of ligands for cellular receptors. A ligand with receptor antagonist properties is a ligand that blocks, reduces or prevents the physiological effects caused by the binding of an agonist (including an endogenous agonist) to a receptor. At the same time, he himself is not obliged (although he can) to produce any physiological effects due to his binding to the receptor (and according to the strict definition, which implies and includes only neutral antagonists, he should not even produce any physiological effects by itself [4]. Suppression of enzyme activity is usually called inhibition, but this is not always correct. An inhibitor is a substance that causes a specific decrease in enzyme activity [5]. Enzyme activators are substances that increase the rate of an enzymatic reaction [6].

Table 2.  Quantitative prediction of antitarget interaction profiles for  (Z)-N,N-dimethyl-2-(perfluorophenyl)-2-(2-phenylhydrazinylidene)acetamide.

*in AD - compound falls in the applicability domain of the model
*out of AD - compound is out of the applicability domain of the model

The total number of antitarget(s): 7
Analysis of Table 2 demonstrates that for a given compound, seven counteractions fall within the scope of the model. For (Z) -N, N-dimethyl-2- (perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide, based on the GUSAR program, it is predicted:
1.    It is a 5-hydroxytryptamine receptor antagonist. 5-HT antagonists are a subtype of serotonin receptors, these are many chemical substances and drugs, in particular, some beta-blockers, some typical and atypical antipsychotics, some anti-migraine [7-12].
2.    The second important effect is an antagonist of alpha-2A adrenergic receptors. It is known that direct antagonists of presynaptic alpha-2-adrenergic receptors mianserin and mirtazapine are widely used as antidepressants [13].
3.    The next is the androgen receptor antagonist. Androgen receptor antagonists are often used in the treatment of diseases caused by excess androgens, such as prostate cancer. Compounds that are full or partial antagonists of androgen receptors are called antiandrogens. Complete AR antagonists are, for example, the non-steroidal compounds hydroxyflutamide, nilutamide, and bicalutamide [14-17].
4.    Dopamine receptor antagonist. Compounds with similar effects are known as anti-dopaminergic and are a type of drug that blocks dopamine receptors. Most antipsychotics are dopamine antagonists, and as such they have found use in the treatment of schizophrenia, bipolar disorder, and stimulant psychosis [18].
5.    Estrogen receptor antagonists. Most often, these are drugs that block estrogen receptors. Estrogen receptor antagonists are commonly used in breast cancer therapy, as androgen receptor antagonists are used in prostate cancer therapy as shown by JB et al [19]. Antiestrogens, also known as estrogen antagonists or estrogen blockers, are a class of drugs that prevent estrogens such as estradiol from mediating their biological effects in the body [20-24].
6.    Further, an inhibitor of the enzyme amine oxidase (flavin-containing). Monoamine oxidase inhibitors are biologically active substances that can inhibit the enzyme monoamine oxidase contained in nerve endings, preventing this enzyme from destroying various monoamines (serotonin, norepinephrine, dopamine, phenylethylamine, tryptamines, octopamine) and thereby increasing their concentration in the synaptic cleft. For this reason, for medical purposes, these substances are used mainly as antidepressants, as well as in the treatment of parkinsonism and narcolepsy [25].
7.    Activator and inhibitor of carbonic anhydrase. This enzyme is known as a substance that acts as a catalyst in living organisms to help speed up chemical reactions as shown by Harvison et al [26]. Carbonic anhydrase is one of the important enzymes found in erythrocytes, gastric mucosa, pancreatic cells, and even in the renal tubules [27]. The main role of carbonic anhydrase in the human body is to catalyze the conversion of carbon dioxide to carbonic acid and vice versa. However, it can also help with the transport of CO 2 in the blood, which in turn promotes respiration. It may even participate in the formation of hydrochloric acid in the stomach as shown by Harvison et al [26]. Thus, the role of carbonic anhydrase depends on where it is located in the body. Carbonic anhydrase inhibitors (CAI), used both systemically and topically, effectively reduce intraocular pressure. Unlike systemic CAI, 2% dorozolamide and 1% brinzolamide, penetrating deep into the tissues of the eye, do not lead to systemic effects, and therefore these drugs are widely used in the treatment of glaucoma [28].

Conclusion

           The results show that seven antitargets are predicted for (Z) -N, N-dimethyl-2- (perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide. It once again confirms the fact that QSAR models can be successfully used to filter chemical compounds using the correction value  estimated by taking an average of three chemicals values from the training set that  the most similar to the chemical under prediction. Thus, without spending quite a lot of time and resources on preclinical studies,  can be to conduct an initial screening of the synthesized compound in order to increase the efficiency of the search for drugs with the desired pharmacological effects.

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14 A.K. Roy, Y. Lavrovsky, C.S. Song, S. Chen, M.H. Jung, N.K. Velu, B. Chatterjee, Vitamins & Hormones 55 (1988) 309.

15 I.J. McEwan, A.O. Brinkmann, Androgen physiology: receptor and metabolic disorders. Endotext [Internet] (2016).

16 C.W. Bardin, T. Brown, V.V. Isomaa, O.A. Jänne, Pharmacology & therapeutics 23(3) (1983) 443.

17 D. Raudrant, T. Rabe, Drugs 63(5) (2003) 463.

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19 L. Goldman (2012). Schafer A. Goldman’s cecil medicine, vol. 1.

20 https://www.oecd.org/dac/31650813.pdf

21 A. Salsali, M. Nathan, American journal of therapeutics, 13(4) (2006) 349.

22 K. McKeage, M.P. Curran, G.L. Plosker, Drugs 64(6) (2004) 633.

23 L. Aguilar, M. Lara-Flores, J. Rendon-von Osten, J.A. Kurczyn, B. Vilela, A.L. da Cruz, Environmental Science and Pollution Research 28(34) (2021) 47262.

24 L.M. Angus, B.J. Nolan, J.D. Zajac, A.S. Cheung, Clinical endocrinology 94(5) (2021) 743.

25 Yu.W. Bykov, R.A. Becker, M.K. Reznikov, Resistant depression. Practical guidance. - Kiev: Medkniga, (2013) 400 p.

26 P.J. Harvison, Journal of the American Chemical Society 127 (10) (2005) 3643.

27 T.H. Maren, Physiological reviews 47(4) (1967) 595.

28 N.I. Kurysheva, Ophthalmology 17(3) (2020) 542.

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Cite this article

U.F. Askerova, Quantitative prediction of counter attack profiles for (Z) -N, N-dimethyl-2- (perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide using the GUSAR program, UNEC J. Eng. Appl. Sci 2(1) (2022) 58-64

  • Received07 Apr 2022
  • Accepted06 May 2022
  • Published26 May 2022

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Keywords

  • computer prediction of counter attack profiles
  • GUSAR program
  • perfluorophenyl derivatives
  • dimethylamino derivatives
Download PDF Quantitative prediction of counter attack profiles for (Z)-N, N-dimethyl-2-(perfluorophenyl) -2- (2-phenylhydrazinylidine) acetamide using the GUSAR program
  1. D.S. Wishart, C. Knox, A.C. Guo, D. Cheng, S. Shrivastava, D. Tzur, M. Hassanali, Nucleic acids research 36(suppl_1) (2008) 901.

  2. A.V. Zakharov, A.A. Lagunin, D.A. Filimonov, V.V. Poroikov, Chemical Research in Toxicology 25(11) (2012) 2378.

  3. Z. Atioglu, M. Akkurt, U.F. Askerova, S.H. Mukhtarova, R.K. Askerov, S. Mlowe, Acta Crystallographica Section E: Crystallographic Communications 77(9) (2021).

  4. S.A. Nickolls, R. Gurrell, G. van Amerongen, J. Kammonen, L. Cao, A.R. Brown, R.P. Butt, British journal of pharmacology 175(4) (2018) 708.

  5. https://biokhimija.ru/fermenty/ingibirovanie-fermentov.html

  6. https://studme.org/167629/geografiya/aktivatory_ingibitory_fermentov

  7. B.T. Shireman, C.A. Dvorak, D.A. Rudolph, P. Bonaventure, D. Nepomuceno, L. Dvorak, N.I. Carruthers, Bioorganic & medicinal chemistry letters 18(6) (2008) 2103.

  8. R.B. Westkaemper, S.P. Runyon, M.L. Bondarev, J.E. Savage, B.L. Roth, R.A. Glennon, European journal of pharmacology 380(1) (1999) R5-R7.

  9. R.B. Westkaemper, R.A. Glennon, Current Topics in Medicinal Chemistry 2(6) (2002) 575.

  10. S. Peddi, B.L. Roth, R.A. Glennon, R.B. Westkaemper, European journal of pharmacology 482(1-3) (2003) 335.

  11. S.P. Runyon, P.D. Mosier, B.L. Roth, R.A. Glennon, R.B. Westkaemper, Journal of medicinal chemistry 51(21) (2008) 6808.

  12. K.J. Wilson, M.B. van Niel, L. Cooper, D. Bloomfield, D. O’Connor, L.R. Fish, A.M. MacLeod, Bioorganic & medicinal chemistry letters 17(9) (2007) 2643.

  13. H. Renz, Praktische Labordiagnostik: Lehrbuch zur Laboratoriumsmedizin, klinischen Chemie und Hämatologie. Walter de Gruyter GmbH & Co KG (2018).

  14. A.K. Roy, Y. Lavrovsky, C.S. Song, S. Chen, M.H. Jung, N.K. Velu, B. Chatterjee, Vitamins & Hormones 55 (1988) 309.

  15. I.J. McEwan, A.O. Brinkmann, Androgen physiology: receptor and metabolic disorders. Endotext [Internet] (2016).

  16. C.W. Bardin, T. Brown, V.V. Isomaa, O.A. Jänne, Pharmacology & therapeutics 23(3) (1983) 443.

  17. D. Raudrant, T. Rabe, Drugs 63(5) (2003) 463.

  18. J.M. Beaulieu, R.R. Gainetdinov, Pharmacological reviews 63(1) (2011) 182.

  19. L. Goldman (2012). Schafer A. Goldman’s cecil medicine, vol. 1.

  20. https://www.oecd.org/dac/31650813.pdf

  21. A. Salsali, M. Nathan, American journal of therapeutics, 13(4) (2006) 349.

  22. K. McKeage, M.P. Curran, G.L. Plosker, Drugs 64(6) (2004) 633.

  23. L. Aguilar, M. Lara-Flores, J. Rendon-von Osten, J.A. Kurczyn, B. Vilela, A.L. da Cruz, Environmental Science and Pollution Research 28(34) (2021) 47262.

  24. L.M. Angus, B.J. Nolan, J.D. Zajac, A.S. Cheung, Clinical endocrinology 94(5) (2021) 743.

  25. Yu.W. Bykov, R.A. Becker, M.K. Reznikov, Resistant depression. Practical guidance. - Kiev: Medkniga, (2013) 400 p.

  26. P.J. Harvison, Journal of the American Chemical Society 127 (10) (2005) 3643.

  27. T.H. Maren, Physiological reviews 47(4) (1967) 595.

  28. N.I. Kurysheva, Ophthalmology 17(3) (2020) 542.