ANALYSIS BY CHEMOINFORMATICS METHODS BIOLOGICAL ACTIVITY INDICATORS OF QUINOLINE AND PYRROLE[1,2–A]QUINOLINE DERIVATIVES

  • M. P. Zavgorodniy Zaporizhzhya National University
  • E. А. Brazhko Zaporizhzhya National University
  • A. S. Yevlash Zaporizhzhya National University
  • Е. М. Аbakumets Zaporizhzhya National University
  • D. R. Borysenko Zaporizhzhya National University
  • S. О. Strelbitskaya Zaporizhzhya National University
  • М. Е. Holovan Zaporizhzhya National University
  • O. A. Brazhko Zaporizhzhya National University
Keywords: quinoline, pyrrole[1,2-a] quinoline, metods of chemoinformatics, toxicity

Abstract

For conducting a computer experiment was used new software tool called QuS (read as “k’us”, abbrev. QSAR Server). One of it’s tasks is to integrate and coordinate the work of other software that performs individual stages of analysis. This development consists of two parts: user interface in form of a webpage and a web-server. Program management (web-server) is carried out through a webpage where are located nessessary tools and the results of the analysis are displayed. This software development is written in the programming language Object Pascal (Web-Server) and JavaScript (User Interface) using Ready-made classes and libraries (Ararat Synapse, LCLBase, SynEdit) with open source code. To perform individual stages of QSAR analysis were used different software tools such as: PaDEL-Descriptor, McQSAR and some others. Verification of the correctness of the program was carried out by conducting QSAR analysis based on existing QSAR results analysis from publication in relevant scientific journals. A number of analyzes were held for obtaining correlation models using genetic algorithm. The statistical characteristic of the obtained equations was compared with the corresponding statistical characteristic of QSAR equations of models with selected for review of publications. The total size of sample that was used for the computer calculations – 64 compounds.

The compounds used as the general sample – derivatives of quinoline and pyrrole[1,2-a]quinolone – are a part of a longstanding experimental base of the laboratory of biotechnology of physiologically active substances of Zaporizhzhya National University.

Investigation was conducted in two stages. The first stage is definition of physico-chemical and toxicity indicators for selected parameters (water solubility, lipophilicity, LC50 for Fathead minnow, Daphnia magna, IGC50 Tetrahymena pyriforms, LD50 bioaccumulation factor, total indicator of embryotoxic and teratogenic activity, an Emmos test). Second – a software algorithm for implementing the developed QSAR methodology study using derivatives of quinoline and pyrrole[1,2-a]quinoline. In the final conclusion presented results regarding using of software development QuS. Several thousand different regression equations were constructed with different statistical reliability and predictive power. First, among them were chosen only those equations, which have the square coefficient of correlation R2 and square coefficient of cross-validation Q2 greater than 0,7, after that was analysed second sample of received models. From the second sample were selected models that used no more than four DMS, R2 and Q2 were greater than 0,75, the equation did not contain specific mathematical functions, which uses in its work the genetic algorithm of McQSAR software (for example, “minimum” and “maximum” functions). Among the received equations were chosen three, which were used as finite regression models. Preliminary analysis of physical-and-chemical properties and obtained values DMS led to the following conclusions about the dependence between structures of the study sample of compounds and LD50 for mice with intraperitoneal administration:

  • the probability of manifestation of the toxic effect increases in case of:
  • the value of molar refraction is less than 70 cm3;
  • the value of lipophilicity (Log P) is greater than one;
  • the total number of atoms is less than 30;
    • the probability of manifestation of the toxic effect is lower in compounds that have:
  • 1) bigger amount of free acidic groups (carboxylic);
  • 2) less value of the integral sum of atomic polarizations molecules, including atoms of the Hydrogen;
  • 3) more methyl groups (CH3) and more Oxygen atoms.

In this way, we can assume that aforementioned factors which correlate with an increase of toxicity, may be related to the best transportation of compounds (which correspond to these factors) through cell membrane: greater lipophilicity, smaller molecule size, less value of refraction (which characterizes the actual volume of the molecule) and, accordingly, the less polarization ability.

References

ЛІТЕРАТУРА
1. Бобкова Л. С., Чекман І. С., Яворовський О. П. Застосування методу QSAR в токсикології. Современные проблемы токсикологии. 2008. № 2. С. 78-86.
2. Бражко О. А. Біологічно активні похідні хіноліну та акридину з азото- та сірковмісними функціональними групами: дис. … д-ра біол. наук: 02.00.10 / Інститут біоорганічної хімії та нафтохімії НАН України. Київ, 2005. 456 с.
3. Zavgorodniy M. P., Brazhko A. A., Veselkov A. V. QuS: A Software for Automated QSAR analysis of Biologically Active Compounds. Chemistry of Nitrogen Containing Heterocycles, CNCH-2015: VII Intern. Conf., 9-13 November, 2015. Book Abstr, Kharkiv: Ekskluziv Publ., 2015. Р 26.
4. Zefirov N. S., Palyulin V. A. Fragmental Approach in QSPR. J. Chem. Inf. Comput. Sci. 2002. Vol. 42 (5). Р. 1112-1122.
5. Arakawa M., Hasegawa K., Funatsu K. The recent trend in QSAR modeling- variable selection and 3D-QSAR methods. Current Computer-Aided Drug Design. 2007. Vol. 3. P. 254-262. DOI: 10.2174/ 157340907782799417
6. Бражко A. А. Синтез, свойства и биологическая активность 2-тио- и 4-тио, 2-гидразино- и 4-гидразинохинолинов и их производных: автореф. дис. … к.ф.н.: 15.00.02 / Національний університете “Львівська політехніка”. Львів, 1989. – 20 с.
7. Gaikwad J. V. Application of chemoinformatics for innovative drug discovery. International Journal of Chemical Sciences and Applications. 2010. Vol. 1. Р. 16-24.
8. Martin T. M. Toxicity Estimation Software Tool (TEST). U.S. Environmental Protection Agency: Washington DC, 2016. (інструмент програмного забезпечення) URL: https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test
9. Tetko I.V. et all. Virtual computational chemistry laboratory - design and description J. Comput. Aid. Mol. Des / Tetko I.V. et all. 2005. Vol. 19. Р. 453-464.
10. Mikko J. Vainio, Mark S. Johnson. McQSAR: A Multiconformational Quantitative Structure-Activity Relationship Engine Driven by Genetic Algorithms. J. Chem. Inf. Model. 2005. Vol. 45. Р. 1953-1961.
11. Prajapati K., Singh S., Pathak A. K., Meht P. QSAR analysis on some 8-methoxyquinoline derivatives as H37RV inhibitors. Int. J. Chem Tech Res. 2011. Vol. 193. Р. 408-422.
12. Abhinav P. M. 2D-QSAR study of 2,5-dihydropyrazolo[4,3-c]quinoline-3-one a novel series of PDE-4-inhibitors. Int. J. Pharmaceutical and Biomedical Sci. 2012. Vol. 3(1). Р. 105-111.
13. Abolghasem J., Mohammad A.F. Experimental and Computational Methods Pertaining to Drug Solubility. Toxicity and Drug Testing. 2012. Р. 187-197.

REFERENCE
1. Bobkova L. S., Chekman І. S., Javorovs’kij O. P. Zastosuvannja metodu QSAR v toksikologії. Sovremennye problemy toksikologii. 2008. № 2. S. 78-86.
2. Brazhko O. A. Bіologіchno aktivnі pohіdnі hіnolіnu ta akridinu z azoto- ta sіrkovmіsnimi funkcіonal’nimi grupami: dis. … d-ra bіol. nauk: 02.00.10 / Іnstitut bіoorganіchnoyi hіmіyi ta naftohіmії NAN Ukrayini. Kiyiv, 2005. 456 s.
3. Zavgorodniy M. P., Brazhko A. A., Veselkov A. V. QuS: A Software for Automated QSAR analysis of Biologically Active Compounds. Chemistry of Nitrogen Containing Heterocycles, CNCH-2015: VII Intern. Conf., 9-13 November, 2015. Book Abstr, Kharkiv: Ekskluziv Publ., 2015. Р 26.
4. Zefirov N. S., Palyulin V. A. Fragmental Approach in QSPR. J. Chem. Inf. Comput. Sci. 2002. Vol. 42 (5). Р. 1112-1122.
5. Arakawa M., Hasegawa K., Funatsu K. The recent trend in QSAR modeling- variable selection and 3D-QSAR methods. Current Computer-Aided Drug Design. 2007. Vol. 3. P. 254-262. DOI: 10.2174/ 157340907782799417
6. Brazhko A. A. Sintez, svojstva i biologicheskaja aktivnost’ 2-tio- i 4-tio, 2-gidrazino- i 4-gidrazinohinolinov i ih proizvodnyh: avtoref. dis. … k.f.n.: 15.00.02 / Nacіonal'nij unіversitete “L’vіvs’ka polіtehnіka”. L’vіv, 1989. – 20 s.
7. Gaikwad J. V. Application of chemoinformatics for innovative drug discovery. International Journal of Chemical Sciences and Applications. 2010. Vol. 1. Р. 16-24.
8. Martin T. M. Toxicity Estimation Software Tool (TEST). U.S. Environmental Protection Agency: Washington DC, 2016. (instrument programnogo zabezpechennya) URL: https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test
9. Tetko I. V. et all. Virtual computational chemistry laboratory - design and description J. Comput. Aid. Mol. Des / Tetko I. V. et all. 2005. Vol. 19. Р. 453-464.
10. Mikko J. Vainio, Mark S. Johnson. McQSAR: A Multiconformational Quantitative Structure-Activity Relationship Engine Driven by Genetic Algorithms. J. Chem. Inf. Model. 2005. Vol. 45. Р. 1953-1961.
11. Prajapati K., Singh S., Pathak A.K., Meht P. QSAR analysis on some 8-methoxyquinoline derivatives as H37RV inhibitors. Int. J. Chem Tech Res. 2011. Vol. 193. Р. 408-422.
12. Abhinav P. M. 2D-QSAR study of 2,5-dihydropyrazolo[4,3-c]quinoline-3-one a novel series of PDE-4-inhibitors. Int. J. Pharmaceutical and Biomedical Sci. 2012. Vol. 3(1). Р. 105-111.
13. Abolghasem J., Mohammad A. F. Experimental and Computational Methods Pertaining to Drug Solubility. Toxicity and Drug Testing. 2012. Р. 187-197.
How to Cite
Zavgorodniy, M. P., BrazhkoE. А., Yevlash, A. S., АbakumetsЕ. М., Borysenko, D. R., StrelbitskayaS. О., HolovanМ. Е., & Brazhko, O. A. (1). ANALYSIS BY CHEMOINFORMATICS METHODS BIOLOGICAL ACTIVITY INDICATORS OF QUINOLINE AND PYRROLE[1,2–A]QUINOLINE DERIVATIVES. Bulletin of Zaporizhzhia National University. Biological Sciences, (2), 104-111. Retrieved from http://journalsofznu.zp.ua/index.php/biology/article/view/103
Section
BIOLOGICAL CHEMISTRY AND BIOACTIVE SUBSTANCES (BAS)