QSAR STUDIES ON DERIVATIVES OF QUINAZOLINE-4(3H)-ONES WITH ANTICONVULSANT ACTIVITIES
DOI:
https://doi.org/10.18540/jcecvl4iss2pp0255-0264Keywords:
Epilepsy, Quantitative structure activity relationship, Kennard-Stone algorithm, Genetic function algorithm, Quinazolinone ring systemAbstract
Quantitative structure-activity relationship study was done on some quinazoline-4(3H)-ones derivatives with anticonvulsant activity against maximal electroshock-induced seizure. The quinazoline derivatives used as dataset and their anticonvulsant activity value were obtained from the literature. The molecular structure of the dataset compounds was generated with Spartan 14 software. This was optimized with PM3 semi-empirical quantum mechanical method available in the software. Molecular descriptors were obtained from the optimized structures using the PaDEL-Descriptor software. Activity values of the compounds and molecular descriptors obtained from the optimized structure made up the database for the study. The database was divided into training and test sets with Kennard Stone algorithm. Genetic function algorithm was used to develop quantitative structure-activity relationship models. The best model obtained was stable, robust and had good statistical parameters including determination coefficient R2 (0.899), adjusted determination coefficient R2adj (0.888), variance ratio F (82.03), leave one out cross-validated determination coefficient Q2 (0.866) and predicted determination coefficient for the test set R2pred (0.7406). The model indicated that the anticonvulsant activity of the studied compounds was dependent on Broto-Moreau autocorrelation-lag2/weighted by Vander Waals volume (ATS2v), average coefficient sum of the last eigenvector from Barysz matrix/weighted by Vander Waals volume (VE2_DZv), largest absolute eigenvalue of Burden matrix-6/weighted by relative atomic mass (SpMax6_Bhm), average valence path of order 6 and radial distribution function at 4.5 interatomic distance weighted by first ionization potential (RDF45i).Downloads
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References
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CHOUDHARY, M.; SHARMA, B. K. QSAR rationales for the 5-HT6 antagonistic activity of Epiminocyclohepta [b] indoles. Journal of Pharmaceutical Chemistry, v. 6, p.321–330, 2014.
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KENNARD, R. W.; STONE, L. A. Computer aided design of experiments. Technometrics, v.11, p. 137-148, 1969.
KRAMER, G. Epilepsy in the elderly: some clinical and pharmacotherapeutic aspects, Epilepsia, v. 42, p. 55-59, 2001.
MOHAMED F. Z. New fluorinated quinazolinone derivatives as anticonvulsant agents. Journal of Taibah University Medical Sciences, v.9, n.2, p.104–109, 2014.
NOOLVI, M. N.; PATEL, H. M.; BHARDWAJ, V. 2D qsar studies on a series of 4-anilino quinazoline derivatives as tyrosine kinase (egfr) inhibitor: an approach to design anticancer agents, Digest Journal of Nonmaterial and Biostructures, v. 5, n.2, p. 387 – 401, 2010.
OPREA, T. I. (2005) Chemoinformatics in drug discovery, WILEY-VCH Verlag GmbH and Co, Weinheim, v. 23, p. 437-453, 2005.
PABLO, R. D.; IBEZIM, E.; ERLINDA, V. O.; EDUARDO, A. C. QSAR on aryl-piperazine derivatives with activity on malaria, Chemometrics and Intelligent Laboratory Systems, v. 110, p. 81–88, 2012.
POURBASHEER, E.; RIAHI, S.; GANJALI, M. R.; NOROUZI, P. Application of genetic algorithm–support vector machine (GA–SVM) for prediction of BK-channels activity. European Journal of Medicinal Chemistry, v. 44, p. 5023–5028, 2009.
RIAHI, S.; POURBASHEER, E.; GANJALI, M. R.; NOROUZI, P. Investigation of different linear and non-linear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. Journal of Hazardous Material, v. 166, p.853–859, 2009.
ROGERS D.; HOPFINGER, A. J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships, Journal of Chemical Information and Computational Science, v.34,p. 854–866, 1994.
ROY, K. On some aspects of validation of predictive QSAR models. Expert Opinion in Drug Discovery, v.2, p.1567-1577, 2007.
ROY, K..; KAR, S.; DAS, R. N. A primer on QSAR/QSPR modeling fundamental concepts, Springer, p. 48-58, 2015.
ROY, K..; MITRA, I.; KAR, S.; OJHA, P. K.; DAS, R. N.; KABIR. H. Comparative studies on some metrics for external validation of QSPR models, Journal of Chemical Informatics and Modeling, v. 52, p. 396–408, 2012.
ROY, K.; DAS, R. N.; AMBURE, P.; AHER, R. B. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, v.152, p.18–33, 2016.
ROY, K.; GHOSH, G. Exploring QSARs with extended topochemical atom (ETA) indices for modeling chemical and drug toxicity, Current Pharmaceutical Discovery,v.16, p.2625-2639, 2010.
ROY, P. P.; LEONARD, J. T.; ROY, K. Exploring the impact of training sets for the development of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems,v.90,p.31-42, 2008.
SHAO, Y.; MOLNAR, L. F.; JUNG, Y.; KUSSMANN, J.; OCHSENFELD, C.; BROWN, S.T. Advances in methods algorithms in modern quantum chemistry program package. Physics Chemistry Chemistry Physics, v. 8, p. 3172, 2006.
SIVAKUMAR, P. M.; BABU, S. K.. G.; MUKESH, D. QSAR studies on chalcones and flavonoids as anti-tuberculosis agents using genetic function approximation (GFA) method. Bulletin of Pharmaceutical Chemistry (Tokyo),v.55,p 44–49,2007.
SPECK-PLANCHE, A.; CORDEIRO, M. N. Computer-aided drug design methodologies toward the design of anti-hepatitis C agents. Current Topic on Medicinal Chemistry, v. 12, p. 802-813, 2012.
THIRUMURUGAN, R.; SRIRAM, D.; STABLES, J. 2,4-dimethoxyphenylsemicarbazones with anticonvulsant activity against three animal models of seizures: Bioorganic and Medicinal Chemistry Journal, v. 14, p. 3106– 3112, 2006.
TODESCHINI, R.; CONSONNI, V. Molecular descriptors for chemo informatics, 2rd Edition, WILEY-VCH Verlag GmbH and Co, Weinheim, v.1 and 2, p. 27-37, 550-726, 2009.
TOPLISS, J.G.; COSTELLO, R.J. Chance correlations in structure-activity studies using multiple regression analysis. Journal of Medicinal Chemistry,v.15, p. 1066–1068, 1972.
TROPSHA A. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics,v. 29, p. 476–488, 2010.
TROPSHA, A.; GRAMATICA, P.; GOMBAR V.K.. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Molecular Informatics, v. 22, p. 69–7, 2003.
YAP, C.W. PaDEL-Descriptor. open source to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, v 32, n.7, p. 1466-1474, 2011.
AL-RASHOOD, S. T.; ABOLDAHAB, I. A.; NAGI, M. N.; ABOUZEID, L. A.; ABDEL-AZIZ, A. A. M.; ABDELHAMID, S. G.; YOUSSEF, K. M.; AL-OBAID, A. M.; EL-SUBBAGH, H. I. Synthesis, dihydrofolate reductase inhibition, antitumor testing, and molecular Modeling Study of Some New 4(3H)- quinazolinone Analog,
Bioorganic and Medicinal Chemistry, v. 14, p. 8608-8621, 2008.
AMBURE, P.; AHER, R. B.; GAJEWICZ, A.; PYZYT, W. R. K. NanoBRIDGES software: open access tools to perform QSAR and nano-QSAR modeling. Chemometry and Intelligent Laboratory System, v147, p. 1-13, 2015.
ARCHANA, S. V. K.; KUMAR, A. Synthesis of newer thiadiazolyl and thiazolidinonyl quinazolin-4(3H)-ones as potential anticonvulsant agents. European Journal of Medicinal Chemistry, v. 37, p. 873-/882, 2002.
ARCHANA, S. V. K.; KUMAR, A. Synthesis of some newer derivatives of substituted quinazolinonyl-2-oxo/thiobarbituric acid as potent anticonvulsant agents. Bioorganic and Medicinal Chemistry, v. 12, p. 1257–1264, 2004.
BAVETSIAS, V.; HENDERSON, E. A.; MCDONALD, E. Cyclopenta[g]quinazolinone-Based Inhibitors of Thymidylate Synthase Targeting ?-Folate Receptor Overexpressing Tumours: Synthetic Approaches to 4-{N [(6RS)-2-Hydroxymethyl-4-oxo-3,4,7,8-tetrahydro-6H-cyclopenta[g]quinazolin-6-yl]-N-(prop-2-ynyl) amino}benzoic acid. Tetrahedron, v.63, p.1537-1543, 2007.
BROWN, T. R.; HOLMES, G. L. N. Epilepsy, English Journal of Medicine, v. 344, p. 1145-1151, 2001.
CHOUDHARY, M.; SHARMA, B. K. QSAR rationales for the 5-HT6 antagonistic activity of Epiminocyclohepta [b] indoles. Journal of Pharmaceutical Chemistry, v. 6, p.321–330, 2014.
EL-HAKIM, A. E.; ABDEL-HAMIDE, S. G.; AL-AZHAR EL-HELBY, A. A. Journal of Pharmaceutical Science, v.14, p.156-163, 1994.
EL-NASER, O.A.R.; EL-SAYED, B.S. Synthesis and anticonvulsant activity of some new 3-(p-Sulfamoylphenyl)-4(3H)-Quinazolinones. Arzneimittelforsch, v. 44, p. 915- 919, 1994.
GAVERNET, L.; CABRERA, M. J. D.; BRUNO-BLANCH, L. E.; ESTIÚ, G. L. 3D-QSAR design of novel antiepileptic sulfamides. Bioorganic and Medicinal Chemistry,v. 15,n.3, p. 1556–1567, 2007.
GEORGEY, H.; ABDEL-GAWAD, N.; ABBAS, S. Synthesis and anticonvulsant activity of some quinazolin-4- (3H)-one derivatives. Molecules, v. 13,p. 2557-2569, 2008.
GRAMATICA, P.; CASSANI, S.; ROY, P.P.; KOVARICH, S.; YAP, C.W.; PAPA, E. QSAR Modeling is not “push a button and find a correlation”: A case study of toxicity of (Benzo)-triazoles on algae, Molecular Informatics, v.31, p. 817-835, 2012.
GÜNGÖR, T.; CHEN, Y.; GOLLA, R.; MA, Z.; CORTE, J.R.; NORTHROP, J.P.; BIN, B.; DICKSON, J.K.; STOUCH, T.; ZHOU, R.; JOHNSON, S.E.; SEETHALA, R.; FEYEN, J.H.M. Synthesis and Characterization of 3-Arylquinazolinone and 3-Arylquinazolinethione Derivatives as selective estrogen receptor beta modulators. Journal of Medicinal Chemistry,v. 49, p. 2440-2455, 2006.
HABIBI, Y. A.; DANANDEH, J. M. Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis. Monatsh Chemistry, v. 140, p. 1279–1288, 2009.
KEIR, L. B.; HALL, L. H. Molecular connectivity in chemistry and drug research. Academic press 111fifth Avenue, New York,v.14, p.47-89, 1976.
KENNARD, R. W.; STONE, L. A. Computer aided design of experiments. Technometrics, v.11, p. 137-148, 1969.
KRAMER, G. Epilepsy in the elderly: some clinical and pharmacotherapeutic aspects, Epilepsia, v. 42, p. 55-59, 2001.
MOHAMED F. Z. New fluorinated quinazolinone derivatives as anticonvulsant agents. Journal of Taibah University Medical Sciences, v.9, n.2, p.104–109, 2014.
NOOLVI, M. N.; PATEL, H. M.; BHARDWAJ, V. 2D qsar studies on a series of 4-anilino quinazoline derivatives as tyrosine kinase (egfr) inhibitor: an approach to design anticancer agents, Digest Journal of Nonmaterial and Biostructures, v. 5, n.2, p. 387 – 401, 2010.
OPREA, T. I. (2005) Chemoinformatics in drug discovery, WILEY-VCH Verlag GmbH and Co, Weinheim, v. 23, p. 437-453, 2005.
PABLO, R. D.; IBEZIM, E.; ERLINDA, V. O.; EDUARDO, A. C. QSAR on aryl-piperazine derivatives with activity on malaria, Chemometrics and Intelligent Laboratory Systems, v. 110, p. 81–88, 2012.
POURBASHEER, E.; RIAHI, S.; GANJALI, M. R.; NOROUZI, P. Application of genetic algorithm–support vector machine (GA–SVM) for prediction of BK-channels activity. European Journal of Medicinal Chemistry, v. 44, p. 5023–5028, 2009.
RIAHI, S.; POURBASHEER, E.; GANJALI, M. R.; NOROUZI, P. Investigation of different linear and non-linear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. Journal of Hazardous Material, v. 166, p.853–859, 2009.
ROGERS D.; HOPFINGER, A. J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships, Journal of Chemical Information and Computational Science, v.34,p. 854–866, 1994.
ROY, K. On some aspects of validation of predictive QSAR models. Expert Opinion in Drug Discovery, v.2, p.1567-1577, 2007.
ROY, K..; KAR, S.; DAS, R. N. A primer on QSAR/QSPR modeling fundamental concepts, Springer, p. 48-58, 2015.
ROY, K..; MITRA, I.; KAR, S.; OJHA, P. K.; DAS, R. N.; KABIR. H. Comparative studies on some metrics for external validation of QSPR models, Journal of Chemical Informatics and Modeling, v. 52, p. 396–408, 2012.
ROY, K.; DAS, R. N.; AMBURE, P.; AHER, R. B. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, v.152, p.18–33, 2016.
ROY, K.; GHOSH, G. Exploring QSARs with extended topochemical atom (ETA) indices for modeling chemical and drug toxicity, Current Pharmaceutical Discovery,v.16, p.2625-2639, 2010.
ROY, P. P.; LEONARD, J. T.; ROY, K. Exploring the impact of training sets for the development of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems,v.90,p.31-42, 2008.
SHAO, Y.; MOLNAR, L. F.; JUNG, Y.; KUSSMANN, J.; OCHSENFELD, C.; BROWN, S.T. Advances in methods algorithms in modern quantum chemistry program package. Physics Chemistry Chemistry Physics, v. 8, p. 3172, 2006.
SIVAKUMAR, P. M.; BABU, S. K.. G.; MUKESH, D. QSAR studies on chalcones and flavonoids as anti-tuberculosis agents using genetic function approximation (GFA) method. Bulletin of Pharmaceutical Chemistry (Tokyo),v.55,p 44–49,2007.
SPECK-PLANCHE, A.; CORDEIRO, M. N. Computer-aided drug design methodologies toward the design of anti-hepatitis C agents. Current Topic on Medicinal Chemistry, v. 12, p. 802-813, 2012.
THIRUMURUGAN, R.; SRIRAM, D.; STABLES, J. 2,4-dimethoxyphenylsemicarbazones with anticonvulsant activity against three animal models of seizures: Bioorganic and Medicinal Chemistry Journal, v. 14, p. 3106– 3112, 2006.
TODESCHINI, R.; CONSONNI, V. Molecular descriptors for chemo informatics, 2rd Edition, WILEY-VCH Verlag GmbH and Co, Weinheim, v.1 and 2, p. 27-37, 550-726, 2009.
TOPLISS, J.G.; COSTELLO, R.J. Chance correlations in structure-activity studies using multiple regression analysis. Journal of Medicinal Chemistry,v.15, p. 1066–1068, 1972.
TROPSHA A. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics,v. 29, p. 476–488, 2010.
TROPSHA, A.; GRAMATICA, P.; GOMBAR V.K.. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Molecular Informatics, v. 22, p. 69–7, 2003.
YAP, C.W. PaDEL-Descriptor. open source to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, v 32, n.7, p. 1466-1474, 2011.
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Published
2018-07-04
How to Cite
Oluwaseye, A., UZAIRU, A., SHALLANGWA, G., & ABECHI, S. (2018). QSAR STUDIES ON DERIVATIVES OF QUINAZOLINE-4(3H)-ONES WITH ANTICONVULSANT ACTIVITIES. The Journal of Engineering and Exact Sciences, 4(2), 0255–0264. https://doi.org/10.18540/jcecvl4iss2pp0255-0264
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Physical Chemistry
