In order to gain further insights into the structural requirements for anti-tuberculosis
activity by chalcone derivatives of 1,3-diphenylprop-2-ene-1-one, quantitative
structure activity relationship (QSAR) was performed using genetic function
approximation (GFA). Geometry optimization was achieved at the density
functional theory (DFT) level using Becke’s three-parameter Lee-Yang- Parr
hybrid functional (B3LYP) in combination with the 6-31G* basis set.
Subsequently, quantum chemical and molecular descriptors were generated and
divided into training and test sets by Kennard Stone algorithm. Internal and
external validations as well as Y-randomization tests were employed in model validation. Five predictive
models were generated by GFA. The generated models showed that constitutional indices, 2D autocorrelations and radial
distribution function (RDF) descriptors were important contributors to anti-tuberculosis
activity of 1,3-diphenylprop-2-ene-1-one derivatives. Based on validation
results, model 4 was chosen as the best of the five models.
Cite this paper
Ogadimma, A. I. and Adamu, U. (2016). Quantitative Structure Activity Relationship Analysis of Selected Chalcone Derivatives as Mycobacterium tuberculosis Inhibitors. Open Access Library Journal, 3, e2432. doi: http://dx.doi.org/10.4236/oalib.1102432.
Mack, U., Migliori, G.B., Sester, M., Rieder, H.L.,
Ehlers, S., Goletti, D., Bossink, A., Magdorf, K., Holscher, C., Kampmann, B.,
Arend, S.M., Detjen, A., Bothamley, G., Zellweger, J.P., Milburn, H., Diel, R.,
Ravn, P., Cobelens, F., Cardona, P.J., Kan, B., Solovic, I., Duarte, R., Cirillo,
D.M. and Lange, C. for the TBNET (2009) LTBI: Latent Tuberculosis Infection or
Lasting Immune Responses to M. Tuberculosis? ATBNET Consensus Statement. European Respiratory Society, 33, 956-973. http://dx.doi.org/10.1183/09031936.00120908
Tripathi, R., Tewari,
N.,
Dwivedi, N. and Tiwari,
V.K. (2005) Fighting Tuberculosis: An Old Disease with New Challenges. Medicinal Research Reviews, 25, 93-131. http://dx.doi.org/10.1002/med.20017
Alejandro,
S.P., Marcus
Tulius, S. and de
Paulo-Emerenciano, V. (2010) Current Pharmaceutical Design of Antituberculosis Drugs: Future
Perspectives. Current Pharmaceutical Design, 16,
2656-2665. http://dx.doi.org/10.2174/138161210792389289
Eric, G.M., Uzairu, A. and Mamza, P.A.P. (2015) Investigation of
the Activity of 8-Methylquinolones against Mycobacteriumtuberculosis Using Theoretical
Molecular Descriptors: A Case Study. European Scientific
Journal September, 11, 1857-7881.
Ravichandran, V., Shalini, S., Sokkalingam, A.D., Harish, R. and Suresh, K. (2014) QSAR Study of 7-Chloroquinoline Derivatives as Antitubercular
Agents. World Journal of Pharmacy and
Pharmaceutical Sciences, 3, 1072-1082.
Ravichandran, V., Shalini, S., Kumar, K.V., Harish, R. and Kumar, K.S.
(2015) QSAR Study on Arylthioquinoline Derivatives
as Anti-Tubercular Agents. PTB Reports, 1, 81-86. http://dx.doi.org/10.5530/PTB.1.2.8
Younes, A., Abdelkader, A., Hayat, L., Ahmed, R., Driss, Z. and Mohamed, Z. (2014) QSAR for
Antimycobacterial Activity of β-Thia
Adduct of Chalcone and Diazachalcone Derivatives. International Journal of
Computational and Theoretical Chemistry, 2, 20-25. http://dx.doi.org/10.11648/j.ijctc.20140203.11
Gupta, R.A. and Kaskhedikar, S.G. (2012) Synthesis,
Evaluation and QSAR Analysis of 5-Nitrofuran-2-Yl/4-Nitro- phenyl Methylene Substituted Hydrazides as
Antitubercular Agents. Asian Journal of Pharmaceutical and Clinical Research, 5, 251-259.
Priyadarsini,
R.,
Tharanib, C.B., Sathya,
S. and Kavithaa,
S. (2012) Pharmacophore Modeling and
3D-QSAR Studies on Substituted Benzothiazole/Benzimidazole Analogues as DHFR
Inhibitors with Antimycobacterial Activity. International Journal of Pharma Sciences
and Research, 3, 4441-4450.
Sawarkar, V.M., Dudhe, P.B., Nagras, M.A., Bhosle, P.V., Jadhav, B. and Meshram, R.S. (2013) 2D & 3D QSAR Studies of Biaryl
Analogues of Pa-824 Having
Various Ether Linkers: An Approach to Design Antitubercular
Agents. Pharmacophore, 4, 92-104.
Rajasekaran, S., Gopalkrishna, R. and Sanjay, P.P.N. (2011) 2D QSAR Studies of Some Novel Quinazolinone
Derivatives as Antitubercular Agents. Journal of Computational Methods in
Molecular Design, 1, 69-82.
Kamalakaran, A.S., Srinivasan, S. and Veluchamy,
A. (2009) QSAR Studies on N-Aryl Derivative Activity towards Alzheimer’s Disease. Molecules, 14, 1448-1455. http://dx.doi.org/10.3390/molecules14041448
Umaa, K., Kavithamani, A., Maida Engels, S.E. and Geetha, G. (2013) Quantitative Structure Activity Studies on
the Anti-Mycobacterial Potentials of Certain Chalcone Derivatives. International Journal of Research in Organic
Chemistry, 3, 6-10.
Ballabio, D., Consonni, V., Mauri, A., Claeys-Bruno, M., Sergent, M. and Todeschini,
R. (2014) A Novel Variable Reduction Method Adapted from Space-Filling Designs. Chemometrics and Intelligent Laboratory
Systems, 136, 147-154. http://dx.doi.org/10.1016/j.chemolab.2014.05.010
Ambure, P., Aher, R.B., Gajewicz, A. and Puzyn, T. (2015) “NanoBRIDGES”
Software: Open Access Tools to Perform QSAR and Nano-QSAR Modeling. Chemometrics and Intelligent Laboratory
Systems, 147, 1-13. http://dx.doi.org/10.1016/j.chemolab.2015.07.007
Todd, M.M., Harten, P., Douglas, M.Y., Muratov, E.N., Golbraikh, A., Zhu, H. and Tropsha, A. (2012) Does Rational Selection of Training
and Test Sets Improve the Outcome of QSAR Modeling? Journal of Chemical
Information and Modeling, 52, 2570-2578. http://dx.doi.org/10.1021/ci300338w
Khaled, K.F. and Abdel-Shafi, N.S. (2011) Quantitative Structure and Activity Relationship Modeling Study of Corrosion
Inhibitors: Genetic Function Approximation and Molecular Dynamics Simulation
Methods. International Journal of Electrochemical
Science, 6, 4077-4094.
Das, R.N. and Roy, K. (2012) Development of Classification and Regression Models for Vibrio fischeri Toxicity of Ionic Liquids: Green Solvents
for the Future. Toxicology Research, 1,
186-195. http://dx.doi.org/10.1039/c2tx20020a
Kar, S. and Roy, K. (2011) Development and Validation of a Robust QSAR Model for Prediction
of Carcinogenicity of Drugs. Indian
Journal of Biochemistry and Biophysics, 48, 111-122.
Roy, P.P. and
Roy, K. (2008) On Some Aspects
of Variable Selection for Partial Least Squares Regression Models. QSAR
& Combinatorial Science, 27, 302-313. http://dx.doi.org/10.1002/qsar.200710043
Indrani, M., Achintya,
S. and
Kunal, R. (2010) Chemometric Modeling of Free Radical Scavenging Activity of Flavone
Derivatives. European Journal of
Medicinal Chemistry, 45,
5071-5079. http://dx.doi.org/10.1016/j.ejmech.2010.08.016
Roy, K. and
Mitra, I. (2011) On
Various Metrics Used for Validation of Predictive QSAR Models with Applications
in Virtual Screening and Focused Library Design. Combinatorial Chemistry
& High Throughput Screening, 14, 450-474. http://dx.doi.org/10.2174/138620711795767893
Roy,
K., Chakraborty, P., Mitra, I., Ojha, P.K., Kar, S. and Das, R.N. (2013) Some Case Studies on
Application of “rm2” Metrics for Judging Quality of
Quantitative Structure-Activity Relationship Predictions: Emphasis on Scaling
of Response Data. Journal of Computational Chemistry, 34, 1071-1082. http://dx.doi.org/10.1002/jcc.23231
Tropsha, A. (2010) Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics, 29,
476-488. http://dx.doi.org/10.1002/minf.201000061
Roy, K., Kar, S. and Das, R.N. (2015) Statistical Methods
in QSAR/QSPR. In: Roy, K., Kar, S. and Das, R.N., Eds., A Primer
on QSAR/QSPR Modeling, Springer
Briefs in Molecular Science,Springer, Berlin, 37-59. http://dx.doi.org/10.1007/978-3-319-17281-1_2
Roy, K. and
Paul, S. (2008) Exploring 2D and 3D QSARs of 2,4-diphenyl-1,3-oxazolines for
Ovicidal Activity against Tetranychus urticae. QSAR &
Combinatorial Science, 28,
406-425. http://dx.doi.org/10.1002/qsar.200810130
Partha, P.R., Somnath, P., Indrani, M. and Kunal, R. (2009) On Two Novel Parameters for Validation of Predictive
QSAR Models. Molecules, 14, 1660-1701. http://dx.doi.org/10.3390/molecules14051660
Roy, K. (2007)On Some
Aspects of Validation of Predictive Quantitative Structure-Activity Relationship Models. Expert
Opinion on Drug Discovery, 2, 1567-1577. http://dx.doi.org/10.1517/17460441.2.12.1567
Singh, P. (2013) Molecular Descriptors in Modelling of
TNF-∝ Converting Enzyme (TACE) Inhibition Activity
of 2-(2-Aminothiazol-4-yl) pyrrolidine-Based Tartrate Diamides. Indian Journal of Chemistry, 52, 1325-1341.
Cheng, Z.J. and Zhang, Y.T. (2010) Classification
Models of Estrogen Receptor-β Ligands
Based on PSO-Adaboost- SVM. Journal of Convergence Information
Technology, 5, 67-83. http://dx.doi.org/10.4156/jcit.vol5.issue2.8
Fernandez, M.,
Caballero, J. and
Tundidor-Camba, A. (2006) Linear and Nonlinear QSAR Study of N-hydroxy-2- [(phenylsulfonyl)amino] Acetamide
Derivatives as Matrix Metalloproteinase Inhibitors. Bioorganic & Medicinal Chemistry, 14, 4137-4150. http://dx.doi.org/10.1016/j.bmc.2006.01.072