QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIPS (QSAR) STUDY ON NOVEL 4-AMIDINOQUINOLINE AND 10-AMIDINOBENZONAPHTHYRIDINE DERIVATIVES AS POTENT ANTIMALARIA AGENT

Authors

  • Aliyu Wappah Mahmud Ahmadu Bello University Zaria, Nigeria
  • Gideon Adamu Shallangwa Ahmadu Bello University Zaria, Nigeria
  • Adamu Uzairu Ahmadu Bello University Zaria, Nigeria

DOI:

https://doi.org/10.18540/jcecvl5iss3pp0271-0282

Keywords:

QSAR, Antimalaria, Plasmodium falciparum and 4-Amidinoquinoline

Abstract

Quantitative structure–activity relationships (QSAR) has been a reliable study in the development of models that predict biological activities of chemical substances based on their structures for the development of novel chemical entities. This  study was carried out on 44 compounds of 4-amidinoquinoline and 10-amidinobenzonaphthyridine derivatives to develop a model that relates their structures to their activities against Plasmodium falciparum. Density Functional Theory (DFT) with basis set B3LYP/6-31G? was used to optimize the compounds. Genetic Function Algorithm (GFA) was employed in selecting descriptors and building the model. Four models were generated and the model with best internal and external validation has internal squared correlation coefficient (

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References

AI, J. L.; VASILIY, N. K.; RAMADAS, S.; LUCIA, G.; DIANA C.; QIGUI, L.; MARA, K. D.; & PHILIP S. Antimalarial Activity of 4-Amidinoquinoline and 10-Amidinobenzonaphthyridine Derivatives. Journal Of Medicinal Chemistry, 2015.

BECKE, A. D. Becke’s three parameter hybrid method using the LYP correlation functional. Journal Of Chemical Physics. 98, 5648–5652. 1993.

COHEN J. M.; SMITH, D. L.; COTTER, C.; WARD, A.; YAMEY, G.; SABOT, O. J.; & MOONEN, B. Malaria resurgence: A systematic review and assessment of its causes. Malaria Journal, 11: 122, 2012.

FIDOCK, D. A. Drug discovery: Priming the antimalarial pipeline. Nature, 465, 297–298, 2010.

FRIEDMAN, J. H. Multivariate adaptive regression splines. Annals Of Statistics, 1–67, 1991.

JALALI-HERAVI, M.; & KYANI, A. Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach. Journal Chemical Information And Computer Sciences, 44, 1328–1335, 2004.

KRAFTS, K.; HEMPELMANN, E.; & SKÓRSKA-STANIA, A. From methylene blue to chloroquine: A brief review of the development of an antimalarial therapy. Journal Of Parasitology Research, 11, 1-6. 2012.

LI, Z.; WAN, H.; SHI, Y.; & OUYANG, P. Personal experience with four kinds of chemical structure drawing software: review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch. Journal Of Chemical Information And Computer Sciences. 44, 1886–1890, 2004.

MISHRA, A.; BATCHU, H.; SRIVASTAVA, K.; SINGH, P.; SHUKLA, P. K,; & BATRA, S. Synthesis and evaluation of new diaryl ether and quinoline hybrids as potential antiplasmodial and antimicrobial agents. Bioorganic And Medicinal Chemistry Letters, 24, 1719–1723, 2014.

PALMER, K. J.; HOLLIDAY, S. M.; & BROGDEN, R. N. Mefloquine: A review of its antimalarial activity, pharmacokinetic properties and therapeutic efficacy. Drugs. 45, 430–475. 1993.

PLOWE, C. V. Antimalarial drug resistance in Africa: strategies for monitoring and deterrence. Current Topics In Microbiology And Immunology, 295, 55–79. 2005.

RACZYNSKA, E. D.; DECOUZON, M.; GAL, J. F.; MARIA, P. C.; WOZNIAK, K.; KURG, R.; & CAIRNS, S. N. Super bases and super acids in gas phase. Trends In Organic Chemistry, 7, 95-103, 1998.

SINGH, P. Quantitative structure-activity relationship study of substituted-[1, 2, 4] oxadiazoles as S1P1 agonists. Journal Current Chemical And Pharmaceutical Sciences, 2013.

TROPSHA, A. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics, 29, 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, 22, 69–77, 2003.

TROTT, O.; & OLSON, A. J. Autodock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. Journal of Computational Chemistry, 22, 455-461, 2010.

UHLEMANN, A. C.; & KRISHNA, S. Antimalarial multi-drug resistance in Asia: mechanisms and assessment. Current. Topics. Microbiology. Immunology, 295, 39–53, 2005.

VEERASAMY, R.; RAJAK, H.; JAIN, A.; SIVADASAN, S.; VARGHESE, C. P.; & AGRAWAL, R. K. Validation of QSAR models-strategies and importance. International Journal Of Drug Design And Discovery. 3, 511–519, 2011.

WORLD HEALTH ORGANIZATION. World Malaria Report, 2018.

YAP, C. W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32, 1466–1474, 2011.

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Published

2019-06-28

How to Cite

Mahmud, A. W., Shallangwa, G. A., & Uzairu, A. (2019). QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIPS (QSAR) STUDY ON NOVEL 4-AMIDINOQUINOLINE AND 10-AMIDINOBENZONAPHTHYRIDINE DERIVATIVES AS POTENT ANTIMALARIA AGENT. The Journal of Engineering and Exact Sciences, 5(3), 0271–0282. https://doi.org/10.18540/jcecvl5iss3pp0271-0282

Issue

Section

Physical Chemistry

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