QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer against Leukemia cell line (K562)

Abdullahi Mustapha; Gideon Shallangwa; Muhammad Tukur Ibrahim; Abdullahi Umar Bello; David Arthur Ebuka; Adamu Uzairu; Paul Mamza

doi:10.18596/jotcsa.457618

Araştırma Makalesi

QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer against Leukemia cell line (K562)

Yıl 2018, Cilt: 5 Sayı: 3, 1387 - 1398, 01.09.2018

Abdullahi Mustapha , Gideon Shallangwa Muhammad Tukur Ibrahim , Abdullahi Umar Bello David Arthur Ebuka , Adamu Uzairu Paul Mamza

https://doi.org/10.18596/jotcsa.457618

Cited By: 10

Öz

This research applied Quantitative Structure Activity Relationship (QSAR) technique in developing a Multiple-Linear Regression (MLR) model using Genetic Functional Algorithm (GFA) method in selecting relevant molecular descriptors from the structures of 24 C14-urea tetrandrine compounds. Firstly, the compounds were optimized at Density Functional Theory (DFT) level using Becke’s three-parameter Lee-Yang-Parr hybrid functional (B3LYP) with the 6-31G* basis set in the Spartan 14 Version 1.1.4 software. The molecular descriptors were calculated using Padel- software, and the results were divided in to training and test set. A model was built from the training set with internal validation parameter R2train as 0.910403. The external validation of the model was carried out using the test set compounds with validation parameter R2test as 0.6443 which passed the criteria for acceptability of a QSAR model globally. The coefficient of determination (𝑐𝑅2𝑝) parameter was calculated as 0.819296 which is greater than 0.5, this affirms that the generated model is robust. Furthermore, AST4p, GATS8v and MLFER are the descriptors in the model with positive mean effect of 0.089972855, 0.909814859 and 0.000212286 respectively. This study inferred that there will be positive influence on the inhibitory concentrations when the each descriptor value increases

Anahtar Kelimeler

QSAR, Mean Effect, Validation, Descriptors, Model

Kaynakça

1. Lookward W. Leukemia (AML, CML, ALL and CLL). www.RN.ORG; ©RN.ORG®, S.A., RN.ORG®, LL; 2015. ISBN: 678-693-1-32.
2. Lozzio CB, Lozzio BB. Human Chronic Myelogenous Leukemia Cell-Line with Positive Philadelphia Chromosome. National Institute of Health. 1975; 45(1): 321–340.
3. Karimiani EG, Marriage F, Merritt AJ, Burthem J, Byers RJ, Day PJ. Single-cell analysis of K562 cells: an imatinib-resistant subpopulation is adherent and has upregulated expression of BCR-ABL mRNA and protein. Experimental Hematology. 2014; 42(3): 183-191
4. Fan Y, Lu H, An L, Wang C, Zhou Z, Feng F, Zhao Q. Effect of active fraction of Eriocaulon sieboldianum on human leukemia K562 cells via proliferation inhibition, cell cycle arrest and apoptosis induction. Environmental Toxicology and Pharmacology. 2016; 4(3):13-20.
5. Liu T, Liu X, Li WH. Tetrandrine, a Chinese Plant-Derived Alkaloid, Is a Potential Candidate for Cancer Chemotherapy, On Co-Target. 2016; 2(7):480100–480115.
6. Perkins R, Fang H, Tong W, Welsh WJ. Quantitative Structure-Activity Relationship Methods: Perspectives on Drug Discovery and Toxicology. 2003; 22(1): 1666–1679.
7. Lan J, Huang L, Lou H, Chen C, Liu T, Hu S, Yao Y, Song J, Luo J, Liu Y, Xia B, Xia L, Zeng X, Ben-David Y, Pan W. Design and Synthesis of Novel Tetrandrine Derivatives as Potential Anti-Tumor Agents against Human Hepatocellular Carcinoma. European Journal of Medicinal Chemistry. 2017 Sep; 3-4.
8. Becke AD. Becke’s three-parameter hybrid method using the LYP correlation functional. Journal of Chemical Physics. 1993; 98: 5648–5652.
9. Lee C, Yang W, Parr RG. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B. 1988; 37:785
10. Alisi IO, Uzairu A, Abechi SE, Idris SO. Quantitative Structure activity relationship analysis of coumarins as free radical scavengers by genetic function algorithm. Iranian Chemical Society. 2018 Mar; 2(6):208–222
11. Ambure P, Rahul BA, Agnieszka G, Tomasz P, Kunal R. “NanoBRIDGES” Software: Open Access Tools to Perform QSAR and Nano-QSAR Modeling. Chemical Intelligent Laboratory Systems. 2015; 147: 1–13.
12. Kennard RW, Stone LA. Computer Aided Design of Experiments. Technometrics. 1969 Feb; 11(1):137–48.
13. Friedman JH, Multivariate Adaptive Regression Splines. The Annals of Statistics. 1991: 1–67.
14. Khaled KF, Abdel-Shafi NS. Quantitative structure and activity relationship modeling study of corrosion inhibitors: Genetic function approximation and molecular dynamics simulation methods. International Journal of Electrochemical Science. 2011; 6:4077-4094
15. Brand V, Orr KA, Comprehensive R archive network (CRAN): http://CRAN.Rproject.org. retrieved; 2015
16. Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics. 2010 Jul 6; 29(6–7):476–88. 17. Minovski N, Župerl Š, Drgan V, Novič M. Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: a case study. Analytica Chimica Acta. 2013; 759:28–42.
18. Myers RH. Classical and modern regression application. 2nd edition. Duxbury Press. CA. 1990
19. Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-baes QSARs. Environmental Health Perspectives 2003; 111:1361-1375
20. Nandi S, Monesi A, Drgan V, Merzel F, Novič M. Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors. Chemistry Central Journal. 2013; 7:1-13.
21. Gramatica P, Giani E., Papa E., Statistical external validation and consensus modeling: A QSPR case study for KOC prediction. Journal of Molecular Graphics and Modelling. 2007; 25:755-66.
22. Todeschini R, Consonni V. Molecular descriptors for chemo-informatics. Weinheim: Wiley- VCH; 2009. (Methods and principles in medicinal chemistry). ISBN: 9783527318520
23. Adeniji SE, Sani U, Uzairu A, QSAR Modeling and Molecular Docking Analysis of Some Active Compounds against Mycobacterium Tuberculosis Receptor (Mtb CYP121). Journal of Pathogens Hindawi. 2018; 24-64.

Yıl 2018, Cilt: 5 Sayı: 3, 1387 - 1398, 01.09.2018

Abdullahi Mustapha , Gideon Shallangwa Muhammad Tukur Ibrahim , Abdullahi Umar Bello David Arthur Ebuka , Adamu Uzairu Paul Mamza

https://doi.org/10.18596/jotcsa.457618

Cited By: 10

Öz

Kaynakça

1. Lookward W. Leukemia (AML, CML, ALL and CLL). www.RN.ORG; ©RN.ORG®, S.A., RN.ORG®, LL; 2015. ISBN: 678-693-1-32.
2. Lozzio CB, Lozzio BB. Human Chronic Myelogenous Leukemia Cell-Line with Positive Philadelphia Chromosome. National Institute of Health. 1975; 45(1): 321–340.
3. Karimiani EG, Marriage F, Merritt AJ, Burthem J, Byers RJ, Day PJ. Single-cell analysis of K562 cells: an imatinib-resistant subpopulation is adherent and has upregulated expression of BCR-ABL mRNA and protein. Experimental Hematology. 2014; 42(3): 183-191
4. Fan Y, Lu H, An L, Wang C, Zhou Z, Feng F, Zhao Q. Effect of active fraction of Eriocaulon sieboldianum on human leukemia K562 cells via proliferation inhibition, cell cycle arrest and apoptosis induction. Environmental Toxicology and Pharmacology. 2016; 4(3):13-20.
5. Liu T, Liu X, Li WH. Tetrandrine, a Chinese Plant-Derived Alkaloid, Is a Potential Candidate for Cancer Chemotherapy, On Co-Target. 2016; 2(7):480100–480115.
6. Perkins R, Fang H, Tong W, Welsh WJ. Quantitative Structure-Activity Relationship Methods: Perspectives on Drug Discovery and Toxicology. 2003; 22(1): 1666–1679.
7. Lan J, Huang L, Lou H, Chen C, Liu T, Hu S, Yao Y, Song J, Luo J, Liu Y, Xia B, Xia L, Zeng X, Ben-David Y, Pan W. Design and Synthesis of Novel Tetrandrine Derivatives as Potential Anti-Tumor Agents against Human Hepatocellular Carcinoma. European Journal of Medicinal Chemistry. 2017 Sep; 3-4.
8. Becke AD. Becke’s three-parameter hybrid method using the LYP correlation functional. Journal of Chemical Physics. 1993; 98: 5648–5652.
9. Lee C, Yang W, Parr RG. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B. 1988; 37:785
10. Alisi IO, Uzairu A, Abechi SE, Idris SO. Quantitative Structure activity relationship analysis of coumarins as free radical scavengers by genetic function algorithm. Iranian Chemical Society. 2018 Mar; 2(6):208–222
11. Ambure P, Rahul BA, Agnieszka G, Tomasz P, Kunal R. “NanoBRIDGES” Software: Open Access Tools to Perform QSAR and Nano-QSAR Modeling. Chemical Intelligent Laboratory Systems. 2015; 147: 1–13.
12. Kennard RW, Stone LA. Computer Aided Design of Experiments. Technometrics. 1969 Feb; 11(1):137–48.
13. Friedman JH, Multivariate Adaptive Regression Splines. The Annals of Statistics. 1991: 1–67.
14. Khaled KF, Abdel-Shafi NS. Quantitative structure and activity relationship modeling study of corrosion inhibitors: Genetic function approximation and molecular dynamics simulation methods. International Journal of Electrochemical Science. 2011; 6:4077-4094
15. Brand V, Orr KA, Comprehensive R archive network (CRAN): http://CRAN.Rproject.org. retrieved; 2015
16. Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics. 2010 Jul 6; 29(6–7):476–88. 17. Minovski N, Župerl Š, Drgan V, Novič M. Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: a case study. Analytica Chimica Acta. 2013; 759:28–42.
18. Myers RH. Classical and modern regression application. 2nd edition. Duxbury Press. CA. 1990
19. Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-baes QSARs. Environmental Health Perspectives 2003; 111:1361-1375
20. Nandi S, Monesi A, Drgan V, Merzel F, Novič M. Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors. Chemistry Central Journal. 2013; 7:1-13.
21. Gramatica P, Giani E., Papa E., Statistical external validation and consensus modeling: A QSPR case study for KOC prediction. Journal of Molecular Graphics and Modelling. 2007; 25:755-66.
22. Todeschini R, Consonni V. Molecular descriptors for chemo-informatics. Weinheim: Wiley- VCH; 2009. (Methods and principles in medicinal chemistry). ISBN: 9783527318520
23. Adeniji SE, Sani U, Uzairu A, QSAR Modeling and Molecular Docking Analysis of Some Active Compounds against Mycobacterium Tuberculosis Receptor (Mtb CYP121). Journal of Pathogens Hindawi. 2018; 24-64.

Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Kimya Mühendisliği
Bölüm	Makaleler
Yazarlar	Abdullahi Mustapha Gideon Shallangwa Bu kişi benim Muhammad Tukur Ibrahim Abdullahi Umar Bello Bu kişi benim David Arthur Ebuka Adamu Uzairu Bu kişi benim Paul Mamza Bu kişi benim
Yayımlanma Tarihi	1 Eylül 2018
Gönderilme Tarihi	6 Eylül 2018
Kabul Tarihi	25 Aralık 2018
Yayımlandığı Sayı	Yıl 2018 Cilt: 5 Sayı: 3

Kaynak Göster

Vancouver	Mustapha A, Shallangwa G, Ibrahim MT, Bello AU, Ebuka DA, Uzairu A, Mamza P. QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer against Leukemia cell line (K562). JOTCSA. 2018;5(3):1387-98.