Development of Predictive in Silico Cytotoxic Activity Model 
to Predict the Cytotoxicity of a Diverse Set of Colchicine 
Binding Site Inhibitors

SINGH, Vijay Kumar; SAHU, Sumanta Kumar; OJHA, Krishna Kumar

doi:10.14744/ejmo.2022.44123

Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors

Sumanta Kumar SAHU, (Department of Bioinformatics Central, University of South Bihar Gaya, Bihar India)

Krishna Kumar OJHA, (Department of Bioinformatics Central, University of South Bihar Gaya, Bihar India)

Vijay Kumar SINGH (Department of Bioinformatics Central, University of South Bihar Gaya, Bihar India)

Eurasian Journal of Medicine and Oncology

1 0

Yıl: 2022 Cilt: 6 Sayı: 2 Sayfa Aralığı: 172 - 181 Metin Dili: İngilizce DOI: 10.14744/ejmo.2022.44123 İndeks Tarihi: 06-07-2022

Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors

Öz:

Microtubule-targeting agents often have limitations to the development of resistance. Colchicine binding site (CBS) agents have several advantages compared with other tubulin inhibitors. Numerous medications in this class are less susceptible to multidrug resistance that restricts the viability of different inhibitors. In the present study, molecules that bind to the CBS of tubulin are collected from PubMed literature against the A549 cancer cell line. Regression models were established between the descriptor and IC50 value of all the compounds present in the training set based on significant molecular fingerprints using multiple linear regression (MLR). Fifteen most significant descriptors selected include Burden modified eigenvalue descriptors, PaDEL-weighted path descriptor, autocorrelation descriptor, topological distance matrix descriptor, MLFER descriptor, Barysz matrix descriptor, chi path cluster descriptor, and validated using internal and external validation parameters. The selected MLR-GA model has R2adjusted = 0.7895, Q2 CV = 0.76577, R2 pred = 0.7419, and R2 tes = 0.77373. An applicability domain is also defined so that it defines the chemical space that the model can predict. The above details suggest a good predictive model for CBS inhibitors that can predict the IC50 value of the unknown chemical compound.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

1. Nogales E. Structural insights into microtubule function. Annu Rev Biochem 2000;69:277–302. [CrossRef]
2. Arnst KE, Wang Y, Hwang DJ, Xue Y, Costello T, Hamilton D, et al. A potent, metabolically stable tubulin inhibitor targets the colchicine binding site and overcomes taxane resistance. Cancer Res 2018;78:265–77. [CrossRef]
3. Field JJ, Díaz JF, Miller JH. The binding sites of microtubule-stabilizing agents. Chem Biol 2013;20:301–15. [CrossRef]
4. McLoughlin EC, O'Boyle NM. Colchicine-binding site inhibitors from chemistry to clinic: a review. Pharmaceuticals (Basel) 2020;13:8.
5. Anzengruber F, Graf V, Hafner J, Meienberger N, Guenova E, Dummer R. Efficacy and safety of colchicine in inflammatory skin diseases: a retrospective, monocentric study in a large tertiary center. J Dermatolog Treat 2021;32:104–9. [CrossRef]
6. Kavallaris M. Microtubules and resistance to tubulin-binding agents. Nat Rev Cancer 2010;10:194–204.
7. Ren J, Wu L, Xin WQ, Chen X, Hu K. Synthesis and biological evaluation of novel 4β-(1,3,4-oxadiazole-2- amino)-podophyllotoxin derivatives. Bioorganic Med Chem Lett 2012;22:4778– 82. [CrossRef]
8. Manzoor S, Bilal A, Khan S, Ullah R, Iftikhar S, Emwas AH, et al. Identification and characterization of SSE15206, a microtubule depolymerizing agent that overcomes multidrug resistance. Sci Rep 2018;8:3305.
9. Wang Z, Chen J, Wang J, Ahn S, Li CM, Lu Y, et al. Novel tubulin polymerization inhibitors overcome multidrug resistance and reduce melanoma lung metastasis. Pharm Res 2012;29:3040– 52. [CrossRef]
10. Eissa IH, Dahab MA, Ibrahim MK, Alsaif NA, Alanazi AZ, Eissa SI, et al. Design and discovery of new antiproliferative 1,2,4-triazin-3(2H)-ones as tubulin polymerization inhibitors targeting colchicine binding site. Bioorg Chem 2021;112:104965.
11. Leoni LM, Hamel E, Genini D, Shih H, Carrera CJ, Cottam HB, et al. Indanocine, a microtubule-binding indanone and a selective inducer of apoptosis in multidrug-resistant cancer cells. J Natl Cancer Inst 2000;92:217–24. [CrossRef]
12. Mahal K, Resch M, Ficner R, Schobert R, Biersack B, Mueller T. Effects of the tumor-vasculature-disrupting agent verubulin and two heteroaryl analogues on cancer cells, endothelial cells, and blood vessels. ChemMedChem 2014;9:847–54.
13. Wu X, Wang Q, Li W. Recent advances in heterocyclic tubulin inhibitors targeting the colchicine binding sit. Anti-Cancer Agents in Medicinal Chemistry. Available at: https://www.ingentaconnect.com/contentone/ben/acamc/2016/00000016/00000010/art00013. Accessed May 20, 2022.
14. Ansari M, Shokrzadeh M, Karima S, Rajaei S, Fallah M, Ghassemi-Barghi N, et al. New thiazole-2(3H)-thiones containin 4-(3,4,5-trimethoxyphenyl) moiety as anticancer agents. Eur J Med Chem. 2020;185:111784. [CrossRef]
15. Bai Z, Liu X, Guan Q, Ding N, Wei Q, Tong B, et al. 5-(3,4,5-trimethoxybenzoyl)-4-methyl-2-(p-tolyl) imidazol (BZML) targets tubulin and DNA to induce anticancer activity and overcome multidrug resistance in colorectal cancer cells. Chem Biol Interact 2020;315:108886. [CrossRef]
16. Lu Y, Chen J, Xiao M, Li W, Miller DD. An overview of tubulin inhibitors that interact with the colchicine binding site. Pharm Res 2012;29:2943–71. [CrossRef]
17. Wang XF, Guan F, Ohkoshi E, Guo W, Wang L, Zhu DQ, et al. Optimization of 4-(N-cycloamino)phenylquinazolines as a novel class of tubulin-polymerization inhibitors targeting the colchicine site. J Med Chem 2014;57:1390–402.
18. Li DD, Qin YJ, Zhang X, Yin Y, Zhu HL, Zhao LG. Combined molecular docking, 3D-QSAR, and pharmacophore model: design of novel tubulin polymerization inhibitors by binding to colchicine-binding site. Chem Biol Drug Des 2015;86:731–45.
19. Tyagi C, Grover S, Dhanjal JK, Goyal S, Goyal M, Grover A. Mechanistic insights into mode of action of novel natural cathepsin L inhibitors. BMC Genomics 2013;14:S10.
20. Li X, Fourches D. Inductive transfer learning for molecular activity prediction: next-gen QSAR models with MolPMoFiT. ChemRxiv. 2019 Oct 16. Doi: 10.26434/chemrxiv.9978743.v1. [Epub ahead of print]. [CrossRef]
21. Idakwo G, Luttrell IV J, Chen M, Hong H, Gong P, Zhang C. A review of feature reduction methods for QSAR-based toxicity prediction. Challenges Adv Comput Chem Phys 2019;30:119– 39. [CrossRef]
22. Khan MF, Verma G, Akhtar W, Shaquiquzzaman M, Akhter M, Rizvi MA, et al. Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1,3,4-thiadiazole amides and sulfonamides as antitubulin agents. Arab J Chem 2019;12:5000–18.
23. Zhang S, Su L, Zhang X, Li C, Qin W, Zhang D, et al. Combined toxicity of nitro-substituted benzenes and zinc to photobacterium phosphoreum: evaluation and QSAR analysis. Int J Environ Res Public Health 2019;16:1041. [CrossRef]
24. Varnek A, Fourches D, Horvath D, Klimchuk O, Gaudin C, Vayer P, et al. ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors. Curr Comput Aided-Drug Des 2008;4:191–8. [CrossRef]
25. Niu MM, Qin JY, Tian CP, Yan XF, Dong FG, Cheng ZQ, et al. Tubulin inhibitors: Pharmacophore modeling, virtual screening and molecular docking. Acta Pharmacol Sin 2014;35:967–79.
26. Liu G, Jiao Y, Huang C, Chang P. Identification of novel and potent small-molecule inhibitors of tubulin with antitumor activities by virtual screening and biological evaluations. J Comput Aided Mol Des 2019;33:659–64. [CrossRef]
27. Behbahani FS, Tabeshpour J, Mirzaei S, Golmakaniyoon S, Tayarani-Najaran Z, Ghasemi A, et al. Synthesis and biological evaluation of novel benzo[c]acridine-diones as potential anticancer agents and tubulin polymerization inhibitors. Arch Pharm (Weinheim) 2019;352:e1800307.
28. Antúnez-Mojica M, Rodríguez-Salarichs J, Redondo-Horcajo M, León A, Barasoain I, Canales Á, et al. Structural and biochemical characterization of the interaction of tubulin with potent natural analogues of podophyllotoxin. J Nat Prod 2016;79:2113–21. [CrossRef]
29. Ahmad FBH, Moghaddam MG, Basri M, Abdul Rahman MB. Anticancer activity of 3-O-acylated betulinic acid derivatives obtained by enzymatic synthesis. Biosci Biotechnol Biochem 2010;74:1025–9. [CrossRef]
30. Xu Q, Bao K, Sun M, Xu J, Wang Y, Tian H, et al. Design, synthesis and structure-Activity relationship of 3,6-diaryl-7H-[1,2,4] triazolo[3,4-b][1,3,4]thiadiazines as novel tubulin inhibitors. Sci Rep 2017;7:11997. [CrossRef]
31. Yang F, Yu LZ, Diao PC, Jian XE, Zhou MF, Jiang CS, et al. Novel [1,2,4]triazolo[1,5-a]pyrimidine derivatives as potent antitubulin agents: Design, multicomponent synthesis and antiproliferative activities. Bioorg Chem 2019;92:103260.
32. Zheng S, Zhong Q, Mottamal M, Zhang Q, Zhang C, Lemelle E, et al. Design, synthesis, and biological evaluation of novel pyridine-bridged analogues of combretastatin-A4 as anticancer agents. J Med Chem 2014;57:3369–81. [CrossRef]
33. Wu GR, Xu B, Yang YQ, Zhang XY, Fang K, Ma T, et al. Synthesis and biological evaluation of podophyllotoxin derivatives as selective antitumor agents. Eur J Med Chem 2018;155:183–96.
34. Huang L, Liu M, Man S, Ma D, Feng D, Sun Z, et al. Design, synthesis and bio-evaluation of novel 2-aryl-4-(3,4,5-trimethoxy-benzoyl)-5-substituted-1,2,3-triazoles as the tubulin polymerization inhibitors. Eur J Med Chem 2020;186:111846.
35. Zhang X, Rakesh KP, Shantharam CS, Manukumar HM, Asiri AM, Marwani HM, et al. Podophyllotoxin derivatives as an excellent anticancer aspirant for future chemotherapy: A key current imminent needs. Bioorganic Med Chem 2018;26:340– 55. [CrossRef]
36. Zhu L, Luo K, Li K, Jin Y, Lin J. Design, synthesis and biological evaluation of 2-phenylquinoline-4-carboxamide derivatives as a new class of tubulin polymerization inhibitors. Bioorganic Med Chem. 2017;25:5939–51. [CrossRef]
37. Zheng YB, Gong JH, Liu XJ, Wu SY, Li Y, Xu XD, et al. A novel nitrobenzoate microtubule inhibitor that overcomes multidrug resistance exhibits antitumor activity. Sci Rep 2016;6:31472.
38. Du J, Li J, Gao M, Guan Q, Liu T, Wu Y, Li Z, Zuo D, Zhang W, Wu Y. MAY, a novel tubulin inhibitor, induces cell apoptosis in A549 and A549/Taxol cells and inhibits epithelial-mesenchymal transition in A549/Taxol cells. Chem Biol Interact. 2020 May 25;323:109074. [CrossRef]
39. Duan YT, Sang YL, Makawana JA, Teraiya SB, Yao YF, Tang DJ, et al. Discovery and molecular modeling of novel 1-indolyl acetate - 5-Nitroimidazole targeting tubulin polymerization as antiproliferative agents. Eur J Med Chem 2014;85:341–51.
40. Wang XF, Wang SB, Ohkoshi E, Wang LT, Hamel E, Qian K, et al. N-aryl-6-methoxy-1,2,3,4-tetrahydroquinolines: A novel class of antitumor agents targeting the colchicine site on tubulin. Eur J Med Chem 2013;67:196–207. [CrossRef]
41. Leardi R, Boggia R, Terrile M. Genetic algorithms as a strategy for feature selection. J. Chemom 1992;6:267–81. [CrossRef]
42. Ahmadi S, Habibpour E. Application of GA-MLR for QSAR modeling of the arylthioindole class of tubulin polymerization inhibitors as anticancer agents. Anticancer Agents Med Chem 2017;17:552–65. [CrossRef]
43. Habibpour E, Ahmadi S. QSAR modeling of the arylthioindole class of colchicine polymerization inhibitors as anticancer agents. Curr Comput Aided Drug Des 2017;13:143–59.
44. Ahmadi S, Khazaei MR, Abdolmaleki A. Quantitative structure–property relationship study on the intercalation of anticancer drugs with ct-DNA. Med Chem Res 2014;23:1148–61.
45. Adeniji SE, Uba S, Uzairu A, Arthur DE. A derived QSAR model for predicting some compounds as potent antagonist against mycobacterium tuberculosis: a theoretical approach. Adv Prev Med 2019;2019:5173786. [CrossRef]
46. Alexander DLJ, Tropsha A, Winkler DA. Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 2015;55:1316–22.
47. Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 2015;145:22–9.
48. Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information. Phys Rev E Stat Nonlin Soft Matter Phys 2004;69:066138. [CrossRef]
49. Pedregosa F, Weiss R, Brucher M. Scikit-learn : machine learning in Python. JMLR 2011;12:2825–30.
50. Toppur B, Jaims KJ. Determining the best set of molecular descriptors for a Toxicity classification problem. RAIRO - Oper Res 2021;55:2769–83.
51. Adedirin O, Uzairu A, Shallangwa GA, Abechi SE. Optimization of the anticonvulsant activity of 2-acetamido- N -benzyl-2- ( 5- methylfuran-2-yl ) acetamide using QSAR modeling and molecular docking techniques. Beni-Suef Univ J Basic Appl Sci 2018;7:430–40. [CrossRef]
52. Adedirin O, Uzairu A, Shallangwa A, Abechi E. A novel QSAR model for designing, evaluating, and predicting the antiMES activity of new 1H-pyrazole-5-carboxylic acid derivatives. JOTCSA 2018;4:437–44.
53. Alisi IO, Uzairu A, Abechi SE, Idris SO. Quantitative structure activity relationship analysis of coumarins as free radical scavengers by genetic function algorithm. Phys Chem Res 2018;6:208–22.
54. Ramrez-Galicia G, Garduo-Juárez R, Correa-Basurto J, Deeb O. Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations. J Enzyme Inhib Med Chem 2012;27:174–86. [CrossRef]
55. Lee W, Park SJ, Hwang JY, Hur KH, Lee YS, Kim J, et al. QSAR model for predicting the cannabinoid receptor 1 binding affinity and dependence potential of synthetic cannabinoids. Molecules 2020;25:6057. [CrossRef]
56. Umar AB, Uzairu A, Shallangwa GA, Uba S. QSAR modelling and molecular docking studies for anti-cancer compounds against melanoma cell line SK-MEL-2. Heliyon 2020;6:e03640.
57. Bello AS, Uzairu A, Ibrahim MT, Gatugel YA. Quantum modelling analysis of some potent indole derivatives on NS5B Polymerase Inhibitors. Sci World J 2019;14:32–7.

APA	SAHU S, OJHA K, SINGH V (2022). Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. , 172 - 181. 10.14744/ejmo.2022.44123
Chicago	SAHU Sumanta Kumar,OJHA Krishna Kumar,SINGH Vijay Kumar Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. (2022): 172 - 181. 10.14744/ejmo.2022.44123
MLA	SAHU Sumanta Kumar,OJHA Krishna Kumar,SINGH Vijay Kumar Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. , 2022, ss.172 - 181. 10.14744/ejmo.2022.44123
AMA	SAHU S,OJHA K,SINGH V Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. . 2022; 172 - 181. 10.14744/ejmo.2022.44123
Vancouver	SAHU S,OJHA K,SINGH V Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. . 2022; 172 - 181. 10.14744/ejmo.2022.44123
IEEE	SAHU S,OJHA K,SINGH V "Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors." , ss.172 - 181, 2022. 10.14744/ejmo.2022.44123
ISNAD	SAHU, Sumanta Kumar vd. "Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors". (2022), 172-181. https://doi.org/10.14744/ejmo.2022.44123

APA	SAHU S, OJHA K, SINGH V (2022). Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. Eurasian Journal of Medicine and Oncology, 6(2), 172 - 181. 10.14744/ejmo.2022.44123
Chicago	SAHU Sumanta Kumar,OJHA Krishna Kumar,SINGH Vijay Kumar Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. Eurasian Journal of Medicine and Oncology 6, no.2 (2022): 172 - 181. 10.14744/ejmo.2022.44123
MLA	SAHU Sumanta Kumar,OJHA Krishna Kumar,SINGH Vijay Kumar Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. Eurasian Journal of Medicine and Oncology, vol.6, no.2, 2022, ss.172 - 181. 10.14744/ejmo.2022.44123
AMA	SAHU S,OJHA K,SINGH V Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. Eurasian Journal of Medicine and Oncology. 2022; 6(2): 172 - 181. 10.14744/ejmo.2022.44123
Vancouver	SAHU S,OJHA K,SINGH V Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors. Eurasian Journal of Medicine and Oncology. 2022; 6(2): 172 - 181. 10.14744/ejmo.2022.44123
IEEE	SAHU S,OJHA K,SINGH V "Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors." Eurasian Journal of Medicine and Oncology, 6, ss.172 - 181, 2022. 10.14744/ejmo.2022.44123
ISNAD	SAHU, Sumanta Kumar vd. "Development of Predictive in Silico Cytotoxic Activity Model to Predict the Cytotoxicity of a Diverse Set of Colchicine Binding Site Inhibitors". Eurasian Journal of Medicine and Oncology 6/2 (2022), 172-181. https://doi.org/10.14744/ejmo.2022.44123