Multivariate Statistical 2D QSAR Analysis of Indenoisoquinoline-based Topoisomerase- I Inhibitors as Anti-lung Cancer Agents


Cite item

Full Text

Abstract

Background: Indenoisoquinoline-based compounds have shown promise as topoisomerase-I inhibitors, presenting an attractive avenue for rational anticancer drug design. However, a detailed QSAR study on these derivatives has not been performed till date.

Objective: :To study aimed to identify crucial molecular features and structural requirements for potent topoisomerase- 1 inhibition.

Methods: A comprehensive two-dimensional (2D) QSAR analysis was performed on a series of 49 indenoisoquinoline derivatives using TSAR3.3 software. A robust QSAR model based on a training set of 33 compounds was developed achieving favorable statistical values: r2 = 0.790, r2CV = 0.722, f = 36.461, and s = 0.461. Validation was conducted using a test set of nine compounds, confirming the predictive capability of the model (r2 = 0.624). Additionally, artificial neural network (ANN) analysis was employed to further validate the significance of the derived descriptors.

Results: The optimized QSAR model revealed the importance of specific descriptors, including molecular volume, Verloop B2, and Weiner topological index, providing essential insights into effective topoisomerase-1 inhibition. We also obtained a robust partial least-square (PLS) analysis model with high predictive ability (r2 = 0.788, r2CV = 0.743). The ANN results further reinforced the significance of the derived descriptors, with strong r2 values for both the training set (r2 = 0.798) and the test set (r2 = 0.669).

Conclusion: :The present 2D QSAR analysis offered valuable molecular insights into indenoisoquinoline-based topoisomerase- I inhibitors, supporting their potential as anti-lung cancer agents. These findings contribute to the rational design of more effective derivatives, advancing the development of targeted therapies for lung cancer treatment.

About the authors

Supriya Singh

Department of Pharmaceutics, Delhi Pharmaceutical Science and Research University

Email: info@benthamscience.net

Bharti Mangla

Department of Pharmaceutics, Delhi Pharmaceutical Science and Research University

Author for correspondence.
Email: info@benthamscience.net

Shamama Javed

Department of Pharmaceutics, College of Pharmacy, Jazan University

Email: info@benthamscience.net

Pankaj Kumar

Department of Pharmaceutics, Delhi Pharmaceutical Science and Research University

Email: info@benthamscience.net

Waquar Ahsan

Department of Pharmaceutical Chemistry, College of Pharmacy, Jazan University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Siegel, R.L.; Miller, K.D.; Wagle, N.S.; Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin., 2023, 73(1), 17-48. doi: 10.3322/caac.21763 PMID: 36633525
  2. Pommier, Y. Topoisomerase I inhibitors: Camptothecins and beyond. Nat. Rev. Cancer, 2006, 6(10), 789-802. doi: 10.1038/nrc1977 PMID: 16990856
  3. Pommier, Y.; Leo, E.; Zhang, H.; Marchand, C. DNA topoisomerases and their poisoning by anticancer and antibacterial drugs. Chem. Biol., 2010, 17(5), 421-433. doi: 10.1016/j.chembiol.2010.04.012 PMID: 20534341
  4. Ling-hua, M.; Zhi-yong, L.; Pommier, Y. Non-camptothecin DNA topoisomerase I inhibitors in cancer therapy. Curr. Top. Med. Chem., 2003, 3(3), 305-320. doi: 10.2174/1568026033452546 PMID: 12570765
  5. Nagarajan, M.; Morrell, A.; Fort, B.C.; Meckley, M.R.; Antony, S.; Kohlhagen, G.; Pommier, Y.; Cushman, M. Synthesis and anticancer activity of simplified indenoisoquinoline topoisomerase I inhibitors lacking substituents on the aromatic rings. J. Med. Chem., 2004, 47(23), 5651-5661. doi: 10.1021/jm040025z PMID: 15509164
  6. Nagarajan, M.; Morrell, A.; Ioanoviciu, A.; Antony, S.; Kohlhagen, G.; Agama, K.; Hollingshead, M.; Pommier, Y.; Cushman, M. Synthesis and evaluation of indenoisoquinoline topoisomerase I inhibitors substituted with nitrogen heterocycles. J. Med. Chem., 2006, 49(21), 6283-6289. doi: 10.1021/jm060564z PMID: 17034134
  7. Soren, B.C.; Babu Dasari, J.; Ottaviani, A.; Messina, B.; Andreotti, G.; Romeo, A.; Iacovelli, F.; Falconi, M.; Desideri, A.; Fiorani, P. In vitro and in silico characterization of an antimalarial compound with antitumor activity targeting human DNA topoisomerase IB. Int. J. Mol. Sci., 2021, 22(14), 7455. doi: 10.3390/ijms22147455 PMID: 34299074
  8. Afantitis, A.; Melagraki, G.; Sarimveis, H.; Koutentis, P.A.; Markopoulos, J.; Igglessi-Markopoulou, O. A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis. Mol. Divers., 2006, 10(3), 405-414. doi: 10.1007/s11030-005-9012-2 PMID: 16896545
  9. Morrell, A.; Placzek, M.; Parmley, S.; Grella, B.; Antony, S.; Pommier, Y.; Cushman, M. Optimization of the indenone ring of indenoisoquinoline topoisomerase I inhibitors. J. Med. Chem., 2007, 50(18), 4388-4404. doi: 10.1021/jm070307+ PMID: 17676830
  10. Dalby, A.; Nourse, J.G.; Hounshell, W.D.; Gushurst, A.K.I.; Grier, D.L.; Leland, B.A.; Laufer, J. Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited. J. Chem. Inf. Comput. Sci., 1992, 32(3), 244-255. doi: 10.1021/ci00007a012
  11. Sadowski, J.; Gasteiger, J. From atoms and bonds to three-dimensional atomic coordinates: Automatic model builders. Chem. Rev., 1993, 93(7), 2567-2581. doi: 10.1021/cr00023a012
  12. Baskin, I.I.; Palyulin, V.A.; Zefirov, N.S. Neural networks in building QSAR models. Methods Mol. Biol., 2008, 458, 137-158. PMID: 19065809
  13. Goodenough, A.E.; Hart, A.G.; Stafford, R. Regression with empirical variable selection: Description of a new method and application to ecological datasets. PLoS One, 2012, 7(3), e34338. doi: 10.1371/journal.pone.0034338 PMID: 22479605
  14. Singh, S.; Das, S.; Pandey, A.; Paliwal, S.; Singh, R. Quantitative structure activity relationship studies of topoisomerase I inhibitors as potent antibreast cancer agents. J. Chem., 2013, 2013, 1-9. doi: 10.1155/2013/849793
  15. Kesar, S.; Paliwal, S.K.; Mishra, P.; Chauhan, M. Quantitative structure-activity relationship analysis of selective Rho kinase inhibitors as neuro-regenerator agents. Turk. J. Pharmac.Sci., 2019, 16(2), 141-154. doi: 10.4274/tjps.galenos.2018.70288 PMID: 32454707
  16. Shahlaei, M.; Fassihi, A.; Saghaie, L.; Zare, A. Prediction of partition coefficient of some 3-hydroxy pyridine-4-one derivatives using combined partial least square regression and genetic algorithm. Res. Pharm. Sci., 2014, 9(2), 143-153. PMID: 25657783
  17. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst., 2001, 58(2), 109-130. doi: 10.1016/S0169-7439(01)00155-1
  18. Kubinyi, H. Evolutionary variable selection in regression and PLS analyses. J. Chemometr., 1996, 10(2), 119-133. doi: 10.1002/(SICI)1099-128X(199603)10:2<119:AID-CEM409>3.0.CO;2-4
  19. Livingstone, D.J.; Salt, D.W. Regression analysis for QSAR using neural networks. Bioorg. Med. Chem. Lett., 1992, 2(3), 213-218. doi: 10.1016/S0960-894X(01)81067-2
  20. Himmelblau, D.M. Accounts of experiences in the application of artificial neural networks in chemical engineering. Ind. Eng. Chem. Res., 2008, 47(16), 5782-5796. doi: 10.1021/ie800076s
  21. Paliwal, S.; Yadav, D.; Yadav, R.; Kaushik, V.; Paliwal, S. Common binding requirements of PPAR-α/δ/γ pan agonists: Quantitative structure–activity relationship analysis of indanylacetic acid derivatives carrying 4-thiazolyl-phenoxy tail group. Med. Chem. Res., 2012, 21(6), 891-907. doi: 10.1007/s00044-011-9599-z
  22. Taillandier, G.; Domard, M.; Boucherle, A. Application des paramètres de Verloop. Comparaison avec les autres paramétres steriques, problemes de leur choix. Farmaco, Sci., 1980, 35(2), 89-109. PMID: 7450026
  23. Brethomé, A.V.; Fletcher, S.P.; Paton, R.S. Conformational effects on physical-organic descriptors: The case of Sterimol steric parameters. ACS Catal., 2019, 9(3), 2313-2323. doi: 10.1021/acscatal.8b04043
  24. Ibrahim, H.; Sharafdini, R.; Réti, T.; Akwu, A. Wiener–Hosoya matrix of connected graphs. Mathematics, 2021, 9(4), 359. doi: 10.3390/math9040359

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2023 Bentham Science Publishers