QSAR of acyl alizarin red biocompound derivatives of Rubia tinctorum roots and its ADMET properties as anti-breast cancer candidates against MMP-9 protein receptor: In Silico study

Alizarin is a polycyclic compound isolated from roots of Rubia tinctorum that has potential as a breast anticancer candidate. Increasing anticancer activity can be done through structural modification to produce derivatives in the form of group substitution in the meta position using acyl. The purpose of this work is to forecast the anticancer activity of alizarin and its derivatives on the MMP-9 receptor using. Important biological activity factors will be identified by Quantitative Structure Activity molecular docking Relationship (QSAR) and projected absorption, distribution, metabolism, elimination, and toxicity (ADMET). Using Molegro Virtual Docker (MVD), molecular docking was carried out on the MMP 9 receptor (4WZV.pdb). LogP, Etot, and MR are the physicochemical parameters that are examined in order to produce QSAR. Statistical Package for the Social Science (SPSS) was used for the QSAR analysis. The pkCSM was utilized to determine ADMET prediction. The acyl alizarin derivatives have a lower rerank score than alizarin, according to the docking results so that they are predicted to have more potent anticancer activity. The QSAR analysis's findings indicated that logP and Etot had the greatest effects on the alizarin compound's and its derivatives' activity. The results of the ADMET prediction indicate that acyl alizarin is less harmful and superior to alizarin. Research findings show that it is possible to synthesize acyl alizarin derivatives, especially alizarin octanoate, which will then be tested in vitro or in vivo to determine its anti-breast cancer activity and toxicity.

1. Hanahan, D., Weinberg, R.A. (2000). The Hallmarks of Cancer. Cell, 100(1), 57-70. https://doi.org/10.1016/S0092-8674(00)81683-9

2. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A. et al. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 71(3), 209-249. https://doi.org/10.3322/caac.21660

3. Yabluchanskiy, A, Ma, Y, Iyer, R.P., Hall, M.E., Lindsey, M.L. (2013). Matrix metalloproteinase-9: Many shades of function in cardiovascular disease. Physiology, 28(6), 391-403. https://doi.org/10.1152/physiol.00029.2013

4. Greenlee, K.J., Corry, D.B., Engler, D.A., Matsunami, R.K., Tessier, P., Cooc, R.G. et al. (2006). Proteomic identification of in vivo substrates for matrix metalloproteinases 2 and 9 reveals a mechanism for resolution of inflammation. The Journal of Immunoljgy, 177(10), 7312-7321. https://doi.org/10.4049/jimmunol.177.10.7312

5. Khandia, R, Munjal, A. (2020). Interplay between inflammation and cancer. Chapter in a book: Advances in Protein Chemistry and Structural Biology, 119, 199-245. https://doi.org/10.1016/bs.apcsb.2019.09.004

6. Kessenbrock, K., Plaks, V., Werb, Z. (2010). Matrix metalloproteinases: Regulators of the tumor microenvironment. Cell, 141(1), 52-67. https://doi.org/10.1016/j.cell.2010.03.015

7. Quintero-Fabian, S., Arreola, R., Becerril-Villanueva, E., Torres-Romero, J.C., Arana-Argae, V., Lara-Riegos, J. et al. (2019). Role of matrix metalloproteinases in angiogenesis and cancer. Frontiers in Oncology, 9, Article 1370. https://doi.org/10.3389/fonc.2019.01370

8. Nelson, A. R., Fingleton, B., Rothenberg, M. L., Matrisian, L. M. (2000). Matrix metalloproteinases: Biologic activity and clinical implications. Journal of Clinical Oncology, 18(5), 1135-1149. https://doi.org/10.1200/jco.2000.18.5.1135

9. Merdad, A., Karim, S., Schulten, H. J., Dallol, A., Buhmeida, A., Al-Thubaity, F. et al. (2014). Expression of matrix metalloproteinases (MMPs) in primary human breast cancer: MMP-9 as a potential biomarker for cancer invasion and metastasis. Anticancer Research, 34(3), 1355-1366.

10. Meng, Q., Liang, C., Hua, J., Zhang, B., Liu, J., Zhang, Y. et al. (2020). A miR-146a-5p/TRAF6/NF-kB p65 axis regulates pancreatic cancer chemoresistance: functional validation and clinical significance. Theranostics, 10(9), 3967-3979. https://doi.org/10.7150/thno.40566

11. Li, K., Zhang, Z., Mei, Y., Yang, Q., Qiao, S., Ni, C. et al. (2021). Metallothionein-1G suppresses pancreatic cancer cell stemness by limiting activin A secretion NF-KB inhibition. Theranostics, 11(7), 3196-2212. https://doi.org/10.7150/thno.51976

12. Lindenmeyer, F., Legrand, Y., Menashi, S. (1997). Upregulation of MMP-9 expression in MDA-MB231 tumor cells by platelet granular membrane. FEBS Letters, 418(1-2), 19-22. https://doi.org/10.1016/s0014-5793(97)01336-7

13. Mishra, S. R., Nandhakumar, P., Yadav, K. P., Barik, S., Kumar, A., Saini, M. et al. (2017). In vitro analysis of alizarin as novel therapeutic agent for murine breast cancer. The Pharma Innovation Journal, 6(10), 345-350.

14. Ekowati, J., Diyah, N. W., Nofianti, K. A., Hamid, I. S., Siswandono (2018). Mo-lecular Docking of Ferulic Acid Derivatives on P2Y12 Receptor and their ADMET Prediction. Journal of Mathematical and Fundamental Sciences, 50(2), 203-219. https://doi.org/10.5614/j.math.fund.sci.2018.50.2.8

15. Kamath, V., Pai, A. (2017). Application of molecular descriptors in modern computational drug design-an overview. Research Journal of Pharmacy and Technology, 10(9), 3237-3241. http://doi.org/10.5958/0974-360X.2017.00574.1

16. Habeela, J.N., Maruga, R.M.K.M. (2018). In silico molecular docking studies on the chemical constituents of clerodendrum phlomidis for its cytotoxic potential against breast cancer markers. Research Journal of Pharmacy and Technology, 11(4), Article 1612-1618. http://doi.org/10.5958/0974-360X.2018.00300.1

17. Hardjono, S. (2012). Modification of the structure of 1 — (benzoyloxy) ureaand quantitative relationship of its structure-cytotoxic activity. Author's abstract of the thesis. Universitas Airlangga, Indonesia, 2012. (In Indonesian)

18. Hardjono, S., Siswodihardjo, S., Pramono, P., Darmanto, W. (2016). Quantitative structure-cytotoxic activity relationship 1-(benzoyloxy) urea and its derivative. Current Drug Discovery Technologies, 13(2), 101-108. https://doi.org/10.2174%2F1570163813666160525112327

19. Pinzi, L., Rastelli, G. (2019). Molecular docking: Shifting paradigms in drug discovery. International Journal of Molecular Sciences, 20(18), Article 4331. https://doi.org/10.3390/ijms2018433

20. Park, K. D., Lee, S. G., Kim, S. U., Kim, S. H., Sun, W. S., Cho, S. J. et al. (2004). Anticancer activity of 3-O-acyl and alkyl-(-)-epicatechin derivatives. Bioorganic and Medicinal Chemistry Letters, 14(20), 5189-5192. https://doi.org/10.1016/j.bmcl.2004.07.063

21. Hoque, I., Chatterjee, A., Bhattacharya, S., Biswas, R. (2017). An approach of computer-aided drug design (CADD) tools for in silico pharmaceutical drug design and development. International Journal of Advanced Research in Biological Sciences, 4(2), 60-71. http://doi.org/10.22192/ijarbs.2017.04.02.009

22. Abdel-Ilah, L., Veljovic, E., Gurbeta, L., Badnjevic, A. (2017). Applications of QSAR study in drug design. International Journal of Engineering Research and Technology (IJERT), Vol. 6(6), 582-587.

23. Verma, J., Khedkar, V. M., Coutinho, E. C. (2010). 3D-QSAR in drug design-a review. Current Topics in Medicinal Chemistry, 10(1), 95-115. https://doi.org/10.2174/156802610790232260

24. Pathan, S., Ali, S. M., Shrivastava, M. (2016). Quantitative structure activity relationship and drug design: A review. International Journal of Research in Biosciences, 5(4), 1-5.

25. Pires, D. E. V., Blundell, T. L., Ascher, D. B. (2015). pkCSM: Predicting small-mol-ecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066-4072. https://doi.org/10.1021/acs.jmedchem.5b00104

26. Pagadala, N. S., Syed, K., Tuszynski, J. (2017). Software for molecular docking: A review. Biophysical Reviews, 9(2), 91-102. https://doi.org/10.1007/s12551-016-0247-1

27. Ramirez, D., Caballero, J. (2018). Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules, 23(5), Article 1038. https://doi.org/10.3390/molecules23051038

28. Patlewicz, G., Jeliazkova, N., Safford, R. J., Worth, A. P., Aleksiev, B. (2008). An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR and QSAR in Environmental Researc, 19(5-6), 495-524. https://doi.org/10.1080/10629360802083871

29. McMurry, J., E., Fay, R., C. (2012). Chemistry. Boston: Prentice Hall, 2012.

30. Klebe, G. (2013). Protein-Ligand Interactions as the Basis for Drug Action. Chapter in a book: Drug Design. Springer, Berlin, Heidelberg, 2013. https://doi.org/10.1007/978-3-642-17907-5_4

31. Grogan, S, Preuss, C.V. (2023). Pharmacokinetics. Chapter in a book: StatPearls [Internet]. StatPearls Publishing LLC., 2023.

32. Paul, A. (2019). Drug Absorption and Bioavailability. Chapter in a book: Introduction to Basics of Pharmacology and Toxicology. Springer, Singapore, 2019. https://doi.org/10.1007/978-981-32-9779-1

33. Chevillard, F., Lagorce, D., Reynes, C., Villoutreix, B. O., Vayer, P., Miteva, M. A. (2012). In silico prediction of aqueous solubility: A multimodel protocol based on chemical similarity. Molecular Pharmaceutics, 9(11), 3127-3135. https://doi.org/10.1021/mp300234q

34. Gleeson, M. P. (2008). Generation of a set of simple, interpretable ADMET rules of thumb. Journal of Medicinal Chemistry, 51(4), 817-834. https://doi.org/10.1021/jm701122q

35. Currie, G. M. (2018). Pharmacology, part 2: Introduction to pharmacokinetics. Journal of Nuclear Medicine Technology, 46(3), 221-230. https://doi.org/10.2967/jnmt.117.199638

36. Smith, D. A., Beaumont, K., Maurer, T. S., Di, L. (2015). Volume of distribution in drug design. Miniperspective. Journal of Medicinal Chemistry, 58(15), 5691-5698. https://doi.org/10.1021/acs.jmedchem.5b00201

37. Jeffrey, P., Summerfield, S. (2010). Assessment of the blood-brain barrier in CNS drug discovery. Neurobiology of Disease, 37(1), 33-37. https://doi.org/10.1016/j.nbd.2009.07.033

38. Wilde, M., Pichini, S., Pacifici, R., Tagliabracci, A., Busardo, F. P., Auwarter, V. et al. (2019). Metabolic Pathways and Potencies of New Fentanyl Analogs. Frontiers in pharmacology, 10, Article 238. https://doi.org/10.3389%2Ffphar.2019.00238

39. Rizzieri, D., Paul, B., Kang, Y. (2019). Metabolic alterations and the potential for targeting metabolic pathways in the treatment of multiple myeloma. Journal of Cancer Metastasis and Treatment, 5, 26. https://doi.org/10.20517/2394-4722.2019.05

40. Garza, A. Z., Park, S. B., Kocz, R. (2023). Drug Elimination. Chapter in a book: StatPearls [Internet]. StatPearls Publishing LLC., 2023.

41. Herdiansyah, M. A., Ansori, A. N. M., Kharisma V. D., Alifiansyah, M. R. T., Anggraini, D., Priyono, Q. A. P., Yusniasari, P. A., Fetty, A. J. T., Zainul, R., Rebezov, M., Kolesnik, E., Maksimiuk, N. (2024). In silico study of cladosporol and its acyl derivatives as anti-breast cancer against alpha-estrogen receptor. Biosaintifika, 15(1), 1-13.

42. Zainul, R., Kharisma, V. D., Ciuputri, P., Ansori, A. N. M., Herdiansyah, M. A., Sahadewa, S., Durry, F. D. (2024). Antiretroviral activity from elderberry (Sambucus nigra L.) flowers against HIV-2 infection via reverse transcriptase inhibition: A viroinformatics study. Healthcare in Low-resource Settings, 1(2024), 1-12. https://doi.org/10.4081/hls.2024.12047

43. Krihariyani, D., Haryanto, E., Sasongkowati, R. (2021). in silico analysis of antiviral activity and pharmacokinetic prediction of brazilein sappan wood (Caesalpinia sappan L.) against SARS-CoV-2 spike glycoproteins. Indonesian Journal of Medical Laboratory Science and Technology, 3(1), 26-37. https://doi.org/10.33086/ijmlst.v3i1.1854