Computational screening and qsar study of bastadins as acat1 inhibitors
DOI:
https://doi.org/10.22399/ijcesen.3994Keywords:
Bastadins, ACAT1 inhibitors, QSAR, ANN, MLR, MNLRAbstract
In the search for new and effective anticancer agents, we performed a QSAR study on a series of sixteen bastadins to evaluate their potential as ACAT1 inhibitors and predict their antiangiogenic activity. Our goal was to establish a clear correlation between their biological responses and a set of molecular descriptors, applying principal component analysis (PCA), multiple linear regression (MLR), multiple nonlinear regression (MNLR), and an artificial neural network (ANN). Among the models generated, the best MLR and MNLR approaches achieved determination coefficients (R²) of 0.71 and 0.91. To further assess their reliability, we performed an external validation on a test set of three compounds, confirmed their predictive accuracy, and yielded R² test values of 0.70 and 0.83, respectively. Furthermore, the ANN model, built with a 4-4-1 architecture, showed excellent performance, achieving a correlation coefficient of 0.96 with leave-one-out cross-validation coefficients (Q²) of 0.79. These results indicate that the selected descriptors and calculated parameters are sufficient to reliably predict the biological activity of bastadins as ACAT1 inhibitors, providing a solid basis for the computer-aided design of novel anticancer agents.
References
[1] Gibbs, J. B. (2000). Mechanism-based target identification and drug discovery in cancer research. Science, 287(5460), 1969–1973.
[2] Myint, K. Z., & Xie, X. Q. (2010). Recent advances in computational fragment-based drug design. International Journal of Molecular Sciences, 11(10), 3846–3866.
[3] Perkins, R., Fang, H., Tong, W., & Welsh, W. J. (2003). Quantitative structure–activity relationship methods: Perspectives on drug discovery and toxicology. Environmental Toxicology and Chemistry, 22(8), 1666–1679.
[4] Salum, L. B., & Andricopulo, A. D. (2009). Fragment-based QSAR: Perspectives in drug design. Molecular Diversity, 13(3), 277–285.
[5] El Mchichi, L., Aouidate, A., Chokrafi, F. Z., Ghaleb, A., Lakhlifi, T., Khalil, F., & Bouachrine, M. (2018). Prediction of biological activity of pyrazolo [3,4-b] quinolinyl acetamide by QSAR results. Rhazes: Green and Applied Chemistry, 3, 79–93.
[6] Ebada, S. S., & Proksch, P. (2012). The chemistry of marine sponges. In E. Fattorusso, W. Gerwick, & O. Taglialatela-Scafati (Eds.), Handbook of Marine Natural Products (pp. 191–293). Springer.
[7] Eguchi, K., Kato, H., Fujiwara, Y., Losung, F., Mangindaan, R. E. P., de Voogd, N. J., Takeya, M., & Tsukamoto, S. (2015). Bastadins, brominated-tyrosine derivatives, suppress accumulation of cholesterol ester in macrophages. Bioorganic & Medicinal Chemistry Letters, 25(22), 5389–5392.
[8] Sun, T., & Xiao, X. (2024). Targeting ACAT1 in cancer: From threat to treatment. Frontiers in Oncology, 14, 1395192.
[9]. Aoki, S., Cho, S.-H., Hiramatsu, A., Kotoku, N., & Kobayashi, M. (2006). Bastadins, cyclic tetramers of brominated-tyrosine derivatives, selectively inhibit the proliferation of endothelial cells. Journal of Natural Medicines, 60(3), 231–235.
[10]. Kotoku, N., Hiramatsu, A., Tsujita, H., Hirakawa, Y., Sanagawa, M., Aoki, S., & Kobayashi, M. (2008). Structure–activity relationship study of bastadin 6, an anti-angiogenic brominated-tyrosine derived metabolite from marine sponge. Archiv der Pharmazie: Chemistry in Life Sciences, 341(9), 568–577.
[11]. Hypercube, Inc. (2008). HyperChem (Molecular Modeling System) [Computer software]. Gainesville, FL: Hypercube, Inc.
[12]. Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., Scalmani, G., Barone, V., Petersson, G. A., Nakatsuji, H., Li, X., Caricato, M., Marenich, A. V., Bloino, J., Janesko, B. G., Gomperts, R., Mennucci, B., Hratchian, H. P., Ortiz, J. V., Izmaylov, A. F., … Fox, D. J. (2009). Gaussian 09 (Revision A.02) [Computer software]. Wallingford, CT: Gaussian, Inc.
[13]. Molinspiration Cheminformatics. (2019). Molinspiration Property Calculator. https://www.molinspiration.com/docu/mipc/index.html
[14]. Viswanadhan, V. N., Ghose, A. K., Revankar, G. R., & Robins, R. K. (1989). Prediction of hydrophobic (LogP) parameters from atomic contributions. Journal of Chemical Information and Computer Sciences, 29(3), 163–172.
[15]. Gavezzotti, A. (1983). The calculation of molecular volumes and the use of volume analysis in the investigation of structured media and of solid-state organic reactivity. Journal of the American Chemical Society, 105(16), 5220–5225.
[16]. Bodor, N., Gabanyi, Z., & Wong, C. K. (1989). A new method for the estimation of partition coefficient. Journal of the American Chemical Society, 111(11), 3783–3786.
[17]. Ooi, T., Obatake, M., Nemethy, G., & Scheraga, H. A. (1987). Accessible surface areas as a measure of the thermodynamic parameters of hydration of peptides. Proceedings of the National Academy of Sciences of the United States of America, 84(10), 3086–3090.
[18]. Tanaka, M., Sugimoto, R., & Nakamura, N. (2025). Spontaneous orientation polarization driven by designing molecular asymmetry. Communications Materials, 6, 92.
[19]. Prasanna, S., & Doerksen, R. J. (2009). Topological polar surface area: A useful descriptor in 2D-QSAR. Current Medicinal Chemistry, 16(1), 21–41.
[20]. Tabani, H., Fernando, L. D., Heiss, C., & Azadi, P. (2025). A review of advanced strategies for molecular weight determination of insoluble polysaccharides: Developments and future trends. International Journal of Biological Macromolecules, 320(Pt 4), 146047.
[21]. Schneider, G., Baringhaus, K. H., & Kubinyi, H. (2008). Molecular design: Concepts and applications (277 p.). Weinheim, Germany: Wiley-VCH.
[22]. Cramer, C. J. (2004). Essentials of computational chemistry: Theories and models (2ᵉ éd., 624 p.). Chichester, UK: Wiley.
[23]. Lee, C., Yang, W., & Parr, R. G. (1988). Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B, 37(2), 785–789.
[24]. Becke, A. D. (1993). Density-functional thermochemistry. III. The role of exact exchange. The Journal of Chemical Physics, 98(7), 5648–5652.
[25]. Hariharan, P. C., & Pople, J. A. (1973). The influence of polarization functions on molecular orbital hydrogenation energies. Theoretica Chimica Acta, 28(3), 213–222.
[26]. Segall, D. M. (2012). [Review of drug design]. Current Pharmaceutical Design, 18(9), 1292–1310.
[27]. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64(Suppl.), 4–17.
[28]. Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615–2623.
[29]. Zerroug, A., Belaidi, S., Benbrahim, I., Sinha, L., & Chtita, S. (2019). Molecular docking, 3D-QSAR, and ADMET studies on new pyridazine derivatives as anti-cancer agents. Journal of King Saud University – Science, 31(4), 595–601.
[30]. Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the Rule of 5 and drugability. Advanced Drug Delivery Reviews, 101, 89–98.
[31]. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46(1–3), 3–26.
[32]. Carr, R. A., Congreve, M., Murray, C. W., & Rees, D. C. (2005). Fragment-based lead discovery: Introduction and overview. Drug Discovery Today, 10(14), 987–992.
[33]. Mortenson, P. N., & Murray, C. W. (2011). Assessing the lipophilicity of fragments and early hits. Journal of Computer-Aided Molecular Design, 25(8), 663–667.
[34]. Schultes, S., Graaf, C., Haaksma, E., Iwan, J. P., & Kramer, O. (2010). Ligand efficiency as a guide in fragment hit selection and optimization. Drug Discovery Today: Technologies, 7(3), e157–e162.
[35]. Koubi, Y., Moukhliss, Y., Sbai, A., El Masaoudy, Y., Maghat, H., El-Mernissi, R., Ajana, M. A., Bouachrine, M., & Lakhlifi, T. (2019). Antimicrobial evaluation against Escherichia coli (MTCC 1652) by using 1,4-disubstituted 1,2,3-triazole and derivatives: QSAR study. Arabian Journal of Chemical and Environmental Research, 6(1), 57–69.
[36]. Renckens, C. N. M. (2007). Alternatieve klinisch-chemische laboratoria: Leveranciers van onjuiste of niet-bestaande diagnosen. Nederlands Tijdschrift voor Geneeskunde, 151(51), 2816–2819.
[37]. He, G., Feng, L., & Chen, H. (2012). Safety management research based on system engineering theory. In Proceedings of the International Symposium on Safety Science and Engineering in China (pp. 204–209). Procedia Engineering, 43.
[38]. Aurélien, R. (2006). Détermination et caractérisation des cibles de DSP1 chez Drosophila melanogaster (Doctoral dissertation, Université d’Orléans).
[39]. Rouane, A., Tchouar, N., Belaidi, S., Kerassa, A., & Lanez, T. (2023). Chemical structure, substitution effect, and drug-likeness applied to quercetin and its derivatives. Journal of Fundamental and Applied Sciences, 15(3), 1–12.
[40]. Fadel, F. Z., Tchouar, N., Belaidi, S., Soualmia, F., Oukil, O., & Ouadah, K. (2021). Computational screening and QSAR study on a series of theophylline derivatives as ALDH1A1 inhibitors. Journal of Fundamental and Applied Sciences, 13(2), 1–12.
[41]. Doak, B. C., Over, B., Giordanetto, F., & Kihlberg, J. (2014). Oral druggable space beyond the rule of 5: Insights from drugs and clinical candidates. Chemistry & Biology, 21(9), 1115–1142.
[42]. Garcia Jimenez, D., Poongavanam, V., & Kihlberg, J. (2023). Macrocycles in drug discovery: Learning from the past for the future. Journal of Medicinal Chemistry, 66(8), 5602–5617.
[43]. Schultes, S., Graaf, C., Haaksma, E., Iwan, J. P., & Kramer, O. (2010). Ligand efficiency as a guide in fragment hit selection and optimization. Drug Discovery Today: Technologies, 7(3), e157–e162.
[44]. Bemis, G. W., & Murcko, M. A. (1996). The properties of known drugs. 1. Molecular frameworks. Journal of Medicinal Chemistry, 39(15), 2887–2893.
[45]. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23(1–3), 3–25.
[46]. Singh, T., Adekoya, O. A., & Jayaram, B. (2015). Protein–ligand interaction fingerprints: Generating insights into binding affinities and mechanisms. Molecular BioSystems, 11(4), 1041–1051.
[47]. Johnson, T. W., Gallego, R. A., & Edwards, M. P. (2018). Lipophilic efficiency as an important metric in drug design. Journal of Medicinal Chemistry, 61(15), 6401–6420.
[48]. Reynolds, C. H. (2015). Ligand efficiency metrics: Why all the fuss? Future Medicinal Chemistry, 7(11), 1363–1365.
[49]. Cramer, C. J. (2013). Essentials of computational chemistry: Theories and models (2nd ed., 624 p.). Chichester, UK; Hoboken, NJ: John Wiley & Sons.
[50]. Hinchliffe, A. (2003). Molecular modelling for beginners (2nd ed., 410 p.). Chichester, UK: John Wiley & Sons.
[51]. Leach, A. R. (2001). Molecular modelling: Principles and applications (2nd ed., 744 p.). Harlow, England: Pearson Education/Prentice Hall.
[52]. Atkins, P., & de Paula, J. (2014). Physical chemistry (10th ed., 972 p.). Oxford, UK: Oxford University Press.
[53]. Kerassa, A., Belaidi, S., Harkati, D., Lanez, T., Prasad, O., & Sinha, L. (2016). Theoretical studies on the electronic and structural properties of bioactive compounds. Reviews in Theoretical Science, 4(1), 85–96.
[54]. Almi, Z., Belaidi, S., & Segueni, L. (2015). Computational investigation of physicochemical descriptors in drug design. Reviews in Theoretical Science, 3(3), 264–272.
[55]. Lee, B., & Richards, F. M. (1971). The interpretation of protein structures: Estimation of static accessibility. Journal of Molecular Biology, 55(3), 379–400.
[56]. Andrasi, M., Buglyo, P., Zekany, L., & Gaspar, A. (2007). Determination of stability constants of metal complexes in solution by potentiometric titration. Journal of Pharmaceutical and Biomedical Analysis, 43(3), 1040–1046.
[57]. Ertl, P., Rohde, B., & Selzer, P. (2000). Fast calculation of molecular polar surface area as a sum of fragment-based contributions. Journal of Medicinal Chemistry, 43(20), 3714–3717.
[58]. Viswanadhan, V. N., Ghose, A. K., Revankar, G. R., & Robins, R. K. (1989). Prediction of hydrophobic (log P) parameters from atomic contributions. Journal of Chemical Information and Computer Sciences, 29(3), 163–172
[59]. Fukui, K. (1982). Role of frontier orbitals in chemical reactions. Science, 218(4574), 747–754.
[60]. Parr, R. G., & Pearson, R. G. (1983). Absolute hardness: Companion parameter to absolute electronegativity. Journal of the American Chemical Society, 105(26), 7512–7516.
[61]. Tabti, K., Sbai, A., Maghat, H., Lakhlifi, T., & Bouachrine, M. (2024). Computational assessment of the reactivity and pharmaceutical potential of novel triazole derivatives: An approach combining DFT calculations, molecular dynamics simulations, and molecular docking. Arabian Journal of Chemistry, 17(1), 105376.
[62]. Vidal, R., Ma, Y., & Sastry, S. S. (2016). Principal component analysis. In Generalized principal component analysis (pp. 25–62). New York, NY: Springer.
[63]. Larif, M., Chtita, S., Adad, A., Hmamouchi, R., Bouachrine, M., & Lakhlifi, T. (2014). Predicting biological activity of chalcone (1,3-diphenyl-2-propen-1-one) derivatives: Cytotoxicity against HT-29 human colon adenocarcinoma cell lines by DFT-QSAR models. Journal of Computational Methods in Molecular Design, 4(4), 121–130.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
