In Silico Study of 4-Chloro-N-(Phenylcarbamothioyl)-benzamide as an Anticancer Candidate: Molecular Docking Analysis and ADMET Profiling

Ida Bagus Oka Wisnu Andhika; Dini Kesuma; Aguslina Kirtishanti

doi:10.33394/j-ps.v14i4.22783

Authors

Ida Bagus Oka Wisnu Andhika University of Surabaya
Dini Kesuma University of Surabaya
Aguslina Kirtishanti University of Surabaya

DOI:

https://doi.org/10.33394/j-ps.v14i4.22783

Keywords:

4-Cl-PCTB, ADMET prediction, Glutaminase, In silico study, Molecular docking

Abstract

Glutaminase (GLS) is an enzyme involved in cellular glutamine metabolism and has been investigated as a potential target in cancer research. This study aimed to conduct an exploratory in silico assessment of 4-chloro-N-(phenylcarbamothioyl)-benzamide (4-Cl-PCTB) through molecular docking against GLS and prediction of its physicochemical and ADMET properties. The molecular structure of 4-Cl-PCTB was prepared using Chem3D version 16.0, while molecular docking was performed using Molegro Virtual Docker version 7.0 with the GLS structure obtained from the Protein Data Bank (PDB ID: 8JUB). The predicted physicochemical and pharmacokinetic properties were evaluated using SwissADME and pkCSM. The reported molecular docking analysis yielded a Rerank Score of −78.023 for 4-Cl-PCTB, compared with −85.728 for lapatinib. The predicted human intestinal absorption of 4-Cl-PCTB was 88.309%, while its molecular weight and consensus LogP were reported as 290.77 Da and 2.73, respectively. These results represent computational predictions and do not establish experimentally confirmed GLS binding affinity, enzyme inhibition, oral bioavailability, or safety. Furthermore, limitations in the available docking documentation and inconsistencies in some reported computational parameters restrict the interpretation of the findings. The comparison with lapatinib should also be interpreted cautiously because it was not established as a GLS-specific reference inhibitor. In conclusion, 4-Cl-PCTB warrants further investigation as a computationally assessed compound; however, its potential as an anticancer candidate remains unconfirmed. Reproducible docking studies, biochemical GLS inhibition assays, and appropriate cellular experiments are required to establish its biological relevance.

References

Agili, F. A. (2024). Biological applications of thiourea derivatives: Detailed review. Chemistry, 6(3), 435–468. https://doi.org/10.3390/chemistry6030025

Altharawi, A., & Alqahtani, S. M. (2026). Integrative computational chemistry approaches in modern drug discovery: Advances in docking, pharmacophore modeling, molecular dynamics, and virtual screening. Pharmaceutics, 18(5), 565. https://doi.org/10.3390/pharmaceutics18050565

Baell, J. B., & Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry, 53(7), 2719–2740. https://doi.org/10.1021/jm901137j

Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., & Hopkins, A. L. (2012). Quantifying the chemical beauty of drugs. Nature Chemistry, 4(2), 90–98. https://doi.org/10.1038/nchem.1243

Bou Zerdan, M., Ghorayeb, T., Saliba, F., Allam, S., Bou Zerdan, M., Yaghi, M., Bilani, N., Jaafar, R., & Nahleh, Z. (2022). Triple negative breast cancer: Updates on classification and treatment in 2021. Cancers, 14(5), 1–21. https://doi.org/10.3390/cancers14051253

Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J., & Wyatt, P. G. (2008). Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem, 3(3), 435–444. https://doi.org/10.1002/cmdc.200700139

Caminero Gomes Soares, A., Marques Sousa, G. H., Calil, R. L., & Goulart Trossini, G. H. (2023). Absorption matters: A closer look at popular oral bioavailability rules for drug approvals. Molecular Informatics, 42(11), 1–12. https://doi.org/10.1002/minf.202300115

Chiodi, D., & Ishihara, Y. (2023). “Magic chloro”: Profound effects of the chlorine atom in drug discovery. Journal of Medicinal Chemistry, 66(8), 5305–5331. https://doi.org/10.1021/acs.jmedchem.2c02015

Creanza, T. M., Delre, P., Ancona, N., Lentini, G., Saviano, M., & Mangiatordi, G. F. (2021). Structure-based prediction of hERG-related cardiotoxicity: A benchmark study. Journal of Chemical Information and Modeling, 61(9), 4758–4770. https://doi.org/10.1021/acs.jcim.1c00744

Daina, A., & Zoete, V. (2016). A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 11(11), 1117–1121. https://doi.org/10.1002/cmdc.201600182

Daina, A., Michielin, O., & Zoete, V. (2014). iLOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. Journal of Chemical Information and Modeling, 54(12), 3284–3301. https://doi.org/10.1021/ci500467k

Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, Article 42717. https://doi.org/10.1038/srep42717

Di Paola, L., De Ruvo, M., Paci, P., Santoni, D., & Giuliani, A. (2013). Protein contact networks: An emerging paradigm in chemistry. Chemical Reviews, 113(3), 1598–1613. https://doi.org/10.1021/cr3002356

Egan, W. J., Merz, K. M., Jr., & Baldwin, J. J. (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry, 43(21), 3867–3877. https://doi.org/10.1021/jm000292e

Ferreira, L. G., dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015). Molecular docking and structure-based drug design strategies. Molecules, 20(7), 13384–13421. https://doi.org/10.3390/molecules200713384

Filho, A. M., Laversanne, M., Ferlay, J., Colombet, M., Piñeros, M., Znaor, A., Parkin, D. M., Soerjomataram, I., & Bray, F. (2025). The GLOBOCAN 2022 cancer estimates: Data sources, methods, and a snapshot of the cancer burden worldwide. International Journal of Cancer, 156(7), 1336–1346. https://doi.org/10.1002/ijc.35278

Freihat, O., Sipos, D., & Kovacs, A. (2025). Global burden and projections of breast cancer incidence and mortality to 2050: a comprehensive analysis of GLOBOCAN data. Frontiers in Public Health, 13(October), 1–12. https://doi.org/10.3389/fpubh.2025.1622954

Gajula, S. N. R., Pillai, M. S., Samanthula, G., & Sonti, R. (2021). Cytochrome P450 enzymes: A review on drug metabolizing enzyme inhibition studies in drug discovery and development. Bioanalysis, 13(17), 1355–1378. https://doi.org/10.4155/bio-2021-0132

Gross, M. I., Demo, S. D., Dennison, J. B., Chen, L., Chernov-Rogan, T., Goyal, B., Janes, J. R., Laidig, G. J., Lewis, E. R., Li, J., Mackinnon, A. L., Parlati, F., Rodriguez, M. L. M., Shwonek, P. J., Sjogren, E. B., Stanton, T. F., Wang, T., Yang, J., Zhao, F., & Bennett, M. K. (2014). Antitumor activity of the glutaminase inhibitor CB-839 in triple-negative breast cancer. Molecular Cancer Therapeutics, 13(4), 890–901. https://doi.org/10.1158/1535-7163.MCT-13-0870

Gumus, I., Solmaz, U., Binzet, G., Keskin, E., Arslan, B., & Arslan, H. (2019). Supramolecular self-assembly of new thiourea derivatives directed by intermolecular hydrogen bonds and weak interactions: crystal structures and Hirshfeld surface analysis. Research on Chemical Intermediates, 45(2), 169–198. https://doi.org/10.1007/s11164-018-3596-5

Hitchcock, S. A., & Pennington, L. D. (2006). Structure–brain exposure relationships. Journal of Medicinal Chemistry, 49(26), 7559–7583. https://doi.org/10.1021/jm060642i

Jin, J., Byun, J. K., Choi, Y. K., & Park, K. G. (2023). Targeting glutamine metabolism as a therapeutic strategy for cancer. Experimental and Molecular Medicine, 55(4), 706–715. https://doi.org/10.1038/s12276-023-00971-9

Kadayat, T. M., Park, S., Jun, K. Y., Magar, T. B. T., Bist, G., Shrestha, A., Na, Y., Kwon, Y., & Lee, E. S. (2016). Effect of chlorine substituent on cytotoxic activities: Design and synthesis of systematically modified 2,4-diphenyl-5H-indeno[1,2-b]pyridines. Bioorganic and Medicinal Chemistry Letters, 26(7), 1726–1731. https://doi.org/10.1016/j.bmcl.2016.02.053

Krishna, S., Borrel, A., Huang, R., Zhao, J., Xia, M., & Kleinstreuer, N. (2022). High-throughput chemical screening and structure-based models to predict hERG inhibition. Biology, 11(2), Article 209. https://doi.org/10.3390/biology11020209

Kumar, M. A., Baba, S. K., Khan, I. R., Khan, M. S., Husain, F. M., Ahmad, S., Haris, M., Singh, M., Akil, A. S. A. S., Macha, M. A., & Bhat, A. A. (2025). Glutamine metabolism: Molecular regulation, biological functions, and diseases. MedComm, 6(7), 1–32. https://doi.org/10.1002/mco2.70120

Lampa, M., Arlt, H., He, T., Ospina, B., Reeves, J., Zhang, B., Murtie, J., Deng, G., Barberis, C., Hoffmann, D., Cheng, H., Pollard, J., Winter, C., Richon, V., Garcia-Escheverria, C., Adrian, F., Wiederschain, D., & Srinivasan, L. (2017). Glutaminase is essential for the growth of triple-negative breast cancer cells with a deregulated glutamine metabolism pathway and its suppression synergizes with mTOR inhibition. PLoS ONE, 12(9). https://doi.org/10.1371/journal.pone.0185092

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. https://doi.org/10.1016/S0169-409X(00)00129-0

Matés, J. M., Campos-Sandoval, J. A., de los Santos-Jiménez, J., & Márquez, J. (2020). Glutaminases regulate glutathione and oxidative stress in cancer. Archives of Toxicology, 94(8), 2603–2623. https://doi.org/10.1007/s00204-020-02838-8

Paggi, J. M., Pandit, A., & Dror, R. O. (2024). The art and science of molecular docking. Annual Review of Biochemistry, 93, 389–410. https://doi.org/10.1146/annurev-biochem-030222-120000

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

Shinada, N. K., De Brevern, A. G., & Schmidtke, P. (2019). Halogens in protein-ligand binding mechanism: A structural perspective. Journal of Medicinal Chemistry, 62(21), 9341–9356. https://doi.org/10.1021/acs.jmedchem.8b01453

Tavčar Kunstič, T., Debeljak, N., & Fon Tacer, K. (2023). Heterogeneity in hormone-dependent breast cancer and therapy: Steroid hormones, HER2, melanoma antigens, and cannabinoid receptors. Advances in Cancer Biology - Metastasis, 7(December 2022). https://doi.org/10.1016/j.adcanc.2022.100086

Thomsen, R., & Christensen, M. H. (2006). MolDock: A new technique for high-accuracy molecular docking. Journal of Medicinal Chemistry, 49(11), 3315–3321. https://doi.org/10.1021/jm051197e

Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334

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. https://doi.org/10.1021/jm020017n

Warren, G. L., Andrews, C. W., Capelli, A.-M., Clarke, B., LaLonde, J., Lambert, M. H., Lindvall, M., Nevins, N., Semus, S. F., Senger, S., Tedesco, G., Wall, I. D., Woolven, J. M., Peishoff, C. E., & Head, M. S. (2006). A critical assessment of docking programs and scoring functions. Journal of Medicinal Chemistry, 49(20), 5912–5931. https://doi.org/10.1021/jm050362n

Xiong, X., Zheng, L. W., Ding, Y., Chen, Y. F., Cai, Y. W., Wang, L. P., Huang, L., Liu, C. C., Shao, Z. M., & Yu, K. D. (2025). Breast cancer: Pathogenesis and treatments. Signal Transduction and Targeted Therapy, 10(1). https://doi.org/10.1038/s41392-024-02108-4

Yang, C., Chen, E. A., & Zhang, Y. (2022). Protein–ligand docking in the machine-learning era. Molecules, 27(14), 4568. https://doi.org/10.3390/molecules27144568

Yoo, H. C., Yu, Y. C., Sung, Y., & Han, J. M. (2020). Glutamine reliance in cell metabolism. Experimental and Molecular Medicine, 52(9), 1496–1516. https://doi.org/10.1038/s12276-020-00504-8

Zhang, J., Li, H., Zhang, Y., Huang, J., Ren, L., Zhang, C., Zou, Q., & Zhang, Y. (2025). Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction. Briefings in Bioinformatics, 26(5). https://doi.org/10.1093/bib/bbaf533

Zhang, X., Shen, C., Zhang, H., Kang, Y., Hsieh, C. Y., & Hou, T. (2024). Advancing ligand docking through deep learning: Challenges and prospects in virtual screening. Accounts of Chemical Research, 57(10), 1500–1509. https://doi.org/10.1021/acs.accounts.4c00093

Zhao, Y. H., Abraham, M. H., Le, J., Hersey, A., Luscombe, C. N., Beck, G., Sherborne, B., & Cooper, I. (2002). Rate-limited steps of human oral absorption and QSAR studies. Pharmaceutical Research, 19(10), 1446–1457. https://doi.org/10.1023/A:1020444330011

Zhu, L., Zhu, X., & Wu, Y. (2022). Effects of glucose metabolism, lipid metabolism, and glutamine metabolism on tumor microenvironment and clinical implications. Biomolecules, 12(4). https://doi.org/10.3390/biom12040580