Journal of Drug Design and Medicinal Chemistry

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Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design

Received: Nov. 28, 2023    Accepted: Dec. 12, 2023    Published: Feb. 05, 2024
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Abstract

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women. It is characterized by hyperandrogenism, ovarian dysfunction, and metabolic abnormalities. Current treatment options have limitations and many women remain undiagnosed or untreated. Cytochrome P450c 17 alpha (CYP17A1) plays a key role in androgen biosynthesis and is a potential therapeutic target for PCOS. Known CYP17A1 inhibitors metformin, spironolactone, and clomiphene were used for scaffold hopping to generate structurally diverse compounds. These were screened against CYP17A1 (PDB code 3RUK) through molecular docking. Hits were subjected to fragment-based de novo design and further docking. Quality parameters, ADMET profiling, and biological activity predictions were evaluated. Scaffold hopping yielded 300 compounds, from which 10 hits were identified. De novo design generated 326 ligands, of which 7 demonstrated superior binding to 3RUK compared to reference drugs. These hits formed favourable interactions within the binding pocket and exhibited drug-like properties. They were predicted to inhibit CYP17A1 and show activity for PCOS-related indications. Toxicity profiling suggested an acceptable safety profile. Through an integrated in silico workflow, this study identified 7 novel CYP17A1 inhibitor scaffolds as potential leads for PCOS treatment. Their predicted bioactivities and properties warrant further experimental validation. This approach provides a foundation for the development of improved PCOS therapeutics targeting androgen biosynthesis.

DOI 10.11648/jddmc.20241001.15
Published in Journal of Drug Design and Medicinal Chemistry ( Volume 10, Issue 1, April 2024 )
Page(s) 31-44
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

17-Alpha Hydroxylase, P-Glycoprotein, Chemotypes and Hepatotoxicity

References
[1] Abad-Zapatero, C., Perišić, O., Wass, J., Bento, A. P., Overington, J., Al-Lazikani, B., & Johnson, M. E. (2010). Ligand efficiency indices for an effective mapping of chemical-biological space: The concept of an atlas-like representation. In Drug Discovery Today (Vol. 15, Issues 19–20). https://doi.org/10.1016/j.drudis.2010.08.004
[2] Baell, J. B. (2016). Feeling Nature’s PAINS: Natural Products, Natural Product Drugs, and Pan Assay Interference Compounds (PAINS). In Journal of Natural Products (Vol. 79, Issue 3). https://doi.org/10.1021/acs.jnatprod.5b00947
[3] Banegas-Luna, A. J., Cerón-Carrasco, J. P., Puertas-Martín, S., & Pérez-Sánchez, H. (2019). BRUSELAS: HPC Generic and Customizable Software Architecture for 3D Ligand-Based Virtual Screening of Large Molecular Databases. Journal of Chemical Information and Modeling, 59(6). https://doi.org/10.1021/acs.jcim.9b00279
[4] Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1). https://doi.org/10.1093/nar/gky318
[5] Basanagouda, M., Jadhav, V. B., Kulkarni, M. V., & Nagendra Rao, R. (2011). Computer-aided prediction of biological activity spectra: Study of correlation between predicted and observed activities for Coumarin-4-Acetic acids. Indian Journal of Pharmaceutical Sciences, 73(1). https://doi.org/10.4103/0250-474x.89764
[6] Burley, S. K., Berman, H. M., Kleywegt, G. J., Markley, J. L., Nakamura, H., & Velankar, S. (2017). Protein Data Bank (PDB): The single global macromolecular structure archive. In Methods in Molecular Biology (Vol. 1607). https://doi.org/10.1007/978-1-4939-7000-1_26
[7] Lipinski, C. A. (2004). Lead- and drug-like compounds: The rule-of-five revolution. In Drug Discovery Today: Technologies (Vol. 1, Issue 4). https://doi.org/10.1016/j.ddtec.2004.11.007
[8] Chang, M. W., Lindstrom, W., Olson, A. J., & Belew, R. K. (2007). Analysis of HIV wild-type and mutant structures via in silico docking against diverse ligand libraries. Journal of Chemical Information and Modeling, 47(3). https://doi.org/10.1021/ci700044s
[9] Cui, W., Aouidate, A., Wang, S., Yu, Q., Li, Y., & Yuan, S. (2020). Discovering Anti-Cancer Drugs via Computational Methods. In Frontiers in Pharmacology (Vol. 11). https://doi.org/10.3389/fphar.2020.00733
[10] Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate the pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7. https://doi.org/10.1038/srep42717
[11] Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F., Bellis, L., & Overington, J. P. (2015). ChEMBL web services: Streamlining access to drug discovery data and utilities. Nucleic Acids Research, 43(W1). https://doi.org/10.1093/nar/gkv352
[12] de Souza Neto, L. R., Moreira-Filho, J. T., Neves, B. J., Maidana, R. L. B. R., Guimarães, A. C. R., Furnham, N., Andrade, C. H., & Silva, F. P. (2020). In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery. In Frontiers in Chemistry (Vol. 8). https://doi.org/10.3389/fchem.2020.00093
[13] Douguet, D. (2010). e-LEA3D: A computational-aided drug design web server. Nucleic Acids Research, 38(SUPPL. 2). https://doi.org/10.1093/nar/gkq322
[14] Du, X., Li, Y., Xia, Y. L., Ai, S. M., Liang, J., Sang, P., Ji, X. L., & Liu, S. Q. (2016). Insights into protein–ligand interactions: Mechanisms, models, and methods. In International Journal of Molecular Sciences (Vol. 17, Issue 2). https://doi.org/10.3390/ijms17020144
[15] Fink, C., Sun, D., Wagner, K., Schneider, M., Bauer, H., Dolgos, H., Mäder, K., & Peters, S. A. (2020). Evaluating the Role of Solubility in Oral Absorption of Poorly Water-Soluble Drugs Using Physiologically-Based Pharmacokinetic Modeling. Clinical Pharmacology and Therapeutics, 107(3). https://doi.org/10.1002/cpt.1672
[16] Garrett M. Morris, Ruth Huey, William Lindstrom, Michel F. Sanner, Richard K. Belew, David S Goodsell, & Arthur J. Olson. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of Computational Chemistry, 30(16).
[17] Ghosh, M., Roy, K., & Roy, S. (2013). Immunomodulatory effects of antileishmanial drugs. Journal of Antimicrobial Chemotherapy, 68(12). https://doi.org/10.1093/jac/dkt262
[18] Harwood, K., Vuguin, P., & DiMartino-Nardi, J. (2007). Current approaches to the diagnosis and treatment of polycystic ovarian syndrome in youth. In Hormone Research (Vol. 68, Issue 5). https://doi.org/10.1159/000101538
[19] Hevener, K. E., Pesavento, R., Ren, J. H., Lee, H., Ratia, K., & Johnson, M. E. (2018). Hit-to-Lead: Hit Validation and Assessment. In Methods in Enzymology (Vol. 610). https://doi.org/10.1016/bs.mie.2018.09.022
[20] Holdgate, G. A., Meek, T. D., & Grimley, R. L. (2018). Mechanistic enzymology in drug discovery: A fresh perspective. In Nature Reviews Drug Discovery (Vol. 17, Issue 2). https://doi.org/10.1038/nrd.2017.219
[21] Jin, P., & Xie, Y. (2018). Treatment strategies for women with polycystic ovary syndrome. In Gynecological Endocrinology (Vol. 34, Issue 4). https://doi.org/10.1080/09513590.2017.1395841
[22] Kamboj, A., Verma, D., Sharma, D., Pant, K., Pant, B., & Kumar, V. (2020). A Molecular Docking Study Towards Finding Herbal Treatment against Polycystic Ovary Syndrome (PCOS). International Journal of Recent Technology and Engineering, 8(2S12), 38–41. https://doi.org/10.35940/ijrte.b1006.0982s1219
[23] Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2021). PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Research, 49(D1). https://doi.org/10.1093/nar/gkaa971
[24] Lagunin, A., Stepanchikova, A., Filimonov, D., & Poroikov, V. (2000). PASS: Prediction of activity spectra for biologically active substances. Bioinformatics, 16(8). https://doi.org/10.1093/bioinformatics/16.8.747
[25] Löbenberg, R., Amidon, G. L., Ferraz, H. G., & Bou-Chacra, N. (2013). Mechanism of gastrointestinal drug absorption and application in therapeutic drug delivery. In Therapeutic Delivery Methods: A Concise Overview of Emerging Areas. https://doi.org/10.4155/EBO.13.349
[26] Lobo, R. A., & Carmina, E. (2000). The importance of diagnosing the polycystic ovary syndrome. Annals of Internal Medicine, 132(12), 989–993. https://doi.org/10.7326/0003-4819-132-12-200006200-00010
[27] Malikova, J., Brixius-Anderko, S., Udhane, S. S., Parween, S., Dick, B., Bernhardt, R., & Pandey, A. V. (2017). CYP17A1 inhibitor abiraterone, an anti-prostate cancer drug, also inhibits the 21-hydroxylase activity of CYP21A2. Journal of Steroid Biochemistry and Molecular Biology, 174. https://doi.org/10.1016/j.jsbmb.2017.09.007
[28] Onakpoya, I. J., Heneghan, C. J., & Aronson, J. K. (2016). Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: A systematic review of the world literature. BMC Medicine, 14(1). https://doi.org/10.1186/s12916-016-0553-2
[29] Pence, H. E., & Williams, A. (2010). ChemSpider: An online chemical information resource. In Journal of Chemical Education (Vol. 87, Issue 11). https://doi.org/10.1021/ed100697w
[30] Poroikov, V., & Filimonov, D. (2005). PASS: Prediction of biological activity spectra for substances. In Predictive Toxicology. https://doi.org/10.1201/9780849350351-15
[31] Rao, M., Broughton, K. S., & LeMieux, M. J. (2020). Cross-sectional Study on the Knowledge and Prevalence of PCOS at a Multiethnic University. Progress in Preventive Medicine, 5(2), e0028. https://doi.org/10.1097/pp9.0000000000000028
[32] Reynolds, C. H., Bembenek, S. D., & Tounge, B. A. (2007). The role of molecular size in ligand efficiency. Bioorganic and Medicinal Chemistry Letters, 17(15). https://doi.org/10.1016/j.bmcl.2007.05.038
[33] Rim, K. T. (2020). In silico prediction of toxicity and its applications for chemicals at work. In Toxicology and Environmental Health Sciences (Vol. 12, Issue 3). https://doi.org/10.1007/s13530-020-00056-4
[34] Sakyi, P. O., Broni, E., Amewu, R. K., Miller, W. A., Wilson, M. D., & Kwofie, S. K. (2022). Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase. Frontiers in Cellular and Infection Microbiology, 12. https://doi.org/10.3389/fcimb.2022.859981
[35] Sander, T., Freyss, J., Von Korff, M., & Rufener, C. (2015). DataWarrior: An open-source program for chemistry-aware data visualization and analysis. Journal of Chemical Information and Modeling, 55(2). https://doi.org/10.1021/ci500588j
[36] Sawale, R. T., Kalyankar, T. M., George, R., & Deosarkar, S. D. (2016). Molar refraction and polarizability of antiemetic drug 4-amino-5-chloro-N-(2-(diethylamino)ethyl)-2 methoxybenzamide hydrochloride monohydrate in (aqueous-sodium or lithium chloride) solutions at 30°c. Journal of Applied Pharmaceutical Science, 6(3). https://doi.org/10.7324/JAPS.2016.60321
[37] Schultes, S., De Graaf, C., Haaksma, E. E. J., De Esch, I. J. P., Leurs, R., & Krämer, O. (2010). Ligand efficiency as a guide in fragment hit selection and optimization. In Drug Discovery Today: Technologies (Vol. 7, Issue 3). https://doi.org/10.1016/j.ddtec.2010.11.003
[38] Sterling, T., & Irwin, J. J. (2015). ZINC 15 - Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 55(11). https://doi.org/10.1021/acs.jcim.5b00559
[39] Šudomová, M., Hassan, S. T. S., Khan, H., Rasekhian, M., & Nabavi, S. M. (2019). A multi-biochemical and in silico study on anti-enzymatic actions of pyroglutamic acid against pde-5, ace, and urease using various analytical techniques: Unexplored pharmacological properties and cytotoxicity evaluation. Biomolecules, 9(9). https://doi.org/10.3390/biom9090392
[40] Trott Oleg, & Arthur J. Olson. (2010). AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31.
[41] van den Anker, J., Reed, M. D., Allegaert, K., & Kearns, G. L. (2018). Developmental Changes in Pharmacokinetics and Pharmacodynamics. Journal of Clinical Pharmacology, 58. https://doi.org/10.1002/jcph.1284
[42] Van Norman, G. A. (2019). Phase II Trials in Drug Development and Adaptive Trial Design. JACC: Basic to Translational Science, 4(3). https://doi.org/10.1016/j.jacbts.2019.02.005
[43] Varma, A. K., Patil, R., Das, S., Stanley, A., Yadav, L., & Sudhakar, A. (2010). Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of Drug-Designing. PLoS ONE, 5(8). https://doi.org/10.1371/journal.pone.0012029
[44] 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). https://doi.org/10.1021/jm020017n
[45] Velázquez M., E., Acosta, A., & Mendoza, S. G. (1997). Menstrual cyclicity after metformin therapy in polycystic ovary syndrome. Obstetrics and Gynecology, 90(3). https://doi.org/10.1016/S0029-7844(97)00296-2
[46] Wasko, M. J., Pellegrene, K. A., Madura, J. D., & Surratt, C. K. (2015). A role for fragment-based drug design in developing novel lead compounds for central nervous system targets. In Frontiers in Neurology (Vol. 6, Issue SEP). https://doi.org/10.3389/fneur.2015.00197
[47] Wawrzkiewicz-Jałowiecka, A., Kowalczyk, K., Trybek, P., Jarosz, T., Radosz, P., Setlak, M., & Madej, P. (2020). In search of new therapeutics—molecular aspects of the pcos pathophysiology: Genetics, hormones, metabolism and beyond. International Journal of Molecular Sciences, 21(19), 1–24. https://doi.org/10.3390/ijms21197054
[48] Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., MacIejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, Di., … Wilson, M. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1). https://doi.org/10.1093/nar/gkx1037
[49] Wróbel, T. M., Jørgensen, F. S., Pandey, A. V., Grudzińska, A., Sharma, K., Yakubu, J., & Björkling, F. (2023). Non-steroidal CYP17A1 Inhibitors: Discovery and Assessment. Journal of Medicinal Chemistry, 66(10), 6542–6566. https://doi.org/10.1021/acs.jmedchem.3c00442
[50] Zyad, A., Leouifoudi, I., Tilaoui, M., Mouse, H. A., Khouchani, M., & Jaafari, A. (2018). Natural Products as Cytotoxic Agents in Chemotherapy against Cancer. In Cytotoxicity. https://doi.org/10.5772/intechopen.72744
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    Bandoma, C. A., Yeboah, P., Gyan, K., Rafiu, S. A., Adusei, J. A., et al. (2024). Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design. Journal of Drug Design and Medicinal Chemistry, 10(1), 31-44. https://doi.org/10.11648/jddmc.20241001.15

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    ACS Style

    Bandoma, C. A.; Yeboah, P.; Gyan, K.; Rafiu, S. A.; Adusei, J. A., et al. Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design. J. Drug Des. Med. Chem. 2024, 10(1), 31-44. doi: 10.11648/jddmc.20241001.15

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    AMA Style

    Bandoma CA, Yeboah P, Gyan K, Rafiu SA, Adusei JA, et al. Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design. J Drug Des Med Chem. 2024;10(1):31-44. doi: 10.11648/jddmc.20241001.15

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  • @article{10.11648/jddmc.20241001.15,
      author = {Comfort Azansuma Bandoma and Prince Yeboah and Kenneth Gyan and Shuraif Abdul Rafiu and Juliana Achiaa Adusei and Gideon Djan},
      title = {Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design},
      journal = {Journal of Drug Design and Medicinal Chemistry},
      volume = {10},
      number = {1},
      pages = {31-44},
      doi = {10.11648/jddmc.20241001.15},
      url = {https://doi.org/10.11648/jddmc.20241001.15},
      eprint = {https://download.sciencepg.com/pdf/10.11648.jddmc.20241001.15},
      abstract = {Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women. It is characterized by hyperandrogenism, ovarian dysfunction, and metabolic abnormalities. Current treatment options have limitations and many women remain undiagnosed or untreated. Cytochrome P450c 17 alpha (CYP17A1) plays a key role in androgen biosynthesis and is a potential therapeutic target for PCOS. Known CYP17A1 inhibitors metformin, spironolactone, and clomiphene were used for scaffold hopping to generate structurally diverse compounds. These were screened against CYP17A1 (PDB code 3RUK) through molecular docking. Hits were subjected to fragment-based de novo design and further docking. Quality parameters, ADMET profiling, and biological activity predictions were evaluated. Scaffold hopping yielded 300 compounds, from which 10 hits were identified. De novo design generated 326 ligands, of which 7 demonstrated superior binding to 3RUK compared to reference drugs. These hits formed favourable interactions within the binding pocket and exhibited drug-like properties. They were predicted to inhibit CYP17A1 and show activity for PCOS-related indications. Toxicity profiling suggested an acceptable safety profile. Through an integrated in silico workflow, this study identified 7 novel CYP17A1 inhibitor scaffolds as potential leads for PCOS treatment. Their predicted bioactivities and properties warrant further experimental validation. This approach provides a foundation for the development of improved PCOS therapeutics targeting androgen biosynthesis.
    },
     year = {2024}
    }
    

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  • TY  - JOUR
    T1  - Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design
    AU  - Comfort Azansuma Bandoma
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    T2  - Journal of Drug Design and Medicinal Chemistry
    JF  - Journal of Drug Design and Medicinal Chemistry
    JO  - Journal of Drug Design and Medicinal Chemistry
    SP  - 31
    EP  - 44
    PB  - Science Publishing Group
    SN  - 2472-3576
    UR  - https://doi.org/10.11648/jddmc.20241001.15
    AB  - Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women. It is characterized by hyperandrogenism, ovarian dysfunction, and metabolic abnormalities. Current treatment options have limitations and many women remain undiagnosed or untreated. Cytochrome P450c 17 alpha (CYP17A1) plays a key role in androgen biosynthesis and is a potential therapeutic target for PCOS. Known CYP17A1 inhibitors metformin, spironolactone, and clomiphene were used for scaffold hopping to generate structurally diverse compounds. These were screened against CYP17A1 (PDB code 3RUK) through molecular docking. Hits were subjected to fragment-based de novo design and further docking. Quality parameters, ADMET profiling, and biological activity predictions were evaluated. Scaffold hopping yielded 300 compounds, from which 10 hits were identified. De novo design generated 326 ligands, of which 7 demonstrated superior binding to 3RUK compared to reference drugs. These hits formed favourable interactions within the binding pocket and exhibited drug-like properties. They were predicted to inhibit CYP17A1 and show activity for PCOS-related indications. Toxicity profiling suggested an acceptable safety profile. Through an integrated in silico workflow, this study identified 7 novel CYP17A1 inhibitor scaffolds as potential leads for PCOS treatment. Their predicted bioactivities and properties warrant further experimental validation. This approach provides a foundation for the development of improved PCOS therapeutics targeting androgen biosynthesis.
    
    VL  - 10
    IS  - 1
    ER  - 

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Author Information
  • Department of Chemical Sciences, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana

  • Department of Chemistry, College of Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

  • Department of Chemical Sciences, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana

  • Department of Chemistry, College of Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

  • Department of Chemistry, College of Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

  • Department of Chemical Sciences, School of Sciences, University of Energy and Natural Resources, Sunyani, Ghana

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