Huntington Hastalığının Tedavisi İçin Yeni Dihidrokromeno¬kromendiol Türevlerinin ADMET Özellikleri ve Geniş Spektrumlu Biyoaktivitesinin In Silico Değerlendirilmesi

Abstract

Bu çalışmada, Huntington hastalığının (HD) tedavisinde potansiyel kullanım olasılığı belir-lenmiş bazı dihidrokromenokromeno-diol türevi bileşiklerin ADMET ve geniş spektrumlu biyoaktivite özellikleri incelenmiştir. Bu amaçla, öncelikle moleküllerin ADMET kapsamındaki fizikokimyasal özellikleri, lipofilisite, suda çözünürlük, absorpsiyon, dağılım, metabolizma ve atılım, toksisite, çevresel toksisite, Tox21 yolakları ve medikal kimya özel-likleri değerlendirilmiştir. Geniş spektrumlu biyoaktivite açısından ise sıçanlarda akut tok-sisite, ilaçların kardiyovasküler ve hepatobiliyer sistemler üzerindeki yan etkileri, antibak-teriyel aktivite, antifungal aktivite, anti-HIV aktivite, antiviral aktivite ve tümör ile tümör olmayan hücre hatlarıyla etkileşim değerleri hesaplanmıştır. Elde edilen tüm hesaplamalar sonucunda, 3 numaralı molekülün(2,7-dietil-3,8-dimetil-5,10-dihidrokromeno[5,4,3-cde]kromene-5,10-diol) ADMET ve geniş spektrumlu biyoaktivite açısından HD tedavisi için ideal bir aday molekül olabileceği sonucuna varılmıştır.

Keywords

• Huntington hastalığı
• ADMET
• biyoaktivite
• dihidrokromenokromeno-diol türevleri
• yeni ilaç

In Silico Evaluation of ADMET and Broad-Spectrum Bioactivity of Some dihydrochromenochromene-diol Derivatives Novel Drug Candidates for Huntington’s Disease Treatment

Abstract

In this study, we determined the ADMET and broad-spectrum bioactivity properties of some dihydrochromenochromene-diol derived compounds that have been identified as having po-tential for use in the treatment of Huntington's disease (HD). To this end, we first examined the physicochemical, lipophilicity, water solubility, absorption, distribution, metabolism and excretion, toxicity, environmental toxicity, Tox21 pathway, and medicinal chemistry proper-ties of the molecules considered within the scope of ADMET. In terms of broad spectrum bioactivity, acute toxicity in rats, side effects of drugs on the cardiovascular and hepatobili-ary systems, antibacterial activity, antifungal activity, anti-HIV activity, antiviral activity, and interaction with tumor and non-tumor cell lines values were calculated. Based on all the calculations obtained, it was concluded that molecule 3 (2,7-diethyl-3,8-dimethyl-5,10-dihydrochromeno[5,4,3-cde]chromene-5,10-diol) could be an ideal molecule for the treat-ment of HD in terms of ADMET and broad spectrum bioactivity.

Keywords

• Huntington's disease
• ADMET
• bioactivity
• dihydrochromenochromene-diol derivatives
• nov-el drug

References

1. Afzal, A. H., Alam, O., Zafar, S., Alam, M. A., Ahmed, K., Khan, J., Khan, R., Shahat, A. A., Alhalmi, A. (2024). Application of machine learning for the prediction of absorption, distribution, metabolism and excretion (ADME) properties from Cichorium intybus plant phytomolecules. Processes, 12, 2488. https://doi.org/10.3390/pr12122488
2. Benet, W. (2005). Predicting drug disposition via application of BCS: Transport, absorption, elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharmaceutical Research, 22, 11-23. https://doi.org/10.1007/s11095-004-9004-4
3. Block, K. I., Gyllenhaal, C., Lowe, L., Amedei, A., Amin, A. R. M. R. (2015). Designing a broad-spectrum integrative approach for cancer prevention and treatment. Seminars in Cancer Biology, 35, S276-S304. https://doi.org/10.1016/j.semcancer.2015.09.007
4. Bužgová, R., Kozáková, R., Juríčková, L. (2019). Quality of life in patients with Huntington's disease. PLoS ONE, 14, e0214395. https://doi.org/10.1371/journal.pone.0214395
5. Cao, D. Y., Wang, J. M., Zhou, R., Li, Y. Y., Yu, H. D. (2012). ADMET evaluation in drug discovery. Journal of Chemical Information and Modeling, 52, 1132-1137. https://doi.org/10.1021/ci200571a
6. Daina, A., Michielin, O., Zoete, V. (2017a). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717. https://doi.org/10.1038/srep42717
7. Daina, A., Michielin, O., Zoete, V. (2017b). SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 45, W296-W302. https://doi.org/10.1093/nar/gkx382
8. Daoud, N. E. H., Borah, P., Deb, P. K., Venugopala, K. N., Hourani, W. (2021). ADMET profiling in drug discovery: Perspectives on current applications and future challenges. Current Drug Metabolism, 22, 503-522. https://doi.org/10.2174/1389200222666210603101739

9. Flores-Holguín, N., Frau, J., Glossman-Mitnik, D. (2021). Chemical reactivity of the new potential antifungal drug molecule. Frontiers in Chemistry, 9, 708364. https://doi.org/10.3389/fchem.2021.708364
10. Firth, A., Prathapan, P. (2021). Drug discovery in the era of artificial intelligence. Current Research in Pharmacology and Drug Discovery, 2, 100011. https://doi.org/10.1016/j.crphar.2020.100011
11. Fu, L., Shi, S., Yi, J., Wang, N., He, Y. (2024). ADMETlab 3.0: A web server for ADMET prediction and optimization. Nucleic Acids Research, 52, W422-W431. https://doi.org/10.1093/nar/gkae236
12. Guan, L., Yang, H., Cai, Y., Sun, L., Di, P. (2018). ADMET-score: A relative drug-likeness score based on ADMET profiles. Medicinal Chemistry Communications, 10, 148-157. https://doi.org/10.1039/c8md00472b
13. Hartung, T. (2024). The misleading role of animal models in drug development. Frontiers in Drug Discovery, 4, 1355044. https://doi.org/10.3389/fddsv.2024.1355044
14. Huang, R., Xia, M., Nguyen, D. T., Zhao, T., Sakamuru, S. (2016). Tox21 challenge to build predictive models. Frontiers in Environmental Science, 3, 85. https://doi.org/10.3389/fenvs.2015.00085
15. İslamoglu, F. (2025). Comprehensive computational profiling of candidate molecules for Huntington’s disease. French-Ukrainian Journal of Chemistry, 13, 31-50. https://doi.org/10.17721/fujcV13I2P31-50
16. Jambhekar, S. S., Breen, P. J. (2013). Drug absorption and bioavailability. Drug Discovery Today, 18, 1173-1184. https://doi.org/10.1016/j.drudis.2013.08.013
17. Karim, T., Almatarneh, M. H., Rahman, S., Alodhayb, A. N., Albrithen, H. (2024). In silico prediction of antibacterial activity. ChemistrySelect, 9, e202402780. https://doi.org/10.1002/slct.202402780
18. Kinnunen, K. M., Schwarz, A. J., Turner, E. C., Pustina, D., Gantman, E. C. (2021). Functional magnetic resonance imaging in Huntington's disease. Frontiers in Neurology, 12, 712555. https://doi.org/10.3389/fneur.2021.712555
19. Lagunin, A., Stepanchikova, A., Filimonov, D., Poroikov, V. (2000). PASS: Prediction of activity spectra for biologically active substances. Bioinformatics, 16, 747-748. https://doi.org/10.1093/bioinformatics/16.8.747
20. Lagunin, A., Zakharov, A., Filimonov, D., Poroikov, V. (2011). QSAR modelling of acute toxicity. Molecular Informatics, 30, 241-250. https://doi.org/10.1002/minf.201000129
21. Lin, J., Sahakian, D. C., Morais, S. M. F., Xu, J. J., Polzer, R. J. (2003). The prediction of drug metabolism using intrinsic and extrinsic molecular properties. Current Topics in Medicinal Chemistry, 3, 1125-1154. https://doi.org/10.2174/1568026033452096
22. Manivannan, H. P., Veeraraghavan, V. P., Francis, A. P. (2025). Prediction of multi-targeting pharmacological activity. Applied Biochemistry and Biotechnology, 197, 2979-3007. https://doi.org/10.1007/s12010-024-05150-8
23. Massey, T. H., McLauchlan, D. J. (2024). Huntington's disease: An update on pathophysiology and treatment. Clinical Medicine, 24, 100200. https://doi.org/10.1016/j.clinme.2024.100200
24. McEuen, K., Borlak, J., Tong, W., Chen, M. (2017). Drug lipophilicity and liver injury. International Journal of Molecular Sciences, 18, 1335. https://doi.org/10.3390/ijms18071335
25. Menden, M. P., Iorio, F., Garnett, M., McDermott, U., Benes, C. H. (2013). Machine learning prediction of cancer cell sensitivity. PLoS ONE, 8,e61318. https://doi.org/10.1371/journal.pone.0061318
26. Odunitan, T. T., Apanisile, B. T., Afolabi, J. A., Adeniwura, P. O., Akinboade, M. W. (2025). Exploring the efficacy of antimicrobial peptides. Chemistry and Biodiversity, 22, e202401349. https://doi.org/10.1002/cbdv.202401349
27. Paprocka, J., Machnikowska-Sokołowska, M., Gruszczyńska, K., Emich-Widera, E. (2020). Juvenile Huntington's disease – A review. Brain Sciences, 10, 849. https://doi.org/10.3390/brainsci10110849
28. Rajaraman, D., Sonadevi, S., Dhandapani, A., Nagaraj, K. (2025). Potential EGFR inhibitor study. Letters in Drug Design and Discovery, e100199. https://doi.org/10.1016/j.lddd.2025.100199
29. Reddy, A. S., Zhang, S. (2013). Polypharmacology: Drug discovery for the future. Expert Review of Clinical Pharmacology, 6, 97-109. https://doi.org/10.1586/ecp.12.74
30. Ryszkiewicz, P., Malinowska, B., Schlicker, E. (2025). Polypharmacology: New drugs. Pharmacological Reports, 77, 543-560. https://doi.org/10.1007/s43440-025-00715-8
31. Wang, J. (2009). In silico ADMET prediction. Current Pharmaceutical Design, 15, 2195-2219. https://doi.org/10.2174/138161209788682514
32. Waring, M. J. (2010). Lipophilicity in drug discovery. Expert Opinion on Drug Discovery, 5, 235-248. https://doi.org/10.1517/17460441003605098
33. Xiao, X., Wang, C., Chang, D., Wang, Y., Dong, X. (2020). Antiviral therapeutic candidates. Frontiers in Immunology, 11, 586572. https://doi.org/10.3389/fimmu.2020.586572
34. Zhang, Z., Tang, W. (2018). Drug metabolism in discovery and development. Acta Pharmaceutica Sinica B, 8, 721-732. https://doi.org/10.1016/j.apsb.2018.04.003
35. Zhong, L., Li, Y., Xiong, L., Wang, W., Wu, M. (2021). Small molecules in targeted cancer therapy. Signal Transduction and Targeted Therapy, 6, 201. https://doi.org/10.1038/s41392-021-00572-w