Uji Aktivitas Senyawa 5-hydroxymethylfurfural dari Tanaman Abiu (Pouteria caimito) Sebagai Kandidat Obat Kanker Tulang Menggunakan Metode In Silico

Shafa Noer; Elsa Nurahma Putri

doi:10.30998/edubiologia.v4i2.24683

Uji Aktivitas Senyawa 5-hydroxymethylfurfural dari Tanaman Abiu (Pouteria caimito) Sebagai Kandidat Obat Kanker Tulang Menggunakan Metode In Silico

Shafa Noer^(1*), Elsa Nurahma Putri⁽²⁾

(1) (Scopus ID: 57204545462, Sinta ID: 5979788) Universitas Indraprasta PGRI
(2) Universitas Indraprasta PGRI
(*) Corresponding Author

Abstract

Kanker tulang merupakan salah satu kasus kanker yang serius dan perlu dicari metode pengobatan yang efektif. Tujuan dari penelitian ini adalah untuk melihat secara in-silico apakah senyawa 5- hydroxymethylfurfural yang terkandung dalam tanaman abiu berpotensi digunakan sebagai kandidat obat kanker tulang. Senyawa alami yang diuji adalah 5-hydroxymethylfurfural dari tanaman abiu (Pouteria caimito), dengan protein target Beta-glucocerebrobsidase. Penelitian ini menggunakan metode eksperimental in-silico dengan lima tahapan: analisis senyawa alami sebagai obat potensial (menggunakan PubChem dan way2drug), identifikasi protein target (Swiss Target Prediction), pemodelan, dan validasi struktur 3D protein target (SWISS, SAVER, PyMOL), molecular docking (PyRx), dan analisis farmakokimia serta toksisitas (SwissADME, ProTox). Dari berbagai hasil yang diperoleh secara in-silico, dapat disimpulkan bahwa senyawa 5-hydroxymethylfurfural berpotensi dijadikan kandidat obat kanker tulang. Uji toksisitas menunjukkan senyawa ini berada di kelas 5 yang berarti aman jika tertelan.

Keywords

5-hydroxymethylfurfural; In-silico; Anti-kanker; Kesehatan

Full Text:

PDF (Indonesian)

References

Abreu, M. M., De Nobrega, P. A., Sales, P. F., De Oliveira, F. R., & Nascimento, A. A. (2019). Antimicrobial and antidiarrheal activities of methanolic fruit peel extract of Pouteria caimito. Pharmacognosy Journal, 11(5), 944–950. https://doi.org/10.5530/pj.2019.11.150

Ambarwati, W. N., & Wardani, E. K. (2014). Efek samping kemoterapi secara fisik pasien penderita kanker servik. In prosiding seminar nasional & internasional, 2(2), 97-106.

American Cancer Society. (2021). About Bone Cancer What Is Bone Cancer? https://www.cancer.org/cancer/types/bone-cancer/about/key-statistics.html. Diakses tanggal 17 Juli 2024.

Arif, A. B., Susanto, S., Matra, D. D., & Widayanti, S. M. (2021). Identifikasi senyawa bioaktif dan manfaatnya dari beberapa bagian tanaman abiu (Pouteria caimito). Jurnal Hortikultura Indonesia, 12(1), 10–20.

Arús-Pous, J., Johansson, S. V., Prykhodko, O., Bjerrum, E. J., Tyrchan, C., Reymond, J. L., Chen, H., & Engkvist, O. (2019). Randomized SMILES strings improve the quality of molecular generative models. Journal of Cheminformatics, 11(1), 1–13. https://doi.org/10.1186/s13321-019-0393-0

Banerjee P., Kemmler E., Dunkel M., & Preissner R. (2024). ProTox 3.0: a web server for the prediction of toxicity of chemicals Nucleic Acids Res (Web server issue 2024); NAR

Cancer Research UK. (2023). Types of cancer. https://www.cancerresearchuk.org/about-cancer/what-is-cancer/how-cancer-starts/types-of-cancer. Diakses tanggal 17 Juli 2024.

Daina, A., Michielin, O., & Zoete, V. (2014). SwissADME: a free web tool to evaluate pharmacokinetics, druglikeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 1-13. https://doi.org/10.1038/srep42717

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(January), 1–13. https://doi.org/10.1038/srep42717

Daina, A., Michielin, O., & Zoete, V. (2019). SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357–W3664. https://doi.org/10.1093/nar/gkz382

Daina, A., & Zoete, V. (2016). A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. Chem Med Chem, 1117–1121.

https://doi.org/10.1002/cmdc.201600182

Daina, A., & Zoete, V. (2024). Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning. Communications Chemistry, 7(1), 1–8. https://doi.org/10.1038/s42004-024-01179-2

Filimonov, D. A., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Druzhilovskii, D. S., Pogodin, P. V., & Poroikov, V. V. (2014). Prediction of the biological activity spectra of organic compounds using the pass online web resource. Chemistry of Heterocyclic Compounds, 50(3), 444–457. https://doi.org/10.1007/s10593-014-1496-1

Kementrian Kesehatan RI. (2024). https://bgsi.kemkes.go.id/id/diseases/detail?link_flag=%2Fdiseases%2Fcancer. Diakses tanggal 17 Juli 2024.

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. https://doi.org/10.1016/j.addr.2012.09.019

Putra, I. G. N. T. M., & Astuti, N. M. W. (2023). Kandungan Fitokimia, aktivitas antioksidan, dan toksisitas ciplukan (Physalis angulata L.). COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat, 3(06), 2168-2179.

PyRx. (2024). Introduction. https://pyrx.sourceforge.io/. Diakses tanggal 22 Juli 2024.

Rakasiwi, N. P. D., Setiawan, G. B., & Aryana, I. G. N. W. (2020). Karakteristik kanker payudara dengan metastasis tulang tahun 2015-2017 di RSUP Sanglah Denpasar. Jurnal Medika Udayana, 9(1).

https://doi.org.10.24843.MU.2020.V9.i1.P04

Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., Bork, P., Jensen, L. J., & Von Mering, C. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research, 51(1 D), D638–D646. https://doi.org/10.1093/nar/gkac1000

Tuli, H. S., Sood, S., Pundir, A., Choudhary, D., Dhama, K., Kaur, G., Seth, P., Vashishth, A., & Kumar, P. (2022). Molecular docking studies of apigenin, kaempferol, and quercetin as potential targets against spike receptor protein of SARS COV. Journal of Experimental Biology and Agricultural Sciences, 10(1), 144–149. https://doi.org/10.18006/2022.10(1).144.149

Wang, X., Shen, Y., Wang, S., Li, S., Zhang, W., Liu, X., Lai, L., Pei, J., & Li, H. (2017). Pharm Mapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Research, 45(W1), W356–W360. https://doi.org/10.1093/nar/gkx374

DOI: http://dx.doi.org/10.30998/edubiologia.v4i2.24683