Toxicity Prediction of Bioactive β-Sitosterol from Saw Palmetto (Serenoa repens)

Yuneka Saristiana; Muh. Deni Kurniawan; Fendy Prasetyawan; Ratna Mildawati; Lisa Savitri; Muhammad Nurul Fadel; Emma Jayanti  Besan

doi:10.30872/jtpc.v10i1.377

Authors

Yuneka Saristiana Universitas Kadiri Author
Muh. Deni Kurniawan Study Program of pharmacy, Faculty of Pharmacy, Universitas Mulawarman Author https://orcid.org/0000-0002-9888-8743
Fendy Prasetyawan Universitas Kadiri Author
Ratna Mildawati STIKes Ganesha Husada Kediri Author
Lisa Savitri Universitas Kadiri Author
Muhammad Nurul Fadel Universitas Muhammadiyah Kudus Author
Emma Jayanti Besan Universitas Muhammadiyah Kudus Author

DOI:

https://doi.org/10.30872/jtpc.v10i1.377

Keywords:

β-sitosterol, Serenoa, Toxicity, Prostate, InSilico

Abstract

β-sitosterol is one of the major bioactive compounds found in Serenoa repens (Saw Palmetto) that has been widely studied for its potential therapeutic effects, particularly in prostate disorders. This study aimed to evaluate the toxicity profile of β-sitosterol using an in silico approach through the ProTox-III platform. Toxicity predictions included organ toxicity and toxicity endpoints. The results showed that β-sitosterol was predicted to be inactive for hepatotoxicity (0.87), nephrotoxicity (0.89), and cardiotoxicity (0.85), indicating a low risk to major organs. Additionally, carcinogenicity (0.60), mutagenicity (0.98), cytotoxicity (0.94), and clinical toxicity (0.52) were also predicted to be inactive, suggesting a favorable safety profile. However, β-sitosterol demonstrated active predictions for neurotoxicity (0.54), respiratory toxicity (0.82), immunotoxicity (0.99), blood-brain barrier penetration (0.91), and nutritional toxicity (0.66), indicating potential risks that require further investigation. Overall, the findings suggest that β-sitosterol has a generally safe toxicity profile but may pose moderate risks in specific toxicity parameters. Further experimental validation through in vitro and in vivo studies is recommended to confirm these findings and support the safe therapeutic use of β-sitosterol.

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References

[1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

[2] Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades. J Nat Prod. 2020;83(3):770–803.

[3] Shoskes DA, Nickel JC, Dolinga R, Prots D. Clinical evaluation of Serenoa repens in prostate disorders. J Urol. 2021;205(4):987–95.

[4] Bin Sayeed MS, Ameen SS. Beta-sitosterol: A promising but orphan nutraceutical to fight against cancer. Nutr Cancer. 2020;72(8):1304–17.

[5] Baskar AA, Ignacimuthu S, Paulraj GM, Numair KS. Chemopreventive potential of beta-sitosterol in cancer. J Agric Food Chem. 2021;69(21):6102–11.

[6] Raies AB, Bajic VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci. 2016;6(2):147–72.

[7] Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: A webserver for prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257–63.

[8] Pires DE, Blundell TL, Ascher DB. pkCSM: Predicting small molecule pharmacokinetic properties. J Med Chem. 2015;58(9):4066–72.

[9] Chen M, Borlak J, Tong W. High lipophilicity and toxicity. Chem Res Toxicol. 2016;29(7):122–32.

[10] Daina A, Michielin O, Zoete V. SwissADME: Evaluation of pharmacokinetics and drug-likeness. Sci Rep. 2017;7(1):42717.

[11] Habib FK, Wyllie MG. Phytotherapy in prostate disease. Curr Opin Urol. 2018;28(3):256–61.

[12] Awad AB, Fink CS. Phytosterols as anticancer agents. Nutr Cancer. 2000;37(2):123–9.

[13] Ekor M. The growing use of herbal medicines: Issues relating to adverse reactions. Front Pharmacol. 2014;4:177.

[14] Todeschini R, Consonni V. Molecular descriptors for chemoinformatics. Wiley-VCH; 2009.

[15] Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, et al. QSAR modeling: where have you been? J Med Chem. 2014;57(12):4977–5010.

[16] Banerjee P, Preissner R. Computational toxicity prediction. Front Pharmacol. 2018;9:1–12.

[17] Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, et al. Exploiting machine learning. J Chem Inf Model. 2019;59(9):3732–46.

[18] Tropsha A. Best practices for QSAR model development. Mol Inform. 2010;29(6–7):476–88.

[19] Valerio LG. In silico toxicology models. Toxicol Appl Pharmacol. 2009;241(3):356–70.

[20] Rawla P. Epidemiology of prostate cancer. World J Oncol. 2019;10(2):63–89.

[21] Pernar CH, Ebot EM, Wilson KM, Mucci LA. The epidemiology of prostate cancer. Cold Spring Harb Perspect Med. 2018;8(12):a030361.

[22] Gupta SC, Hevia D, Patchva S, Park B, Koh W, Aggarwal BB. Upsides and downsides of natural antioxidants. Biochem Pharmacol. 2018;98(2):151–60.

[23] Mayr A, Klambauer G, Unterthiner T, Steijaert M, Wegner JK, Ceulemans H, et al. Deep learning in toxicity prediction. Chem Sci. 2016;7(2):785–95.

[24] Wu K, Wei GW. Quantitative toxicity prediction. J Chem Inf Model. 2018;58(2):520–31.

[25] Fourches D, Muratov E, Tropsha A. Trustworthy computational toxicology. Nat Chem Biol. 2015;11(8):535–7.

[26] Prasetyawan F, Salmasfattah N, Muklish FA, Saristiana Y. Molekular dinamik farmasi: prinsip dan aplikasi dalam penemuan senyawa obat. Borneo Novelty Publishing; 2024.