Toxicity of Anti-Inflammatory Substances in Hemigraphis Alternata Leaves: In Silico Study Using ProTox-II

Authors

  • Yeni Yeni Department of Pharmacy, Faculty of Pharmacy and Science, Universitas Muhammadiyah Prof. DR. HAMKA, Jakarta, Indonesia Author https://orcid.org/0000-0001-9042-4824
  • Rizky Arcinthya Rachmania Department of Pharmacy, Faculty of Pharmacy and Science, Universitas Muhammadiyah Prof. DR. HAMKA, Jakarta, Indonesia Author

DOI:

https://doi.org/10.30872/jsk.v5i5.564

Keywords:

in silico, toxicity, Hemigraphis alternata, anti-inflammatory, ProTox-II

Abstract

Hemigraphis alternata is empirically used to treat wounds. Hemigraphis alternata leaves ethyl acetate extract can assist in resolving the inflammatory process by inhibiting enzymes that play a role in the inflammatory cycle. Twenty-two substances found in the leaves of Hemigraphis alternata were predicted to have an anti-inflammatory effect by inhibiting cyclooxygenase-1 (COX-1) or 5-lipoxygenase (5-LOX) as an enzyme target. In-silico toxicology was carried out to acquire new anti-inflammatory drugs with low toxicity from 22 compounds. ProTox-II was utilized to measure the level of toxicity of these drugs at many endpoints. In this study, five compounds have LD50 > 5000 mg/kg body weight, toxicity class 5-6, and inactive for cytotoxicity, carcinogenicity, hepatotoxicity, mutagenicity and immunotoxicity parameters. They are 2-methyleneoctanenitrile, nerolidol, 2,7-dioxa-tricyclo[4.4.0.0(3,8)]deca-4,9-diene, 9,9-dimethoxybicyclo[3.3.1]nonane-2,4-dione, and phytol.

References

Di, L., Kerns, E.H., 2016. Toxicity. Drug-Like Prop. Elsevier Inc.

Zhang, Y., 2018. Cell toxicity mechanism and biomarker. Clin. Transl. Med., 7(1), 1–6.

Yang, L., 2015. Safety of nanotechnology-enhanced orthopedic materials. Mater. Woodhead Publishing.

Chinedu, E., Arome, D., Ameh, F.S., 2013. A new method for determining acute toxicity in animal models. Toxicol. Int., 20(3), 224-26.

Wu, Y., Wang, G., 2018. Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. Int. J. Mol. Sci., 19(8), 2358.

Zhang, L., Zhang, H., Ai, H., Hu, H., Li, S., Zhao, J., Liu, H., 2018. Applications of machine learning methods in drug toxicity prediction. Curr Top Med Chem, 18(12).

Yeni, Supandi, Merdekawati, F., 2018. In silico toxicity prediction of 1-phenyl-1- (quinazolin-4-yl) ethanol compounds by using Toxtree , pkCSM and preADMET. Pharmaciana, 8(2), 205-16.

Supandi, Yeni, Merdekawati, F., 2018. In silico study of pyrazolylaminoquinazoline toxicity by Lazar ,Protox, and Admet Predictor. J. Appl. Pharm. Sci., 8(09), 119-29.

Boobis, A., Gundert-Remy, U., Kremers, P., Macheras, P., Pelkonen, O., 2002. In silico prediction of ADME and pharmacokinetics: Report of an expert meeting organised by COST B15. Eur. J. Pharm. Sci. 17, 183-93.

Banerjee, P., Eckert, A.O., Schrey, A.K., Preissner, R., 2018. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res., 46(W1), W257-63.

Goswami, M.R., 2019. An easy screening throughin silico study for predictive toxicity mechanisms of different phthalate compounds by using online tool (Protox-II webserver). J. Adv. Sci. Res., 10(4), 246-53.

Biswas, S., Talapatra, S.N., 2019. Microbial volatile organic compounds as indoor air pollutants: prediction of acute oral toxicity, hepatotoxicity, immunotoxicity, genetic toxicity endpoints, nuclear receptor signalling and stress response pathways by using Protox-II webserver. J. Adv. Sci. Res., 10(3), 186-95.

Ghosh, S., Tripathi, P., Talukdar, P., Talapatra, S.N., 2019. In silico study by using ProTox-II webserver for oral acute toxicity, organ toxicity, immunotoxicity, genetic toxicity endpoints, nuclear receptor signalling and stress response pathways of synthetic pyrethroids. World Sci. News, 132, 35-51.

Knoops, B., Argyropoulou, V., Becker, S., Ferté, L., Kuznetsova, O., 2016. Multiple roles of peroxiredoxins in inflammation. Mol. Cells., 39(1), 60-4.

Leonard, B.E., 2018. Inflammation and depression: A causal or coincidental link to the pathophysiology?. Acta Neuropsychiatr., 30(1), 1-6.

Loi, F., Córdova, L.A., Pajarinen, J., Lin, T.H., Yao, Z., Goodman, S.B., 2016. Inflammation, fracture and bone repair. Bone, 86, 119-30.

Praja, M.H., Oktarlina, R.Z., 2017. Uji Efektivitas daun petai cina (Laucaena glauca) sebagai antiinflamasi dalam pengobatan luka bengkak. Jurnal Majority, 6(1), 60-3.

Efron, N., 2017. Contact lens wear is intrinsically inflammatory. Clin. Exp. Optom., 100(1), 3-19.

Grosser, T., Theken, K.N., FitzGerald, G.A., 2017. Cyclooxygenase inhibition: Pain, inflammation, and the cardiovascular system. Clin. Pharmacol. Ther., 102(4), 611-22.

Ming, W.K., 2019. Bioassay-guided purification and identification of chemical constituents from Hemigraphis alternata (Doctoral dissertation, Dissertation).

Adangampurath, S., Sudhakaran, S., 2018. Anti-inflammatory potential of flavonoids from Hemigraphis colorata. Int. J. Life Sci., 6(2), 569-74.

Arun, K.K., Nimmanapalli, P.R., Chaitanya, R.K., Roy, K., 2013. Ethyl acetate extract of Hemigraphis colorata leaves shows anti-inflammatory and wound healing properties and inhibits 5-lipoxygenase and cyclooxygenase-1 and 2 enzymes. J. Med. Plants Res., 7(37), 2783-91.

Yeni, Y., Rachmania, R.A., Yanuar, D.Y.M.R., 2021. In silico study of compounds contained in Hemigraphis alternata leaves against 5-LOX for anti-inflammatory. Indones. J. Pharm. Sci. Technol., 8(1), 34-41.

Yeni, Y., Rachmania, R.A., Mochamad, D.Y.M.R., 2021. Affinity of compounds in Hemigraphis alternata (Burm. F.) T. Ander leaves to cyclooxygenase 1 (COX-1): In silico approach . Proc. 4th Int. Conf. Sustain. Innov. 2020–Health Sci. Nurs. (ICoSIHSN 2020), 33, 552-55.

Banerjee, P., Dehnbostel, F.O., Preissner, R., 2018. Prediction is a balancing act: Importance of sampling methods to balance sensitivity and specificity of predictive models based on imbalanced chemical data sets. Front. Chem., 6, 362.

Drwal, M.N., Banerjee, P., Dunkel, M., Wettig, M.R., Preissner, R., 2014. ProTox: A web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res., 42(W1), W53-8.

Idakwo, G., Luttrell, J., Chen, M., Hong, H., Zhou, Z., Gong, P., Zhang, C., 2018. A review on machine learning methods for in silico toxicity prediction. J. Environ. Sci. Heal., Part C, 36(4), 169-91.

Team ProTox-II. ProTox-II. Available at: https://tox-new.charite.de/protox_II/ index.php?site=faq#Toxicological_endpoints. Accessed March 6, 2023.

Chmiel, J.A., Daisley, B.A., Pitek, A.P., Thompson, G.J., Reid, G., 2020. Understanding the effects of sublethal pesticide exposure on honey bees: A role for probiotics as mediators of environmental stress. Front. Ecol. Evol., 8, 22.

Akhila, J.S., Shyamjith, D., Alwar, M.C., 2007. Acute toxicity studies and determination of median lethal dose. Curr. Sci., 93(7), 917-20.

Kumar, N., Tomar, R., Pandey, A., Tomar, V., Singh, V.K., Chandra, R., 2018. Preclinical evaluation and molecular docking of 1,3-benzodioxole propargyl ether derivatives as novel inhibitor for combating the histone deacetylase enzyme in cancer. Artif. Cells., Nanomedicine, Biotechnol., 46(6), 1288-99.

Gadaleta, D., Vukovi?, K., Toma, C., Lavado, G.J., Karmaus, A.L., Mansouri, K., Kleinstreuer, N.C., Benfenati, E., Roncaglioni, A., 2019. SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. J. Cheminform., 11(1), 1–16.

Morris-Schaffer, K., McCoy, M.J., 2021. A Review of the LD50 and its current role in hazard communication. ACS Chem. Heal. Saf., 28(1), 25-33.

Strickland, J., Paris, M.W., Allen, D., Casey, W., 2019. Approaches to reducing animal use for acute toxicity testing: Retrospective analyses of pesticide data. Altern. to Anim. Test., 37-9.

Hodgson, E., Levi, P.E., 2004. Hepatotoxicity. A Textb. Mod. Toxicol. John Wiley & Sons, Ltd.

Roberts, S.M., James, R.C., Franklin, M.R., 2000. Hepatotoxicity: Toxic effects on the liver. Princ. Toxicol. John Wiley & Sons, Ltd.

Çelik, T.A., 2018. Introductory chapter: Cytotoxicity. IntechOpen.

Hsu, K.H., Su, B.H., Tu, Y.S., Lin, O.A., Tseng, Y.J., 2016. Mutagenicity in a molecule: Identification of core structural features of mutagenicity using a scaffold analysis. PLoS One, 11(2), e0148900.

Honma, M., 2016. Thresholds of toxicological concern for genotoxic impurities in pharmaceuticals. Threshold. Genotoxic Carcinog From Mech to Regul. Academic Press.

Kilcoyne, A., O’Connor, D., Ambery, P., 2013. Carcinogenicity. Pharmaceut Med Oxford. Oxford University Press.

Dieter, S., 2018. What is the meaning of ‘A compound is carcinogenic’?. Toxicol. Reports, 5, 504-11.

Gulati, K., Ray, A., 2009. Immunotoxicity. Handb. Toxicol. Chem. Warf. Agents. Academic Press.

Abdollahi, M., Behboudi, A.F., 2014. Nitroglycerin Encycl. Toxicol. Third Ed. Academic Press.

Brown, J.G., 2013. Impact of product attributes on preclinical safety evaluation. A Compr. Guid. to Toxicol. Preclin. Drug Dev. Academic Press.

Volger, O.L., 2014. ’Omics-based testing for direct immunotoxicity. Toxicogenomics-Based Cell Model. Academic Press.

Downloads

Published

2024-11-15

Issue

Vol. 5 No. 5 (2023): J. Sains Kes.

Section

Articles

How to Cite

[1]
Y. Yeni and R. A. . Rachmania, “Toxicity of Anti-Inflammatory Substances in Hemigraphis Alternata Leaves: In Silico Study Using ProTox-II”, J. Sains. Kes, vol. 5, no. 5, pp. 810–815, Nov. 2024, doi: 10.30872/jsk.v5i5.564.

Similar Articles

141-150 of 246

You may also start an advanced similarity search for this article.