The Prediction of Pharmacokinetic Properties of Compounds in Hemigraphis alternata (Burm.F.) T. Ander Leaves Using pkCSM

https://doi.org/10.22146/ijc.73117

Yeni Yeni(1*), Rizky Arcinthya Rachmania(2)

(1) Department of Pharmacy, Universitas Muhammadiyah Prof. DR. HAMKA, Jl. Delima II/IV, Jakarta 13460, Indonesia
(2) Department of Pharmacy, Universitas Muhammadiyah Prof. DR. HAMKA, Jl. Delima II/IV, Jakarta 13460, Indonesia
(*) Corresponding Author

Abstract


The inflammatory process aids in healing and maintains the body's balance. Untreated acute inflammation can cause organ disease, which can lead to a chronic inflammatory phenotype. Hemigraphis alternata is a plant that has anti-inflammatory activity. The compounds contained in H. alternata leaves have been predicted to have an affinity for receptors involved in the inflammatory process. A large number of drug candidates were withdrawn from preclinical trials due to their poor pharmacokinetic profiles. Drug compounds must cross the barriers that exist in the body to reach their biological targets so that they can generate a biological effect. The pharmacokinetic features of 22 components in H. alternata leaves were predicted in order to search for inflammatory medication candidates with suitable pharmacokinetic profiles. The pkCSM, a strategy for predicting and optimizing the pharmacokinetic properties of small molecules based on distance-based graph signatures was used in this work. The pkCSM employed 20 predictors separated into four groups: absorption, distribution, metabolism, and excretion. Based on the prediction findings, there are five substances with the best pharmacokinetic features, 8a-methyl-3,4,4a,5,6,7-hexahydro-2H-naphthalene-1,8-dione, (E)-3,7,11,15-tetramethylhexadec-2-en-1-ol, 2-methylenecholestan-3-ol, 5-(hydroxymethyl) furan-2-carbaldehyde and 2,3-dihydro-2,5-dimethyl-5H-1,4-dioxepin.


Keywords


Hemigraphis alternata; pharmacokinetic profiles; pkCSM

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References

[1] Chen, L., Deng, H., Cui, H., Fang, J., Zuo, Z., Deng, J., Li, Y., Wang, X., and Zhao, L., 2018, Inflammatory responses and inflammation-associated diseases in organs, Oncotarget, 9 (6), 7204–7218.

[2] Antonelli, M., and Kushner, I., 2017, It's time to redefine inflammation, FASEB J., 31 (5), 1787–1791.

[3] Serhan, C.N., Gupta, S.K., Perretti, M., Godson, C., Brennan, E., Li, Y., Soehnlein, O., Shimizu, T., Werz, O., Chiurchiù, V., Azzi, A., Dubourdeau, M., Gupta, S.S., Schopohl, P., Hoch, M., Gjorgevikj, D., Khan, F.M., Brauer, D., Tripathi, A., Cesnulevicius, K., Lescheid, D., Schultz, M., Särndahl, E., Repsilber, D., Kruse, R., Sala, A., Haeggström, J.Z., Levy, B.D., Filep, J.G., and Wolkenhauer, O., 2020, The atlas of inflammation resolution (AIR), Mol. Aspects Med., 74, 100894.

[4] Rahman, S.M.M., Atikullah, M., Islam, M.N., Mohaimenul, M., Ahammad, F., Islam, M.S., Saha, B., and Rahman, M.H., 2019, Anti-inflammatory, antinociceptive and antidiarrhoeal activities of methanol and ethyl acetate extract of Hemigraphis alternata leaves in mice, Clin. Phytosci., 5 (1), 16.

[5] Ming, W.K., 2019, Bioassay-guided purification and identification of chemical constituents from Hemigraphis alternata, Dissertation, Monash University, Malaysia.

[6] Yeni, Y., Rachmania, R.A., and Mochamad, D.Y.M., 2021, Affinity of compounds in Hemigraphis alternata (Burm.F.) T. Ander leaves to cyclooxygenase 1 (COX-1): In silico approach, Proceedings of the 4th International Conference on Sustainable Innovation 2020–Health Science and Nursing (ICoSIHSN 2020), Atlantis Press, 552–555.

[7] Yeni, Y., Rachmania, R., and Yanuar, M.D., 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.

[8] Pires, D.E.V., Blundell, T.L., and Ascher, D.B., 2015, pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures, J. Med. Chem., 58 (9), 4066–4072.

[9] Boobis, A., Gundert-Remy, U., Kremers, P., Macheras, P., and Pelkonen, O., 2002, In silico prediction of ADME and pharmacokinetics: Report of an expert meeting organised by COST B15, Eur. J. Pharm. Sci., 17 (4-5), 183–193.

[10] Brogi, S., Ramalho, T.C., Kuca, K., Medina-Franco, J.L., and Valko, M., 2020, Editorial: In silico methods for drug design and discovery, Front. Chem., 8, 612.

[11] Chandrasekaran, B., Abed, S.N., Al-Attraqchi, O., Kuche, K., and Tekade, R.K., 2018, “Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties” in Dosage Form Design Parameters: Advances in Pharmaceutical Product Development and Research, vol. II, Academic Press, Cambridge, United States, 731–755.

[12] Shaker, B., Ahmad, S., Lee, J., Jung, C., and Na, D., 2021, In silico methods and tools for drug discovery, Comput. Biol. Med., 137, 104851.

[13] 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., and Silva, F.P., 2020, In silico strategies to support fragment-to-lead optimization in drug discovery, Front. Chem., 8, 93.

[14] Mvondo, J.G.M., Matondo, A., Mawete, D.T., Bambi, S.M.N., Mbala, B.M., and Lohohola, P.O., 2021, In silico ADME/T properties of quinine derivatives using SwissADME and pkCSM Webservers, Int. J. Trop. Dis. Health, 42 (11), 1–12.

[15] Pires, D.E., Kaminskas, L.M., and Ascher, D.B., 2018, “Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand” in Computational Drug Discovery and Design, Eds. Gore, M., and Jagtap, U., pp. 271–284, Humana Press, New York, United States.

[16] Udrea, A.M., Gradisteanu Pircalabioru, G., Boboc, A.A., Mares, C., Dinache, A., Mernea, M., and Avram, S., 2021, Advanced bioinformatics tools in the pharmacokinetic profiles of natural and synthetic compounds with anti-diabetic activity, Biomolecules, 11 (11), 1692.

[17] Udrea, A.M., Puia, A., Shaposhnikov, S., and Avram, S.P., 2018, Computational approaches of new perspectives in the treatment of depression during pregnancy, Farmacia, 66 (4), 680–687.

[18] Domínguez-Villa, F.X., Durán-Iturbide, N.A., and Ávila-Zárraga, J.G., 2021, Synthesis, molecular docking, and in silico ADME/Tox profiling studies of new 1-aryl-5-(3-azidopropyl) indol-4-ones: Potential inhibitors of SARS CoV-2 main protease, Bioorg. Chem., 106, 104497.

[19] Mansour, M.A., AboulMagd, A.M., and Abdel-Rahman, H.M., 2020, Quinazoline-Schiff base conjugates: In silico study and ADMET predictions as multi-target inhibitors of coronavirus (SARS-CoV-2) proteins, RSC Adv., 10 (56), 34033–34045.

[20] Tripathy, D., Nayak, B.S., Mohanty, B., and Mishra, B., 2019, Solid dispersion: A technology for improving aqueous solubility of drug, J. Pharm. Adv. Res., 2 (7), 577–586.

[21] Henriques, J., Falé, P.L., Pacheco, R., Florêncio, M.H., and Serralheiro, M.L., 2018, Phenolic compounds from Actinidia deliciosa leaves: Caco-2 permeability, enzyme inhibitory activity and cell protein profile studies, J. King Saud Univ., Sci., 30 (4), 513–518.

[22] Awortwe, C., Fasinu, P.S., and Rosenkranz, B., 2014, Application of Caco-2 cell line in herb-drug interaction studies: Current approaches and challenges, J. Pharm. Pharm. Sci., 17 (1), 1–19.

[23] Pecoraro, B., Tutone, M., Hoffman, E., Hutter, V., Almerico, A.M., and Traynor, M., 2019, Predicting skin permeability by means of computational approaches: Reliability and caveats in pharmaceutical studies, J. Chem. Inf. Model., 59 (5), 1759–1771.

[24] Berezhkovskiy, L.M., 2007, The connection between the steady state (Vss) and terminal (Vβ) volumes of distribution in linear pharmacokinetics and the general proof that Vβ ≥ Vss, J. Pharm. Sci., 96 (6), 1638–1652.

[25] Guo, T., Wang, Y., Guo, Y., Wu, S., Chen, W., Liu, N., Wang, Y., and Geng, D., 2018, 1, 25-D3 protects from cerebral ischemia by maintaining BBB permeability via PPAR-γ activation, Front. Cell. Neurosci., 12, 480.

[26] Ju, F., Ran, Y., Zhu, L., Cheng, X., Gao, H., Xi, X., Yang, Z., and Zhang, S., 2018, Increased BBB permeability enhances activation of microglia and exacerbates loss of dendritic spines after transient global cerebral ischemia, Front. Cell. Neurosci., 12, 236.

[27] Bhosle, V.K., Altit, G., Autmizguine, J., and Chemtob, S., 2017, “Basic Pharmacologic Principles” in Fetal and Neonatal Physiology, 5th Ed., Eds. Polin, R.A., Abman, S.H., Rowitch, D.H., Benitz, W.E., and Fox, W.W., Elsevier, Philadelphia, United States, 187–201.



DOI: https://doi.org/10.22146/ijc.73117

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