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Licensed Unlicensed Requires Authentication Published by De Gruyter December 18, 2019

ADMET properties of novel 5-O-benzoylpinostrobin derivatives

  • Mohammad Rizki Fadhil Pratama ORCID logo , Hadi Poerwono ORCID logo and Siswandono Siswodiharjo ORCID logo EMAIL logo



Prediction of the properties of absorption, distribution, metabolism, excretion, and toxicity (ADMET) from a compound is essential, especially for modified novel compounds. Previous research has successfully designed several modified compounds of 5-O-benzoyl derivatives from pinostrobin, a flavanone that has cytotoxic activity. This study aims to describe the properties of ADMET from the 5-O-benzoylpinostrobin derivative.


Prediction of the properties of ADMET was carried out using three web servers consisting of SwissADME, pkCSM, and ProTox-II. The observed parameters are divided into ADMET parameters.


In general, absorption parameters indicate that the 5-O-benzoylpinostrobin derivative has lower water solubility than the parent pinostrobin. Distribution parameters show mixed results for distribution through the blood-brain barrier. Metabolism parameters showed different results with generally inhibitory activity shown in CYP2C19, CYP2C9, and CYP3A4. The excretion parameters showed a higher total clearance than pinostrobin except in the trifluoromethyl derivative. The toxicity parameters showed both pinostrobin and the 5-O-benzoylpinostrobin derivatives, including the class IV toxicity category with the lowest LD50 value indicated by the nitro derivative of 1500, with the possible target of the androgen receptor and prostaglandin G/H synthase 1.


Overall, the 5-O-benzoylpinostrobin derivative has the predicted ADMET profile that is relatively similar to pinostrobin, with the most noticeable difference being shown in the absorption parameters where all 5-O-benzoylpinostrobin derivatives have lower water solubility than pinostrobin.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.


[1] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017;7:42717.10.1038/srep42717Search in Google Scholar PubMed PubMed Central

[2] Wang Y, Xing J, Xu Y, Zhou N, Peng J, Xiong Z, et al. In silico ADME/T modelling for rational drug design. Q Rev Biophys 2015;48:488–515.10.1017/S0033583515000190Search in Google Scholar PubMed

[3] Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 2015;58:4066–72.10.1021/acs.jmedchem.5b00104Search in Google Scholar PubMed PubMed Central

[4] Jaudan A, Sharma S, Malek SN, Dixit A. Induction of apoptosis by pinostrobin in human cervical cancer cells: possible mechanism of action. PLoS One 2018;13:e0191523.10.1371/journal.pone.0191523Search in Google Scholar PubMed PubMed Central

[5] Nurrachma MY, Fadliyah H, Meiyanto E. Fingerroot (Boesenbergia pandurata): a prospective anticancer therapy. Indones I Cancer Chemoprevent 2018;9:102–9.10.14499/indonesianjcanchemoprev9iss2pp102-109Search in Google Scholar

[6] Poerwono H, Sasaki S, Hattori Y, Higashiyama K. Efficient microwave-assisted prenylation of pinostrobin and biological evaluation of its derivatives as antitumor agents. Bioorg Med Chem Lett 2010;20:2086–9.10.1016/j.bmcl.2010.02.068Search in Google Scholar PubMed

[7] Guan L, Yang H, Cai Y, Sun L, Di P, Li W, et al. ADMET-score – a comprehensive scoring function for evaluation of chemical drug-likeness. MedChemComm 2019;10:148–57.10.1039/C8MD00472BSearch in Google Scholar

[8] O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: an open chemical toolbox. J Cheminform 2011;3:33.10.1186/1758-2946-3-33Search in Google Scholar PubMed PubMed Central

[9] Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 2018;46:257–63.10.1093/nar/gky318Search in Google Scholar PubMed PubMed Central

[10] Djoumbou-Feunang Y, Fiamoncini J, Gil de la Fuente A, Greiner R, Manach C, Wishart DS. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminform 2019;11:2.10.1186/s13321-018-0324-5Search in Google Scholar PubMed PubMed Central

[11] Maki T, Takeda K Benzoic Acid and Derivatives. In: Elvers B, Bellussi G, Bus J, Drauz K, Greim H, Hessel V, editor(s). Ullmann’s encyclopedia of industrial chemistry. Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA, 2000:329–42.Search in Google Scholar

[12] Beaumont K, Webster R, Gardner I, Dack K. Design of ester prodrugs to enhance oral absorption of poorly permeable compounds: challenges to the discovery scientist. Curr Drug Metab 2003;4:461–85.10.2174/1389200033489253Search in Google Scholar PubMed

[13] Savjani KT, Gajjar AK, Savjani JK. Drug solubility: importance and enhancement techniques. ISRN Pharm 2012;2012:195727.10.5402/2012/195727Search in Google Scholar PubMed PubMed Central

[14] Alavijeh MS, Christy M, Qaiser MZ, Palmer AM. Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery. NeuroRX 2005;2:554–71.10.1602/neurorx.2.4.554Search in Google Scholar PubMed PubMed Central

[15] Ekowati J, Diyah NW, Nofianti KA, Hamid IS, Siswandono S. Molecular docking of ferulic acid derivatives on P2Y12 receptor and their ADMET prediction. J Math Fundam Sci 2018;50:203–19.10.5614/ in Google Scholar

[16] Schyman P, Liu R, Desai V, Wallqvist A. vNN web server for ADMET predictions. Front Pharmacol 2017;8:889.10.3389/fphar.2017.00889Search in Google Scholar PubMed PubMed Central

[17] Daina A, Zoete V. A BOILED-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 2016;11:1117–21.10.1002/cmdc.201600182Search in Google Scholar PubMed PubMed Central

[18] Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther 2013;138:103–41.10.1016/j.pharmthera.2012.12.007Search in Google Scholar PubMed

[19] Lombardo F, Obach RS, Varma MV, Stringer R, Berellini G. Clearance mechanism assignment and total clearance prediction in human based upon in silico models. J Med Chem 2014;57:4397–405.10.1021/jm500436vSearch in Google Scholar PubMed

[20] Wahl J, Smiesko M. Endocrine disruption at the androgen receptor: employing molecular dynamics and docking for improved virtual screening and toxicity prediction. Int J Mol Sci 2018;19:1784.10.3390/ijms19061784Search in Google Scholar PubMed PubMed Central

[21] Liedtke AJ, Crews BC, Daniel CM, Blobaum AL, Kingsley PJ, Ghebreselasie K, et al. Cyclooxygenase-1-selective inhibitors based on the (E)-2′-des-methyl-sulindac sulfide scaffold. J Med Chem 2012;55:2287–300.10.1021/jm201528bSearch in Google Scholar PubMed PubMed Central

Received: 2019-09-05
Accepted: 2019-10-06
Published Online: 2019-12-18

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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