ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM

Overview

Journal ACS Omega

Publisher American Chemical Society

Specialty Chemistry

Date 2020 Jul 14

PMID 32656429

Citations 48

Authors

Noemi Angeles Duran-Iturbide

Barbara I Diaz-Eufracio

Jose L Medina-Franco

Affiliations

Soon will be listed here.

Abstract

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

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References

Fatima S, Gupta P, Sharma S, Sharma A, Agarwal S . ADMET profiling of geographically diverse phytochemical using chemoinformatic tools. Future Med Chem. 2019; 12(1):69-87. DOI: 10.4155/fmc-2019-0206. View

Pires D, Blundell T, Ascher D . pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem. 2015; 58(9):4066-72. PMC: 4434528. DOI: 10.1021/acs.jmedchem.5b00104. View

Ntie-Kang F, Zofou D, Babiaka S, Meudom R, Scharfe M, Lifongo L . AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS One. 2013; 8(10):e78085. PMC: 3813505. DOI: 10.1371/journal.pone.0078085. View

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. PMC: 5335600. DOI: 10.1038/srep42717. View

Sanchez-Cruz N, Pilon-Jimenez B, Medina-Franco J . Functional group and diversity analysis of BIOFACQUIM: A Mexican natural product database. F1000Res. 2020; 8. PMC: 6993822. DOI: 10.12688/f1000research.21540.2. View

Lagorce D, Douguet D, Miteva M, Villoutreix B . Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Sci Rep. 2017; 7:46277. PMC: 5387685. DOI: 10.1038/srep46277. View

Daly A, Rettie A, Fowler D, Miners J . Pharmacogenomics of CYP2C9: Functional and Clinical Considerations. J Pers Med. 2017; 8(1). PMC: 5872075. DOI: 10.3390/jpm8010001. View

Wang Y, Xing J, Xu Y, Zhou N, Peng J, Xiong Z . In silico ADME/T modelling for rational drug design. Q Rev Biophys. 2015; 48(4):488-515. DOI: 10.1017/S0033583515000190. View

Raies A, Bajic V . In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci. 2016; 6(2):147-172. PMC: 4785608. DOI: 10.1002/wcms.1240. View

10.

Newman D, Cragg G . Natural Products as Sources of New Drugs from 1981 to 2014. J Nat Prod. 2016; 79(3):629-61. DOI: 10.1021/acs.jnatprod.5b01055. View

11.

Bergstrom C, Larsson P . Computational prediction of drug solubility in water-based systems: Qualitative and quantitative approaches used in the current drug discovery and development setting. Int J Pharm. 2018; 540(1-2):185-193. PMC: 5861307. DOI: 10.1016/j.ijpharm.2018.01.044. View

12.

Sorokina M, Steinbeck C . Review on natural products databases: where to find data in 2020. J Cheminform. 2021; 12(1):20. PMC: 7118820. DOI: 10.1186/s13321-020-00424-9. View

13.

Lahlou M . Screening of natural products for drug discovery. Expert Opin Drug Discov. 2013; 2(5):697-705. DOI: 10.1517/17460441.2.5.697. View

14.

Yu H, Zou B, Wang X, Li M . Investigation of miscellaneous hERG inhibition in large diverse compound collection using automated patch-clamp assay. Acta Pharmacol Sin. 2016; 37(1):111-23. PMC: 4722980. DOI: 10.1038/aps.2015.143. View

15.

Pilon-Jimenez B, Saldivar-Gonzalez F, Diaz-Eufracio B, Medina-Franco J . BIOFACQUIM: A Mexican Compound Database of Natural Products. Biomolecules. 2019; 9(1). PMC: 6358837. DOI: 10.3390/biom9010031. View

16.

Gadaleta D, Lombardo A, Toma C, Benfenati E . A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications. J Cheminform. 2018; 10(1):60. PMC: 6503381. DOI: 10.1186/s13321-018-0315-6. View

17.

He S, Ye T, Wang R, Zhang C, Zhang X, Sun G . An In Silico Model for Predicting Drug-Induced Hepatotoxicity. Int J Mol Sci. 2019; 20(8). PMC: 6515336. DOI: 10.3390/ijms20081897. View

18.

Thorn C, Aklillu E, Klein T, Altman R . PharmGKB summary: very important pharmacogene information for CYP1A2. Pharmacogenet Genomics. 2011; 22(1):73-7. PMC: 3346273. DOI: 10.1097/FPC.0b013e32834c6efd. View

19.

Zhang Z, Tang W . Drug metabolism in drug discovery and development. Acta Pharm Sin B. 2018; 8(5):721-732. PMC: 6146880. DOI: 10.1016/j.apsb.2018.04.003. View

20.

Bocci G, Carosati E, Vayer P, Arrault A, Lozano S, Cruciani G . ADME-Space: a new tool for medicinal chemists to explore ADME properties. Sci Rep. 2017; 7(1):6359. PMC: 5527008. DOI: 10.1038/s41598-017-06692-0. View