Abstract
Natural compounds are promising leads in drug discovery due to their low toxicity and synergistic effects existing in nature, providing efficient and low-cost therapeutic solutions. Synergistic effects are observed in highly similar or closely related compounds where the combined effect is much more significant than individual usage. However, multiple hurdles exist in the identification of similar compounds, in particular, accumulation of large volumes of compounds, procurement of authentic information, diversity and complexity of the compounds, convoluted mechanism of action, need of high-throughput screening and validation techniques, most importantly incompleteness of critical information like indications for the natural compounds. Currently, not many comprehensive computational pipelines are available for drug discovery using natural products. To overcome these challenges, in this study, we focus on predicting highly similar candidate compounds with synergistic effects useful in combinatorial/alternative therapies. We developed a molecular compound similarity prediction model for computing four different compound-compound similarity scores based on (i) bioactivity, (ii) chemical structure, (iii) target enzyme, and (iv) protein functional domain, using the data from public repositories. The calculated scores are combined efficiently for predicting highly similar compound pairs with similar biological or physicochemical properties. We evaluate the accuracy of our model with pharmacological and bioassay results, and manually curated literature from PubChem, NCBI, etc. As a use case, we selected 415 compounds based on 13 functional categories, out of which 66 natural compounds with 198 compound-compound similarity scores were identified as top candidates based on similar bioactivities, chemical substructures, targets, and protein functional sites. Statistical analysis of the scores revealed a significant difference in the mean similarity scores for all four categories. Twenty-eight closely interacting compounds, including Quercetin, Apigenin, etc. were identified as candidates for combinational therapies showing synergistic effects. Herbs, including Dill, Basil, Garlic, Mint, etc., were predicted as potential combinations for achieving synergistic effects. Twenty-four compounds with unknown pharmacological effects were associated with 58 potential new pharmacological effects/indications. If applied broadly, this model can address many problems in chemogenomics and help in identifying novel drug targets and indications, which is a critical step in natural drug discovery research and evidence for drug-repurposing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
National Center for Biotechnology Information. PubChem Database. Rutin, CIDÂ =Â 5280805, https://pubchem.ncbi.nlm.nih.gov/compound/Rutin (accessed on Feb. 4, 2020).
References
Pezzani, R., et al.: Synergistic effects of plant derivatives and conventional chemotherapeutic agents: an update on the cancer perspective. Medicina 55(4), 110 (2019)
Bender, A., Jenkins, J.L., Scheiber, J., Sukuru, S.C.K., Glick, M., Davies, J.W.: How similar are similarity searching methods? a principal component analysis of molecular descriptor space. J. Chem. Inf. Model. 49(1), 108–119 (2009)
Gilissen, C., Hoischen, A., Brunner, H.G., Veltman, J.A.: Disease gene identification strategies for exome sequencing. Eur. J. Hum. Genet. 20(5), 490–497 (2012)
Jachak, S.M., Saklani, A.: Challenges and opportunities in drug discovery from plants. Curr. Sci. 92(9), 1251–1257 (2007)
Medina-Franco, J.L., Giulianotti, M.A., Welmaker, G.S., Houghten, R.A.: Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov. Today 18(9–10), 495–501 (2013)
Keri, R.S., Quintanova, C., Chaves, S., Silva, D.F., Cardoso, S.M., Santos, M.A.: New tacrine hybrids with natural-based cysteine derivatives as multitargeted drugs for potential treatment of Alzheimer’s disease. Chem. Biolo. Drug Des. 87(1), 101–111 (2016)
Öztürk, H., Ozkirimli, E., Özgür, A.: A comparative study of smiles-based compound similarity functions for drug-target interaction prediction. BMC Bioinform. 17(1), 128 (2016)
Yamanishi, Y., Pauwels, E., Saigo, H., Stoven, V.: Identification of chemogenomic features from drug-target interaction networks by sparse canonical correspondence analysis. Mach. Learn. Syst. Biol. 28(18), 87 (2011)
Kanehisa, M.: KEGG bioinformatics resource for plant genomics and metabolomics. In: Edwards, D. (ed.) Plant Bioinformatics. MMB, vol. 1374, pp. 55–70. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3167-5_3
Marchler-Bauer, A., et al.: CDD: NCBI’s conserved domain database. Nucleic Acids Res. 43(D1), D222–D226 (2015)
Heymans, M., Singh, A.K.: Deriving phylogenetic trees from the similarity analysis of metabolic pathways. Bioinformatics 19(suppl-1), i138–i146 (2003)
Liu, Y., Zhao, H.: Predicting synergistic effects between compounds through their structural similarity and effects on transcriptomes. Bioinformatics 32(24), 3782–3789 (2016)
Ackland, M.L., Van De Waarsenburg, S., Jones, R.: Synergistic antiproliferative action of the flavonols quercetin and kaempferol in cultured human cancer cell lines. Vivo 19(1), 69–76 (2005)
Tang, Q., Ji, F., Wang, J., Guo, L., Li, Y., Bao, Y.: Quercetin exerts synergetic anti-cancer activity with 10-hydroxy camptothecin. Eur. J. Pharm. Sci. 109, 223–232 (2017)
Che, C.-T., Wang, Z.J., Chow, M.S.S., Lam, C.W.K.: Herb-herb combination for therapeutic enhancement and advancement: theory, practice and future perspectives. Molecules 18(5), 5125–5141 (2013)
HemaIswarya, S., Doble, M.: Potential synergism of natural products in the treatment of cancer. Phytotherapy Res. Int. J. Devoted Pharmacol. Toxicol. Eval. Natural Product Deriv. 20(4), 239–249 (2006)
Corsale, I., et al.: Flavonoid mixture (diosmin, troxerutin, rutin, hesperidin, quercetin) in the treatment of I–III degree hemorroidal disease: a double-blind multicenter prospective comparative study. Int. J. Colorectal Dis. 33(11), 1595–1600 (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chandrababu, S., Bastola, D. (2020). A Novel Prediction Model for Discovering Beneficial Effects of Natural Compounds in Drug Repurposing. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_72
Download citation
DOI: https://doi.org/10.1007/978-3-030-45385-5_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45384-8
Online ISBN: 978-3-030-45385-5
eBook Packages: Computer ScienceComputer Science (R0)