Use Of Machine Learning For Intelligence Detection For Pharmaceutical Drug-Drug Interactions
Main Article Content
Abstract
Artificial neural networks (ANNs) have been developed to predict the clinical significance of drug-drug interactions (DDIs) for a set of 35 pharmaceutical drugs using data compiled from the Web-based resources, Lexi- comp and Vidal, with inputs furnished by various drug pharmacokinetic (PK) and/or pharmacodynamic (PD) properties, and/or drug-enzyme interaction data. Success in prediction of DDI significance was found to vary according to the drug properties used as ANN input, and also varied with the DDI dataset used in training. The Lexicomp® dataset is found to give predictions marginally better than those obtained using the Vidal® dataset, with the best prediction of minor DDIs achieved using a multi-layer perceptron (MLP) model trained using enzyme variables alone (F1 82%) and the best prediction of major DDIs achieved using a MLP model trained on PK/PD properties alone (F1 54%). Given a more comprehensive and more consistent dataset of DDI data, we conclude that machine learning tools could be used to acquire new knowledge on DDIs, and could thus facilitate the regulatory agencies, and pharmacovigilance of newly licensed drugs.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
D. S. Wishart, C. Knox, A. C. Guo, S. Shrivastava, M.Hassanali, P. Stothard, Z. Chang, and J. Woolsey, “DrugBank: a comprehensive resource for in silico drug discovery and exploration.,” Nucleic Acids Res., vol. 34, no. Database issue, pp. D668–D672, 2006.
T. Liu, Y. Lin, X. Wen, R. N. Jorissen, and M. K. Gilson, “BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities.,” Nucleic Acids Res., vol. 35, no. Database issue, pp. D198–201, Jan. 2007.
A. Gaulton, L. J. Bellis, a P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, and J. P. Overington, “ChEMBL: a large-scale bioactivity database for drug discovery.,” Nucleic Acids Res., vol. 40, no. Database issue, pp. D1100–7, Jan. 2012.
M. Kuhn, M. Campillos, I. Letunic, L. J. Jensen, and P. Bork, “A side effect resource to capture phenotypic effects of drugs,” Mol. Syst. Biol., vol. 6, no. 343, pp. 1–6, 2010.
R. Xu and Q. Wang, “Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection,” BMC Bioinformatics, vol. 15, no. 1, p. 17, 2014.
T. I. Oprea, S. Kim Nielsen, O. Ursu, J. J. Yang, O. Taboureau,S. L. Mathias, I. Kouskoumvekaki, L. a. Sklar, and C. G. Bologa, “Associating drugs, targets and clinical outcomes into an integrated network affords a new platform for computer-aided drug repurposing,” Mol. Inform., vol. 30, no. 2–3, pp. 100–111, 2011.
P. Sanseau and J. Koehler, “Editorial: Computational methods for drug repurposing,” Brief. Bioinform., vol. 12, no. 4, pp. 301– 302, 2011.
M. Campillos, M. Kuhn, A. Gavin, L. J. Jensen, and P. Bork, “Drug Target Identification Using Side-Effect Similarity,” Science (80-. )., vol. 321, no. 5886, pp. 263–266, 2008.
Z. Li, M. Huang, W. Zhong, Z. Liu, Y. Xie, Z. Dai, and X. Zou, “Identification of drug–target interaction from interactome network with ‘guilt-by-association’ principle and topology features,” Bioinformatics, vol. 32, no. 7, pp. 1057–1064, Apr.2016.
WHO, “The Importance of Pharmacovigilance: Safety Monitoring of Medicinal Products,” 2002.
K. Baxter, Stockley’s Drug Interactions, 10th ed. London: Pharmaceutical Press, 2013.
M. Lea, S. E. Rognan, R. Koristovic, T. B. Wyller, and E. Molden, “Severity and Management of Drug–Drug Interactions in Acute Geriatric Patients,” Drugs Aging, vol. 30, no. 9, pp. 721–727, Sep. 2013.
T. O’Hare, A. S. Corbin, and B. J. Druker, “Targeted CML therapy: controlling drug resistance, seeking cure,” Curr. Opin. Genet. Dev., vol. 16, no. 1, pp. 92–99, Feb. 2006.
D. Tatro, Drug interaction facts 2010: The Authority on Drug Interactions. St. Louis, MO: Wolters Kluwer Health, 2010.
A. I. Vitry, “Comparative assessment of four drug interaction compendia,” Br. J. Clin. Pharmacol., vol. 63, no. 6, pp. 709– 714, Jun. 2007.
P. L. Smithburger, S. L. Kane-Gill, N. J. Benedict, B. A.Falcione, and A. L. Seybert, “Grading the Severity of Drug-Drug Interactions in the Intensive Care Unit: A Comparison Between Clinician Assessment and Proprietary Database Severity Rankings,” Ann. Pharmacother., vol. 44, no. 11, pp. 1718–1724,Nov. 2010.
F. Cheng and Z. Zhao, “Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic,therapeutic, chemical, and genomic properties,” J. Am. Med. Informatics Assoc., vol. 21, no. e2, pp. e278–e286, Oct. 2014.
D. Sridhar, S. Fakhraei, and L. Getoor, “A probabilistic approach for collective similarity-based drug–drug interaction prediction,” Bioinformatics, vol. 450, no. June, p. btw342, Jun. 2016.
S. Hunta, N. Aunsri, and T. Yooyativong, “Drug-Drug Interactions prediction from enzyme action crossing through machine learning approaches,” in 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2015, pp. 1–4.
S. Polak, J. Brandys, and a. Mendyk, “Neural System for in silico Drug-Drug Interaction Screening,” Int. Conf. Comput. Intell. Model. Control Autom. Int. Conf. Intell. Agents, Web Technol. Internet Commer., vol. 2, 2005.
M. Herrero-Zazo, I. Segura-Bedmar, J. Hastings, and P.Martínez, “DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug–Drug Interactions and Their Mechanisms,” J. Chem. Inf. Model., vol. 55, no. 8, pp. 1698–1707, 2015.
E. Sweetman and S. Martindale, “Martindale: the complete drug reference.,” 2016. .
“Clarke’s Analysis of Drugs and Poisons,” 2016.
R. S. Obach, F. Lombardo, and N. J. Waters, “Trend Analysis of a Database of Intravenous Pharmacokinetic,” Pharmacology, vol. 36, no. 7, pp. 1385–1405, 2008.
S. Zhou, S. Yung Chan, B. Cher Goh, E. Chan, W. Duan, M. Huang, and H. L. McLeod, “Mechanism-based inhibition of cytochrome P450 3A4 by therapeutic drugs.,” Clin. Pharmacokinet., vol. 44, no. 3, pp. 279–304, Jan. 2005.
S. Preissner, K. Kroll, M. Dunkel, C. Senger, G. Goldsobel, D. Kuzman, S. Guenther, R. Winnenburg, M. Schroeder, and R.Preissner, “SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP- drug interactions,” Nucleic Acids Res., vol. 38, no. Database, pp. D237–D243, Jan. 2010.
Lexicomp, “Lexi-InteractTM Online.
Vidal, “Interctions medicamenteuses.
C. M. Bishop, Neural networks for pattern recognition. Oxford : Clarendon Press, 1995.
P. Zhang, F. Wang, J. Hu, and R. Sorrentino, “Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects,” Sci. Rep., vol. 5, p. 12339, Jul. 2015.