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A survey on adverse drug reaction studies: data, tasks and machine learning methods

Nguyen, Duc Anh Nguyen, Canh Hao Mamitsuka, Hiroshi 京都大学 DOI:10.1093/bib/bbz140

2021.01

概要

MOTIVATION: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. RESULTS: In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug-ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug-ADR prediction task. Finally, we discussed open problems for further ADR studies. AVAILABILITY: Data and code are available at https://github.com/anhnda/ADRPModels.

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参考文献

[1]World Health Organization: WHO (1972).

Definitions.

http:

//www.who.int/medicines/areas/quality_safety/

safety_efficacy/trainingcourses/definitions.pdf

(10 Sep 2019, date last accessed).

[2]Poudel, D. R., Acharya, P., Ghimire, S., et al. (2017). Burden of

hospitalizations related to adverse drug events in the usa: a retrospective

analysis from large inpatient database. Pharmacoepidemiology and drug

safety, 26(6), 635–641.

[3]Weiss, A., Elixhauser, A., Bae, J., et al. (2013). Origin of adverse drug

events in us hospitals, 2011. HCUP Statistical Brief , 158.

[4]Hoots, B. E., Xu, L., Kariisa, M., et al. (2018). 2018 annual surveillance

report of drug-related risks and outcomes–united states. CDC National

Center for Injury Prevention and Control.

[5]Rieder, M. J. (1994). Mechanisms of unpredictable adverse drug

reactions. Drug Safety, 11(3), 196–212.

[6]Mann, R. D. and Andrews, E. B. (2007). Pharmacovigilance. John

Wiley & Sons.

[7]Poloju, N. and Muniganti, P. (2018). Adverse drug reaction detection

using data mining approaches: A survey. Internatinal journal of recent

trends in engineering and research, 2018.

[8]Chen, Y. G., Wang, Y. Y., and Zhao, X. M. (2016). A survey on

computational approaches to predicting adverse drug reactions. Current

topics in medicinal chemistry, 16(30), 3629–3635.

[9]Ho, T. B., Le, L., Thai, D. T., et al. (2016). Data-driven approach

to detect and predict adverse drug reactions. Current pharmaceutical

design, 22(23), 3498–3526.

[10]Wang, X., Thijssen, B., and Yu, H. (2013). Target essentiality and

centrality characterize drug side effects. PLoS computational biology,

9(7), e1003119.

[11]Alberti, P. and Cavaletti, G. (2014). Management of side effects

in the personalized medicine era: chemotherapy-induced peripheral

neuropathy. In Pharmacogenomics in Drug Discovery and Development,

pages 301–322. Springer.

[12]Bao, Y., Kuang, Z., Peissig, P., et al. (2017). Hawkes process

modeling of adverse drug reactions with longitudinal observational data.

In Machine learning for healthcare conference, volume 2017, pages

177–190. Proceedings of Machine Learning Research.

[13]U.S. Food and Drug Administration (2019).

Questions and

answers on fda’s adverse event reporting system (faers).

https://www.fda.gov/drugs/surveillance/

fda-adverse-event-reporting-system-faers, 10 Sep

2019, date last accessed.

[14]Stang, P. E., Ryan, P. B., Racoosin, J. A., et al. (2010). Advancing the

science for active surveillance: rationale and design for the observational

medical outcomes partnership. Annals of internal medicine, 153(9),

600–606.

[15]Hripcsak, G., Duke, J. D., Shah, N. H., et al. (2015).

Observational health data sciences and informatics (ohdsi): opportunities

for observational researchers.

Studies in health technology and

informatics, 216, 574.

[16]Kuhn, M., Letunic, I., Jensen, L. J., et al. (2015). The sider database of

drugs and side effects. Nucleic acids research, 44(D1), D1075–D1079.

A Self-archived copy in

Kyoto University Research Information Repository

https://repository.kulib.kyoto-u.ac.jp

“main” — 2019/11/18 — 13:41 — page 11 — #11

A survey on ADR

[17]Liu, M., Wu, Y., Chen, Y., et al. (2012). Large-scale prediction

of adverse drug reactions using chemical, biological, and phenotypic

properties of drugs. Journal of the American Medical Informatics

Association, 19(e1), e28–e35.

[18]Banda, J. M., Evans, L., Vanguri, R. S., et al. (2016). A curated

and standardized adverse drug event resource to accelerate drug safety

research. Scientific data, 3, 160026.

[19]Tatonetti, N. P., Patrick, P. Y., Daneshjou, R., and Altman, R. B.

(2012). Data-driven prediction of drug effects and interactions. Science

translational medicine, 4(125), 125ra31–125ra31.

[20]Wishart, D. S., Knox, C., Guo, A. C., et al. (2007). Drugbank: a

knowledgebase for drugs, drug actions and drug targets. Nucleic acids

research, 36(suppl_1), D901–D906.

[21]Kim, S., Thiessen, P. A., Bolton, E. E., et al. (2015). Pubchem

substance and compound databases. Nucleic acids research, 44(D1),

D1202–D1213.

[22]Berman, H. M., Westbrook, J., Feng, Z., et al. (2000). The protein

data bank. Nucleic acids research, 28(1), 235–242.

[23]Liu, T., Lin, Y., Wen, X., et al. (2006). Bindingdb: a web-accessible

database of experimentally determined protein–ligand binding affinities.

Nucleic acids research, 35(suppl_1), D198–D201.

[24]Keshava Prasad, T., Goel, R., Kandasamy, K., et al. (2008). Human

protein reference database⣔2009 update. Nucleic acids research,

37(suppl_1), D767–D772.

[25]Davis, A. P., Murphy, C. G., Saraceni-Richards, C. A., et al. (2008).

Comparative toxicogenomics database: a knowledgebase and discovery

tool for chemical–gene–disease networks. Nucleic acids research,

37(suppl_1), D786–D792.

[26]Kanehisa, M. and Goto, S. (2000). Kegg: kyoto encyclopedia of genes

and genomes. Nucleic acids research, 28(1), 27–30.

[27]Günther, S., Kuhn, M., Dunkel, M., et al. (2007). Supertarget and

matador: resources for exploring drug-target relationships. Nucleic acids

research, 36(suppl_1), D919–D922.

[28]Cai, M.-C., Xu, Q., Pan, Y.-J., et al. (2014). Adrecs: an ontology

database for aiding standardization and hierarchical classification of

adverse drug reaction terms. Nucleic acids research, 43(D1), D907–

D913.

[29]Ji, Z. L., Han, L. Y., Yap, C. W., et al. (2003). Drug adverse reaction

target database (dart). Drug safety, 26(10), 685–690.

[30]Chen, X., Ji, Z. L., and Chen, Y. Z. (2002). Ttd: therapeutic target

database. Nucleic acids research, 30(1), 412–415.

[31]Belleau, F., Nolin, M.-A., Tourigny, N., et al. (2008). Bio2rdf:

towards a mashup to build bioinformatics knowledge systems. Journal

of biomedical informatics, 41(5), 706–716.

[32]Simpson, S. E., Madigan, D., Zorych, I., et al. (2013). Multiple

self-controlled case series for large-scale longitudinal observational

databases. Biometrics, 69(4), 893–902.

[33]Huynh, T., He, Y., Willis, A., et al. (2016). Adverse drug reaction

classification with deep neural networks. In Proceedings of COLING.

Coling, COLING.

[34]Lee, K., Qadir, A., Hasan, S. A., et al. (2017). Adverse drug event

detection in tweets with semi-supervised convolutional neural networks.

In Proceedings of the 26th International Conference on World Wide Web,

pages 705–714. International World Wide Web Conferences Steering

Committee, WWW.

[35]Emadzadeh, E., Sarker, A., Nikfarjam, A., and Gonzalez, G.

(2017). Hybrid semantic analysis for mapping adverse drug reaction

mentions in tweets to medical terminology. In AMIA Annual Symposium

Proceedings, volume 2017, page 679. American Medical Informatics

Association, PubMed Central.

[36]Ring, J. and Brockow, K. (2002). Adverse drug reactions: mechanisms

and assessment. European surgical research, 34(1-2), 170–175.

11

[37]Testa, B. and Kier, L. B. (1991). The concept of molecular structure

in structure–activity relationship studies and drug design. Medicinal

research reviews, 11(1), 35–48.

[38]Todeschini, R. and Consonni, V. (2008). Handbook of molecular

descriptors, volume 11. John Wiley & Sons.

[39]Grisoni, F., Ballabio, D., Todeschini, R., et al. (2018). Molecular

descriptors for structure–activity applications: A hands-on approach. In

Computational Toxicology, pages 3–53. Springer.

[40]Steinbeck, C., Han, Y., Kuhn, S., et al. (2003). The chemistry

development kit (cdk): An open-source java library for chemo-and

bioinformatics. Journal of chemical information and computer sciences,

43(2), 493–500.

[41]Daylight Chemica Information System, Inc (2018). Daylight theory:

Fingerprint.

http://www.daylight.com/dayhtml/doc/

theory/theory.finger.html (10 Sep 2019, date last accessed).

[42]Hall, L. H. and Kier, L. B. (1995). Electrotopological state indices for

atom types: a novel combination of electronic, topological, and valence

state information. Journal of Chemical Information and Computer

Sciences, 35(6), 1039–1045.

[43]Klekota, J. and Roth, F. P. (2008). Chemical substructures that enrich

for biological activity. Bioinformatics, 24(21), 2518–2525.

[44]Durant, J. L., Leland, B. A., Henry, D. R., and Nourse, J. G. (2002).

Reoptimization of mdl keys for use in drug discovery. Journal of chemical

information and computer sciences, 42(6), 1273–1280.

[45]Bolton, E. E., Kim, S., and Bryant, S. H. (2011). Pubchem3d:

conformer generation. Journal of cheminformatics, 3(1), 4.

[46]Openeye scientific software (2018). OMEGA. https://www.

eyesopen.com/omega (10 Nov 2018, date last accessed).

[47]Wermuth, C. G. (2011). The practice of medicinal chemistry.

Academic Press.

[48]Goodford, P. J. (1985). A computational procedure for determining

energetically favorable binding sites on biologically important

macromolecules. Journal of medicinal chemistry, 28(7), 849–857.

[49]Crivori, P., Cruciani, G., Carrupt, P. A., et al. (2000). Predicting bloodbrain barrier permeation from three-dimensional molecular structure.

Journal of medicinal chemistry, 43(11), 2204–2216.

[50]Kubinyi, H. (1998). Comparative molecular field analysis (comfa).

The encyclopedia of computational chemistry, 1, 448–460.

[51]Cruciani, G., Carosati, E., De Boeck, B., et al. (2005). Metasite:

understanding metabolism in human cytochromes from the perspective

of the chemist. Journal of medicinal chemistry, 48(22), 6970–6979.

[52]Young, R. C., Mitchell, R. C., Brown, T. H., et al. (1988).

Development of a new physicochemical model for brain penetration and

its application to the design of centrally acting h2 receptor histamine

antagonists. Journal of medicinal chemistry, 31(3), 656–671.

[53]Testa, B., Caron, G., Crivori, P., et al. (2000). Lipophilicity and related

molecular properties as determinants of pharmacokinetic behaviour.

CHIMIA International Journal for Chemistry, 54(11), 672–677.

[54]van de Waterbeemd, H. and Kansy, M. (1992). Hydrogen-bonding

capacity and brain penetration. CHIMIA International Journal for

Chemistry, 46(7-8), 299–303.

[55]Lipinski, C. A., Lombardo, F., Dominy, B. W., et al. (1997).

Experimental and computational approaches to estimate solubility and

permeability in drug discovery and development settings. Advanced drug

delivery reviews, 23(1-3), 3–25.

[56]WHO Collaborating Centre for Drug Statistics Methodology

(2019).

Guidelines for atc classification and ddd assignment.

https://www.whocc.no/filearchive/publications/

2019_guidelines_web.pdf, (10 Sep 2019, date last accessed).

[57]Yamanishi, Y., Pauwels, E., and Kotera, M. (2012). Drug side-effect

prediction based on the integration of chemical and biological spaces.

Journal of chemical information and modeling, 52(12), 3284–3292.

A Self-archived copy in

Kyoto University Research Information Repository

https://repository.kulib.kyoto-u.ac.jp

“main” — 2019/11/18 — 13:41 — page 12 — #12

12

[58]Montastruc, J.-L., Sommet, A., Bagheri, H., and Lapeyre-Mestre,

M. (2011). Benefits and strengths of the disproportionality analysis for

identification of adverse drug reactions in a pharmacovigilance database.

British journal of clinical pharmacology, 72(6), 905–908.

[59]Agresti, A. (1992). A survey of exact inference for contingency tables.

Statistical science, 7(1), 131–153.

[60]Kuhn, M., Campillos, M., Letunic, I., et al. (2010). A side effect

resource to capture phenotypic effects of drugs. Molecular systems

biology, 6(1), 343.

[61]Yang, F., Yu, X., and Karypis, G. (2014). Signaling adverse drug

reactions with novel feature-based similarity model. In Bioinformatics

and Biomedicine (BIBM), 2014 IEEE International Conference on, pages

593–596. IEEE, IEEE.

[62]Chen, A. W. (2018). Predicting adverse drug reaction outcomes with

machine learning. International Journal Of Community Medicine And

Public Health, 5(3), 901–904.

[63]Huba, G., Wingard, J. A., and Bentler, P. M. (1981). A comparison

of two latent variable causal models for adolescent drug use. Journal of

Personality and Social Psychology, 40(1), 180.

[64]Zhang, W., Liu, F., Luo, L., et al. (2015). Predicting drug side effects

by multi-label learning and ensemble learning. BMC bioinformatics,

16(1), 365.

[65]Muñoz, E., Nováˇcek, V., and Vandenbussche, P.-Y. (2017).

Facilitating prediction of adverse drug reactions by using knowledge

graphs and multi-label learning models. Briefings in bioinformatics,

20(1), 190–202.

[66]Cao, D., Xiao, N., Li, Y., et al. (2015). Integrating multiple

evidence sources to predict adverse drug reactions based on a systems

pharmacology model. CPT: pharmacometrics & systems pharmacology,

4(9), 498–506.

[67]Pauwels, E., Stoven, V., and Yamanishi, Y. (2011). Predicting

drug side-effect profiles: a chemical fragment-based approach. BMC

bioinformatics, 12(1), 169.

[68]Muñoz, E., Nováˇcek, V., and Vandenbussche, P.-Y. (2016). Using drug

similarities for discovery of possible adverse reactions. In AMIA Annual

Symposium Proceedings, volume 2016, page 924. American Medical

Informatics Association.

[69]Zhang, W., Chen, Y., Tu, S., et al. (2016). Drug side effect prediction

through linear neighborhoods and multiple data source integration. In

2016 IEEE international conference on bioinformatics and biomedicine

(BIBM), pages 427–434. IEEE.

[70]Jahid, M. J. and Ruan, J. (2013). An ensemble approach for drug

side effect prediction. In Bioinformatics and Biomedicine (BIBM), 2013

IEEE International Conference on, volume 2013, pages 440–445. IEEE,

IEEE.

[71]Cami, A., Arnold, A., Manzi, S., and Reis, B. (2011). Predicting

adverse drug events using pharmacological network models. Science

translational medicine, 3(114), 114ra127–114ra127.

[72]Lin, J., Kuang, Q., Li, Y., and et al. (2013). Prediction of adverse drug

reactions by a network based external link prediction method. Analytical

Nguyen et al.

Methods, 5(21), 6120–6127.

[73]Davazdahemami, B. and Delen, D. (2018). A chronological

pharmacovigilance network analytics approach for predicting adverse

drug events. Journal of the American Medical Informatics Association,

25(10), 1311–1321.

[74]Tong, H., Faloutsos, C., and Pan, J.-Y. (2006). Fast random walk with

restart and its applications. In Sixth International Conference on Data

Mining (ICDM’06), pages 613–622. IEEE, IEEE.

[75]Rahmani, H., Weiss, G., Méndez-Lucio, O., et al. (2016). Arwar: A

network approach for predicting adverse drug reactions. Computers in

biology and medicine, 68, 101–108.

[76]Poleksic, A. and Xie, L. (2018). Predicting serious rare adverse

reactions of novel chemicals. Bioinformatics, 1, 8.

[77]Zitnik, M., Agrawal, M., and Leskovec, J. (2018). Modeling

polypharmacy side effects with graph convolutional networks.

Bioinformatics, 34(13), i457–i466.

[78]Wang, C.-S., Lin, P.-J., Cheng, C.-L., et al. (2019). Detecting potential

adverse drug reactions using a deep neural network model. Journal of

medical Internet research, 21(2), e11016.

[79]Dey, S., Luo, H., Fokoue, A., et al. (2018). Predicting adverse

drug reactions through interpretable deep learning framework. BMC

bioinformatics, 19(21), 476.

[80]Dimitri, G. M. and Lió, P. (2017). Drugclust: a machine learning

approach for drugs side effects prediction. Computational biology and

chemistry, 68, 204–210.

[81]Xiao, C., Zhang, P., Chaowalitwongse, W. A., et al. (2017). Adverse

drug reaction prediction with symbolic latent dirichlet allocation.

In Proceedings of the Thirty-First AAAI Conference on Artificial

Intelligence. AAAI Press.

[82]Shaked, I., Oberhardt, M. A., Atias, N., et al. (2016). Metabolic

network prediction of drug side effects. Cell systems, 2(3), 209–213.

[83]Mahadevan, R. and Schilling, C. (2003). The effects of alternate

optimal solutions in constraint-based genome-scale metabolic models.

Metabolic engineering, 5(4), 264–276.

[84]Wallach, I., Jaitly, N., and Lilien, R. (2010). A structure-based

approach for mapping adverse drug reactions to the perturbation of

underlying biological pathways. PloS one, 5(8), e12063.

[85]Zheng, H., Wang, H., Xu, H., et al. (2014). Linking biochemical

pathways and networks to adverse drug reactions. IEEE transactions on

nanobioscience, 13(2), 131–137.

[86]Chen, X., Liu, X., Jia, X., et al. (2013). Network characteristic

analysis of adr-related proteins and identification of adr-adr associations.

Scientific reports, 3, 1744.

[87]Jiang, Y., Li, Y., Kuang, Q., et al. (2014). Predicting putative adverse

drug reaction related proteins based on network topological properties.

Analytical Methods, 6(8), 2692–2698.

[88]Wang, J., Li, Z.-x., Qiu, C.-x., et al. (2011). The relationship between

rational drug design and drug side effects. Briefings in bioinformatics,

13(3), 377–382.

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