Data mining for potential adverse drug–drug interactions
Abstract
INTRODUCTION: Patients, in particular elderly ones, frequently receive more than one drug at a time. With each drug added to a regime, the number of potential drug-drug interactions (DDIs) increases by a power law. Early prediction of relevant interactions by computerized tools greatly aids clinicians and can guide their prescribing choices. AREAS COVERED: In this article, we discuss different types of DDIs, on which levels they can arise and what efforts have been made in the past to detect and predict them. The emphasis is on data mining technology and network analysis, but overlaps with traditional pharmacovigilance are also discussed. Finally, we discuss strategies to focus and simplify mining efforts to get meaningful results with less effort. EXPERT OPINION: The necessary technology for detecting adverse DDIs exists and is quite refined, although it is more often implied in lower risk scenarios (such as syntactic analysis in web searches and online libraries). Data mining for DDIs, on the other hand, still requires a great deal of human intervention, not only to validate the results but also, more importantly, to separate the relevant from the spurious. The fields of network analysis and graph theory show great promise but have not yet shown much beyond descriptive analyses.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Awareness of drug interaction effects is a difficult and growing issue, particularly in geriatrics.
“Awareness of drug interaction effects is a difficult and growing issue181–184, particularly in geriatrics, which gave rise to Beer’s criteria185.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [184]
Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167 · see citing works
Shimada, T., Yamazaki, H., Mimura, M., Inui, Y., & Guengerich, F. P. (1994) · The Journal of Pharmacology and Experimental Therapeutics · doi:10.1016/s0022-3565(25)22379-5 · Cited by 2,785 · cited 1× · see citing works
“It has been estimated that 75 -- 90% of oxidative metabolism is performed by only a handful of isoforms (CYP 3A4, 2D6, 1A2, 2C9, 2C19 and 2E1) [4,5], and these are important to assess in drug safety.”
BackgroundADMET in silico modelling: towards prediction paradise?
Waterbeemd, H. V. D., & Gifford, E. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1032 · Cited by 1,894 · see citing works
Cytochrome P450 and Chemical Toxicology
Guengerich, F. P. (2007) · Chemical Research in Toxicology · doi:10.1021/tx700079z · Cited by 1,726 · see citing works
Drugs Behave as Substrates, Inhibitors and Inducers of Human Cytochrome P450 3A4
Zhou, S. (2008) · Current Drug Metabolism · doi:10.2174/138920008784220664 · Cited by 721 · see citing works
The effects of age and liver disease on the disposition and elimination of diazepam in adult man.
Klotz, U. E., Avant, G. R., Hoyumpa, A. M., Schenker, S., & Wilkinson, G. R. (1975) · Journal of Clinical Investigation · doi:10.1172/jci107938 · Cited by 693 · see citing works
A statistical methodology for drug–drug interaction surveillance
Norén, G. N., Sundberg, R., Bate, A., & Edwards, I. R. (2008) · Statistics in Medicine · doi:10.1002/sim.3247 · Cited by 202 · see citing works
Tatonetti, N. P., Denny, J. C., Murphy, S. N., Fernald, G. H., Krishnan, G., Castro, V., Yue, P., Tsau, P. S., Kohane, I. S., Roden, D. M., et al. (2011) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2011.83 · Cited by 202 · see citing works
Web-scale pharmacovigilance: listening to signals from the crowd
White, R. W., Tatonetti, N. P., Shah, N. H., Altman, R. B., & Horvitz, E. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001482 · Cited by 200 · see citing works
Predicting and Preventing Adverse Drug Reactions in the Very Old
Merle, L., Laroche, M., DANTOINE, T. F., & Charmes, J. (2005) · Drugs & Aging · doi:10.2165/00002512-200522050-00003 · Cited by 147 · see citing works
Text-mining approaches in molecular biology and biomedicine
Krallinger, M., Erhardt, R. A.-A., & Valencia, A. (2005) · Drug Discovery Today · doi:10.1016/s1359-6446(05)03376-3 · Cited by 147 · see citing works
Clinically important drug-disease interactions and their prevalence in older adults
Lindblad, C. I., Hanlon, J. T., Gross, C. R., Sloane, R. J., Pieper, C. F., Hajjar, E. R., Ruby, C. M., Schmader, K. E., & Panel, M. C. (2006) · Clinical Therapeutics · doi:10.1016/j.clinthera.2006.08.006 · Cited by 136 · see citing works
Duke, J. D., Han, X., Wang, Z. P., Subhadarshini, A., Karnik, S., Li, X., Hall, S. D., Jin, Y., Callaghan, J. T., Overhage, J. M., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002614 · Cited by 128 · see citing works
Identifying Adverse Drug Reactions Associated with Drug-Drug Interactions
Leone, R., Magro, L., Moretti, U., Cutroneo, P. M., Moschini, M., Motola, D., Tuccori, M., & Conforti, A. (2010) · Drug Safety · doi:10.2165/11534400-000000000-00000 · Cited by 96 · see citing works
Sedykh, A., Fourches, D., Duan, J., Hucke, O., Garneau, M., Zhu, H., Bonneau, P. R., & Tropsha, A. (2012) · Pharmaceutical Research · doi:10.1007/s11095-012-0935-x · Cited by 95 · see citing works
A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents
Segura-Bédmar, I., Martı́nez, P., & de Pablo-Sánchez, C. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s2-s1 · Cited by 73 · see citing works
Identification of Adverse Drug Reactions in Geriatric Inpatients Using a Computerised Drug Database
Egger, T., Dormann, H., Ahne, G., Runge, U., Neubert, A. C., Criegee‐Rieck, M., Gaßmann, K., & Brune, K. (2003) · Drugs & Aging · doi:10.2165/00002512-200320100-00005 · Cited by 71 · see citing works
Zakrzewski-Jakubiak, H., Doan, J., Lamoureux, P., Singh, D. K., Turgeon, J., & Tannenbaum, C. (2011) · The American Journal of Geriatric Pharmacotherapy · doi:10.1016/j.amjopharm.2011.09.006 · Cited by 60 · see citing works
Network-Based Analysis and Characterization of Adverse Drug–Drug Interactions
Takarabe, M., Shigemizu, D., Kotera, M., Goto, S., & Kanehisa, M. I. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200367w · Cited by 59 · see citing works
Harpaz, R., Haerian, K., Chase, H. S., & Friedman, C. (2010) · PubMed Central · Cited by 52 · see citing works
(1982) · Annals of Internal Medicine · doi:10.7326/0003-4819-97-1-164_1 · Cited by 47 · see citing works
Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases.
Duda, S. N., Aliferis, C., Miller, R. A., Statnikov, A., & Johnson, K. (2005) · PubMed Central · Cited by 38 · see citing works
Qian, Y., Ye, X., Du, W., Ren, J., Sun, Y., Wang, H., Luo, B., Gao, Q.-B., Wu, M., & He, J. (2009) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.2009.03557.x · Cited by 37 · see citing works
A hybrid method for relation extraction from biomedical literature
Huang, M., Zhu, X., & LI, M. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2005.06.010 · Cited by 31 · see citing works
The Development and Evaluation of Triage Algorithms for Early Discovery of Adverse Drug Interactions
Strandell, J., Caster, O., Hopstadius, J., Edwards, I. R., & Norén, G. N. (2013) · Drug Safety · doi:10.1007/s40264-013-0053-7 · Cited by 31 · see citing works
Pearson, R. (1982) · BMJ · doi:10.1136/bmj.285.6344.757 · Cited by 28 · see citing works
A Single Kernel-Based Approach to Extract Drug-Drug Interactions from Biomedical Literature
Zhang, Y., Lin, H., Yang, Z., Wang, J., & Li, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048901 · Cited by 28 · see citing works
Fiß, T., Ritter, C. A., Alte, D., van den Berg, N., & Hoffmann, W. U. (2010) · Pharmacy World & Science · doi:10.1007/s11096-010-9409-6 · Cited by 25 · see citing works
Yamashita, F., Feng, C., Yoshida, S., Itoh, T., & Hashida, M. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci100334z · Cited by 20 · see citing works
Architecture of the drug-drug interaction network
Hu, T., & Hayton, W. L. (2009) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1111/j.1365-2710.2009.01103.x · Cited by 14 · see citing works
Wiegrebe, W. (1992) · Archiv der Pharmazie · doi:10.1002/ardp.19923250214 · Cited by 14 · see citing works
The accuracy of information on current drug therapy in geriatric out-patients' records
Hasan, M., & WOODHOUSE, K. W. (1996) · Archives of Gerontology and Geriatrics · doi:10.1016/0167-4943(95)00673-7 · Cited by 3 · see citing works
Choi, C. A., Chang, M. J., Choi, H. D., Chung, W., & Shin, W. G. (2013) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2013.08.009 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
“Awareness of drug interaction effects is a dif fi cult and growing issue 181 – 184 , particularly in geriatrics, which gave rise to Beer ’ s criteria 185 .”
BackgroundVilar, S., Friedman, C., & Hripcsak, G. M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx010 · Cited by 155 · see citing works
Deep learning for drug–drug interaction extraction from the literature: a review
Zhang, T., Leng, J., & Liu, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz087 · Cited by 121 · see citing works
Drug-Drug Interaction Associated with Mold-Active Triazoles among Hospitalized Patients
Andes, D. R., Azie, N. E., Yang, H., Harrington, R. L., Kelley, C., Tan, R., Wu, E. Q., Franks, B., Kristy, R. M., Lee, E. W., et al. (2016) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00054-16 · Cited by 56 · see citing works
Ai, N., Fan, X., & Ekins, S. (2015) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2015.03.006 · Cited by 47 · see citing works
Identifying side effects of commonly used drugs in the treatment of Covid 19
Aygün, İ., Kaya, M., & Alhajj, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-78697-1 · Cited by 47 · see citing works
Stojanovich, L., Kontić, M., Smiljanic, D., Djoković, A., Stamenkovic, B. N., & Marisavljević, D. (2013) · PubMed · Cited by 22 · see citing works
Integration of flow studies for robust selection of mechanoresponsive genes
Maimari, N., Pedrigi, R. M., Russo, A., Broda, K. B., & Krams, R. (2016) · Thrombosis and Haemostasis · doi:10.1160/th15-09-0704 · Cited by 15 · see citing works
Mixture drug‐count response model for the high‐dimensional drug combinatory effect on myopathy
Wang, X., Zhang, P., Chiang, C., Wu, H., Shen, L., Ning, X., Zeng, D., Wang, L., Quinney, S. K., Feng, W., et al. (2017) · Statistics in Medicine · doi:10.1002/sim.7545 · Cited by 15 · see citing works
Deep Learning for High-Order Drug-Drug Interaction Prediction
Peng, B., & Ning, X. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342136 · Cited by 14 · see citing works
Muñoz, M. A., Tonning, J. M., Brinker, A. D., Delaney, J. A., Gatti, J. C., & Avigan, M. (2016) · Pharmaceutical Medicine · doi:10.1007/s40290-016-0162-6 · Cited by 9 · see citing works
Drug-drug interaction prediction based on co-medication patterns and graph matching
Chiang, W. H., Shen, L., Li, L., & Ning, X. (2020) · International Journal of Computational Biology and Drug Design · doi:10.1504/ijcbdd.2020.105093 · Cited by 9 · see citing works
Ramachandran, R. A., Tell, L. A., Rai, S., Gedara, N. I. M., Xu, X., Riviere, J. E., & Jaberi‐Douraki, M. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15051384 · Cited by 9 · see citing works
Pattern Discovery from Directional High-Order Drug-Drug Interaction Relations
Ning, X., Schleyer, T., Shen, L., & Li, L. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.20 · Cited by 6 · see citing works
Pattern Discovery from High-Order Drug-Drug Interaction Relations
Chiang, W.-H., Schleyer, T., Shen, L., Li, L., & Ning, X. (2018) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-018-0020-2 · Cited by 5 · see citing works
Porcalla, A., Barshteyn, N., Snyder, S., & Bhattacharya, M. (2017) · Therapeutic Innovation & Regulatory Science · doi:10.1177/2168479017706717 · Cited by 4 · see citing works
Vildhede, A. M. (2015) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Identifying Side Effects of Some Drugs Used in Covid-19 TreatmentPreprint
Aygün, İ., Kaya, M., & Alhajj, R. (2020) · doi:10.21203/rs.3.rs-40903/v1
Mining medicine prescriptions to enhance predictive management of COVID-19 patients in the UAEPreprint
AlBataineh, M., Ruta, D., Salameh, L. I. J., Zalloua, P., Safar, H. A., Ibrahim, S. M., & Mahboub, B. H. (2023) · Research Square · doi:10.21203/rs.3.rs-3312811/v1
- Authors
- Hammann, F., & Drewe, J.
- Affiliations
- University Hospital of Basel (CH)
- ORCID
- Hammann, Felix
- Venue
- Expert Opinion on Drug Metabolism & Toxicology
- Publisher
- Informa UK Limited
- Published
- 2014-03-04
- Volume
- 10
- Issue
- 5
- Pages
- 665-671
- ISSN
- 1742-5255 (ISSN-L)
- 1744-7607
- OpenAlex
- W2007921205
- Cited by
- 19
- References
- 34
- Citations by year
- 2023: 2 · 2022: 1 · 2020: 3 · 2019: 2 · 2018: 1 · 2017: 4 · 2016: 3 · 2015: 2 · 2013: 1
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Opinion on Drug Metabolism & Toxicology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- drug-drug interactions, adverse drug interactions, data mining, network analysis, graph theory, pharmacovigilance, polypharmacy, elderly patients, interaction prediction, syntactic analysis
- MeSH terms
- Drug Interactions, Geriatrics, Humans, Models, Biological, Pharmacology, Clinical, Data Mining, Pharmacovigilance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
