Cosmetics; Risk Assessment; Humans; Animals; Animal Testing Alternatives/methods; Quantitative Structure-Activity Relationship; Cosmetics/chemistry; Cosmetics/adverse effects; Animal Testing Alternatives; Information Systems; Biochemistry, Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)
Abstract :
[en] The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data. TOXIN KG uses graph-structured semantic technology and integrates toxicological data through ontologies, ensuring interoperable representation. The primary data source is safety information on cosmetic ingredients from scientific opinions issued by the Scientific Committee on Consumer Safety between 2009 and 2019. The ToxRTool automates the reliability assessment of toxicity studies, while the Simplified Molecular Input Line Entry System (SMILES) notation standardizes chemical identification, enabling in silico prediction of repeated-dose toxicity via the implementation of the Organization for Economic Co-operation and Development Quantitative Structure-Activity Relationship Toolbox (OECD QSAR Toolbox). The ToXic Process Ontology, enriched with relevant biological repositories, is employed to represent toxicological concepts systematically. Search filters allow the identification of cosmetic compounds potentially linked to liver toxicity. Data visualization is achieved through Ontodia, a JavaScript library. TOXIN KG, filled with information for 88 cosmetic ingredients, allowed us to identify 53 compounds affecting at least one liver toxicity parameter in a 90-day repeated-dose animal study. For one compound, we illustrate how TOXIN KG links this observation to hepatic cholestasis as an adverse outcome. In an ab initio NGRA context, follow-up in vitro studies using human-based NAMs would be necessary to understand the compound's biological activity and the molecular mechanism leading to the adverse effect. In summary, TOXIN KG emerges as a valuable tool for advancing the reusability of cosmetics safety data, providing knowledge in support of NAM-based hazard and risk assessments. Database URL: https://toxin-search.netlify.app/.
Disciplines :
Life sciences: Multidisciplinary, general & others Computer science
Author, co-author :
Sepehri, Sara ; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Heymans, Anja; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Win, Dinja De; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Maushagen, Jan; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Sanctorum, Audrey; WISE lab, Department of Computer Science, Vrije Universiteit Brussel, Pleinlaan 9, Brussels 1050, Belgium
Debruyne, Christophe ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Rodrigues, Robim M; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Kock, Joery De; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Rogiers, Vera; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Troyer, Olga De; WISE lab, Department of Computer Science, Vrije Universiteit Brussel, Pleinlaan 9, Brussels 1050, Belgium
Vanhaecke, Tamara ; Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium
Language :
English
Title :
The TOXIN knowledge graph: supporting animal-free risk assessment of cosmetics.
Publication date :
28 January 2025
Journal title :
Database: The Journal of Biological Databases and Curation
This work was financially supported by Onderzoeksraad Vrije Universiteit Brussel and Research Chair Mireille Aerens for the development of Alternatives to Animal Testing. The research of Audrey Sanctorum has been funded by an FWO Postdoc Fellowship (1276721N) of the Research Foundation Flanders.
SCCS/1647/22. SCCS Notes of Guidance for the Testing of Cosmetic Ingredients and their Safety Evaluation 12th revision, 2023.
Silva RJ, Tamburic S. A state-of-the-art review on the alternatives to animal testing for the safety assessment of cosmetics. Cosmetics 2022;9:90. https://doi.org/10.3390/cosmetics9050090
OECD. Overview of concepts and available guidance related to Integrated Approaches to Testing and Assessment (IATA). Environment, Health and Safety, Environment Directorate, OECD (329). 2020.
Cronin MTD, Bajot F, Enoch SJ et al. The in chemico-in silico interface: challenges for integrating experimental and computational chemistry to identify toxicity. Altern Lab Anim 2009;37:513–21. https://doi.org/10.1177/026119290903700508
NNLM. Data Curation. 2022. https://www.nnlm.gov/guides/dataglossary/data-curation (24 October 2023, date last accessed).
Gustafson E, Debruyne C, De Troyer O et al. Screening of repeated dose toxicity data in safety evaluation reports of cosmetic ingredients issued by the Scientific Committee on Consumer Safety between 2009 and 2019. Arch Toxicol 2020;94:3723–35. https://doi.org/10.1007/s00204-020-02868-2
SCCS/1322/10. SCCS – Opinion on HC Yellow no 13. 2011. https://health.ec.europa.eu/scientific-committees/scientificcommittee-consumer-safety-sccs/sccs-opinions_en (13 November 2024, date last accessed)
Microsoft Support. Excel Specifications and Limits. https://support.microsoft.com/en-us/office/excel-specifications-and-limits-1672b34d-7043-467e-8e27-269d656771c3 (11 October 2024, date last accessed)
Das S, Cyganiak R, and Sundara S. R2RML: RDB to RDF Mapping Language. 2012. https://www.w3.org/TR/r2rml/ (11 October 2024, date last accessed).
Debruyne C, and O'Sullivan D. R2RML-F: towards shar-ing and executing domain logic in R2RML mappings. In: LDOW@WWW 1593. Montreal, Canada, 12 April 2016. CEUR-WS.org, 2016. https://ceur-ws.org/Vol-1593/article-13.pdf (11 October 2024, date last accessed).
Sanctorum A, Riggio J, Maushagen J et al. End-user engineering of ontology-based knowledge bases. Behaviour Inf Technol 2022;41:1811–29. https://doi.org/10.1080/0144929X.2022. 2092032
OECD. Test No. 408: Repeated Dose 90-Day Oral Toxicity Study in Rodents. 2018 (OECD Guidelines for the Testing of Chemicals, Section 4). https://www.oecd-ilibrary.org/environment/ test-no-408-repeated-dose-90-day-oral-toxicity-study-in-rodents_ 9789264070707-en (28 September 2024, date last accessed).
Shen Y, Li Z, Wang X et al. DataType-aware knowledge graph representation learning in hyperbolic space. In: International Conference on Information and Knowledge Management, Proceedings Online, 1–5 November 2021. pp. 1630–39. Gold Coast, Queensland, Australia: ACM Digital Library, 2021.
Sanctorum A, Riggio J, Sepehri S et al. A jigsaw-based end-user tool for the development of ontology-based knowledge bases. In: Fogli D, Tetteroo D, Barricelli BR, Borsci S, Markopoulos P and Papadopoulos GA (eds), End-User Development IS-EUD Gewerbestrasse 11, Cham, Ch 6330. Lecture Notes in Computer Science. Vol. 12724. Switzerland: Springer, Cham. 2021, pp. 169–84.
Collier ZA, Gruss RJ, and Abrahams AS. How Good Are Large Language Models at Product Risk Assessment? Risk Analysis. https://onlinelibrary.wiley.com/doi/full/10.1111/risa.14351 (12 November 2024, date last accessed)
Fu X, Wojak A, Neagu D et al. Data governance in predictive toxicology: a review. J Cheminform 2011;3:24. https://doi.org/10.1186/1758-2946-3-24
Kumar BS, and Kumar BS. Introductory Chapter: Data Integrity and Data Governance. Data Integrity and Data Governance. https://www.intechopen.com/chapters/86485 (17 November 2024, date last accessed).
Nikam NR, Patil PR, Vakhariya R et al. Data integrity: an overview. Int J Recent Sci Res 2020;11:38762–67.
Yamagata Y, Yamada H, Horii I. Current status and future perspective of computational toxicology in drug safety assessment under ontological intellection. J Toxicol Sci 2019;44:721–35. https://doi.org/10.2131/jts.44.721
Staal YCM, Pennings JLA, Hessel EVS et al. Advanced toxicological risk assessment by implementation of ontologies operationalized in computational models. Appl In Vitro Toxicol 2017;3:325–32. https://doi.org/10.1089/aivt.2017.0019
Boyles RR, Thessen AE, Waldrop A et al. Ontology-based data integration for advancing toxicological knowledge. Curr Opin Toxicol 2019;16:67–74. https://doi.org/10.1016/j.cotox.2019.05.005
Hardy B, Apic G, Carthew P et al. Toxicology ontology perspectives. Altex 2012;29:139–56.
Bretones Cassoli B. Knowledge Graphs for Data And Knowledge Management in Cyber-Physical Production Systems. 2022 https://www.repo.uni-hannover.de/handle/123456789/12278 (17 November 2024, date last accessed).
Mungall CJ, Torniai C, Gkoutos GV et al. Uberon, an integrative multi-species anatomy ontology. Genome Biol 2012; 13:1–20.
Hastings J, De Matos P, Dekker A et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 2012;41:D456. https://doi.org/10.1093/nar/gks1146
Ashburner M, Ball CA, Blake JA et al. Gene ontology: tool for the unification of biology. Nature Genet 2000;25:25–29. https://doi.org/10.1038/75556
Consortium TGO, Aleksander SA, Balhoff J et al. The gene ontology knowledgebase in 2023. Genetics 2023;224:iyad031 https://doi.org/10.1093/genetics/iyad031.
Yamamoto S, Sakai N, Nakamura H et al. Inoh: Ontology-Based Highly Structured Database of Signal Transduction Pathways. Database (Oxford). 2011. /pmc/articles/PMC3225078/ (29 September 2024, date last accessed)
He Yongqun, Liu Yue, and Zhao Bin. OGG: A Biological Ontology for Representing Genes and Genomes in Specific Organ-isms International Conference on Biomedical Ontology (ICBO). In: CEUR Workshop Proceedings 1327, 8–9 October 2014, 13–20. Houston, Texas, USA, 2014.
Schriml LM, Munro JB, Schor M et al. The human disease ontology 2022 update. Nucleic Acids Res 2022;50:D1255. https://doi.org/10.1093/nar/gkab1063
Yamagata Y, Yamada H. Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management. Sci Rep 2020;10:1–14. https://doi.org/10.1038/s41598-020-71370-7
Bateman A, Martin MJ, Orchard S et al. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res 2023;51:D523–31.
Kanehisa M, Furumichi M, Sato Y et al. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res 2023;51:D587–92. https://doi.org/10.1093/nar/gkac963
Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci 2019;28:1947–51. https://doi.org/10.1002/pro.3715
Kanehisa M, Goto S, Kawashima S et al. The KEGG databases at GenomeNet. Nucleic Acids Res 2002;30:42–46. https://doi.org/10.1093/nar/30.1.42
Kanehisa M, Goto S. Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27–30. https://doi.org/10.1093/nar/28.1.27
Gillespie M, Jassal B, Stephan R et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res 2022;50:D687–92. https://doi.org/10.1093/nar/gkab1028
Debruyne C, Munnelly G, Kilgallon L et al. Creating a knowledge graph for Ireland's lost history: knowledge engineering and curation in the beyond 2022 Project. J Comput Cult Herit 2022;15:1 - 25/25. https://doi.org/10.1145/3474829.
Sanctorum A, Maushagen J, Sepehri S et al. Knowledge Management in the Context of Toxicity Testing. From Theory of Knowledge Management to Practice. 2023. https://www.intechopen.com/chapters/1147484 (3 September 2024, date last accessed)
Weininger D. SMILES, a chemical language and information system: 1: introduction to methodology and encoding rules. J Chem Inf Comput Sci 1988;28:31–36. https://doi.org/10.1021/ ci00057a005
Born J, Markert G, Janakarajan N et al. Chemical representation learning for toxicity prediction. Digit Discov 2023;2:674–91. https://doi.org/10.1039/D2DD00099G
O'Boyle NM. Towards a Universal SMILES representation – a standard method to generate canonical SMILES based on the InChI. J Cheminform 2012;4:1–14.
Klimisch H-J, Andreae M, Tillmann U. A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 1997;25:1–5. https://doi.org/10.1006/rtph.1996.1076
Schneider K, Schwarz M, Burkholder I et al. “ToxRTool”, a new tool to assess the reliability of toxicological data. Toxicol Lett 2009;189:138–44. https://doi.org/10.1016/j.toxlet.2009.05.013
Segal D, Makris SL, Kraft AD et al. Evaluation of the ToxRTool's ability to rate the reliability of toxicological data for human health hazard assessments. Regul Toxicol Pharmacol 2015;72:94–101. https://doi.org/10.1016/j.yrtph.2015.03.005
Johnson C, Anger LT, Benigni R et al. Evaluating confidence in toxicity assessments based on experimental data and in silico predictions. Comput Toxicol 2022;21:100204.
Dimitrov SD, Diderich R, Sobanski T et al. QSAR toolbox – workflow and major functionalities. SAR QSAR Environ Res 2016;27:203–19. https://doi.org/10.1080/1062936X.2015. 1136680
OECD, ECHA, Laboratory of Mathematical Chemistry. Profilers QSAR Toolbox. 2024. https://qsartoolbox.org/resources/profilers/ (6 September 2024, date last accessed).
Safety Assessment Division CMCNI of T and E. Hazard Evaluation Support System Integrated Platform (HESS). 2023. https://www.nite.go.jp/en/chem/qsar/hess-e.html (6 September 2024, date last accessed).
More SJ, Bampidis V, Benford D et al. Guidance on the use of the threshold of toxicological concern approach in food safety assessment. EFSA J 2019;17:e05708.
Cramer GM, Ford RA, Hall RL. Estimation of toxic hazard—a decision tree approach. Food Cosmet Toxicol 1976;16:255–76. https://doi.org/10.1016/S0015-6264(76)80522-6
Munro IC, Ford RA, Kennepohl E et al. Thresholds of toxicological concern based on structure-activity relationships. Drug Metab Rev 1996;28:209–17. https://doi.org/10.3109/03602539608994000
OECD, ECHA, Laboratory of Mathematical Chemistry. Application manual of OECD QSAR Toolbox v.4 (F1 help). 2017.
Aljallal MA, Price NR, Chaudhry Q. Assessment of performance of the profilers provided in the OECD QSAR toolbox for category formation of chemicals [Internet], nature portfolio. Sci Rep 2024;14:18330. https://doi.org/10.1038/s41598-024-69157-1
Sakuratani Y, Zhang HQ, Nishikawa S et al. Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories. SAR QSAR Environ Res 2013;24:351–63. https://doi.org/10.1080/1062936X.2013. 773375
Vinken M, Pauwels M, Ates G et al. Screening of repeated dose toxicity data present in SCC(NF)P/SCCS safety evaluations of cosmetic ingredients. Arch Toxicol 2012;86:405–12. https://doi.org/10.1007/s00204-011-0769-z
Pognan F, Beilmann M, Boonen HCM et al. The evolving role of investigative toxicology in the pharmaceutical industry. Nat Rev Drug Discov 2023;22:317–35. https://doi.org/10.1038/s41573-022-00633-x
Van Norman GA. Limitations of animal studies for predicting toxicity in clinical trials: is it time to rethink our current approach? JACC Basic Transl Sci 2019;4:845. https://doi.org/10.1016/j.jacbts.2019.10.008
Akhtar A. The flaws and human harms of animal experimentation. Cam Q Healthc Ethics 2015;24:407. https://doi.org/10.1017/ S0963180115000079
Timón-Reina S, Rincón M, and Martínez-Tomás R. An overview of graph databases and their applications in the biomedical domain. Database 2021;2021:26.
Mouromtsev D, Pavlov D, Emelyanov Y et al. The simple web-based tool for visualization and sharing of semantic data and ontologies. International Workshop on the Semantic Web.
Komninos A. Interaction Design Foundation. An Introduction to Usability | IxDF. 2020. https://www.interaction-design.org/literature/article/an-introduction-to-usability (21 September 2024, date last accessed).
Quesenbery W. What does usability mean: looking beyond ‘Ease of Use' – Whitney interactive design. In: Proceedings of the 48th Annual Conference, Society for Technical Communication Chicago 13-16 May 2001, 2001.
Boyce M, Favela KA, Bonzo JA et al. Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis. Front Toxicol 2023;5:1051483. https://doi.org/10.3389/ftox.2023.1051483
Boyce M, Meyer B, Grulke C et al. Comparing the performance and coverage of selected in silico (liver) metabolism tools relative to reported studies in the literature to inform analogue selection in read-across: a case study. Comput Toxicol 2022;21:100208. https://doi.org/10.1016/j.comtox.2021.100208
Euzenat J, and Shvaiko P. Matching strategies. In: Ontology Matching. Berlin, Heidelberg: Springer, 117–150.
Probst D, Reymond JL. SmilesDrawer: parsing and drawing SMILES-encoded molecular structures using client-side JavaScript. J Chem Inf Model 2018;58:1–7. https://doi.org/10.1021/acs.jcim. 7b00425
Lin TS, Coley CW, Mochigase H et al. BigSMILES: a structurally-based line notation for describing macromolecules. ACS Cent Sci 2019;5:1523–31. https://doi.org/10.1021/acscentsci.9b00476
Lin TS, Rebello NJ, Lee GH et al. Canonicalizing BigSMILES for polymers with defined backbones. ACS Polymers Au 2022;2:486–500. https://doi.org/10.1021/acspolymersau.2c00009
Ryan C, Grant R, Carragáin E et al. Linked data authority records for Irish place names. Int J Digit Libr 2015;15:73–85. https://doi.org/10.1007/s00799-014-0129-8
Yang S, Teng M, Dong X et al. LLM-based SPARQL generation with selected schema from large scale knowledge base: 304–16.
Romano JD, Hao Y, Moore JH et al. Automating predictive toxicology using ComptoxAI. Chem Res Toxicol 2022;35:1370–82. https://doi.org/10.1021/acs.chemrestox.2c00074
Walker VR, Schmitt CP, Wolfe MS et al. Evaluation of a semi-automated data extraction tool for public health literature-based reviews: Dextr. Environ Int 2022;159:107025. https://doi.org/10.1016/j.envint.2021.107025
Xu Y, Liu X, Cao X et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation 2021;2:100179. https://doi.org/10.1016/j.xinn.2021.100179
Basile AO, Yahi A, Tatonetti NP. Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci 2019;40:624. https://doi.org/10.1016/j.tips.2019.07.005
Grüninger M, and Fox MS. The role of competency questions in enterprise engineering. In: Asbjørn Rolstadås (ed.), Benchmarking — Theory and Practice. IFIP Advances in Information and Communication Technology ((IFIPAICT)). Boston, MA: Springer, 1995, 22–31. https://doi.org/10.1007/978-0-387-3484 7-6_3
Vrandeci´vrande ̌ ci´c ̌ D. Ontology evaluation. In: Staab Steffen, Studer Rudi (eds), Handbook on Ontologies International Handbooks on Information Systems. Springer, 2009, 293–313.
Tamura YO, Sugama J, Iwasaki S et al. Selective acetyl-CoA carboxylase 1 inhibitor improves hepatic steatosis and hepatic fibrosis in a pre-clinical NASH model. J Pharmacol Exp Ther 2021;379:280–89. https://doi.org/10.1124/jpet.121.000786
Gnoni A, Di Chiara Stanca B, Giannotti L et al. Quercetin reduces lipid accumulation in a cell model of NAFLD by inhibiting de novo fatty acid synthesis through the acetyl-CoA carboxylase 1/AMPK/PP2A axis. Int J Mol Sci 2022;23:1044. https://doi.org/10.3390/ijms23031044
Prins GH, Luangmonkong T, Oosterhuis D et al. A pathophysiological model of non-alcoholic fatty liver disease using precision-cut liver slices. Nutrients 2019;11:507. https://doi.org/10.3390/ nu11030507
Wohlleben W, Mehling A, Landsiedel R. Lessons learned from the grouping of chemicals to assess risks to human health. Angew Chem Int Ed 2023;62:e202210651. https://doi.org/10.1002/anie. 202210651
Knudsen TB. Ontologies as the Basis for in Silico Reconstruction of Tissue Dynamics. 2023. https://epa.figshare.com/articles/presentation/Ontologies_as_the_basis_for_in_silico_ reconstruction_of_tissue_dynamics/24201369?file=42472152 (23 September 2024, date last accessed).
Pawar G, Madden JC, Ebbrell D et al. In silico toxicology data resources to support read-across and (Q)SAR. Front Pharmacol 2019;10:449547.
Cronin MTD, Belfield SJ, Briggs KA et al. Making in silico predictive models for toxicology FAIR. Regul Toxicol Pharmacol 2023;140:105385. https://doi.org/10.1016/j.yrtph.2023. 105385
Iglesias-Molina A, Van Assche D, Arenas-Guerrero J et al. The RML ontology: a community-driven modular redesign after a decade of experience in mapping heterogeneous data to RDF. In: ISWC 2023, 22th International Semantic Web Confernece, 14266 Athens Greece, Greece Nov 2023, LNCS, 2023.
Debruyne C, and Van Assche D. The Conformance of an RML Processor Built from Scratch to Validate Specifications and Test Cases 5th International Workshop on Knowledge Graph Construction co-located with 21th Extended Semantic Web Conference (ESWC 2024) 3718 of CEUR. In: Workshop Proceedings Heraklion, Greece, May 26-27 2024, 2024.
Devlin Jacob, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: North American Chapter of the Association for Computational Linguistics (2019), Proceedings of NAACL-HLT 2019, Minneapolis, Minnesota, June 2 - June 7, 2019, pp. 4171–4186. 2019.
Brown TB, Mann B, Ryder N et al. Language models are few-shot learners. 2020. https://arxiv.org/abs/2005.14165 (15 November 2024, date last accessed).
Scalia G. Machine learning for scientific data analy-sis. In: Piroddi L (ed.), Special Topics in Information Technology, SpringerBriefs in Applied Sciences and Technology (BRIEF-SPOLIMI). Cham, Switzerland: Springer, 2022, 115–26.
Sendak M, Sirdeshmukh G, Ochoa T et al. Development and validation of ML-DQA-a machine learning data quality assurance framework for healthcare ML-DQA for healthcare ML-DQA for healthcare. Proc Mach Learn Res 2022;182:1–19.
Hornbæk K. Current practice in measuring usability: challenges to usability studies and research. Int J Hum Comput Stud 2006;64:79–102. https://doi.org/10.1016/j.ijhcs.2005.06.002
Bastien JMC. Usability testing: a review of some methodological and technical aspects of the method. Int J Med Inform 2010;79:e18–23.
