From Chaos to Order: Mapping Extracted Data from Systematic Literature Reviews for Application to the AOP Framework
Abstract
Introduction:
Systematic reviews (SR) collect and integrate data corpuses into consistent, computable, and comparable datasets. The adverse outcome pathway (AOP) framework facilitates the linking of data describing molecular initiating events, through one or more key events (KEs), to adverse biological outcomes. To explore the potential application of data from SRs to the AOP framework, a case study was conducted to explore mapping SR to existing AOP KEs.
Methods:
SR data consisted of in vitro and in vivo androgen receptor (AR) toxicity information from nonmammalian vertebrate species collected as described by the authors, limiting data comparability. Data were standardized and mapped to terms for Level of Biological Organization, Object, Process, and Action using existing KEs in the AOP-Wiki as a source for endpoint terms.
Results:
In vitro SR data had 131 of 264 records that mapped to AR transactivation, while in vivo data had 226 of 1891 records directly mappable to 31 different KEs (e.g., increased vitellogenin messenger RNA). When no appropriate terms existed in the AOP-Wiki, standardized terms were proposed for future use. For unstructured data, mapping and standardization required additional interpretation.
Conclusions:
This study highlights the difficulties in aligning heterogeneously extracted SR data with a structured framework. This work highlights the need for language standardization and the adoption of clear data collection guidance prior to, and during, the SR to enhance data comparability and computability. The adoption of such efforts can advance the ability of resulting data to be reused and applied to frameworks such as AOPs. Lessons learned in this case study are applicable to similar efforts examining the use of automation in data extraction and evaluation.
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Disclaimer
Contractor’s roles did not include establishing Agency policy. All authors received their typical and usual salaries from their respective institutions for the development of the research and writing of the article. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA nor does the mention of trade names or commercial products indicate endorsement by the federal government. This article was not reviewed by and does not reflect the view of 3M or Underwriters Laboratories Research Institutes.
References
1.Haddaway NR, Westgate MJ. Predicting the time needed for environmental systematic reviews and systematic maps. Conserv Biol 2019;33(2):434–443;
2.Kumar G, Basri S, Imam AA, et al. Data harmonization for heterogeneous datasets: A systematic literature review. Applied Sciences 2021;11(17):8275;
3.Lorenc T, Felix L, Petticrew M, et al. Meta-analysis, complexity, and heterogeneity: A qualitative interview study of researchers’ methodological values and practices. Syst Rev 2016;5(1):192;
4.Ives C, Campia I, Wang R-L, et al. Creating a structured AOP knowledgebase via ontology-based annotations. Appl In Vitro Toxicol 2017;3(4):298–311;
5.Ankley GT, Bennett RS, Erickson RJ, et al. Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 2010;29(3):730–741;
6.Collaborative Adverse Outcome Pathway Wiki. Available from: https://aopwiki.org/ [Last accessed: December31, 2025].
7.Tollefsen KE, Scholz S, Cronin MT, et al. Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA). Regul Toxicol Pharmacol 2014;70(3):629–640;
8.Ellison CM, Piechota P, Madden JC, et al. Adverse Outcome Pathway (AOP) informed modeling of aquatic toxicology: QSARs, read-across, and interspecies verification of modes of action. Environ Sci Technol 2016;50(7):3995–4007;
9.Seo M, Chae CH, Lee Y, et al. Novel QSAR models for molecular initiating event modeling in two intersecting adverse outcome pathways based pulmonary fibrosis prediction for biocidal mixtures. Toxics 2021;9(3):59;
10.Vinken M. Adverse outcome pathways as tools to assess drug-induced toxicity. Methods Mol Biol 2016;1425:325–337;
11.Perkins EJ, Antczak P, Burgoon L, et al. Adverse outcome pathways for regulatory applications: Examination of four case studies with different degrees of completeness and scientific confidence. Toxicol Sci 2015;148(1):14–25;
12.(OECD) TOfECaD. Users’ Handbook supplement to the Guidance Document for developing and assessing Adverse Outcome Pathways. Report no. 1, 2018/02/13. OECD; 2018.
13.Knapen D, Angrish MM, Fortin MC, et al. Adverse outcome pathway networks I: Development and applications. Environ Toxicol Chem 2018;37(6):1723–1733;
14.Vliet SMF, Markey KJ, Lynn SG, et al. Weight of evidence for cross-species conservation of androgen receptor-based biological activity. Toxicol Sci 2023;193(2):131–145;
15.Cohen Hubal EA, Frank JJ, Nachman R, et al. Advancing systematic-review methodology in exposure science for environmental health decision making. J Expo Sci Environ Epidemiol 2020;30(6):906–916;
16.Mortensen HM, Martens M, Senn J, et al. The AOP-DB RDF: Applying FAIR principles to the semantic integration of AOP data using the research description framework. Front Toxicol 2022;4:803983;
17.Pittman ME, Edwards SW, Ives C, et al. AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks. Toxicol Appl Pharmacol 2018;343:71–83;
18.Watford S, Edwards S, Angrish M, et al. Progress in data interoperability to support computational toxicology and chemical safety evaluation. Toxicol Appl Pharmacol 2019;380:114707;
19.Whaley P, Edwards SW, Kraft A, et al. Knowledge organization systems for systematic chemical assessments. Environ Health Perspect 2020;128(12):125001;
20.Wittwehr C, Clerbaux L-A, Edwards S, et al. Why adverse outcome pathways need to be FAIR [Internet]. Altex 2024;41(1):50–56; doi: 10.14573/altex.2307131
21.Karmaus AL, Bisson W, Braeuning A, et al. Methods2AOP: A collaboration to strengthen the integration of test methods into the adverse outcome pathway framework. F1000Res 2025;14:1375; doi: 10.12688/f1000research.172881.1
22.Angrish M, Burns S, Cleland J, et al. An environmental health vocabulary and its semi-automated curation workflow. Evid Based Toxicol 2025;3(1):2485111;
23.Wittwehr C. AI4AOP—Artificial Intelligence for AOPs—Background and Overview. 2024. Available from: https://zenodo.org/records/14515001/files/AI4AOP%20Thoughtstarter.pdf
24.Mortensen H. Environmental Health Language Collective (EHLC) Adverse Outcome Pathway (AOP) Standards Workshop Report. Zenodo; 2025. Available from: https://zenodo.org/records/17940827
25.Evidence-based Toxicology Collaboration. Available from: https://www.ebtox.org/ [Last accessed: December31, 2025].
26.Monarch Initiative. Available from: https://monarchinitiative.org [Last accessed: December31, 2025].
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Article first published online: March 8, 2026
Issue published: March 1, 2026
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Authors’ Contributions
P.C.: Conceptualization, data curation (lead), methodology (lead), and writing—original draft (lead). J.A.: Data curation, writing—original draft, and writing—review and editing. S.B.: Project administration, writing—original draft, and writing—review and editing. B.C.: Data curation and writing—review and editing. S.E.: Supervision and methodology. V.H.: Writing—review and editing. S.G.L.: Supervision, funding acquisition, and writing—review and editing. K.M.: Conceptualization, methodology, funding acquisition, and writing—review and editing. S.M.F.V.: Conceptualization, data curation, methodology, and writing—review and editing.
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