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Comparison of piecewise structural equation modeling and Bayesian network for de novo construction of a quantitative adverse outcome pathway network
Örebro University, School of Medical Sciences. Örebro University Hospital. Clinical Epidemiology and Biostatistics.ORCID iD: 0000-0002-3552-9153
Norwegian Institute for Water Research (NIVA), Oslo, Norway.
Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
Norwegian Institute for Water Research (NIVA), Oslo, Norway; Centre for Environmental Radioactivity (CERAD), Norwegian University of Life Sciences (NMBU), Ås, Norway; Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Ås, Norway.
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2023 (English)In: Altex, ISSN 1868-596X, E-ISSN 1868-8551, Vol. 40, no 2, p. 287-298Article in journal (Refereed) Published
Abstract [en]

Quantitative adverse outcome pathway network (qAOPN) is gaining momentum due to the predictive nature, alignment with quantitative risk assessment and great potential as a computational new approach methodology (NAM) to reduce laboratory animal tests. The present work aimed to demonstrate two advanced modeling approaches, piecewise structural equation modeling (PSEM) and Bayesian network (BN), for de novo qAOPN model construction based on routine ecotoxicological data. A previously published AOP network comprised of four linear AOPs linking excessive reactive oxygen species production to mortality in aquatic organisms was employed as a case study. The demonstrative case study intended to answer: Which linear AOP in the network contributed the most to the AO? Can any of the upstream KEs accurately predict the AO? What are the advantages and limitations of PSEM or BN in qAOPN development? The outcomes from the two approaches showed that both PSEM and Bayesian network were suitable for constructing a complex qAOPN based on limited experimental data. Besides quantification of response-response relationships, both approaches were capable of identifying the most influencing linear AOP in a complex network and evaluating the predictive ability of the AOP, albeit some discrepancies in predictive ability were identified for the two approaches using this specific dataset. The PROs and CONs of the two approaches for qAOPN construction were discussed in detail and suggestions on optimal workflows of PSEM and BN were provided to guide future qAOPN development.

Place, publisher, year, edition, pages
Springer Spektrum , 2023. Vol. 40, no 2, p. 287-298
Keywords [en]
Bayesian network, animal alternative, new approach methodology, piecewise structural equation modeling, quantitative adverse outcome pathway, response-response relationship
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:oru:diva-102163DOI: 10.14573/altex.2207113ISI: 000983400500008PubMedID: 36317504Scopus ID: 2-s2.0-85152173693OAI: oai:DiVA.org:oru-102163DiVA, id: diva2:1710172
Funder
The Research Council of Norway
Note

Funding agencies:

NIVA Computational Toxicology Program, NCTP 5 (RCN) 301397

RCN-funded Centre for Environmental Radioactivity (CERAD) 160016 223268

Available from: 2022-11-11 Created: 2022-11-11 Last updated: 2023-06-01Bibliographically approved

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Cao, Yang

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