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A Method to Calibrate Chemical-Agnostic Quantitative Adverse Outcome Pathways on Multiple Chemical Data

Zhou, Zheng LU orcid and Sahlin, Ullrika LU orcid (2026) In Chemical Research in Toxicology 39(5). p.852-861
Abstract

Quantitative Adverse Outcome Pathways (qAOPs) can support next-generation risk assessment by integrating new approach methods (NAMs) for deriving points of departure. To be useful, a qAOP should be chemical-agnostic. However, existing qAOP studies often pool multichemical data without adequately addressing cross-chemical heterogeneity. Consequently, pathway relationships become obscured by heterogeneity-induced variations, thereby compromising model reliability and generalizability. We developed a calibration approach to address this challenge by leveraging hierarchical structures to systematically separate chemical-specific heterogeneity from the underlying pathway effects. Chemical-specific deviations are explicitly modeled as random... (More)

Quantitative Adverse Outcome Pathways (qAOPs) can support next-generation risk assessment by integrating new approach methods (NAMs) for deriving points of departure. To be useful, a qAOP should be chemical-agnostic. However, existing qAOP studies often pool multichemical data without adequately addressing cross-chemical heterogeneity. Consequently, pathway relationships become obscured by heterogeneity-induced variations, thereby compromising model reliability and generalizability. We developed a calibration approach to address this challenge by leveraging hierarchical structures to systematically separate chemical-specific heterogeneity from the underlying pathway effects. Chemical-specific deviations are explicitly modeled as random effects, enabling the extraction of pathway-level parameters that represent core mechanistic relationships independent of the individual chemical properties. We demonstrated through a simulation study that performance differences between models with and without hierarchical calibration can reveal the magnitude of the heterogeneity in the data. When heterogeneity is substantial, an uncalibrated qAOP should not be considered truly chemical-agnostic in practice, as it confounds pathway-level effects with chemical-specific variations. Finally, we demonstrated the application of this calibration approach through deriving points of departure to a case study of nonmutagenic liver tumor qAOP.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Chemical Research in Toxicology
volume
39
issue
5
pages
10 pages
publisher
The American Chemical Society (ACS)
external identifiers
  • pmid:41979413
  • scopus:105036365133
ISSN
0893-228X
DOI
10.1021/acs.chemrestox.5c00435
language
English
LU publication?
yes
id
1446df1d-f3d2-468a-8124-2df35246f1b6
date added to LUP
2026-06-26 14:02:08
date last changed
2026-06-27 03:27:52
@article{1446df1d-f3d2-468a-8124-2df35246f1b6,
  abstract     = {{<p>Quantitative Adverse Outcome Pathways (qAOPs) can support next-generation risk assessment by integrating new approach methods (NAMs) for deriving points of departure. To be useful, a qAOP should be chemical-agnostic. However, existing qAOP studies often pool multichemical data without adequately addressing cross-chemical heterogeneity. Consequently, pathway relationships become obscured by heterogeneity-induced variations, thereby compromising model reliability and generalizability. We developed a calibration approach to address this challenge by leveraging hierarchical structures to systematically separate chemical-specific heterogeneity from the underlying pathway effects. Chemical-specific deviations are explicitly modeled as random effects, enabling the extraction of pathway-level parameters that represent core mechanistic relationships independent of the individual chemical properties. We demonstrated through a simulation study that performance differences between models with and without hierarchical calibration can reveal the magnitude of the heterogeneity in the data. When heterogeneity is substantial, an uncalibrated qAOP should not be considered truly chemical-agnostic in practice, as it confounds pathway-level effects with chemical-specific variations. Finally, we demonstrated the application of this calibration approach through deriving points of departure to a case study of nonmutagenic liver tumor qAOP.</p>}},
  author       = {{Zhou, Zheng and Sahlin, Ullrika}},
  issn         = {{0893-228X}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{852--861}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Chemical Research in Toxicology}},
  title        = {{A Method to Calibrate Chemical-Agnostic Quantitative Adverse Outcome Pathways on Multiple Chemical Data}},
  url          = {{http://dx.doi.org/10.1021/acs.chemrestox.5c00435}},
  doi          = {{10.1021/acs.chemrestox.5c00435}},
  volume       = {{39}},
  year         = {{2026}},
}