Sahlin, U. | Search bioRxiv

bioRxiv2025

A METHOD TO CALIBRATE CHEMICAL AGNOSTIC QUANTITATIVE ADVERSE OUTCOME PATHWAYS ON MULTIPLE CHEMICAL DOSE-RESPONSE DATA

Quantitative Adverse Outcome Pathways (qAOPs) may support next-generation risk assessment by integrating New Approach Methodologies (NAMs) for derivation of points of departure. To be useful, a qAOP should be chemical-agnostic. However, existing qAOP studies often pool multi-chemical data without adequately addressing inter-chemical heterogeneity. Consequently, fundamental pathway relationships become obscured by heterogeneity-induced noise, thereby compromising the reliability of chemical-agnostic predictions. We developed a chemical-agnostic calibration approach to addresses this challenge by leveraging hierarchical structures to systematically separate chemical-specific heterogeneity from underlying pathway effects. Through this methodological framework, chemical-specific deviations are explicitly modeled as random effects, enabling the extraction of pathway-level parameters that represent core mechanistic relationships independent of individual chemical properties. Through simulation studies across varying heterogeneity levels, we demonstrate that performance differences between models with and without hierarchical calibration reveal the magnitude of heterogeneity in the data. Moreover, 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 variation. We demonstrated the application of this calibration approach through a case study of non-mutagenic liver tumor. The framework proposed in this study enhances qAOP generalizability while preserving the chemical-agnostic principle, supporting robust NAMs-based next-generation risk assessments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/642550v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@e7011eorg.highwire.dtl.DTLVardef@1fa7be9org.highwire.dtl.DTLVardef@1dd0b52org.highwire.dtl.DTLVardef@1123a93_HPS_FORMAT_FIGEXP M_FIG Figure 1: For TOC only C_FIG

bioinformatics↗

bioRxiv2024

Causal, Predictive Or Observational? Different Understandings Of Key Event Relationships For Adverse Outcome Pathways

The Adverse Outcome Pathways (AOPs) framework is pivotal in toxicology, but the terminology describing Key Event Relationships (KERs) varies within AOP guidelines. This study examined the usage of causal, observational and predictive terms in AOP documentation and their adaptation in AOP development. A literature search and text analysis of key AOP guidance documents revealed nuanced usage of these terms, with KERs often described as both causal and predictive. The adaptation of terminology varies across AOP development stages. Evaluation of KER causality often relies targeted blocking experiments and weight-of-evidence assessments in the putative and qualitative stages. Our findings highlight a potential mismatch between terminology in guidelines and methodologies in practice, particularly in inferring causality from predictive models. We argue for careful consideration of terms like causal and essential to facilitate interdisciplinary communication. Furthermore, integrating known causality into quantitative AOP models remains a challenge.

pharmacology and toxicology↗