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<p>This paper examines why robust causality assessment is central to toxicology yet increasingly difficult in networked biological systems, including Adverse Outcome Pathway (AOP) networks and multi-omics datasets. Classical causal frameworks (e.g., Koch/Dale reasoning and Bradford Hill-type considerations) remain useful for scoping, but they offer limited operational guidance for multifactorial, nonlinear, and feedback-dominated mechanisms. We therefore synthesize a modern toolbox for causality analysis in toxicology spanning directed acyclic graphs for explicit causal assumptions, quantitative and probabilistic approaches for integrating evidence across key events, and network/<i>dynamical</i> methods that help identify influential hubs and points of vulnerability in AOP networks. Building on evidence-based toxicology, we propose an operational workflow that links (i) structured scoping and model specification, (ii) protocolized evidence retrieval across in vivo, in vitro, in silico and human data streams, (iii) risk-of-bias appraisal and quantitative synthesis of effect sizes and dose–response, (iv) mechanistic integration and targeted perturbation assays, and (v) translation via dual-strand certainty rating (mechanistic vs difference-making evidence) and Evidence-to-Decision tables. We discuss how explainable AI can support scalable integration and transparency. A worked developmental neurotoxicity example illustrates how this pipeline can support regulatory recommendations while explicitly documenting uncertainty.</p>

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Causality analysis of toxicological mechanisms in networked systems such as adverse outcome pathway networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Thomas Hartung,
  • Karolina Kopańska,
  • Alexandra Maertens,
  • Paul Whaley,
  • Sebastian Hoffmann

摘要

This paper examines why robust causality assessment is central to toxicology yet increasingly difficult in networked biological systems, including Adverse Outcome Pathway (AOP) networks and multi-omics datasets. Classical causal frameworks (e.g., Koch/Dale reasoning and Bradford Hill-type considerations) remain useful for scoping, but they offer limited operational guidance for multifactorial, nonlinear, and feedback-dominated mechanisms. We therefore synthesize a modern toolbox for causality analysis in toxicology spanning directed acyclic graphs for explicit causal assumptions, quantitative and probabilistic approaches for integrating evidence across key events, and network/dynamical methods that help identify influential hubs and points of vulnerability in AOP networks. Building on evidence-based toxicology, we propose an operational workflow that links (i) structured scoping and model specification, (ii) protocolized evidence retrieval across in vivo, in vitro, in silico and human data streams, (iii) risk-of-bias appraisal and quantitative synthesis of effect sizes and dose–response, (iv) mechanistic integration and targeted perturbation assays, and (v) translation via dual-strand certainty rating (mechanistic vs difference-making evidence) and Evidence-to-Decision tables. We discuss how explainable AI can support scalable integration and transparency. A worked developmental neurotoxicity example illustrates how this pipeline can support regulatory recommendations while explicitly documenting uncertainty.