Cardiotoxicity adverse outcome pathway network: towards mechanistic and quantitative modelling

Luiz Ladeira1, Devon A Barnes2, Rosalinde Masereeuw2

  • 1Biomechanics Research Unit, GIGA Institute, University of Liège, Liège, Belgium.

Abstract

Insights

A new Adverse Outcome Pathway (AOP) network reveals key biological events driving chemical-induced heart toxicity. This resource aids in developing advanced, animal-free safety testing strategies for drug development and environmental assessment.

Area of Science:

  • Toxicology
  • Biomedical Science
  • Environmental Health

Background:

  • Chemical-induced heart toxicity poses significant challenges in drug development and environmental safety.
  • Current testing methods often fail to capture the complex biological progression of toxicities due to narrow, late-stage endpoints.

Purpose of the Study:

  • To develop a comprehensive Adverse Outcome Pathway (AOP) network to map the progression of chemical-induced heart toxicity.
  • To identify critical biological pathways and biomarkers for improved cardiotoxicity assessment.
  • To create a practical, accessible resource for designing human-relevant, animal-free testing strategies.

Main Methods:

  • Integrated data from the OECD AOP-Wiki to construct a network of 64 biological events and 94 relationships.
  • Identified central biological events and 'crossroads' where different toxic chemicals converge.
  • Developed a methods catalogue linking biological events to laboratory assays.
  • Hosted the network on an interactive, FAIR-aligned web platform.

Main Results:

  • Revealed a core set of biological events, including oxidative stress and mitochondrial dysfunction, as primary drivers of cardiac injury.
  • Demonstrated how systemic factors and inter-organ interactions (e.g., with kidneys) contribute to cardiotoxicity.
  • Established a network that moves beyond linear pathways to illustrate complex toxicological interactions.

Conclusions:

  • The developed AOP network provides a clear scaffold for understanding heart safety.
  • This resource facilitates the design of more human-relevant, animal-free testing strategies.
  • Enables prioritization of impactful biomarkers for future safety assessments, enhancing drug development and environmental safety evaluations.

Related Concept Videos

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...