Internal exposure dynamics drive the Adverse Outcome Pathways of synthetic glucocorticoids in fish

Abstract

The Adverse Outcome Pathway (AOP) framework represents a valuable conceptual tool to systematically integrate existing toxicological knowledge from a mechanistic perspective to facilitate predictions of chemical-induced effects across species. However, its application for decision-making requires the transition from qualitative to quantitative AOP (qAOP). Here we used a fish model and the synthetic glucocorticoid beclomethasone dipropionate (BDP) to investigate the role of chemical-specific properties, pharmacokinetics, and internal exposure dynamics in the development of qAOPs. We generated a qAOP network based on drug plasma concentrations and focused on immunodepression, skin androgenisation, disruption of gluconeogenesis and reproductive performance. We showed that internal exposure dynamics and chemical-specific properties influence the development of qAOPs and their predictive power. Comparing the effects of two different glucocorticoids, we highlight how relatively similar in vitro hazard-based indicators can lead to different in vivo risk. This discrepancy can be predicted by their different uptake potential, pharmacokinetic (PK) and pharmacodynamic (PD) profiles. We recommend that the development phase of qAOPs should include the application of species-species uptake and physiologically-based PK/PD models. This integration will significantly enhance the predictive power, enabling a more accurate assessment of the risk and the reliable transferability of qAOPs across chemicals.

Adaptive capabilities and fitness consequences associated with pollution exposure in fish

“…These processes together describe levels and kinetics of internal chemical exposure and clearance, and hence influence toxicity. In general, concentrations of the respective chemical (or its metabolites) in the plasma of exposed fish are good predictors of biological effects [41] but they can also show considerable intra-individual variation. As an example, individual plasma concentrations in fathead minnow ( Pimephales promelas ) exposed to a synthetic glucocorticoid differed by as much as eightfold, which may in part be attributed to high variability in absorption and excretion dynamics [41].…”

Adverse outcome pathway networks I: Development and applications

“…Data generated during drug development were used to identify the cascades of key events likely to be triggered, and this information was organized within an AOP network. Chronic in vivo exposures to beclomethasone dipropionate were then carried out to generate a quantitative AOP network, which provided evidence that the polypharmacology profile of the beclomethasone dipropionate was indeed critically important to interpret and accurately predict the toxicological profile of the drug (Margiotta‐Casaluci et al ).…”

Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment

“…The AOP framework was conceptualized by integrating concepts across (eco)toxicology (including the mode-of-action framework), which originally included chemical-specific mechanistic information, into hazard assessment to aid in risk assessment and understanding of stressor-mediated adverse outcomes (Meek et al 2003;Seed et al 2005;Boobis et al 2006Boobis et al , 2008. For example, adverse outcomes may include cellular endpoints such as skin sensitization or abnormal cell proliferation (Nel and Malloy 2017), but how those interactions translate into effects on organismal performance and ecological processes remains uncertain (Margiotta-Casaluci et al 2016). The AOP framework has been used as an effective tool for arranging information at the suborganismal levels of organization and as an aid in interpreting data from high-throughput screening methods for the purpose of risk assessment.…”

“…Quantifying the detailed mechanistic information necessary to develop qAOPs on a single species is challenging, expensive, and labor and time intensive (Margiotta-Casaluci et al 2016), and it is impractical to repeat for thousands of species. Existing AOPs generally converge on single biological endpoints as if independent (e.g., growth or reproduction) and thus ignore the trade-offs implied by resource limitation, e.g., through the competition for energy among physiological processes.…”