In Silico Toxicity Profiling of Industrial Pollutants: A Computational NAM Approach to Assessing PFAS Ecological Risks in Zebrafish (Danio rerio)

The intensified release of industrial pollutants into natural habitats creates a severe threat to global biodiversity and ecosystem health. Traditional ecotoxicological assessments relying on in vivo animal testing are often time-consuming, ethically constrained, and lack the scalability required to evaluate the sheer volume of emerging contaminants. To address this, New Approach Methodologies (NAMs) utilising computational biophysics offer a highly effective, visionary alternative. This study employs an in silico toxicity profiling framework to evaluate the ecological risk of the per- and polyfluoroalkyl substances (PFAS), specifically Perfluorooctanoic acid (PFOA) and Perfluorooctanesulfonic acid (PFOS), to the sentinel wildlife species. Using advanced molecular docking and computational modelling, we assessed the binding affinities and the structural interactions of these industrial pollutants against Peroxisome proliferator-activated receptors (PPARs), specifically PPARα and PPARγ, associated with the aquatic bio-indicator Zebrafish (Danio rerio). Our predictive modelling demonstrates that PFOA and PFOS exhibit notably high binding affinity to the ligand-binding domains of these nuclear receptors. This strong molecular interaction suggests a severe potential for the disruption of lipid metabolism and critical endocrine signalling pathways in exposed aquatic life. By integrating molecular-level in silico profiling with the macro-level wildlife ecotoxicology, this approach successfully bridges the gap between computational structural biology and environmental conservation. Finally, this next-generation risk assessment tool provides a rapid, ethical, and highly scalable mechanism to predict chemical hazards, thereby informing proactive environmental policy and habitat protection strategies without relying on traditional animal models.