Predictive endocrine remodeling and regulatory intelligence: From Nanoremediation to digital–twin toxicology and adaptive environmental governance

Microplastics and nanoplastics are becoming significant environmental contaminants capable of transporting endocrine–disrupting chemicals, and interact directly with biological systems thereby perturbing hormone receptor signaling and inducing systemic endocrine dysfunction through mechanisms such as oxidative stress, mitochondrial damage, sustained inflammation, and epigenetic modifications. While conventional remediation strategies primarily mitigate environmental contamination and provide limited capacity into how residual particules disrupt endocrine homeostasis across biological systems. Here, we propose an Advanced Predictive Nanotechnological Endocrine Remediation (APN–NER) framework as a conceptual, rather than experimentally validated, paradigm for integrating remediation with predictive endocrine risk assessment. The framework combines 3D–engineered nanomaterials, intelligent remediation platforms, systems endocrinology, multiomics, artificial intelligence, and digital–twin toxicology to connect contaminant capture with mechanistic characterization and endocrine outcome prediction. We critically examine how remediation–generated chemical and biological data could inform computational models of exposure, molecular disruption, and endocrine network responses. APN–NER therefore reframes remediation as both an environmental intervention and a potential source of mechanistic intelligence, providing a proposed pathway toward adaptive, mechanism–informed monitoring and future. However, susbtantial methodological and regulatory validation remains necessary.