A new study by researchers at the Chinese Research Academy of Environmental Sciences and Beijing Normal University presents a non-animal assessment framework designed to detect hazardous chemicals faster and more precisely. By merging high-throughput screening, AI-enabled toxicity mapping, and quantitative in vitro–to–in vivo risk modeling, the framework uncovers priority pollutants, reveals their toxicity mechanism, and predicts risk thresholds. With these tools, researchers have a potential foundation for a predictive, prevention-oriented chemical governance system that could potentially change the way chemical risk assessments are made in the future. The research was published in the November 2025 edition of Environmental Science and Ecotechnology.

According to a new release outlining the research, the number of chemicals released into global supply chains continues to increase. Animal testing — long considered the backbone of chemical safety — is strained by a long testing period, high costs, and low throughput. Additionally, modern contaminants increasingly display nontraditional toxicity behaviors, from non-monotonic dose responses to complex multi-target biological interactions. These scientific hurdles collide with ethical debates and regulatory momentum to reduce reliance on animals. Because of these challenges, there has emerged a need to develop new, integrative approaches for chemical hazard identification and risk assessment.

In the study, researchers unveiled a unified, non-animal framework that reimagines how chemical risks are evaluated. The system brings together high-throughput computational screening, multi-omics toxicity insights, and advanced in vitro models powered by AI. By bridging mechanistic understanding with quantitative exposure predictions, the framework offers regulators and scientists a cohesive roadmap for rapidly identifying harmful chemicals and determining safe environmental thresholds.

According to the news release, "the authors presented a three-part framework that transforms chemical assessment from isolated tests into a predictive, mechanism-driven pipeline. The first module looks at the global toxicity data gap by cross-referencing chemical inventories from multiple databases with quantitative structure–activity relationship (QSAR) models, read-across methods and bioactivity clustering. The high-throughput process rapidly flags chemicals lacking safety data and nominates candidates requiring urgent evaluation. The second module applies artificial intelligence to multi-source toxicological evidence — including genomics, transcriptomics, proteomics, and adverse outcome pathways. Machine learning models uncover patterns too complex for traditional methods, pinpointing toxic structural features, molecular biomarkers, and mechanistic pathways. Advanced models such as ToxACoL demonstrate the ability to predict toxicity across species and experimental conditions, even when datasets are limited or imbalanced. The final module bridges laboratory findings with real-world exposure. By integrating ADME factors, physiologically based toxicokinetic models, and organoid or organ-on-chip validation, the system translates in vitro responses into in vivo-equivalent doses. Environmental concentrations can then be compared against these predicted thresholds to quantify ecological and human risks with unprecedented speed."

With these modules, researchers can create a toxicology model that is anticipatory and scalable, which can replace the slower, reactive testing with a more proactive chemical management system.

According to a scientist involved in the study, the new, integrated framework represents a critical turning point for global chemical governance. The expert noted that "high-quality, multimodal NAM datasets — when standardized, validated, and shared transparently — can deliver predictions that rival or surpass traditional animal studies. Yet, they also highlight key challenges, including data harmonization, model interpretability, and the need for interdisciplinary talent trained in toxicology, AI, and regulatory science." According to the scientist, sustained investment and clear policy guidance could help to make the new digital framework a "cornerstone of next-generation chemical safety evaluation."

Additionally, the study's framework "opens a pathway toward chemical safety assessments that are faster, more humane, and more scientifically robust." It has the potential to help regulators quickly "pinpoint high-risk pollutants, guide safer chemical design in industry, and provide early warnings for emerging contaminants before they become widespread hazards." Using a technique of linking predictive toxicology with identified principles, this new system could support more integrated protection of humans, ecosystems, and biodiversity, according to the scientists. Additionally, the successful implementation of this framework has the potential to "strengthen climate resilience, reduce pollutant-driven ecosystem damage, and accelerate global sustainability efforts aimed at managing chemical risks in a rapidly changing world."