There has been a clear shift over recent years in how scientists assess the risks of engineered nanoparticles, calling for toxicity evaluations that are predictive, mechanism-based, and grounded in real-life exposure scenarios.
Study: Predictive and Mechanism-Based Toxicity Evaluation of Engineered Nanoparticles. Image Credit: TheCorgi/Shutterstock.com
An editorial published in Nanomaterials synthesizes findings from a recent Special Issue, reflecting how the field is moving beyond short-term toxicity tests toward more integrated and forward-looking safety frameworks.
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Engineered nanoparticles (NPs), typically measuring between 1 and 100 nanometers, behave very differently from their bulk material counterparts. Their high surface area and reactive surfaces enable novel interactions with biological systems, affecting how they are absorbed, transported, and processed in cells and organisms.
With nanomaterials increasingly being used in energy technologies, electronics, medicine, and consumer products, their release into the environment during manufacturing, use, and disposal has become unavoidable. Concerns about long-term ecological and human health risks continue to intensify.
Acute Toxicity Translated to System-Levels
The review highlights how nanotoxicology has evolved from an early focus on acute cytotoxicity toward examining chronic, low-dose, and system-level effects.
Evidence shows that nanoparticle toxicity is highly context-dependent, shaped by material composition, size, shape, and exposure conditions.
Several examples illustrate this complexity: Silver nanomaterials exhibit pronounced shape-dependent toxicity, with different morphologies triggering distinct biological responses; carbon-based nanoparticles, a major component of fine particulate air pollution, are linked to adverse respiratory, cardiovascular, and neurological effects.
For emerging materials such as MXenes, toxicity appears closely tied to environmental stability and degradation pathways rather than composition alone.
Modeling in Toxicity Testing
A central message in the review is that the choice of biological model can strongly influence toxicity outcomes.
Studies reviewed span conventional in vitro systems, including A549 lung and HepG2 liver cells, as well as whole-organism models such as rats, zebrafish, and aquatic plants.
Differences in nanoparticle uptake, intracellular processing, and stress responses between primary cells, cancer cell lines, and intact organisms complicate cross-study comparisons and limit straightforward extrapolation.
To address these challenges, the article looks at integrating experimental data with computational approaches.
Tools such as quantitative structure–activity relationship (QSAR) modeling, machine learning, and deep learning are presented as complementary methods that can help screen and prioritize nanomaterials, rather than replacing laboratory- or organism-based testing.
Persistent Gaps in Mechanistic Understanding
Despite significant progress, there are several unresolved challenges.
Nanoparticle heterogeneity – encompassing size, shape, surface chemistry, and colloidal stability – continues to undermine reproducibility and predictive accuracy.
Detecting and characterizing nanoparticles within complex biological environments remains technically demanding, particularly when particles aggregate, chemically transform, or degrade after exposure.
While oxidative stress, inflammatory signaling, endocrine disruption, and reproductive effects are frequently observed, these responses are not yet integrated into a unified, system-level mechanistic framework.
These combined challenges make it difficult to predict long-term outcomes from short-term assays.
Life-Cycle Risk Assessment Approaches
Looking ahead, the authors argue that nanoparticle safety evaluation must adopt a life-cycle perspective. This means accounting not only for pristine materials, but also for their transformation products and dynamic behavior across biological and ecological systems.
Mechanism-based frameworks that combine experimental toxicology with advanced computational tools are seen as essential for improving hazard ranking, exposure-effect prediction, and risk assessment.
As engineered nanomaterials continue to enter commercial and environmental pathways, the need for reliable, predictive toxicity frameworks becomes increasingly urgent. The review suggests that machine learning-assisted screening could help prioritize materials for early-stage assessment, supporting safer design choices before large-scale deployment.
Nanotoxicology is framed as a collaborative, interdisciplinary effort in this review.
Progress will depend on closer integration among toxicologists, materials scientists, computational researchers, and regulators to ensure that technological innovation advances alongside stronger health and environmental protection.
Journal Reference
Yan, B. & Liu, R. (2026). Predictive and Mechanism-Based Toxicity Evaluation of Engineered Nanoparticles. Nanomaterials, 16(3), 185. DOI: 10.3390/nano16030185
