Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles

TL;DR

This paper uses natural language processing to analyze nanomaterial toxicity from scientific literature, creating a structured database for data-driven hazard predictions.

Contribution

A novel NLP pipeline that combines topic modeling and LLM prompts to extract nanotoxicology data at scale.

Findings

Ag-based nanomaterials show the lowest MIC and MBC compared to ZnO, TiO2, and Au.

Nanomaterials under 50 nm often have lower MIC and LC50, but toxicity varies widely due to surface properties.

Ag and TiO2 are linked to reproductive and developmental toxicity in model organisms.

Abstract

Natural language processing (NLP) pipelines can mine
the nanotoxicology
literature at a scale and resolution that cannot be achieved by manual
curation. Here, we established a NLP pipeline that coupled topic modeling
and end point-specific extraction LLM prompts to convert ∼106 sentences extracted from abstracts of scientific articles
into a structured knowledge base containing 13 nanotoxicology features.
The pipeline is capable of analyzing and extracting nanomaterial descriptors
such as size, ζ potential, and surface area, along with biological
end points such as minimum inhibitory concentration (MIC), minimum
bactericidal concentration (MBC) and lethal concentration 50% (LC50). Statistical convergence across multiple quantitative end
points - MIC, MBC, microbial log reduction, and biofilm killing efficiency
- shows that Ag-based nanomaterials are the most potent antimicrobial
agents, showing lower MIC and MBC than ZnO, TiO2 and Au
analogs. This trend was also observed for individual pathogens such
as Escherichia coli and Staphylococcus aureus. Most nanomaterials are within
1 to 100 nm, with nanoparticles featured in >80% of the studies.
Although
nanomaterials <50 nm often produce the lowest MIC and LC50, toxicity within a single size class spans orders of magnitude,
underscoring the influence of surface chemistry, coatings, and colloidal
behavior. In addition, adverse reproductive effects in Caenorhabditis elegans and Daphnia
magna, and developmental abnormalities in Danio rerio, are predominantly related to Ag and
TiO2. In general, our automated data extraction and the
curation strategy transforms disparate literature into a machine-readable
knowledge base that paves the way for data-driven predictions of nanomaterial
hazards.

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