AI Insight
ToxCastLite is a newly developed computational tool that converts the large ToxCast MySQL database into a portable, semantically structured system combining SQLite databases with an RDF knowledge graph, reducing the data footprint from approximately 100 GB to around 3 GB for targeted use cases such as developmental neurotoxicity. The system integrates in vitro bioactivity data from ToxCast with in vivo toxicity evidence from ToxRefDB v3.0 and product-use context from CPDat v4.0, all linked through standardized chemical identifiers. A prototype interface allows users to query this integrated dataset using natural language, which is translated into SPARQL queries via a locally deployed large language model, enabling cross-domain evidence mining without requiring expertise in SQL or SPARQL.
Why it matters
ToxCastLite lowers the technical barrier for researchers and regulatory scientists to perform integrated chemical safety assessments by connecting laboratory bioactivity data, animal study results, and real-world exposure context in a single accessible framework. This kind of integrated evidence system supports the development and evaluation of new approach methodologies intended to reduce reliance on traditional animal testing in toxicology.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Motivation: The ToxCast database is a valuable resource for computational toxicology and new approach methodologies (NAMs), but the approximately 100GB MySQL distribution is difficult to use for portable local analysis and cross-domain evidence mining. Many practical questions concern chemicals, in vitro bioactivity, in vivo toxicological evidence, and exposure-relevant product-use context rather than raw database keys. Results: We present ToxCastLite, a portable semantic evidence-access system that combines assay-scoped SQLite databases with a compact RDF layer for GraphDB-based querying. The system streams large ToxCast/invitrodb MySQL dumps into curated SQLite profiles, reducing the footprint to approximately 3~GB for focused use cases such as developmental neurotoxicity. Dense numerical evidence, including concentration–response rows, remains in SQLite, while the RDF projection exposes linked semantic entities such as chemicals, assays, endpoints, model results, potency parameters (AC50), and MC6 quality flags. We further extend the graph with CPDat v4.0 product-use and functional-use evidence and ToxRefDB v3.0 in vivo toxicity evidence, including processed studies, point-of-departure records, effect summaries, and observation summaries. These layers are linked through DSSTox Substance Identifiers, enabling integrated queries across NAM bioactivity, curated animal-study evidence, and exposure/use context. A Streamlit prototype supports exploration through a locally deployed LLM that translates natural-language questions into SPARQL, grounded by a versioned RDF schema to reduce hallucination risk. Case studies in developmental neurotoxicity demonstrate how ToxCastLite identifies concordance between high-confidence in vitro DNT activity and positive in vivo apical evidence, detects in vitro DNT activity beyond available DNT-specific in vivo evidence, and prioritizes chemicals where NAM signals, ToxRefDB evidence, and CPDat product-use context intersect. For selected results, users can drill down from the semantic graph to the underlying SQLite records and retrieve concentration–response curves for expert inspection without manually writing SQL or SPARQL.