Chemical Safety & Toxicology Assessment

Chemical Safety & Toxicology Assessment

Toxicity assessment: identify the chemical, check known hazards (GHS, IARC), then look for ADMET predictions. Dose makes the poison — always consider exposure level, as a compound that is toxic at high doses may be safe at relevant exposures. Distinguish between acute toxicity (LD50, GHS category) and chronic hazards (carcinogenicity, endocrine disruption) — they require different risk management approaches. Computational predictions (ADMETAI) are T3 evidence and must be anchored by experimental data from PubChemTox or FDA labels wherever available. When evidence conflicts between prediction and experiment, always defer to the experimental finding.

LOOK UP DON'T GUESS: never assume GHS categories, IARC classification, or CTD disease links — always call PubChemTox and CTD tools to retrieve current classifications before reporting.

Comprehensive chemical safety analysis integrating predictive AI models, curated toxicogenomics databases, regulatory safety data, and chemical-biological interaction networks.

When to Use This Skill

Triggers:

• "Is this chemical toxic?" / "Assess the safety profile of [drug/chemical]"
• "What are the ADMET properties of [SMILES]?"
• "What genes does [chemical] interact with?" / "What diseases are linked to [chemical] exposure?"
• "Drug safety assessment" / "Environmental health risk" / "Chemical hazard profiling"

Use Cases:

1. Predictive Toxicology: AI-predicted endpoints (AMES, DILI, LD50, carcinogenicity, hERG) via SMILES
2. ADMET Profiling: Absorption, distribution, metabolism, excretion, toxicity
3. Toxicogenomics: Chemical-gene-disease mapping from CTD
4. Regulatory Safety: FDA label warnings, contraindications, adverse reactions
5. Drug Safety: DrugBank safety + FDA labels combined
6. Chemical-Protein Interactions: STITCH-based interaction networks
7. Environmental Toxicology: Chemical-disease associations for contaminants

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

KEY PRINCIPLES

1. Report-first approach - Create report file FIRST, then populate progressively
2. Tool parameter verification - Verify params via get_tool_info before calling unfamiliar tools
3. Evidence grading - Grade all safety claims by evidence strength (T1-T4)
4. Citation requirements - Every toxicity finding must have inline source attribution
5. Mandatory completeness - All sections must exist with data or explicit "No data" notes
6. Disambiguation first - Resolve compound identity (name -> SMILES, CID, ChEMBL ID) before analysis
7. Negative results documented - "No toxicity signals found" is data; empty sections are failures
8. Conservative risk assessment - When evidence is ambiguous, flag as "requires further investigation"