Tooluniverse Toxicology

Toxicology Assessment via Adverse Outcome Pathways & Signal Detection

Systematic toxicology analysis that links molecular initiating events (MIEs) through adverse outcome pathways (AOPs) to apical adverse outcomes, then triangulates with real-world FAERS signals, FDA label data, and toxicogenomic associations.

Domain Reasoning

Toxicity has many mechanisms, and the first interpretive question is temporal: is this acute toxicity (immediate effect from a high dose) or chronic toxicity (cumulative damage from long-term low-dose exposure)? Acute and chronic toxicity operate through different mechanisms — acute hepatotoxicity may reflect direct mitochondrial damage, while chronic hepatotoxicity may involve fibrosis from repeated low-level inflammation. They also have different regulatory frameworks: acute toxicity is captured by LD50 and emergency protocols, while chronic toxicity requires long-term carcinogenicity and repeat-dose studies.

LOOK UP DON'T GUESS

• Adverse outcome pathways for a chemical: query AOPWiki_list_aops and AOPWiki_get_aop; do not describe mechanisms from memory.
• FAERS adverse event signals: retrieve from FAERS_count_reactions_by_drug_event and FAERS_calculate_disproportionality; never estimate PRR values.
• FDA label warnings: call DailyMed_parse_adverse_reactions and related tools; do not state boxed warnings from memory.
• CTD chemical-gene and chemical-disease associations: query CTD_get_chemical_gene_interactions and CTD_get_chemical_diseases; do not infer gene targets without database evidence.

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.

When to Use This Skill

Triggers:

• "What are the toxicity mechanisms for [drug/chemical]?"
• "Find adverse outcome pathways for [chemical]"
• "What AOPs are relevant to [target/organ/effect]?"
• "FAERS signal analysis for [drug]"
• "Toxicogenomic profile for [chemical]"
• "What is the mechanism of hepatotoxicity / cardiotoxicity / neurotoxicity for [drug]?"

Use Cases:

1. AOP Tracing: Map chemical MIE through key events to apical outcome using AOPWiki
2. Real-World Signal Detection: Quantify FAERS adverse event signals with PRR/ROR
3. Label Safety Mining: Extract FDA boxed warnings, contraindications, nonclinical toxicology
4. Toxicogenomics: Chemical-gene-disease associations from CTD
5. Integrated Mechanism Report: Combine AOP pathway + real-world signals into unified narrative

KEY PRINCIPLES

1. AOP-first thinking - Frame all toxicity in terms of MIE → Key Events → Adverse Outcome
2. Report-first approach - Create report file FIRST, update progressively
3. Evidence grading mandatory - T1 (regulatory/clinical) through T4 (computational/AOP annotation)
4. Distinguish mechanism from signal - AOPWiki = mechanism; FAERS = real-world signal
5. Disambiguation first - Resolve drug/chemical identity before any queries
6. English-first queries - Always use English names in tool calls

Evidence Grading

Workflow Overview

Chemical/Drug Query
|
+-- PHASE 0: Disambiguation
|   Resolve name -> identifiers (ChEMBL, PubChem CID, SMILES)
|
+-- PHASE 1: Adverse Outcome Pathway Mapping (AOPWiki)
|   List AOPs by keyword; retrieve key events, MIEs, and biological plausibility scores
|
+-- PHASE 2: Real-World Adverse Event Signals (FAERS)
|   Top reactions by drug; disproportionality (PRR); serious event filter
|
+-- PHASE 3: FDA Label Safety Mining
|   Boxed warnings, contraindications, nonclinical toxicology, adverse reactions
|
+-- PHASE 4: Toxicogenomics (CTD)
|   Chemical-gene interactions; chemical-disease associations
|
+-- SYNTHESIS: Integrated Toxicology Report
    AOP-linked mechanism + FAERS signal + CTD gene targets + Risk classification

Phase 0: Disambiguation

Objective: Establish compound identity before any database queries.

Tools:

• PubChem_get_CID_by_compound_name (name: str) — get CID + SMILES
• ChEMBL_search_drugs (query: str) — get ChEMBL ID and max phase

Capture: generic name, SMILES, PubChem CID, ChEMBL ID, drug class.

Phase 1: Adverse Outcome Pathway Mapping

Objective: Find AOPs relevant to the chemical's known or suspected toxicity mechanisms.

Tools

AOPWiki_list_aops:

• Input: keyword (str) — e.g., organ ("liver", "kidney"), effect ("apoptosis", "inflammation"), or target ("AhR", "PPARalpha")
• Output: List of AOP IDs, titles, and short descriptions
• Use: Discovery scan to identify candidate AOPs

AOPWiki_get_aop:

• Input: aop_id (int) — ID from list_aops result
• Output: Full AOP details including MIE, key events (KEs), key event relationships (KERs), biological plausibility, and weight-of-evidence
• Use: Retrieve mechanistic pathway details for selected AOPs

Workflow

1. Query AOPWiki_list_aops with organ-level keyword (e.g., "hepatotoxicity", "nephrotoxicity")
2. Query again with mechanism-level keyword (e.g., "oxidative stress", "mitochondria")
3. Select top 3-5 most relevant AOPs by title relevance
4. Call AOPWiki_get_aop for each selected AOP
5. Extract: MIE (molecular initiating event), key events in order, apical adverse outcome, biological plausibility score

Decision Logic

• AOP found: Extract full pathway; note plausibility level (high/moderate/low)
• No direct AOP match: Try broader organ or mechanism terms; document as "no AOP directly mapped"
• Multiple AOPs: Report all; highlight shared key events as high-confidence mechanisms

AOP Table Format

Phase 2: Real-World Adverse Event Signals (FAERS)

Objective: Quantify observed adverse events with statistical signal measures.

Tools

FAERS_count_reactions_by_drug_event:

• Input: drug_name (str), limit (int, default 50)
• Output: Top adverse reactions with counts
• Note: param is drug_name not drug

FAERS_calculate_disproportionality:

• Input: drug_name (str), reaction_meddra_pt (str)
• Output: PRR, ROR, IC with confidence intervals

FAERS_filter_serious_events:

• Input: drug_name (str), serious_type (str: "death", "hospitalization", "life-threatening")
• Output: Serious event count and case details

FAERS_stratify_by_demographics:

• Input: drug_name (str), reaction_meddra_pt (str)
• Output: Age/sex breakdown for specific reaction

Workflow

1. Get top 25 reactions via FAERS_count_reactions_by_drug_event
2. Filter to organ-system clusters matching the AOP outcomes from Phase 1
3. Calculate PRR for top 10 reactions via FAERS_calculate_disproportionality
4. Check serious events (deaths, hospitalizations) for highest-PRR reactions

Signal Thresholds

Phase 3: FDA Label Safety Mining

Objective: Extract regulatory safety findings from approved drug labels.

Tools

• DailyMed_parse_adverse_reactions (drug_name: str)
• DailyMed_parse_contraindications (drug_name: str)
• DailyMed_parse_clinical_pharmacology (drug_name: str)
• DailyMed_parse_drug_interactions (drug_name: str)

Note: These tools apply to FDA-approved drugs only. Environmental chemicals will have no label data — document explicitly.

Workflow

1. Extract adverse reactions and note which match FAERS signals
2. Extract contraindications (highest evidence tier [T1])
3. Note pharmacological mechanism from clinical pharmacology section

Phase 4: Toxicogenomics (CTD)

Objective: Map chemical-gene interactions and chemical-disease associations.

Tools

CTD_get_chemical_gene_interactions:

• Input: input_terms (str) — chemical name or MeSH ID
• Output: Gene targets with interaction type (increases/decreases expression)
• Use: Find molecular targets mediating toxicity

CTD_get_chemical_diseases:

• Input: input_terms (str) — chemical name or MeSH ID
• Output: Disease associations with evidence type (curated/inferred)
• Use: Find downstream disease endpoints

Workflow

1. Query CTD with compound name; note curated (higher confidence) vs inferred entries
2. Cross-reference gene targets with Phase 1 AOP key events
3. Note which CTD disease endpoints match AOP apical outcomes

Synthesis: Integrated Toxicology Report

Structure:

# Toxicology Report: [Compound Name]
**Generated**: YYYY-MM-DD

## Executive Summary
Risk tier: CRITICAL / HIGH / MEDIUM / LOW / INSUFFICIENT DATA
Key finding summary (2-3 sentences)

## 1. Compound Identity
(disambiguation table)

## 2. Adverse Outcome Pathways [T3-T4]
(AOP table; pathway diagrams in text form)

## 3. Real-World Adverse Event Signals [T1-T2]
(FAERS top reactions + PRR table + serious events)

## 4. FDA Label Safety [T1]
(boxed warnings, contraindications, adverse reactions)

## 5. Toxicogenomics [T2-T4]
(CTD gene targets + disease associations)

## 6. Mechanistic Integration
(How AOP key events map to observed FAERS signals and CTD gene targets)

## 7. Risk Classification
(Final tier with rationale)

## Data Gaps & Limitations
(Missing data, confidence caveats)

Risk Classification

Fallback Chains

Tool Parameter Reference (Critical)

Limitations

• AOPWiki: AOPs are in development; many lack high plausibility scores
• FAERS: Observational data; confounding by indication; underreporting bias
• CTD: Inferred associations have high false-positive rate
• DailyMed: FDA-approved drugs only; no environmental chemical coverage
• Environmental chemicals: Primarily Phase 1 (AOP) + Phase 4 (CTD) data available

References

• AOPWiki: https://aopwiki.org
• FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers
• CTD: http://ctdbase.org
• DailyMed: https://dailymed.nlm.nih.gov
• OpenFDA: https://open.fda.gov