Toxicology Assessment via Adverse Outcome Pathways & Signal Detection

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