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name tooluniverse-chemical-safety
description Comprehensive chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, FDA label safety data, DrugBank safety profiles, and STITCH chemical-protein interactions. Performs predictive toxicology (AMES, DILI, LD50, carcinogenicity), organ/system toxicity profiling, chemical-gene-disease relationship mapping, regulatory safety extraction, and environmental hazard assessment. Use when asked about chemical toxicity, drug safety profiling, ADMET properties, environmental health risks, chemical hazard assessment, or toxicogenomic analysis.

Chemical Safety & Toxicology Assessment

Comprehensive chemical safety and toxicology analysis integrating predictive AI models, curated toxicogenomics databases, regulatory safety data, and chemical-biological interaction networks. Generates structured risk assessment reports with evidence grading.

When to Use This Skill

Triggers:

  • "Is this chemical toxic?" / "What are the toxicity endpoints for [compound]?"
  • "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?"
  • "Predict toxicity for these molecules"
  • "Drug safety assessment for [drug name]"
  • "Environmental health risk of [chemical]"
  • "Chemical hazard profiling"
  • "Toxicogenomic analysis of [compound]"

Use Cases:

  1. Predictive Toxicology: AI-predicted toxicity endpoints (AMES mutagenicity, DILI, LD50, carcinogenicity, skin reactions) for novel compounds via SMILES
  2. ADMET Profiling: Full absorption, distribution, metabolism, excretion, toxicity characterization
  3. Toxicogenomics: Chemical-gene interaction mapping, gene-disease associations from CTD
  4. Regulatory Safety: FDA label warnings, boxed warnings, contraindications, adverse reactions
  5. Drug Safety Assessment: Combined DrugBank safety + FDA labels + adverse event data
  6. Chemical-Protein Interactions: STITCH-based chemical-protein binding and interaction networks
  7. Environmental Toxicology: Chemical-disease associations for environmental contaminants

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 minimums 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"
  9. English-first queries - Always use English chemical/drug names in tool calls

Evidence Grading System (MANDATORY)

Grade every toxicity claim by evidence strength:

Tier Symbol Criteria Examples
T1 [T1] Direct human evidence, regulatory finding FDA boxed warning, clinical trial toxicity, human case reports
T2 [T2] Animal studies, validated in vitro Nonclinical toxicology, AMES positive, animal LD50
T3 [T3] Computational prediction, association data ADMET-AI prediction, CTD association, QSAR model
T4 [T4] Database annotation, text-mined Literature mention, database entry without validation

Required Evidence Grading Locations

Evidence grades MUST appear in:

  1. Executive Summary - Key toxicity findings graded
  2. Toxicity Predictions - Every ADMET-AI endpoint with confidence note
  3. Regulatory Safety - FDA findings marked [T1]
  4. Chemical-Gene Interactions - CTD data marked by curation status
  5. Risk Assessment - Final risk classification with supporting evidence tiers

Core Strategy: 8 Research Dimensions

Chemical/Drug Query
|
+-- PHASE 0: Compound Disambiguation (ALWAYS FIRST)
|   +-- Resolve name -> SMILES, PubChem CID, ChEMBL ID
|   +-- Get molecular formula, weight, canonical structure
|
+-- PHASE 1: Predictive Toxicology (ADMET-AI)
|   +-- Mutagenicity (AMES)
|   +-- Hepatotoxicity (DILI, ClinTox)
|   +-- Carcinogenicity
|   +-- Acute toxicity (LD50)
|   +-- Skin reactions
|   +-- Stress response pathways
|   +-- Nuclear receptor activity
|
+-- PHASE 2: ADMET Properties
|   +-- Absorption: BBB penetrance, bioavailability
|   +-- Distribution: clearance, volume of distribution
|   +-- Metabolism: CYP interactions (1A2, 2C9, 2C19, 2D6, 3A4)
|   +-- Physicochemical: solubility, lipophilicity, pKa
|
+-- PHASE 3: Toxicogenomics (CTD)
|   +-- Chemical-gene interactions
|   +-- Chemical-disease associations
|   +-- Affected biological pathways
|
+-- PHASE 4: Regulatory Safety (FDA Labels)
|   +-- Boxed warnings (Black Box)
|   +-- Contraindications
|   +-- Adverse reactions
|   +-- Warnings and precautions
|   +-- Nonclinical toxicology
|
+-- PHASE 5: Drug Safety Profile (DrugBank)
|   +-- Toxicity data
|   +-- Contraindications
|   +-- Drug interactions affecting safety
|
+-- PHASE 6: Chemical-Protein Interactions (STITCH)
|   +-- Direct chemical-protein binding
|   +-- Interaction confidence scores
|   +-- Off-target effects
|
+-- PHASE 7: Structural Alerts (ChEMBL)
|   +-- Known toxic substructures (PAINS, Brenk)
|   +-- Structural alert flags
|
+-- SYNTHESIS: Integrated Risk Assessment
    +-- Aggregate all evidence tiers
    +-- Risk classification (Low/Medium/High/Critical)
    +-- Data gaps and recommendations

Phase 0: Compound Disambiguation (ALWAYS FIRST)

CRITICAL: Resolve compound identity before any analysis.

Input Types Handled

Input Format Resolution Strategy
Drug name (e.g., "Aspirin") PubChem_get_CID_by_compound_name -> get SMILES from properties
SMILES string Use directly for ADMET-AI; resolve to CID for other tools
PubChem CID PubChem_get_compound_properties_by_CID -> get SMILES + name
ChEMBL ID ChEMBL_get_molecule -> get SMILES + properties

Resolution Steps

  1. Input detection: Determine if input is name, SMILES, CID, or ChEMBL ID
    • SMILES: contains typical SMILES characters (=, #, [, ], (, ), c, n, o and no spaces in middle)
    • CID: numeric only
    • ChEMBL: starts with "CHEMBL"
    • Otherwise: treat as compound name
  2. Name to CID: PubChem_get_CID_by_compound_name(name=<compound_name>)
  3. CID to properties: PubChem_get_compound_properties_by_CID(cid=<cid>)
  4. Extract SMILES: Get SMILES from PubChem properties (field: ConnectivitySMILES, CanonicalSMILES, or IsomericSMILES depending on response format)
  5. Store resolved IDs: Maintain dict with name, smiles, cid, formula, weight, inchi

Disambiguation Output

## Compound Identity

| Property | Value |
|----------|-------|
| **Name** | Acetaminophen |
| **PubChem CID** | 1983 |
| **SMILES** | CC(=O)Nc1ccc(O)cc1 |
| **Formula** | C8H9NO2 |
| **Molecular Weight** | 151.16 |
| **InChI** | InChI=1S/C8H9NO2/... |

Phase 1: Predictive Toxicology (ADMET-AI)

When: SMILES is available (from Phase 0 or provided directly)

Objective: Run comprehensive AI-predicted toxicity endpoints

Tools Used

All ADMET-AI tools take the same parameter format:

Tool Predicted Endpoints Parameter
ADMETAI_predict_toxicity AMES, Carcinogens_Lagunin, ClinTox, DILI, LD50_Zhu, Skin_Reaction, hERG smiles: list[str]
ADMETAI_predict_stress_response Stress response pathway activation (ARE, ATAD5, HSE, MMP, p53) smiles: list[str]
ADMETAI_predict_nuclear_receptor_activity AhR, AR, ER, PPARg, Aromatase nuclear receptor activity smiles: list[str]

Workflow

  1. Call ADMETAI_predict_toxicity(smiles=[resolved_smiles])
  2. Call ADMETAI_predict_stress_response(smiles=[resolved_smiles])
  3. Call ADMETAI_predict_nuclear_receptor_activity(smiles=[resolved_smiles])
  4. For each endpoint, interpret prediction:
    • Classification endpoints: Active (1) = toxic signal, Inactive (0) = no signal
    • Regression endpoints (LD50): Report numerical value with context
    • All predictions graded [T3] (computational prediction)

Decision Logic

  • Multiple SMILES: Can batch up to ~10 SMILES in single call
  • Failed prediction: If ADMET-AI fails, note "prediction unavailable" (don't fail entire report)
  • Confidence: Note that AI predictions are [T3] evidence, not definitive
  • hERG flag: If hERG = Active, flag prominently (cardiac safety risk)
  • AMES flag: If AMES = Active, flag prominently (mutagenicity concern)
  • DILI flag: If DILI = Active, flag prominently (liver toxicity concern)

Output Table

### Toxicity Predictions [T3]

| Endpoint | Prediction | Interpretation | Concern Level |
|----------|-----------|---------------|---------------|
| AMES Mutagenicity | Inactive | No mutagenic signal | Low |
| Carcinogenicity | Inactive | No carcinogenic signal | Low |
| ClinTox | Active | Clinical toxicity signal | HIGH |
| DILI | Active | Drug-induced liver injury risk | HIGH |
| LD50 (Zhu) | 2.45 log(mg/kg) | ~282 mg/kg (moderate) | Medium |
| Skin Reaction | Inactive | No skin sensitization signal | Low |
| hERG Inhibition | Active | Cardiac arrhythmia risk | HIGH |

*All predictions from ADMET-AI. Evidence tier: [T3] (computational prediction)*

Phase 2: ADMET Properties

When: SMILES is available

Objective: Full ADMET characterization beyond toxicity

Tools Used

Tool Properties Predicted Parameter
ADMETAI_predict_BBB_penetrance Blood-brain barrier crossing probability smiles: list[str]
ADMETAI_predict_bioavailability Oral bioavailability (F20%, F30%) smiles: list[str]
ADMETAI_predict_clearance_distribution Clearance, VDss, half-life, PPB smiles: list[str]
ADMETAI_predict_CYP_interactions CYP1A2, 2C9, 2C19, 2D6, 3A4 inhibition/substrate smiles: list[str]
ADMETAI_predict_physicochemical_properties LogP, LogD, LogS, MW, pKa smiles: list[str]
ADMETAI_predict_solubility_lipophilicity_hydration Aqueous solubility, lipophilicity, hydration free energy smiles: list[str]

Workflow

  1. Call all 6 ADMET tools in parallel (independent calls)
  2. Compile results into Absorption / Distribution / Metabolism / Excretion sections
  3. Assess Lipinski Rule of 5 compliance from physicochemical properties
  4. Flag drug-drug interaction risks from CYP inhibition profiles

Decision Logic

  • BBB penetrant + toxicity: If BBB = Yes and any CNS toxicity endpoint active, flag as neurotoxicity risk
  • Low bioavailability: If F20% = Low, note absorption concerns
  • CYP inhibitor: If CYP3A4 inhibitor = Yes, flag high DDI risk
  • Lipinski violations: Count violations and report drug-likeness assessment

Output Format

### ADMET Profile [T3]

#### Absorption
| Property | Value | Interpretation |
|----------|-------|----------------|
| BBB Penetrance | Yes | Crosses blood-brain barrier |
| Bioavailability (F20%) | 85% | Good oral absorption |

#### Distribution
| Property | Value | Interpretation |
|----------|-------|----------------|
| VDss | 1.2 L/kg | Moderate tissue distribution |
| PPB | 92% | Highly protein bound |

#### Metabolism
| CYP Enzyme | Substrate | Inhibitor |
|------------|-----------|-----------|
| CYP1A2 | No | No |
| CYP2C9 | Yes | No |
| CYP2C19 | No | No |
| CYP2D6 | No | No |
| CYP3A4 | Yes | Yes (DDI risk) |

#### Excretion
| Property | Value | Interpretation |
|----------|-------|----------------|
| Clearance | 8.5 mL/min/kg | Moderate clearance |
| Half-life | 6.2 h | Moderate half-life |

Phase 3: Toxicogenomics (CTD)

When: Compound name is resolved

Objective: Map chemical-gene-disease relationships from curated CTD data

Tools Used

Tool Function Parameter
CTD_get_chemical_gene_interactions Genes affected by chemical input_terms: str (chemical name)
CTD_get_chemical_diseases Diseases linked to chemical exposure input_terms: str (chemical name)

Workflow

  1. Call CTD_get_chemical_gene_interactions(input_terms=compound_name)
  2. Call CTD_get_chemical_diseases(input_terms=compound_name)
  3. Parse gene interactions: extract gene symbols, interaction types (increases/decreases expression, binding, etc.)
  4. Parse disease associations: extract disease names, evidence types (marker/mechanism/therapeutic)
  5. Identify most affected biological processes from gene list

Decision Logic

  • Direct evidence vs inferred: CTD separates curated direct evidence from inferred associations
  • Therapeutic vs toxic: Disease associations can be therapeutic (drug treats disease) or adverse (chemical causes disease)
  • Gene interaction types: Distinguish between expression changes, binding, and activity modulation
  • Prioritize marker/mechanism: These indicate stronger causal evidence than simple associations
  • Grade curated as [T2]: Direct curated CTD evidence from literature
  • Grade inferred as [T3]: Computationally inferred associations

Output Format

### Toxicogenomics (CTD) [T2/T3]

#### Chemical-Gene Interactions (Top 20)
| Gene | Interaction | Type | Evidence |
|------|------------|------|----------|
| CYP1A2 | increases expression | mRNA | [T2] curated |
| TP53 | affects activity | protein | [T2] curated |
| ...  | ... | ... | ... |

**Total interactions found**: 156
**Top affected pathways**: Xenobiotic metabolism, Apoptosis, DNA damage response

#### Chemical-Disease Associations (Top 10)
| Disease | Association Type | Evidence |
|---------|-----------------|----------|
| Liver Neoplasms | marker/mechanism | [T2] curated |
| Contact Dermatitis | therapeutic | [T2] curated |
| ... | ... | ... |

Phase 4: Regulatory Safety (FDA Labels)

When: Compound has an approved drug name

Objective: Extract regulatory safety information from FDA drug labels

Tools Used

Tool Information Retrieved Parameter
FDA_get_boxed_warning_info_by_drug_name Black box warnings (most serious) drug_name: str
FDA_get_contraindications_by_drug_name Absolute contraindications drug_name: str
FDA_get_adverse_reactions_by_drug_name Known adverse reactions drug_name: str
FDA_get_warnings_by_drug_name Warnings and precautions drug_name: str
FDA_get_nonclinical_toxicology_info_by_drug_name Animal toxicology data drug_name: str
FDA_get_carcinogenic_mutagenic_fertility_by_drug_name Carcinogenicity/mutagenicity/fertility data drug_name: str

Workflow

  1. Call all 6 FDA tools in parallel (independent queries by drug name)
  2. Parse and structure each response
  3. Prioritize: Boxed Warnings > Contraindications > Warnings > Adverse Reactions
  4. All FDA label data is [T1] evidence (regulatory finding based on human/animal data)

Decision Logic

  • Boxed warning present: Flag as CRITICAL safety concern in executive summary
  • No FDA data: Chemical may not be an approved drug; note "Not an FDA-approved drug" and continue with other phases
  • Multiple warnings: Categorize by organ system (hepatic, cardiac, renal, CNS, etc.)
  • Nonclinical toxicology: Grade as [T2] (animal data supporting human risk)

Output Format

### Regulatory Safety (FDA) [T1]

#### Boxed Warning
**PRESENT** - Hepatotoxicity risk with doses >4g/day. Liver failure reported. [T1]

#### Contraindications
- Severe hepatic impairment [T1]
- Known hypersensitivity [T1]

#### Adverse Reactions (by frequency)
| Reaction | Frequency | Severity |
|----------|-----------|----------|
| Nausea | Common (>1%) | Mild |
| Hepatotoxicity | Rare (<0.1%) | Severe |
| ... | ... | ... |

#### Nonclinical Toxicology [T2]
- **Carcinogenicity**: No carcinogenic potential in 2-year rat/mouse studies
- **Mutagenicity**: Negative in Ames assay and in vivo micronucleus test
- **Fertility**: No effects on fertility at doses up to 10x human dose

Phase 5: Drug Safety Profile (DrugBank)

When: Compound is a known drug

Objective: Retrieve curated drug safety data from DrugBank

Tools Used

Tool Information Parameters
drugbank_get_safety_by_drug_name_or_drugbank_id Toxicity, contraindications query: str, case_sensitive: bool, exact_match: bool, limit: int

Workflow

  1. Call drugbank_get_safety_by_drug_name_or_drugbank_id(query=drug_name, case_sensitive=False, exact_match=False, limit=5)
  2. Parse toxicity information, overdose data, contraindications
  3. Cross-reference with FDA data from Phase 4

Decision Logic

  • Toxicity field: Contains LD50 values, overdose symptoms, organ toxicity data
  • DrugBank ID: Note if found for cross-referencing
  • Conflict with FDA: If DrugBank and FDA disagree, note discrepancy and defer to FDA [T1]
  • Not found: Chemical may not be in DrugBank; continue with other phases

Phase 6: Chemical-Protein Interactions (STITCH)

When: Compound can be identified by name or SMILES

Objective: Map chemical-protein interaction network for off-target assessment

Tools Used

Tool Function Parameters
STITCH_resolve_identifier Resolve chemical name to STITCH ID identifier: str, species: int (9606=human)
STITCH_get_chemical_protein_interactions Get chemical-protein interactions identifiers: list[str], species: int, required_score: int
STITCH_get_interaction_partners Get interaction network identifiers: list[str], species: int, limit: int

Workflow

  1. Resolve compound: STITCH_resolve_identifier(identifier=compound_name, species=9606)
  2. Get interactions: STITCH_get_chemical_protein_interactions(identifiers=[stitch_id], species=9606, required_score=700)
  3. Identify off-target proteins (not the intended drug target)
  4. Flag safety-relevant targets: hERG (cardiac), CYP enzymes (metabolism), nuclear receptors (endocrine)

Decision Logic

  • High confidence (>900): Well-established interaction [T2]
  • Medium confidence (700-900): Probable interaction [T3]
  • Low confidence (400-700): Possible interaction, needs validation [T4]
  • Safety-relevant targets: Flag interactions with known safety targets
  • No STITCH data: Chemical may be too novel; note and continue

Phase 7: Structural Alerts (ChEMBL)

When: ChEMBL molecule ID is available (from Phase 0)

Objective: Check for known toxic substructures

Tools Used

Tool Function Parameters
ChEMBL_search_compound_structural_alerts Find structural alert matches molecule_chembl_id: str, limit: int

Workflow

  1. If ChEMBL ID available: ChEMBL_search_compound_structural_alerts(molecule_chembl_id=chembl_id, limit=20)
  2. Parse alert types: PAINS (pan-assay interference), Brenk (medicinal chemistry), Glaxo (GSK structural alerts)
  3. Categorize severity: Some alerts are informational, others indicate likely toxicity

Decision Logic

  • PAINS alerts: May cause false positives in screening; note for medicinal chemistry
  • Brenk alerts: Known problematic substructures; flag if present
  • No alerts: Good sign but not definitive proof of safety
  • No ChEMBL ID: Skip this phase gracefully; note "structural alert analysis not available"

Synthesis: Integrated Risk Assessment (MANDATORY)

Always the final section. Integrates all evidence into actionable risk classification.

Risk Classification Matrix

Risk Level Criteria
CRITICAL FDA boxed warning present OR multiple [T1] toxicity findings OR active DILI + active hERG
HIGH FDA warnings present OR [T2] animal toxicity OR multiple active ADMET endpoints
MEDIUM Some [T3] predictions positive OR CTD disease associations OR structural alerts
LOW All ADMET endpoints negative AND no FDA/DrugBank safety flags AND no CTD concerns
INSUFFICIENT DATA Fewer than 3 phases returned data; cannot make confident assessment

Synthesis Template

## Integrated Risk Assessment

### Overall Risk Classification: [HIGH]

### Evidence Summary
| Dimension | Finding | Evidence Tier | Concern |
|-----------|---------|--------------|---------|
| ADMET Toxicity | DILI active, hERG active | [T3] | HIGH |
| FDA Label | Boxed warning for hepatotoxicity | [T1] | CRITICAL |
| CTD Toxicogenomics | 156 gene interactions, liver neoplasms | [T2] | HIGH |
| DrugBank | Known hepatotoxicity at high doses | [T2] | HIGH |
| STITCH | Binds CYP3A4, hERG | [T3] | MEDIUM |
| Structural Alerts | 2 Brenk alerts | [T3] | MEDIUM |

### Key Safety Concerns
1. **Hepatotoxicity** [T1]: FDA boxed warning + ADMET-AI DILI prediction + CTD liver disease associations
2. **Cardiac Risk** [T3]: ADMET-AI hERG prediction + STITCH hERG interaction
3. **Drug Interactions** [T3]: CYP3A4 substrate/inhibitor, potential DDI risk

### Data Gaps
- [ ] No in vivo genotoxicity data available
- [ ] STITCH interaction scores moderate (700-900)
- [ ] No environmental exposure data

### Recommendations
1. Avoid doses >4g/day (hepatotoxicity threshold) [T1]
2. Monitor liver function in chronic use [T1]
3. Screen for CYP3A4 interactions before co-administration [T3]
4. Consider cardiac monitoring for at-risk patients [T3]

Mandatory Completeness Checklist

Before finalizing any report, verify:

  • Phase 0: Compound fully disambiguated (SMILES + CID at minimum)
  • Phase 1: At least 5 toxicity endpoints reported or "prediction unavailable" noted
  • Phase 2: ADMET profile with A/D/M/E sections or "not available" noted
  • Phase 3: CTD queried; gene interactions and disease associations reported or "no data in CTD"
  • Phase 4: FDA labels queried; results or "not an FDA-approved drug" noted
  • Phase 5: DrugBank queried; results or "not found in DrugBank" noted
  • Phase 6: STITCH queried; results or "no STITCH data available" noted
  • Phase 7: Structural alerts checked or "ChEMBL ID not available" noted
  • Synthesis: Risk classification provided with evidence summary
  • Evidence Grading: All findings have [T1]-[T4] annotations
  • Data Gaps: Explicitly listed in synthesis section

Tool Parameter Reference

Critical Parameter Notes (verified from source code):

Tool Parameter Name Type Notes
All ADMETAI tools smiles list[str] Always a list, even for single compound
All CTD tools input_terms str Chemical name, MeSH name, CAS RN, or MeSH ID
All FDA tools drug_name str Brand or generic drug name
drugbank_get_safety_* query, case_sensitive, exact_match, limit str, bool, bool, int All 4 required
STITCH_resolve_identifier identifier, species str, int species=9606 for human
STITCH_get_chemical_protein_interactions identifiers, species, required_score list[str], int, int required_score=400 default
PubChem_get_CID_by_compound_name name str Compound name (not SMILES)
PubChem_get_compound_properties_by_CID cid int Numeric CID
ChEMBL_search_compound_structural_alerts molecule_chembl_id str ChEMBL ID (e.g., "CHEMBL112")

Response Format Notes

  • ADMET-AI: Returns {status: "success", data: {...}} with prediction values
  • CTD: Returns list of interaction/association objects
  • FDA: Returns {status, data} with label text
  • DrugBank: Returns {data: [...]} with drug records
  • STITCH: Returns list of interaction objects with scores
  • PubChem CID lookup: Returns {IdentifierList: {CID: [...]}} (may or may not have data wrapper)
  • PubChem properties: Returns dict with CID, MolecularWeight, ConnectivitySMILES, IUPACName

Fallback Strategies

Compound Resolution

  • Primary: PubChem by name -> CID -> properties -> SMILES
  • Fallback 1: ChEMBL search by name -> molecule -> SMILES
  • Fallback 2: If SMILES provided directly, skip name resolution

Toxicity Prediction

  • Primary: All 9 ADMET-AI endpoints
  • Fallback: If ADMET-AI fails for a compound, note "prediction failed" and continue with database evidence
  • Note: ADMET-AI may fail for very large or unusual SMILES

Regulatory Data

  • Primary: FDA labels by drug name
  • Fallback: If FDA returns no data, try alternative drug names (brand vs generic)
  • Note: Non-drug chemicals (pesticides, industrial) will not have FDA labels

CTD Data

  • Primary: Search by common chemical name
  • Fallback: Try MeSH name if common name fails
  • Note: Novel compounds may not be in CTD

Common Use Patterns

Pattern 1: Novel Compound Assessment

Input: SMILES string for new molecule
Workflow: Phase 0 (SMILES->CID) -> Phase 1 (toxicity) -> Phase 2 (ADMET) -> Phase 7 (structural alerts) -> Synthesis
Output: Predictive safety profile for novel compound

Pattern 2: Approved Drug Safety Review

Input: Drug name (e.g., "Acetaminophen")
Workflow: All phases (0-7 + Synthesis)
Output: Complete safety dossier with regulatory + predictive + database evidence

Pattern 3: Environmental Chemical Risk

Input: Chemical name (e.g., "Bisphenol A")
Workflow: Phase 0 -> Phase 1 -> Phase 2 -> Phase 3 (CTD, key for env chemicals) -> Phase 6 -> Synthesis
Output: Environmental health risk assessment focused on gene-disease associations

Pattern 4: Batch Toxicity Screening

Input: Multiple SMILES strings
Workflow: Phase 0 -> Phase 1 (batch) -> Phase 2 (batch) -> Comparative table -> Synthesis
Output: Comparative toxicity table ranking compounds by safety

Pattern 5: Toxicogenomic Deep-Dive

Input: Chemical name + specific gene or disease interest
Workflow: Phase 0 -> Phase 3 (CTD expanded) -> Literature search -> Synthesis
Output: Detailed chemical-gene-disease mechanistic analysis

Output Report Structure

All analyses generate a structured markdown report with progressive sections:

# Chemical Safety & Toxicology Report: [Compound Name]

**Generated**: YYYY-MM-DD HH:MM
**Compound**: [Name] | SMILES: [SMILES] | CID: [CID]

## Executive Summary
[2-3 sentence overview with risk classification and key findings, all graded]

## 1. Compound Identity
[Phase 0 results - disambiguation table]

## 2. Predictive Toxicology
[Phase 1 results - ADMET-AI toxicity endpoints]

## 3. ADMET Profile
[Phase 2 results - absorption, distribution, metabolism, excretion]

## 4. Toxicogenomics
[Phase 3 results - CTD chemical-gene-disease relationships]

## 5. Regulatory Safety
[Phase 4 results - FDA label information]

## 6. Drug Safety Profile
[Phase 5 results - DrugBank data]

## 7. Chemical-Protein Interactions
[Phase 6 results - STITCH network]

## 8. Structural Alerts
[Phase 7 results - ChEMBL alerts]

## 9. Integrated Risk Assessment
[Synthesis - risk classification, evidence summary, data gaps, recommendations]

## Appendix: Methods and Data Sources
[Tool versions, databases queried, date of access]

Limitations & Known Issues

Tool-Specific

  • ADMET-AI: Predictions are computational [T3]; should not replace experimental testing
  • CTD: Curated but may lag behind latest literature by 6-12 months
  • FDA: Only covers FDA-approved drugs; not applicable to environmental chemicals or supplements
  • DrugBank: Primarily drugs; limited coverage of industrial chemicals
  • STITCH: Score thresholds affect sensitivity; lower scores increase false positives
  • ChEMBL: Structural alerts require ChEMBL ID; not all compounds have one

Analysis

  • Novel compounds: May only have ADMET-AI predictions (no database evidence)
  • Environmental chemicals: FDA/DrugBank phases will be empty; rely on CTD and ADMET-AI
  • Batch mode: ADMET-AI can handle batches; other tools require individual queries
  • Species specificity: Most data is human-centric; animal data noted where applicable

Technical

  • SMILES validity: Invalid SMILES will cause ADMET-AI failures
  • Name ambiguity: Chemical names can be ambiguous; always verify with CID
  • Rate limits: Some FDA endpoints may rate-limit for rapid queries

Summary

Chemical Safety & Toxicology Assessment Skill provides comprehensive safety evaluation by integrating:

  1. Predictive toxicology (ADMET-AI) - 9 tools covering toxicity, ADMET, physicochemical properties
  2. Toxicogenomics (CTD) - Chemical-gene-disease relationship mapping
  3. Regulatory safety (FDA) - 6 tools for label-based safety extraction
  4. Drug safety (DrugBank) - Curated toxicity and contraindication data
  5. Chemical interactions (STITCH) - Chemical-protein interaction networks
  6. Structural alerts (ChEMBL) - Known toxic substructure detection

Outputs: Structured markdown report with risk classification, evidence grading, and actionable recommendations

Best for: Drug safety assessment, chemical hazard profiling, environmental toxicology, ADMET characterization, toxicogenomic analysis

Total tools integrated: 25+ tools across 6 databases