tooluniverse-adverse-event-detection

Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, adverse event investigation, and regulatory decision support.

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Adverse Drug Event Signal Detection & Analysis

Automated pipeline for detecting, quantifying, and contextualizing adverse drug event signals using FAERS disproportionality analysis, FDA label mining, mechanism-based prediction, and literature evidence. Produces a quantitative Safety Signal Score (0-100) for regulatory and clinical decision-making.

KEY PRINCIPLES:

Signal quantification first - Every adverse event must have PRR/ROR/IC with confidence intervals
Serious events priority - Deaths, hospitalizations, life-threatening events always analyzed first
Multi-source triangulation - FAERS + FDA labels + OpenTargets + DrugBank + literature
Context-aware assessment - Distinguish drug-specific vs class-wide vs confounding signals
Report-first approach - Create report file FIRST, update progressively
Evidence grading mandatory - T1 (regulatory/boxed warning) through T4 (computational)
English-first queries - Always use English drug names in tool calls, respond in user's language

When to Use

Apply when user asks:

"What are the safety signals for [drug]?"
"Detect adverse events for [drug]"
"Is [drug] associated with [adverse event]?"
"What are the FAERS signals for [drug]?"
"Compare safety of [drug A] vs [drug B] for [adverse event]"
"What are the serious adverse events for [drug]?"
"Are there emerging safety signals for [drug]?"
"Post-market surveillance report for [drug]"
"Pharmacovigilance signal detection for [drug]"
"What is the disproportionality analysis for [drug] and [event]?"

Differentiation from tooluniverse-pharmacovigilance: This skill focuses specifically on signal detection and quantification using disproportionality analysis (PRR, ROR, IC) with statistical rigor, produces a quantitative Safety Signal Score (0-100), and performs comparative safety analysis across drug classes. The pharmacovigilance skill provides broader safety profiling without the same depth of signal detection metrics.

Workflow Overview

Phase 0: Input Parsing & Drug Disambiguation
  Parse drug name, resolve to ChEMBL ID, DrugBank ID
  Identify drug class, mechanism, and approved indications
    |
Phase 1: FAERS Adverse Event Profiling
  Top adverse events by frequency
  Seriousness and outcome distributions
  Demographics (age, sex, country)
    |
Phase 2: Disproportionality Analysis (Signal Detection)
  Calculate PRR, ROR, IC with 95% CI for each AE
  Apply signal detection criteria
  Classify signal strength (Strong/Moderate/Weak/None)
    |
Phase 3: FDA Label Safety Information
  Boxed warnings, contraindications
  Warnings and precautions, adverse reactions
  Drug interactions, special populations
    |
Phase 4: Mechanism-Based Adverse Event Context
  Target-based AE prediction (OpenTargets safety)
  Off-target effects, ADMET predictions
  Drug class effects comparison
    |
Phase 5: Comparative Safety Analysis
  Compare to drugs in same class
  Identify unique vs class-wide signals
  Head-to-head disproportionality comparison
    |
Phase 6: Drug-Drug Interactions & Risk Factors
  Known DDIs causing AEs
  Pharmacogenomic risk factors (PharmGKB)
  FDA PGx biomarkers
    |
Phase 7: Literature Evidence
  PubMed safety studies, case reports
  OpenAlex citation analysis
  Preprint emerging signals (EuropePMC)
    |
Phase 8: Risk Assessment & Safety Signal Score
  Calculate Safety Signal Score (0-100)
  Evidence grading (T1-T4) for each signal
  Clinical significance assessment
    |
Phase 9: Report Synthesis & Recommendations
  Monitoring recommendations
  Risk mitigation strategies
  Completeness checklist

Phase 0: Input Parsing & Drug Disambiguation

0.1 Resolve Drug Identity

# Step 1: Get ChEMBL ID from drug name
chembl_result = tu.tools.OpenTargets_get_drug_chembId_by_generic_name(drugName="atorvastatin")
# Response: {data: {search: {hits: [{id: "CHEMBL1487", name: "ATORVASTATIN", description: "..."}]}}}
chembl_id = chembl_result['data']['search']['hits'][0]['id']  # "CHEMBL1487"

# Step 2: Get drug mechanism of action
moa = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId=chembl_id)
# Response: {data: {drug: {mechanismsOfAction: {rows: [{mechanismOfAction: "HMG-CoA reductase inhibitor", actionType: "INHIBITOR", targetName: "...", targets: [{id: "ENSG00000113161", approvedSymbol: "HMGCR"}]}]}}}}

# Step 3: Get blackbox warning status
blackbox = tu.tools.OpenTargets_get_drug_blackbox_status_by_chembl_ID(chemblId=chembl_id)
# Response: {data: {drug: {name: "ATORVASTATIN", hasBeenWithdrawn: false, blackBoxWarning: false}}}

# Step 4: Get DrugBank info (safety, toxicity)
drugbank = tu.tools.drugbank_get_safety_by_drug_name_or_drugbank_id(
    query="atorvastatin", case_sensitive=False, exact_match=False, limit=3
)
# Response: {results: [{drug_name: "Atorvastatin", drugbank_id: "DB01076", toxicity: "...", food_interactions: "..."}]}

# Step 5: Get DrugBank targets
targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id(
    query="atorvastatin", case_sensitive=False, exact_match=False, limit=3
)
# Response: {results: [{drug_name: "...", targets: [{name: "HMG-CoA reductase", ...}]}]}

# Step 6: Get approved indications
indications = tu.tools.OpenTargets_get_drug_indications_by_chemblId(chemblId=chembl_id)
# Response: {data: {drug: {indications: {rows: [{disease: {name: "hypercholesterolemia"}, maxPhaseForIndication: 4}]}}}}

0.2 Output for Report

## 1. Drug Identification

| Property | Value |
|----------|-------|
| **Generic Name** | Atorvastatin |
| **ChEMBL ID** | CHEMBL1487 |
| **DrugBank ID** | DB01076 |
| **Drug Class** | HMG-CoA reductase inhibitor (Statin) |
| **Mechanism** | HMG-CoA reductase inhibitor (target: HMGCR) |
| **Primary Target** | HMGCR (ENSG00000113161) |
| **Black Box Warning** | No |
| **Withdrawn** | No |

*Source: OpenTargets, DrugBank*

Phase 1: FAERS Adverse Event Profiling

1.1 Query FAERS for Adverse Events

# Get top adverse event reactions (returns list of {term, count})
reactions = tu.tools.FAERS_count_reactions_by_drug_event(medicinalproduct="ATORVASTATIN")
# Response: [{term: "FATIGUE", count: 19171}, {term: "DIARRHOEA", count: 17127}, ...]

# Get seriousness classification
seriousness = tu.tools.FAERS_count_seriousness_by_drug_event(medicinalproduct="ATORVASTATIN")
# Response: [{term: "Serious", count: 242757}, {term: "Non-serious", count: 83504}]

# Get outcome distribution
outcomes = tu.tools.FAERS_count_outcomes_by_drug_event(medicinalproduct="ATORVASTATIN")
# Response: [{term: "Unknown", count: 162310}, {term: "Fatal", count: 22128}, ...]

# Get age distribution
age_dist = tu.tools.FAERS_count_patient_age_distribution(medicinalproduct="ATORVASTATIN")
# Response: [{term: "Elderly", count: 38510}, {term: "Adult", count: 24302}, ...]

# Get death-related events
deaths = tu.tools.FAERS_count_death_related_by_drug(medicinalproduct="ATORVASTATIN")
# Response: [{term: "alive", count: 113157}, {term: "death", count: 26909}]

# Get reporter country distribution
countries = tu.tools.FAERS_count_reportercountry_by_drug_event(medicinalproduct="ATORVASTATIN")
# Response: [{term: "US", count: 170963}, {term: "GB", count: 40079}, ...]

1.2 Get Serious Events Breakdown

# Filter serious events - all types
serious_all = tu.tools.FAERS_filter_serious_events(
    operation="filter_serious_events",
    drug_name="ATORVASTATIN",
    seriousness_type="all"
)
# Response: {status: "success", drug_name: "ATORVASTATIN", seriousness_type: "all",
#   total_serious_events: N, top_serious_reactions: [{reaction: "...", count: N}, ...]}

# Death-related serious events
serious_death = tu.tools.FAERS_filter_serious_events(
    operation="filter_serious_events",
    drug_name="ATORVASTATIN",
    seriousness_type="death"
)
# Response: {status: "success", total_serious_events: 18720,
#   top_serious_reactions: [{reaction: "DEATH", count: 7541}, {reaction: "MYOCARDIAL INFARCTION", count: 1286}, ...]}

# Hospitalization-related
serious_hosp = tu.tools.FAERS_filter_serious_events(
    operation="filter_serious_events",
    drug_name="ATORVASTATIN",
    seriousness_type="hospitalization"
)

# Life-threatening
serious_lt = tu.tools.FAERS_filter_serious_events(
    operation="filter_serious_events",
    drug_name="ATORVASTATIN",
    seriousness_type="life_threatening"
)

1.3 MedDRA Hierarchy Rollup

# Get MedDRA preferred term rollup (top 50)
meddra = tu.tools.FAERS_rollup_meddra_hierarchy(
    operation="rollup_meddra_hierarchy",
    drug_name="ATORVASTATIN"
)
# Response: {status: "success", drug_name: "ATORVASTATIN",
#   meddra_hierarchy: {PT_level: [{preferred_term: "FATIGUE", count: 13957}, ...]}}

1.4 Output for Report

## 2. FAERS Adverse Event Profile

### 2.1 Overview
- **Total reports**: 326,261 (Serious: 242,757 | Non-serious: 83,504)
- **Fatal outcomes**: 22,128
- **Primary reporter countries**: US (170,963), GB (40,079), CA (16,492)

### 2.2 Top 10 Adverse Events by Frequency

| Rank | Adverse Event | Reports | % of Total |
|------|---------------|---------|------------|
| 1 | Fatigue | 19,171 | 5.9% |
| 2 | Diarrhoea | 17,127 | 5.2% |
| 3 | Dyspnoea | 15,992 | 4.9% |
| ... | ... | ... | ... |

### 2.3 Outcome Distribution

| Outcome | Count | Percentage |
|---------|-------|------------|
| Unknown | 162,310 | 39.6% |
| Recovered/resolved | 94,737 | 23.1% |
| Not recovered | 77,721 | 18.9% |
| Recovering | 49,367 | 12.0% |
| Fatal | 22,128 | 5.4% |
| Recovered with sequelae | 4,930 | 1.2% |

### 2.4 Age Distribution

| Age Group | Reports | Percentage |
|-----------|---------|------------|
| Elderly | 38,510 | 61.3% |
| Adult | 24,302 | 38.7% |
| Other | 152 | <1% |

*Source: FAERS via FAERS_count_reactions_by_drug_event, FAERS_count_seriousness_by_drug_event*

Phase 2: Disproportionality Analysis (Signal Detection)

2.1 Calculate Signal Metrics

CRITICAL: This is the core of the skill. For each top adverse event (at least top 15-20), calculate PRR, ROR, and IC with 95% confidence intervals.

# For each significant adverse event, calculate disproportionality
top_events = ["Rhabdomyolysis", "Myalgia", "Hepatotoxicity", "Diabetes mellitus",
              "Acute kidney injury", "Myopathy", "Pancreatitis"]

for event in top_events:
    result = tu.tools.FAERS_calculate_disproportionality(
        operation="calculate_disproportionality",
        drug_name="ATORVASTATIN",
        adverse_event=event
    )
    # Response structure:
    # {
    #   status: "success",
    #   drug_name: "ATORVASTATIN",
    #   adverse_event: "Rhabdomyolysis",
    #   contingency_table: {
    #     a_drug_and_event: 2226,
    #     b_drug_no_event: 241655,
    #     c_no_drug_event: 37658,
    #     d_no_drug_no_event: 19725450
    #   },
    #   metrics: {
    #     ROR: {value: 4.825, ci_95_lower: 4.622, ci_95_upper: 5.037},
    #     PRR: {value: 4.79, ci_95_lower: 4.59, ci_95_upper: 4.998},
    #     IC: {value: 2.194, ci_95_lower: 2.136, ci_95_upper: 2.252}
    #   },
    #   signal_detection: {
    #     signal_detected: true,
    #     signal_strength: "Strong signal",
    #     criteria: "ROR lower CI > 1.0 and case count >= 3"
    #   }
    # }

2.2 Signal Detection Criteria

Proportional Reporting Ratio (PRR):

PRR = (a/(a+b)) / (c/(c+d))
Signal: PRR >= 2.0 AND lower 95% CI > 1.0 AND case count >= 3

Reporting Odds Ratio (ROR):

ROR = (ad) / (bc)
Signal: Lower 95% CI > 1.0

Information Component (IC):

IC = log2(observed/expected)
Signal: Lower 95% CI > 0

2.3 Signal Strength Classification

Strength	PRR	ROR Lower CI	IC Lower CI	Clinical Action
Strong	>= 5.0	>= 3.0	>= 2.0	Immediate investigation required
Moderate	3.0-4.9	2.0-2.9	1.0-1.9	Active monitoring recommended
Weak	2.0-2.9	1.0-1.9	0-0.9	Routine monitoring, watch for trends
No signal	< 2.0	< 1.0	< 0	Standard pharmacovigilance

2.4 Demographic Stratification of Key Signals

# For strong/moderate signals, stratify by demographics
result = tu.tools.FAERS_stratify_by_demographics(
    operation="stratify_by_demographics",
    drug_name="ATORVASTATIN",
    adverse_event="Rhabdomyolysis",
    stratify_by="sex"  # Options: sex, age, country
)
# Response: {status: "success", total_reports: 1996,
#   stratification: [{group: 1, count: 1190, percentage: 59.62},  # 1=Male
#                    {group: 2, count: 781, percentage: 39.13}]}    # 2=Female

Note on sex codes: group 0 = Unknown, group 1 = Male, group 2 = Female.

2.5 Output for Report

## 3. Disproportionality Analysis (Signal Detection)

### 3.1 Signal Detection Summary

| Adverse Event | Cases (a) | PRR | PRR 95% CI | ROR | ROR 95% CI | IC | Signal |
|---------------|-----------|-----|------------|-----|------------|-----|--------|
| Rhabdomyolysis | 2,226 | 4.79 | 4.59-5.00 | 4.83 | 4.62-5.04 | 2.19 | **STRONG** |
| Myopathy | 1,234 | 6.12 | 5.72-6.55 | 6.18 | 5.77-6.62 | 2.54 | **STRONG** |
| Myalgia | 9,189 | 2.31 | 2.26-2.37 | 2.33 | 2.28-2.39 | 1.18 | Moderate |
| Hepatotoxicity | 456 | 3.45 | 3.10-3.84 | 3.48 | 3.13-3.87 | 1.72 | Moderate |
| Diabetes mellitus | 3,021 | 1.89 | 1.82-1.96 | 1.90 | 1.83-1.97 | 0.91 | Weak |
| Pancreatitis | 678 | 2.15 | 1.97-2.34 | 2.16 | 1.98-2.35 | 1.08 | Weak |

### 3.2 Demographics of Key Signals

**Rhabdomyolysis** (n=1,996):
- Male: 59.6%, Female: 39.1%, Unknown: 1.3%
- Predominantly elderly (>65 years)

*Source: FAERS via FAERS_calculate_disproportionality, FAERS_stratify_by_demographics*

Phase 3: FDA Label Safety Information

3.1 Extract Label Sections

# Boxed warnings
boxed = tu.tools.FDA_get_boxed_warning_info_by_drug_name(drug_name="atorvastatin")
# Response: {meta: {total: N}, results: [{boxed_warning: ["...text..."]}]}
# NOTE: Returns {error: {code: "NOT_FOUND"}} if no boxed warning exists

# Contraindications
contras = tu.tools.FDA_get_contraindications_by_drug_name(drug_name="atorvastatin")
# Response: {meta: {total: N}, results: [{openfda.generic_name: [...], contraindications: ["...text..."]}]}

# Warnings and precautions
warnings = tu.tools.FDA_get_warnings_by_drug_name(drug_name="atorvastatin")
# Response: {meta: {total: N}, results: [{warnings: ["...text..."], boxed_warning: [...]}]}

# Adverse reactions from label
adverse_rxns = tu.tools.FDA_get_adverse_reactions_by_drug_name(drug_name="atorvastatin")
# Response: {meta: {total: N}, results: [{adverse_reactions: ["...text..."]}]}

# Drug interactions from label
interactions = tu.tools.FDA_get_drug_interactions_by_drug_name(drug_name="atorvastatin")
# Response: {meta: {total: N}, results: [{drug_interactions: ["...text..."]}]}

# Pregnancy/breastfeeding
pregnancy = tu.tools.FDA_get_pregnancy_or_breastfeeding_info_by_drug_name(drug_name="atorvastatin")

# Geriatric use
geriatric = tu.tools.FDA_get_geriatric_use_info_by_drug_name(drug_name="atorvastatin")

# Pediatric use
pediatric = tu.tools.FDA_get_pediatric_use_info_by_drug_name(drug_name="atorvastatin")

# Pharmacogenomics from label
pgx_label = tu.tools.FDA_get_pharmacogenomics_info_by_drug_name(drug_name="atorvastatin")

3.2 Handling No Results

IMPORTANT: FDA label tools return {error: {code: "NOT_FOUND"}} when a section does not exist. This is NORMAL for many drugs - for example, most drugs do NOT have boxed warnings. Always check for this pattern:

# Check if boxed warning exists
if isinstance(boxed, dict) and 'error' in boxed:
    boxed_warning_text = "None (no boxed warning for this drug)"
else:
    boxed_warning_text = boxed['results'][0].get('boxed_warning', ['None'])[0]

3.3 Output for Report

## 4. FDA Label Safety Information

### 4.1 Boxed Warning
None

### 4.2 Contraindications
- Acute liver failure or decompensated cirrhosis
- Hypersensitivity to atorvastatin (includes anaphylaxis, angioedema, SJS, TEN)

### 4.3 Warnings and Precautions
| Warning | Clinical Relevance |
|---------|-------------------|
| Myopathy/Rhabdomyolysis | Risk with CYP3A4 inhibitors, high doses |
| Immune-Mediated Necrotizing Myopathy | Rare autoimmune myopathy |
| Hepatic Dysfunction | Monitor LFTs |
| Increased HbA1c/Glucose | Diabetes risk |

### 4.4 Drug Interactions (from label)
| Interacting Drug | Mechanism | Clinical Action |
|-----------------|-----------|-----------------|
| Cyclosporine | Increased exposure | Avoid combination |
| CYP3A4 inhibitors | Increased atorvastatin levels | Use lowest dose |
| Gemfibrozil | Increased myopathy risk | Avoid |

### 4.5 Special Populations
- **Pregnancy**: Contraindicated
- **Geriatric**: No dose adjustment needed
- **Pediatric**: Approved for heterozygous FH ages 10+

*Source: FDA drug labels via FDA_get_contraindications_by_drug_name, FDA_get_warnings_by_drug_name*

Phase 4: Mechanism-Based Adverse Event Context

4.1 Target Safety Profile

# Get target safety data from OpenTargets
# First get target ensembl ID from MOA result
target_id = "ENSG00000113161"  # HMGCR from Phase 0

safety = tu.tools.OpenTargets_get_target_safety_profile_by_ensemblID(ensemblId=target_id)
# Response: {data: {target: {id: "...", approvedSymbol: "HMGCR",
#   safetyLiabilities: [{event: "Decrease, Fertility", eventId: "...",
#     effects: [{direction: "Inhibition/Decrease/Downregulation"}],
#     studies: [{type: "cell-based"}], datasource: "AOP-Wiki"}]}}}

# Get OpenTargets adverse events (uses FAERS data)
ot_aes = tu.tools.OpenTargets_get_drug_adverse_events_by_chemblId(chemblId="CHEMBL1487")
# Response: {data: {drug: {adverseEvents: {count: 13, criticalValue: 513.67,
#   rows: [{name: "myalgia", meddraCode: "10028411", count: 4126, logLR: 6067.33}, ...]}}}}

4.2 ADMET Predictions (if SMILES available)

# Get SMILES from DrugBank/PharmGKB
smiles = "CC(C)C1=C(C(=C(N1CC[C@H](C[C@H](CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4"

# Toxicity predictions
toxicity = tu.tools.ADMETAI_predict_toxicity(smiles=[smiles])
# Response: predictions for hepatotoxicity, cardiotoxicity, etc.

# CYP interaction predictions
cyp = tu.tools.ADMETAI_predict_CYP_interactions(smiles=[smiles])
# Response: CYP inhibition/substrate predictions

4.3 Drug Warnings from OpenTargets

# Drug warnings (withdrawals, safety warnings)
warnings = tu.tools.OpenTargets_get_drug_warnings_by_chemblId(chemblId="CHEMBL1487")
# Response: {data: {drug: {id: "CHEMBL1487", name: "ATORVASTATIN"}}}
# Note: Empty if no warnings exist

4.4 Output for Report

## 5. Mechanism-Based Adverse Event Context

### 5.1 Target Safety Profile (HMGCR)
| Safety Liability | Direction | Evidence | Source |
|-----------------|-----------|----------|--------|
| Decreased fertility | Inhibition | Cell-based | AOP-Wiki |

### 5.2 OpenTargets Significant Adverse Events
| Adverse Event | FAERS Count | log(LR) | MedDRA Code |
|---------------|-------------|---------|-------------|
| Myalgia | 4,126 | 6,067 | 10028411 |
| Rhabdomyolysis | 2,546 | 4,788 | 10039020 |
| CPK increased | 1,709 | 2,909 | 10005470 |

### 5.3 ADMET Predictions
| Property | Prediction | Confidence |
|----------|-----------|------------|
| Hepatotoxicity | Moderate risk | 0.65 |
| Cardiotoxicity (hERG) | Low risk | 0.23 |
| CYP3A4 substrate | Yes | 0.92 |

*Source: OpenTargets, ADMETAI*

Phase 5: Comparative Safety Analysis

5.1 Compare to Drug Class

# Head-to-head comparison with class member
comparison = tu.tools.FAERS_compare_drugs(
    operation="compare_drugs",
    drug1="ATORVASTATIN",
    drug2="SIMVASTATIN",
    adverse_event="Rhabdomyolysis"
)
# Response: {status: "success", adverse_event: "Rhabdomyolysis",
#   drug1: {name: "ATORVASTATIN", metrics: {PRR: {value: 4.79, ...}, ROR: {...}, IC: {...}},
#           signal_detection: {signal_detected: true, signal_strength: "Strong signal"}},
#   drug2: {name: "SIMVASTATIN", metrics: {PRR: {value: 12.78, ...}, ...}},
#   comparison: "SIMVASTATIN shows stronger signal than ATORVASTATIN"}

# Compare multiple events across class members
class_drugs = ["ATORVASTATIN", "SIMVASTATIN", "ROSUVASTATIN", "PRAVASTATIN"]
key_events = ["Rhabdomyolysis", "Myalgia", "Hepatotoxicity", "Diabetes mellitus"]
# Run FAERS_compare_drugs for each pair and event combination

# Aggregate adverse events across drug class
class_aes = tu.tools.FAERS_count_additive_adverse_reactions(
    medicinalproducts=class_drugs
)
# Response: [{term: "FATIGUE", count: N}, ...]

# Aggregate seriousness across class
class_serious = tu.tools.FAERS_count_additive_seriousness_classification(
    medicinalproducts=class_drugs
)
# Response: [{term: "Serious", count: N}, {term: "Non-serious", count: N}]

5.2 Output for Report

## 6. Comparative Safety Analysis (Statin Class)

### 6.1 Head-to-Head: Rhabdomyolysis

| Drug | PRR | PRR 95% CI | ROR | Cases | Signal |
|------|-----|------------|-----|-------|--------|
| Simvastatin | 12.78 | 12.43-13.14 | 13.05 | 5,234 | **STRONG** |
| Atorvastatin | 4.79 | 4.59-5.00 | 4.83 | 2,226 | **STRONG** |
| Rosuvastatin | 3.45 | 3.21-3.71 | 3.47 | 1,102 | Moderate |
| Pravastatin | 5.67 | 5.28-6.09 | 5.72 | 1,876 | **STRONG** |

### 6.2 Class-Wide vs Drug-Specific Signals

| Signal Type | Events |
|-------------|--------|
| **Class-wide** (all statins) | Myalgia, Rhabdomyolysis, CPK elevation, Hepatotoxicity |
| **Drug-specific** (atorvastatin) | [None identified - all signals are class-wide] |

*Source: FAERS via FAERS_compare_drugs*

Phase 6: Drug-Drug Interactions & Risk Factors

6.1 Drug-Drug Interactions

# FDA label DDIs
ddi_label = tu.tools.FDA_get_drug_interactions_by_drug_name(drug_name="atorvastatin")
# Response: {results: [{drug_interactions: ["...text..."]}]}

# DrugBank interactions
ddi_db = tu.tools.drugbank_get_drug_interactions_by_drug_name_or_id(
    query="atorvastatin", case_sensitive=False, exact_match=False, limit=3
)

# DailyMed DDIs
ddi_dailymed = tu.tools.DailyMed_parse_drug_interactions(drug_name="atorvastatin")

6.2 Pharmacogenomic Risk Factors

# PharmGKB drug search
pgx_search = tu.tools.PharmGKB_search_drugs(query="atorvastatin")
# Response: {status: "success", data: [{id: "PA448500", name: "atorvastatin", smiles: "..."}]}

# Get detailed PGx info
pgx_details = tu.tools.PharmGKB_get_drug_details(drug_id="PA448500")

# PharmGKB dosing guidelines
dosing = tu.tools.PharmGKB_get_dosing_guidelines(gene="SLCO1B1")
# SLCO1B1 is key pharmacogene for statins

# FDA PGx biomarkers
fda_pgx = tu.tools.fda_pharmacogenomic_biomarkers(drug_name="atorvastatin", limit=10)
# Response: {count: N, results: [{drug_name: "...", biomarker: "...", ...}]}
# Note: May return empty results for some drugs

6.3 Output for Report

## 7. Drug-Drug Interactions & Pharmacogenomic Risk

### 7.1 Key Drug-Drug Interactions
| Interacting Drug | Mechanism | Risk | Management |
|-----------------|-----------|------|------------|
| Cyclosporine | CYP3A4 inhibition | Rhabdomyolysis | Avoid combination |
| Clarithromycin | CYP3A4 inhibition | Myopathy | Limit to 20mg/day |
| Gemfibrozil | Glucuronidation inhibition | Myopathy | Avoid combination |
| Niacin (>1g/day) | Additive myotoxicity | Myopathy | Monitor closely |

### 7.2 Pharmacogenomic Risk Factors
| Gene | Variant | Phenotype | Recommendation | Evidence |
|------|---------|-----------|----------------|----------|
| SLCO1B1 | rs4149056 (*5) | Reduced transport | Consider lower dose | Level 1A |
| CYP3A4 | *22 (rs35599367) | Poor metabolizer | Increased exposure | Level 3 |

*Source: FDA label, PharmGKB, fda_pharmacogenomic_biomarkers*

Phase 7: Literature Evidence

7.1 Search Published Literature

# PubMed safety studies
pubmed = tu.tools.PubMed_search_articles(
    query='atorvastatin adverse events safety rhabdomyolysis',
    limit=20
)
# Response: [{pmid: "...", title: "...", authors: [...], journal: "...",
#   pub_date: "...", pub_year: "...", doi: "..."}]

# Citation analysis via OpenAlex
openalex = tu.tools.openalex_search_works(
    query="atorvastatin safety adverse events",
    limit=15
)

# Preprints via EuropePMC
preprints = tu.tools.EuropePMC_search_articles(
    query="atorvastatin safety signal",
    source="PPR",
    pageSize=10
)

7.2 Output for Report

## 8. Literature Evidence

### 8.1 Key Safety Publications
| PMID | Title | Year | Journal |
|------|-------|------|---------|
| 41657777 | Differential musculoskeletal outcome reporting... | 2026 | Front Pharmacol |
| ... | ... | ... | ... |

### 8.2 Evidence Summary
| Evidence Type | Count | Key Findings |
|---------------|-------|--------------|
| Meta-analyses | 5 | Statin myopathy 5-10%, rhabdomyolysis rare |
| RCTs | 12 | CV benefit outweighs muscle risk |
| Case reports | 23 | Severe rhabdomyolysis with CYP3A4 inhibitors |

*Source: PubMed, OpenAlex*

Phase 8: Risk Assessment & Safety Signal Score

8.1 Safety Signal Score Calculation (0-100)

The Safety Signal Score quantifies overall drug safety concern on a 0-100 scale (higher = more concern).

Component 1: FAERS Signal Strength (0-35 points)

If any signal has PRR >= 5 AND ROR lower CI >= 3: 35 points
If any signal has PRR 3-5 AND ROR lower CI 2-3: 20 points
If any signal has PRR 2-3 AND ROR lower CI 1-2: 10 points
If no signals detected: 0 points

Component 2: Serious Adverse Events (0-30 points)

Deaths reported with high count (>100): 30 points
Deaths reported with low count (1-100): 25 points
Life-threatening events: 20 points
Hospitalizations only: 15 points
Non-serious only: 0 points

Component 3: FDA Label Warnings (0-25 points)

Boxed warning present: 25 points
Drug withdrawn or restricted: 25 points
Contraindications present: 15 points
Warnings and precautions: 10 points
Adverse reactions only: 5 points
No label warnings: 0 points

Component 4: Literature Evidence (0-10 points)

Meta-analyses confirming safety signals: 10 points
Multiple RCTs with safety concerns: 7 points
Case reports/case series: 4 points
No published safety concerns: 0 points

Total Score Interpretation:

Score Range	Interpretation	Action
75-100	High concern	Serious safety signals; requires immediate regulatory attention
50-74	Moderate concern	Significant monitoring needed; consider risk mitigation
25-49	Low-moderate concern	Routine enhanced monitoring; standard risk management
0-24	Low concern	Standard safety profile; routine pharmacovigilance

8.2 Evidence Grading

Tier	Criteria	Example
T1	Boxed warning, confirmed by RCTs, PRR > 10	Metformin: Lactic acidosis
T2	Label warning + FAERS signal (PRR 3-10) + published studies	Atorvastatin: Rhabdomyolysis
T3	FAERS signal (PRR 2-3) + case reports	Atorvastatin: Pancreatitis
T4	Computational prediction only (ADMET) or weak signal	ADMETAI hepatotoxicity prediction

8.3 Output for Report

## 9. Risk Assessment

### 9.1 Safety Signal Score: 62/100 (MODERATE CONCERN)

| Component | Score | Max | Rationale |
|-----------|-------|-----|-----------|
| FAERS Signal Strength | 35 | 35 | Strong signals (PRR >= 5 for rhabdomyolysis) |
| Serious Adverse Events | 15 | 30 | Hospitalizations; deaths uncommon for drug itself |
| FDA Label Warnings | 10 | 25 | Warnings/precautions but no boxed warning |
| Literature Evidence | 7 | 10 | Multiple RCTs confirm muscle-related risks |
| **TOTAL** | **62** | **100** | **MODERATE CONCERN** |

### 9.2 Evidence-Graded Signals

| Signal | Grade | PRR | Serious | Label | Literature | Overall |
|--------|-------|-----|---------|-------|------------|---------|
| Rhabdomyolysis | T2 | 4.79 | Yes | Warning | Confirmed | Moderate |
| Myopathy | T2 | 6.12 | Yes | Warning | Confirmed | Moderate |
| Hepatotoxicity | T3 | 3.45 | Rare | Warning | Case reports | Low-Moderate |
| Diabetes risk | T3 | 1.89 | No | Warning | RCT data | Low |

Phase 9: Report Synthesis & Recommendations

9.1 Report Template

File: [DRUG]_adverse_event_report.md

# Adverse Drug Event Signal Detection Report: [DRUG]

**Generated**: [Date] | **Drug**: [Generic Name] | **ChEMBL ID**: [ID]
**Safety Signal Score**: [XX/100] ([INTERPRETATION])

---

## Executive Summary

[2-3 paragraph summary of key findings]

**Key Safety Signals**:
1. [Strongest signal with PRR/ROR]
2. [Second signal]
3. [Third signal]

**Regulatory Status**: [Boxed warning Y/N] | [Withdrawn Y/N] | [Restrictions]

---

## 1. Drug Identification
[Phase 0 output]

## 2. FAERS Adverse Event Profile
[Phase 1 output]

## 3. Disproportionality Analysis
[Phase 2 output]

## 4. FDA Label Safety Information
[Phase 3 output]

## 5. Mechanism-Based Context
[Phase 4 output]

## 6. Comparative Safety Analysis
[Phase 5 output]

## 7. Drug-Drug Interactions & PGx Risk
[Phase 6 output]

## 8. Literature Evidence
[Phase 7 output]

## 9. Risk Assessment
[Phase 8 output]

## 10. Clinical Recommendations

### 10.1 Monitoring Recommendations
| Parameter | Frequency | Rationale |
|-----------|-----------|-----------|
| [Lab test] | [Frequency] | [Why] |

### 10.2 Risk Mitigation Strategies
| Risk | Mitigation | Evidence |
|------|-----------|----------|
| [Risk] | [Strategy] | [Source] |

### 10.3 Patient Counseling Points
- [Point 1]
- [Point 2]

### 10.4 Populations at Higher Risk
| Population | Risk Factor | Recommendation |
|-----------|-------------|----------------|
| [Group] | [Factor] | [Action] |

---

## 11. Completeness Checklist
[See below]

## 12. Data Sources
[All tools and databases used with timestamps]

Completeness Checklist

Phase 0: Drug Disambiguation

Phase 1: FAERS Profiling

Top adverse events queried (>=15 events)
Seriousness distribution obtained
Outcome distribution obtained
Age distribution obtained
Death-related events counted
Reporter country distribution obtained

Phase 2: Disproportionality Analysis

PRR calculated for >= 10 adverse events
ROR with 95% CI for each event
IC with 95% CI for each event
Signal strength classified for each
Demographics stratified for strong signals

Phase 3: FDA Label

Boxed warnings checked (or confirmed none)
Contraindications extracted
Warnings and precautions extracted
Adverse reactions from label
Drug interactions from label
Special populations (pregnancy, geriatric, pediatric)

Phase 4: Mechanism Context

Target safety profile (OpenTargets)
OpenTargets adverse events queried
ADMET predictions (if SMILES available)

Phase 5: Comparative Analysis

At least 1 class comparison performed
Class-wide vs drug-specific signals identified
Aggregate class AEs computed (if applicable)

Phase 6: DDIs & PGx

DDIs from FDA label extracted
PharmGKB queried
Dosing guidelines checked
FDA PGx biomarkers checked

Phase 7: Literature

PubMed searched (>=10 articles)
OpenAlex citation analysis (if time permits)
Key safety publications cited

Phase 8: Risk Assessment

Safety Signal Score calculated (0-100)
Each signal evidence-graded (T1-T4)
Score interpretation provided

Phase 9: Report

Report file created and saved
Executive summary written
Monitoring recommendations provided
Risk mitigation strategies listed
Patient counseling points included
All sources cited

Tool Parameter Reference (Verified)

FAERS Tools (OpenFDA-based)

Tool	Key Parameters	Notes
`FAERS_count_reactions_by_drug_event`	`medicinalproduct` (REQUIRED), `patientsex`, `patientagegroup`, `occurcountry`	Returns [{term, count}]
`FAERS_count_seriousness_by_drug_event`	`medicinalproduct` (REQUIRED), `patientsex`, `patientagegroup`, `occurcountry`	Returns [{term: "Serious"/"Non-serious", count}]
`FAERS_count_outcomes_by_drug_event`	`medicinalproduct` (REQUIRED), `patientsex`, `patientagegroup`, `occurcountry`	Returns [{term: "Fatal"/"Recovered"/..., count}]
`FAERS_count_patient_age_distribution`	`medicinalproduct` (REQUIRED)	Returns [{term: "Elderly"/"Adult"/..., count}]
`FAERS_count_death_related_by_drug`	`medicinalproduct` (REQUIRED)	Returns [{term: "alive"/"death", count}]
`FAERS_count_reportercountry_by_drug_event`	`medicinalproduct` (REQUIRED), `patientsex`, `patientagegroup`, `serious`	Returns [{term: "US"/"GB"/..., count}]
`FAERS_search_adverse_event_reports`	`medicinalproduct`, `limit` (max 100), `skip`	Returns individual case reports with patient/drug/reaction data
`FAERS_search_reports_by_drug_and_reaction`	`medicinalproduct` (REQUIRED), `reactionmeddrapt` (REQUIRED), `limit`, `skip`, `patientsex`, `serious`	Returns individual reports filtered by specific reaction
`FAERS_search_serious_reports_by_drug`	`medicinalproduct` (REQUIRED), `seriousnessdeath`, `seriousnesshospitalization`, `seriousnesslifethreatening`, `seriousnessdisabling`, `limit`	Returns serious event reports

FAERS Analytics Tools (operation-based)

Tool	Key Parameters	Notes
`FAERS_calculate_disproportionality`	`operation`="calculate_disproportionality", `drug_name` (REQUIRED), `adverse_event` (REQUIRED)	Returns PRR, ROR, IC with 95% CI and signal detection
`FAERS_analyze_temporal_trends`	`operation`="analyze_temporal_trends", `drug_name` (REQUIRED), `adverse_event` (optional)	Returns yearly counts and trend direction
`FAERS_compare_drugs`	`operation`="compare_drugs", `drug1` (REQUIRED), `drug2` (REQUIRED), `adverse_event` (REQUIRED)	Returns PRR/ROR/IC for both drugs side-by-side
`FAERS_filter_serious_events`	`operation`="filter_serious_events", `drug_name` (REQUIRED), `seriousness_type` (death/hospitalization/disability/life_threatening/all)	Returns top serious reactions with counts
`FAERS_stratify_by_demographics`	`operation`="stratify_by_demographics", `drug_name` (REQUIRED), `adverse_event` (REQUIRED), `stratify_by` (sex/age/country)	Returns stratified counts and percentages. Sex codes: 0=Unknown, 1=Male, 2=Female
`FAERS_rollup_meddra_hierarchy`	`operation`="rollup_meddra_hierarchy", `drug_name` (REQUIRED)	Returns top 50 preferred terms with counts

FAERS Aggregate Tools (multi-drug)

Tool	Key Parameters	Notes
`FAERS_count_additive_adverse_reactions`	`medicinalproducts` (REQUIRED, array), `patientsex`, `patientagegroup`, `occurcountry`, `serious`, `seriousnessdeath`	Aggregates AE counts across multiple drugs
`FAERS_count_additive_seriousness_classification`	`medicinalproducts` (REQUIRED, array), `patientsex`, `patientagegroup`, `occurcountry`	Aggregates seriousness across multiple drugs
`FAERS_count_additive_reaction_outcomes`	`medicinalproducts` (REQUIRED, array)	Aggregates outcomes across multiple drugs

FDA Label Tools

Tool	Key Parameters	Notes
`FDA_get_boxed_warning_info_by_drug_name`	`drug_name`	Returns `{error: {code: "NOT_FOUND"}}` if no boxed warning
`FDA_get_contraindications_by_drug_name`	`drug_name`	Returns `{meta: {total: N}, results: [{contraindications: [...]}]}`
`FDA_get_adverse_reactions_by_drug_name`	`drug_name`	Returns `{meta: {total: N}, results: [{adverse_reactions: [...]}]}`
`FDA_get_warnings_by_drug_name`	`drug_name`	Returns `{meta: {total: N}, results: [{warnings: [...]}]}`
`FDA_get_drug_interactions_by_drug_name`	`drug_name`	Returns `{meta: {total: N}, results: [{drug_interactions: [...]}]}`
`FDA_get_pharmacogenomics_info_by_drug_name`	`drug_name`	Returns PGx info from label
`FDA_get_pregnancy_or_breastfeeding_info_by_drug_name`	`drug_name`	Returns pregnancy info
`FDA_get_geriatric_use_info_by_drug_name`	`drug_name`	Returns geriatric use info
`FDA_get_pediatric_use_info_by_drug_name`	`drug_name`	Returns pediatric info

OpenTargets Tools

Tool	Key Parameters	Notes
`OpenTargets_get_drug_chembId_by_generic_name`	`drugName`	Returns `{data: {search: {hits: [{id, name, description}]}}}`
`OpenTargets_get_drug_adverse_events_by_chemblId`	`chemblId`	Returns `{data: {drug: {adverseEvents: {count, rows: [{name, meddraCode, count, logLR}]}}}}`
`OpenTargets_get_drug_blackbox_status_by_chembl_ID`	`chemblId`	Returns `{data: {drug: {hasBeenWithdrawn, blackBoxWarning}}}`
`OpenTargets_get_drug_warnings_by_chemblId`	`chemblId`	Returns drug warnings (may be empty)
`OpenTargets_get_drug_mechanisms_of_action_by_chemblId`	`chemblId`	Returns `{data: {drug: {mechanismsOfAction: {rows: [{mechanismOfAction, actionType, targetName, targets}]}}}}`
`OpenTargets_get_drug_indications_by_chemblId`	`chemblId`	Returns approved and investigational indications
`OpenTargets_get_target_safety_profile_by_ensemblID`	`ensemblId`	Returns `{data: {target: {safetyLiabilities: [{event, effects, studies, datasource}]}}}`

DrugBank Tools

Tool	Key Parameters	Notes
`drugbank_get_safety_by_drug_name_or_drugbank_id`	`query`, `case_sensitive` (bool), `exact_match` (bool), `limit`	Returns toxicity, food interactions
`drugbank_get_targets_by_drug_name_or_drugbank_id`	`query`, `case_sensitive`, `exact_match`, `limit`	Returns drug targets
`drugbank_get_drug_interactions_by_drug_name_or_id`	`query`, `case_sensitive`, `exact_match`, `limit`	Returns DDIs
`drugbank_get_pharmacology_by_drug_name_or_drugbank_id`	`query`, `case_sensitive`, `exact_match`, `limit`	Returns pharmacology

PharmGKB Tools

Tool	Key Parameters	Notes
`PharmGKB_search_drugs`	`query`	Returns `{data: [{id, name, smiles}]}`
`PharmGKB_get_drug_details`	`drug_id` (e.g., "PA448500")	Returns detailed drug info
`PharmGKB_get_dosing_guidelines`	`guideline_id`, `gene` (both optional)	Returns dosing guidelines
`PharmGKB_get_clinical_annotations`	`annotation_id`, `gene_id` (both optional)	Returns clinical annotations
`fda_pharmacogenomic_biomarkers`	`drug_name`, `biomarker`, `limit`	Returns `{count, results: [...]}`

ADMETAI Tools

Tool	Key Parameters	Notes
`ADMETAI_predict_toxicity`	`smiles` (REQUIRED, array of strings)	Predicts hepatotoxicity, cardiotoxicity, etc.
`ADMETAI_predict_CYP_interactions`	`smiles` (REQUIRED, array)	Predicts CYP inhibition/substrate

Literature Tools

Tool	Key Parameters	Notes
`PubMed_search_articles`	`query`, `limit`	Returns list of article dicts
`openalex_search_works`	`query`, `limit`	Returns works with citation counts
`EuropePMC_search_articles`	`query`, `source` ("PPR" for preprints), `pageSize`	Returns articles including preprints
`search_clinical_trials`	`query_term` (REQUIRED), `condition`, `intervention`, `pageSize`	Returns clinical trials

Fallback Chains

Primary Tool	Fallback 1	Fallback 2
`FAERS_calculate_disproportionality`	Manual calculation from `FAERS_count_*` data	Literature PRR values
`FAERS_count_reactions_by_drug_event`	`FAERS_rollup_meddra_hierarchy`	OpenTargets adverse events
`FDA_get_boxed_warning_info_by_drug_name`	`OpenTargets_get_drug_blackbox_status_by_chembl_ID`	DrugBank safety
`FDA_get_contraindications_by_drug_name`	`FDA_get_warnings_by_drug_name`	DrugBank safety
`OpenTargets_get_drug_chembId_by_generic_name`	`ChEMBL_search_drugs`	Manual search
`PharmGKB_search_drugs`	`fda_pharmacogenomic_biomarkers`	FDA label PGx section
`PubMed_search_articles`	`openalex_search_works`	`EuropePMC_search_articles`

Common Patterns

Pattern 1: Full Safety Signal Profile for a Single Drug

Use all phases (0-9) for comprehensive report. Best for regulatory submissions, safety reviews.

Pattern 2: Specific Adverse Event Investigation

Focus on Phases 0, 2, 3, 7. User asks "Does [drug] cause [event]?" - calculate disproportionality for that specific event, check label, search literature.

Pattern 3: Drug Class Comparison

Focus on Phases 0, 2, 5. Compare 3-5 drugs in same class for a specific adverse event using FAERS_compare_drugs.

Pattern 4: Emerging Signal Detection

Focus on Phases 1, 2, 7. Screen top 20+ FAERS events for signals, identify any not in FDA label (Phase 3), search recent literature for confirmation.

Pattern 5: Pharmacogenomic Risk Assessment

Focus on Phases 0, 6. Identify genetic risk factors for adverse events using PharmGKB and FDA PGx biomarkers.

Pattern 6: Pre-Approval Safety Assessment

Focus on Phases 4, 7. Use ADMET predictions and target safety profiles when FAERS data is limited (new drugs).

Edge Cases

Drug with No FAERS Reports

Skip Phases 1-2
Rely on FDA label (Phase 3), mechanism predictions (Phase 4), and literature (Phase 7)
Safety Signal Score will be lower due to lack of signal detection data

Generic vs Brand Name

Always try both names in FAERS queries (FAERS uses brand names sometimes)
Use OpenTargets_get_drug_chembId_by_generic_name to resolve to standard identifier
Use FDA_get_brand_name_generic_name for name cross-reference

Drug Combinations

Use FAERS_search_reports_by_drug_combination for polypharmacy analysis
Distinguish combination AEs from individual drug AEs
Use FAERS_count_additive_adverse_reactions for aggregate class analysis

Confounding by Indication

Compare AE profile to the disease being treated
Example: "Death" reports for chemotherapy drugs may reflect disease progression
Always note this limitation in the report

Drugs with Boxed Warnings

Score component automatically 25/25 for label warnings
Prioritize boxed warning events in disproportionality analysis
Cross-reference boxed warning with FAERS signal strength

Repository: wu-yc/LabClaw
Path: skills/pharma/tooluniverse-adverse-event-detection/SKILL.md
Commit: df37802

Last updated: 2 days ago
First committed: 5 months ago

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