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

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

2.3 Outcome Distribution

2.4 Age Distribution

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

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

4.4 Drug Interactions (from label)

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(N1CCC@HO)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)

5.2 OpenTargets Significant Adverse Events

5.3 ADMET Predictions

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

6.2 Class-Wide vs Drug-Specific Signals

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

7.2 Pharmacogenomic Risk Factors

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

8.2 Evidence Summary

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)

9.2 Evidence-Graded Signals

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

10.2 Risk Mitigation Strategies

10.3 Patient Counseling Points

• [Point 1]
• [Point 2]

10.4 Populations at Higher Risk

11. Completeness Checklist

[See below]

12. Data Sources

[All tools and databases used with timestamps]

Completeness Checklist

Phase 0: Drug Disambiguation

• Generic name resolved

• ChEMBL ID obtained

• DrugBank ID obtained

• Drug class identified

• Mechanism of action stated

• Primary target identified

• Blackbox/withdrawal status checked

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