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Tooluniverse Pharmacovigilance

mims-harvard/tooluniverse

  • 372 installs
  • 1.6k repo stars
  • Updated August 4, 2026
  • mims-harvard/tooluniverse

tooluniverse-pharmacovigilance is a ToolUniverse agent skill that monitors adverse events, drug safety signals, and pharmacovigilance datasets for developers triaging post-market safety concerns and summarizing risk patt

About

tooluniverse-pharmacovigilance is an agent skill from mims-harvard/tooluniverse for querying and summarizing pharmacovigilance data through the ToolUniverse platform. It helps developers and research engineers monitor adverse drug events, detect safety signals, and triage post-market risk patterns from pharmacovigilance datasets. Teams reach for tooluniverse-pharmacovigilance when building health-tech agents or data pipelines that need structured access to drug safety evidence rather than manual literature searches. The skill connects agent workflows to ToolUniverse pharmacovigilance tooling for safety triage and risk summarization.

  • Adverse event and safety signal exploration
  • Post-market and trial safety surveillance support
  • ToolUniverse pharmacovigilance data access
  • Agent-assisted safety summarization
  • Ongoing risk pattern triage

Tooluniverse Pharmacovigilance by the numbers

  • 372 all-time installs (skills.sh)
  • +8 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #526 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs372
repo stars★ 1.6k
Last updatedAugust 4, 2026
Repositorymims-harvard/tooluniverse ↗

How do you monitor pharmacovigilance adverse event data?

Monitor adverse events, drug safety signals, and pharmacovigilance datasets through ToolUniverse when triaging safety concerns or summarizing post-market risk patterns.

Who is it for?

Health-tech and bioinformatics developers building agents that triage drug safety data through ToolUniverse pharmacovigilance tools.

Skip if: General web app development, non-pharma software projects, or teams without ToolUniverse access or pharmacovigilance data needs.

When should I use this skill?

The user asks to monitor adverse events, triage drug safety signals, or summarize pharmacovigilance datasets via ToolUniverse.

What you get

Adverse event summaries, drug safety signal reports, and pharmacovigilance dataset query results.

  • adverse event summary
  • safety signal report
  • dataset query results

Files

SKILL.mdMarkdownGitHub ↗

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Pharmacovigilance Safety Analyzer

Systematic drug safety analysis using FAERS adverse event data, FDA labeling, PharmGKB pharmacogenomics, and clinical trial safety signals.

KEY PRINCIPLES: 1. Report-first approach - Create report file FIRST, update progressively 2. Signal quantification - Use disproportionality measures (PRR, ROR) 3. Severity stratification - Prioritize serious/fatal events 4. Multi-source triangulation - FAERS, labels, trials, literature 5. Pharmacogenomic context - Include genetic risk factors 6. Actionable output - Risk-benefit summary with recommendations 7. English-first queries - Always use English drug names in tool calls

---

When to Use

Apply when user asks:

  • "What are the safety concerns for [drug]?"
  • "What adverse events are associated with [drug]?"
  • "Is [drug] safe? What are the risks?"
  • "Compare safety profiles of [drug A] vs [drug B]"
  • "Pharmacovigilance analysis for [drug]"

---

Clinical Reasoning Framework

Reasoning Strategy 1: On-Target vs Off-Target Thinking

Ask: is this adverse effect a predictable extension of the drug's mechanism (on-target), or something the mechanism doesn't explain (off-target)? On-target effects are dose-dependent and predictable. Off-target effects are often idiosyncratic and harder to predict.

How to apply this: 1. Look up the drug's primary mechanism of action (use ChEMBL or DailyMed label) 2. For each reported adverse event, ask: "Does this follow logically from what the drug does to its target?" If yes, it is on-target toxicity — expect dose-dependence and manage with dose reduction 3. If the adverse event cannot be explained by the primary mechanism, consider off-target receptor binding or reactive metabolite formation. These require different management (drug discontinuation, not dose adjustment) 4. Use KEGG pathway data to identify metabolic routes that could produce toxic intermediates

---

Reasoning Strategy 2: Timeline as Diagnostic Tool

When did the adverse event start relative to drug initiation? The timeline alone narrows the mechanism:

  • Hours = anaphylaxis, immediate hypersensitivity, or direct pharmacological overshoot
  • Days = serum sickness, cytotoxic reactions, cumulative pharmacological effects
  • 1-6 weeks = delayed hypersensitivity (SJS/TEN, DRESS), organ accumulation
  • Months = chronic toxicity, cumulative organ damage
  • Years = long-term cumulative effects

How to apply this: When reviewing FAERS case reports, always check the time_to_onset field. If the reported timeline is biologically implausible for the proposed mechanism, suspect confounding or misattribution. A reaction appearing years after drug start is unlikely to be immune-mediated but could be chronic accumulation.

---

Reasoning Strategy 3: Dose-Dependent vs Idiosyncratic Classification

This distinction determines monitoring strategy and management:

  • Dose-dependent (Type A): Predictable from pharmacology. Dose-response relationship exists. Can be managed by dose reduction. These are on-target toxicities pushed too far.
  • Idiosyncratic (Type B): Not predictable from pharmacology alone. No clear dose-response. Often immune-mediated or due to metabolic idiosyncrasy (e.g., genetic variation in drug metabolism). Drug must be stopped — dose reduction will not help.
  • Mixed: Some reactions are dose-dependent in most patients but become idiosyncratic in genetically susceptible individuals. When you see a "Type A" reaction occurring at unexpectedly low doses, suspect a pharmacogenomic contributor.

How to apply this: When evaluating a safety signal, classify it as Type A or B. This determines whether you recommend dose adjustment (Type A) or drug avoidance with potential pharmacogenomic screening (Type B).

---

Reasoning Strategy 4: The Naranjo Algorithm for Causality Classification

When investigating a suspected drug adverse event, the Naranjo algorithm asks: (1) Did the event appear after the drug was given? (2) Did it improve when the drug was stopped? (3) Did it reappear when restarted? (4) Could other causes explain it? Score each question to classify causality.

Reasoning Strategy 5: The Rechallenge Question

Did the event recur when the drug was restarted? Positive rechallenge is the strongest evidence for causation in an individual case. But rechallenge is often unethical for serious reactions, so absence of rechallenge data doesn't exonerate the drug.

How to apply this: When reviewing case narratives or FAERS reports, check for dechallenge (did the event resolve when the drug was stopped?) and rechallenge (did it recur on re-exposure?). A positive dechallenge + positive rechallenge is near-definitive. Negative dechallenge weakens the causal link considerably.

---

Reasoning Strategy 5: Disproportionality Reasoning

A signal in FAERS means the drug-event pair is REPORTED more than expected. It does not mean the drug CAUSES the event. Think about reporting biases:

  • Serious events get reported more than mild ones
  • New drugs get reported more than old ones (Weber effect)
  • Drugs prescribed to sick populations get events attributed to them that may reflect the underlying disease
  • Media attention or regulatory alerts create reporting spikes

How to apply this: Always ask — what is the base rate of this event in the untreated population? A high PRR for "cardiac arrest" in a drug used by ICU patients may reflect the patient population, not the drug. Cross-reference with clinical trial placebo-arm rates when available.

---

Reasoning Strategy 6: When to Use Tools vs Reason

Use FAERS/OpenFDA tools to QUANTIFY a signal you have already hypothesized based on mechanism. Do not mine FAERS without a hypothesis — you will find spurious associations.

The correct sequence: 1. Reason about mechanism first (what adverse events are plausible given this drug's pharmacology?) 2. Form specific hypotheses (e.g., "this drug may cause QT prolongation because it blocks hERG channels") 3. Query tools to test each hypothesis (FAERS for reporting frequency, DailyMed for label warnings, PharmGKB for genetic risk factors) 4. Interpret results in context (is the signal consistent with the mechanism? Is the timeline plausible? Are there confounders?)

---

Reasoning Strategy 7: Pharmacogenomic Risk Assessment

Rather than memorizing gene-drug pairs, apply this reasoning framework:

1. Identify the drug's metabolic pathway (use KEGG or DailyMed label): Which CYP enzymes metabolize it? Is it a prodrug requiring activation? 2. Assess the consequence of altered metabolism: For active drugs, poor metabolizers accumulate the drug (toxicity risk). For prodrugs, poor metabolizers fail to activate (efficacy failure). Ultra-rapid metabolizers show the opposite pattern. 3. Check for immune-mediated risk: If the drug is associated with severe cutaneous reactions (SJS/TEN, DRESS) or hypersensitivity syndrome, query PharmGKB for HLA associations. These are population-specific. 4. Use PharmGKB evidence levels to guide action: Level 1A/1B (guideline-based) = actionable now. Level 2A/2B = may inform. Level 3 = not clinically actionable yet.

Query PharmGKB_search_drugs(query=...) and CPIC_list_guidelines to get current pharmacogenomic annotations rather than relying on memorized associations, which may be outdated.

---

Critical Workflow Requirements

Report-First Approach (MANDATORY)

1. Create [DRUG]_safety_report.md FIRST with all section headers and [Researching...] placeholders 2. Apply mechanistic reasoning first (on-target toxicity, time-to-onset, dose vs. idiosyncratic, PGx) 3. Progressively update as data is gathered 4. Output separate data files: [DRUG]_adverse_events.csv and [DRUG]_pharmacogenomics.csv

Citation Requirements (MANDATORY)

Every safety signal MUST include source tool, data period, PRR, case counts, and serious/fatal breakdown.

---

Tool Parameter Reference (CRITICAL)

ToolWRONG ParameterCORRECT Parameter
FAERS_count_reactions_by_drug_eventdrugdrug_name
FAERS_filter_serious_eventsAmerican spelling (e.g., "Hemorrhage")MedDRA British spelling (e.g., "Haemorrhage")
FAERS_stratify_by_demographicsRequiring adverse_eventadverse_event is optional (omit for all-event stratification)
DailyMed_search_splsnamedrug_name
PharmGKB_search_drugsdrugquery
OpenFDA_search_drug_eventsdrug_namesearch

---

Workflow Overview

Phase 0: Mechanistic Reasoning (BEFORE tools)
  On-target toxicity, time-to-onset, dose vs idiosyncratic, PGx risk

Phase 1: Drug Disambiguation
  -> Resolve drug name, get identifiers (ChEMBL, DrugBank)

Phase 2: Adverse Event Profiling (FAERS)
  -> Query FAERS, calculate PRR, stratify by seriousness

Phase 3: Label Warning Extraction
  -> DailyMed boxed warnings, contraindications, precautions

Phase 4: Pharmacogenomic Risk
  -> PharmGKB clinical annotations, high-risk genotypes

Phase 5: Clinical Trial Safety
  -> ClinicalTrials.gov Phase 3/4 safety data

Phase 5.5: Pathway & Mechanism Context
  -> KEGG drug metabolism, target pathway analysis

Phase 5.6: Literature Intelligence
  -> PubMed, BioRxiv/MedRxiv, OpenAlex citation analysis

Phase 6: Signal Prioritization
  -> Rank by PRR x severity x frequency

Phase 7: Report Synthesis

---

Phase 0: Mechanistic Reasoning (DO THIS BEFORE TOOLS)

1. Identify drug class and primary mechanism (use DailyMed label or ChEMBL) 2. Apply on-target vs off-target thinking (Strategy 1) to predict plausible adverse events 3. Estimate expected time-to-onset for each predicted event (Strategy 2) 4. Classify each as dose-dependent vs idiosyncratic (Strategy 3) 5. Formulate specific, testable safety hypotheses to guide tool queries (Strategy 6)

Phase 1: Drug Disambiguation

1. Search DailyMed via DailyMed_search_spls(drug_name=...) for NDC, SPL setid, generic name 2. Search ChEMBL via ChEMBL_search_drugs(query=...) for molecule ID, max phase 3. Document: generic name, brand names, drug class, mechanism, approval date

Phase 2: Adverse Event Profiling (FAERS)

1. Query FAERS_count_reactions_by_drug_event(drug_name=..., limit=50) for top events 2. For each event, get detailed breakdown (serious, fatal, hospitalization counts) 3. Calculate PRR: (A/B) / (C/D) where A=drug+event, B=drug+any, C=event+any_other, D=total_other 4. Apply signal thresholds: PRR > 2.0 (signal), > 3.0 (strong signal), case count >= 3

Severity classification:

  • Fatal (highest priority), Life-threatening, Hospitalization, Disability, Other serious, Non-serious

FAERS filter_serious_events -- MedDRA Spelling (CRITICAL)

FAERS_filter_serious_events uses MedDRA preferred terms which follow British English spelling conventions. Common examples:

Incorrect (American)Correct (MedDRA/British)
HEMORRHAGEHaemorrhage
ANEMIAAnaemia
EDEMAOedema
DIARRHEADiarrhoea
LEUKOPENIALeucopenia
ESOPHAGITISOesophagitis

The adverse_event parameter should use the exact MedDRA preferred term spelling. When in doubt, first query FAERS_count_reactions_by_drug_event to see the exact event names as they appear in the FAERS database, then use those exact strings.

Additional FAERS notes:

  • adverse_event is now correctly appended to the OpenFDA query in _filter_serious_events
  • FAERS_stratify_by_demographics: adverse_event is optional — when omitted, stratification covers all events for the drug. Sex codes: 0=Unknown, 1=Male, 2=Female

See SIGNAL_DETECTION.md for detailed disproportionality formulas and example output tables.

Phase 3: Label Warning Extraction

1. Get label via DailyMed_get_spl_by_setid(setid=...) 2. Extract: boxed warnings, contraindications, warnings/precautions, drug interactions 3. Categorize severity: Boxed Warning > Contraindication > Warning > Precaution

Phase 4: Pharmacogenomic Risk

1. Search PharmGKB_search_drugs(query=...) for clinical annotations 2. Document actionable variants with evidence levels (1A/1B/2A/2B/3) 3. Note CPIC/DPWG guideline status

PGx Evidence Levels:

LevelDescriptionAction
1ACPIC/DPWG guideline, implementableFollow guideline
1BCPIC/DPWG guideline, annotationConsider testing
2AVIP annotation, moderate evidenceMay inform
2BVIP annotation, weaker evidenceResearch
3Low-level annotationNot actionable

Phase 5: Clinical Trial Safety

1. Search search_clinical_trials(intervention=..., phase="Phase 3", status="Completed") 2. Extract serious AE rates, discontinuation rates, deaths 3. Compare drug vs placebo rates

Phase 5.5: Pathway & Mechanism Context

1. Query KEGG for drug metabolism pathways 2. Analyze target pathways for mechanistic basis of AEs 3. Document pathway-AE relationships

Phase 5.6: Literature Intelligence

1. PubMed: PubMed_search_articles(query='"[drug]" AND (safety OR adverse OR toxicity)') 2. BioRxiv/MedRxiv: Search for recent preprints (flag as not peer-reviewed) 3. OpenAlex: Citation analysis for key safety papers

Phase 6: Signal Prioritization

Signal Score = PRR x Severity_Weight x log10(Case_Count + 1)

Severity weights: Fatal=10, Life-threatening=8, Hospitalization=5, Disability=5, Other serious=3, Non-serious=1

Categorize signals:

  • Critical (immediate attention): High PRR + fatal outcomes
  • Moderate (monitor): Moderate PRR + serious outcomes
  • Known/Expected (manage clinically): Low PRR, in label

Cross-check against mechanistic prediction: A signal not predicted mechanistically warrants additional scrutiny (possible confounding, reporting bias, or genuinely novel finding).

---

Output Report

Save as [DRUG]_safety_report.md. See REPORT_TEMPLATES.md for the full report structure and example outputs.

---

Evidence Grading

TierCriteriaExample
T1PRR >10, fatal outcomes, boxed warningLactic acidosis
T2PRR 3-10, serious outcomesHepatotoxicity
T3PRR 2-3, moderate concernHypoglycemia
T4PRR <2, known/expectedGI side effects

---

Fallback Chains

Primary ToolFallback 1Fallback 2
FAERS_count_reactions_by_drug_eventOpenFDA_search_drug_eventsLiterature search
DailyMed_search_splsOpenFDA_search_drug_labelsDailyMed website
PharmGKB_search_drugsCPIC_list_guidelinesLiterature search
search_clinical_trialsClinicalTrials.gov APIPubMed for trial results

---

Completeness Checklist

See CHECKLIST.md for the full phase-by-phase verification checklist.

---

References

  • FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers
  • DailyMed: https://dailymed.nlm.nih.gov
  • PharmGKB: https://www.pharmgkb.org
  • ClinicalTrials.gov: https://clinicaltrials.gov
  • OpenFDA: https://open.fda.gov
  • KEGG Drug: https://www.genome.jp/kegg/drug
  • Tool documentation: TOOLS_REFERENCE.md

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How it compares

Use tooluniverse-pharmacovigilance for drug safety datasets via ToolUniverse; use general analytics skills for non-pharma data pipelines.

FAQ

What does tooluniverse-pharmacovigilance monitor?

tooluniverse-pharmacovigilance monitors adverse drug events and pharmacovigilance safety signals through ToolUniverse datasets. The skill helps developers triage post-market safety concerns and summarize risk patterns in agent workflows.

Who should use tooluniverse-pharmacovigilance?

tooluniverse-pharmacovigilance suits health-tech and bioinformatics developers building ToolUniverse-connected agents for drug safety surveillance. The skill targets post-market adverse event analysis rather than general software development.

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