--- name: pharmacovigilance-guide-0c6576730fe3 description: "Adverse drug event detection, safety signal mining, and drug monitoring" metadata: openclaw: emoji: "⚠️" category: "domains" subcategory: "pharma" keywords: ["pharmacovigilance", "adverse-events", "drug-safety", "faers", "signal-detection", "disproportionality"] source: "wentor" --- # Pharmacovigilance Guide A skill for computational pharmacovigilance research, covering adverse drug event (ADE) databases, signal detection algorithms, disproportionality analysis, and safety surveillance methods used in post-market drug monitoring. ## Adverse Event Data Sources ### Key Databases | Database | Operator | Coverage | Access | |----------|----------|----------|--------| | FAERS (FDA Adverse Event Reporting System) | FDA | US spontaneous reports | Free quarterly downloads | | EudraVigilance | EMA | European reports | Research access via application | | VigiBase | WHO-UMC | Global (150+ countries) | Research license | | VAERS | CDC/FDA | US vaccine adverse events | Free download | | MAUDE | FDA | Medical device reports | Free download | ### Loading FAERS Data ```python import pandas as pd import zipfile import os def load_faers_quarter(data_dir: str, year: int, quarter: int) -> dict: """ Load FAERS quarterly data files into DataFrames. Downloads available from: fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html Returns dict of DataFrames for each file type. """ prefix = f"faers_ascii_{year}Q{quarter}" tables = {} file_map = { "DEMO": "demographics", # Patient demographics "DRUG": "drugs", # Drug information "REAC": "reactions", # Adverse reactions (MedDRA terms) "OUTC": "outcomes", # Patient outcomes "INDI": "indications", # Drug indications "THER": "therapy", # Therapy dates "RPSR": "report_sources", # Report source } for suffix, name in file_map.items(): filepath = os.path.join(data_dir, f"{suffix}{year}Q{quarter}.txt") if os.path.exists(filepath): tables[name] = pd.read_csv( filepath, sep="$", encoding="latin-1", low_memory=False, on_error="warn" ) return tables # Example: Load and inspect faers = load_faers_quarter("./faers_data", 2024, 3) print(f"Reports: {len(faers['demographics']):,}") print(f"Drug-reaction pairs: {len(faers['reactions']):,}") ``` ## Signal Detection Methods ### Disproportionality Analysis Disproportionality measures compare the observed frequency of a drug-event pair against the expected frequency under independence: ```python import numpy as np from scipy.stats import chi2 def compute_disproportionality(a: int, b: int, c: int, d: int) -> dict: """ Compute disproportionality measures from a 2x2 contingency table: Event+ Event- Drug+ a b Drug- c d a: reports with both the drug and the event b: reports with the drug but not the event c: reports with the event but not the drug d: reports with neither """ n = a + b + c + d expected = (a + b) * (a + c) / n if n > 0 else 0 # Reporting Odds Ratio (ROR) ror = (a * d) / (b * c) if b * c > 0 else float("inf") ln_ror = np.log(ror) if ror > 0 and ror != float("inf") else 0 se_ln_ror = np.sqrt(1/a + 1/b + 1/c + 1/d) if min(a, b, c, d) > 0 else float("inf") ror_lower = np.exp(ln_ror - 1.96 * se_ln_ror) # Proportional Reporting Ratio (PRR) prr = (a / (a + b)) / (c / (c + d)) if (a + b) > 0 and (c + d) > 0 else 0 # Information Component (IC, Bayesian shrinkage) ic = np.log2((a + 0.5) / (expected + 0.5)) if expected > 0 else 0 # Chi-squared with Yates correction chi2_val = (n * (abs(a * d - b * c) - n / 2) ** 2) / ( (a + b) * (c + d) * (a + c) * (b + d) ) if min(a + b, c + d, a + c, b + d) > 0 else 0 return { "a": a, "b": b, "c": c, "d": d, "expected": round(expected, 2), "ROR": round(ror, 3), "ROR_lower_95": round(ror_lower, 3), "PRR": round(prr, 3), "IC": round(ic, 3), "chi2": round(chi2_val, 3), "signal": ror_lower > 1 and a >= 3 and chi2_val > 3.84, } ``` ### Multi-Item Gamma Poisson Shrinker (MGPS) The MGPS method (used by FDA) applies empirical Bayesian shrinkage to stabilize estimates for rare events: ```python def empirical_bayes_geometric_mean(observed: np.ndarray, expected: np.ndarray) -> np.ndarray: """ Simplified EBGM computation. Shrinks observed/expected ratios toward the overall mean, reducing false positives from small counts. """ # Raw ratio rr = observed / np.maximum(expected, 0.01) # Empirical Bayes shrinkage (simplified two-component mixture) # Full implementation uses EM algorithm to fit mixture of gammas global_mean = np.mean(rr) shrinkage = expected / (expected + 1) # more shrinkage for small expected ebgm = shrinkage * rr + (1 - shrinkage) * global_mean return ebgm ``` ## MedDRA Terminology ### Medical Dictionary for Regulatory Activities MedDRA provides the standardized terminology for adverse event coding: ``` Hierarchy (5 levels): System Organ Class (SOC) -- e.g., "Cardiac disorders" High Level Group Term (HLGT) -- e.g., "Cardiac arrhythmias" High Level Term (HLT) -- e.g., "Supraventricular tachyarrhythmias" Preferred Term (PT) -- e.g., "Atrial fibrillation" Lowest Level Term (LLT) -- e.g., "Auricular fibrillation" ``` ### Standardized MedDRA Queries (SMQs) Pre-defined search strategies for known safety topics: - **Anaphylactic reaction (SMQ)**: Broad and narrow search terms - **Drug-induced liver injury (SMQ)**: Hy's Law criteria - **Torsade de pointes / QT prolongation (SMQ)**: Cardiac safety signals - **Rhabdomyolysis (SMQ)**: Muscle-related adverse events ## Temporal Pattern Analysis ### Time-to-Onset Analysis ```python def time_to_onset_analysis(drug_start_dates: pd.Series, event_dates: pd.Series) -> dict: """ Analyze time-to-onset distribution for a drug-event pair. Useful for distinguishing causal signals from coincidental reports. """ ttp = (event_dates - drug_start_dates).dt.days ttp = ttp[ttp >= 0] # exclude negative (data quality issue) return { "n_reports": len(ttp), "median_days": ttp.median(), "mean_days": ttp.mean(), "q25_days": ttp.quantile(0.25), "q75_days": ttp.quantile(0.75), "within_30_days_pct": (ttp <= 30).mean() * 100, "within_90_days_pct": (ttp <= 90).mean() * 100, } ``` ## Causality Assessment Standard frameworks for evaluating whether a drug caused an adverse event: | Method | Type | Key Criteria | |--------|------|-------------| | WHO-UMC | Algorithmic | Temporal, dechallenge, rechallenge, alternative causes | | Naranjo Score | Scoring scale | 10 questions, score 0-13 (definite/probable/possible/doubtful) | | Bradford Hill | Principles | Strength, consistency, specificity, temporality, biological gradient | ## Tools and Resources - **openFDA API**: Direct access to FAERS data via REST - **OHDSI / OMOP CDM**: Standardized observational health data for pharmacoepidemiology - **PhViD (R package)**: Pharmacovigilance signal detection methods - **EHRtemporalVariability**: R package for temporal data quality in EHR - **VigiRank**: WHO-UMC signal prioritization algorithm - **AEOLUS**: Standardized and cleaned version of FAERS data --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/pharma/pharmacovigilance-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/pharma/pharmacovigilance-guide/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Healthcare & Life Sciences Roles: Clinical Research, Data Scientist, Health Informatics Specialties: API, Database Tools: Faers Country applicability: United Kingdom, United States Classification basis: Source catalog labels Instruction SHA-256: 57ebc80961b00ef1512f20821abd3612560d22fa91f2ba69f939642b4422636a