Data Points
Continuously updated
AI Models
Domain-specific
Therapeutic Areas
Validated coverage
DDI Detection
vs manual review
AS Profiling Base 100b®
The Largest Integrated Pharmacovigilance Dataset
100+ billion data points from clinical trials, spontaneous adverse event reports, published literature, electronic health records, and regulatory submissions — harmonized, deduplicated, and continuously updated.
Every data point is traceable to its source with full provenance tracking. All transformations are auditable with complete data lineage from raw source to analytical output.
Request a Data Source Briefing →Clinical Trial Data
ClinicalTrials.gov, EudraCT, client CSR databases, study-level and patient-level data with adverse event narratives and demographics.
Spontaneous Reporting
FAERS (FDA), EudraVigilance (EMA), VigiBase (WHO-UMC), JADER (PMDA) — updated quarterly with full case-level detail and MedDRA coding.
Published Literature
PubMed, Embase, Cochrane Library, regulatory agency websites — NLP extraction from full-text articles and abstracts with automated citation tracking.
Real-World Evidence
CPRD, Optum, MarketScan, claims databases, EHR feeds — with patient-level longitudinal data and exposure-outcome linkage.
Regulatory Submissions
FDA approval packages, EMA assessment reports, PMDA reviews — extracted from publicly available documents with structured data fields.
24 Domain-Specific AI Models
Organized in Four Complementary Categories
Each model is purpose-trained on pharmacovigilance data with regulatory use cases in mind. Not general-purpose LLMs adapted to healthcare.
NLP Models
Text Extraction
Extract structured data from unstructured case narratives, PDFs, and regulatory documents.
Entity Recognition
Identify drugs, adverse events, patient characteristics, and causality indicators.
Classification
MedDRA coding, seriousness assessment, expectedness determination, regulatory categorization.
Sentiment Analysis
Assess severity language, clinical significance indicators, and outcome severity scoring.
Summarization
Generate executive summaries from multi-source evidence for regulatory submissions.
Translation
Multi-language support for global pharmacovigilance with domain-specific terminology.
Statistical Models
Bayesian Inference
Prior-informed signal detection and benefit-risk quantification with uncertainty propagation.
Frequentist Analysis
Classical hypothesis testing, confidence intervals, p-values for regulatory documentation.
Survival Analysis
Time-to-event modeling, Kaplan-Meier estimation, Cox regression for long-term safety.
Dose-Response
Non-linear modeling, threshold detection, safety margin estimation.
Meta-Analysis
Fixed and random effects models for evidence synthesis across multiple studies.
Regression
Multivariable adjustment for confounding, subgroup identification, interaction testing.
Predictive Models
Signal Detection
Disproportionality analysis, multi-item gamma Poisson shrinker, Bayesian confidence propagation.
Trend Forecasting
Time series analysis for emerging safety signals and epidemiological trend prediction.
Risk Scoring
Patient-level risk stratification for adverse event probability and severity.
Patient Stratification
Subgroup identification based on demographics, comorbidities, and exposure patterns.
Outcome Prediction
Machine learning models for benefit and risk outcome probability estimation.
Comparative Effectiveness
Real-world evidence synthesis for head-to-head comparisons and network meta-analysis.
Validation Models
Consistency Checks
Cross-reference validation across data sources to detect discrepancies and duplicates.
Cross-Validation
Model performance evaluation with k-fold cross-validation and holdout testing.
Bias Detection
Identify reporting bias, selection bias, and confounding in observational data.
Completeness Scoring
Assess data quality and field completeness for regulatory submission readiness.
Confidence Calibration
Ensure model uncertainty estimates are well-calibrated for risk communication.
Uncertainty Quantification
Bayesian and bootstrap methods for propagating uncertainty through analysis pipeline.
Drug-Drug Interaction Detection
3x Improvement Over Manual Literature Review
ArcaScience's DDI detection models scan 100+ billion data points including spontaneous reports, literature, and clinical trial data to identify potential drug-drug interactions with higher sensitivity and specificity than manual review.
Validated against known DDI databases (DrugBank, TWOSIDES, FDA Adverse Event Reporting System) with continuous retraining as new evidence emerges.
Request a DDI Analysis Demo →Detection rate vs manual review
Sensitivity (true positive rate)
Specificity (true negative rate)
Data Quality & Curation
Continuous Validation and Quality Assurance
Every data point undergoes automated quality checks before integration. All transformations are auditable with complete data lineage.
Automated QC
Completeness scoring, consistency checks, duplicate detection, outlier identification, and cross-source validation.
99.7% data quality score
Continuous Updates
Quarterly updates for regulatory databases, weekly updates for literature, daily updates for RWE feeds. Versioned snapshots for reproducibility.
Updated continuously
Full Traceability
Every analytical result traces back to source data with complete audit trail. 21 CFR Part 11 compliant data lineage tracking.
100% auditable