AI-Powered Pharmacovigilance: How Agentic AI Enhances Drug Safety Processes

The pharmaceutical industry is navigating an era of digital transformation, with artificial intelligence (AI) emerging as a cornerstone of innovation. One of the most promising applications is in pharmacovigilance (PV)—the science of detecting, assessing, understanding, and preventing adverse effects or any other drug-related problems

Among the latest advancements in this space is Agentic AI—a more autonomous, intelligent form of AI designed to handle complex, evolving workflows with minimal human input.

Agentic AI not only automates manual tasks but also adapts, learns, and makes informed decisions. This is revolutionizing how pharmaceutical companies approach drug safety. In this blog, we explore how Agentic AI enhances the pharmacovigilance lifecycle—from case intake and processing to signal detection, literature screening, aggregate reporting, and regulatory compliance.

1. Intelligent Case Intake

The first step in any PV workflow is case intake, where adverse event (AE) reports are collected from various sources such as emails, contact centers, healthcare professionals, literature, and social media. Manual intake is often slow and inconsistent, especially when handling unstructured data.

How Agentic AI Improves It:

• Multichannel Data Collection: Captures AE reports from structured and unstructured data sources.

• Natural Language Understanding: Uses NLP to extract relevant details like reporter identity, event date, drug names, and reaction terms.

• Smart Triage: Assesses seriousness and routes the case to appropriate workflows based on regulatory timelines and therapeutic area.

The result is faster and more accurate data entry, reduced intake lag time, and improved compliance with global reporting timelines.

2. Automated ICSR Processing

Individual Case Safety Reports (ICSRs) are central to pharmacovigilance. Traditionally, processing these reports involves manual data entry, validation, coding, and narrative writing—an effort-intensive process prone to inconsistencies.

Agentic AI's Capabilities:

• Auto-Population of Fields: Extracts and populates MedDRA/WHO-DD coded data directly into safety databases.

• Duplication Detection: Identifies potential duplicate reports across different data sources.

• Narrative Generation: Uses contextual data to draft preliminary narratives, reducing manual documentation time.

• Error Flagging: Highlights missing or conflicting data for human review.

By automating routine tasks, Agentic AI accelerates case closure while improving data accuracy and quality.

3. Signal Detection and Management

Detecting potential safety signals early is critical to protecting public health. However, traditional methods rely heavily on statistical thresholds and manual review, which may delay risk identification.

Agentic AI Adds Intelligence:

• Real-Time Pattern Analysis: Identifies abnormal trends or clusters in adverse events across patient populations, regions, and drug types.

• Machine Learning Algorithms: Learns from historical data to refine thresholds and prioritize cases for review.

• Causal Inference Modeling: Helps determine whether observed AEs are likely related to the drug in question.

These capabilities allow for earlier detection of risks, enabling timely interventions and enhanced patient safety.

4. Literature Monitoring at Scale

Scientific literature is a key source of pharmacovigilance insights. However, screening thousands of publications manually is resource-heavy and error-prone.

What Agentic AI Can Do:

• Continuous Journal Scanning: Monitors indexed literature databases like PubMed, Embase, and others in real time.

• Contextual Relevance Matching: Filters and flags articles based on product name, molecule, or AE keywords.

• Automated Data Extraction: Identifies safety-relevant information such as case reports or safety concerns and structures them for ICSR processing.

This reduces the risk of missed safety signals and ensures comprehensive literature surveillance with minimal effort.

5. Accelerated Aggregate Report Generation

Periodic reports like PSURs, DSURs, and PADERs require compiling data from multiple systems and sources. This process involves labor-intensive collation, analysis, and formatting.

How Agentic AI Enhances It:

• Automated Data Aggregation: Collects relevant data from safety databases, clinical trials, and literature.

• Pre-Drafted Content: Creates structured templates and content blocks for regulatory reports.

• Compliance Validation: Ensures the report adheres to the latest ICH, EMA, and FDA guidelines.

This not only speeds up report generation but also ensures consistency, quality, and compliance—reducing the likelihood of regulatory rejections.

Conclusion:

A Safer, Smarter Future for Drug Safety

Agentic AI is not just a buzzword—it’s a game-changing technology reshaping the future of pharmacovigilance. By automating routine processes, enhancing data analysis, and ensuring compliance, it enables pharma companies to improve patient safety while operating more efficiently.

Contact us now to schedule your personalized demo.

As data volumes continue to grow and regulatory landscapes evolve, adopting Agentic AI will be critical for staying competitive and compliant. Whether it’s streamlining case processing or empowering real-time regulatory intelligence, the impact of Agentic AI is already visible—and it’s only just beginning.