24 Mar, 2025

Role of artificial intelligence in pharmacovigilance: Chartered and Unchartered areas

Role of artificial intelligence in pharmacovigilance: Chartered and Unchartered areas

Artificial intelligence (AI) is playing an increasingly important role in pharmacovigilance (PV), transforming how adverse drug reactions (ADRs) are detected, assessed, and reported. There are many tools coming up in the market having great claims on improvement of productivity, quality and turn around time in various pharmacovigilance processes like Intake, Literature Search and signal detection. Below are the areas where lot development is ongoing for AI enabled systems:

1. Adverse Event (AE) Detection and Reporting

AI can automatically extract and identify potential adverse events from:

  • Electronic health records (EHRs)
  • Medical literature
  • Social media and patient forums
  • Spontaneous reporting databases

Natural Language Processing (NLP) tools are especially useful in analysing unstructured data (e.g., clinical notes, social media posts) for ADR signals.

2. Case Processing Automation

Routine PV tasks are being automated with AI, including:

  • Data entry and case triage
  • Medical coding (e.g., MedDRA terms)
  • Narrative generation for case reports
  • Automated submission of reports to regulatory agencies (e.g., FDA, EMA)

3. Data Mining and Integration

AI helps in:

  • Combining multiple data sources (e.g., spontaneous reports, literature, claims data).
  • Enhancing data quality through de-duplication, standardization, and cleaning.
  • Using predictive analytics to foresee safety issues before they manifest widely.

4. Signal Detection and Prioritization

AI algorithms, particularly machine learning (ML) models, can analyse massive datasets to detect safety signals faster and more accurately than traditional methods. AI can:

  • Identify trends and patterns indicating new or rare ADRs.
  • Rank and prioritize signals based on severity and frequency.
  • Reduce false positives and improve signal validation.

5. Automated Literature Screening

Pharma companies use AI to screen PubMed, Embase, and journals for safety-related publications.

  • Reduces the burden of manual literature reviews by automating relevant article identification.

Apart from all these areas, there are other areas where AI can definitely help in reducing manual intervention and increase productivity. Below are few examples:

1. Predictive Safety Profiling

AI models can predict potential ADRs in:

  • Pre-clinical or early clinical phases using in silico modelling.
  • Based on chemical structure, pharmacological targets, and patient genetics. This supports safer drug design and early risk mitigation.
  • Easy identification of drug interactions and class related adverse events

2. AI enabled chat boats for medical information centres

AI chatbots are emerging as powerful tools for patient-reported adverse drug reaction (ADR) collection, making pharmacovigilance more patient-centric, real-time, and scalable. Here's a deep dive into how they work, their benefits, and examples:

  • Patient Interaction: Chatbots interact with patients via web apps, mobile apps, or messaging platforms (e.g., WhatsApp, Facebook Messenger, SMS). They prompt patients to report side effects using natural language or guided forms.
  • Data Capture: Collect structured and unstructured data: symptoms, timing, medication, dosage, demographics, medical history. NLP algorithms interpret and code the data using systems like MedDRA for regulatory reporting.
  • Automated Case Processing: The chatbot flags serious ADRs for rapid follow-up. Can generate narrative summaries for PV case files. Integrates with safety databases (e.g., Argus, Veeva Vault Safety) for downstream processing.

Some benefits of using AI Chatbots are as below:

  • Increased Reporting Rates: Patients are more likely to report ADRs via chatbots than traditional channels.
  • Faster Detection: Real-time data collection accelerates signal detection.
  • Cost Efficiency: Reduces need for call centres and manual data entry.
  • Enhanced Data Quality: Consistent, structured data collection improves accuracy.
  • Patient Engagement: Educates patients on drug safety and follow-up actions.

3. Case allocation and tracking in ICSR processing

AI is playing an increasingly impactful role in case allocation and tracking in Individual Case Safety Report (ICSR) processing, helping pharmacovigilance (PV) teams manage high volumes of cases efficiently, accurately, and in compliance with regulatory timelines.

Here’s how AI contributes to case allocation and tracking in ICSR workflows:

A. AI in Case Allocation

a. Automated Case Triage & Prioritization

AI systems analyse incoming ICSRs to:

  • Determine seriousness, expectedness, and regulatory deadlines.
  • Classify cases by source (e.g., spontaneous, literature, clinical trial) and region (to comply with local reporting rules).
  • Predict complexity level based on data completeness, co-medications, and narratives.

?? Result: Cases are prioritized and routed to the right safety reviewers (e.g., senior vs. junior staff, specific region-focused teams).

b. Intelligent Workload Distribution

AI algorithms assess:

  • Team member availability, current workload, expertise, and past performance.
  • Allocates cases to ensure balanced workloads, adherence to SLA (Service Level Agreements), and efficient use of resources.

?? Often powered by machine learning (ML) and business rules engines integrated into PV systems like Argus, Veeva Vault Safety, etc.

c. Escalation Management

AI can detect potential case processing bottlenecks (e.g., aging cases, approaching deadlines) and automatically escalate to managers or reassign cases for quicker action.

B. AI in Case Tracking

a. Real-Time Dashboarding & Alerts

AI-powered dashboards track:

  • Case status (intake, triage, assessment, submission stages).
  • Turnaround times and regulatory timelines (e.g., 15-day, 7-day deadlines).
  • AI detects processing delays and triggers alerts for proactive intervention.

b. Predictive Analytics

AI can forecast:

  • Future case volumes (based on seasonality, product launches, campaigns).
  • Potential resource gaps and processing delays, enabling better planning.
  • Compliance risks for late reporting to regulators.

c. Quality Monitoring

AI systems audit cases in real time for completeness and consistency:

  • Detect missing mandatory fields (e.g., reporter info, suspect drug).
  • Flag duplicates, inconsistent coding, or narrative discrepancies.
  • Provide feedback loops for continuous process improvement.

Below are benefits of use of AI in case allocation and tracking:

  • Faster Turnaround - Reduced cycle times in case processing and regulatory submissions.
  • Improved Compliance - Minimized risk of late submissions, regulatory non-compliance
  • Resource Optimization - Better utilization of PV teams, avoiding overload or underuse
  • Scalability - Easily handles volume surges (e.g., post-marketing safety events)
  • Enhanced Visibility - Real-time insights for KPIs, audit readiness, trend analysis

4. Regulatory Intelligence mining

Regulatory Intelligence (RI) mining is a critical function in pharmacovigilance (PV) that involves systematically gathering, analysing, and applying regulatory information to ensure compliance and optimize drug safety processes globally. With increasing complexity in global regulations, AI-powered regulatory intelligence mining is becoming essential for efficient PV operations. Below are some areas where AI can help:

A. Monitoring and Tracking Global PV Regulations

  • Objective: Stay updated with evolving PV laws, guidelines, and submission requirements across countries (e.g., FDA, EMA, PMDA, CDSCO, MHRA).
  • AI Role: Automatically scans regulatory agency websites, guidelines, legislation portals, and newsfeeds.
  • Provides real-time alerts on changes (e.g., updated reporting timelines, new E2B standards).

B. Impact Analysis and Compliance Planning

  • Objective: Assess how regulatory changes affect current PV processes and ensure timely adaptation.
  • AI Role: Uses Natural Language Processing (NLP) to analyse new regulations, compare with existing policies, and suggest process updates or SOP revisions.
  • Supports risk assessment and change management planning.

C. Automation of Literature and Document Review

  • Objective: Identify relevant guidelines, updates, and scientific publications impacting drug safety.
  • AI Role: Mines PubMed, Embase, government portals using keyword extraction, topic modelling, and semantic search to extract actionable intelligence.
  • Reduces manual review time and improves relevance filtering.

D. Supporting Regulatory Submissions

  • Objective: Ensure accurate and timely submission of ICSRs, PSURs, DSURs, RMPs, and other PV deliverables.
  • AI Role: Maps regulatory requirements to submission checklists, ensures regional compliance, and flags missing elements before filing.
  • Ensures alignment with local PV reporting formats and deadlines.

E. Competitive Intelligence

  • Objective: Understand how other companies handle PV compliance and monitor benchmark practices.
  • AI Role: Mines public inspection reports, warning letters, and enforcement actions from agencies (e.g., FDA 483s).
  • Highlights compliance trends and common pitfalls to avoid.

F. Decision Support for PV Strategy

  • Objective: Enable proactive decision-making for global safety strategies, such as product launches, expansions, or audits.
  • AI Role: Aggregates and presents regulatory landscapes by country or region, helping PV leaders assess regulatory risk profiles.
  • Supports audit readiness and inspection preparation.

Judicious and cautious use of AI can help in automizing non-value-added tasks and lead to optimal utilization of subject matter expertise of resources. However, technology must comply with GDPR, HIPAA, and regional data protection laws and of course, human oversight is must !!!.