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Success Story

Improving Speed and Efficiency of Drug Safety Signals Detection 

A global pharmaceutical company used Graphwise’s GraphDB to create an automated ADR monitoring system for scientific literature. It helped them deliver real-time adverse event detection, reduced validation time, and enhanced regulatory compliance throughout the drug development lifecycle.

The Client

Global pharmaceutical company required to monitor Adverse Drug Reactions (ADRs) in scientific literature and provide strict signal detection processes for identifying new adverse events

The Challenge

The company needed a more efficient solution for systematic and comprehensive ADR monitoring in scientific literature to meet FDA and European Medicinal Agency pharmacovigilance requirements and quickly assess signals for prompt notification to regulatory bodies

The Solution

Using Graphwise’s GraphDB and knowledge graph-based semantic text analysis, the company created a highly configurable scientific literature monitoring system that processed real-time publication feeds and identifies potential drug adverse events automatically

Technical capabilities

  • Real-time processing of publications from NCBI PubMed and Elsevier EMBASE with automatic ADR identification
  • ICSR schema recognition extracting patient data, drug information, and administration details using UMLS, RxNorm, and proprietary terminologies

Business outcomes

  • Reduced time and effort in identifying and validating adverse events through automated extraction  
  • Enhanced regulatory compliance adhering to FDA and EMA pharmacovigilance guidelines

The Challenge

Adverse Drug Reaction (ADR) monitoring requires constant surveillance for undesirable drug effects post-licensing. The company’s existing process for screening scientific literature was time-consuming and struggled to meet goals.

The challenges included exponentially growing safety information in diverse formats, incomplete coverage of important ADR data elements, ambiguous drug naming using generic and brand names interchangeably. Additionally, the existing system had difficulty capturing complex causal relations between entities in case reports consistently.

The Solution

The solution leveraged Graphwise’s GraphDB and a knowledge graph-based semantic text analysis that goes beyond standard content indexing. It processes real-time publication feeds from multiple sources. It handles both article content and metadata while recognizing relevant data elements from Individual Case Safety Reports (ICSRs).

The solution integrates established reference datasets such as UMLS and RxNorm with proprietary client terminologies. Then it complements extracted data with structured ADR knowledge from public reporting systems. The automatic ADR extraction provides normalized, structured data that only requires validation by subject matter experts. As a result, the overall process is significantly accelerated.

The Impact

The new monitoring system delivered substantial improvements across multiple areas. It provided comprehensive drug safety knowledge by utilizing publicly available information to build an up-to-date safety knowledge graph for causality assessment. It also successfully connected proprietary and public data, linking internal safety data with large repositories of semi-structured adverse reaction information.

The Graphwise solution enabled lifecycle-wide safety tracking through a single intelligent system with automated signal detection as new data entered the knowledge graph. The automated identification of adverse events became a key operational benefit, significantly reducing validation time and effort. As a result, the solution enhanced regulatory compliance while improving overall patient Healthcare.

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Facing Similar Challenges?

Struggling with time-consuming manual processes for drug safety monitoring and regulatory compliance in pharmaceutical development?

Whether you're a pharma company, biotech firm, or healthcare organization, Graphwise can help you:

  • Automate adverse event detection from scientific literature using semantic text analysis 
  • Integrate proprietary safety data with public ADR repositories for comprehensive insights   
  • Accelerate regulatory compliance with real-time signal detection and validation processes   
  • Enhance patient safety through systematic, lifecycle-wide drug safety monitoring

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