Tracking and reporting adverse events
In recent years, regulatory authorities such as the FDA and EMA have placed an increased emphasis on drug safety of marketed products, particularly the tracking and reporting of adverse events. Pharmaceutical companies are expected to regularly screen the worldwide scientific literature for potential adverse drug reactions, at least every two weeks. The use of text mining and other tools to streamline the literature review process for pharmacovigilance is more crucial than ever in order to ensure patient safety, without overloading drug safety teams.
Manual review of adverse events is time-consuming
Eric Lewis (Safety Development Leader at GlaxoSmithKline) talked at the Linguamatics Text Mining Summit about the challenges of reviewing medical literature for safety signals. For example, he looked for literature for a sample of just 20 marketed products across a 300-day period. Eric found that there were on average 60 new references per day (with a total of over 11,000 documents). He found that manual review time was 1.2 to 1.6 minutes per abstract. He extrapolated this to a typical pharma company product portfolio of 200 marketed products, and showed that this volume of literature would take over 2,200 hours to review – hugely time-consuming.
NLP finds relationships between drug and adverse event
Eric went on to describe how using NLP, it’s possible to use the linguistic processing to focus in much more specifically on potential drug-related adverse events, by searching for the most appropriate relationships between a drug and an adverse event. Eric presented a specific search, to find the adverse events associated with the selective androgen receptor modifier, Enobosarm (an investigational drug also known as MK-2866 or Ostarine). Searching manually across literature databases, Eric pulled out 132 abstracts, but manual review (3 hours) found that only about 30% of these were relevant and actually described an association with an adverse event. Using I2E to index and query for a precise and accurate pattern took just a few minutes, and provided a structured results table for rapid final review.
Effective drug safety across drug development
This use case demonstrates the value of I2E within a pharmacovigilance application. Many Linguamatics customers use I2E for safety-related applications, such as gaining better value from internal safety reports (e.g. Merck), alerts for possible severe adverse events during clinical trials (Agios) or comparing adverse reactions from clinical trials to patient-reported outcomes (AstraZeneca).
Please access our application note to find out more.
Linguamatics I2E provides powerful linguistic tools to capture numerics. The screenshot shows structured normalised dose information for olanzapine, extracted from recent MEDLINE abstracts. The ability to capture both dose details and causal relationships (e.g. drug x causes adverse event y) in structured form means that safety assessment teams can quickly and effectively review literature reports for safety signals.
Read more about text mining, drug safety and pharmacovigilance.
Learn more about how Agios Pharmaceuticals used text mining for automated identification of potential drug safety events:
