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Drug Safety Landscapes – Painting the Picture with Natural Language Processing

Drug Safety Landscapes – Painting the Picture with Natural Language Processing

Pharmaceutical companies have a problem: there are a plethora of free insights at their fingertips from widely available sources – like scientific papers, clinical trial databases, regulatory reviews and drug labels, or internal legacy reports – that can help them improve drug safety, but this valuable information is often buried in these documents as unstructured text. 

Understanding the safety landscape around any drug, adverse event (AE), or drug target is a critical part of drug development, from initial therapeutic target selection to post-market surveillance. Effective access to the right information brings value in safety assessment and risk management. 

However, to uncover this information, pharmaceutical companies need tools that will help them unlock data that was previously inaccessible to obtain new insights, break down silos, and boost innovation. For example, manual search and review of safety literature is slow and repetitive, abstracts can take one-to-two minutes to review, and full articles around 15-to-20 minutes. To solve these problems, many pharma companies have turned to natural language processing (NLP), an artificial intelligence-based technology that automates data mining.  

Getting the right mix of NLP, content, ontologies and questions  

Leveraging NLP, Linguamatics Safety Intelligence Hub integrates the right content, including literature, drug labels, FDA and EMA regulatory approval packages, with ontologies, intuitive queries, and easy-to-use searches, to rapidly deliver the information that safety teams need. This provides a dedicated hub of information that enables safety assessment teams, medical reviewers, drug safety experts, and toxicologists to contextualize and understand safety signals. 

The hub helps safety teams to reduce time spent on accessing the right information from key safety data sources, to answer important questions such as: 

  • Has this event been seen before? 

  • With this drug or drug class? 

  • In what patients, or preclinical species? 

  • How can I understand the mechanism of action, and critically, the risk liability? 

NLP enriches relevant data sources with life science ontologies around drugs and AEs, including MedDRA, making them easy to search effectively. FDA reviews, letters and labels, and FDA and EMA drug labels, contain rich safety information on marketed drugs. Post-market AE data can be found in FAERS, and includes information on AEs reported in clinical trials. Literature sources provide current and legacy scientific research in abstracts, full-text articles, and preprints. Search across these data sources provides information on the context around any drug, target, or disease. 

Benefits of accessing safety vigilance information from the Safety Intelligence Hub include: 

  • Access to award-winning NLP technology 

  • Multiple data sources available to search across, with ontologies to boost recall  

  • Flexible search in an intuitive interface 

  • Value across all stages of safety 

  • Reduced manual effort and time to insights  

Numerous pharmaceutical companies have implemented NLP technology to mine literature to better understand safety signals and improve adverse event detection. Learn more about how the Safety Intelligence Hub can help you enhance drug safety. 

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