There surely can’t be anyone in the pharma industry who hasn’t heard the story of thalidomide. The disaster that followed the release onto the market of thalidomide in 1959 triggered a wave of regulatory changes to ensure reliable evidence of drug safety, efficacy and chemical purity, before a new drug is released onto the market.
While failure of clinical efficacy is the major cause of drug attrition, a poor safety profile is also a major factor in failure of drugs in development, at all stages from initial lead candidate through preclinical and clinical development to post-marketing surveillance. In order to ensure the safety of drugs on the market, rigorous testing is carried out throughout the pipeline, and can be categorized into preclinical safety/toxicology in animal models, clinical safety in human subjects, and then post-market pharmacovigilance, to look for safety signals across a wide patient population (see schematic below).
At every stage, critical data is being both generated and sought from unstructured text – from internal safety reports, scientific literature, individual case safety reports, clinical investigator brochures, patient forum, social media, conference abstracts. Intelligent search across these hundreds of thousands of pages can provide the information for key decision support. Many of our customers are using the power of Linguamatics Natural Language Processing (NLP) solution to transform the unstructured text into actionable structured data that can be rapidly visualized and analyzed, at every stage through the safety lifecycle of a drug.
NLP to enhance the searchability of internal preclinical toxicology safety reports
There is a wealth of valuable information in legacy preclinical safety reports. Locked away in these historical data are answers to questions such as: Has this particular organ toxicity been seen before? In what species, and with what chemistry? Many pharma organizations use document repositories to store their preclinical tox studies, but the search functionality of many of these document management systems is limited, hindering access. Eric Su, Principal Research Scientist at Eli Lilly and Company, talked recently about how NLP can help solve this challenge. At Lilly, they use a search portal (learn more about user search and visualization here) to enable rapid effective searches over the NLP-enabled. This intuitive GUI allows scientists who aren’t expert in NLP to run effective searches and pull back the data and documents of interest, for downstream review and analysis.
Agios Pharmaceuticals use of Natural Language Processing for Clinical Safety
At Agios, Linguamatics NLP has been used for 10 years. Stuart Murray (Director, Informatics) said that one reason NLP is used is speed: to get decision support as fast and as comprehensively as possible. “We’ve used NLP from very early exploratory research to discover targets for our pipeline through to pre-clinical development looking for safety signals, and now most recently for pharmacovigilance to understand what is going on in our clinical trials”. In clinical safety workflows, NLP is being used to mine AE reports, extract case data from call center records, and assist with initial coding of reported events and WHO drugs. A recent use case explored the risk of a rare (potentially life-threatening) adverse event, Differentiation Syndrome, in patients on trial of Agios’s IDH1-inhibitor AG120. NLP was used to extract key information from Serious Adverse Event (SAE) Report Forms. The extracted data was visualized as networks in Cytoscape and enabled clinicians to explore the patterns of symptoms between patients, and critically, identify those at risk.
NLP to support Medical Coding in Adverse Event Report Processing
Once a drug is on the market, pharma companies need to screen huge volumes of reports for potential adverse events from patients in the real world. Within pharmacovigilance workflows, reports are received from many sources – call center feeds, emails, regulatory AE reports and more. These are often in everyday language, and so have to be coded into a standardized format to allow database processing. For the adverse event, indication, medical history, etc. the Medical Dictionary for Regulatory Activities (MedDRA) must be used. Most of the coding is manual and time consuming. Only when the verbatim exactly matches a MedDRA term, is coding automatic. For example, a verbatim might read: “I really got ill the other day, had a horrible headache and couldn’t sleep for two days.” “Headache” has an exact match in MedDRA, so can be auto-encoded, but “couldn’t sleep” is not a MedDRA term and so has to be manually coded to “sleeplessness”. Linguamatics worked with CSL Behring to develop an NLP workflow that doubled the level of auto-coding (from only 30% of adverse events, to over 60%). Use of NLP reduced manual time by 50%, improved coding consistency, and can reduce risk for case processing and medical evaluation.
Digital Transformation for Safety
Safety is assessed at all stages in the life history of a drug. The ultimate test comes only after the drug has been marketed and used in a clinical setting in many thousands of patients, across broader indications, and in combinations with other drugs. There will always be risks; but the more data that can be gathered, analyzed, and transformed into actionable information, the greater the chance of lowering that risk.
If you’d like to know more about any of these safety use cases, or hear about the variety of ways to access the power of NLP for safety assessment, please contact us.
Schematic showing the timing of the main safety assessment studies during drug discovery and development, through discovery, clinical development, and into post-market surveillance and pharmacovigilance. At all stages, project teams need the most comprehensive view of relevant data available; and text mining plays a key role in access to actionable insights for safety.
Learn more about drug development safety
