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 categorised 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.
Assessing Non-Clinical Safety to Advance High Quality Drug Candidates at Merck MSD
At Merck MSD, the Safety Assessment and Laboratory Animals Resources (SALAR) division helps advance high quality drug candidates into development by defining the non-clinical safety and selectivity of lead compounds. Merck uses AMP in an automated workflow to extract unstructured conclusions and interpretations from final study reports, antemortem reports, postmortem reports and protocols stored in a Documentum-based electronic official file repository. The I2E queries developed were able to identify, extract, and normalize study annotation metadata and organ pathology findings. The results are combined with structured output, loaded into a SALAR knowledgebase, and visualised via dashboards for the safety assessment teams.
This workflow provides significant business impact, for example:
- Driving regulatory change to take work out of safety assessment systems without compromising human safety
- Providing historical summary of findings to assess significance of new findings on pipeline compounds
Agios Pharmaceuticals use of Natural Language Processing for Clinical Safety
At Agios, Linguamatics I2E has been used for 10 years. Stuart Murray (Director, Informatics) said that one reason I2E is used is speed: to get decision support as fast and as comprehensively as possible. “We’ve used I2E 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. I2E was used to extract key information from Serious Adverse Event (SAE) Report Forms. The extracted data was visualised as networks in Cytoscape and enabled clinicians to explore the patterns of symptoms between patients, and critically, identify those at risk.
Text Mining for Post-Market Surveillance at Pfizer
The increasing prevalence of real world data for pharmacovigilance and post-market surveillance provides pharma companies and healthcare organisations a rich seam of data to monitor and mine. Regulations insist that all pharma companies monitor the scientific literature regularly, for example to search for possible adverse events that aren’t reported on drug labels. Linguamatics customers use I2E to extract structured information from patient forum, call center feeds, social media tweets, or electronic medical records and patient narratives.
For example, Pfizer used I2E to categorise and tag call center feeds for key metadata such as caller demographics and reason for calling (e.g. complaint, formulation information, side effect, drug-drug interactions). Text analytics enabled the medical affairs researchers to deepen the relationship for drug-disease associations, by looking within the call logs for information on pre-existing conditions, and relating these to the potential side effects reported in the call log. These associations enabled over 70% of the reported side effects to be related to underlying pre-existing conditions – and not an ADR.
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, analysed, 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 I2E’s NLP for safety assessment, please contact us.
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