Ensuring patient safety is the highest priority for drug companies and prescribers – and obviously for patients themselves – so any steps that can give scientists and clinicians more accurate, well rounded descriptions of safety data should be welcomed by all parties. AstraZeneca (AZ) wanted test the hypothesis that adverse reaction (AR) information from patients could effectively supplement information from clinical trials, and a key challenge was assembling comparable data sets. AZ studied the commonly reported adverse reaction “nausea”: it is associated with many drugs, and there is a wealth of documented information – albeit in a variety of formats. It is also often debilitating, so anything to reduce its occurrence would be of value to patients.
Patient-reported Real-World Evidence
AZ worked with the patient-generated health data in the PatientsLikeMe system and looked for records reporting nausea as an adverse reaction. Because the PatientsLikeMe system is very well structured, it was relatively simple to extract a clean nausea AR data set that was amenable to comparison.
Clinical Trial Events
Adverse reactions observed in clinical trials are included on drug labels and the data is then listed in the online DailyMed repository maintained by the National Library of Medicine. FDA only offers guidance on how to submit the data, so the content and formats are highly variable, and this complicated creating a well-structured data set to compare with the PatientsLikeMe real-world data.
Using Natural Language Processing (NLP) to make sense of the data
AZ opted to use Linguamatics I2E NLP solution to extract the relevant AR information from the DailyMed unstructured repository into a structured and analytics-friendly format. I2E is ideally suited to deal with the variety and variability found in drug-related text and data where drug names, concepts, and dosing regimens can be expressed differently, and the same word may have different meanings. I2E queries searched DailyMed for nausea as an AR, and then extracted and formatted the required information and useful related data to give a set of well formatted data on some 200 drugs with nausea as a reported AR, ready for comparison with the PatientsLikeMe data.
Comparing Real-World Evidence and Clinical Trial Events
AZ compared the two data set using analytical and statistical tools and found that there were quantifiable differences between the nausea AR reporting rates on drug labels and those self-reported in PatientsLikeMe.
To learn more about this AZ study, how they used I2E, and the conclusions they reached about the relationship between the patient-reported ARs and clinical trial events, please download the AZ case study or watch the recorded webinar.
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