Clinical Trials text mining can speed key decisions, effective site selection and trial design
Clinical trials form the cornerstone of evidence-based medicine, and are essential to establishing the safety and efficacy of new drugs. Each new drug, before being approved by regulatory agencies, must pass through a set of gates. At the very basic level these include phase 1 for first-in-human safety; phase 2 for efficacy and biological activity against the target; and phase 3 for safety, efficacy and effectiveness of the new therapeutic.
At each of these phases, careful planning is essential for a successful study. The clinical study protocol covers objective(s), design, methodology, statistical considerations and organization of a clinical trial, and ensures the safety of the trial subjects and integrity of the data collected.
Over recent years, clinical trial designs and procedures have become more diverse and more complex. The impact of precision medicine means trials have to be more carefully planned to ensure adequate statistical power for smaller patients groups, and adaptive, umbrella, basket and n-of-1 trials are now more frequent.
As with all research, being able to utilize the experience of other researchers and clinical trials is hugely valuable. We are fortunate in having public access to current and historic clinical trial data, through various clinical trial registries such as US NIH ClinicalTrials.gov, and the WHO ICTRP.
These clinical trial registries contain abundant valuable information that can be used in protocol development - for example trial site selection, principal investigator identification, or assessment of patient inclusion and exclusion criteria for particular therapeutic areas. Other applications of these data include competitive intelligence around another company’s therapeutic pipeline, or information on potential in-licensing opportunities.
Addressing clinical trial registry search challenges with text analytics
However, there are issues regarding effective search. For example, search vocabularies are incomplete and much of the information within records is unstructured and not easily extracted using the registry-supplied search interfaces.
Many pharma and biotech organizations are using Linguamatics text analytics with clinical trial registries to gain access to valuable data in a more effective and efficient process.
At Linguamatics we work closely with organizations like Merck to provide a software platform and content that helps design better clinical trials more efficiently and intelligently. We help identify, pinpoint, and extract information that’s usually unavailable or really hard to get to.
Read how Merck used Linguamatics I2E to reveal a completely new trial site for diabetes.
Eric Su, Principal Research Scientist at Eli Lilly, said that “I2E provides data that would take tens or hundreds of times longer with tedious manual work,” and Cassie Gregson, Principal Informatics Scientist at AstraZeneca, said working with Linguamatics “….makes it possible to find high value information which was previously difficult to find or unobtainable.”