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Enabling a new way to select fit-for-purpose COAs to simplify clinical protocols

For clinical researchers, getting to a finished protocol is a delicate art in navigating research, regulatory precedence, and institutional knowledge. And complexity continues to grow - more regulations, more endpoints per trial, more amendments, and more options (in-person or remote assessments). The clinical protocol has become everything but the kitchen sink. So how can we simplify while maintaining data quality and integrity? We need reliable recommendations so that we can make decisions without wading through every piece of research and data available.

ICON knows that ontologies are a great way to organize large datasets to build insights through computation. ICON’s Atlas™ platform organizes >3.5k Digital Health Technologies (DHTs) across measures (passively-monitored COAs and digital biomarkers), medical conditions, and vendors. While DHTs are one piece of the puzzle, holistic protocol design requires more breadth. When selecting which COAs will represent study endpoints, a questionnaire, standardized task, DHT, or a combination thereof could be used to measure the same or related outcome:

tool-selection-blog-with background
Actively collected COAs (the DLQI, MOS Sleep Scale, or Worst Itch NRS) or passive-monitoring COAs (accelerometry & PPG-derived heart rate or actigraphy) can all report measures relevant to scratching in sleep for atopic dermatitis patients

And yet, no single resource exists to compare them side-by-side to help researchers pick the right tool for the job. Thus, we started brainstorming ways to expand the precision measurement capabilities of Atlas Network to include traditional, actively collected COAs.

ICON-sponsored MAPI Research Trust’s ePROVIDE™ database maintains the world’s largest library of >5k actively collected COAs. Turns out, five of the data elements in ePROVIDE™ and Atlas™ can be perfectly overlaid: Therapeutic area(s), medical condition(s), concepts of interest/domains, measures, and populations (Fig 2). These data elements create a backbone for a shared framework — or what we call “an aligned ontology”:

active passive ontologies landscape image for table
Mapping the conceptual overlap between actively collected and passive monitoring COA ontologies

We validated this conceptual alignment and selected atopic dermatitis as a proof-of-concept. As described in our paper published in Nature’s npj Digital Medicine, this framework allows researchers to compare all relevant actively-collected and passively-monitored COAs and pick the best one for their study. Together with our co-authors we imagined how this aligned ontology could answer these questions:

  • Which questionnaire(s), DHT(s), and standardized tasks have been used to measure COAs for atopic dermatitis?
  • How has scratching behavior been measured actively and/or passively?
  • Which tool(s) are well validated for my target population?
  • Which tool(s) have data robust enough to support regulatory approval and reimbursement?

As well as practical considerations, such as:

  • Can the tool be deployed (e.g., shipped, translated, operated, etc.) in the geographic region(s) needed for my trial?

Both actively-collected and passive-monitoring COAs can be critical components to bringing patient-centred treatments to market and aligning them is a big step in helping the industry select the right tool for the job — such as picking a DHT to passively collect data when a PRO might be difficult to administer or using both to contextualize passively-collected data to a participant’s reported experience.

This new way to research, identify, and select fit-for-purpose measures with confidence will reduce the number of endpoints in clinical studies without sacrificing quality and data integrity. Simpler protocols lessen the burden on participants and sites, can increase response rates and retention, and reduce issues with missing data along with many other downstream improvements to effort and cost.

Now imagine this aligned ontology persisted as a living repository mapping evidence collected as these decisions are made, the trials are conducted, and the results are published. Additional datasets - like imaging, labs, or real-world data - can be added and aligned. This dataset could be used to train language models and/or generative AI, leading to insights and predictive recommendations for better drug discovery and development. This is one way our industry gets closer to the vision of precision measures for patient-centered drug development.

Blog authored by Courtney Webster, Compliance, ICON. Originally written for HumanFirst, a company acquired by Icon in january 2024.
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