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Kerus: Design of Real World Clinical Trials

The background:

Kerus started out life as a software tool built within the pharmaceutical industry to address some immediate and pressing concerns. Multiple pressures are making modern clinical trials rapidly become more complex. These include personalised medicine, inclusion of real world data and an urgent need to streamline the development pipeline by making each trial answer more questions.

The task set for Dr Beattie was to take this prototype software and develop it into a more generalised product that would meet an unmet need in the pharmaceutical industry.

With an existing prototype available the first task was to review all the associated literature, including feedback from early market testing and the experience of the researcher involved in the early prototype, Dr Aiden Flynn.

This review sought to not only understand what had been done, but also what:

  • Competitor products were available
  • Gaps existed in the original conception
  • Key unmet market needs were
  • Opportunities were present

Schematic showing the agile workflow used in Kerus development, iterating though defining needs,designing solutions, building that solution before deploying it and getting customer feedback

Kerus Development Strategy

The main outcomes were:

  • Commercial products supporting clinical trial design were not capable of meeting the needs that had been identified
  • The main competitor was in fact expert statisticians within the pharmaceutical teams who could manually programme simulations and subsequent analysis
  • The original prototype was designed to handle specific types of clinical trial designs for a limited range of data types, a commercial product needed to generalise the solution
  • This generalisation had to allow flexible trial designs, data types, interactions, analyses and ways of designing how success could be measured
  • There was a gap for a tool that could allow designers of trials to build in real-world complications and require realistic multilevel outcomes to support decision making
  • This tool would need to be able to allow expert statisticians and domain knowledge experts to collaborate and produce outputs that could be used to communicate with management.
  • The tool was essential for many cutting edge advancements in biomedical technology, including personalised medicine, companion diagnostics, ‘omics based research and benefit-risk analysis