ML could facilitate increased adoption of adaptive clinical trials

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A new machine learning (ML) software platform has been launched to facilitate wider adoption of advanced and adaptive clinical trials.

The latest version of AdaptV includes an advanced mode to support more in-depth modifications of relevant algorithmic and statistical parameters that go into trial design, accounting for the unique characteristics of a specific trial.

It also incorporates a new proprietary algorithm for several types of adaptive trial designs:

  • Group Sequential Design (GSD), an adaptive design that improves accuracy in small sample sizes and supports multi-arm trials while maintaining strict control for avoiding success declarations when the drug does not work (type-I error).
  • Novel Response Adaptive Randomization (RAR) capabilities that support multi-interims in a trial while maintaining interactivity and accuracy at the highest standards.
  • Bayesian Optimal Interval Phase I/II trial design (BOIN12) aligned with the FDA project Optimus, for optimisation and dose selection for oncology drug development.

“Our platform enables clinical trial teams and strategic stakeholders to better understand the trade-offs of key decisions required throughout adaptive trial design and to make optimal decisions at any point during the trial,” said Raviv Pryluk, CEO and Co-founder of PhaseV.

“By helping to overcome the inherent complexities, we unlock the tremendous potential of adaptive trials and facilitate increased adoption to support more efficient drug development and commercialisation.”

Diana Spencer, Senior Digital Content Editor, DDW

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