Clinical trials with virtual comparator arms via artificial intelligence will improve data quality, go/no go confidence, trial design – experts
Artificial intelligence/machine learning (AI/ML) advances that allow clinical trial sponsors to create synthetic control arms (SCAs) -- instead of enrolling patients on active comparators – are expected to provide better quality data for drug development “go”/“no-go” decisions, experts said. SCAs will also allow sponsors to generate investor confidence in early-phase assets, they said.
Sponsors are also using AI/ML’s rapid problem-detecting and -solving abilities to improve clinical trial design by identifying the best patient subpopulations and comparator drugs, and to alert sponsors to enrolment and safety problems with ongoing trials, experts added.
Companies planning the use of AI/ML for clinical trials include service provider Medidata Solutions (NASDAQ:MDSO) working with companies like Roche (VTX:ROG) and London-based BenevolentAI is using AI/ML for its in-house drug pipeline.
SCAs are created by aggregating patient data from past clinical trials to create a virtual control arm representing a comparator drug, which can be used for comparison in the trial of an investigational drug, eliminating the need to enrol patients into an active comparator arm, experts said.