Using Machine Learning to Identify a Suitable Patient Population for Anakinra for the Treatment of COVID-19 Under the Emergency Use Authorization

https://www.fda.gov/drugs/spotlight-cder-science/using-machine-learning-identify-suitable-patient-population-anakinra-treatment-covid-19-under?utm_medium=email&utm_source=govdelivery In this Spotlight on CDER Science, CDER reviewers combined the predictive ability of artificial intelligence/machine learning (AI/ML) with appropriate validation processes to develop a method to identify a patient population who will likely benefit from a drug therapy. On November 8, 2022, FDA issued an Emergency Use Authorization (EUA) for anakinra (Kineret) for the treatment of COVID-19 in hospitalized adults with pneumonia requiring supplemental oxygen (low- or high-flow oxygen) who are at risk of progressing to severe respiratory failure and likely to have an elevated plasma soluble urokinase plasminogen activator receptor (suPAR). However, an approved suPAR commercial test is not available in the U.S., which created a challenge during CDER’s review of the EUA application when identifying the patient population most likely to benefit from anakinra. To ensure patients’ timely access to this treatment, the CDER review team used AI/ML to facilitate the identification of patients who could receive the drug under the EUA. The goal was to develop a scoring rule that would ensure a high proportion of patients meeting the criteria would have a suPAR ≥ 6 ng/mL. This was the first time that CDER used AI/ML for a regulatory decision.