Abstract
BackgroundWhile clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary approach to predict 135 adverse events using post-market safety data from marketed drugs. Adverse event information available from FDA product labels and scientific literature for drugs that have the same activity at one or more of the same targets, structural and target similarities, and the duration of post market experience were used as features for a classifier algorithm. The proposed method was studied using 54 drugs and a probabilistic approach of performance evaluation using bootstrapping with 10,000 iterations.ResultsOut of 135 adverse events, 53 had high probability of having high positive predictive value. Cross validation showed that 32% of the model-predicted safety label changes occurred within four to nine years of approval (median: six years).ConclusionsThis approach predicts 53 serious adverse events with high positive predictive values where well-characterized target-event relationships exist. Adverse events with well-defined target-event associations were better predicted compared to adverse events that may be idiosyncratic or related to secondary target effects that were poorly captured. Further enhancement of this model with additional features, such as target prediction and drug binding data, may increase accuracy.
Highlights
While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events
Some adverse events may have high prevalence in specific subpopulations who were not enrolled in the clinical trials or subgroups who cannot be identified based on information collected from patients in Daluwatte et al BMC Bioinformatics (2020) 21:163 the trials
We may consider alternative groupings or adding additional terms to complete a mechanistically-related grouping. This classifier algorithm predicts significant adverse events that are of high priority for regulatory monitoring, some of which may be difficult to observe in clinical trials
Summary
While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary approach to predict 135 adverse events using post-market safety data from marketed drugs. Adverse event information available from FDA product labels and scientific literature for drugs that have the same activity at one or more of the same targets, structural and target similarities, and the duration of post market experience were used as features for a classifier algorithm. The Food and Drug Administration’s (FDA) proposed process modernization to support new drug development involves establishing a unified post-market safety surveillance framework to monitor the benefits and risks of drugs across their lifecycles [1]. While clinical trials are considered the gold standard for detecting and labeling adverse events, these trials are not sufficiently powered to detect less common adverse events.
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