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Identifying and Mitigating Potential Biases in Predicting Drug Approvals
Ist Teil von
Drug safety, 2022-05, Vol.45 (5), p.521-533
Ort / Verlag
Cham: Springer International Publishing
Erscheinungsjahr
2022
Link zum Volltext
Quelle
SpringerLink (Online service)
Beschreibungen/Notizen
Introduction
Machine learning models are increasingly applied to predict the drug development outcomes based on intermediary clinical trial results. A key challenge to this task is to address various forms of bias in the historical drug approval data.
Objective
We aimed to identify and mitigate the bias in drug approval predictions and quantify the impacts of debiasing in terms of financial value and drug safety.
Methods
We instantiated the Debiasing Variational Autoencoder, the state-of-the-art model for automated debiasing. We trained and evaluated the model on the Citeline dataset provided by Informa Pharma Intelligence to predict the final drug development outcome from phase II trial results.
Results
The debiased Debiasing Variational Autoencoder model achieved better performance (measured by the
F
1
score 0.48) in predicting the drug development outcomes than its un-debiased baseline (
F
1
score 0.25). It had a much higher true-positive rate than baseline (60% vs 15%), while its true-negative rate was slightly lower (88% vs 99%). The Debiasing Variational Autoencoder distinguished between drugs developed by large pharmaceutical firms and those by small biotech companies. The model prediction is strongly influenced by multiple factors such as prior approval of the drug for another indication, whether the trial meets the positive/negative endpoints, and the year when the trial is completed. We estimate that the debiased model generates financial value for the drug developer in six major therapeutic areas, with a range of US$763–1,365 million.
Conclusions
Our analysis shows that debiasing improves the financial efficiency of late-stage drug development. From the pharmacovigilance perspective, the debiased model is more likely to identify drugs that are both safe and effective. Meanwhile, it may predict a higher probability of success for drugs with potential adverse effects (because of its lower true-negative rate), thus it must be used with caution to predict the development outcomes of drug candidates currently in the pipeline.