Drug safety is, understandably, a prime concern for pharma organizations, regulators, health authorities and patients alike. While there is always risk associated with any medication or treatment, the aim is always to understand any risk so that it can be handled or mitigated appropriately.
The holy grail is, then, how can we predict risk effectively? This is a huge focus of many research initiatives and is being address at many levels – drug target, molecule, patient, population. With the recent flourishing of AI/ML, we’ve seen a blossoming of models to enable risk prediction.
Pilot paper demonstrates use of NLP for adverse events to feed machine learning
There is ongoing work at the FDA to develop models that can predict adverse events (AEs) using post-market safety data, for new drugs coming on the market. Two papers published this year use a combination of AI/ML tools, including NLP, ensemble models and classification algorithms. Both papers build upon pilot work. The pilot study of six drugs demonstrated that pharmacological target AE profiles, based on marketed drugs, can be used to predict unlabelled adverse events for a new drug at the time of approval.