Summary
In this chapter, we went into the details of how a new drug is tested for safety and efficacy before it can be launched in the market. We understood the various phases in the clinical trial workflow and looked at how regulatory agencies make policies to ensure the safety of patients and trial participants. We understood the importance of PV in the overall monitoring of the drug and looked into the details of real-world data. Additionally, we learned about how ML can optimize the clinical trial workflow and make it safer and more efficient. Finally, we learned about the new features of SageMaker called SageMaker Pipelines and Model Registry, which can aid in these processes. We also built a sample workflow to cluster adverse event data about drugs.
In Chapter 10, Utilizing Machine Learning in the Pharmaceutical Supply Chain, we will look at how pharma manufacturers are utilizing ML to maximize the return on multi-year investments and launching a new drug on the market.
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