In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence. Key Takeaways Understand how machine learning differs from traditional statistical approaches, and why it is parti...
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In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence. Key Takeaways Understand how machine learning differs from traditional statistical approaches, and why it is parti...
Episode 22: Quality Tolerance Limits in Clinical Trials
QCast: Data-Driven Dialogue in Drug Development
9 minutes
1 month ago
Episode 22: Quality Tolerance Limits in Clinical Trials
In this QCast episode, Jullia and Tom explore quality tolerance limits in clinical trials, explaining what they are, how they support risk based quality management, and how to define, monitor and govern them so they genuinely protect participant safety and trial integrity rather than becoming a tick box exercise. Key Takeaways Define QTLs from the risk assessment, link them to truly critical to quality parameters.Set a small number of clear, study level limits, document rationale and calculat...
QCast: Data-Driven Dialogue in Drug Development
In this QCast episode, Jullia and Tom unpack how machine learning is being applied across the pharmaceutical industry. They discuss what machine learning means in a regulated drug development context, where it is already supporting discovery, development, and trial operations, and how teams can use these methods responsibly without undermining scientific or regulatory confidence. Key Takeaways Understand how machine learning differs from traditional statistical approaches, and why it is parti...
