JMP for Drug Optimization, Manufacturing and Quality: Part 2
Created:
Nov 9, 2021 04:20 PMLast Modified: Dec 19, 2023 4:00 PM
This video is part of a series of three presentations for the pharmaceutical industry.
In the first session, we present the design of experiments, adjustment of a statistical model, and optimization of the process based on the model.
In this second session for drug manufacturing, we explore and visualize manufacturing data, and then use data mining for root cause analysis.
Suppose we are part of a drug company that is experiencing a high batch rejection rate due to problems with tablet dissolution. The lower specification limit is 70%, meaning the dissolution rate of the tablets must be at least 70, and lots with a dissolution less than 70 are rejected.
Rather than optimizing the process using a design of experiments, we wish to identify the major influences on dissolution and identify short-term corrective measures to implement immediately to reduce the batch rejection rate.
Finally, we look at how to use a Monte Carlo simulation to account for variation in manufacturing variables, incorporate process knowledge, and get a more realistic idea of how the changes we implement will affect the manufacturing process in the long term.
Pharma II SP demo only.mp4
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This post originally written in Spanish and has been translated for your convenience. When you reply, it will also be translated back to Spanish.
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