Non-clinical pharmaceutical statistics

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By Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry, Alexander Schacht, Benjamin Piske, and Leaders in the pharma industry. Discovered by Player FM and our community — copyright is owned by the publisher, not Player FM, and audio is streamed directly from their servers. Hit the Subscribe button to track updates in Player FM, or paste the feed URL into other podcast apps.
Interview with Sam Gardner

What is non-clinical pharmaceutical statistics?
Join us while I talk about this topic with Sam and dive deeper into these points:

  • Non-clinical statistics is the application of statistics in pharmaceutical R&D and manufacturing and that is not directly related to planning and analyzing clinical trials.
  • There are numerous statistical issues to deal with in these areas, and often regulatory expectations require the use of statistical methods.
  • In this episode we discussed example areas for Non-Clinical Statistics in the Manufacturing and Quality area (also known as Chemistry, Manufacturing and Controls (CM&C))
    • Analytical Methods for assessing quality
      • Understanding the science and technology is important (understanding how the data is actually generated or "how data happens").
      • Determining appropriate specifications for a drug product and ensuring that the natural variation in the measurements will not lead to failing specifications.
      • Qualifying and determining the defined content of chemical reference standards (using appropriate experimental designs)
    • Ongoing Manufacturing Processes
      • Using control charts for on-going monitoring of manufacturing processes
      • Understanding sources of variability that impact the output of the manufacturing process
      • When something goes wrong: for example there is a bad batch that fails to meet its quality specification, and when this happen, an investigation has to be conducted, and statistical thinking, data gathering and combining, data visualization, data mining and experimental design can be used as part of the investigation
      • Building tools to automatically build data sets from manufacturing data and visualize that data
    • Designing new manufacturing processes

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