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
- Applying the principles of good process design.
- Highly recommend book for any statistician: Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services
- More on this will be covered in a later episode.
- Analytical Methods for assessing quality
Founder, Owner, and Principal Consultant for Wildstat Consulting, LLC.
Sam holds a BS in Mathematics and Chemistry from Purdue University, a MS in Mathematics from Creighton University, and a MS in Statistics from the University of Kentucky.
Sam has held numerous roles in his career. Right out of college, he earned a commission as an officer in the United States Air Force. For 12 years, he worked as a military scientific analyst in several roles, including weapon systems modeling and simulation, flight test planning and data analysis, and human factors research and development. He was also a member of the faculty at the Air Force Institute of Technology, where he taught statistics and experimental design to fellow officers in the graduate engineering, science, and logistics programs.
After his military service, Sam joined Eli Lilly and Company and worked in research and development and manufacturing as a statistician and as a process chemist. He later made a career transition to the SAS Institute, where was a technical expert in the JMP Division, and that role gave him a broad exposure to hundreds of companies that are using statistics to solve business problems. He returned to Eli Lilly in the Advanced Analytics group, focusing on the application of predictive modeling in support of sales and marketing. He then moved back into research and development at Elanco Animal Health, where he led the Experimental Design and Statistics team with a focus on applying Quality by Design to the development of new pharmaceutical products.
After three decades of work in government and industry, Sam decided to launch his own business to provide consulting services that utilize his talents in statistical thinking, problem solving, experimental design, and statistics and predictive modeling.
Sam is a member of the American Statistical Association, and he also serves as a volunteer member of the United States Pharmacopoeia’s Statistics Expert Committee.
Sam lives in Lafayette, Indiana, USA, with his wife, Susan, and together they have four adult children.
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