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PHARMACEUTICAL INDUSTRY :
Process Capability and Control |
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Client: |
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A major global pharmaceutical company with facilities in the U.S., Puerto Rico and overseas manufacturing ethical drugs in a variety of dosage forms.
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Problem: |
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An API with a long history of stable and predictable production was exhibiting levels of variation in a critical release parameter never before experienced, resulting in the possible rejection of many batches of material. The process was complex with over 15 major unit operations. A key raw material came from a natural source. An investigation was launched to identify the root cause. Batch records contained hundreds of process data elements. The company’s investigation analyzed data for selected process parameters in an effort to find a correlation with the observed variation in problem release parameter. Because the investigation followed a “hunt and peck” approach to root cause analysis, as opposed to a “whole process” view of the problem, it achieved only limited success. The client, needing the problem solved in eight weeks or less, turned to Tunnell.
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Approach: |
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The Tunnell team included a pharmaceutical process engineer, a statistician, a chemist, and a data analyst. They used a proven Tunnell methodology for identifying drivers of process parameter variation. Focus interviews and focus groups were conducted to mine the collective insight of the client organization. Process control analysis was performed to identify obvious and unusual trends in key process parameters. In-depth process walk-throughs by experienced pharmaceutical scientists were conducted and observations logged. A database of over 1000 data fields was populated from existing batch record data. Advanced statistical analysis was performed on the database…and more. Tunnell’s approach to integrating these activities resulted in identification of the source of the problem in six weeks.
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Results: |
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Six critical drivers of variation were identified, many of which were not originally anticipated by the client investigation team because several of the drivers of variation exhibited unexpected interactions, which defeated traditional approaches to root cause analysis.
A high level of process understanding was attained about cause-and-effect relationships between key process parameters.
Specific recommendations were developed and implemented to address the problem, which resulted in significant improvement in process capability and reduced the risk of batch rejection and investigations.
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