Statistical Procedures Needed for Compliance with the Guidance on Process Validation

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  •   Duration: 90 Minutes  
  • Recorded Access recorded version only for one participant; unlimited viewing for 6 months ( Access information will be emailed 24 hours after the completion of live webinar)
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Overview:

This guidance document contains several statements that make it clear that statistical procedures will be expected. Recently, FDA issued new draft guidance on process validation. When finalized, it will supersede the previously issued guidance documents on process validation. It is expected that the final guidance will not be much different from the draft.

This guidance document contains several statements that make it clear that statistical procedures will be expected. Some of these procedures are of an advanced nature and companies need to be prepared to meet these requirements.

Areas Covered in the Session:
  • In general what topics does the new draft guidance cover?
  • What are the specific statements?
  • What are these statistical procedures, and how are we expected to apply them?
  • If we lack statistical expertise, what can we do to ensure compliance?
Who Will Benefit:
  • Personnel who will need to plan and execute process validation studies.
  • Supervisors who will need to review process validation reports and execute studies.
  • Quality assurance and control personnel.
  • Managers responsible for quality operations.
  • Auditors and others who will need to periodically review or check studies.
Steven S. Kuwahara, Ph.D. is the founder and Principal of GXP BioTechnology LLC, a consulting firm that works in the areas covered by the GLP and GMP of drugs, biologics, and nutraceuticals. Steve has over 30 years of experience in supervising quality control laboratories, including an animal testing facility, and in performing GLP and GMP audits of internal and external testing laboratories . Steve has participated in the development of drugs and biologicals through all phases of clinical research and final product production.


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