Data integrity is fundamental in a Pharmaceutical Quality System which ensures that medicines are of the required quality. This requires an understanding the expectations for data integrity from a CGMP perspective. Reviewing FDA guidance documents and citations in FDA warning letters will facilitate this understanding. Providing practical steps which can be taken will help your organization reduce the likelihood of receiving a Warning Letter due to Data Integrity issues.
Upon completion of this course the learner should:
Understand what Data Integrity is, and why it is so important.
Be familiar with regulatory expectations for laboratory notebooks and computer systems.
Be familiar with issues that resulted in warning letters from the FDA.
Understand some practical steps that can be taken to facilitate good documentation and reduce the likelihood of receiving a warning letter for data integrity issues.
Areas Covered in the Webinar:
What is meant by ‘Data Integrity’?
Data integrity expectations from the FDA guidance
Discussion on computer systems, notebooks and spreadsheets
FDA warning letters citing data integrity issues
Practical steps you can take to avoid being cited
Questions and discussion
In addition, he has volunteered for the USP for over 10 years, and currently serves on the General Chapters – Chemical Analysis Expert Committee, and serves on Expert Panels on Validation and Verification, Residual Solvents and Use of Enzymes for Dissolution Testing of Gelatin Capsules. He also serves on the Steering Committee of the AAPS IN Vitro Release and Dissolution Testing Focus Group.
He has particular interest in QbD/Lean approaches to dissolution testing, method lifecycle (development/validation/transfer), impurity testing and instrument qualification, and is passionate about using good science and sound logic to achieve high quality results, consistent with cGMPs, while minimizing resources. Mr. Martin is has presented at several scientific meetings, and authored of several papers in the areas of dissolution and analytical method validation.