Examples of factors that can increase the risk of data integrity error are complex, inconsistent processes with open and subjective results. Simple, consistent, clearly defined and objective tasks result in reduced risk. Several CMOs and order laboratories participated throughout the value chain in the final disposition and release of the finished product. The file conformity assessment was conducted in preparation for the submission of the NDA, during which the record batch review process and executed batch records were reviewed in detail. Identical data elements (e.g. molecular weight .B on a Certificate of Analysis [CoA]) identified at different locations along the value chain no longer matched. Data security measures should be at least equivalent to those applied in earlier phases of the data lifecycle. Subsequent changes to the data (e.g. B through an IT helpdesk or database changes) should be controlled by the pharmaceutical quality system, with appropriate segregation of duties and approval processes.
The approach to risk identification, reduction, verification and communication should be iterative and integrated into the pharmaceutical quality system. This should provide oversight from senior management and allow for a balance between data integrity and overall GMP priorities, in line with the principles of ICH Q9 &Q10. At the same time (data is produced at the time of carrying out the activity) This data integrity error allowed errors and misinterpretations that eventually led to further verification of the batch registration, which led to errors and made it difficult to compile, verify and submit the NDA submission, which was eventually delayed. KIP WOLF is Principal at Tunnell Consulting, where he leads the data integrity practice. This is a special consideration when computerized systems draw the user`s attention to input outside the specifications (i.e. the user “stores” the data entry) or stores the data set in temporary memory before the end of the data entry process. A CDMO in long-standing contact with the various partners in a joint vaccine development programme exchanged regulatory data between them (. IND i.e.
the effective date), to facilitate the planning of the availability of clinical trial equipment for the initiation of the study and the date of submission of the IND`s annual report. To understand the data integrity error described here, we must first understand the IND`s annual reporting requirements. Similarly, “validated systems” that do not allow the user to make changes to the data may be compromised if the user can choose which data is printed, reported or transmitted for processing. This involves running the activity multiple times as a separate event and reporting a desired result from one of these repetitions. Many companies have eliminated, for economic reasons, having a person in the factory; Perhaps this approach should be reconsidered. It may be useful to have a person in the facility to evaluate the processing data and raw data for the first batches produced, in order to develop an additional level of confidence in the contractor`s operation, which can be terminated. For those who have a routine person in the facility during manufacturing and testing, that person may also be responsible for verifying selected original data, both in manufacturing and in the laboratory, at the draw. We will come back to discuss how we can verify our supplier`s data so that we are confident that the product is available for clinical trials, either for commercial distribution. With respect to regulation, 21 CFR 200.10 deal with contract establishments used by pharmaceutical companies. It says, “The Food and Drug Administration.
considers extramural installations as an extension of the manufacturer`s own installation. THE FDA`s Guidelines on Drug Manufacturing Contracts: Quality agreements embarrass the fact that contract laboratories must “apply appropriate controls to ensure that test data and results are reliable and maintained in accordance with CGMP requirements…