Validation data integrity

Reference Application Kinetics Property predictions Fractionation modeling Validation data Integration with production planning... 

Microbiological aspects will need to be discussed, but the amount of information will depend on the type of product. For nonsterile products there will need to be a description of the microbiological attributes of the product and, if appropriate, a rationale for not performing microbial limit tests. For preserved products the selection of the antimicrobial preservatives will need to be discussed and the effectiveness of the selected system demonstrated. For sterile products there will need to be appropriate process validation data and information on the integrity of the container-closure system. 

ActivityBase enables the capture, validation, and visualization of high-throughput screening data. Integration with Microsoft Excel provides flexibility analysis template design. The chemically... 

Documentation comprises procedures, instructions, test methods,batch records, and so on that are documented and controlled. Documentation is prepared, reviewed, and approved by qualified personnel. Approved copies of documents are distributed to relevant departments and superseded copies are retrieved and archived. The retention period for each type of document is specified. Documents are issued with document and version numbers for ease of identification and reference. Master copies of documents are filed at secured locations with authorized access. Master copies stored in electronic media require validation in accordance with FDA regulation 21 CFR Part 11 (see Section 9.6.3) to assess the security of access and data integrity. Operators are trained and retrained to only apply the latest approved documents. 

To provide a practical, understandable and common way of measurement uncertainty calculations, mainly based on already existing quality control and validation data covering all uncertainty sources in a integral way... 

Once data have been appropriately and accurately entered in the system, processed, and stored, they are presumably available for later comparison, analysis, or combination. That presumption is based on the confidence that the system does not in any way corrupt or modify the data, however. Validation requires evidence of continued data integrity. 

Data Integrity To demonstrate that records are valid and trustworthy. A record is a combination of raw data and the metadata (processing parameters plus other related information necessary to reconstruct the records) ... 

Defining and adhering to a validation plan to control the application and system operation, including GMP risk and validation rationale Documenting the validation life-cycle steps to provide evidence of system accuracy, reliability, repeatability, and data integrity... 

So, what level of data integrity is acceptable The total percentage of errors we found in our study was 5%. Is this satisfactory Is this the best we can hope for Hopefully not, especially as more people become dependent on databases and the rate of production of data becomes ever faster. Clearly, there is a need for a system that will better validate data being entered in the most used CAS databases. It is desirable that the quality of the databases increases at the same time as they are mushrooming in size. 

The ChemHTS-1 module of Chem-X provides integration to plate samples, copy plates, load analysis validation data and request biological testing. A simple set of ORACLE tables ensure speed and flexibility (tables 5-7). 

These do not normally need extensive validation if the version to be acquired has already been exposed to the marketplace for an extended period. However, new versions are a different matter and should be treated with caution. Validation effort should concentrate on functionality, critical algorithms and parameters, data integrity (security, accuracy, and reliability), and operational... 

If the existing system is not validated, the integrity of the data within the system eannot be relied npon. Data eaimot simply be transferred to a new electronic repository withont data verihcation. 

When analyzing samples, the data shonld be validated. The validation process includes documentation and checks for data plansibility, data integrity, traceability, and security. 

Assure validity, security, integrity, and traceability of data. 

Apart from the failure to validate the CDS application there was also a prominent issue with security and data integrity. Protection of electronic records created by any CDS is vitally important. 

Due to the critical nature of the data stored in the LIMS database, data integrity must be assured. Current technology allows for disk mirroring to ensure that any database additions and changes (and therefore changes to electronic records) are copied to additional, and possibly remote, locations. The function and use of each mirror for backup, for disaster recovery, and for system startup after shutdown must be understood. Each of these scenarios must be tested as part of the validation. 

Data Integrity Checks (e.g., Range Checks, Validation of Inputs)... 

Validation of clinical research computer systems should demonstrate that the compnter system is suitable for its intended purpose." Validation is achieved throngh a life-cycle approach to compnter system development, operation, and maintenance. The varions international GCP/GLP reqnirements also emphasize the importance of data integrity. This covers data inpnt, manipnlation, ontpnt, and archiving. 

Figure 29.2 shows how a high-level risk assessment may be documented (see Chapter 6 for Validation Determination Statement). The seven questions cover virtually every contingency that could necessitate validation. A yes answer to any of the questions indicates that the system requires validation. The integrated chromatography data system used as an example in this case study clearly meets a regulatory documentation expectation and impacts release decisions, and thus must be validated. 

Such a situation offers both benefits and challenges. Parallel testing, i.e., the running of two systems in parallel and comparing the outputs, offers the opportunity of increasing the level of confidence that the system meets all expectations and user requirements. However, at some point, cut-over to sole operation of the new or modified database will have to be achieved. A crucial consideration here is the assurance that all records from the previous system (be they manual, from a different database, or from a smaller version of the new database) are available for further processing, inspection, and manipulation, and most importantly, have had their data integrity preserved. This is usually achieved by validation of the process used to transfer the data into the new system. 

Instruments should be validated. Data transfer after acquisition should have integrity checking. This is normally facilitated through industry standard protocols. Suppher auditing might be appropriate for new instrument developments. 

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