By Arvilla Trag, RAC, Principal Consultant, CMC Compliance Services
Good Documentation Practices and Data Integrity (GDP/DI) are inseparable elements of data management, the foundation of any quality system and are applicable to all departments. While it is possible to have GDP without data integrity, data integrity without GDP is not possible. This provides a sound argument for companies to combine GDP and data integrity into a single SOP and ensure strict adherence to the procedures.
GDP/DI are regulatory requirements, called for in ICH Q7 Good Manufacturing Practices for Active Pharmaceutical Ingredients, 21 CFR Part 211 Current Good Manufacturing Practices for Finished Pharmaceuticals, 21 CFR Part 58 Good Laboratory Practices, EudraLex Volume 4 Good Manufacturing Practices, and PIC/S PE 009-8 Guide to GMP for Medicinal Products. The fact that GDP/DI are codified in so many regulatory documents illustrates the significance regulators place on them.
What exactly is documentation? Many would respond with “I wrote it down, therefore it is documented.” Documentation is not the single action of writing down everything you do. Good Documentation Practices are methods for recording, correcting and managing data, documents and records, to ensure the reliability and integrity of information and data throughout all aspects of a product's lifecycle. It is a multi-step process that includes the recording of data, issuance, review, approval, presentation and disposal of documents, and the ability to retrieve documents.
There are some very specific requirements for data capture to follow GDP.
The Eight Deadly Sins of GDP:
- Use of White-Out®
- Data obscured
- No date, or post-dated, or pre-dated
- No initials/signatures
- No explanation for changes in recorded data
- Use of sticky notes instead of QA-approved forms/notebooks
When and where data are documented are as important as how they are documented. The purpose of documentation is to create a record of facts that proves procedures and processes were correctly executed, thereby substantiating the resulting data. The short version of this description is “If it isn’t documented, it didn’t happen.”
FDA defines data integrity as “The completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate.” This is also known by the acronym ALCOA:
Attributable, Legible, Contemporaneous, Original and Accurate. These elements go hand-in-hand with GDP.
Validated Part 11-compliant electronic data systems address the attributes of GDP and data integrity, but what if your company is still using pen-to-paper data capture? Regardless of where data is manually captured — R&D, materials management, laboratories, or manufacturing – the importance of GDP/DI cannot be overstated.
Most companies with GMPs in place understand this, but small/virtual drug development companies often do not realize that GDP/DI are not just for large or commercial operations. Scientists moving from academia to industry often bristle at the mere suggestion that they are not using good documentation practices, without understanding that what constituted good documentation in their post-doctoral work is not necessarily the same as what FDA considers good documentation.
GDP/DI are not only important regulatory requirements, but they are also important business requirements. Solid GDP/DI improves risk management by virtue of supplying more information. It improves resource optimization by creating a record of what worked and what didn’t work, and what it took to make it work, such as costs and lead times on components and materials. GDP/DI also provides key information for critical decisions and technology transfer.
GDP/DI enhances patent protection, increases the value of technology that may be sold or outlicensed, and can expedite new drug approval by virtue of creating a cohesive and thorough collection of developmental data.
Let’s look at patent protection for a biotechnology product. Because biotechnology is considered more unpredictable than other fields, more detail and evidence that the claims are enabled is typically required than for non-biotech products. GDP/DI can play a critical role in supporting what was discovered/invented and when by documenting dates, who performed what, and what the results were. This can be especially important in patent infringement cases where “prior art” is in question.
When technology is sold or outlicensed, the buyer typically performs due diligence on the technology and the data supporting it to determine if it is worth the asking price (among other reasons). A weak data package may not support the value of the technology in question and will command a lower price than a data package that is thorough and 100% GDP/DI compliant.
One of the largest and most complex sections of a marketing application is 3.2.S.2.6 Process Development. This section is often the most difficult to write due to a lack of clear, complete, and retrievable documentation. The complete drug substance manufacturing history – which can go back 20 years or even more, including any process changes and comparability assessments must be presented in detail, including justification for process, materials and analytical methods changes. The process of identifying missing data and reports, locating them, and determining the sequence of events can slow down the authoring of this section dramatically. Even when reports are available, they are often incomplete in terms of GDP/DI, lacking justification supported by data, authorship, adequate information regarding when changes were made and why, and how CQAs and CPPs were identified.
Even though most bench research never makes it to market, assuming every project has the potential for further development and utilizing GDP/DI from the onset of research is a good business practice. Should the project move forward to IND or BLA/NDA filing, this will enable the Process Development section to be completed much more quickly. A thorough Process Development section also can be used to support information required in other sections, such as risk assessment, the Target Quality Product Profile (TQPP), and both in-process and product specifications.
When a CDMO performs process development for a sponsor, the early development reports are sometimes haphazard, lacking details that will be impossible to retrieve a few years later when a marketing application is being prepared. Specifying the format and content of development reports in the Quality Agreement can help forestall this. The requirement for GDP/DI should be included in all Quality Agreements.
When FDA sends a sponsor a Request For Information (RFI) it is frequently because the data originally submitted was insufficient to support the conclusions drawn. Receiving an RFI can slow the review process considerably while the requested information is located and summarized; this can be avoided with a comprehensive data package generated utilizing GDP/DI.
GDP/DI also protects against employee turnover. When a lead scientist leaves a company, his/her research remains behind, but it if it is unclear or incomplete it is no simple matter to interpret or recreate it. Establishing a policy of required GDP/DI for all employees from the outset is cheap insurance for the future of your projects.