The Food and Drug Administration (FDA) recently announced a new guidance program for the life science industry regarding data integrity. This FDA guidance draft is currently open & available for the industry to comment on and provide feedback. Additionally, the FDA regulatory process that looks to be initially targeted by the food and drug authority includes:
- Investigational New Drug Application (IND)
- New Drug Application (NDA)
- Abbreviated New Drug Application (ANDA)
- Biologic License Application (BLA)
Code of Federal Regulations
The guidance draft cites several different code of federal regulation (CFR) that are being reviewed and analyzed during this process. Specific CFR federal regulations mentioned include:
- 21 CFR 11
- 21 CFR 50
- 21 CFR 56
- 21 CFR 58
- 21 CFR 210
- 21 CFR 211
- 21 CFR 312
- 21 CFR 601
- 21 CFR 820
Data Integrity
Specific use cases mentioned by FDA include data integrity throughout the clinical data management process in bioavailability (BA) & bioequivalence (BE) studies. Further, the FDA guidance provides initial suggestions to optimize data integrity success by focusing on three specific drivers:
- Users or applicants in clinical trial
- Site management of clinical trial
- Quality management system for clinical trial
Finally, FDA provides best practices & recommendations for risk control and risk management. Understanding how to maintain good lab practice & clinical research success is essential for all participants.
FDA Guidelines
One of the primary objectives of the data integrity draft is to align FDA expectations with the clinical data being submitted in pharmacologic studies. Additionally, clinical data submitted by a drug sponsor for FDA drug approval must be:
“accurate, complete, and reliable, and that industry maintain data integrity throughout the data lifecycle of the product(s) or biologic therapeutic(s).”
Finally, new concerns are being raised about the evaluation of bioequivalence and bioavailability study data submitted during the FDA regulatory process. FDA highlights specific comments about regulatory concerns during in vivo pharmacology studies. The regulatory documentation lists specific areas adding to the erosion of data integrity based on FDA inspection experiences in the field:
- Patient testing sites
- Clinical testing sites
- Analytical testing sites
Legal Regulation
The FDA guidance begins with reminding drug sponsors that they bear the responsibility of quality assurance even if a contact manufacturing organization (CMO) is engaged. Additionally, confirming a contract development and manufacturing organization (CDMO) has confidentiality processes in place is essential.
For drug sponsors who are submitting a FDA new drug application, ICH guidelines and FDA guidance should be followed by external vendors who are conducting the elements of study-related activity. Finally, clinical data being generated through bioavailability and bioequivalence studies must be durable & reliable in nature, and properly documented in a quality management system (QMS).
Testing Site
The regulatory guidance goes on to elaborate about the monitoring and oversight strategy needed for clinical testing. Specific drivers listed for analysis when selecting a drug manufacturing partner include:
- Personnel Education
- Personnel Experience
- Personnel Training
Manufacturing standards and expectations should be documented clearly in a formal agreement. Additionally, FDA highlights that regulatory requirements for clinical study activities should be documented and followed, including:
- Clinical study protocol
- Clinical study procedure
- Clinical study process
Finally, FDA recommends that bioanalytical analysis should include closer oversight and monitoring. Any CMO or CDMO must have documented analytical methods in their quality management system. Further, all analytical methodologies must follow applicable FDA regulations and be included in the QMS standard operating procedures (SOP).
Quality Management System
The FDA guidance transitions to describe database integrity and why any clinical services provider you choose must “use a quality management system to help ensure data integrity”. This includes testing sites managing a quality management system and efficacy of “data governance throughout the data lifecycle”.
Records Management
The operations team supporting the clinical research management should review the QMS using a pre-planned schedule, per the FDA guidance. All data governance used to store and retrieve information in the clinical trial management system should replicate the reliability & durability of the QMS.
Additionally, data integrity across the QMS would focus on each unique phase of the collection process. FDA recommends employees responsible for quality updates consider isolating job responsibilities between the separate data lifecycle phases to help reduce unintentional mishaps.
Clinical Technology
Using modern technology is recommended for all employees who are responsible & can impact data quality. Specific systems mentioned in the regulatory guidance that can improve data integrity include:
- Computer hardware or related systems
- 362 software for security or performance
- Peripheral devices, networks or cloud infrastructure for connectivity
- Any associated IT documents for usability (e.g. SOP manual)
Metadata
The guidance goes on to explain more about classification & ensuring that metadata is correct. Metadata, as described by FDA, is “data about data” and provides clarifying information to the agency. Common types of metadata quality concerns are listed as examples:
- Missing date/time stamp for when data was acquired
- Incorrect measurement units or documentation
- Missing user ID’s of the team who conducted data testing or data analysis
Sample Analysis
An important facet of the FDA guidance discusses sample analysis & if the clinical evaluation is completed at a location different from the original testing site. Employees conducting the clinical evaluation should be familiar with good clinical practice & fully understand the study protocol, various test methods that were used, and any quality management system SOPs to follow.
Further, different types of instruments & clinical equipment used during the sample analysis should be “calibrated, maintained and serviced” per FDA. The regulatory guidance cites several examples that should follow manufacturer service guidelines, including:
- Balances & pipettes
- Centrifuges & spectrometers
- Liquid chromatographs
- Refrigerators & storage freezers
Quality Assurance and Quality Control
Managing the risk associated with a QMS is an important facet of the FDA guidance. A quality assurance program should be implemented, including standard operating procedures that limit access control & processes to isolate a data breach. Further, implementing a quality control program can help find and remedy data integrity flaws. An efficient quality control program should include:
- Mapping of standard processes & risk control
- Employee training & knowledge gaps
- Difference between unintentional & intentionally compromised data
Corrective Action and Preventative Action
If data integrity deviations are identified in a quality control analysis, FDA recommends a corrective and preventative action (CAPA) program be launched. Additionally, the CAPA would look at all relevant processes and tasks as a whole rather than isolating a repeat occurrence. The recommended best practices for a quality control plan include:
- Assemble & examine information for review
- Identify real & possible problems during the review
- Explore problems & create appropriate CAPA
- Verify & validate the success of the CAPA
- Communicate updated CAPA success to applicable teams
- Document activity & intelligence for management review