Data Reporting, Integrity and Compliance
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Last Updated: April 17, 2024
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Published: January 5, 2024
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To ensure the data integrity and compliance, laboratories should ensure that any process from sampling to reporting is fully controlled. Automating and digitalizing these processes can result in a more efficient and effective workflow.
This article examines how analytical scientists can enhance their data sharing and reporting practices, leading to improvements in data integrity and an easier route to compliance within a regulated good manufacturing practice (GMP) environment.
Download this article to learn more about:
- Data integrity and compliance challenges
- Digitalization and automation principles
- Industry case studies demonstrating successful data sharing methods
Data Reporting, Integrity and
Compliance
Article Published: November 15, 2022 | Bob McDowall, PhD
In this article, we examine how analytical scientists can enhance their data sharing
and reporting practices, leading to improvements in data integrity and an easier
route to compliance within a regulated good manufacturing practice (GMP)
environment.
To ensure the integrity and compliance of reported data, each GMP-regulated
laboratory should ensure that any process from sampling to reporting is fully
controlled. The best way to achieve this is to use the technical controls in
laboratory informatics software to achieve data integrity and regulatory
compliance.
To illustrate this, we will explore how two departments regulated by GMP,
analytical development and quality control, should work and, where appropriate,
collaborate. While these two departments may appear to have similar analytical
roles, they have different objectives due to their function in development and
production respectively. Both departments are responsible for analysis of raw
materials, intermediates and formulations. Analytical development is also
responsible for generating regulatory submission data for product approval while
quality control must generate annual product reviews (APR) under 21 CFR
211.180(e) or product quality reviews (PQR) for EU GMP Chapter 1.10.1,2 The
differences between the two reviews are that the former assesses representative
batches whilst the latter must review all batches. The two departments must also
collaborate for technology transfer of analytical procedures and any follow-up
troubleshooting and therefore must share data and reports with each other. The
focus in this article will be on the development, validation and application of
analytical procedures and not on confirmation of new molecular entity (NME)
chemical structure.
To help in preparation of this article, Dr. Christine Mladek from Boehringer
Ingelheim Pharma GmbH & Co.KG (BI) and Dr. Markus Dathe from F. Hoffman La
Roche AG (Roche) were interviewed for their views and experiences in these
areas.
Functions of analytical development and quality control
All outputs from both departments are totally dependent on the trustworthiness,
reliability, integrity and compliance of the underlying records and data in both
paper and electronic formats used to generate them. Furthermore, speed is also a
key factor – analytical development can accelerate time to register and quality
control can accelerate time to release product.
Figure 1: Overview of the functions of analytical development and quality control
within pharmaceutical research, development and production.
Table 1: Comparison of analytical development and quality control department
functions.
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Problems with report data integrity and compliance
However, some of the barriers to achieve the goals listed in Table 1 are:
Paper processes: Blank forms are not acceptable in a regulated
environment. To meet standards, they must be uniquely numbered, issued
and reconciled.3,4 This requires a high administrative overhead to use while
ensuring compliance. Data on paper are difficult to share.
•
Hybrid systems: These consist of electronic records that are linked to
signed paper printouts; again, the data on a paper printout is difficult to
share. The controls required to ensure data integrity are higher cost than
an electronic system due to the two-record media used.4
•
Spreadsheets: These are widely available and easily used and abused;
spreadsheets are ubiquitous in most laboratories, but they are hybrid
systems. Typically, data are entered manually which results in the need for
transcription error checking.
•
Transcription error checks: Data manually entered into a computerized
system or transcribed from one printout to another must be checked
carefully, creating bottlenecks, especially when errors are found and must
be corrected and rechecked.
•
All of these barriers combine to ensure that any business process in either
department is slow and inefficient.
Principles of digitalization/automation
To ensure that a report meets integrity, quality, speed and compliance criteria, it is
essential to automate or digitalize the process(es) that acquire, process, calculate
and report the results. When designing electronic workflows, three principles of
laboratory digitalization must be followed:
Data acquisition at the point of origin
Eliminate paper records
Always interface instruments to acquire and process analytical data
1.
Never transcribe data
All data must be transferred electronically between systems using validated
processes to avoid making transcription errors
Eliminate spreadsheets by using calculations in informatics applications
2.
Know where the data are stored
This may involve location and file naming conventions so that data can be
retrieved for audit or inspection easily
This is essential for reporting results of analyses but also technology
transfer reports and generating analytical regulatory submission packages
and PQRs
3.
Data process mapping should be used to identify data vulnerabilities in the current
process as well as process inefficiencies. By using the three automation principles
above, the vulnerabilities can be eliminated and data integrity and regulatory
compliance achieved. This results in a more efficient and effective process to aid
the compliance, integrity, quality and speed of reporting.
Figure 2 illustrates this process by showing before and after chromatographic
analyses where calculations have been incorporated into the chromatography data
system (CDS) and electronic signatures have been implemented. There is an option
to print the final report if required.
Figure 2: Redesign of a chromatography analysis to eliminate spreadsheet
calculations and paper printouts.
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Sharing informatics applications?
Although the analytical processes in the two departments appear similar, one
approach is to share the same informatics applications such as a laboratory
information management system (LIMS), laboratory execution system (LES) or
electronic lab notebook (ELN). There are some obvious advantages; a single system
validation covering the two departments, information sharing and negotiating
license costs. Although the main analytical processes are similar, there are
differences that need to be considered.
Analytical development is at the start of the development process and, as
analytical procedures are still being developed, require flexible working, meaning
that any competent analyst can work on analysis. In contrast, quality control uses
registered analytical procedures and may want to limit an analyst to specific
analytical procedures for which they have been certified.
These are very practical considerations that should be used to select the right
application for the job. Can the two departments with different working practices
be accommodated? This must be made clear in the selection process and
evaluated with shortlisted systems. One possibility would be to establish two
groups with different user roles and access privileges in a selected application. The
issue then becomes how to share data between the two for analytical method
transfer etc.
Remote working triggers system design changes
The COVID-19 pandemic has taught us that remote working requires electronic
data and communication. It is not practical or realistic to send paper records
between a laboratory and an analyst’s house, as the metadata including audit trail
entries must be included. Moreover, standalone systems do not facilitate remote
working as the data are difficult to share. Therefore, as working practices evolve,
system design must evolve from standalone to network operation. The ability to
review all data and metadata remotely and sign reports electronically is a critical
driver for laboratories to allow effective remote working for data sharing and
collaboration.
Suppliers are market driven and if customers don’t ask for features like this, they
won’t be delivered.
Technology transfer and data sharing
One area where it is essential for the two departments to collaborate is technology
transfer of analytical procedures. In an ideal world, technology transfer would be
facilitated by using the same analytical instruments and software so that the
instrument parameters can be transferred electronically alongside example data.
This would give the receiving laboratory more detail than is usually in a written
document. This process can easily become a car crash where the originating
department sends the receiving laboratory the analytical report and leaves them
to it. What better ways are there for collaborating between the two departments
that BI and Roche can tell us?
Dr. Markus Dathe stated that the ideal approach is to share the overall
validation between the two departments. The main work is conducted by
analytical development but intermediate precision involves quality control
staff working with their instruments in their laboratories. Both departments
are co-signatories of the final validation report.
•
Alternatively, Dr. Christine Mladek suggested that a member of the
receiving laboratory could work in the originating laboratory to learn and
understand the procedure and speed establishment in their own
laboratory, simplifying the transfer protocol.
•
Both interviewees agreed that having the same instrument data system
means that data from development and validation experiments can be
easily shared between the two departments.
•
Ideally the same make and model of instruments should be present in both
departments to aid transfers. One problem noted by Mladek was that
difficulties were encountered when trying to transfer gas chromatography
(GC) methods when the originating and receiving laboratories used
different supplier’s instruments.
•
Roche’s analytical development support their quality control colleagues for
five years after transfer of the procedure. BI quality control staff need
access to development records in case quality control identify an impurity
in production and need to validate it with development. Therefore, ongoing
data sharing and collaboration is essential and critical to success for
both organizations.
•
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Analytical development: regulatory submissions
Development of analytical procedures requires exploratory work that may lead to
further experimentation or a dead end. Development of the design space,
including identification of parameters that need control and critical quality
attributes (CQA), is important for the validation.
Data and reports from the development and validation of analytical procedures for
NMEs as well as summary results from analysis of the validation batches from
initial production are used in a regulatory submission for new products. It’s
important from both companies’ perspectives that these data be accessible by
quality control.
If a marketing authorization is required for the United States market, the FDA will
perform a pre-approval inspection following compliance program guide (CPG)
7346.832.5 Here, the three objectives (readiness for commercial manufacturing,
conformance to the application and a data integrity audit) all involve assessment of
the integrity of the data submitted to the FDA and if relevant, any referenced data
are also in scope. Rapid retrieval of electronic data is required for the inspector to
review and therefore it is essential to know where the electronic data are located.
Quality control: product quality reviews
Under EU and FDA GMP regulations companies are required to perform APRs (FDA
GMP) or PQRs (EU GMP). There are differences between the two types of review:
21 CFR 211.180(e)(1): “A review of a representative number of batches,
whether approved or rejected, and, where applicable, records associated
with the batch.”1
•
EU GMP Chapter 1 clause 1.10: “Regular periodic or rolling quality reviews
of all authorized medicinal products, …. objective of verifying the
consistency of the existing process, the appropriateness of current
specifications for both starting materials and finished product, to highlight
any trends and to identify product and process improvements. Such
reviews should normally be conducted and documented annually.”2
•
The EU requirement for PQR is more comprehensive and requires substantial data
retrieval and trending of data. The requirement for a PQR must be designed into
laboratory informatics applications so that a multitude of spreadsheets are
generated for PQRs.
Mladek and Dathe mentioned that both their companies are involved in the
development of additional databases or data lakes for handling PQR data. Data
are abstracted from the source systems such as enterprise resource planning (ERP)
and LIMS then transferred to the data repositories. It is important to understand
that data integrity must be maintained during the transfer so that any conclusions
made are based on firm data. Furthermore, both interviewees mentioned that
interpretation of analytical data needs review by analytical scientists who
understand the meaning of the data rather than statisticians who do not.
Summary
Collaboration between regulated analytical development and quality control
laboratories requires integrity and compliance of the data shared. Method transfer
between the two departments is enhanced by using the same informatics
solutions. This facilitates ease of data sharing, not just during the method transfer
process but in the years afterwards, in case of problems and unknown impurities
observed.
References
1. 21 CFR 211 Current Good Manufacturing Practice for Finished Pharmaceutical
Products. 2008, Food and Drug Administration: Sliver Spring, MD.
2. EudraLex - Volume 4 Good Manufacturing Practice (GMP) Guidelines, Chapter 1
Pharmaceutical Quality System. 2013, European Commission: Brussels.
3. FDA Guidance for Industry Data Integrity and Compliance With Drug CGMP Questions
and Answers 2018, Food and Drug Administration: Silver Spring, MD.
4. PIC/S PI-041 Good Practices for Data Management and Integrity in Regulated GMP /
GDP Environments Draft. 2021, Pharmaceutical Inspection Convention /
Pharmaceutical Inspection Cooperation Scheme: Geneva.
5. FDA Compliance Program Guide CPG 7346.832 Pre-Approval Inspections. 2019, Food
and Drug Administration: Sliver Spring, MD.
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