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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5577_Библиотеки_им_академика_М_И_Перельмана.pdf
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Manufacture and Supply, Science and Reg ulation Towards High-Qua lity Medicinal Products
systems which have external personnel authorized to access and edit,
and systems that are employed within the company. EU Falsified
Medicines Directive regulates the labelling and distribution practices of drugs, namely ensuring that packaging cannot be tampered
without being noticed, the identity of the contents in the packaging are accurate and attributable, and documentation that assures
Internet sales of pharmaceutical products is validated by relevant
authorities [47]. The Drug Data Management Standard of China,
as translated by the China Working Group of Rx-360, provides regulations to promote DI [48]. It regulates documentation of various
processes such as training of personnel, validation of computerized
systems and data management, and CAPA when DI violations are
found. One section specifically provides examples on how DI would
be maintained using ALCOA+ as a guide. In Article 7, it specifically
promotes whistleblowing as part of the culture for pharmaceutical
manufacturing companies as well [48].
Guidance documents
As legislation tend to be generic to facilitate application to a wide
variety of pharmaceutical companies, guidance documents have
been published to clarify legislative requirements [49]. In general,
guidance documents encourage voluntary compliance and can be
adapted to suit the company’s culture and manufacturing processes.
Guidance documents published to promote GMP include the WHO
Guidance on Good Data and Record Management Practices (WHO
Technical Report Series 996, Annex 5) [1], FDA Data Integrity and
Compliance with CGMP Guidance for Industry, MHRA GxP Data
Integrity Guidance and Definitions, the Pharmaceutical Inspection
Convention and Pharmaceutical Inspection Co-operation Scheme
(PIC/S) Guide to Good Manufacturing Practice for Medicinal
Good Documentation Practice and Pharmaceutical Data Integ rity
Products [50], PIC/S Guide to Good Manufacturing Practice for
Active Pharmaceutical Ingredients [51], the latter is equivalent to
the International Council on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q7 – Good Manufacturing Practice Guide for Active Pharmaceutical Ingredients [52].
The WHO Guidance on Data and Record Management Practices
also promotes a company culture of integrity and provides links to
relevant legislation and guidance documents [1]. The PIC/S Guide
to GMP for Active Pharmaceutical Ingredients has been established
for many years already and it addresses the same issues as ICH Q7
[53, 54].
There are also guidance that clarify specific portions of the GMP,
including PIC/S Good Practices for Computerized Systems in Regulated GxP Environment [55], FDA Standardization of Data and
Documentation Practices for Product Tracing Guidance for Industry [56], FDA Contract Manufacturing Arrangements for Drugs:
Quality Agreements — Guidance for Industry [57], and ICH Q9 –
Quality Risk Management [58]. These documents provide in-depth
guidance to the various aspects of GMP, with due consideration for
the respective country’s regulation. However, as mentioned earlier
in this paper, there are some guidance that specifically focus on DI.
These include the PIC/S Good Practices for Data Management and
Integrity in Regulated GMP/ GDP Environment [59], FDA Data
Integrity and Compliance with cGMP Guidance for Industry [9],
and MHRA ‘GxP’ Data Integrity Guidance and Definitions [60], all
recently published due to increasing attention on DI [3]. Generally,
these guidance documents discuss audit requirements, personnel
responsibility in promoting DI, and validation of computerized systems and other GMP processes. The WHO, PIC/S and FDA further
provide clarification on CAPA to be taken when DI violations are
found [9, 59], with PIC/S providing added clarification on outsourced
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Manufacture and Supply, Science and Reg ulation Towards High-Qua lity Medicinal Products
processes and promotion of quality culture [59]. Some guidance documents also help companies to understand the legal requirements.
For example, the Orange Guide [61] compiles relevant legislation
and guidance notes for manufacturers planning to enter the UK
pharmaceutical market, increasing the ease for manufacturers to
understand the regulations by providing relevant guidance and
binding legislation.
Overall, the legislation and guidance documents appear to be
comprehensive in assuring and promoting DI. However, they are
unable to prevent DI violations alone. Most DI issues only surface
during on-site audits [18] or from whistleblowing [15], and by then,
non-compliant pharmaceutical products would have already been
distributed, with potentially substandard products having been
consumed by patients. Hence, legislation and guidance must be
supplemented with other approaches to promote and assure DI at a
higher level.
Proposed Solutions to Better Promote and Assure Data Integrity
Culture of integrity
A study was conducted by the Parenteral Drug Association (PDA)
to assess the eectiveness of its published DI guidance document.
Although more than 90% found this guidance helpful in promoting
DI, some remarked that a culture of integrity is required to truly
attain DI [62]. Incentives, including recognition for companies if no
DI issues have been found for a consecutive number of years, could
be introduced to encourage companies to follow the guidance.
Good Documentation Practice and Pharmaceutical Data Integ rity
As mentioned in some legislation and guidance documents, a culture of integrity is required in a company to make regulations work
[62]. Setting a culture of integrity is important so that management would treat DI seriously [3, 63], and employees would then
feel obligated to do the same [64]. According to a study by Yang,
Sun and Eppler, for any strategy to be implemented successfully, the
formulation needs to be of a certain standard, and inter and intradepartment relationships should be cordial [62]. Middle management is noted to be the main drivers for implementation [62],
and close collaboration with the top management increases its
eectiveness [62]. However, if management ignores the DI issues,
implementation would be hindered [65]. Open and supportive communication between employees and management aid in eective
strategy implementation [62, 66]. Providing internal whistleblowing opportunities to flag any DI issues will further promote a company’s culture of integrity [67, 68].
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This method is adopted by FDA, where under the Dodd-Frank Wall
Street and False Claims Act, monetary rewards are used to promote
whistleblowing behavior [67].
Having a culture of integrity within the company will reduce DI
issues, and ultimately bring about a positive perception of the company’s pharmaceutical products [63]. However, for a large company,
it is dicult to start a culture of integrity if this culture was absent
in the first place, as the implementation of such a culture requires
some time before the eects are fully felt [65]. Furthermore, old
habits may cause top management to resist adopting such a culture
unless specific incentives are provided [65]. Therefore, some regulations should be in place to start this culture of integrity within the
company [64].
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Manufacture and Supply, Science and Reg ulation Towards High-Qua lity Medicinal Products
Database management systems
Another proposed solution to promote DI is by having good and
eective database management. A database management system
(DBMS) stores data [69] and presents them in an understandable
format when accessed [112]. With data becoming larger in volume
and variety in the pharmaceutical manufacturing industry [70],
user-friendly and ecient DBMS are in high demand [71]. With validated DBMS, manufacturers and regulators would be better able to
focus on other DI-related issues.
This paper also evaluates some of these DBMS, and their eectiveness
in promoting DI below. Specifically, the advantages and complications
of 3 major categories of DBMS are compared, see Figure 2.
Non-Relational Dat italeResaba onal Database
NoSQL
Graph
Database
Document
Database
Key-Value Store
Wide-
Column
Store
Blockchain
ODBMS ORDBMS
NewSQL
smetsyStnemeganaMsmetsyStnemeganaM
Figure 2: Examples of database management systems [72–76]
ODBMS: object database management system; ORDBMS: object relational database
management.
Good Documentation Practice and Pharmaceutical Data Integ rity
Relational and non-relational database management system
Relational database management systems store data in either a
two-dimensional table or a three-dimensional ‘object’ [72, 77].
Non-relational database management system on the other hand
does not have a specified structure of storing data. Further elaboration is provided in Tables 4, 5 and 6.
With a wide variety of DBMS choices currently in the market, adopting one that keeps data ALCOA+ throughout its lifespan would
minimize the cost required to maintain it manually [95].
Blockchain technology
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Blockchain is hypothesized as the next pharmaceutical manufacturing DBMS innovation [96, 97]. It is a decentralized record of digital events, with validation by the participants occurring before it
is recorded [98], making manipulation of previously verified transactions including data entry or movement very hard, and cannot
be deleted [99]. Blockchain has three main ways to ensure data
security. Firstly, it has a hash function, which identifies blocks,
and calculation of hashes involves the previous block’s hash [100].
Secondly, it has a peer-to-peer network to verify before it is added to
the current blockchain as a legitimate block [97], removing the need
for an authorized person for approval of transaction [98]. Once a
block is added, it is added to all the copies of the verified blockchain
across the entire network [101], hence remaining in the system indefinitely. Thirdly, as only pre-approved participants can participate in
adding new blocks, the identity of the node adding the block would
be documented [102], which ensures data attributability.
Table 4: Comparison between relational database management systems
Supports ACID transactions — Atomicity (all-or-nothing), Consistency (only validated results recorded), Isolation (independent from other concurrent transactions), and Durability (able to survive malfunctions) properties [73, 78].
— Ensures data is accurate, consistent and enduring.
Short
Description
Relational database
management systems
(RDBMS)
NewSQL (structure
query language) column-
oriented RDBMS
Tabulated DBMS [72] Merger of RDBMS and
NoSQL [73]; scalable
tabulated DBMS [75]
Object database
management system
(ODBMS)
Stores data as an
‘object’ [77]
Object relational
database management
system (ORDBMS)
Merger of RDBMS and
ODBMS; objectoriented tabulated
Data [79]
Examples Oracle [80] VoltDB [81] DB4O [82] PostgreSQL [83]
How it assures
ALCOA+
Missing headers or data
would bring up an error
message and recorded
in the audit log [72],
which ensures accurate
Similar to RDBMS,
maintaining ACID
Transactions [75]
Able to collate a more
complete dataset
eciently, reducing
unnecessary duplicates of data [84]
Eciently stores tabu-
lar data in the form
of objects, reducing
unnecessary duplicates of data [83]
and attributable data
Advantages
Simple DBMS for data
which requires little
processing [72]
Duplicates of data
store preserves ACID
transactions [75]
Able to store nonvalue files like
images [87]
Merges advantages
experienced from
both RDBMS and
ODBMS [79]
(Continued)
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Manufacture and Supply, Science and Reg ulation Towards High-Qua lity Medicinal Products
Table 4: (Continued)
Complications
Relational database
management systems
(RDBMS)
Multiple user interfaces
in the market [72]
Has integrated functions to generate audit
report [85]
Can test system without altering stored
data [85]
Requires in- depth
RDBMS knowledge
to utilize its
functions [85]
Unable to be upscaled
with increasing data
volume [75, 88]
Unable to store
non-value data [74]
NewSQL (structure
query language) column-
oriented RDBMS
Large volume of data
can be input at a
fast rate, even with a
large volume of data
already stored [86]
Able to retain data
if system disconnect
abruptly [79]
Changes to database
structure requires
revalidat- ion of existing data against new
structure [89, 90]
There is an
eventual limit to its
scalability [90]
Object database
management system
(ODBMS)
More ecient
that RDBMS by
storing data as
objects rather than
tables [87]
More data can be
captured for
more complete
database [87]
Changes to database structure may
modify data and
operations [74]
Lack user- friendly
interface requires
programming
language proficiency [74]
Object relational
database management
system (ORDBMS)
Able to tweak database structure to
their needs [83]
Increased complexity and hence financially taxing [74]
Good Documentation Practice and Pharmaceutical Data Integ rity
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Manufacture and Supply, Science and Reg ulation Towards High-Qua lity Medicinal Products
Table 5: Characteristics of non-relational database management systems
Support BASE transactions – Basically Available (reduce data unavailability by
duplicating and partitioning of data), Soft State (allowing for inconsistencies),
and Eventual Consistency (consistency among nodes is guaranteed only in some
undefined states) [62].
— Would lead to a point in time where data stored would be inconsistent across
data duplicates, possibly requiring an authorized person to deconflict any issues
that may arise.
NoSQL
Short Description Non-tabulated DBMS [73] with reduced complexity [74]
Examples
Riak (Key-Value) [91, 92]
Cassandra (Wide Column) [91, 92]
MongoDB (Document Oriented) [91, 92] Neo4j (Graph
Oriented) [91, 92]
HBase (Key-Value, Wide Column) [78, 92]
How it assures
ALCOA-plus
Copies of data are kept on the server, which helps the data
to be available and consistent [75]
Advantages Variety of models that can be adopted to best suit compa-
ny’s needs [75, 91]
Complications Auditing logs are not provided [93]
More prone to unauthorized editing [80]
No innate data ciphering available [94]
*See Table 6 for description on the dierent NoSQL models.
Table 6: Descriptions of dierent NoSQL Categories [75]
Category
Description
Key-value Optimized for fast retrieval
Wide Column also known
as Column families
Document-oriented
Eciently storing sparse, non-transactional, heter-
ogenous data to support partial record access
Extension of key-value to manage semi-structured,
arbitrarily nested hierarchical document data
*Hypothesized to be best suited for pharmaceutical
manufacturing industry record-keeping purposes
Graph oriented Eciently store and query relationship-rich data
Good Documentation Practice and Pharmaceutical Data Integ rity
Furthermore, by using block chain-utilizing smart contracts, DI
can be enforced [103], using blockchain technology to ensure all
components of the contract are met before transactions such as
approvals occur [103, 104].
This can also be employed for auditing as well, where, if certain
values deviate from the acceptable range, they would be flagged
up for inspection [100]. Companies such as BlockVerify [103] and
One Network Enterprises [96] have started to employ Blockchain
to maintain DI in the pharmaceutical market. Blockchain has also
been applied in promoting DI in the distribution of pharmaceutical
products through modium.io AG [105], making use of an array of
sensors to ensure erroneous data would not be entered into the system in the first place [103]. In the future, blockchain could even be
used to supplement guidance documents [106, 107].
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However, handling large volumes of information and simultaneous
transactions is slow with current blockchain technology [108], and
with more data being generated in pharmaceutical manufacturing
companies, this translates to lower eciency of maintaining DI for
large data stores. Furthermore, the diculty in comprehending and
using the code gives the developer the power to maintain DI [109],
rendering both authorities and companies incapable of maintaining DBMS DI themselves. Additionally, having a private blockchain
requires data encryption [109] to protect data from unauthorized
access [107, 110].
In general, having a good DBMS promotes DI as it streamlines
audits. Guidance in the form of questions is available to help companies find the best DBMS options available for them [75]. Furthermore, it is common to use multiple databases for dierent functions