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DATA INTEGRITY - ALCOA+
In 2013, The US-FDA reported that laboratory processes and deficiencies associated with laboratory
controls were ranked in the top three most frequent causes of observations following US-FDA
inspections. And same report also cited that an increase of 50% in warning letters related data
integrity.
During Annual meeting of International Society of Pharmaceutical engineering (ISPE) held in 2014 at
Las Vegas, it was reported that the FDA has identified that a dozen Indian pharmaceutical
manufacturers who had problem with data integrity practices at their facilities. That number is
significant, since India and china account for 80% of API production.
Other regulatory bodies, including the European Medicine Agency have made similar observations. It
is expected that this trend will continue to grow.
Data integrity is currently one of the highest cited area in regulatory observations yet data integrity is
not a new requirement.
For years the basic principles have been described in international GMP guidelines. Here I will
highlight the meaning and principles of Data Integrity.
Definitions:
DATA:
Data is the information derived or obtained from ‘Raw data’.
RAW DATA:
Original records and documentation retained in the format in which they were originally generated
(Paper or electronic) or as a ‘True copy’.
META DATA:
Meta data is the data that describes the attributes of other data and provide context and meaning.
Examples:
1. For example, Analyst-A reported impurity-A results as 0.05% from HPLC chromatogram.
Raw data: HPLC Chromatogram
Data: 0.05%
Meta Data: Analyst-A, Impurity-A
2. For example, Operator-A recorded in BPCR as Reactor-A temperature raised to 20°C and
maintained for 1 hr.
Raw data: BPCR
Data: 20°C, 1 hr
Meta Data: Operator-A, Recorder-A
DATA INTEGRITY:
1. Data integrity is a policy of the firm which assurance that all data are accurate, complete, intact
and maintained within their original context including their relationship to other data records
throughout the data life cycle.
2. In short, data integrity aims to prevent unintentional changes to data or information. i.e ensuring
data integrity means protecting original data from accidental or intentional modification,
alteration, malicious intent (fraud) or even data deletion (data loss).
ALCOA+ Principle:
1. As per Regulatory, data should meet certain fundamental elements of quality as
follows whether they are recorded on paper or electronically.
2. ALCOA is commonly used acronym short for “Accurate, Legible, Contemporaneous, Original and
Attributable.
3. Later on Complete, Consistent, Enduring and available also added to ALCOA principle which
then termed as ALCOA+.
4. As per ALCOA+ Principle data should be Accurate, Legible, Contemporaneous, Original,
Attributable, Complete, Consistent, Enduring and available.
ACCURATE:
The term Accurate means data are correct, truthful, valid and reliable. This means an honest,
accurate and thorough representation of facts describing conduct of study.
Example-1:
A manufacturing instruction state as follows
1. Take 25 gram of RM1 and add to 100 L water
2. Mix for 20 min. Check complete dissolution
3. Heat to 70°C.
Now while the solution was being heated for whatever reason the temperature rose to 72°C.
Is it deviation? Obviously Yes! So what does one do? Report? Ideally Report? What happens then
Investigation, Risk assessment, CAPA, Massive documentation and probability of Auditors
comments?
So, is there an easier remedy? Simply write 70°C in BPCR instead 72°C?
Example-2:
The result of Impurity from a HPLC chromatogram getting out of specification results as 0.11%
against limit 0.10%. Ideally OOS initiation, Investigation, Impact assessment, CAPA and training.
So, is there an easier remedy? Simply write adjust integration parameters and adjust impurity
result to 0.09% instead of 0.11%.
Other examples for inaccuracy:
· Not or inadequately qualified/ calibrated / maintained equipment or instruments used.
· Not or inadequately validated method / process used.
· Investigation of OOS results & Deviation not done or doubtful.
So, never compromise accuracy at any situation record actual accurate details.
There will be times when source documents are in complete, inconsistent, or wrong. If changes
need to be made modifying a document always need to done in complaint manner. When the
source is electronic, Audit trails can provide transparency to prevent data from being altered in a
way that it is difficult to detect.
Finally, Data must correctly reflect the action / observation made.
LEGIBLE:
Data should be readable and understandable and must be possible to interpret data after it is
recorded.
Example-1:
The typo error in date was identified in the document as 20/09/2016 instead of 19/09/2016. During
the correction good documentation practices were not followed due to that old entries are not
readable or not understandable. i.e. generated inlegibel document
Example-2:
During the issuance BPCR, it was noticed that the Xerox (True copy) of the master BPCR not legible
due to printer problem but same was issued. i.e. generated inlegible document.
CONTEMPORANEOUS:
Data must be recorded at the time it was generated and observed. The documentation should serve
as an accurate attestation of what was done and what was decided and why i.e. what influenced the
decision at that time.
Example-1:
A manufacturing instruction state as follows
1. Take 25 gram of RM1 and add to 100 L water
2. Mix for 20 min. Check complete dissolution
3. Heat to 70°C and maintain for 15 min.
But during recording of BPCR, Ideally record 70°C without noticing online for actual temperature.
Example-2:
During the HPLC analysis, the online entries (Updation of balance usage, pH meter usage logbooks)
were not made and all entries were made after completion of analysis.
ORIGINAL:
Original Record can describe the first source capture of data or information. If corrections or revisions
need to be made to original record, changes should not obscure prior entries.
Example:
In case of HPLC, The first source data is electronic copy of chromatograms and in case of balance
the first source data is paper weight print which comes under original data.
ATTRIBUTABLE:
Attributable means information relating to originator of the data. i.e. when documenting data on a
paper every written element is need to be tracked back to the authorized individual who is responsible
for recording it. It requires the signature and the date.
Audit trail in the electronic system make it very obvious who created record, when it was created, who
made a change, when the change was made and reason or the change. A complaint system will
automatically track this information and enable electronic signature. Data is attributable to a unique
user with secure password and role based permissions.
COMPLETE:
Complete data can be describe all relevant data is present and available. i.e. Complete data is data
with all required data.
Example:
In case BPCR, BPCR is only a data not a complete data, a complete data includes Raw material
issuance slips, on demand slips, in-process analysis reports, Labels..etc.
CONSISTENT:
All elements of record, such as sequence of events follow on and are dated or time stamped in
expected sequence. i.e consistent practices to be followed like Good documentation practices…etc.
For example correction of wrong entries to be done in same manner for all documents.
AVAILABLE:
Data/Documents should readily available for review and auditor or inspection over the lifetime of
document.
Records must be available for review at any time during the required retention period, accessible in
readable format to all applicable persons who are responsible for their review whether for routine
release decisions, investigations, trending, annual reports, audits or inspections.
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6 comments:
Unknown September 23, 2016 at 11:41 PM
meta data understanding looks different than that mentioned in FDA draft.
Reply
Reddy
September 24, 2016 at 10:21 AM
It is inline with FDA draft only. As per FDA 'Meta data is data about data and Meta data for the particular
piece of data that could include date & time stamp of when were data acquired, user ID of the person who
conducted the test, and instrument ID, Audit trail etc'.
FDA has given example 23 mg as example. If you look at only 23 mg, 'mg' comes under meta data. But in
an example like 'I bought 23 mg of powder'. 23 mg is data and I & powder comes under meta data. Meta
data depends on information available and your reporting data.
Reply
Reddy
September 24, 2016 at 10:22 AM