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.