What is ALCOA+?
ALCOA+ is a system that is used to implement data integrity in the pharmaceutical manufacturing industry. The term ALCOA was introduced by US-FDA in the starting. In the earlier stage, it was ALCOA only, after some period of time ALCOA+ was introduced with some powerful tools that made it more efficient. The main motive of this framework was to integrate data and data security.
These days, It has become more essential to adopt ALCOA+ for compliance with FDA and GMP practices. Principles of ALCOA+ are being more important to follow GMP and GLP practices also.
The ALCOA stands for A Attributable, L Legible, C Contemporaneous, O Original, and A Accurate. However, ALCOA+ is added with some more significant features that are Complete, Consistent, Enduring, and Available.
Why is ALCOA+ important in validation and data integrity ?
Pharmaceutical Industry was struggling for a long time for data management before the introduction of the ALCOA+ framework. It was very hard to compare whole data offline for validation and research purposes. To meet up every compliance it is very important to manage data integrity. With the passage of time, the pharmaceutical industry bears a big burden of data management due to lots of regulatory compliance. ALCOA+ allows the industry to integrate their whole data.
After the success in the pharmaceutical industry, other industries also adopted ALCOA+ as a framework for data integration and the security of data. Some big regulatory bodies like FDA-US Food and Drug Administration, WHO, GAMP also used and pushed to adopt the ALCOA+ framework.
The ALCOA+ Principle
The Term ALCOA stands for naturally five basic principles for data integrity. Those five principles are as follow:
After the update of the original ALCOA principles to ALCOA+. The ALCOA principles remain as and four additions are :
ALCOA+ can be described in such a manner every attribute of this system is illustrated here as:
Attributable represents here that when the data is recorded or collected the identity of the individual can be recorded for further investigations on any alteration found in data in the future. The responsible person can be traced easily. The place or the section is also included, the date and time of data entry are also registered in this system.
The legible term is to ensure that data can be easily read and understandable by anyone, the manual raw data should be protected to verify electronic data. The data should be relevant and understood even after a long time. Clear and transparent words used in data collections are much helpful.
The data collected should have an accurate time and date. The data in contemporary nature is more usable. This indicates that data recording should have the time of collection in records.
When a person collects the data, the time of collection or activity should be recorded in electronic data as well as in manual records on papers.
The data recorded for integration should be original and relevant to the activity performed. That should not be a copy of similar activity performed earlier. The original data remains more durable than a copy or transcript. If the data is found to be copied the data recorder should have to prove the authenticity of the data preserved. The copied or transcript data can create errors in the whole system in the future.
Every data collected for data integrity should be free from any error, the data should show the activity truly. Any editing that misleads in data integrity should be avoided. The information should be complete and viable. If any necessary editing or change takes place that should refer to the original information. During electronic data recording, proper verification should be ensured by various accuracy checks.
The data collected should be complete, there should not be missing any activity or deleted in between. The whole process of that date of documentation should be completely recorded.
This indicates any rectification that was done during the whole life of the data.
The data should be consistent in nature that has to be chronological. Data should have time stamps and dates in sequence. Various audits should be carried out consistently.
The quality of paper is high so that it does not lose data for the next few decades or years, the readability of data should remain intact for a long time. The electronic system where data is recorded should be durable for a long time to ensure protecting data loss.
Data recorded needs to be easily accessible for future needs, If the data is easily available it will prove much useful. Proper indexing on offline data should be marked on it.
Data Integrity And Validation
In the era of the information revolution, Data integrity is very crucial for every validation process in every manufacturing industry. Data integrity allows manufacturers to review their in-process loss, quality errors, cost efficiency, and future plans. ALCOA+ is a quite good framework to ensure data security and data integrity with fewer efforts. If the data is available easily and complete in nature that helps to make corrective action plans for past discrepancies.
Frequently asked questions
What is attributable meaning in Alcoa?
The meaning of attributable in ALCOA is that any data collected for further digital use can be identified who registered the data, the sequences of data collection can be investigated if in the future it is required.
What is the difference between ALCOA and ALCOA Plus?
The term ALCOA is brought into use with its five core attributes including Attributable, Legible, Contemporaneous, Original, Accurate, and Complete. After an update in the original ALCOA, basic five concepts remain included in the frame, four more attributes are included as Complete, Consistent, Enduring, Available. The advanced ALCOA+ contains nine features in its principle.
What are the 5 principles of data integrity?
As per the basic principle of ALCOA, the data should have five main features to maintain data integrity that are including Attributable, Legible, Contemporaneous, Original, and Accurate
What is ALCOA Plus for MHRA?
ALCOA was historically regarded as defining the attributes of data quality that are suitable for regulatory purposes. There is no fundamental difference between ALCOA and ‘+’. ‘+’ is subsequently added to emphasize the requirements. Therefore, according to MHRA governance measures
should ensure that data is complete, consistent, enduring, and available throughout the data life cycle.