Implementing Pharmaceutical Data Integrity Principles In an Era of Advanced Technology

Introduction

Data integrity is of paramount importance in pharmaceutical industry as seen through the rigorous guidelines which are issued by regulatory bodies such as the ICH and USP. With the advancement in technologies such as cloud computing and artificial intelligence, more challenges arise on how best these technologies can be incorporated in data management whilst adhering to set guidelines. This article will discuss key principles in data integrity and how best it can be implemented in an era of fast-paced technological advancements. Enjoy

Data Integrity

According to WHO, pharmaceutical data integrity means data must be complete, consistent, accurate, reliable and trustworthy throughout its lifecycle

Key Principle: ALCOA+ Framework

Attributable

Data should be traced to the person who created it to the exact time and date of action. Data should have an individual responsible for its recording or retrieval. This is responsible for accountability

Legible

Data recorded should be permanent and recorded in a durable medium. This applies to both handwritten and electronic records

Contemporaneous

Data should be recorded at the exact time of action or observation. This prevents backdating or delayed entries which may result in omission of important data

Original

Data should be available be the original record form or a certified copy of the original

Accurate

Data recorded should be a representation of the true action or observation which occurred, free from errors or editing without justification and documentation.

 Consistency

Data provided should be coherent throughout the process being recorded, showing a coherent timeline of events. 

Complete

All the data which is required to be recorded should be captured and reported

Enduring and Available

Data should be able to last throughout its required lifetime and be able to be retrieved easily when required

Using Technology in Implementing Data Integrity Principles

Cloud-Based Data Management

Pharmaceutical companies can adopt validated cloud solutions for storing laboratory, clinical or manufacturing data. This ensures data is legible, contemporaneous, original, consistent enduring and available

This can be achieved by ensuring:

• Data encryption at all times

• Automated data backup and disaster recovery solution

• Sourcing an approved GxP-compliant cloud provider

Secure Access Control

Secure access control ensures data remains attributable

This can be done by having:

• User electronic signatures

• Multifactor authentication

• Biometric login for critical systems

• AI-driven anomaly detection to flag suspicious logins or data changes

Audit trails and Metadata capture

Ensures data remains contemporaneous, complete, consistency and accurate

This is done by having:

• Cloud logs automatically captured and monitored

• Audit trail software integrated into the main pharmaceutical system

• AI-driven anomaly detection to flag unusual trends in data modification

AI in Validation and Monitoring

Ensures data is accurate

This is achieved by:

• Machine learning for predictive data quality monitoring

• Natural language processing (NLP) to check compliance of free text entries with SOP standards

Data Governance Framework

Data should be managed during its lifetime to ensure its in line with the company policies and SOPs. Internal pharmaceutical regulatory departments can use such technologies to ensure that the whole organization is in compliance with set guidelines by authority on data integrity

Integration of technology involves:

• Digital SOP platform

• Cloud dashboards to track compliance metrics

• AI-based compliance bots to scan data for gaps in integrity

Cybersecurity Integration

As companies use cloud computing and AI tools, it is important that they are able to detect and prevent data breaches

This can be done by:

• AI-powered intrusion detection system

• Zero trust architecture

• Endpoint detection and response (EDR) tools to secure lab instruments and computers connected to network

Data Lifecycle Management

This helps in keeping data enduring & available

Data lifecycle management can be achieved by:

• Cloud archiving with retention locks

• Automated alerts before data expiry

• Blockchain timestamping for long term authentication

Hybrid Systems Validation

The use of cloud and AI systems will need to be validated so as to be accepted by regulatory bodies

System validation of such technologies will involve:

• AI/Machine Learning frameworks

• Continuous performance qualification as the technologies evolve with automated scripts

• Following GAMP 5 guidelines for computerized system validation

Staff Training

Since staff is responsible for operating these technologies, it is important that they understand how to use such technologies and importance of digital compliance requirements

This often involves:

• E-learning platforms with interactive AI tutors

• Cloud based competency tracking

• Digital simulations of data breaches scenarios

Conclusion

No doubt that technologies such as cloud computing and Artificial Intelligence are defining data integrity practices in pharmaceutical sector. Nonetheless, the ALCOA+ principles which are the cornerstone of data integrity still applies even when using such technologies.

It is therefore the responsibility of the pharmaceutical company to ensure the ALCOA+ principles are embedded in their digital systems, ensuring strong cybersecurity and creating a culture of digital compliance