Executive Overview

The pharmaceutical industry relies heavily on data, both for compliance and innovation. The growing reliance on digital technologies makes data integrity even more crucial. Mismanagement, whether through deletion, manipulation, or inadequate documentation, can lead to regulatory and quality setbacks. Companies need a robust quality and continuous improvement culture to address these challenges. Additional guidance in the ISPE GAMP® 5 document complements and enhances the maturity guidance [1] discussed in this paper.

The International Society for Pharmaceutical Engineering (ISPE) offers guidance through its maturity models, which address distinct yet complementary aspects of modern manufacturing: the ISPE Data Integrity (DI) Maturity Model [3] and the ISPE Pharma 4.0 Model [2]. The DI Model addresses data integrity throughout the lifecycle to address regulatory compliance. The Pharma 4.0 Model supports seamless integration of Industry 4.0 technologies, which enhances operational efficiency and innovation.

Understanding these two models and looking for ways to integrate them can help companies navigate the complexities of digital transformation and regulatory compliance. This white paper compares both models’ scope, implementation strategies, and benefits. Additionally, it explores ways to combine the two models to help pharmaceutical companies safeguard their data and transform manufacturing by implementing Pharma 4.0 concepts to address the next generation of innovation.

Introduction

Understanding the different maturity models can help the pharmaceutical industry move toward integrated digital operations. Each model focuses on different aspects of organization maturity: the ISPE DI model emphasizes safeguarding the integrity and regulatory compliance of critical data.  By comparison, the ISPE Pharma 4.0 model addresses the complexities of implementing Industry 4.0 digital technologies to enhance and automate pharmaceutical manufacturing.

Overview of the Models

ISPE Data Integrity Maturity Model

The ISPE DI Maturity Model ensures that data critical for regulatory compliance and business operations is accurate, complete, and reliable throughout its lifecycle [3]. The DI model addresses essential areas such as data creation, processing, maintenance, compliance, and reporting through various controls, such as data governance and risk management, focused on regulatory compliance and inspection readiness. The model further outlines a path from basic, uncontrolled processes to a mature, proactive state where data integrity is embedded in pharmaceutical operations through a culture of continuous improvement.

ISPE Pharma 4.0 Maturity Model

The ISPE Pharma 4.0 Model provides a framework for adopting digital technologies in pharmaceutical manufacturing and addresses industry-specific guidance for Industry 4.0 implementation. It spans various organizational disciplines and processes, including Quality Management, the human element, operating model principles, and elements and enablers specific to the pharmaceutical industry [2]. The goal is to transform from traditional manufacturing processes to an optimized, fully integrated digital enterprise that leverages data, connectivity, and scalable systems for operational excellence.

Areas of Focus Comparison

The table below outlines the focus areas for the two maturity models, examining scope and application, implementation strategy, and the outcomes and benefits of each model.

Studies and Articles on Data Integrity and Pharma 4.0

A study published in the Journal of Open Innovation12 examined the impact of implementing Industry 4.0 concepts in Biopharmaceutical companies. The study indicates that increased productivity, enhanced competitiveness, and improved quality are benefits realized from implementing Industry 4.0 concepts.

The concepts presented in this paper are not without challenges. Companies produce a significant amount of data related to manufacturing operations. Managing that data is crucial and can contribute to the success of an organization. An article published in ISPE’s Pharmaceutical Engineering, titled Data Integrity in Cloud and Big Data Applications [1] [3], discusses using artificial intelligence and machine learning to analyze real-time data and predict trends to improve processes and operations. The article discusses the crucial nature of data integrity to GMP operations and the application of risk-based approaches to manufacturing and process intelligence so that companies can implement Pharma 4.0 concepts.

Regulatory Guidance

Any discussion of pharmaceutical manufacturing, data integrity, and advanced technologies requires understanding the regulatory landscape governing companies. The following table highlights some regulatory guidelines for compliance and discussion points for using AI in drug manufacturing.

Future Direction of Regulatory Guidance

Regulatory bodies face challenges in addressing the use of advanced technology available to pharmaceutical companies.  Regulatory agencies are playing catch-up with the fast-paced direction of technology and the changes proposed by Industry 4.0 and Pharma 4.0. 

The use of Artificial Intelligence (AI) in the making of drugs is the topic of a discussion paper published by the Center for Drug Evaluation and Research (CDER), a division of the US Food and Drug Administration (FDA).  The paper aims to inform the development of a regulatory framework for the use of AI in the manufacturing of drugs14.

A paper published by the National Institute of Health (NIH), National Library of Medicine titled “Future directions in regulatory affairs” in part discusses global regulatory harmonization and convergence as regulatory bodies look to manage the regulatory changes needed to the advances in technology as the pharmaceutical industry moves toward Industry 4.0 and Pharma 4.015.  

Some agencies that offer harmonized guidance specifically for regulatory agencies include the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) and the Pharmaceutical Inspection Co-operation Scheme (PIC/S).  ICH Q99 - Quality Risk Management and Q1010 - Pharmaceutical Quality System are the foundation for many regulatory agencies' guidance, and ISPE references these in many ISPE guidance documents.  PIC/S offers Data Integrity specific guidance in their good practices for data management and integrity in regulated GMP/GDP environments11 (published in July 2021).

The table below outlines some of the challenges that future regulations need to address to allow for innovation and safety while adapting to the transformative impact of Industry 4.0 and Pharma 4.0.

Integration Opportunities and Cultural Change

Each model offers unique benefits and focus areas. Integrating the two models may benefit organizations looking to achieve overall digital maturity as they look to the future of pharmaceutical manufacturing. Implementing the DI model within the Pharma 4.0 operational framework ensures that organizations adopt advanced technology and maintain control over their data and its integrity. The concepts around IT and automation infrastructures promoted by Pharma 4.0 can enhance the effectiveness of data integrity efforts.

The table below illustrates cultural implications and strategies for successfully implementing the DI and Pharma 4.0 Maturity Models. Organizations looking to advance their maturity levels around people, technologies, and processes must change their cultural norms and strategic initiatives. 

Conclusion

The ISPE DI and Pharma 4.0 Maturity Models focus on different aspects of pharmaceutical manufacturing. While they concentrate on different areas, they complement each other. Organizations do not need to choose between the two models. Instead, they need to understand how these models can be part of a broader strategy to achieve compliance and operational maturity that addresses the competitive landscape offered by the promise of Industry 4.0 concepts like smart manufacturing and digital transformation.

This white paper instead suggests a pathway to evaluate current practice against these models and devise a strategy to integrate both. Integrating the two models ensures that digital transformation goes hand-in-hand with uncompromised data integrity, compliance, and organizational maturity. 

Pharmaceutical organizations should assess their current maturity levels across both models. They should identify integration points that allow the organization to leverage each of its strengths, thereby ensuring a balanced approach to digital transformation and regulatory compliance. 

Adopting elements from both models can ensure the regulatory focus and inspection readiness offered by the DI model, while also incorporating the digital transformation offered by the Pharma 4.0 model.

References

1. ISPE  GAMP® 5: A Risk-Based Approach to Compliant GxP Computerized Systems 

2. ISPE Baseline Guide: Pharma 4.0

3. ISPE  GAMP®: Records and Data Integrity Guide

4. ISPE APQ Guide Series: Change Management System

5. ISPE APQ Guide Series: Corrective Action and Preventive Action (CAPA) System

6. ISPE APQ Guide Series: Cultural Excellence

7. ISPE APQ Guide Series: Management Responsibilities and Management Review

8. ISPE APQ Guide Series: Process Performance and Product Quality Monitoring System

9. ICH Q9 Guideline - Quality Risk Management

10. ICH Q10 Guideline - Pharmaceutical Quality System

11. PIC/S Guidance - Good practices for data management and integrity in regulated GMP/GDP environments. - July 2021

12. Silva F, Resende D, Amorim M, Borges M. A Field Study on the Impacts of Implementing Concepts and Elements of Industry 4.0 in the Biopharmaceutical Sector. Journal of Open Innovation: Technology, Market, and Complexity. 2020; 6(4):175. https://doi.org/10.3390/joitmc6040175

13. Manzano, T, & Langer, G (2019). Data Integrity in Cloud and Big Data Applications. Pharmaceutical Engineering, 39(2), 1-8. https://www.ispe.gr.jp/ISPE/02_katsudou/pdf/201812_en.pdf

14. Title: Artificial Intelligence in Drug Manufacturing; Author: Center for Drug Evaluation and Research (CDER); Year: 2023 Publication Type: Discussion Paper; Publisher: U.S. Food and Drug Administration (FDA) (see link in table above)

15. Chisholm, O., & Critchley, H. (2022). Future directions in regulatory affairs. Frontiers in Medicine, 9. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868628/pdf/fmed-09-1082384.pdf)