ICH Q7 Training Week
From 19-23 June 2023, the ICH Q7 training week took place in Munich, Germany. Many interested participants from a large variety of countries got together with the speakers to discuss and debate well-established and current topics in the field of chemical and biotech APIs and their legal GMP requirements. The event was supported by the representative of the APIC Task Force "Third Party Manufacturing" Mauro Menichelli.
This time, as part of the compliance courses, Dr Markus Dathe held the presentation “Data Integrity in the light of ICH Q7” and pointed out the following:
“The ICH Q7 guideline1 has been existing since 2000 and was amended in 2015 with a Questions and Answers document2 supporting a clear interpretation and modernizing the guideline. ICH Q7 was unique at its time because it included in a holistic and comprehensive way the modern elements of quality assurance and quality management: for example risk control, computer system validation and integrated quality approaches. The Q&A document officially adapted the ICH Q93 and Q104 risk-based approaches and clearly integrated Q7 in the ICH Pharma Quality System (PQS). ICH Q7 anticipates major elements of Data Integrity (DI), even though it was created just before Data Integrity became a major topic in the pharmaceutical industry.
The question we want to explore is whether Data Integrity is a completely new approach or just a different perspective on already existing GMP requirements as those arising from ICH Q7.
Data Integrity can be described as "the opposite of data corruption, which is a form of data loss. … In short, data integrity aims to prevent unintentional changes to information." And "Data integrity refers to maintaining and assuring the accuracy and consistency of data over its entire life-cycle, and is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data."5
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In general, Data Integrity elements are categorized by
- Physical Integrity (e.g. safety, security, durability)
- Logical Integrity (e.g. context, plausibility)
- Scientific Integrity (e.g. correctness, accuracy)
It is important to understand that Data Integrity is not Data Quality, even though it is one of the elements and a prerequisite to it. Using data, we collect information which is aggregated to knowledge; if we use wrong data, we may use wrong information to conclude wrong things. This can be a threat to patients at the end.
Fig.1: ICH Q7 Training Week in Munich, June 2023.
Data Integrity requirements and measures can be divided into the following facts:
- organizational
- technical
- records/documents/data
If you compare these Data Integrity elements with Q7, represented by the chapters where organizational elements are marked yellow, system and process relevant green, and the records/documents/ data are labelled blue
it is quite interesting, how much alignment between Data Integrity and the Q7 elements are to be found. Furthermore, ICH Q7 is one of the first examples for a systematic quality risk management. In particular, it can be seen as an application of pragmatic risk categorization determined by the distance to the patient and the influence on the quality of the (medicinal) product. A principle, which is nowadays the standard for many DI guidelines (compared to e.g. FDA’s and MHRA’s guidelines) which are using the concepts of direct and indirect data (direct influence on patient safety and product quality or not), adding complexity as a risk criterium via the static and dynamic (interactive) data.
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In their 2004 document “PHARMACEUTICAL CGMPS FOR THE 21ST CENTURY — A RISK-BASED APPROACH”6 , the FDA explains their “Strategic Action Plan for Protecting and Advancing America’s Health”. The Agency’s Strategic Plan identified efficient risk management as a key element: “Efficient risk management requires using the best scientific data, developing quality standards, and using efficient systems and practices that provide clear and consistent decisions …”. Taking this plan into consideration, risk management, best scientific data and consistent decision making is calling out already for what we later call Data Integrity.
It is important to understand that (quality) risk management is essential to the implementation and maintenance of Data Integrity concepts. In order to control the risks they need to be categorized.
Such a categorization must be simple and easy to be applied fast, consistently and reliably. A systematic approach is necessary to implement a holistic Data Integrity strategy. Such holistic and systematic approaches are based on a Data Integrity risk analysis, which is again the base for deriving governance concepts including master data. For the data (quality) strategy risks are collected in a register/inventory which also comprises measures and uses classifications to maintain an overview of the organization’s data, systems, and processes throughout the lifecycle and to consistently control them. Libraries of such risks, measures, methods to GMP and Data Integrity ensure systematic, efficient and effective results. This includes the acceptance of risks!
As per practical experience, hybrid records (i.e. systems maintaining both paper and electronic records, typically with “paper lead”) bear the highest risks to data and records, and are for that reason in the main focus of inspections and audits. Due to high efforts for the second person review of both paper and electronic records, and due to the need to assure that both are correct, consistent and synchronized, they expose the organizations to high financial and regulatory risks.
Since classical GMP regulations like ICH Q7 and the current Data Integrity standards are founded on the same basic principles, it rather seems to be a natural extension of the GMPs to the 21st century than a completely new metamorphosis – set by the authorities to assure the safety of the public and the best product quality.
This presentation was one of the lectures of the joint sessions of the Compliance Courses for APIs produced by chemical synthesis and cell culture/fermentation. As usually, these courses were followed by the Auditor Training which was characterized by many role plays and discussion sessions. Once again, the ICH Q7 Training Week was full of interesting topics and beneficial for both speakers and attendees.
| Note: The upcoming ICH Q7 Training Week will take place live online in November 2024. For more information, please visit the ICH Q7 Week website. |
About the Authors
Anne Günster joined CONCEPT HEIDELBERG in 2019 and organises and conducts courses and conferences on behalf of the ECA Academy in the areas API Manufacturing, Regulatory Affairs, Documentation and Laboratory Data Integrity.
Dr Markus Dathe has been GMP Sytems Coordinator in the Synthetic Small Molecules Development at Roche since 2011.
Notes:
1 ICH Q7 Good manufacturing practice for active pharmaceutical ingredients - Scientific guideline, 200
2 ICH guideline Q7 on good manufacturing practice for active pharmaceutical ingredients – questions and answers, 2015
3 ICH guideline Q9 on quality risk management, 2005
4 ICH guideline Q10 on pharmaceutical quality system, 2008
5 Wikipedia: Data Integrity, https://en.wikipedia.org/wiki/Data_integrity, called on 10-Oct-2023
6 FDA, PHARMACEUTICAL CGMPS FOR THE 21ST CENTURY — A RISK-BASED APPROACH, FINAL REPORT, 2024
