The Clinical Leader interviewed Frank Henrichmann, Senior Executive Consultant at QFINITY and Co-Chair of the GAMP Global Steering Committee, on the occasion of the second edition of the ISPE GAMP Good Practice Guide for computerized GCP systems. The conversation explores how clinical trials are changing and the role of validated computer systems.<\/p>\n
Mr. Henrichmann works as a Senior Executive Consultant at Q-FINITY Quality Management<\/a>. He is an expert in quality management, computer system validation, and compliance, particularly in the context of clinical trials and pharmacovigilance. Over the course of more than 22 years, he has gained extensive experience with strategies, projects, and measures for GxP-regulated environments, both at a CRO and at a major pharmaceutical company. In his current role, he assists life sciences companies and supports technology providers in finding innovative solutions to quality and validation challenges. He is a qualified ISPE trainer, a member of the ISPE Clinical Systems Special Interest Group (SIG), and a co-author of the ISPE GAMP Good Practice Guide: Validation and Compliance of Computerized GCP Systems and Data<\/a>. He has been a member of ISPE since 2001 and currently serves as co-chair of the GAMP Global Steering Committee. <\/p>\n Frank Henrichmann is one of the lead authors of the ISPE GAMP Good Practice Guide: Validation and Compliance of Computerized GCP Systems and Data – Good eClinical Practice (Second Edition). To mark the occasion, he was invited by Clinical Leader to participate in an interview. In this interview, Henrichmann provides insights into the challenges and changes in the field of clinical trials. The interview highlights the importance of computer systems in clinical trials. Read the full interview at Clinical Leader<\/span>↗<\/span><\/a><\/p>\n The second edition of the “GAMP Good Practice Guide: Computerized GCP Systems & Data” has been published. Why is now the right time for an update? <\/strong><\/p>\n Frank Henrichmann: The way we design, plan, and conduct clinical trials today differs from how we conducted them 15, 10, or even five years ago. The pharmaceutical products being developed in clinical trials have changed significantly and now include more innovative cell and gene therapies (CGTs). This requires a completely different approach to clinical trial design, and the tools we use must adequately support these new designs and meet the new requirements. <\/p>\n The challenges posed by the COVID-19 pandemic required the industry to rethink its approach to data collection and management. Pandemic-related lockdowns limited study participants’ ability to visit clinical facilities for treatments and examinations, leading to an accelerated shift toward decentralized data collection via participants’ homes, mobile clinics, wearables, sensors, and telemedicine. This brought additional challenges for data integrity and data management. While participants in traditional systems were typically identified by an anonymous number, these new systems collect and process personally identifiable information to ensure the delivery of devices or investigational products and to enable telemedicine. These are just a few examples of what has changed in recent years since the publication of the first edition of our guide. Of course, regulatory authorities have also responded to these changes and issued guidelines to address these new elements and challenges. <\/p>\n What are some of the new topics covered in the second issue?<\/strong><\/p>\n The comprehensive new guide covers more topics than the first edition. While the structure of the first edition has remained the same, the content has been expanded to include topics such as decentralized trials, the evolution of data management toward data science using AI-enabled solutions, and guidelines for generating real-world evidence (RWE) from real-world data (RWD) available from electronic health records (EHRs), patient registries, and other non-regulated data sources. <\/p>\n In addition, the guidance document now also covers oversight activities such as audits and assessments and includes practical guidance and questions to consider when evaluating computer systems in clinical settings. The guidance document also includes guidance on data protection in the context of clinical trials. And while the validation of AI\/ML-based systems is addressed in other ISPE guidelines, the considerations regarding AI-enabled systems used in clinical trials are specific to this comprehensive guideline. <\/p>\n Today, an increasing number of users are becoming involved in clinical trial processes – from pharmaceutical companies, CROs, technology service providers, and clinical research organizations to trial participants. How does this new issue take this growing number of end users into account and address their needs? <\/strong><\/p>\n As more and more stakeholders interact with the systems and data during the collection and processing of clinical trial data, greater challenges arise for data integrity. For example, in decentralized clinical trials (DCTs), sensors and wearables are often used to collect data directly from participants. This data must be securely and accurately transferred to the systems that manage data from all trial participants, such as an EDC system. <\/p>\n Because these sensors and wearables take frequent measurements, the amount of data collected has increased exponentially. In a traditional study, for example, blood glucose levels or an ECG were measured only during clinic visits. With wearables and sensors, these measurements can be recorded with high accuracy at much shorter intervals – almost continuously. The massive increase in data volume has led to traditional data management evolving into a data science activity, in which specialized tools – previously used primarily for analyzing big data – are now being used to analyze study data in order to identify patterns or discrepancies. Today, the industry is rapidly adopting AI-enabled solutions to manage and analyze these large volumes of data more quickly, so that patient safety and well-being can be better protected and critical decisions can be made faster based on reliable and trustworthy data. <\/p>\n The massive increase in data volume has led to traditional data management evolving into a data science activity.<\/p>\n<\/blockquote>\n Clinical data often comes from systems owned and operated by a healthcare facility. These may include instruments and devices used in the daily care of patients, as well as electronic health records in hospitals. If data generated or processed by these systems is to be used in a clinical trial, the sponsor must verify the suitability of these systems in advance. This requirement presents several challenges, given that clinical trials often involve hundreds of sites around the globe. Efficient yet reliable methods and processes must be implemented to ensure the necessary control and monitoring while avoiding overburdening the limited resources of the trial sites. The guidance document provides practical guidance and a list of factors to consider when evaluating such systems at clinical sites to ensure the necessary control. <\/p>\n With regard to the overlap between these systems and those of pharmaceutical manufacturers and laboratories – which generally comply with GMP and GLP, respectively – how does this guide assist users working in these related fields?<\/strong><\/p>\n We have tried not to reinvent the wheel and have focused on the systems directly related to the conduct of the clinical trial. However, there is an overlap with drug manufacturing, as investigational products must also be manufactured in appropriate, albeit small, quantities. The manufacture of the investigational product must comply with established GMP guidelines and expectations and has not been covered in detail in this guide, as this topic is already addressed in other ISPE guides. However, the investigational product may need to be packaged and labeled differently from marketed drugs in order to establish and maintain the blinding of a clinical trial. This is a critical aspect in many clinical trials, as it ensures that neither the participant nor the investigator can determine which participant is receiving the new investigational product, the standard of care, or a placebo. In our guideline, we have described the systems involved in this randomization process, explained the key risks associated with the process and the supporting systems, and presented possible validation approaches. <\/p>\n Similarly, we addressed the interface with the laboratories that analyze the samples collected from participants. The validation of the systems used within the laboratory itself was not covered in our guidance, as it has already been addressed in other ISPE guidance documents. However, the specific requirements for data transfer and the management of this laboratory data in a GCP system, while ensuring data integrity, were described in the section on good clinical laboratory practice in the guideline. <\/p>\n New in this issue is a focus on “ensuring compliance with applicable regulations, with a particular emphasis on data integrity and data flows, taking into account challenges posed by outsourcing services and technology.” What is the reason for including this additional objective? <\/strong><\/p>\n GCP systems have a broader user base than most other regulatory systems, including sponsors, clinical service providers such as CROs, clinical sites, and trial participants. Because of this broad user base, it has become standard practice to outsource nearly all computer systems required for the collection and management of clinical trial data. Specialized technology service providers have developed web-based, zero-footprint systems that all authorized users can easily access, regardless of local infrastructure or device. All collected data is analyzed and processed by clinical research service providers such as CROs and, of course, the sponsor. These data transfers and activities must be assessed for potential risks to data integrity. This is important because the data must be collected and analyzed accurately and in a timely manner to identify possible adverse events or a lack of efficacy. Furthermore, this data often forms the basis for regulatory submissions to regulatory authorities. The quality and integrity of the data therefore have potential implications for the health and well-being of future users of the drug. <\/p>\n The quality and integrity of the data have potential implications for the health and well-being of future users of the drug.<\/p>\n<\/blockquote>\n And finally, when and where will the second edition be available?<\/strong><\/p>\n After 18 months of intensive work by a team of nearly 50 industry experts from sponsors, CROs, technology providers, and consultants, the guidance was published by ISPE on July 31, 2024. However, this is not the end of our team’s activities. We are currently planning a series of webinars on specific topics covered in the guidance document. These will be posted at ISPE.org\/webinars as soon as they become available, such as decentralized trials and AI-enabled solutions in data management. In addition, there will be presentations on similar topics at various ISPE conferences and events around the world. As this field continues to evolve, we will cover new developments in future articles for ISPE’s Pharmaceutical Engineering Magazine or other ISPE publications. <\/p>\n Frank Henrichmann was pleased to have been invited to this interview.<\/p>\n The webinar series on the 2nd edition has since taken place. The recordings are available to ISPE members (sign-in required).<\/p>\n Webinar: Introduction to the 2nd Edition of the GAMP GPG on GCP Systems (ISPE members)<\/span>↗<\/span><\/a> The Clinical Leader interviewed Frank Henrichmann, Senior Executive Consultant at QFINITY and Co-Chair of the GAMP Global Steering Committee, on the occasion of the second edition of the ISPE GAMP Good Practice Guide for computerized GCP systems. The conversation explores how clinical trials are changing and the role of validated computer systems. Mr. Henrichmann works […]<\/p>\n","protected":false},"author":5,"featured_media":13355,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[39],"tags":[],"class_list":["post-15701","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-category"],"yoast_head":"\nThe Interview<\/h3>\n
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Related ISPE webinars<\/h2>\n
\nWebinar: Audit and Oversight Challenges in GCP Systems (ISPE members)<\/span>↗<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"