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NEXT EDITION FEBRUARY 2025
CARBON CAPTURE QUASAR #168
Ashok Kumar, Milind Nadgouda
Clinical Site Audit Selection in Pharmaceuticals Elina Beletski, Björn Koneswarakantha, Hangyu Liu, Ofure Obazee, Michael Pelosi, Lucie Regne-Martos
REGULARS
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WELCOME
Vision – To inspire quality, integrity and compliance in scientific research.
Mission – To provide news, knowledge and learning in the scientific research community and build expertise through training, communication, engagement and collaboration.
Dear Members,
It is worthwhile reflecting upon the Vision and Mission that RQA is continuing to strive to fulfil.
RQA’s Vision is to inspire quality, integrity and compliance in scientific research.
Our Mission is to provide news, knowledge and learning in the scientific research community and build expertise through training, communication, engagement and collaboration.
Our focus has been on how we can better serve our members and customers to provide more of the required services and information required.
One major factor that has impacted upon our operations in meeting this vision is the membership base and customer profile of the Association. Over recent years this has changed significantly from a traditional UK/ European focus, to a growing global operation as our membership profile has changed. It also recognises that our UK members are no longer just interested in changes and developments within the UK regulatory arena but also the global regulatory scene.
This has required us to modify how we operate to provide a more global picture. Over the pandemic and post-pandemic period we introduced new technology which has permitted us to communicate and interact more dynamically with members and customers worldwide. In the coming months, we will be launching an RQA App which will improve the communications and process of interacting with members and customers.
The RQA Community Hub has been a great success in the way in which members can meet and exchange ideas and opinion. The introduction of ‘Question Time’ sessions, which are open discussions on a variety of topics, have been well attended and well received by those taking part. Notification of these events are sent to all members and I encourage you to join in and share your views and opinions. One recent example topic has been about AI’s role in improving quality processes, its ethical implications and how it’s shaping the future of industries, this interactive Q&A sparked insightful discussions and collective learning. All levels of knowledge and curiosity are welcome to attend. If you have any suggestions for future topics, please do contact the RQA Office.
The RQA Board has been working closely with Committee Chairs to implement changes in the way we interact and communicate as a team to identify, coordinate, develop and deliver new products and services to members and customers. Part of this change is to ensure ‘Hot Topics’ right across the spectrum of membership interests, be they changes to Regulations, improvements in best practice or developments to existing products, are identified and delivered in the most appropriate and timely way. Alongside the work of committees, the number and contribution of Special Interest Groups
(SIGs) continues to develop and deliver valuable products and ideas. These SIGs address and develop both the scope of our activities whilst also providing a different perspective of many areas of interest to the membership.
Virtual events, the Community Hub, Quality Conversations, Quasar, our Professional Development programme and conferences are just some of the methods that enable RQA to offer a broad range of services and information, not just within the UK, but globally to the entire community.
One of the strengths of RQA has always been the number of volunteers who kindly share their time and knowledge in producing and delivering numerous publications, activities and events. We are always seeking new volunteers to contribute to RQA activities. If you are interested in contributing, please don't hesitate to get in touch with the RQA Office. We welcome all contributions.
Kindest regards, Tim
ANNUAL GENERAL MEETING 2024
All Annual General Meetings (AGMs) in the UK are governed by the Companies Act 2006 and the company’s Articles of Association. Due to the various requirements and circumstances, planning, conducting and reporting AGMs can be a complex series of processes.
In essence, the routine business of an RQA AGM relates to Ordinary Resolutions adopting the Director’s Report and Financial Statements contained within the Statutory Accounts. The RQA AGM is also used to appoint new Board Members when required. The other business that can be conducted at an AGM includes Special Resolutions to amend the Articles of Association and Ordinary Resolutions to accept the RQA Annual Report, which incorporates reports from the Chair, Treasurer and Committees.
Taking all the above into account, the Agenda for the RQA AGM 2024 is:
• Ordinary Resolution 1: To accept the Minutes of the RQA AGM 2023
• Ordinary Resolution 2: To adopt the Annual Report for 2023-2024
• Ordinary Resolution 3: To adopt the Statutory Accounts for 2023-2024
• Questions relating to the AGM.
The RQA AGM 2024 will be held at 12 noon (GMT) on Friday 13th December 2024 as a remote event.
A Notice of AGM has recently been circulated to all RQA members and each has the right to vote on the three resolutions. All voting will take place online using the Online AGM Voting Form on the RQA website: www.therqa.com/membership/members-area/ agm/ (member login required). Voting closes on 6th December 2024.
FIRST PRINCIPLES OF QUALITY
Rebuilding foundations for a data-driven future.
Fabrizio Maniglio
In the rapidly evolving life sciences industry, pursuing excellence in quality management has never been more critical. As technological advancements and regulatory demands continue to reshape the sector, organisations face the challenge of not just maintaining quality standards, but continuously redefining and elevating them. This article explores the concept of ‘first principles thinking’ as a foundational approach to reconstructing quality systems in the life sciences sector, focusing on leveraging data analytics, artificial intelligence and cutting-edge technologies to drive innovation and excellence.
The life sciences industry, encompassing pharmaceuticals, biotechnology, medical devices and related fields, operates in an environment where the stakes are incredibly high. The quality of products and processes directly impacts human health and well-being, making it imperative that quality management systems are not just robust, but also adaptive and forward-thinking.
By returning to first principles and rebuilding our approach to quality from the ground up, we can create systems that comply with current regulations and are flexible enough to meet future challenges and opportunities.
This article will delve into the core concepts of first principles thinking, examine the current state of quality management in life sciences and explore how data-driven approaches and artificial intelligence are revolutionising the field. We will discuss practical strategies for implementing these new approaches, address compliance concerns and look at real-world case studies of successful applications. By the end, readers will have a comprehensive understanding of how to leverage principles of thinking and cutting-edge technologies to build quality management systems that are fit for the future.
UNDERSTANDING FIRST PRINCIPLES THINKING
First principles thinking is a problem-solving approach that involves breaking down complex problems into their most basic elements and then reassembling them from the ground up. This method, often associated with innovators like Elon Musk, encourages thinkers to challenge assumptions and think creatively about solutions.
In the context of quality management, first principles thinking involves stripping away layers of established processes and regulations to understand a quality system’s fundamental goals and requirements. By doing so, we can identify inefficiencies, outdated practices and areas where innovation can have the most significant impact (see Figure 1).
By adopting a first-principles approach, organisations can move beyond incremental improvements and achieve transformative change in their quality management systems.
By returning to first principles and rebuilding our approach to quality from the ground up, we can create systems that comply with current regulations and are flexible enough to meet future challenges and opportunities.
THE CURRENT STATE OF QUALITY MANAGEMENT IN LIFE SCIENCES
The life sciences industry has long been at the forefront of quality management, driven by stringent regulatory requirements and the critical nature of its products. However, the current state of quality management in many organisations is characterised by:
1. Complex, paper-based systems: many quality processes still rely heavily on manual documentation and paper trails.
2. Siloed data: information is often scattered across different departments and systems, making gaining a holistic view of quality difficult.
3. Reactive approaches: quality issues are often addressed after they occur rather than being proactively prevented.
4. Compliance-focused mindset: many organisations prioritise meeting regulatory requirements over driving continuous improvement.
5. Resistance to change: established processes and systems can be difficult to modify, even when they are no longer optimal.
While these approaches have served the industry well in the past, they are increasingly inadequate in the face of new challenges such as:
1. Rapidly evolving regulatory landscapes.
2. Increasing product complexity.
3. Globalised supply chains.
4. Rising customer expectations.
5. Pressure to reduce costs and improve efficiency.
To address these challenges, a fundamental rethinking of quality management is necessary, and this is where first principles thinking comes into play.
FIGURE 1. KEY ASPECTS OF FIRST PRINCIPLES THINKING IN QUALITY MANAGEMENT
REBUILDING QUALITY SYSTEMS: A FIRST PRINCIPLES APPROACH
Applying first principles thinking to quality management involves breaking down the core objectives of a quality system and rebuilding it with modern tools and approaches. The fundamental goals of a quality management system in life sciences remain constant (see Figure 2). However, the methods for achieving these goals can be radically reimagined. By starting from these core objectives, we can design systems that are:
• Data-driven: leveraging real-time data to inform decision-making and identify trends
• Proactive: using predictive analytics to anticipate and prevent quality issues before they occur
• Integrated: breaking down silos to create a holistic view of quality across the organisation
• Flexible: able to adapt quickly to changing regulatory requirements and market conditions
• Efficient: streamlining processes to reduce waste and improve productivity. This approach allows organisations to move beyond traditional quality control and assurance models towards a more holistic, integrated quality management system that is better suited to the challenges of the modern life sciences industry.
THE ROLE OF DATA ANALYTICS IN MODERN QUALITY MANAGEMENT
Data analytics plays a crucial role in the reimagined quality management system. By harnessing the power of big data, organisations can:
1. Gain real-time insights: continuous monitoring of processes and products allows for immediate identification of deviations or trends.
2. Enhance decision-making: data-driven decisions are more objective and can lead to better outcomes.
3. Improve traceability: advanced analytics can track products and materials throughout the supply chain, enhancing accountability and facilitating recalls if necessary.
4. Optimise processes: by analysing large datasets, organisations can identify inefficiencies and opportunities for improvement.
5. Predict and prevent issues: predictive analytics can forecast potential quality issues before they occur, allowing for proactive interventions.
‘By leveraging data analytics, organisations can move from reactive to proactive quality management, ultimately leading to improved outcomes and reduced costs.’
Implementing data analytics in quality management requires:
1. Robust data collection systems: ensuring that relevant data is captured accurately and consistently across all processes.
2. Advanced analytics tools: utilising machine learning and statistical analysis to derive meaningful insights from large datasets.
3. Data visualisation: presenting complex data in easily understandable formats to facilitate decision-making.
4. Integration of disparate data sources: combining data from various systems to create a comprehensive view of quality.
5. Data quality: ensuring the accuracy, completeness, timeliness and consistency of data throughout a product or process’s lifecycle.
By leveraging data analytics, organisations can move from reactive to proactive quality management, ultimately leading to improved outcomes and reduced costs.
THE IMPACT OF AI ON RETHINKING PROCESSES AND SYSTEMS
AI is revolutionising quality management in the life sciences industry by enabling organisations to rethink and optimise their processes and systems in unprecedented ways. AI’s impact on quality management can be examined through three key lenses: enhancing efficiency and effectiveness, tackling previously unsolvable problems and fundamentally rethinking traditional approaches.
FIGURE 2. FUNDAMENTAL GOALS OF A QUALITY MANAGEMENT SYSTEM
ENHANCING EFFICIENCY AND EFFECTIVENESS OF SYSTEMS AND PROCESSES
AI is dramatically improving the efficiency and effectiveness of quality management systems:
Automated Quality Control: AI-powered computer vision systems can perform visual inspections of products with greater speed and accuracy than human inspectors. These systems can detect defects, inconsistencies, or contamination that the human eye might miss.
Predictive Maintenance: machine learning algorithms can analyse data from equipment sensors to predict when maintenance is needed, reducing downtime and preventing quality issues caused by equipment failure.
Process Optimisation: AI can analyse vast amounts of historical process data to identify optimal operating conditions, leading to improved product quality and consistency.
Natural Language Processing (NLP) for Documentation: AI-powered NLP can automate the review and analysis of quality-related documents, ensuring compliance and identifying potential issues more efficiently than manual review.
Intelligent Anomaly Detection: Advanced AI algorithms can detect subtle patterns and anomalies in manufacturing processes that might indicate potential quality issues, allowing for early intervention.
TACKLING NEW PROBLEMS
AI is enabling the life sciences industry to address challenges that were previously considered too complex or resource-intensive:
Personalised Medicine: in pharmaceuticals, AI enables the development of personalised treatments by analysing patient data and predicting optimal drug formulations for individual genetic profiles.
Complex Supply Chain Management: AI can enhance supply chain management by predicting demand, optimising inventory levels and identifying potential disruptions that could impact product quality across global, multi-tiered supply networks.
Real-time Quality Monitoring: AI systems can continuously monitor and analyse data from multiple sources in real-time, providing instant alerts and insights that would be impossible for human operators to achieve.
Adaptive Clinical Trials: AI can help design and manage adaptive clinical trials, adjusting parameters in real-time based on incoming data to optimise efficacy and safety outcomes.
Predictive Quality Assurance: by analysing historical data and identifying complex patterns, AI can predict potential quality issues before they occur, allowing for proactive interventions that were not possible with traditional methods.
RETHINKING TRADITIONAL APPROACHES
AI is not just improving existing processes but is also enabling a fundamental rethinking of how quality management is approached: Just-In-Time Instructions: instead of relying on static SOPs, AI systems can provide context-aware, just-in-time instructions to operators. These dynamic guides adapt to the specific situation, equipment status and operator experience level, ensuring optimal procedures are followed.
Question-Based Systems: rather than searching through documents for answers, AI-powered systems can allow users to ask natural language questions and receive precise, contextual answers. This approach can significantly reduce time spent on information retrieval and improve decision making.
Continuous Learning Systems: traditional quality systems are often static, but AI enables developing systems that continuously learn and adapt. These systems can automatically update based on new data, regulatory changes and emerging best practices.
Predictive Compliance: instead of reactive compliance checks, AI can predict potential compliance issues based on current processes and upcoming regulatory changes, allowing organisations to adjust their practices proactively.
Holistic Quality View: AI can integrate data from various sources (manufacturing, supply chain, post-market surveillance) to provide a comprehensive view of product quality throughout its lifecycle, breaking down traditional silos in quality management.
Risk-Based Approach: AI can enable a more sophisticated, data-driven approach to risk management, continuously assessing and prioritising quality risks across the organisation.
Integrating AI into quality management systems allows organisations to move beyond traditional, rule-based approaches to more dynamic, adaptive systems that can handle complexity and uncertainty. This shift enables:
• More efficient resource allocation by focusing human expertise on high-value tasks while automating routine quality checks
• Faster response times to quality issues, potentially preventing costly recalls or production delays
• Improved decision-making through data-driven insights and predictive analytics
• Enhanced ability to manage the increasing complexity of modern life sciences products and processes.
However, the implementation of AI in quality management also presents challenges:
• Ensuring the reliability and transparency of AI decision-making processes
• Managing the ethical implications of AI-driven quality systems
• Addressing potential regulatory concerns about the use of AI in quality-critical processes
• Developing the necessary skills and expertise within the organisation to effectively implement and manage AI systems.
As AI continues to evolve, its role in quality management will likely expand, offering new opportunities for innovation and excellence in the life sciences industry. Organisations that successfully integrate AI into their quality management systems will be well-positioned to lead in an increasingly complex and competitive landscape.
PRACTICAL STRATEGIES FOR IMPLEMENTING DATA-DRIVEN QUALITY SYSTEMS
Implementing a data-driven quality system based on first principles requires a strategic approach. Here are some practical strategies: Assess Current Systems: conduct a thorough evaluation of existing quality processes to identify areas for improvement and digitisation.
Define Clear Objectives: establish specific, measurable goals for the new quality management system.
Invest in Infrastructure: implement robust data collection and analysis tools, including IoT sensors, cloud computing and analytics platforms.
Develop Data Governance Policies: establish clear guidelines for data collection, storage and usage to ensure data integrity and compliance.
Train Staff: provide comprehensive training to employees on new systems and data-driven decision-making processes.
Start with Pilot Projects: begin with small-scale implementations to test and refine approaches before full-scale rollout.
Foster Cross-Functional Collaboration: encourage cooperation between quality, IT and other departments to ensure successful implementation.
Continuously Evaluate and Improve: regularly assess the new system’s performance and make iterative improvements.
ENSURING COMPLIANCE IN A DATA-DRIVEN ENVIRONMENT
While data-driven quality systems offer numerous benefits, they must also meet stringent regulatory requirements. Strategies for ensuring compliance include:
Building Compliance into System Design: consider regulatory requirements from the outset when designing new quality processes.
Implementing Robust Data Integrity Measures: ensure that data is accurate, complete and tamper-proof through technologies like blockchain.
Maintaining Audit Trails: implement systems that automatically track all changes and actions for full traceability.
Building Trust in AI and Machine Learning Models: develop rigorous risk-based, use case-dependent validation or verification processes for AI-driven quality systems to ensure reliability and regulatory acceptance.
Engaging with Regulatory Bodies: proactively communicate with regulators about new approaches to quality management to ensure alignment.
Partnering with a Trusted Life Sciences Partner: leverage the expertise and experience of an established digital solutions provider for quality management. A trusted partner can help you design, implement and optimise your AI-driven quality systems and provide ongoing support and training. A trusted partner can also help you navigate the regulatory landscape and ensure compliance with relevant standards and guidelines. Partnering with a trusted life sciences partner can accelerate your digital transformation and enable you to achieve higher quality, efficiency and innovation levels.
‘A trusted partner can also help you navigate the regulatory landscape and ensure compliance with relevant standards and guidelines.’
FOSTERING A CULTURE OF CONTINUOUS IMPROVEMENT
Implementing a first-principles approach to quality management requires more than just technological changes, it demands a shift in organisational culture. Key aspects of fostering a culture of continuous improvement include:
Leadership Commitment: senior management must visibly support and champion the new approach.
Empowering Employees: encourage staff at all levels to identify and suggest improvements.
Celebrating Successes: recognise and reward innovative ideas and successful implementations.
Promoting Transparency: share quality metrics and improvement initiatives across the organisation.
Encouraging Experimentation: create safe spaces for testing new ideas without fear of failure.
THE FUTURE OF QUALITY MANAGEMENT: TRENDS AND PREDICTIONS
As we look to the future of quality management in the life sciences industry, we can anticipate transformative changes driven by technological advancements and evolving regulatory landscapes. Let’s explore the potential developments in the short-term, medium-term and long-term.
SHORT-TERM PREDICTIONS (1-3 YEARS)
Increased Automation: more quality processes will be automated, with AI playing a central role in routine decision making and data analysis.
Real-time Quality Monitoring: IoT sensors and advanced analytics will enable continuous, real-time monitoring of product quality throughout the supply chain.
Data Integration: organisations will focus on integrating disparate data sources to create a more holistic view of quality across the product lifecycle.
Regulatory Adaptation: regulators will begin to provide more guidance on the use of AI and machine learning in quality management systems.
Skill Development: companies will invest heavily in upskilling their workforce to handle new technologies and data-driven approaches to quality management.
‘Blockchain technology will be increasingly used to ensure data integrity and traceability across the entire supply chain.’
MEDIUM-TERM PREDICTIONS
Predictive Quality Assurance: AI-driven predictive models will become sophisticated enough to anticipate and prevent quality issues before they occur, significantly reducing quality-related incidents.
Personalised Quality Standards: as personalised medicine advances, quality systems will adapt to ensure consistency in individualised products, with AI managing the complexity of varying specifications.
Blockchain Integration: blockchain technology will be increasingly used to ensure data integrity and traceability across the entire supply chain.
Elevating Quality Personnel: quality professionals will be increasingly freed up from routine tasks, allowing them to focus on high-value activities, such as strategic planning, complex problem-solving and innovation in quality processes.
Adaptive Regulatory Compliance: AI systems will enable real-time adaptation to changing regulatory requirements, automatically updating processes and documentation as needed.
Virtual and Augmented Reality in Quality Control: VR and AR technologies will be integrated into quality control processes, enabling more efficient and accurate inspections and training.
LONG-TERM PREDICTIONS
Autonomous Quality Systems: fully autonomous quality management systems will emerge, capable of making complex decisions and adjustments with minimal human intervention.
Quality-Embedded Products: products will have built-in quality monitoring capabilities, continuously assessing their own performance and safety throughout their lifecycle.
Quantum Computing in Quality: quantum computing will enable unprecedented data processing and simulation levels, revolutionising areas like drug discovery and quality risk assessment.
Global Quality Ecosystem: a globally interconnected quality ecosystem will emerge, allowing for real-time industry sharing of quality data and best practices.
Artificial General Intelligence (AGI) in Quality: as AGI develops, it could lead to quality systems that can reason about quality in ways that surpass human capabilities, potentially redefining our understanding of what constitutes quality.
Seamless Quality Systems: quality systems will become virtually invisible to end-users, supporting them at every point with the information needed, when needed and in the format required. Documentation and traceability will happen passively in the background without user input.
Biologically Inspired Quality Systems: quality management may incorporate principles from biological systems, creating self-healing and self-optimising processes that can autonomously adapt to changing conditions.
These predictions highlight a future where quality management becomes increasingly proactive, personalised and integrated into every aspect of the life sciences industry. The role of quality professionals will evolve from enforcing compliance to driving innovation and strategic value.
However, this future also presents challenges:
• Ethical considerations in AI decision-making
• Ensuring human oversight and intervention capabilities in highly automated systems
• Managing the security and privacy implications of interconnected quality ecosystems
• Adapting regulatory frameworks to keep pace with rapid technological advancements
• Addressing potential workforce disruptions as roles and required skills change dramatically.
Organisations that successfully navigate these challenges and embrace the transformative potential of new technologies will be well-positioned to lead in an increasingly complex and competitive landscape. The future of quality management in life sciences promises not just incremental improvements, but also a fundamental reimagining of how we ensure and enhance the quality of products that impact human health and well-being.
CHALLENGES AND CONSIDERATIONS
While the potential benefits of a first principles approach to quality management are significant, there are also challenges to consider:
Initial Costs: implementing new systems and technologies can require substantial upfront investment.
Resistance to Change: overcoming organisational inertia and resistance to new approaches can be difficult.
Data Security and Privacy: as more data is collected and analysed, ensuring its security and privacy becomes increasingly important.
Skill Gaps: organisations may struggle to find talent with the necessary skills to implement and manage advanced quality systems.
Regulatory Uncertainty: the regulatory landscape for AI and other advanced technologies in quality management is still evolving.
CONCLUSION
The application of first-principles thinking to quality management in the life sciences industry represents a significant opportunity to drive innovation, improve efficiency and enhance product quality. By leveraging data analytics, AI and other advanced technologies, organisations can build quality systems that are not only compliant with current regulations but also adaptable to future challenges.
The journey towards a truly data-driven quality management system is complex and challenging, but the potential rewards are substantial. Organisations that successfully navigate this transition will be well-positioned to lead in an increasingly competitive and technologically advanced industry landscape.
As we move forward, it is clear that the future of quality management lies not in incremental improvements to existing systems, but in fundamentally rethinking our approach based on first principles. By embracing this mindset and leveraging the power of data and AI, the life sciences industry can usher in a new era of quality management – one that is proactive, efficient and capable of meeting the evolving needs of patients and healthcare providers worldwide.
‘As we move forward, it is clear that the future of quality management lies not in incremental improvements to existing systems, but in fundamentally rethinking our approach based on first principles.’
PROFILE
Fabrizio is an industry thought leader and the Director of Industry and Business Development at Honeywell. He leverages vast subject matter expertise to drive innovation for the industry and within Honeywell, where he continuously monitors the evolution of the ever-changing healthcare and life-sciences sectors. Fabrizio fosters contacts with other industry thought leaders and regulators to collaborate and influence the future of our industry. He contributed five years as an Expert Solutions Engineer, deepening his understanding of quality life cycle management and the life science industries and was a key differentiator in helping customers achieve their quality management goals.
Before Honeywell, Fabrizio served nine years at a leading European-based Contract Manufacturing Organisation (CMO) in the pharma, biotech and specialty ingredients industry. He served in numerous global quality-related roles, spanning Deviations Management, Head of Audit QA and Compliance, and most recently, as QA Manager for Data Integrity and Computer Systems Validation in Switzerland, the UK and China.
As a result, Fabrizio provides a blend of deep industry, QMS and cutting-edge technical knowledge and is uniquely placed to advise customers and the industry on the future of quality.
THE DATA DRIVEN JOURNEY
Penelope Hutton
One would have to be living in a vacuum to be unaware of the industry shift towards data-driven approaches to Quality Assurance (QA). I align with this shift, because I feel I have been fortunate to be a part of this from the start of my career. As a Data Manager (DM) I was involved in the evolution of data-led approaches, piloting remote data entry at site which has evolved into the Electronic Data Capture (EDC) we see at sites today and the use of data-driven reviews by operations and clinical science via visualisation tools. The data analytic paradigm is probably one of the most significant shifts in approaches to data quality in clinical trials since the move away from Case Report Form (CRF) double data entry.
As my career progressed and I moved into the QA world, the skills gained as a DM were invaluable. Requesting data listings when possible was an easy way to get an overview of the data from different perspectives via the power of the pivot table, to me it seemed quite a natural approach. One of my projects was to review the approach to Investigator Site Audits (ISA), incorporating a data review by including a request for data listings and using them alongside traditional auditing activities. I was fortunate to work with like-minded individuals, who were taking the data driven approach to the next level and I had the opportunity to develop, in a limited way, my programming skills and increase my knowledge and skill level with excel.
During my time at Roche, I was privileged to be one of the first students to complete the Data Analytics university courses. This course was another step in further developing my knowledge and interest in the data analytics space. I was fortunate to work with some incredibly knowledgeable people, who helped me to also develop the knowledge to create data solutions and apply them to daily activities as a QA professional. This knowledge has helped me act as a quality enabler and lead the drive in innovation to complement and enhance traditional quality methods and how they are delivered within organisations.
With the industry shift to data-driven approaches continuing to gain momentum, sponsors and Clinical Research Organisation’s (CRO’s) are driving the paradox change forward. For example, the Inter coMPany quALity Analytics (IMPALA) Consortium, established in July 2019, is a group of biopharmaceutical organisations with the common goal to share knowledge and better understand opportunities in applying advanced analytics for QA.
The IMPALA consortium is currently limited to sponsor organisations and not all sponsors have the capacity to take advantage of what is on offer. In addition, many of the industry-leading CROs have developed their own approach to data-driven solutions and utilise in-house tools to do this.
In my new organisation, ADAMAS, I work with individuals who believe in and implement the data-driven approach to QA activities. We work with clients that, due to many reasons; size, experience, resources, to name but a few, don’t have the option to pursue and leverage the resources that are out there. I was more than delighted to find that when moving to ADAMAS, I didn’t have to give up on my curiosity with data for quality because there were other individuals who shared my passion and were already using data-driven approaches to enhance their services. For example, Routine Data Assessments (RDA), developed by ADAMAS and implementing the principles outlined in ICH E6 (R2), include the review of cumulative data for critical processes against Key Risk Indicators (KRIs) to identify data anomalies and data integrity issues. These insights can be found in the blogs https://www.adamasconsulting. com/adamas-news/kris-and-qtls-simplified-2/ and https://www.adamasconsulting.com/ adamas-news/data-driven-quality-assurance/ This data analytics approach to quality assurance complements the principles outlined in ICH E6(R2) and allows the quality professional to review and assess data fields across sites, subjects and even at the program level to determine the quality of trial results, supporting the study endpoints and highlighting potential subject safety issues or trends in the form of abnormalities. Consequently, patient safety issues, data corruption and rising risk recovery costs can be prevented.
The ICH E6(R3) revision, once issued, will be a significant update to the ICH E6(R2) guideline. The extensive rewrite of the ICH E6 guidance, introduces, expands and clarifies a number of areas. Focusing in terms of the quality activities, the revision expands on Quality Management System (QMS) concepts, encourages the risk-based approach and highlights the importance of data quality and integrity and the need for systems to ensure this. This revision is an ideal opportunity to incorporate data-driven approaches by quality professionals, complementing the traditional audit approach. The classical ISA, where √(n+1) is used to select the number of patient/subjects as the audit sample to be reviewed only permits a narrow review of a limited amount of data for a small number of patients at site. By following a data-driven approach when undertaking QA activities, the data reviewed by the quality professional can be focused on the critical data supporting the study endpoints and
to ensure compliance with the protocol, applicable regulations, guidelines, policies and procedures supporting clinical trial activities ensuring patient safety and data integrity remain at the forefront of QA activities.
This is just one example of the simplest way data-driven approaches can be incorporated into quality activities. The approach can be extrapolated as already explored in the RDA and services that ADAMAS deliver to ensure our clients compliance activities remain at the forefront of industry best practice.
With Joanne North, an industry expert in Clinical Quality Analytics and Clinical Quality Assurance and former member of the IMPALA consortium, joining the team at ADAMAS, we are continuing to develop our data-driven quality expertise and expand on our data-driven quality solutions by harnessing the latest technology.
The revision to ICH E3 has shown that regulators are looking at clinical trials differently, this is the ideal time for QA professionals to do the same. Regulatory agencies themselves are increasingly leveraging data analytics in their activities, developing tools that integrate data from multiple sources, allowing them to quickly assess and analyse clinical sites, identify trends, detect anomalies and make data-driven decisions. Innovative and independent review of data by QA professionals is therefore critical in the fast-moving world of clinical trials to support sponsors in getting treatment to patients. Data Analytics provides an elegant solution to this review and utilisation of data in drug development, and with the future continued development and application of data driven methodologies, the possibilities are limitless.
PROFILE
Penelope is an experienced QA Professional within Pharmaceutical Industry, having worked in both sponsor and consultancy organisations. Specialist in providing compliance advice, strategy insights and internal and external audits as a GxP auditor, she is passionate about working with clients to ensure that their quality activities support Sponsors in getting their products to market and benefitting patients.
AUDITING A PHARMACOVIGILANCE DATABASE – SOME CONSIDERATIONS
Milind Nadgouda
Ashok Kumar
Loveleen Kukreja
Auditing of safety databases can be a complex proposition for many of us. Assessing safety requirements is one major part of it; the added complexity of understanding the database build, the technology involved and the controls around it are the other major part of the audit.
It requires a balance between understanding safety requirements and the technical infrastructure that supports them. Safety databases, especially in the pharmaceutical and healthcare industries, manage large volumes of sensitive data, including adverse event reports, product complaints and regulatory compliance information. As regulations evolve, the importance of effectively auditing these databases has grown, making the role of auditors vital in ensuring compliance, data integrity and system security.
During auditing of safety databases, auditing the technology, understanding the workflow and the process controls are key. Elements of the database build and how the documentation for the database is organised is a factor that cannot be ignored. In addition, the aspects of access control, network security and how the application sits within the framework are important factors to evaluate.
KEY CHALLENGES IN AUDITING SAFETY DATABASES
Auditing safety databases can present several challenges (see Figure 1), including:
1. Technical Complexity: understanding the underlying technology, including how the database is built and how it interacts with other systems, requires specialised expertise.
2. Large Data Volumes: safety databases contain vast amounts of data, making it difficult to thoroughly review and verify all records within a limited timeframe.
3. Evolving Regulations: constantly changing pharmacovigilance and data privacy regulations, such as the European Medicines Agency (EMA) Good Pharmacovigilance Practices (GPvP) and GDPR, add layers of complexity.
4. Data Privacy Concerns: the sensitive nature of safety data requires strict adherence to privacy regulations, ensuring that Personally Identifiable Information (PII) is protected.
5. Limited Access: auditors often have restricted access to database functionalities, making it challenging to perform a comprehensive audit without collaboration from the IT department.
6. Implementation of New Technologies: there are recent trends that indicate a global rise and shift of industry towards new technological advancements. This further adds to the complexity for understanding requirements specific to these advanced technologies.
7. Need for Specialised Expertise: a safety database audit requires a mix of pharmacovigilance, regulatory compliance and IT security knowledge.
8. The Configuration of Safety Database Platforms adds an Additional Layer of Complexity: a safety database is highly configurable to meet specific regulatory and operational needs, but this flexibility can lead to inconsistencies or misconfigurations if not properly managed. Auditors need to ensure that the system is correctly setup to handle case processing, reporting workflows and regulatory requirements across different regions. Evaluating system validation, user access controls and data integrity within the safety database environment is critical but can be challenging due to its technical depth and customisation options.
‘As regulations evolve, the importance of effectively auditing these databases has grown, making the role of auditors vital in ensuring compliance, data integrity and system security.’
FIGURE 1. KEY CHALLENGES IN AUDITING SAFETY DATABASES
Let us start with what a global safety database is.
A global safety database, especially in the context of pharmaceuticals and medical devices, is an essential system used to collect, manage and analyse safety-related information. Its primary function is to support the pharmacovigilance process, ensuring that any potential safety risks or adverse events related to a product are identified, assessed and addressed in compliance with regulatory standards.
In a pharmaceutical setting, such a database centralises the storage of safety data from various sources, including clinical trials, post-market surveillance and spontaneous adverse event reports. It enables companies to track Adverse Drug Reactions (ADRs) and safety issues across different regions, supporting a unified global approach to managing drug safety.
Key components of a global safety database include (see Figure 2):
1. Centralised Data Repository: all safety-related data, from clinical trials to post-marketing reports, is consolidated in one system, providing a single source of truth for safety information.
2. Adverse Event Management: the database facilitates the processing, evaluation and classification of adverse event reports. This includes tasks such as coding medical terms using standardised vocabularies like MedDRA (Medical Dictionary for Regulatory Activities) and conducting signal detection to identify trends or unexpected risks.
3. Risk Assessment and Management: plays a critical role in monitoring and mitigating risks by continuously analysing data to detect any safety signals that may indicate emerging concerns with a product.
4. Regulatory Compliance: the system is designed to ensure that companies adhere to safety reporting requirements imposed by regulatory agencies such as the Food and Drug Administration (FDA), European Medicines Agency (EMA), and International Council for Harmonisation (ICH). It automates and streamlines the submission of Individual Case Safety Reports (ICSRs) and Periodic Safety Update Reports (PSURs/PADERs).
5. Global Accessibility and Collaboration: a global safety database is accessible by various stakeholders worldwide, allowing pharmaceutical companies, regulatory bodies and healthcare providers to access safety data in real time. This facilitates collaborative efforts in monitoring and improving product safety.
6. Advanced Reporting and Analytics: the system often includes tools for performing complex data analysis, visualisation and reporting, which help in detecting trends, assessing safety signals and making informed decisions regarding product safety.
‘Of the respondents who were using or exploring AI or ML technologies, 43% were doing this for the collection and collation of adverse drug reactions, including medical information and product quality.’
TECHNOLOGIES AND TRENDS IN SAFETY DATABASES
During the 2024 GPvP symposium, delegates were live polled on the use of AI within their organisations, with fascinating results. It was noted that 27% of respondents’ organisations were currently using AI or machine learning (ML) technologies for the conduct of pharmacovigilance tasks. 44% of respondents’ organisations were currently exploring or developing AI or machine learning technologies for the conduct of pharmacovigilance tasks. Of the respondents who were using or exploring AI or ML technologies, 43% were doing this for the collection and collation of adverse drug reactions, including medical information and product quality. This data represents the increasing technological advancements and trends that are used in safety databases. Organisations are focussed on some of the following key advancements:
1. Cloud-based Systems: many organisations are adopting cloud-based safety databases for scalability, flexibility and remote access.
2. Artificial Intelligence (AI) and Machine Learning (ML): these technologies are being used to enhance signal detection, automate routine processes and predict adverse events more accurately.
3. Integration with Electronic Health Records (EHRs): safety databases are increasingly integrated with EHR systems to streamline the reporting of adverse events directly from healthcare providers.
4. Blockchain for Data Integrity: blockchain technology is being explored to enhance the security and integrity of safety data, ensuring that it cannot be tampered with or altered.
FIGURE 2. KEY COMPONENTS OF A GLOBAL SAFETY DATABASE
Let us look at the data flow within the safety database before we start into how to audit the data. Pharmacovigilance data flow in the global safety database typically follows the following steps:
The general workflow of a pharmacovigilance database is described below (see Figure 3):
1. Data Collection: gathering information on the substance and its use is the first stage. This includes details regarding the medication’s active ingredients, dosage, delivery method, indications and contraindications. Clinical trials, post-marketing research, impromptu accounts from patients and healthcare professionals and published literature are just a few examples of the many sources from which data can be gathered.
2. Data Entry: the obtained data must be placed into the pharmacovigilance database. The data is normally verified for accuracy and completeness by experienced professionals.
3. Case Processing: the data is processed into individual safety cases, which include information such as patient demographics, the product(s) involved, adverse event(s) and any concomitant medications.
4. Medical Coding: the safety case information is coded using standardised medical terminology, such as the Medical Dictionary for Regulatory Activities (MedDRA) coding system.
5. Signal Detection: after the data has been stored in the database, signal detection methods are utilised to find potential safety concerns or ‘signals’. These algorithms can identify unique patterns of adverse events that might point to a fresh or unidentified drug-related issue.
6. Signal Evaluation: the signals discovered in the preceding step must be assessed to ascertain their clinical importance and whether additional research is required. Reviewing the information at hand and speaking with medical professionals are part of this process.
7. Risk Assessment: if the signal is thought to be clinically important, a risk evaluation is carried out to ascertain the degree of risk connected with the medication. The severity of the adverse event, the number of patients affected and other pertinent factors are all taken into account in this evaluation.
8. Risk Management: appropriate risk management methods are designed based on the risk assessment to reduce the risk to patients. This can entail labelling modifications, usage limitations or even the drug's removal from the market.
9. Data Analysis and Reporting: the safety case data is analysed and reported using various tools and techniques, such as data visualisation and trend analysis, to support decision-making related to product safety.
10. Reporting: finally, reports are generated to inform healthcare professionals, regulatory authorities and the public of any safety concerns identified, with the actions taken to manage the risk. These reports might be distributed directly to patients and healthcare professionals, published in medical journals or posted on the FDA’s website.
FIGURE 3. DATA FLOW WITHIN THE SAFETY DATABASE
Findings were related to the following areas:
• Inadequate documentation or data entry errors
• Insufficient training of staff involved in pharmcovigilance activities
• Inadequate quality control of the database systems
• Inadequate signal detection and management processes.
SIGNIFICANCE OF AUDITING SAFETY DATABASE
Auditing safety databases has become a critical component of pharmacovigilance systems, ensuring that pharmaceutical companies maintain the highest standards of drug safety monitoring. Safety databases serve as the central repository for collecting, storing and managing adverse event reports, signal detection, and risk management activities. Regulatory bodies like the EMA and the FDA place significant emphasis on the integrity and functionality of these databases during inspections, as they are essential for timely identification of safety signals and reporting of Adverse Drug Reactions (ADRs).
Through inspections, regulators frequently identify deficiencies in how companies manage their safety databases, underscoring the need for regular internal and external audits. These audits help ensure that data entered into the system is accurate, complete and accessible for regulatory reporting, aligning the companies with global safety standards. By linking inspection findings to the broader framework of database audits, pharmaceutical organisations can mitigate compliance risks and protect patient safety while adhering to regulatory expectations (see Figure 4).
Findings were related to the following areas:
• Data quality issues, such as incomplete or inaccurate data
• Inadequate documentation or data entry errors
• Insufficient training of staff involved in pharmacovigilance activities
• Inadequate quality control of the database systems
• Inadequate signal detection and management processes.
AUDITING METHODOLOGY
A well-structured approach is essential when auditing safety databases. The following key areas should be part of the audit:
1. Database Build and Functionality
• Validation: ensure the safety database is validated to comply with industry regulations (e.g. FDA’s 21 CFR Part 11, ICH E2B (R3), GAMP5) This involves confirming that the system performs its intended functions consistently and accurately.
• Change Control: review change management procedures to verify that any modifications to the database (software updates, patches) are appropriately documented, tested and approved.
• Documentation: assess how well the database’s documentation is organised. This includes system design specifications, validation protocols and user manuals.
‘Regularly review access logs and ensure there is a process for revoking access when staff leave or change roles.’
2. Access Control and Security
• User Access: verify that user access levels are properly defined, with clear distinctions between data entry, review and administrative rights. Regularly review access logs and ensure there is a process for revoking access when staff leave or change roles.
• Network Security: evaluate network security protocols to ensure the safety database is protected from unauthorised access. This includes assessing firewalls, encryption and multi-factor authentication systems.
• Audit Trails: an audit trail logs every interaction with the database, including data entries, modifications, deletions and user access. Auditors ensure that the audit trail is functioning properly, capturing essential information such as timestamps, user IDs and reasons for changes. They confirm the system’s ability to track user actions, with audit trails capturing who accessed or modified data and when. They also verify that the audit trail is reviewable and that the organisation conducts regular audits of the logs to detect and investigate suspicious activity. This is critical for compliance with regulatory requirements.
‘Any deviation from these principles could compromise data integrity and risk regulatory violations.’
3. Data Integrity
• ALCOA+ Principles: ensure data adheres to the ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, complete, consistent, enduring and available). Any deviation from these principles could compromise data integrity and risk regulatory violations. Review the accuracy, completeness and consistency of the data entered into the safety database. The database is audited for duplicate entries, incorrect classifications or missing information related to adverse events, drug interactions and device malfunctions. A focus is placed on data integrity to ensure that input errors are minimised.
• Data Migration: if the database has been upgraded or migrated, review the migration process to ensure that no data was lost or corrupted in the transfer.
4. Compliance with Regulations
• Regulatory Standards: confirm that the safety database meets the requirements of relevant regulatory bodies such as EMA (GVP Module VI), FDA (21 CFR Part 11) and other applicable global standards. Auditors evaluate the safety database’s ability to generate timely and accurate regulatory reports. This includes reviewing workflows for generating and submitting ICSRs, PSURs and other regulatory documents. Auditors ensure that case narratives, source documentation and any supplementary material are accurate and properly linked within the system.
• Data Privacy: ensure compliance with data privacy regulations like GDPR and HIPAA, particularly in relation to how adverse event reports and patient data are stored and shared.
5. Testing and Validation of Controls
• Periodic Testing: conduct regular testing of controls, including security measures and data backup processes. This should include penetration testing to assess system vulnerabilities.
• Disaster Recovery: review the disaster recovery plan to ensure that the database can be restored in the event of data loss or system failure.
6. Signal Detection and Risk Management Audit:
• Procedural Adequacy: assess the processes for signal detection, ensuring that the company is identifying and evaluating adverse events and safety signals in a timely manner. The audit examines how the database supports statistical signal detection, disproportionality analysis, and trend analysis. The Risk Management Plan (RMP) is reviewed to ensure that identified safety signals are appropriately escalated and addressed.
7. Training and SOPs:
• Adequate Training: evaluate whether employees managing the safety database have received adequate training on its use, as well as on adverse event reporting requirements.
• Adequate Processes: Standard Operating Procedures (SOPs) are examined to ensure they align with best practices and regulatory requirements, covering tasks such as data entry, signal detection and regulatory reporting.
Auditing new technology trends in safety databases involves evaluating how the latest advancements enhance the system’s functionality, compliance, security and data integrity. New technologies such as cloud computing, artificial intelligence (AI), machine learning (ML), blockchain and data integration systems introduce opportunities and challenges. Here’s how to audit these technologies:
1. Cloud-Based Safety Databases:
• Data Security and Privacy: assess whether the cloud provider adheres to Good Pharmacovigilance Practices (GVP) and regulations such as GDPR, HIPAA and 21 CFR Part 11. Check for encryption (data at rest and in transit) and data backup mechanisms.
• Access Control: review role-based access control systems and ensure that only authorised personnel have access to sensitive data.
• Disaster Recovery and Business Continuity: audit the cloud provider’s disaster recovery protocols to ensure that the system can handle data loss or downtime.
‘The Risk Management Plan (RMP) is reviewed to ensure that identified safety signals are appropriately escalated and addressed.’
‘Ensure that models are properly validated and continuously monitored to avoid bias or incorrect predictions.’
2. Artificial Intelligence (AI) and Machine Learning (ML):
• Algorithm Validation: evaluate the robustness of AI/ML models used for signal detection and Adverse Event (AE) reporting. Ensure that models are properly validated and continuously monitored to avoid bias or incorrect predictions.
• Auditability: check if the system retains transparent logs for AI-driven decisions, ensuring that actions taken by algorithms can be audited and explained.
• Automation of Adverse Event Reporting: review how AI is integrated into case processing, ensuring data accuracy and timely reporting to regulatory authorities.
3. Blockchain Technology:
• Data Integrity and Immutability: assess how blockchain is used to maintain data integrity by ensuring that all records (such as adverse event reports) are tamper-proof. Blockchain can create an immutable ledger of safety events, which auditors can verify.
• Compliance with GxP: ensure blockchain systems comply with Good Documentation Practices (GDP) and other GxP requirements, particularly around record keeping and traceability.
4. Integration with Electronic Health Records (EHRs):
• Data Interoperability: evaluate how well the safety database integrates with EHR systems to streamline adverse event reporting. Verify that the data exchanged between the systems is accurate and complete.
• Compliance with Health Standards: check compliance with health information standards such as HL7 (the Health Level 7 standards organisation) and FHIR (Fast Healthcare Interoperability Resources) to ensure seamless data exchange without compromising safety data quality.
• Real-Time Adverse Event Monitoring: assess whether the integration with EHRs allows for real-time or near-real-time monitoring of adverse events and drug safety signals.
5. Automation and Robotic Process Automation (RPA):
• Process Efficiency: audit the automation of routine tasks such as data entry, case processing and report generation. Verify that automated processes follow SOPs and regulatory guidelines.
• Error Reduction: evaluate whether automation is reducing human error and improving the accuracy of data entry and reporting.
• Change Management: review the change management system for automated processes to ensure any updates are tested and documented.
6. Regulatory Reporting:
• Automated ICSR and PSUR Generation: check how the system generates and submits Individual Case Safety Reports (ICSRs) and Periodic Safety Update Reports (PSURs). Ensure that the reports are timely and meet regulatory formatting and content requirements.
• Data Retention: ensure that the technology complies with data retention requirements, maintaining audit trails for all safety reports.
PROFILES
Loveleen Kukreja
With 11+ years of QA and Quality Systems, Loveleen has extensive experience conducting GVP, GCP and GCLP audits across multiple countries. As a CRA, she gained a strong foundation in clinical trial monitoring, site management and regulatory compliance and has delivered conventional GCP training, inspection readiness training and hosted competent authority inspections.
Loveleen’s expertise lies not only in audit execution but also in strategic planning, risk management and maintenance of GxP compliance in an evolving regulatory environment. She has collaborated with senior leadership and diverse stakeholders to design, implement and refine quality systems that meet international regulatory standards and has successfully maintained a state of inspection readiness for internal and external audits, delivering results that align with organisational and regulatory expectations. Loveleen is currently working as Principal QA auditor at RiverArk.
7. Cybersecurity in New Technologies:
• Vulnerability Assessment: conduct regular penetration testing and vulnerability assessments to ensure that new technologies (e.g. cloud or AI) are secure against cyber threats.
• Data Breach Response: review the company’s breach response plan to ensure prompt action in case of a data breach involving the safety database.
‘A structured, risk-based approach, coupled with continuous learning and collaboration, will lead to more effective audits and ultimately better patient safety outcomes.’
CONCLUSION
Auditing safety databases is a complex but critical task. By focusing on the core elements – database validation, access controls, data integrity and compliance with evolving regulations – auditors can ensure that safety databases function as intended while safeguarding sensitive data. With increasing reliance on new technologies like cloud computing, AI and blockchain, audits ensure that these advancements contribute effectively to data accuracy, timely reporting and patient safety. By focusing on areas such as data quality, system validation, regulatory compliance and cybersecurity, regular audits help organisations identify and mitigate risks while preparing for regulatory inspections. A structured, risk-based approach, coupled with continuous learning and collaboration, will lead to more effective audits and ultimately better patient safety outcomes.
REFERENCE
Ashok Kumar
Ashok is an experienced QA professional with over 17 years of expertise in Pharmacovigilance, specialising in GxP audits including GVP, GCP and GMP audits spanning Asia and Europe. Transitioning from a CRA role, Ashok brings valuable insights into clinical operations, enriching their approach to QA. He has previously managed PV contracts across Asia, EU, Middle East, Japan and Brazil.
Ashok also has expertise in case processing, medical assessment, CAPA management and inspection readiness. He has been pivotal to client-facing QA, performing risk-based gap analyses and process reviews.
Ashok holds a Bachelor of Dental Surgery (B.D.S), a PG Diploma in Clinical Research, GCP Certification and is an active member of RQA. He is currently working as Principal GxP QA auditor at RiverArk.
Milind Nadgouda
Milind is a seasoned quality and regulatory professional with 26 years of industry experience. As Co-founder and Director at RiverArk, he has established a reputation for delivering strategic and practical solutions within the GxP space, tailored to meet the complex regulatory needs of global organisations. His core areas of specialisation are Risk Strategy, Regulatory Agency Inspection Readiness and QMS GAP analysis. With expertise in all GxPs, he has consulted and led quality initiatives across all GxP domains.
Milind has worked on other quality methodologies, such Six Sigma, Kaizen and Lean. He’s been both on the frontline and in the backroom supporting teams during GxP inspections from global authorities, such as USFDA, EMA, MHRA et.al. AstraZeneca, Bristol Myers Squibb, Ranbaxy and Eli Lilly are few of the companies Milind has worked at and consulted. He sees himself as a problem solver and a solution provider.
Pharmacovigilance unravelled: highlights of the 2024 MHRA GPvP Symposium Soe Hamill, 4 April 2024 - Good pharmacovigilance practice, Inside the Inspectorate.
THE POWER OF RISK-BASED QUALITY MANAGEMENT
Clinical research has advanced significantly in recent years, making it imperative for stakeholders, such as sponsors, Contract Research Organisations (CROs) and other entities involved to prioritise proactive risk management strategies.
Leire Zúñiga
In this context, Risk-Based Quality Management (RBQM) has emerged as a critical methodology to ensure study participants safety, maintain data integrity and reliability, and comply with regulatory requirements.
This methodology allows for the identification and early mitigation of potential problems during clinical trials, preventing them from becoming more serious. RBQM represents a major shift from traditional, reactive methods towards a more dynamic and proactive approach.
As the final draft of ICH GCP E6 (R3) strongly advocates for RBQM, the urgency for its widespread adoption has become increasingly evident. However, an international survey across the industry revealed that 45% of research organisations have yet to begin their transition to risk-based methodologies. This prompts a critical question:
‘What steps are still necessary to achieve higher-quality, more efficient and faster clinical trials?’
Achieving this will require the alignment of processes, people and technology to fully embrace a risk-based approach.
EVOLUTION OF QUALITY MANAGEMENT IN CLINICAL TRIALS
Traditionally, quality management in clinical trials has been a retrospective process, focused on detecting and correcting issues once they arise. While this approach has been effective in certain aspects, it has limitations in addressing emerging and dynamic risks that develop during a trial. The shift toward RBQM emphasises the importance of using data, analytics and risk assessment strategies throughout the entire trial process, from planning to execution. Unlike traditional approaches, RBQM is not just a monitoring tool, but a comprehensive system that manages quality organisation-wide.
A key aspect of RBQM involves identifying and evaluating potential risks during the trial planning phase. At this stage, it is crucial to identify Critical to Quality Factors (CQFs) – the processes and data that directly impact participant protection, trial outcomes and data integrity. This assessment should consider, for instance, the protocol design, patient population characteristics, investigational product attributes and operational factors that could influence trial outcomes.
PROPORTIONATE RISK-BASED APPROACH
Over the past few years, regulatory agencies such as the FDA, EMA, and MHRA have actively promoted a risk-based approach to quality management. This proportionate risk-based approach allows for more efficient allocation of resources while tailoring control and mitigation measures to the magnitude of identified risks.
The importance of this approach was highlighted during a joint Good Clinical Practice (GCP) and Pharmacovigilance (PV) symposium organised by the FDA, MHRA, and Health Canada in February 2024, where it was emphasised that risk management should not be a one-time activity, but a continuous and dynamic process throughout the trial.
A common finding from regulatory inspections is the failure to update risk assessments during the course of a trial, even when substantial amendments or significant issues are identified. This underscores the need to maintain the risk assessment as a ‘living document’, continuously reviewed and updated to reflect changing circumstances. Risk assessment during the planning phase is only the first step. Clinical trials are inherently dynamic environments where changes in study conditions or outcomes can introduce new risks. For this reason, continuous and documented risk review is a core aspect of the RBQM approach. Ongoing risk assessment enables sponsors and CROs to adjust their risk control strategies, not only in response to new emergent risks but also based on the effectiveness checks that should be conducted on the implemented risk controls to verify that these strategies are working as intended.
CONTINUOUS RISK ASSESSMENT: A CORE COMPONENT
IMPLEMENTING RBQM: CHALLENGES AND SOLUTIONS
Successfully implementing RBQM goes beyond its adoption at the trial level, it must be deeply embedded in the organisation’s culture. This requires not only a shift in processes, but also a comprehensive cultural transformation across the entire organisation.
Organisations need to establish supportive processes that facilitate RBQM at both trial and organisational levels. This includes proactively identifying risks, implementing appropriate control strategies and continuously verifying the effectiveness of these strategies.
‘A successful transition requires the alignment of processes, people and technology/ software, each playing a critical role in ensuring the model’s success.’
However, the transition to an RBQM model comes with challenges, especially for organisations accustomed to traditional approaches. The cultural shift necessary for RBQM can lead to resistance, making effective change management strategies crucial to overcoming these obstacles. A successful transition requires the alignment of processes, people and technology/software, each playing a critical role in ensuring the model’ s success. By fostering a strong organisational risk culture, where risk management is prioritised and embraced at every level, organisations can ensure a smoother and more effective transition to RBQM, ultimately enhancing the quality and efficiency of their clinical trials.
PROCESSES
Aligning processes for the transition to RBQM in clinical trials requires rethinking traditional workflows. Instead of relying on reactive quality management approaches, organisations must integrate risk assessment and monitoring into every phase of the trial – from planning and design to execution and closeout. This shift involves creating adaptable, streamlined processes that support the continuous identification, control and management of risks.
To effectively align processes with RBQM, organisations need to adopt several key strategies.
First, Quality by Design (QbD) should be implemented early, during the planning phase of the trial, to ensure risks are identified and mitigated from the outset.
Second, adaptive processes should be built to allow flexibility, enabling trials to adjust to emerging risks and changes in real time.
Third, integrating risk-based decision-making frameworks is essential, allowing teams to respond swiftly to identified risks with clear protocols and actions. Additionally, fostering cross-functional collaboration ensures that all departments work together cohesively in managing risks.
Finally, embedding continuous risk review into processes guarantees that risk assessments are revisited and updated throughout the trial’s lifecycle.
These aligned processes enable a proactive approach, focusing on CQFs and ensuring that resources are directed toward areas that truly impact the trial’s success and the safety of participants.
PEOPLE
The success of RBQM depends heavily on the people involved. It is crucial to foster a risk-aware culture within the organisation. This means providing comprehensive training and education to staff at all levels, all business functions and ensuring they understand the principles of RBQM and how to implement them in their daily tasks.
Cross-functional collaboration is key, as RBQM requires the involvement of diverse teams such as, for example, clinical operations, data management, biostats, regulatory affairs and quality assurance. Engaging employees in the planning and execution phases gives them ownership of the process, helping to reduce resistance and align everyone with the RBQM objectives.
Implementing pilot programmes can also be an excellent way to demonstrate the benefits of RBQM and fine tune the approach, based on initial outcomes.
Strong leadership support is also crucial. Leaders must embrace the new processes early on and foster a culture of continuous improvement and adaptability within the organisation.
TECHNOLOGY
Investing in the right technology platform is crucial for effectively digitalising RBQM processes. A well-chosen technological solution or software application is instrumental in supporting the entire RBQM cycle, ensuring seamless integration of risk management into clinical trials.
The platform should facilitate continuous oversight, enabling teams to monitor risk assessments in real time and make proactive and informed decisions.
Moreover, it must foster cross-functional collaboration, allowing different teams to work together efficiently, ensuring that risks are managed holistically.
Manual trackers like Microsoft Excel® are not suitable for use in GCP-regulated environments due to its vulnerability to human error, lack of audit trails and difficulty in maintaining compliance with regulatory requirements for data integrity and traceability.
The market currently offers only a limited number of software solutions specifically designed for digitalising RBQM processes.
CONCLUSION
Regulators now expect sponsors to actively identify and manage risks, making proactive risk assessment no longer optional but a critical requirement. Organisations that have traditionally been risk-averse must shift their mindset, embracing a culture that prioritises early risk identification and continuous management. This shift is not only essential for meeting evolving regulatory standards, but also for ensuring the overall success and quality of clinical trials.
The adoption of RBQM represents a fundamental transformation in how quality is managed. By focusing on early risk detection and targeted control measures, sponsors and CROs can significantly enhance operational efficiency, reduce costs and, most importantly, safeguard participants safety and data integrity. While the transition to RBQM may pose challenges, with thorough planning, a risk-aligned culture and support from all stakeholders, the long-term benefits far outweigh the difficulties.
Crucially, RBQM is not about increasing the volume of tasks but about doing the right tasks. The approach emphasises focusing efforts on the factors that truly impact quality, avoiding unnecessary burdens on the system. By prioritising key risks and tailoring interventions, organisations can achieve meaningful quality improvements without added complexity or inflated costs.
Ultimately, RBQM is more than just a methodology for enhancing clinical trials – it is a strategic approach that will shape the future of clinical research.
‘By focusing on early risk detection and targeted control measures, sponsors and CROs can significantly enhance operational efficiency, reduce costs and, most importantly, safeguard participants safety and data integrity.’
REFERENCES
ICH Harmonised Guideline Integrated Addendum to ICH E6(R1): Guideline for Good Clinical Practice E6 (R2).
ICH Harmonised Guideline Integrated Addendum to ICH E6(R3) Draft.
ICH Harmonised Guideline General Considerations for Clinical Studies E8 (R1).
European Medicines Agency (EMA). Reflection Paper on Risk Based Quality Management in Clinical Trials. EMA/269011/2013.
Suprin, B., Kazenwadel, S., & Lis, C. "Quality Risk Management Framework: Guidance for Successful Implementation of Risk Management in Clinical Development." Therapeutic Innovation and Regulatory Science, vol. 53, 2019.
Torok, M., Miller, S., & Spencer, R. "Translating a Culture of Quality to Clinical Research Conduct: Expanding the Clinical Development Quality Framework." Therapeutic Innovation and Regulatory Science, vol. 58, 2024.
Leire holds a PhD in Pharmacy and boasts over 20 years of experience in Quality Assurance within the pharmaceutical and biotechnological industries. She is the Managing Director of Pharmity, a Spainish-based consulting firm specialising in clinical trials, offering global consulting, auditing and training services focused on quality. Additionally, since 2023, Leire has served as the Chief Quality Officer and a founding partner at Qlarix, a company guiding organisations in embracing a risk-based culture and providing software solutions for clinical trials oversight based on RBQM methodology.
HOW TO GAIN THE MOST BENEFIT FROM YOUR QA NETWORK
Introducing the RQA Support and Grow Special Interest Group
Earlier this year, a group of RQA members came together who, amongst all things Quality Assurance, also shared another passion: supporting the development of other professionals. Thus, the Support and Grow Special Interest Group was established with one key guiding principle: ‘We are committed to creating a collaborative environment where everyone can thrive, innovate, network and achieve their full potential, ensuring long-term success and satisfaction for all within the RQA community’.
John Cheshire
Christine Mitchell
Paul Davidson Frankie Drake
I
n this article, some of the founding members from the Support and Grow group share their thoughts and insights into how anyone can gain more benefit from their new or existing QA networks.
PAUL DAVIDSON
In the fast-paced world of Quality Assurance (QA), having a strong professional network is one of the best assets you can have. This network isn’t just a list of names and contacts; it’s a resource for knowledge, support and growth. Here’s a personal story to show how one connection can make a big difference in your career.
IT STARTS WITH ONE
When I first moved from being a QA manager in a corporate setting to becoming a consultant, it felt like stepping into the unknown. My network was full of great colleagues who knew me well, but they weren’t the ‘consultant hirers’ I needed to know. It was a tough time, full of doubts about how to successfully make this transition.
‘My network was full of great colleagues who knew me well, but they weren't the ‘consultant hirers’ I needed to know.’
Thankfully, there was a consultant in my network who had audited my work a few times. Through previous professional interactions, she trusted my skills. Knowing she couldn’t take on a particular client due to her schedule, she took a chance and recommended me. This was a huge moment for me because it was an opportunity to prove myself as a consultant, even though I had no prior experience in that role. Taking on that first consultancy contract was intimidating. There were no guarantees I’d perform well and I was very aware of the risk my colleague took by recommending me. But the project went well and it was the breakthrough I needed. I started to build my own connections within the consultancy network, using the success of that initial contract as a stepping stone.
In the eleven years since then, my network has grown and has become an essential source of advice, support and further opportunities. Each new connection has played a key role in navigating the complexities of the QA industry. But it all started with that one brave recommendation from a trusted colleague.
BUILDING AND LEVERAGING YOUR NETWORK
The key lesson from my experience is that making the effort to establish and nurture that first connection can open doors you never imagined. Here are some practical steps to help you build and use your network effectively:
1. Stay in Touch: Regularly check in with your contacts. Share updates, ask for their opinions and offer your insights. This helps to keep your relationships strong.
2. Attend Industry Events: Conferences, webinars and workshops are great places to expand your network. Don’t just attend – engage with speakers and fellow attendees.
3. Use Social Media: Platforms like LinkedIn are valuable for maintaining professional relationships and staying informed about industry trends.
4. Offer Help: Don’t just seek assistance – be willing to provide it. Sharing your own experiences and knowledge can strengthen your network and reputation.
5. Follow Up: After meeting someone new, send a follow-up message. It shows your genuine interest in maintaining the connection.
ENCOURAGEMENT FOR NEWCOMERS
For those who might feel intimidated or lack confidence, remember that everyone starts somewhere. The QA industry can seem overwhelming, but every seasoned professional was once in your shoes. Your network is there to support you, not judge you. Use your connections to build your confidence. Ask questions, seek advice and learn from others’ experiences. Over time, you’ll find that your own expertise grows and with it, your confidence.
In conclusion, effectively leveraging your QA network involves both seeking help and offering it. By confidently reaching out to others, bridging knowledge gaps and paying it forward, you can build a strong professional network that supports your growth and contributes to the broader QA community. Remember, our industry thrives on collaboration and mutual support, so take the plunge and make those connections.
CHRISTINE MITCHELL
NETWORK LIFE EXPERIENCES MIRRORED IN QUALITY
Networking is something we all do from an early age, and we may not even realise we are doing it. My first experience was at primary school, just five years old, in a playground, full of unknown characters. Who would I talk to first; who looked like the fun one; who was the most popular?
During my teens, I built new networks at athletics and swimming club. I was looking for support and encouragement and even competition to achieve my goals.
When I moved to the Netherlands, expats became part of my network, I felt comfortable with them; we all had something in common, our roots were in a different country. In reality, to really settle in, I needed to network with the locals, learn their language and get an understanding of their culture.
These network life experiences are mirrored in my professional quality career today. My first conference felt the same as the playground, I was a little nervous but excited. There were now different questions: who are my clients; who can support me in my career; who can I learn from?
As a consultant, I benefit from having people in my network who are competitors but supportive and encouraging. Maria Veleva is a good example with her Consultant Special Interest Group on the RQA Community Hub.
Being an industry member of the OECD GLP Discussion Group on Harmonisation Issues, has given me the opportunity to network globally and learn how the language of quality is interpreted by different cultures. This helps me to understand those I am auditing and how I should communicate with them, wherever they are located.
This year, together with an enthusiastic core team, we set up the Support and Grow Special Interest Group on the RQA Community Hub. Just like with my expats, this is a networking platform where I feel comfortable. We can support each other, and we all have one thing in common, quality!
‘In reality, to really settle in, I needed to network with the locals, learn their language and get an understanding of their culture.’
FRANKIE DRAKE
Throughout my career, I’ve learned that the key to getting the most out of your QA network is to approach it with authenticity. In an industry where relationships are built on trust and mutual respect, being yourself is one of the most effective ways to connect with others. Authenticity fosters genuine relationships, and these are the connections that have provided me with the most support and insight throughout my career. When I first had the opportunity to present at a RQA conference, it was not only my first time presenting, but also my first time attending. Naturally I was nervous about standing in front of an audience of peers. To prepare, I practised in front of colleagues, one who encouraged me to not worry about being overly professional and instead to try and let my personality come through more. Initially I hesitated, thinking I needed to maintain a strictly professional facade. But I took that advice to heart, allowing myself to be more relaxed and authentic during the presentation. The result was not only what I hope was a more engaging talk, but it also gave me a stronger connection with the audience.
This experience taught me a valuable lesson that I now apply in all my interactions, even in professional settings: being authentic doesn’t mean being unprofessional. On the contrary, it can make you more approachable and trustworthy, which in turn strengthens your network.
I also believe in embracing a mindset of continuous learning. The field of QA is always evolving and staying up-to-date with the latest trends, regulations and best practices is essential. However, learning shouldn’t be a one-way street – sharing your own insights and experiences is just as important.
Engaging with platforms like the RQA Community Hub, attending conferences and making the effort to connect with other people in the industry are excellent ways to both learn and contribute to the field. The more you share, the more you’ll find others are willing to share with you. This not only keeps you up to date in the industry, but also strengthens your own support network. Sharing your insights also allows you to build relationships with like-minded people who share the same passion for QA. These connections can lead to collaborations and a deeper sense of belonging within the QA community. By contributing your knowledge and experiences, you’ll create a strong network, that’s not only a valuable resource, but a community who encourage and support each other’s growth.
JOHN CHESHIRE
As bizarre as it sounds, quite possibly the best advice I’ve received in my professional career so far was ‘keep being annoying!’. Not annoying in the literal sense of course (at least I hope it wasn’t meant in that way!!) – but meaning I should trust my gut, keep on persevering if I wanted something strongly enough and to never give up in the face of criticism. Navigating your QA career and your network can at times feel like an impossible battle and whilst it may seem futile to persevere, if you trust your gut, you will always make the best decisions and achieve your goals.
Like many of us, my introduction to the wider QA community was through the RQA. My first time attending the International QA Conference was daunting and it was no exaggeration to say I didn’t know anyone there outside of my own company. Despite the nerves, I knew I was lucky to be surrounded by so much knowledge and brilliant individuals, and I would be a fool to not make the most of the time I was there for. So, I put on my brave face, trusted my gut and said “Hi” to as many people as I could. One of those individuals was Hans de Raad whom had presented earlier in the conference and I just thought he was fantastic! We kept in touch on LinkedIn afterwards and since then I’ve been fortunate enough to present alongside Hans on multiple occasions and also lucky enough to join him as a member of the RQA IT Committee.
I consider a lot of my career success to be a product of the support I’ve received from my own QA network. Keeping engaged with industry trends and the interpretations of others has strengthened my own abilities and opened my eyes to new and exciting approaches to QA that continue to make working in this career a delight.
One of the reasons we started the Support and Grow Special Interest Group was to ensure there was an accessible platform for likeminded individuals to meet and learn from each other. I’ve had the opportunity to meet and learn from so many interesting and passionate people and I hope many more in my network do the same.
Whether you are attending your first conference this November or a seasoned conference-pro(!), I challenge you all to speak to someone new this year, add them on LinkedIn and continue to engage with them after November. Some of your best career decisions come from engaging with your QA network. You never know what’s just around the corner!
PROFILES
John has been engaged in the GxP space for six years where he has developed a reliable skillset in IT Quality Assurance and Computerised Systems Validation (CSV). John began his career at Resolian where he successfully led the global CSV team and championed the adoption of risk-based validation. More recently, he has transitioned into consultancy at Headway Quality Evolution to support organisations in identifying their CSV and Data Integrity solutions. In addition to the RQA Support and Grow Special Interest Group, John is also a member of the RQA IT Committee and both the ISPE GAMP UK and C&Q UK COP Steering Committees.
Paul founded Headway Quality Evolution, a quality consultancy firm, in 2013. Since then he has continued to curate a small team of experienced quality professionals offering services across non-regulated research, the GxPs and ISO standards. Paul has been a member of the RQA GLP Committee since 2011, is a member of two RQA Regional Forum committees, a core team member of the Support and Grow SIG and has recently been appointed as the Conference Programme Committee Chair. Prior to HeadwayQE, Paul’s entire career had been focused in academic or commercial research environments.
Frankie is a Quality Consultant at Headway Quality Evolution. She graduated with a BSc (Hons) in Biomedical Science and has nearly nine years’ experience working in quality assurance. Frankie began her career at Charles River Laboratories as an Associate QA Auditor, later progressing to a management role before moving into consultancy. She is also a core member of the RQA Support and Grow Special Interest Group.
Since 1985 Christine has been involved in GXP audits, management of Quality Assurance programmes and providing training in quality systems. She is a fellow member of RQA and an active member of the DARQA GLP Committee. She is the Dutch representative for Industry Members of OECD GLP Discussion Group on Harmonisation Issues. Since 2020 she started her own consultancy company, ChrisalisQAdvice.
TRANSFORMING TRIAL AUDITS: A DEEP DIVE INTO DATA-DRIVEN CLINICAL SITE AUDIT SELECTION IN
PHARMACEUTICALS
Michael Pelosi
Hangyu Liu
Elina Beletski Björn Koneswarakantha
Lucie Regne-Martos
Ofure Obazee
The pharmaceutical industry is witnessing an evolution in data-driven clinical site audit selection. This white paper delves into the transformative shift from traditional data-driven methods to more advanced strategies, exemplified by the challenges of automating clinical site audit selection and giving insights into the current practices of the IMPALA consortium member companies. It highlights the need for continuous innovation and industry collaboration to fully realise the potential of data-driven methodologies in enhancing the quality and integrity of clinical trials.
Challenges: the transition to advanced data-driven processes comes with challenges. Common issues include collecting necessary data, creating robust data management systems and data pipelines, managing vast data sets, maintaining global compliance, resistance to digital transformation and adapting to diverse regulatory environments. This article explores these challenges in depth, offering insights into how each company addresses them, thus providing a base for future solutions.
Current Practices and Innovations: each member company showcases methodologies in leveraging data for clinical site audit selection. Data-centric methods are common and use a blend of historical data trends and current operational metrics to assess site performance, thereby enhancing the efficiency of the selection process. Large reports and dashboards are reviewed to balance study-specific risk factors with site risk levelling while continuously assessing critical data points about patient safety, protocol compliance and data integrity. Risk assessment tools identify potential impacts on safety, quality, efficacy, data integrity or performance in selecting clinical investigator sites for an audit. Comprehensive risk trackers meticulously focus on critical aspects, such as crucial clinical endpoint protection, data integrity and safety. Studies are also categorised into risk levels using a blend of intrinsic and compliance factors, guiding audit coverage with precision based on detailed criteria, such as enrolment figures and adverse event rates.
Integrating dynamic platforms is another approach companies employ for predictive audit scheduling, harnessing algorithms and multiple data sources to streamline site selection, based on risk insights and critical milestones. These approaches highlight the industry’s varied yet convergent paths toward adopting more nuanced, data-centric methodologies in clinical site audit selection.
Vision for the Future: this article casts a vision for the future of clinical site audit selection, one that calls for more efficient and accurate approaches associated with risk. This vision includes integrating sophisticated data analytics, artificial intelligence and machine learning to select audit sites, thereby streamlining risk-based approaches and enhancing trial integrity.
Advantages and Considerations: the benefits of this shift are manifold, including improved efficiency, accuracy, better compliance and cost-effectiveness towards what matters for audit purposes. However, transitioning to an advanced, data-driven approach requires data quality, technology infrastructure, change management and personnel training.
INTRODUCTION
In the fast-evolving landscape of the pharmaceutical industry, specifically clinical trials, the role of data-driven clinical site audit selection has become increasingly crucial. The efficacy and safety of new developments and therapies are evaluated in clinical trials. Traditionally, clinical trials are carried out entirely by clinical sites selected beforehand during the feasibility process, e.g. analyses of patient population availability and previous experience (Dombernowsky et al. 2019).
The increasing complexity of clinical trials can be described as a growth volume of 7-8% per year (Hein et al. 2023) caused by accelerating innovation, e.g. compound development. One of the significant challenges in clinical trials is patient retention (Dombernowsky et al. 2019). Fuelled by the COVID-19 pandemic, alternative designs gained interest, e.g. hybrid trial designs, including more decentralised approaches to clinical trial conduct (Ng et al. 2023).
The increasing complexity of clinical trials, particularly their design, extended the need for data driven feasibility and clinical site selection in quality assurance, i.e. clinical site audits and inspections. Influencing factors, i.e. decentralised trials, risk-based monitoring and advanced digitalisation, are calling for automating the data-driven, but yet manual revision-based clinical site audit selection approaches are currently applied across the industry. Audit and inspection programs start to align with risk-based monitoring increasingly (Higa et al. 2020; FDA, 2024), making room for efforts to review risk levels and their interaction with commonly used data points, e.g. ensuring that chosen sites align not only with the regulatory standards, but also with the specific needs and risks of individual trials, leaving sponsors at a turning point for quality assurance (QA) clinical site audit selection. It is a fundamental shift in thinking, emphasising the value of data integrity and analytical prediction in clinical trial management.
As we explore the current practices of leading pharmaceutical companies like Amgen, Astellas, Bayer, Biogen, Johnson & Johnson, Merck Healthcare KGaA, Roche and Sanofi, this article aims to provide a comprehensive understanding of the challenges, the present state and the future possibilities of automated, risk-based and data-driven clinical site audit selection from a QA perspective. The insights drawn from these industry leaders will shed light on the varied methodologies employed, the commonalities in their approaches and the unique challenges they face in this transition.
This article is organised as follows. First, the Inter-Company Quality Analytics (IMPALA) consortium and related work are described. Second, challenges in accelerating data-driven QA clinical site audit selection are summarised as a significant outcome of
the industry-wide collaboration facilitated through the IMPALA consortium. Third, current practices by consortium members are presented and assessed according to the feasibility of automation. Ultimately, an outlook, sorted from lower hanging to higher hanging fruits, is provided to lay out future possibilities to advance this complex topic to a set of industry-aligned research outcomes, whereby this article represents the basic outline of shared understanding.
The Inter CoMPany quALity Analytics (IMPALA) Consortium is a collaborative initiative that brings together leading biopharmaceutical companies with the mission to transform the Clinical Quality Assurance process in the GCP and GPvP areas by leveraging advanced analytics and best practices. Currently, seventeen companies are members of the consortium and company representatives include functional heads of quality, data analytics, data science leaders and professionals. IMPALA provides an environment for its members to work collectively across the biopharmaceutical industry to develop innovative solutions, methodologies and best practices to address clinical QA changes through advanced analytics.
The consortium engages with health authorities, promoting a collaborative approach to enhance the quality and integrity of clinical trials for patients. This engagement is crucial in fostering a regulatory environment that supports the adoption of advanced analytics and best practices in clinical trials.
The IMPALA Consortium is a pioneering biopharmaceutical industry initiative that revolutionises QA in clinical trials. Through its collaborative approach and innovative projects, IMPALA is making significant strides in enhancing the quality and integrity of clinical trial data, ultimately improving patient care and outcomes. For more information, visit the IMPALA website at https://impala-consortium.org/
Traditionally, investigator site audits and monitoring activities have been conducted on-site and involve extensive source data verification and review exercises. Following the COVID-19 pandemic, traditional on-site activities have been replaced by remote risk-based activities, often leveraging data analytics (Stansbury et al. 2022). To embrace the full potential of clinical and operational study data, several analytical tools that facilitate remote review of clinical data or site risk assessment have been published by the industry as open-source packages (Janitza et al. 2021; Wu et al. 2024; Kirkpatrick et al. 2023). The IMPALA Consortium is an active contributor to this suite of statistical tools, having published the {simaerep} package for the detection of AE under-reporting sites and the {etas} package detecting sites and patients with anomalous time-series in
clinical data (Koneswarakantha et al. 2024; Koneswarakantha et al. 2020; Tiikkainen & Koneswarakantha, 2024). As the adoption of these tools is increasing and the spirit of pre-competitive collaboration continues to be fostered within the industry, we expect those tools to be increasingly integrated into future audit target site selection strategies.
CHALLENGES IN AUDIT CLINICAL SITE SELECTION
This chapter highlights the challenges with the analysis of clinical site data for QA activities, gathered as a result of the exchange within the IMPALA Consortium, literature view and sorted according to the assessed relevance to future automation enhancement. Clinical site audit selection is performed mainly by the quality organisation within R&D, which is undergoing fundamental changes. More and more QA activities have become holistic services that require a skill set beyond traditional QA professionals’ capabilities. There is a shift to include more advanced analytics, machine learning and predictive systems to make informed decisions for risk mitigation. The industry collectively starts to facilitate this change, e.g. by introducing the IMPALA Education Work Product Team (WPT) and establishing technical QA departments consisting of IT personnel with QA process experience and vice versa.
While the conduct of QA audits must remain neutral, there is a requirement to integrate quality data and clinical data to ensure a holistic QA service, the clinical site selection for audits represents current developments in the industry, i.e. Risk-Based Monitoring (RBM) and Decentralised Trials (DCT). These requirements must not be perceived as trade-offs but as contemporary opportunities to further strengthen the role of quality as an essential partner in clinical studies conducted within research pharmaceutical companies and fundamental to Risk Based Quality Management (RBQM). Even though global regulatory agencies have been calling for an increased implementation of RBQM for over a decade, recent analyses performed by The Tufts Center for the Study of Drug Development show an RBQM implementation rate of 57% (Dirks et al. 2024). The low adoption rate is mainly explained by lack of knowledge of RBQM components and tools during clinical trial planning and execution; the functional area with the lowest trust in RBQM remains site management and site monitoring. It is recommended to tailor communication and education based on functional area, as well as to promote successful RBQM implementations (Dirks et al. 2024).
According to a survey conducted among the pharma companies in the WPT, approximately 70% find their clinical site audit selection process is data-driven. However, 50% think that data access is a significant challenge. A scattered system landscape impedes reliable quality decisions and could affect study outcomes due to incorrect clinical site audit selections. Various trial and patient management systems, including system training and role-based permission restrictions, must be accessed. More provisioned integration platforms must be available across clinical operations and quality, offering central access to information. In more prominent companies, some traditional database management infrastructures are provided, e.g., extraction, transformation and data loading (an ETL pipeline). Comprehensive risk levelling and corresponding data points are crucial for making informed choices about clinical sites for auditing, but they are often difficult to gather, especially for small companies. Smaller start-ups face these challenges on an even grander scale. With limited resources and often needing more IT infrastructure, these companies find it harder to implement sophisticated data analytics solutions or access large datasets. This puts them at a disadvantage compared to larger, more established companies. IT infrastructures commonly used today to offer standardised and secure interactions with databases and platforms, e.g. RESTful APIs, are yet to be implemented as a common source for any application development project within pharmaceutical R&D.
The clinical site audit selection as part of site audit planning is a manual, subjective selection. Being responsible for a large and complicated study and putting site-level anomalies into context with the study design, product and portfolio, therapeutic area and country specifics could mean managing many megabyte CSV files. These large files are often hard to reverse engineer and the individual logic applied to clinical site audit selection needs to be documented. A practical example of missing integration is the need for one unique site identification. Quality systems are historically kept as stand-alone databases, whereby sites can only be approximately identified by the site name and PI name, making integration and data analytics tasks unnecessarily complex, e.g. whether a site has been audited before. The pharmaceutical industry is heavily regulated and ensuring data handling processes meet these standards is no small feat. Standards like ALCOA+ are crucial here; they demand accurate, legible and reliable data. Keeping up with these evolving regulations, especially across different countries, adds another layer of complexity. Data management systems are also under pressure due to the sheer volume of data that needs to be handled.
Ensuring this data is consistent across various systems within a company and adheres to all applicable standards is a significant task. Given these varied and complex challenges, the industry has yet to have a one-size-fits-all solution. Depending on its size and resources, each company must find its path in integrating data-driven strategies with automation techniques in its clinical site audit selection processes. The industry needs to keep improving its flexibility towards new technology and processes to handle these challenges better. By doing so, they can make more informed decisions when selecting audit sites, leading to more efficient and compliant operations in clinical trials.
DATA-DRIVEN CLINICAL SITE AUDIT SELECTION: CURRENT STRATEGIES AND IMPLEMENTATIONS
In exploring the diverse array of strategies utilised for data-driven clinical site audit selection in the pharmaceutical industry, this article highlights the current states of multiple leading organisations: Amgen, Astellas, Bayer, Biogen, Johnson & Johnson, Merck Healthcare KGaA, Roche and Sanofi. These industry leaders have each leveraged data analytics to enhance their clinical site audit selection, establishing new standards and profoundly influencing the evolution of this process within the sector.
Audits are vital to identifying potential issues and ensuring adherence to GCP guidelines. To optimise resource allocation and maximise audit effectiveness, comprehensive approaches to clinical site audit selection ensure oversight of clinical trials.
A core clinical site audit selection component is a robust risk assessment with a data-driven foundation. A comprehensive data set is analysed to pinpoint sites with a higher likelihood of non-compliance with GCP guidelines. Past audit reports offer valuable insights into past issues or areas of non-compliance at a site. Sites with a history of significant deviations from the protocol or data integrity concerns become prime candidates for recurring audits to ensure corrective actions have been implemented effectively and to identify any emerging concerns. Clinical Research Associates (CRAs) are crucial in overseeing ongoing trials. Their reports precisely document observations made during site visits, any deviations from the protocol that may have occurred and any concerns raised regarding data quality or subject safety. Frequent deviations or unresolved issues identified by CRAs can flag a site for a more in-depth audit to investigate the root causes and ensure appropriate corrective measures are taken.
Studies with complex protocols involving numerous procedures, stringent inclusion/ exclusion criteria or complex data collection methods inherently carry a higher risk of deviations. These complexities can create challenges for investigators and site personnel, potentially leading to unintentional errors or misinterpretations of the protocol. Sites conducting such trials may require closer scrutiny during audits to ensure proper implementation and adherence to the intricate details of the protocol. The experience and qualifications of the investigators and site personnel significantly impact the conduct of a trial. Sites with limited experience of handling complex protocols or a history of non-compliance in previous studies might benefit from a targeted audit. Audits at such sites can identify potential knowledge gaps or areas where additional training or support might be necessary to ensure proper protocol execution and GCP compliance.
The vulnerability of the study population can also influence audit prioritisation. Trials involving populations with an increased need for protection, such as children, pregnant women or individuals with severe illnesses, might warrant more frequent or comprehensive audits. This ensures that their safety and ethical treatment are precisely monitored throughout the trial. Audits at these sites can focus on informed consent procedures, adherence to ethical guidelines regarding participant welfare and adequate safeguards to minimise potential risks. While risk assessment is a powerful tool, a well-rounded approach to clinical site audit selection goes beyond identifying high-risk sites. Staying informed about current regulatory requirements and focusing audits on areas where compliance is critical is essential. This ensures alignment with the latest regulations and addresses potential concerns about data integrity or subject safety. Sponsors use this information to update their auditing strategies and prioritise areas where new or revised regulations require heightened scrutiny. Balancing risk assessment with available resources is crucial. While high-risk sites demand attention, efficient use of audit personnel needs prioritisation based on the severity of potential risks and the number of available auditors. This ensures that resources are allocated strategically to maximise the impact of audits.
Sponsors and regulatory bodies can reap several critical benefits by employing a comprehensive clinical site audit selection approach. Focusing on high-risk sites and areas of critical compliance allows audits to identify and address potential issues early on, preventing them from escalating and compromising the integrity of the research.
This ultimately leads to higher-quality data and more reliable results from clinical trials. Prioritising high-risk sites ensures that auditors can focus their expertise where needed most, optimising the use of limited resources. Identifying and addressing potential issues early on helps to prevent serious safety concerns for participants and ensures the validity of the research findings. This, in turn, fosters public trust in the clinical research process. Employing a comprehensive approach to clinical site audit selection that combines risk assessment with considerations for regulatory compliance and resource optimisation is vital. These aspects safeguard the integrity of clinical trials, foster public trust and ultimately pave the way for developing safe and effective new treatments for the future.
These sponsors share a common understanding for approaching risk-based, data-driven clinical site audit selection, facilitated through data analytics, whereby patient safety, data integrity and quality compliance are the significant factors applied across products, studies and sites. Current approaches differ because of the distinctive focus on products and indications across the companies, resulting in different risks and weighing factors. Also, some approaches heavily rely on the collaboration between QA and the study teams, while others rely more on pre-developed dashboards and Business Intelligence (BI).
Similar site-level data points are used for data analytics. However, overarching risk categories, i.e. product, country, therapeutic area and study risk, are partially applied and their corresponding data points need to be clustered to an extent that facilitates exploring the feasibility of automation as of now. Furthermore, given the challenges in the previous chapter, the shortlisted sites result from BI integrations, e.g. dashboards or CSV reports and manual review, combined with previous experience while conducting a specific study or programme.
Advancing together to strive for continuous improvement to create an everyday basis for innovation paints a picture of an industry committed to advancing clinical trial quality and safety through flexible, innovative and tailored data-driven clinical site audit selection solutions.
‘Identifying and addressing potential issues early on helps to prevent serious safety concerns for participants and ensures the validity of the research findings.’
VISION FOR A DATA-DRIVEN CLINICAL SITE AUDIT SELECTION
In envisioning a data-driven future for clinical site audit selection in the pharmaceutical industry, we look at a transformative shift toward more innovative, efficient processes. This vision is not just about technology; it is about revolutionising how we approach quality assurance in clinical trials, making them safer, more compliant and more successful.
One first attempt of this transformation can be the semi-automated site audit selection algorithm. Picture a scenario where a sophisticated system recommends audit sites based on your chosen criteria – geographic location, patient enrolment rates or any other specific factor. This is not just a list of random suggestions. The system filters and presents sites that align perfectly with your risk assessments and criteria. However, it does not end there; you, as the quality professional, have the final call to determine the actual sites for auditing. This blend of automated intelligence and human judgment ensures that the chosen sites are both data-approved and expert-endorsed.
What if we could go a step further? Imagine a dynamic world where audit scheduling and scope are not set in stone but evolve based on real-time risks. This approach is not just about being reactive, it is about being proactive. By continuously analysing data, this system can anticipate changes and adapt audit plans accordingly. It is like having a living, breathing process that is always one step ahead, ensuring resources are used efficiently and effectively.
Now, let us talk about the role of Artificial Intelligence (AI). These are not just buzzwords, they can be game changers in the long-term vision. By training AI models with an array of site risk factors, we empower them to make sense of complex data in ways humans never could. These models can analyse the patterns of various risk factors, like under-reporting of adverse events, protocol deviations per patient visits and so on, and quantify them into a recommendation score. Attention needs to also be placed on score adjustments per clinical study, in order to achieve a balanced distribution of selected sites. This score is not just a number but a beacon guiding the clinical site audit selection, offering unprecedented insight and foresight. It must be analysed to what extent AI models support decision-making and to what extent they are a risk due to their limitations. There are ethical considerations to clinical site audit selection; decisions based on AI should not dictate the selection, human expertise and considerations regarding patient safety and study integrity remain top priorities. Data quality is a significant problem, contributing to the lower-hanging
fruit of our vision to invest in data lakes, data cleaning and preparation methods that enhance accessibility and interoperability. While it is a broad task to accomplish and likely to be the most cumbersome, our vision’s higher-hanging fruit is to provide explainable Al, mainly because of the delicate field in which we operate. More concretely speaking, performing sophisticated feature engineering and documentation during model development is not advisable while not fully engaging in data pre-processing and platform integration efforts.
However, it is more than just the known, it is also about exploring the unknown. Using unsupervised learning to delve into historical data, we can unearth patterns and similarities between sites we might have missed. By understanding the risk levels associated with these similar sites, companies can apply a more informed, data-driven approach to site audit selection, especially in areas where they have limited experience. This approach is particularly beneficial when there is limited or no prior experience with specific areas, enabling companies to gauge the risk level of new sites based on their resemblance to previously audited ones.
As the pharmaceutical industry continues to embrace the potential of advanced analytics, including AI and machine learning, we are on the brink of a new era in clinical trial and quality management. The data-driven approach transforms how we select sites for audits and think about the entire clinical trial process. It is a story of progress, of technology hand-in-hand with expertise and mindset change leading us towards a future where every decision is informed, every risk is calculated and every trial is a step closer to success.
CONCLUSION
This article explores the transformative potential of data-driven clinical site audit selection in the pharmaceutical industry. We examine industry leaders’ current practices, highlighting their unique approaches and common challenges.
The challenges identified, ranging from data availability to regulatory complexities, emphasise the need for continuous innovation and adaptation. The envisioned future, where advanced data analytics and machine learning play a central role in clinical site audit selection, promises a more streamlined, compliant and efficient process.
As we move forward, industry leaders must join forces, sharing insights and best practices to elevate the standards of clinical site audit selection. Together, we can ensure a future where clinical site audit selection is about meeting regulatory requirements and upholding the integrity and success of clinical trials. This collaborative effort will be the cornerstone of a data-centric, transparent and practical approach in the pharmaceutical industry.
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Stansbury, N., Barnes, B., Adams, A., Berlien, R., Branco, D., Brown, D., Butler, P., Garson, L., Jendrasek, D., Manasco, G., Ramirez, N., Sanjuan, N., Worman, G., & Adelfio, A. (2022). RiskBased Monitoring in Clinical Trials: Increased Adoption Throughout 2020.
Tiikkainen P (2024). Finding anomalies and outliers in clinical trial time series data. https://phuse.s3.eu-central=mazonaws.com/Archive/2022/ Connect/EU/Belfast/PAP AR04.pdf, Last Accessed On Mar 28, 2024.
Tiikkainen P, Koneswarakantha B (2024). etas: Identifying anomalous clinical time series. R package version 0.2.1. https:// github.com/IMPALA-Consortium/etas, Last Accessed on Mar 24, 2024.
Wu G, Wildfire J, Roumaya M, Kosiba N, Sanders D, Childress S, Ge L, Wang Z, McLaughlin C, Dickens C, Gans M, Anderson J (2024). Gsm: Good Statistical Monitoring. R package version 1.9.2. https://github.com/Gilead-BioStats/gsm, Last Accessed on Mar 24, 2024.
PROFILES
Elina is a Solution Engineer at Bayer Pharmaceuticals, with over seven years of industry experience. Her expertise lies in designing and integrating cloud-based infrastructure, web applications and advanced analytics tools for the QA audits and inspections domain. Elina holds a MSc in computer information systems, specialising in digital health systems design and blockchain technology, providing her with a broad perspective on innovative solutions in higher regulated fields.
Björn is a Principal Quality Data Scientist at Roche with over six years of industry experience, specializing in developing advanced analytics solutions for stakeholders in the GCP and GVP domains. He has co-authored several statistical open-source tools and published numerous scientific articles on quality statistics. Björn holds a PhD in neurobiology and has a substantial publication record in this field, reflecting his strong foundation in research and data science.
Hangyu is an Associate Director leading Artificial Intelligence innovation at Biogen. Prior to joining Biogen, he worked as a Data Scientist in both the life science and eCommerce industries. Hangyu is a co-author of top-tier machine learning papers and a healthcare AI innovation challenge winner. With his expertise, Hangyu leads analytical initiatives that improve efficiency in the GxP quality domain using both traditional statistical methods and cutting-edge techniques, including Large Language Models.
Ofure is a Quality Strategy Lead (Data Analytics) at Merck Healthcare KGaA, with over five years of industry experience providing capabilities for predictive use cases, identification of data trends and insights for the Research & Development Quality function, as well as driving Data Literacy within global teams. She has over eight years of academic experience in Cancer genetic epidemiology where she co-authored several publications. Ofure holds a Doctoral degree in Pharmacogenetics and other degrees in Cell Biology and Genetics, as well as Applied Biotechnology. At her core she is an ‘equipper’ and ‘optimiser’ who is passionate about adding value, which is evident in her significant professional and social contributions.
Michael is the Astellas lead for Global Quality Analytics, overseeing the strategy and implementation of analytics and digital transformation capabilities to enhance quality activities across the Quality function. As part of Quality Governance, he also supports Management Review, electronic Quality Management System activities and work with external consortia. Michael has a diverse educational background, holding degrees in biology, medical humanities, computer information systems and an MBA.
Lucie has worked in Healthcare for over 12 years and is currently the Clinical Quality Performance Group Head within Sanofi. She has a diverse background spanning from clinical operations, clinical audit and risk management. In her current position, Lucie plays a pivotal role in developing and executing the annual strategy for clinical quality performance and risk management within clinical operations. Her innovative approach and strategic thinking have driven transformative changes in organisational processes, making her a key contributor to fostering a culture of excellence and continuous improvement in clinical settings.
A DAY IN THE LIFE
CRISTINA REALINGO CLINICAL TRIAL MONITOR
Name: Cristina Realingo
Job Title: Clinical Trial Monitor
Company: CRUK + UCL CTC
Location: London, UK
Years in industry: 2 years, 5 months
MORNING ROUTINE
What does your typical morning routine look like before heading to work?
My mornings start early, typically around 5:30am to ensure that I have ample time to prepare for the day ahead. Given the nature of my work which involves constant travel, both physically and virtually, to various investigative sites, I rely on my routine that aids in grounding me and sets a calm, focused tone for the day. I begin with light exercise plus mindful journalling followed by a nutritious breakfast.
How do you prepare yourself mentally for your day?
After breakfast, I sit down with my planner to review the day’s schedule. Whether I’m preparing for an on-site visit, a remote monitoring session or a no-visit day focused on documentation and training, I find that mentally walking through the day’s tasks helps me anticipate challenges and prioritise my time effectively. I also take a moment to check my email for any urgent updates from sites, internal colleagues or external Sponsors that may have come in overnight.
By 7:30am, I am typically ready to head out if I have an on-site visit or to log in if I’m working remotely. Regardless of the location of my work at any timepoint, I always ensure I have a clear plan for the day and the flexibility to adapt as needed, which is an essential part of succeeding in this role.
JOB RESPONSIBILITIES
Can you describe your main responsibilities?
As a Clinical Trial Monitor in the field of oncology, my main responsibilities revolve around ensuring that clinical trials are conducted in compliance with the trial protocol, GCP guidance and all relevant regulatory requirements. My role involves frequent on-site monitoring visits, where I review the trial’s progress at various clinical sites across the UK. This includes verifying that the data collected is accurate and that it corresponds with the patient’s medical notes (Source Data Verification). I also ensure that the informed consent process is properly documented and that patients have a clear understanding of what participation in the trial entails.
‘Following a visit, I prepare a detailed monitoring report documenting any findings, deviations from the protocol or areas for improvement.’
Another critical aspect of my job is to ensure patient safety. This involves monitoring the reporting of Adverse Events (AEs) and Serious Adverse Events (SAEs) to ensure they are recorded and reported promptly and accurately. Oncology trials often involve experimental treatments with significant risks, making this a particularly important part of my role.
Additionally, I oversee the handling, storage and dispensing of the Investigational Medicinal Products (IMP) and/or Advance Therapy IMP (ATIMP)/Non-IMPs (NIMP), to ensure compliance with the protocol, pharmacy manual and regulatory requirements. This includes auditing drug accountability logs and verifying that the IMP/ATIMP/NIMP is stored under the correct conditions and its journey is fully traceable.
My responsibilities also extend to training and supporting site staff, ensuring that they are well-versed in the trial protocol and GCP guidelines. I provide guidance on best practices in data collection, patient recruitment, and AE/SAE reporting, and I work closely with them to resolve any issues that arise during the trial. Following a visit, I prepare a detailed monitoring report documenting any findings, deviations from the protocol or areas for improvement.
What types of projects or processes do you usually audit/get involved in?
Apart from those mentioned (i.e. informed consent process, SDV, IMP/ATIMP/NIMP management and safety reporting), I am also involved in auditing the trial’s compliance with regulatory requirements, ensuring that all necessary approvals are in place and that any amendments to the protocol and/or Urgent Safety Measures (USMs) are properly communicated, implemented and documented. Further to this, I identify areas where site operation can be improved, such as enhancing data entry procedures. I work with site staff to implement these improvements, ensuring that the trial runs more efficiently and that the data quality is maintained.
Overall, the trials and processes I audit are all geared towards ensuring that the clinical trial is conducted in a way that is scientifically sound, ethically responsible and compliant with all applicable regulations. My role is to identify any potential issues and to work with the site team to implement solutions that will keep the trial on track.
CHALLENGES AND REWARDS
What are the most challenging aspects of your role?
One of the most challenging aspects of my role is managing the complexity of oncology trials. These trials often involve multiple sites, each with different patient populations and varying levels of staff experience. Ensuring consistency and accuracy across all sites can be difficult, especially when dealing with large volumes of data that need to be verified and analysed.
Another challenge is maintaining strict adherence to the trial protocol while being adaptable to the unique needs of each site. This requires a delicate balance between being thorough and precise while also being flexible and responsive to the realities on the ground. For instance, dealing with discrepancies in data collection or deviations from the protocol can be particularly challenging, requiring quick thinking and effective problem-solving.
The emotional aspect of the job can also be challenging. In oncology trials, you are working with confidential information of patients who often have serious, life-threatening conditions. This can be emotionally taxing especially when adverse events occur or when the patients do not respond well to the treatment. Maintaining a professional demeanour whilst holding the patients’ safety as a top priority requires a strong emotional resilience.
What do you find most rewarding about your job?
Despite the challenges, there are many rewarding aspects to being a Clinical Trial Monitor in oncology. One of the most fulfilling parts of the job is knowing that the work I do contributes to the development of new treatments that can significantly improve, and even save lives.
Another rewarding aspect is the relationships I build with the site staff and the broader clinical team. Working collaboratively with dedicated professionals who are all striving towards the same goal is deeply satisfying. These relationships often lead to a strong sense of mutual respect, which makes the day-to-day challenges easier to manage.
I also find great satisfaction in problem-solving. Whether it’s finding a solution to a data discrepancy or helping a site improve its processes, overcoming obstacles and contributing to the trial’s success is highly rewarding. It’s these moments of success, big or small, that make the challenges of the job worthwhile.
TOOLS AND TECHNIQUES
What tools or software do you use in your daily work?
As a Clinical Trial Monitor, I rely on a variety of tools and software to perform my duties effectively. Some of the key tools include:
• Microsoft OneNote: This is my primary tool for managing the various aspects a trial. It helps me keep track of site visits, monitor trial progress and it acts as a point of reference for the data that needs to be reviewed at every visit
• Electronic Data Capture (EDC) Systems: These systems are used to collect and manage the trial data. I use EDC systems to verify the accuracy of data entered by the site staff and to ensure that it aligns with the source documents
• Microsoft Excel and Word: I use these tools extensively for data recording and report writing. Excel is particularly useful for organising and analysing large datasets, while Word is used for creating monitoring reports and other trial documentation
• Remote Monitoring Tools: With the increasing shift towards remote monitoring, I use various software platforms that allow me to access and review data remotely. This includes secure file-sharing systems and video conferencing tools that enable me to communicate with site staff without being physically present, whilst ensuring that all activities are performed with adherence to relevant regulations.
Are there specific auditing techniques that you find particularly effective?
Yes, there are several techniques that I find particularly effective in ensuring the integrity of the trial and the accuracy of the data. These includes:
• Risk-Based Monitoring (RBM): This approach focuses on identifying and monitoring the areas of the trial that are most likely to impact patient safety or data integrity. By concentrating resources on high-risk areas, I can ensure that potential issues are addressed proactively
• Data Trend Analysis: By analysing trends in the data, I can identify potential discrepancies or deviations from the protocol early on. This technique allows me to spot issues before they become significant concerns.
COLLABORATION AND COMMUNICATION
How do you collaborate with other teams or departments during the auditing process?
Collaboration is a crucial part of my role as a Clinical Trial Monitor. Effective communication with various teams is key to ensuring that the trial runs smoothly and adheres to protocol and regulatory requirements.
When conducting visits, I work closely with the site’s Principal Investigators, data managers, research and pharmacy team leads to verify that the study is being conducted according to the protocol and GCP guidelines. This often involves detailed discussions about patient recruitment, informed consent and data collection procedures, as well as monitoring findings which may include deviations from the protocol. Appropriate Corrective Actions and Preventative Actions (CAPA) are suggested to the site and they must consider how these can be implemented effectively without disrupting the study’s progress.
Overall, my approach to collaboration is proactive and supportive with the aim to foster a positive working relationship with all stakeholders in the trial, ensuring that we work together towards the common goal of successfully completing the trial while maintaining the highest standards of data integrity and patient safety.
‘By analysing trends in the data, I can identify potential discrepancies or deviations from the protocol early on.’
What communication skills do you find most important in your role?
In my role, several communication skills are particularly important:
• Clarity and Written Communication: Being able to convey complex information in a clear and concise manner is essential. Whether this is to explain a protocol requirement to site staff or documenting the findings on the monitoring report, clarity ensures that everyone understands the key points and what actions need to be taken
• Active Listening: It is important to listen intently to the concerns and insights of the site staff and ensure that any actions from my end are followed through as not only does this help in understanding the context of any issues that arise and in developing solutions that are both practical and compliant, but it is vital in building trust and maintaining the rapport with the site team
• Diplomacy: Oftentimes, delivering feedback, especially when it involves bringing non- compliance to light, can be sensitive. Diplomacy allows me to present my findings in a constructive manner that encourages cooperation rather than defensiveness
• Persuasiveness: There are occasions where monitoring findings require changes to the current practice at site. Being persuasive helps in driving adherence to the protocols and ensuring that the trial remains compliant.
ADAPTABILITY
How do you handle unexpected challenges or changes during an audit?
Being flexible and having a solution-oriented approach is invaluable in this role. In the field of oncology trials, where patient safety and data integrity are paramount, quick and effective responses to the challenges are crucial.
‘Diplomacy allows me to present my findings in a constructive manner that encourages cooperation rather than defensiveness.’
‘Once I have a clear understanding of the problem, I consult with the relevant site staff and suggest appropriate CAPA or proceed to the escalation process where necessary.’
When an unexpected issue arises, such as protocol deviation or data discrepancies, I first assess the severity of the situation. This involves determining whether the issue impacts patient safety, data integrity or regulatory compliance. Once I have a clear understanding of the problem, I consult with the relevant site staff and suggest appropriate CAPA or proceed to the escalation process where necessary. For instance, if incorrect adverse events reporting practices were discovered, I would immediately work with the site’s clinical research coordinator and/ or data managers to correct the process. This might involve retraining the staff on proper documentation procedures and ensuring that all past events are recorded accurately. Flexibility is key in this role as sometimes, there will be instances where solutions to findings may involve making adjustments to the monitoring plan guidelines (following approval from Sponsor or internal trial teams), such as revisiting the site sooner than planned to ensure that the proposed CAPA have been implemented effectively.
Can you share a specific instance where your adaptability was crucial?
One instance where my adaptability was crucial occurred during a site visit where I discovered that the site had mistakenly performed an imaging test much earlier than those stipulated on the protocol. This was a significant issue due to its potential to have an impact on the patient safety, as well as the overall results of the trial. Upon discovering the error, the Investigator and relevant site staff were notified and discussed the implications of this deviation. We quickly worked together to assess the extent of the issue, identifying all patients affected by this error and determining the necessary steps to correct the situation.
Given the severity of the deviation, the Sponsor had to be notified and thus I assisted the site to report this accordingly. Throughout this process, my ability to remain calm and focused allowed us to manage the situation effectively, minimising the impact on the trial and ensuring that the patient safety was not compromised.
BALANCING ACCURACY AND EFFICIENCY
How do you ensure accuracy in your audits while also meeting deadlines? Ensuring accuracy while meeting deadlines requires meticulous planning and prioritisation. Before each visit, I review the trial protocol, the site’s previous performance and any unresolved findings from previous visits. This preparation allows me to focus on the most critical aspects of the visit. During the visit, I follow a structured approach, using a personalised checklists to ensure that I cover all necessary areas. I also use risk-based monitoring techniques to focus on the most significant risks to patient safety and data integrity. This allows me to allocate my time effectively, ensuring that high-risk areas are thoroughly examined without neglecting other important aspects of the visit.
To maintain efficiency, I also make use of technology (e.g. remote monitoring tools and data management systems) that streamline the process and reduce the time needed to review documents and data. Are there specific strategies you use to maintain efficiency?
Yes, several strategies help me maintain efficiency:
• Prioritisation: Tasks are prioritised based on their impact on patient safety and data integrity. This ensures that the most critical tasks are completed first, even if time is limited
• Use of Technology: Leveraging technology (e.g. EDC systems and remote monitoring tools) allows me to access and review data more quickly and accurately
• Time Management: I allocate specific time slots for each part of the visit, helping me stay on schedule and avoid spending too much time on less critical areas
• Regular Communication: Maintaining regular communication with the site staff helps to quickly resolve any issues that arise, preventing delays in the visit process
• Continuous Learning: By staying updated on the latest best practices and regulatory changes, I can streamline my processes and avoid unnecessary steps during visits.
ADVICE FOR ASPIRING QA AUDITORS
What advice would you give to someone considering a career in QA auditing?
For anyone considering a career in Monitoring, especially in oncology clinical trials, my advice would be:
• Develop a Strong Foundation in GCP: A deep understanding of Good Clinical Practice (GCP) guidelines is essential. This forms the foundation of all clinical trial monitoring activities, ensuring that trials are conducted ethically and in compliance with regulatory requirements
• Be Detail-Oriented: Clinical Trial Monitoring requires meticulous attention to detail. Small errors can have significant consequences, so it is imperative to develop the ability to spot even the smallest discrepancies in data or documentation
• Cultivate Strong Communication Skills: Effective communication is key in this role. You need to be able to convey complex information clearly and diplomatically, whether you’re writing reports or discussing findings with site staff
• Stay Adaptable: Clinical trials are dynamic environments where unexpected issues can arise. Being adaptable and solution-oriented is crucial to successfully navigating the challenges that come your way
• Pursue Continuous Learning: The field of clinical trials is constantly evolving, with new regulations, technologies and best practices emerging regularly. Staying informed and continuously improving your knowledge and skills will help you stay ahead in this field.
‘You need to be able to convey complex information clearly and diplomatically, whether you’re writing reports or discussing findings with site staff.’
Are there specific skills or qualities that are crucial for success in this field?
Yes, several skills and qualities are crucial for success as a Clinical Trial Monitor:
• Attention to Detail: The ability to notice and address small discrepancies is vital for maintaining the integrity of the trial data
• Analytical Thinking: Being able to analyse data and identify trends or potential issues is important for proactive monitoring and problem-solving
• Ethical Judgement: A strong ethical compass is essential, as your work has direct implication for patient safety and the validity of the research
• Time Management: Effective time management allows you to balance multiple tasks and meet deadlines without compromising on the quality of your work
• Interpersonal Skills: Building positive relationships with site staff and other team members is important for fostering cooperation and ensuring that the trial runs smoothly.
MEMORABLE MOMENTS
Can you share a memorable experience or success story from your time as a QA auditor?
Although there is no one experience or success story that comes to mind, being able to follow each and every patient that I monitor and knowing that they are doing well (or otherwise) with the trial treatment underscores the human aspect of clinical trials. There may be cases that a particular trial will not lead to the immediate approval of the drug, the data collected is invaluable for understanding the treatment’s effects and the challenges faced in treating different forms of cancers. Every patient’s participation in the trial contributes to a better understanding of the disease and helps refine the approach for future studies.
It makes me appreciate that while not every trial leads to a breakthrough, the work we do is essential in the broader journey toward finding effective treatments and also highlights the importance of patient safety, ethical considerations and collaborative efforts required to navigate the complexities of clinical research.
Are there any particularly challenging audits that stand out in your memory?
A particularly challenging visit that stands out in my memory involved a trial site that was struggling with significant compliance issues. Due to the complexity of the trial along with a skeleton workforce with a high patient recruitment, this led to inconsistencies in data entry and several protocol deviations.
This was a critical situation because these deviations could have had serious implications for patient safety and the integrity of the trial data. I had to act swiftly to address these issues, which involved conducting thorough retraining sessions with the site staff, implementing stricter oversight’s protocols and closely monitoring the site’s corrective actions over the following months.
Despite the challenges, the site eventually managed to get back on track. The experience was intense and required a great deal of patience, clear communication and problem-solving skills. It reinforced the importance of vigilance and the need for continuous support and training at trial sites to ensure compliance and high standards throughout the study.
WORK-LIFE BALANCE
How do you maintain a healthy work-life balance in your role?
Maintaining a healthy work-life balance as a Clinical Trial Monitor can be challenging, given the demanding nature of the role, but it is something I prioritise. One strategy I use is setting clear boundaries between work and personal time. While I often travel and work long hours, I make it a point to disconnect from work during weekends and after work hours unless there is an urgent matter that needs immediate attention.
Time management is another key aspect. I plan my weeks carefully, allocating time for site visits, report writing and administrative tasks in a way that ensures I can meet deadline without consistently working overtime. Moreover, I find that regular short breaks during the day help me stay focused and prevents burnout.
What activities or hobbies help you unwind after a working day?
To unwind after a demanding day of work, I enjoy several activities and hobbies that help me relax and recharge. One of my favourite activities is going for a long walk. This not only helps clear my mind but also reduces stress and keeps me physically fit. I am also an avid podcast listener and I find that immersing myself in a good podcast or audio book provides a great escape from the day-to-day pressures of work. It allows me to relax and shift my focus to something entirely different, which is incredibly refreshing. Another hobby that helps me unwind is macramé. I find it therapeutic to switch off and only focus on the intricate patterns and process. It’s a creative outlet that also results in something tangible and rewarding as I often gift the finished product to friends and family.
KEEPING YOU POSTED REGULATIONS AND GUIDELINES
Monjit Summy, GCP Committee
REG AUTH ISSUE OR PUBLISHED DATE EFFECTIVE DATE TOPIC
MHRA 30th August 2024 1st January 2025 UK-Wide licensing for human medicines
This guidance is designed to provide information on the implementation of changes to the licensing of medicines for human use in the UK following the agreement of the Windsor Framework. From 1st January 2025 the Medicines and Healthcare products Regulatory Agency (MHRA) will license medicines across the whole of the UK. This guidance should be used in conjunction with the Human Medicines Regulations 2012 legislation, as amended by the Human Medicines (Amendments relating to the Windsor Framework) Regulations 2024 and other relevant MHRA guidance, including the labelling and packaging guidance published in July 2023.
GP vP
Jana Hyankova, GVP Committee Chair
Supported by Paulina Valuskova, iVigee and Jana Freeman, iVigee
Armenia Effective Jun 2024
The new Center of Drug and Medical Technology Expertise SNPO announced that it has taken over the functions of the former Scientific Center of Drug and Medical Technology Expertise as of 25-Apr-2024.
All correspondence should now be sent to the new center at 49/5 Komitas Avenue, Yerevan.
The website www.pharm.am will remain active until the new official site is launched and emails can continue through info@ampra.am and vigilance@pharm.am
Additionally, recent amendments to the Law ‘On Medicines’ and the Law ‘On State Duty’ will introduce a state tax to replace the current assessment fee.
The Central American Intergration System (SICA) published information about the launch of Noti-FACEDRA 2.0. The Noti-FACEDRA is the Regional Online Notification Portal of Suspected Adverse Reactions to Medicines and Vaccines used by Belize, Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama and the Dominican Republic.
Among the new functionalities, the following stand out:
1. Updated pharmaceutical industry notification forms with user self-management features and improved case follow-up on the cases reported through the Noti-FACEDRA 2.0 portal.
2. A new health professional registration form enabling access to all reported adverse reactions drugs reported in Noti-FACEDRA 2.0.
Access to version 2.0 remains the same.
A user manual for version 2.0 is available.
The Superintendency of Sanitary Regulation (SRS) will become the new National Regulatory Authority in El Salvador, consolidating responsibilities for health regulation previously spread across various agencies, including the National Directorate of Medicines (DNM) and the National Center for Pharmacovigilance (CNFV). The transition follows the approval of the SRS Law on 19th June 2024.
Key updates include:
1. Pharmacovigilance oversight: All communications must be sent to farmacovigilancia@srs.gob.sv
2. Reporting ADRs: The Noti-FACEDRA platform will continue to be used for reporting Adverse Drug Reactions and related issues.
3. Risk Management Plans and Safety Reports: These will be submitted through the electronic portal at https://ventanilla.srs. gob.sv, which remains functional.
4. Dissolution of DNM: The National Directorate of Medicines will be dissolved within 120 business days after the law’s enforcement. The SRS will regulate medicines, medical devices, nutritional supplements and other health-related products.
EMA published the updated documents GVP Module XVI – Risk Minimisation Methods (Rev.3) and GVP Module XVI Addendum II – Methods for evaluating effectiveness of risk minimisation measures. The revised final guidance is applicable to new applications for marketing authorisation, new risk minimisation measures and new studies evaluating risk minimisation measures for authorised medicinal products but not immediately applicable to existing risk minimisation measures and ongoing activities regarding risk minimisation measures; however, where existing risk minimisation measures are amended, the revised guidance should be taken into account and applied if this is considered likely to increase the effectiveness of the risk minimisation measure without jeopardising its familiarity for patients and healthcare professionals using the concerned medicinal product.
Honduras Effective 25th Jun 2024 ARSA announced in AVISO A-ARSA-009-V1 the implementation of the Periodic Safety Report (IPS) for monitoring the safety profile of registered human medications, biologicals and biotechnological products. This new mechanism aims to ensure the continuous updating and guaranteeing of product quality, safety and efficacy. The process is effective immediately and can be conducted online.
Guidelines regarding the IPS have not been published yet.
Japan Effective 28th Jun 2024 PMDA published a partial revision regarding updates to the display of materials for additional risk minimisation activities in Risk Management Plans (RMP). The revised guidelines allow for the creation of RMP materials that include information not directly related to risk minimisation activities (referred to as ‘supplementary sections’), with updated display methods for the RMP mark and the table of contents. To facilitate this, the display methods for the RMP mark and the table of contents have been updated to indicate the presence of such supplementary sections.
Latvia Effective 25th Jun 2024 ZVA published updated versions of the Submission and approval of educational materials stipulated by the risk management plan and the Submission and agreement of Direct Health Care Professionals Communication (versions 2.1 and 1.1, respectively). The updates include clarifications regarding image requirements of an orange palm, QR code and the submission process.
Saudi Arabia Effective Aug 2024 SFDA announced a new email address (DS@sfda.gov.sa) for specific communications to the drug safety department, replacing the general email (NPC@sfda.gov.sa). This new email should be used for:
• Submission of PSUR/PBRER
• Submission of PSUR cycle
• Receiving requirements/responses from MAHs that were sent from the Drug Safety department email
• Send Safety variation requests to MAHs
• Responding to inquiries and consultations regarding the department’s tasks
• Requests to object to the Authority’s decisions regarding the safety and effectiveness of medicines after marketing.
Swissmedic released updated Guidance for the electronic exchange of Individual Case Safety Reports (ICSRs) through the PV Gateway, version 6.0. The update includes a new layout. No content adjustments were made to the previous version. The document became effective as of 16-Aug-2024.
Swissmedic also released new Guidance for Industry on the Electronic Exchange of ICSRs in E2B(R3) Format through B2B Gateway, version 1.0. The document became effective as of 26 Aug 2024.
Turkey Effective June 2024 TITCK announced that, starting 6th June 2024, pharmaceutical companies must enter information about pharmacovigilance officers and their deputies into the ESY system under the ‘Pharmacovigilance Transactions’ section. Data entry should also be done retrospectively for previously notified officers. For guidance, companies can refer to the TITCK ESY Project Company User Guide, available on the login screen.
Source: iViReg (Pharmacovigilance Regulatory Intelligence Knowledge Management System) developed by iVigee Services a.s.
THE ROLE AND IMPORTANCE OF REGULATORY
INTELLIGENCE (RI) IN GOOD PHARMACOVIGILANCE
PRACTICE (GPvP)
Jana Hyankova, RQA GPvP Committee and Paulina Valuskova
Are you interested in the latest Regulatory Intelligence (RI) updates published quarterly in Quasar? Would you like to explore how these updates can help pharmaceutical companies address compliance challenges and improve safety practices? Are you curious to learn what an ideal RI process should look like to maximise its impact on compliance and safety? This summary offers a comprehensive exploration of these topics, highlighting the essential role RI plays in maintaining compliance and enhancing pharmacovigilance practices.
RI involves systematically gathering, analysing and interpreting regulatory information from various sources, such as regulatory and health authorities, industry associations and scientific publications. Regulatory intelligence is a critical component of GVP, playing a vital role in ensuring compliance, managing risks, supporting strategic decision-making and driving continuous improvement in pharmacovigilance (PV) systems. It allows pharmaceutical companies to navigate complex regulatory landscapes effectively, ensuring the safety of their products and maintaining trust with regulatory authorities and the public.
CULTIVATE COMPLIANCE
In the context of GVP, RI helps ensure that pharmaceutical companies stay compliant with ever-evolving global regulations and guidelines. RI provides insights into changes in regulations or notificiation of new guidelines that could affect PV systems, procedures and activities. For instance, updates to reporting requirements, signal detection methods or risk management plans (RMPs) may be swiftly integrated into pharmaceutical companies’ PV Quality Management Systems (QMS).
PROACTIVE RISK MANAGEMENT
By staying informed about upcoming regulatory changes, pharma companies can proactively adjust their PV systems to better manage risks. Fit for purpose RI allows companies to foresee potential compliance issues and address them before they become problematic, in turn minimising the risk of non-compliance penalties. Good RI also aids in understanding compliance expectations around the benefit-risk assessment of medicinal products, enabling companies to make informed decisions about product safety.
STRATEGIC AND GLOBAL DECISION-MAKING
The right RI informs strategic decisions about product development, market entry, and lifecycle management. A proper understanding of the regulatory landscape can help pharma companies decide which markets to enter, based on the complexity and stringency of PV requirements. It also supports decisions on labelling changes, communication strategies and interactions with regulatory authorities by providing evidence-based insights into the expectations and precedents set by regulators.
Pharma companies with global ambitions must navigate different regulatory environments. Suitable RI facilitates harmonisation of PV practices across regions, resulting in consistency in safety reporting and risk management. Legitimate insights into local regulations, such as variations in reporting timelines or the need for region-specific Risk Minimisation Measures (RMM), will necessarily affect global PV strategies.
CROSS-FUNCTIONAL COLLABORATION
Effective RI supports collaboration among divisions within a pharmaceutical company, such as regulatory affairs, safety, clinical development and quality assurance (QA). This collaboration is essential for implementing GVP across the organisation. Overall, it safeguards interdepartmental alignment with the latest regulatory requirements and best practices, reducing the likelihood of errors or oversight in all phases of drug development, registration and market entry.
CONTINUOUS IMPROVEMENT
GVP emphasises the importance of continuous improvement in PV practices. RI contributes to this by identifying trends, emerging issues and best practices in the industry, which can be adopted to enhance a company’s PV system. By monitoring regulatory changes and updates, as well as enforcement actions and common compliance issues, RI helps prevent similar pitfalls and drives the continuous refinement of PV processes.
HOW DO YOU GATHER RI TO SUPPORT YOUR PV SYSTEMS AND PROCESSES, AND IS THE RI YOU GATHER FIT FOR THIS PURPOSE?
To maximise budget utilisation, most pharmaceutical companies rely on Contract Research Organisations (CROs), consultancies and service providers to gather and interpret RI on their behalf. These partners generally apply similar methods to the collection, assessment and reporting of RI. But similar methods do not necessarily result in the same quality of outcome and all too often, input from multiple providers becomes necessary, which adds time and cost to the process. So how are pharma companies supposed to decide how best to comply with PV RI mandates?
Industry experts believe the most important factors to consider when deciding which method is the best fit are:
1. Speed of access to the latest intelligence.
2. Simplicity of workflow management and alert summaries.
3. Reliability of impact assessments of the RI.
The most innovative and cost-effective RI gathering and reporting systems are automated. The best systems are those that enable on-demand, remote and secure access from any location at any time. The industry-leading systems are also scalable, adaptable and customisable in a manner that facilitates comprehensive workflow tracking, covering every step from data entry to impact assessment where every action is documented with a complete audit trail. And, to maintain inspection-proof compliance, these automated systems must reside on a Good Practice (GxP) validated platform. All told though, speed of access and ease of use are meaningless if the available intelligence is improperly assessed, which is why it is imperative that the RI is properly assessed and reported by first-in-class global and local experts.
A BIT OF FUN – ANSWER THESE QUESTIONS TO TEST YOUR UNDERSTANDING OF THE ARTICLES FROM THE LAST EDITION OF QUASAR. (ANSWERS BELOW)
Question 3 – Navigating the Future of Quality: A Look towards the Horizon in Clinical Research and Development
Which of the following records transactions across many computers so that the record cannot be altered retroactively?
a) Artificial Intelligence
b) Machine Learning
Question 1 – The Role of Pharmacovigilance in Oncology Drug Trials
Which trials are designed to evaluate multiple treatments simultaneously?
If you would like to submit an article for a future edition of Quasar on a topic you feel would be of interest to our membership, please contact the editor: editor@therqa.com
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