What are the current trends and innovations in computer system validation methodologies and tools?

In 2023, one of the main trends has been the adoption of the CSA approach which combines critical thinking and a Risk-based approach to validation. Though the Risk-Based approach has been recommended by GAMP 5 previously as well, the focus on this approach has increased now. Apart from this, the use of SaaS has been a major trend in 2023, and most of the Life Sciences vendors are offering SaaS versions of their products. Leveraging vendor deliverables is also a trend which can reduce the validation effort and timelines. The other trend which is evolving fast is the use of Validation Life Cycle Management Tools which reduce paperwork and help to reduce the effort required for CSV projects.

Much of the Pharmaceutical industry has been slow to adopt a risk-based approach to computer system validation, even though regulatory guidance has permitted a risk-based approach for many years. E.g. EU GMP Annex 11, Effective 30 June 2011: "As part of a risk management system, decisions on the extent of validation and data integrity controls should be based on a justified and documented risk assessment of the computerised system". "Manufacturers should be able to justify their standards, protocols, acceptance criteria, procedures and records based on their risk assessment". The focus needs to shift to requirements, risk, and testing with records to show systems are fit for intended use, rather than generating documentation.

One key asset for Computer System Validation (CSV) professionals, in addition to technical expertise, is keeping abreast of industry regulations and standards. This involves understanding guidelines like GAMP and ISO standards pertinent to software validation, such as ISO 13485 for medical devices and ISO 9001 for quality management systems. Familiarity with regulatory mandates from entities like the USFDA and the EMA is crucial, especially in sectors like pharmaceuticals and medical devices. This knowledge empowers CSV professionals to ensure compliance, develop effective validation strategies, conduct thorough risk assessments, and uphold quality standards within regulated environments.

Here are the key trends and innovations in computer system validation methodologies and tools: Risk-based approach: Focuses on critical risks, reducing scope and cost of validation activities. Agile methods: Integrates iterative development and feedback, enhancing speed and flexibility in validation. Automation tools: Automates documentation, testing, and reporting, improving efficiency and consistency. Artificial intelligence: Utilizes AI for data generation and analysis, though it raises ethical and technical concerns. Lean documentation: Emphasizes simplified, standardised templates to streamline validation processes.

I have to agree with Munya’s point that the entire organization needs to be Invested in a risk-based approach. In this case the heavy lifting of the project must be done up front with the intended use (and functionality) clearly outlined and thought out before testing. Whereas up to this, typically entire systems are qualified based on generic requirements, then the end users were trained, then procedures are written and the system goes live. A risk based testing strategy would alleviate extraneous testing and prevent production issues after go-live.

Having worked with a wide range of organisations my observation and experience is that a risk-based approach can only be success if implemented collaboratively across cross-functional teams. It is more than just tools and frameworks. Everyone has significant input as subject matter expert of their functions. Quality/CSV should facilitate an environment of openness, honesty and responsibility. For some organisations risk-based approach is a paradigm shift, any change must be aligned with the organisation Mission, Objectives and Values. This ensures that the approach is better understood and supported by the organisation’s leadership. Most of the heavy lifting is associated with ensuring that there is enough stakeholder buying-in.

The Life Science industry has a strong legacy of rigorous Computer Systems Validation (CSV) practices, but the traditional "test everything approach" has become outdated, leaving GMP manufacturing facilities spending more time documenting than testing. Traditional CSV methodologies can see manufacturers spending up to 80% of their time documenting processes and only 20% of their time testing the efficacy of the solutions. An industry shift in validation practices is happening, as Life Sciences clients successfully embrace a "risk-based Computer Systems Validation (CSV) approach." This shift in focus has reported improved product quality, speed of delivery, cost and time savings.

Drawing from my CSV experience, a risk-based approach allows us to prioritize efforts and allocate resources appropriately, ensuring that critical and high-risk areas receive the necessary attention and validation. A well-performed risk assessment enables us to establish effective risk control measures. Regulators explicitly expect that risk control should be commensurate to the level of risk. Embracing a risk-based approach enables us to tailor our validation efforts effectively. Recognizing that 'one size does not fit all,' we can perform validation at the right scale, ensuring efficiency and accuracy for specific systems.

Something I’ve observed frequently is that organisations do not consider the complexity or risk of systems early on. They therefore cut corners when it comes to creating their requirement specifications, believing that high-level requirements will be sufficient. This is always an error, as there’s no way to test a high-level or vague requirement, nor is there any way to assess the risk of those rules, or to calculate or offer assurance of coverage. If your requirement looks anything like: “the system must have rules to manage x” then either someone is going to have to outline the specific rules (be they 10, or 5,000) later on, or you will not have assurance that they have been appropriately risk-assessed or tested.

Recent trend in CSV is Risk-Based approach following the publication of GAMP 5 edition 2 in September 2022. Some companies have adopted this approach in the past as well but it was not being followed globally. In 2024, We will see more companies following this trend.

In my experience, utilizing agile methodologies are essential to adopt for the future of innovation within regulated software assurance. Working within an agile framework is critical to modernize business processes to meet the needs of rapid testing cycles. Making this shift doesn’t necessitate a change of tools and technology to capture the CSV activities it does however absolutely require a different mindset and business process in order to achieve it.

The issue of improving the quality of the software is separate from the development methodology, tools, or automation. The quality of the software is related to the robustness of the software quality processes and the correct implementation by the stakeholders. This has been the issue since the inception of the first formal system development lifecycle methodology. Regarding the improvement of risk assessments by Agile, how specifically is this accomplished? The risk assessment methodologies are independent of the software development methodology.

Agile in GxP brings speed, collaboration, adaptability, and improved quality and risk management, ensuring a more efficient and compliant software development process. Speed and Responsiveness: Agile's iterative nature allows for faster development, ensuring quicker response to changing regulatory or market demands. Enhanced Collaboration: Cross-functional teams promote better communication and knowledge sharing, reducing the risk of compliance gaps. Adaptability: Agile accommodates evolving requirements and priorities, enabling organizations to remain agile and responsive. Quality and Risk Management: Continuous testing and feedback help identify and address issues early, enhancing product quality and reducing compliance risks.

I would like to add one more as exploratory testing and changes along with an iterative approach....which is also defined in updated guidance.

Biopharmaceutical operations (e.g. manufacturing, clinical research) continue to rely heavily on technology. Opportunities for growth, new product lines and new lines of business often depend on getting to market first, so any software solutions that support these activities must be implemented quickly to help achieve this goal. The Agile approach aggregates the ‘Agile software development approach’ with the traditional validation stages: validation planning; design, build and test; and validation reporting. Whereas the validation planning and validation reporting stages remain unchanged, the design, build and test stage will be adapted by using an Agile software development approach.

Traditionally, Waterfall methodology has been followed by companies where projects need to move in phases. Now, Agile methodology is prefer for validation projects especially as it saves time.

In the evolving landscape of Computer System Validation (CSV), the integration of Agile methodologies is a key trend. Remote Agile adapts to distributed teams and improves collaboration. AI and ML in Agile project management automate tasks for efficiency and predictive insights. Value Stream Management streamlines the process from concept to delivery. Innovations such as Agile Bots, VR, and AR are revolutionizing Agile processes. Automated CSV is emerging, using AI for accuracy and efficiency, and aligning with Agile for streamlined processes. New methodologies such as Business Agility, Disciplined Agile, and Domain-Driven Design are extending Agile principles beyond software, shaping the future of CSV toward adaptability and efficiency.

Agile methodology, renowned for its iterative and adaptable approach to software development, is equally valuable in Computer System Validation (CSV), particularly within regulated sectors such as pharmaceuticals and healthcare. Here's how Agile principles can be effectively applied in CSV practices: Iterative Validation Continuous Feedback and Collaboration Adaptability Cross-Functional Teams Agile Tools Examples of Agile Practices in CSV: Sprint planning meetings. Daily stand-up meetings and issue resolution. Use of automated testing tools. Adoption of faster feedback loops and integration testing. Incorporation of end-user feedback loops to enhance system acceptance and usability.

The usage of appropriate supporting tools made up of a minimum of validated supporting computer systems intended to manage the required electronic supporting documentation/records is highly recommended. It achieves more flexibility and speed in the management and control of the required deliverables. These deliverables do not need to be paper or electronic documents, they can also be electronic records in a computer system with the correct electronic signatures and audit trail. This opens the way to increased paperless validation.

In CSV, automation tools revolutionize efficiency and accuracy by automating tasks such as documentation and testing. Advanced tools, such as Validify, improve risk management and compliance by adapting to regulatory changes. The integration of predictive analytics and machine learning further streamlines risk assessment. Real-world case studies demonstrate these benefits. Emerging technologies will expand the role of automation in CSV, underscoring its growing importance in modern validation strategies.

From my perspective, the use of specific tools such as Application Lifecycle Management (ALM) or test automation becomes indispensable at a certain level of complexity or scale. This is especially true when the deployed IT solution is SaaS-based or a cloud-based solution, as the manual testing effort becomes unsustainable with the regular updates from the service provider, and an automated testing solution should be implemented. The key advantages here are: - Reduction of manual effort by up to 75% - Ensuring compliance - Faster release cycles - Higher test coverage through parallelization - Consistency and repeatability - Integration into CI/CD pipelines - Freeing up resources for value-adding tasks

For cloud Computing like XaaS, risk is more about data privacy and data security as regulated companies are more dependent on vendor procedures...

Data security and privacy, downtime and service availability, and vulnerabilities are the main threats in cloud computing. Performance enhancement and reliability must be prioritized during implementation. Public and shared cloud services require heightened surveillance and vigilance to defend against cyber attacks and ensure data persistence.

Here, the question arises whether cloud computing is part of the validation process or if a cloud-based solution must be validated. In the latter case, particular attention must be paid to the challenges in the GxP-regulated environment regarding change management. The goal is for a system to maintain its validated status. This means that the system must behave at all times as specified by the requirements and as expected. Approach: 1. Change Impact Analysis 2. Test Scope Definition 3. Testing and Documentation 4. Approval

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction Within cloud computing, there are three service models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Validating a cloud software system is a partnership between you and the SaaS vendor. To make the shortlist at a life sciences company, the software company must have a clear validation strategy as well as the processes and documentation to support it.

In the regulated industry, we can't rely solely on the Cloud Service Provider (CSP) for compliance and validation. In my experience, this is a crucial practice. When using cloud services, specific steps are essential: Audit the CSP-We ensure the CSP has a robust process for managing changes, verifying they follow industry standards. Service Agreement-This guarantees timely and adequate information and documents from the CSP about planned changes. It aids in assessing risks and planning validation activities. I've followed this approach throughout my career, crucial for compliance, data security, and reliability in the life science industry. These steps help us balance the benefits of cloud services while retaining control over compliance.

In 2024, life sciences will increasingly adopt cloud computing, integrating AI/ML and edge computing for improved performance and flexibility. Hybrid and multi-cloud strategies are popular for their cost effectiveness and security. Edge computing is critical for efficient data processing, especially in real-time applications. The integration of AI and ML into cloud platforms is accelerating, supporting data-driven decisions and task automation. Cloud security is paramount, with a focus on robust protection against cyber threats. The emergence of quantum cloud computing services will revolutionize areas such as drug discovery, highlighting the growing impact of cloud computing in life sciences.

In life sciences CSV, the top trends for 2023 are Risk-based and Cloud Computing. While Agile, Automation, and AI are present, their adoption is gradual. The real innovation lies in combining these trends. I've experienced over 50% effort savings and over 75% defect detection by blending Risk-based, Cloud Computing, and Agile. Embracing these trends is the path forward, and companies are adapting to these new changes for substantial savings and efficiency.

Artificial Intelligence (AI) is rapidly revolutionizing the landscape of Computer System Validation (CSV) by introducing innovative capabilities that streamline processes, enhance accuracy, and improve overall efficiency. One of the primary areas where AI makes a significant impact is in automated testing. AI-driven testing tools and frameworks have the capacity to automate various aspects of validation testing, from generating test cases to executing tests, analyzing results, and even adapting testing strategies based on evolving system behaviour. This automation not only accelerates the validation process but also reduces manual effort and human errors, thereby improving the reliability and accuracy of validation outcomes.

At the Validation and Regulatory Compliance Professionals group, I conducted a poll on Artificial Intelligence and 80 plus percent of our 11, 752 members indicated that they did not use AI. AI can potentially revolutionize CSV activities, by providing new ways of generating, analyzing, and validating data and information. Artificial Intelligence (AI) has the potential to revolutionize Computer System Validation (CSV) activities in the realm of ensuring the integrity, reliability, and compliance of computer systems. For a detailed post on how AI can provide new ways of generating, analyzing, and validating data and information in the context of CSV visit the Validation and Regulatory Compliance Professionals group.

In exploring AI for CSV in life sciences, key trends for 2024 include the use of generative AI to maximize the value of data and its integration with legacy AI models for improved processes. Challenges lie in linking AI to specific business outcomes and require robust data strategies, especially in healthcare. Effective AI application requires a blend of diverse perspectives, cost-effective solutions, and a focus on business-technology collaboration. Emphasis on digital literacy, talent acquisition, and redefining IT roles around data-driven use cases is critical to successful AI implementation in CSV, ensuring both technological advancement and compliance with industry standards.

any suggestion for online courses related to CSV which help me in my pharma industry. I am working in pharmaceutical company in QA department.