Limitations of Current Digital Validation Tools

However, early systems, such as electronic document management systems (EDMS), often lacked functionalities like direct testing within platforms or effective document data management, resulting in persistent challenges around data integrity and efficiency. Many solutions were custom-coded applications that fell short of meeting the comprehensive needs of the validation lifecycle.

In recent years, commercially available off-the-shelf DVTs have emerged, designed for the digital management of validation data—from creation and execution to oversight. These tools enhance efficiency, compliance, and traceability in ways paper-based processes could not achieve. Yet, limitations remain. Below, we explore several of the major limitations that the industry is currently facing:

• Improved execution logics within DVTs
• Data harmonization
• Data structure: enabling machine learning (ML) and AI
• Reporting: real-time visibility
• Audit readiness
• Flexible pricing models for small and mid-market organizations

Improved Execution Logics within a DVT

Current DVTs often lack robust execution logic during form filling, resulting in incomplete or out-of-specification (OOS) data submissions. This technology is widely used in the banking sector and the general public use it every day when purchasing using a credit card to ensure the fields are completed correctly. These issues are frequently identified during the review process, leading to delays and rework.

Enhanced execution logic, inspired by sectors like banking, could ensure that all required data is correctly entered before submission. Key features for example include:

Challenges

1. Human error: High rates of incomplete or incorrect data due to inadequate validation rules.
2. Rework costs: Significant delays and expenses associated with identifying and correcting errors post-submission.
3. Inconsistent interfaces: Poorly designed forms lead to confusion and increase error likelihood.

How Vendors Can Help

1. Advanced validation engines: Integrate more sophisticated validation logic to catch errors at the point of entry
2. AI-powered assistance: Implements AI-driven suggestions and real-time error predictions to guide users through data entry
3. Enhanced user experience: Design interfaces that simplify workflows, group related fields logically, and use visual aids like progress indicators.
4. Automated quality checks: Develop tools that preemptively flag potential issues before submission

Data Harmonization

The integration of DVTs into the broader digital ecosystem remains underdeveloped. A harmonized approach would enable seamless data exchange between DVTs and other enterprise systems, such as QMS/EDMS systems, manufacturing execution systems (MES), and enterprise resource planning (ERP) applications.

Harmonization involves creating a unified framework where disparate systems, processes, and data sources can work together seamlessly. This includes standardizing data formats, aligning metadata structures, and ensuring interoperability between platforms. Without this, inefficiencies such as data silos, manual workflows, and inconsistent standards hinder decision-making and compliance.

Challenges

1. Siloed systems: Lack of interoperability leads to duplicated data and manual workarounds.
2. Inconsistent standards: Disparate formats across systems hinder data integration.
3. Limited collaboration: Teams struggle to access unified datasets for decision-making.

How Vendors Can Help

1. Develop interoperability standards: Implement robust APIs and middleware to facilitate seamless integration
2. Provide centralized data repositories: Offer solutions that enable real-time access and synchronization of data across platforms
3. Enable enhanced collaboration: Introduce tools that allow cross-functional teams to interact with unified datasets efficiently.
4. Offer training and support: Equip teams with the knowledge to implement and utilize harmonized systems effectively

Example: EDMS Integration: Breaking Down Walls

Despite advancements, many DVTs remain isolated from EDMS platforms. The EDMS system is typically the first step where Vendor engineering data is harnessed and imported into a client system. However, this data is rarely validated once and typically departments repeat this validation step multiple times. For example, in some organizations, the validation team and maintenance teams may check the exact same asset details at different timepoints on a project, with no added value. This lack of integration hinders seamless workflows and creates inefficiencies in document management and traceability.

Integrating DVTs with maintenance, calibration, and periodic validation processes is crucial for achieving a holistic and efficient validation ecosystem. Traditionally, organizations often segregate these activities into separate departments, leading to the development of discrete methodologies that do not immediately interact, despite all supporting the common goal of ensuring product quality.

Challenges

Despite advancements, integrating a data management system (EDMS) into a DVT presents significant challenges. Many DVTs remain disconnected from EDMS platforms, preventing the efficient flow of vendor engineering data into client systems. This disconnection forces multiple departments to independently validate the same data, introducing redundancy and inconsistencies. Furthermore, a lack of integration complicates traceability and hampers seamless workflows, increasing the potential for errors and delays. Such fragmented processes make it difficult for organizations to achieve a unified approach to validation and data integrity.

Integrating DVTs with maintenance, calibration, and periodic validation processes is crucial for achieving a holistic and efficient validation ecosystem. Traditionally, organizations often segregate these activities into separate departments, leading to the development of discrete methodologies that do not immediately interact, despite all supporting the common goal of ensuring product quality.

Benefits:

Integrating DVTs with EDMS platforms unlocks substantial benefits by fostering streamlined and interconnected workflows. This integration enables real-time validation, reducing redundancy and improving data accuracy across departments. This integration enhances traceability, ensuring regulatory compliance and facilitating audits with greater ease. Additionally, combining DVTs with maintenance, calibration, and periodic review and/or requalification processes promotes a comprehensive approach to validation, supporting better resource allocation and improving overall operational efficiency. Ultimately, the alignment of these systems strengthens an organization’s ability to ensure consistent product quality and adherence to industry standards.

Challenges:

1. Data Silos: Limited interoperability between DVTs and EDMS results in duplicated effort and fragmented data.
2. Manual Workflows: Teams often resort to time-consuming manual processes to synchronize information across systems.
3. Traceability Gaps: Inconsistent data flows can lead to audit risks and compliance challenges.
4. Costly Custom Solutions: Organizations often need expensive custom integrations to achieve even basic interoperability.

How Vendors Can Help:

1. Develop Open APIs: Facilitate seamless integration between DVTs and EDMS by providing robust, well-documented APIs.
2. Streamline Workflows: Build tools that automate data exchange and reduce manual intervention, ensuring efficiency.
3. Enable Bidirectional Data Exchange: Create systems that allow for real-time updates and synchronization across platforms to enhance traceability.
4. Provide Scalable Integration Solutions: Offer out-of-the-box integrations that can be easily configured for diverse organizational needs.
5. Deliver Training and Support: Educate teams on best practices for leveraging integrated systems effectively, minimizing errors and maximizing utility.

A harmonized approach would enable seamless data exchange between DVTs and other enterprise systems, such as EDMS, MES, and ERP tools. It also involves adopting industry-wide standards and frameworks to ensure consistency across platforms, reducing inefficiencies and fostering improved collaboration. Ultimately, harmonization supports real-time decision-making, enhances data integrity, and paves the way for advanced analytics and AI applications.

Conclusion

A harmonized approach to integrating DVTs with enterprise systems such as EDMS, MES, and ERP tools is essential for addressing these challenges. This requires adopting industry-wide standards and frameworks to ensure consistency across platforms, thereby reducing inefficiencies and fostering collaboration. By enabling seamless data exchange and real-time decision-making, organizations can enhance data integrity, streamline operations, and unlock the potential for advanced analytics and AI applications. The path forward lies in leveraging scalable, vendor-supported solutions that empower organizations to achieve holistic and efficient validation ecosystems.

Example: Shattering Paper-On-Glass

Early integration of DVTs may still rely on the paper-on-glass method of data capture, where digital records are generated that still closely resemble the structure and layout of a paper-based workflow. In general, the wider industry is still relatively reluctant to transition away from paper-like records out of process familiarity and uncertainty of regulatory scrutiny.

Whilst DVTs have assisted the transition of many paper-based processes, the paper-on-glass method is still heavily limited in how digital validation data can be effectively utilized. Modern facilities that intend to utilize DVTs to revolutionize their approach to validation need to look beyond paper-on-glass and aim for data-centric capture methods instead.

Challenges

1. Limited design: Data capture is limited by the design of the digital record which often originates from the design of a previous paper record
2. Ineffective extraction: Data extracted from paper-on-glass records tend to require manual intervention thus reducing the effectiveness of data utilization
3. User error: Many paper-on-glass records may lack sufficient logic to prevent avoidable data capture errors
4. Inflated record generation: Utilizing a paper-on-glass still tends to lead to the generation of unnecessary records of inflated sizes (e.g., front cover pages)
5. Slow validation process: Content has to be fully controlled and managed manually; no advantage from data-centric (content-based) solutions can be taken

How Vendors Can Help

1. Offer data centric solutions: Promote the benefits of utilizing data centric approaches over paper-on-glass
2. Simplify workflow build: Reduce administrative burden of workflow design by simplifying the process for developing configured data capture workflows
3. Engage with regulatory authorities: Involve representative engagement from regulatory authorities to reduce reluctance from industry on adopting data-centric DVTs
4. Deliver training and support: Educate teams on best practices for designing and utilizing data-centric workflows effectively, minimizing errors and reducing paper-on-glass driven burden

By transitioning away from paper-on-glass solutions, validation data can be more effectively collected, organized, and analyzed without being limited by structural limitations. By removing these limitations, validation activities can be designed with quality built in from the onset and continue to support dynamic and effective risk-based decision making.

Data Structure: Enabling ML and AI

Data structure refers to how data is organized, stored, and retrieved within a system. For DVTs, enabling next-generation ML and AI requires robust, standardized, and scalable data structures that can handle diverse datasets and complex analytics. To enable validation 4.0, the implementation is just a primary starting step to achieving the principle of real-time verification. To truly enable real-time verification a possible solution is to combine this with emerging Technologies such as large language models and machine learning. Current DVT data structures often hinder advanced analytics by lacking uniformity, metadata tagging, and integration capabilities essential for modern technologies.

Challenges

1. Lack of standardization: Disparate data formats make integration and analysis cumbersome, limiting the usability of DVT data for AI applications
2. Data silos: Data stored in isolated systems restricts its accessibility for cross-functional insights.
3. Insufficient metadata tagging: Inconsistent or missing metadata impedes data discovery and classification for advanced analytics
4. Scalability issues: Legacy systems often fail to handle the large and complex datasets required for effective ML/AI implementation

How Vendors Can Help

1. Standardized data formats: Provide tools and guidelines to enforce consistent data structure across all DVTs
2. Metadata tagging frameworks: Introduce automated tagging solutions to improve data discoverability and ensure completeness
3. Data lake integration: Offer seamless connectivity to centralized data lakes, enabling cross-system data sharing and analytics
4. Scalable architectures: Design DVTs with cloud-based, scalable infrastructures to accommodate increasing data volumes and computational demands
5. AI-ready pipelines: Develop built-in workflows to preprocess and structure data for ML/AI models, reducing implementation effort
6. Continuous support: Provide training and ongoing updates to ensure users can adapt to evolving ML/AI technologies

Reporting: Real-Time Visibility

Real-time reporting capabilities remain a critical gap in many DVTs. While DVTs offer reporting within their systems, they do not have a streamlined integration with client scheduling software which renders DVT ineffective in managing resource loading or identifying constraints in the review/approval system. Enhanced reporting features for example could include:

Real-time reporting empowers sponsors to monitor the actual status of validation activities and make informed, risk-based decisions. By presenting live data on critical metrics—such as test execution progress, defect resolution rates, and approval cycles—sponsors can quickly identify areas requiring intervention or adjustment. This visibility reduces decision-making delays and helps align validation efforts with strategic objectives.

Benefits for Risk-Based Decision-Making:

• Immediate risk identification: Highlight emerging risks, such as delayed approvals or unresolved defects, before they escalate
• Resource allocation: Use real-time data to allocate resources more effectively to critical areas of need
• Trend analysis: Continuously track trends in validation performance to proactively address recurring challenges
• Enhanced collaboration: Provide transparent data to all stakeholders, fostering better alignment and communication across teams

By integrating these real-time reporting capabilities, DVTs can significantly enhance oversight, ensuring that sponsors can confidently manage their validation processes while mitigating risks.

Challenges

1. Delayed decision-making: Stakeholders often lack immediate visibility into critical metrics, leading to bottlenecks
2. Fragmented data sources: Reports frequently pull from multiple, disconnected systems, increasing complexity
3. Limited customization: Standardized reporting fails to meet the specific needs of diverse teams

How Vendors Can Help

1. Unified reporting frameworks: Develop integrated solutions that consolidate data from various systems into a single platform.
2. Real-time data sync: Implement tools that update reporting dashboards in real time to avoid lags
3. Flexible customization options: Offer modular reporting tools that allow stakeholders to create views tailored to their specific roles and responsibilities
4. Training and support: Provide guidance on how to leverage reporting tools effectively for actionable insights

These advancements would streamline oversight and improve transparency, enabling faster, informed decision-making while addressing current limitations in reporting frameworks.

Audit Readiness

The most common consistent item to overcome is Audit Readiness. Preparing for audits remains a challenge with current DVTs. Enhanced features for example should consider including:

Challenges

1. Fragmented audit data: Data scattered across systems makes consolidating audit evidence time-consuming and prone to errors
2. Lack of real-time updates: Audit trails and compliance metrics are often not updated in real-time, leading to outdated information
3. Complex regulatory requirements: Navigating varying compliance standards adds to the complexity of audit readiness
4. Resource-intensive preparation: Manual efforts to prepare for audits consume significant time and resources

How Vendors Can Help

1. Centralized audit management tools: Provide platforms that consolidate all audit-related data in one location
2. Real-time monitoring systems: Enable tools that continuously update audit trails and compliance dashboards to provide up-to-the-minute information
3. Customizable regulatory frameworks: Offer configurable templates and workflows that cater to different regulatory environments
4. Automated reporting features: Introduce systems that generate detailed audit reports with minimal manual intervention
5. Continuous support and updates: Provide ongoing training and regular updates to ensure users remain compliant with evolving regulations
6. Embeds AI as a chatbot operating on the database and quickly provides the first answer to a given question

Flexible Pricing Models for Small to Mid-Market Organizations

Small to mid-size organizations often face unique challenges when adopting DVTs due to limited budgets and resource constraints. Unlike larger organizations with dedicated validation teams and substantial budgets, smaller sponsors must operate within tighter financial and operational margins.

Challenges

1. High implementation costs: Advanced DVT systems often require significant upfront investments, including software licenses, hardware upgrades, and onboarding costs, which are prohibitive for smaller sponsors
2. Limited IT infrastructure: Smaller sponsors may not have the robust IT frameworks necessary to support DVT deployment, leading to additional expenses for infrastructure upgrades
3. Complex interfaces: Many existing DVTs are designed with enterprise-scale users in mind, resulting in steep learning curves for smaller teams
4. Resource constraints: Limited personnel and expertise make it challenging to manage the implementation and ongoing maintenance of DVT systems
5. Scalability challenges: Most DVTs are not tailored to scale down effectively, making them impractical for small-scale operations

How Vendors Can Help

1. Affordable pricing models: Offer flexible payment options, such as tiered pricing, pay-as-you-go plans, or subscription-based models, to lower the financial barrier for smaller sponsors
2. Tailored solutions: Develop simplified DVT versions with core functionalities, eliminating unnecessary complexity while meeting the specific needs of smaller sponsors
3. Turnkey implementations: Provide end-to-end deployment packages that include installation, configuration, and initial training to minimize the burden on internal teams
4. Robust training programs: Create easy-to-understand, role-specific training materials and offer virtual support sessions to ensure teams can efficiently use DVTs
5. Cloud-based solutions: Introduce cloud-hosted DVTs that reduce the need for significant infrastructure investment and provide scalable resources on demand
6. Community support networks: Foster online communities or forums where smaller sponsors can share knowledge, collaborate, and troubleshoot common challenges.

Conclusion

By addressing these challenges, vendors can empower smaller sponsors to adopt DVTs confidently, ensuring they can compete in an increasingly digital landscape. Expanding access to DVTs will not only enhance validation processes but also promote greater inclusivity and innovation across the industry.

While DVTs have transformed the pharmaceutical industry, significant limitations persist that hinder their full potential. These gaps include deficiencies in execution logic, the lack of seamless data harmonization, challenges with AI readiness, real-time reporting inefficiencies, barriers for smaller sponsors, EDMS integration difficulties, and the complexities of audit readiness. Addressing these issues is critical for achieving the next level of digital transformation.

DVTs have shown tremendous promise in enhancing compliance, improving efficiency, and fostering innovation. However, the path forward lies in addressing the specific challenges that inhibit widespread adoption and optimal functionality. These include high implementation costs for smaller sponsors, siloed systems that block interoperability, and legacy structures that limit the application of AI and advanced analytics.

By addressing these challenges with innovative strategies, DVTs have the potential to revolutionize the pharmaceutical industry’s validation processes, driving forward a new era of digital validation that is efficient, compliant, and future-ready.

By proactively addressing these areas, DVTs can achieve their true potential, enabling the pharmaceutical industry to enhance compliance, improve efficiency, and drive innovation. With vendors playing a pivotal role in overcoming these challenges, the next decade could see DVTs revolutionizing validation processes on a global scale.

The authors are part of the ISPE Commissioning and Qualification Subcommittee on Digital Validation. This sub-committee has is currently developing the ISPE Good Practice Guide on Digital Validation Tools which is expected to be published in Q2 2025.

Review recently published and upcoming ISPE guidance documents