The reality is that changing your technology platform becomes significantly harder and more disruptive as your biotech company and its pipeline mature. Instead of waiting until your pipeline expands, establish deep technical roots before scaling. Standardizing on the right EDC platform across all clinical phases and studies – rather than switching tools based on early phase needs – reduces long-term complexity, lowers your total cost of ownership (TCO), and effectively prepares your biotech for sustainable growth.

The cost of fragmented systems

While this study-by-study approach might seem practical, it introduces hidden inefficiencies across your portfolio. Instead of building momentum, clinical teams lose valuable time starting from scratch for every single study because they lack a base standards library to build from.

This technical fragmentation quickly trickles down into everyday operations. Inconsistency across phases leads to disjointed downstream systems and data integrations. Critical data governance is often overlooked until data issues emerge late in the pipeline. Ultimately, the constant state of change management forces teams to waste resources on recurring vendor contracts, distinct system validations, and repetitive user training.

How a small biotech standardized for long-term scale

Benefits of broad EDC standardization

By standardizing EDC early, your data management team can begin contributing to a global standards library from day one. Instead of rebuilding the wheel for every trial, this foundational setup reduces the configuration and build effort required for all subsequent studies. Because most EDCs share similar core functionality, user preference among site staff and CRAs is heavily driven by familiarity. Keeping the same system across phases provides a consistent user experience. This is especially crucial for early-phase oncology trials where investigative sites are managing actual patients rather than healthy volunteers.

Beyond day-to-day usability, broad standardization improves your inspection readiness and financial efficiency. Having a single, unbroken audit trail across all clinical phases makes it much easier for your team to speak confidently to regulators during inspections. This approach also delivers a much lower TCO. By eliminating the operational drag of managing multiple software vendors, executing completely separate system validations, and maintaining scattered integrations, emerging biopharmas can protect both their timelines and their budgets.

Looking beyond the next study

Even if your company’s long-term vision isn’t fully certain, or if a phase II program is still three years away, planning for EDC standardization pays off unless your immediate strategy is to sell off the asset. Taking a short-term view of clinical technology only creates compounding technical debt. Emerging biopharmas do not need to navigate this journey alone. A comprehensive system like �Թϱ��� EDC provides the necessary speed and affordability upfront, while offering a clear offramp to transition complete data ownership back to the sponsor over time as your internal team and expertise grow.

Watch the demo to see how �Թϱ��� EDC can streamline your data management processes.

The post A Business Case to Standardize EDC Across Study Phases first appeared on �Թϱ��� Systems Europe.

From managing regulatory complexity to Clinical Trials Information System (CTIS) limitations, here are five key topics disclosure leaders discussed at �Թϱ���’s recent R&D and Quality Summit.

#1. Prepare for manual CTIS workflows as the EMA prioritizes stability

The European Medicines Agency (EMA) is prioritizing operational reliability and core CTIS modules over new innovation.

A key challenge is the lack of bidirectional API functionality between CTIS and sponsor systems. Because the EMA is unlikely to overhaul these APIs before 2028-2029, sponsors and CROs must prepare for heavily manual workflows.

#2. Leverage structured data for CTIS preparation

Without bidirectional APIs, managing data governance across TMF and RIM systems is resource-intensive and error-prone. Simple document misclassifications create major compliance and confidentiality risks.

Sponsors can mitigate this by pulling structured data directly from CTMS to auto-populate disclosures. This replaces manual entry risks with accurate, pre-packaged PDFs ready for CTIS upload.

#3. Bridge the gap between clinical operations and regulatory affairs

Silos between clinical operations and regulatory affairs cause process disconnects and ambiguous CTIS ownership. With health authorities requiring responses within 48 hours, organizations must unify workflows to meet RFI timelines.

Connecting start-up documentation with RIM data gives teams real-time visibility into protocol changes for faster turnaround times.

#4. Anticipate IVDR as the next major regulatory risk

In Vitro Diagnostic Regulation (IVDR) requirements are already active, even though the European Database on Medical Devices’ portal won’t launch until late 2026 or 2027. This gap forces organizations to navigate fragmented local ethics processes.

Additionally, IVDR performance studies do not map cleanly to existing clinical and regulatory architectures. Sponsors anticipate this will become an operational hurdle over the next 12-24 months.

#5. Put redaction and AI on your roadmap

Managing commercially confidential information is a massive administrative drain. �Թϱ��� is tackling this burden by introducing and expanding the integration with Real Life Sciences (RLS) anonymization tools.

Looking further ahead, context-aware AI initiatives with �Թϱ��� Falcon will support SOP-driven automation and interpret evolving confidentiality rules.

Want to keep up with changing disclosure requirements? �Թϱ��� Disclosures is expanding support beyond ClinicalTrials.gov and CTIS to include Japan, the UK, Germany, and China. These improvements help teams manage evolving global requirements from a single, unified ecosystem.

The post Navigating the Post-EU CTR Reality: 5 Takeaways from �Թϱ��� Summit first appeared on �Թϱ��� Systems Europe.

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When I joined my new company, my goal was to streamline regulatory operations and turn our team into a strategic partner for the organization. After successfully transforming regulatory operations at my previous company, I knew that with the right tools, regulatory operations could accelerate innovation and speed therapies to patients.

When I joined, my company had �Թϱ��� Submissions, Submissions Archive, and Registrations. I learned the company’s existing workflows and provided recommendations to streamline end-to-end processes by capitalizing on features built within �Թϱ��� RIM.

The key was to show how regulatory operations can be a strategic asset for the organization, so we needed to understand how to maximize the �Թϱ��� RIM platform when we added �Թϱ��� Submission Publishing. And we wanted to implement it in record-setting time.

Next, I wanted to pressure check the “validation paradigm.” Previously, the team did testing and validation that really didn’t return any benefit, and because �Թϱ��� is a validated environment it was somewhat redundant. So, we adopted a risk-based approach for system changes. This allows the team to automatically adopt upgrades from �Թϱ���’s three releases a year with a streamlined validation process.

“�Թϱ��� Submission Publishing can be a strategic partner to accelerate innovation and speed to patients.”
Matt Neal
Executive Director, Global Regulatory Operations Strategy & Innovation

Getting leadership buy-in

The leadership team and I established a mutual understanding early on to pave the way for these transformational plans. Together, we developed goals and I educated them more deeply on what regulatory operations and the RIM system does today. Then we discussed how it can improve their visibility into what’s happening, and provide valuable data for insight and decision-making.

From that point, I created a plan to show how regulatory operations could be leveraged as a strategic partner to change our submission development process. Because we took the time to define the organization’s needs and align them to our goals, we had leadership’s backing throughout the process.

After successfully implementing �Թϱ��� RIM in a previous company, I was confident in the value we could bring by adjusting our processes. At that company, we were able to spend more time on submission quality versus rote tasks, and submit a breakthrough product Biologics License Application (BLA) faster than expected. Seeing continuous publishing in action for five years at my previous company, I knew we could improve the process here.

Changing the publishing mindset

While we were updating and improving the regulatory operations process, we were always focused on the goal of end-to-end continuous publishing. This meant more productivity and higher quality, as well as reduced stress on the publishing team. I wanted to bring publishing out of the world of heroics, late nights, and weekends and to smooth submissions operations.

I also planned a “team of rivals” style roll-out to help demonstrate the value. One group ran the new process alongside another group running the old process. The goal was to dispel concerns and win them over to the new way of working. I knew that even the skeptics would come around once they saw �Թϱ��� Publishing in action, and sure enough, they all wanted to use the new system when they saw the time savings.

Experiencing a fast and successful implementation

After reconfiguring �Թϱ��� Registrations, Submissions, and Submissions Archive, the regulatory team was ready to implement �Թϱ��� Submissions Publishing. As a company focused on expanding the highest quality oncology therapies to people around the world through “persistent innovation and challenging the status quo,” leadership was behind the plan to move fast.

We implemented Publishing eLearning and used that to self-pace learning alongside the implementation activities with �Թϱ��� and the process exercises. We also loaded a sandbox with our actual documents and data so that the teams could work in a “real world” environment while doing the process exercises. As a result of this “self-guided” approach, conversations with �Թϱ��� became more focused and we were able to quickly execute effective change management, rather than sitting through long educational workshops.

I like to say that it was unique, slightly insane, and incredibly successful. As a result, we implemented �Թϱ��� Submission Publishing in 16 weeks, and I think we could’ve done it faster. �Թϱ��� pushed to keep some live education as a buffer to ensure our success, but the eLearning materials were so thorough that we didn’t even need it.

Outcome: regulatory operations as an asset vs. a cost center

With the backing of senior leadership and my experience with the system, the regulatory operations team successfully implemented the full �Թϱ��� RIM platform to accelerate speed to market. Publishers no longer spend late nights preparing submissions — with continuous publishing, they proactively manage the submission. We now have a single source of truth that acts as the collective memory of the product. The company identified unrealized value within its existing RIM platform by adding �Թϱ��� Submissions Publishing, creating an end-to-end process that transformed regulatory operations into a strategic partner.

About the author:

Matt is an experienced regulatory operations leader with a track record of driving innovation across the pharmaceutical and biotech industries. As executive director of global regulatory operations, strategy and innovation, he leads global initiatives focused on digital transformation, operational excellence, and regulatory modernization. His career spans leadership roles at GSK, Amgen, and Atara, and he was recognized as a �Թϱ��� Hero in Regulatory in 2023. Matt is passionate about advancing regulatory science through technology, collaboration, and bold thinking.

The post Turning Regulatory Ops into a Strategic Partner with Continuous Publishing first appeared on �Թϱ��� Systems Europe.

From streamlining CDMO-sponsor collaboration to accelerating batch release and integrating AI, the key takeaway was that simplifying and standardizing quality processes drives a tangible business impact. As life sciences leaders explore and implement new applications to improve quality in their organizations, the industry is poised for an exciting evolution.

Data as the critical foundation for AI

Quality innovation using AI surfaced repeatedly as a discussion point at this year’s Summit. During the Quality Keynote, we heard from two industry leaders on successful AI strategies for quality.

Magalie Blackie, head of global quality services at Haleon, explained that in a quality context, AI is only as valuable and transformational as the data feeding it, while premature deployment amplifies existing data issues.

Sanofi’s Head of Quality Performance Transformation, Jean-Philippe Francou, recommended that quality leaders focus their AI implementation time and effort on laying a strong data foundation ahead of launch and roll-out across the organization.

“You can’t do anything with AI without quality data. That’s #1.

And to get quality data, you need standardized processes and digital solutions.”
Magalie Blackie
Head of Global Quality Services, Haleon

Magalie Blackie of Haleon and Jean-Philippe Francou of Sanofi
join �Թϱ���’s President of Quality Cloud Mike Jovanis in the Quality Keynote, May 2026.

to hear more AI quality strategy insights.

In a Quality Zone session about solving the right quality management pain points with AI, Novo Nordisk Director Hasse Schøbel reminded us that more data does not automatically equal a ‘better’ AI implementation. Instead, he recommends prioritizing data scope and quality over volume, and applying quality thresholds and controls when data is created so that it can work as an effective AI building block.

Quality leaders now recognize that standardized processes, and having the correct underlying applications, help deliver clean and connected datasets for deeper digital innovation.

Quality Cloud innovations and updates

For a conservative area like quality, understanding how to introduce AI safely and effectively remains a top focus for our customers. During the Quality Keynote, I shared our vision, strategy, and guiding principles for Vault AI. This covered specific agentic Vault AI capabilities we’re introducing and how they will increase efficiency across the board.

Presenting Vault AI’s agentic capabilities in the Quality Keynote

Alongside these AI innovations, the Quality Keynote outlined the broader �Թϱ��� Quality Cloud vision of optimizing and simplifying quality processes for greater efficiency. This includes recent advancements in our core applications, including:

• The new Document Viewer in �Թϱ��� QualityDocs
• Collaborative supplier questionnaires for expanded external collaboration in �Թϱ��� QMS
• How �Թϱ��� Validation Management, �Թϱ��� LIMS, and �Թϱ��� Batch Release reduce time to market

to learn more about new and upcoming Quality Cloud product updates.

How life sciences leaders are modernizing quality

Thirteen �Թϱ��� customers shared their stories at Summit, explaining how modernizing quality management is improving efficiency for their organizations:

• AstraZeneca’s IT Director Paula Haynes shared her organization’s transformation of its validation approach, from using a bespoke tool to adopting Validation Management. It expects to cut cycle times by 20% and reduce event volumes with more right-first-time processes.
• Aenova Group’s VP of Quality Systems Excellence, Letizia Caccialupi, detailed her company’s migration to Quality Cloud. Aenova uses QMS, QualityDocs, and �Թϱ��� Training, and moved from a mixture of highly localized process variations to a single, globally standardized model requiring just 10 SOPs (down from 140 SOPs previously) for the processes implemented in �Թϱ���.
• Sobi’s Petter Gallon, director of global quality systems, outlined why the company’s shift to fully outsourced manufacturing was underpinned by its launch of Batch Release. Sobi now has a single source of truth housing all QA data and supporting automated jurisdictional control, which makes batch release decision-making faster and more accurate than before.

Best practices from quality leaders

Quality leaders from a range of biopharma organizations shared other important insights with peers. Highlights include:

• Novo Nordisk’s Hasse Schøbel recommends maximizing AI impact by serving “the many, not the few” and applying AI to near-universal processes and pain points. Examples of high-impact areas to prioritize include document searching, reporting/trend generation, complaint handling, batch review, and writing/review assistance.
• A discussion group exchanged strategies for modernizing the QC lab, and identified lack of standardization as the biggest barrier to digital uptake in an industry area still largely dominated by paper-based processes.
• UCB’s Nadia Williams, head of IT compliance solutions, identifies digital validation processes as key for maximizing speed and inspection readiness. Nadia shared how UCB’s transition from “scattered” to “centralized” processes with Validation Management cut cycle time by 15%, with the company now targeting a 30% drop in CAPAs and audit actions related to validation.

Celebrating 2026 Quality Heroes

Every year, �Թϱ��� recognizes pioneering customers helping to advance life sciences. Our 2026 Quality Heroes pushed quality to new levels in their organizations, challenging the status quo to deliver better processes and outcomes.

• Strengthening global site collaboration to cut approval times: Birjo Backasch, director of global QA systems at medac, supported the move from fragmented, paper-based legacy systems to a unified platform that drives efficiency and compliance at scale.
• Modernizing quality processes for 9,000+ users across three diverse sectors: Holger Peitz, director of governance framework at Merck KGaA, replaced on-premises legacy systems with a modern, cloud-based quality management system spanning 66 countries.

Quality Heroes celebrated on stage

for more insights from the Quality Zone, including product roadmaps, keynotes, and customer and innovation theater sessions.

The post The Next Era of Quality:
Insights From Industry Leaders at the 2026 R&D and Quality Summit, Europe first appeared on �Թϱ��� Systems Europe.

The life sciences industry is on the brink of an operational paradigm shift. Driven by the rapid acceleration of AI in molecule discovery and the subsequent explosion of data from digital twins, biomarkers, and decentralized clinical trials, the volume and velocity of research output are reaching unprecedented levels.

Simultaneously, health authorities worldwide are modernizing their infrastructures, moving away from static document submissions toward rolling, real-time data submissions, leveraging AI for internal reviews. For regulatory affairs and regulatory operations leaders, this environment creates an opportunity to shift human involvement in regulatory processes further up the value chain. The regulatory organization of tomorrow will be an AI-first, data-driven, strategic partner to R&D.

Macro trends reshaping the regulatory organization

The traditional regulatory organization model is built on a document-centric foundation where submissions are treated as discrete, linear projects. As AI matures, this model will dissolve. Submissions will increasingly transition from independent, heavily managed projects to continuous, digital byproducts of the core R&D lifecycle. And there will be many submissions that leadership will expect regulatory teams to handle while maintaining headcount.

To handle these trends, organizations need to evolve how they think about working with AI. There are three distinct approaches for this:

• Human-in-the-loop: The current, proven standard where AI suggests content or data patterns, and a human manually reviews, edits, and approves every individual step. This is a great model for high-risk, complex regulatory tasks that may impact patient safety and hence benefit from human judgment, regulatory intent assessment, and experience with health authorities.
• Human-on-the-loop: A collaborative approach being piloted by some leading pharma companies. AI autonomously completes routine tasks within human-defined guardrails, while a central team with a mix of agent stewardship and process expertise continuously monitors their progress and intervenes as they deem necessary.
• Human-by-exception: An emerging operational state where AI handles end-to-end task execution but notifies a designated subject matter expert (SME) as its “human manager,” seeking aid when exceptions with lower-than-expected task quality are detected. This is an excellent model for low-risk, high-volume, laborious tasks.

It is essential to use the right collaboration approach with AI based on the risk profile of the task at hand. And as AI technology becomes more proficient, health authority guidance on AI oversight will also evolve. As this new world of working along AI agents as interns, assistants, and colleagues becomes more accepted, more tasks will move from left to right (in picture below).

During this shift, organizations will see the compounded value of using AI to improve human productivity and accelerate processes. It will give regulatory teams much-needed bandwidth to manage their growing portfolio without excessive hiring and evolve from a reactive, manual cycle of “submit, reject, and fix” to a proactive, automated approach of “predict, prevent, and submit.”

An AI-enabled organization model

Regulatory organizations have historically been organized by linear processes and have been execution-focused.

Consequently, the organizational chart must be reimagined. The traditional separation between regulatory strategy, regulatory information management (RIM), submissions management, and publishing will diminish, and teams will flatten into a networked, horizontal function. Teams will become more blended, integrated directly within therapeutic areas to act collaboratively alongside regulatory affairs partners from the beginning of the product lifecycle.

This new structure will result in new and modified roles:

• Regulatory data stewards and architects: As publishing specialists and submission managers move away from being “last-minute heroes” who compile and format final documents, they will advance to upstream data architects. These specialists will own data flows across R&D and ensure information is structured correctly from the outset.
• Content stewards: This role merges authoring and data stewardship across all data disciplines: medical writing, RA CMC, or non-clinical. Content stewards will leverage tools and data with AI-generated baselines to synthesize compliance narratives without the constraints of legacy document formats or functional silos.
• Agent managers: A new but critical governance role will emerge, responsible for understanding how specific AI agents operate, testing their functional boundaries, and providing feedback during practice runs. This role will also manage model drift and justify AI-driven decisions to health authority auditors during inspections.

Human involvement will systematically move up the value chain. As automated systems handle preparation, assembly, and routine compliance checks, human teams will focus primarily on data stewardship at the source, regulatory strategy, intent validation, and decision ownership.

The skillsets and mindsets of an AI-enabled team

For individual professionals, the rise of AI should be viewed as an energizing opportunity. The prevailing sentiment among industry experts is clear: AI will not replace people, but will enhance the way people work and solve problems. Skilled and experienced humans will be indispensable for their advanced judgment, creative thinking, and knowledge. They will work alongside the next generation of hires who bring in their AI and digital fluency skills.

To prepare for this future, regulatory professionals must build a multi-faceted skillset grounded in data fluency and cognitive agility. Essential skills include:

• Systems and data mindset: An understanding of how regulatory strategy translates into interconnected data models with lineage across multiple systems, recognizing how a change in one area impacts the entire ecosystem.
• Critical thinking and skepticism: An ability to evaluate AI outputs to identify errors, counter bias, and understand the strengths and weaknesses of different agentic frameworks.
• AI literacy: A mastery of effective prompting and mentoring of AI assistants, combined with the ability to monitor evolving Health Authority expectations and confidently explain AI-driven decisions to auditors.
• Adaptive reframing: The flexibility to reframe objectives and achieve controlled disruption within established regulatory boundaries set by health authorities.
• Resilience: The capacity to embrace uncertainty and evolve with curiosity and flexibility.

Organizations should seek “neural-network thinkers” who possess strong regulatory fundamentals but are fluid and adaptive, hiring for “capability adjacency” by embedding digitally skilled talent into expert regulatory teams to build highly innovative, hybrid units.

What leaders can do today

Regulatory leaders can execute specific strategic moves today to use AI as a competitive advantage. Instead of waiting for a perfect future state, leaders can reshape organizational blueprints now through deliberate, progressive shifts.

• Facilitate AI literacy, up and down: Arm executives with AI enabled systems to allow for self-driven realization of the value of AI. Upskill experienced regulatory professionals for AI fluency, while hiring smartly to build hybrid talent. This will build confidence, spark curiosity, and foster an adaptive culture ready for deeper collaboration with AI.
• Break down data siloes across R&D: AI is only as good as the data feeding it. Invest in cleaning datasets to ensure consistency and completeness and establish clear ownership and lineage.
• Baseline current processes: Don’t just automate a broken workflow. Before applying AI to any process, re-examine the workflow holistically to identify bottlenecks and top pain points and measure its current state. This ensures readiness to prove positive ROI.

Ultimately, introducing AI into regulatory operations is about increasing efficiency and speed in getting new therapies to market. But, it’s also about elevating the value of human input by reducing manual labor, unifying data, and creating better conditions for clearer judgment.

Learn more about purpose-built AI for regulatory teams.

The post Orchestrating an AI-First Regulatory Organization first appeared on �Թϱ��� Systems Europe.

We shared key innovations that will finally bridge the current gap between these stakeholders and address the lack of a global standard to connect and map clinical data. For example, with �Թϱ��� eSource, sites are in control of the technology and whether they choose to adopt it as their standard. This will help eSource adoption to scale where previous sponsor-owned (and imposed) tools have not.

Risk-based quality management (RBQM) is a capability that has been around for many years, but still with relatively low industry adoption. �Թϱ��� RBQM will be delivered within �Թϱ��� CTMS and �Թϱ��� DQS without adding another system to the clinical technology landscape.

Alex Franklin, director data management at GSK, said that, “Having �Թϱ��� DQS as a center for data orchestration and high level management of risk – interlinking with different functions – gives a connected landscape that will provide oversight of the data. That value-driven cleaning in a risk-based approach will be key for assessing quality and driving change.”

By focusing on innovating for value, we will deliver three AI agents this year to automate processes across study build, testing, data collection, and data cleaning. These AI agent-based automations will help reduce burden for sites and data managers.

These much anticipated innovations will add to the value now being achieved by the industry using the �Թϱ��� Clinical Platform:

Modernizing eCOA operations and site experiences

Fortrea: By changing ways of working, Fortrea is accelerating eCOA timelines and improving the user experience.

Across their portfolio of �Թϱ��� eCOA studies, Fortrea is delivering consistently with high patient compliance and de-risking the studies. Key results include:

• Kickoff to final specification in ~23 days
• Specification to UAT in ~8 days
• 96% average compliance across studies (reaching 98% for a study running nearly two years)

“Our compliance rates show that eCOA is a product that patients want to use and isn’t a burden to them. That ease of doing something is what makes people come back to the app.” — Clare Campbell-Cooper, Global Head, Digital Health & Innovation

Improving end-to-end efficiency with a connected ecosystem

Top 20 biopharma: Building on the success of its clinical data program, the company scaled up change management and achieved its first �Թϱ��� EDC and DQS production trial within eight months. The biopharma will have 100% of study starts in �Թϱ��� EDC and DQS in 2027, and has eliminated custom trial integration with metadata-driven configuration. The clinical data team is on course to achieve its goal of a 4-6 week study build time.

The company’s product director says: “We had a trial go from protocol finalization to EDC go-live in 7 weeks. It’s really impressive what the data management organization has done.”

Global ophthalmology biopharma: With ~200 studies in �Թϱ��� EDC and ~60 studies in �Թϱ��� DQS, the company averages 10 days from last patient last visit (LPLV) to database lock (DBL). The team recently expanded its use of �Թϱ���’s Clinical Platform to include �Թϱ��� RTSM and �Թϱ��� eCOA.

�Թϱ��� eCOA is now mandated for all new studies and the team averages 3-5 days for IRB-ready survey builds, and can shorten that to under a day in critical situations. By leveraging a reusable library and advanced eCOA dynamics, the company has eliminated 90% EDC edits across questionnaires and maintains 95% compliance across studies.

Boehringer Ingelheim: The company consolidated 40+ systems and 70+ interfaces into a single clinical platform, and saved more than 100 hours on data transfer, monitoring, and support by automating site setup from �Թϱ��� CTMS to EDC.

“When you see the end-to-end processes work, it’s like music.” — Julian Righetti, Regions Head, Clinical Development Operations, Boehringer Ingelheim

Gaining clinical data ownership and oversight

Recordati: The company moved from full service outsourcing (FSO) to bringing ownership of systems in-house for the �Թϱ��� Clinical Platform: CTMS, Study Training, Study Startup, EDC, DQS, eCOA, and RTSM. Recordati demonstrated how a connected clinical platform supports data accessibility and risk-based decision making, remaining overall cost neutral while achieving faster study build timelines and strengthening inspection readiness, including successful regulatory inspections.

Continuing the journey

Biopharma companies and CROs are achieving faster study timelines and improved patient experiences with a connected platform approach. These outcomes highlight the advantages of focusing on user needs and building strong partnerships. If you’re looking to transform your clinical trials and learn more from industry leaders who are driving change, �Թϱ��� R&D and Quality Summit provides the opportunity for insights and connections to start your own platform journey.

Register for �Թϱ��� R&D and Quality Summit in Boston.

The post A New Wave of Clinical Data Innovation: Faster Timelines and Better Experiences first appeared on �Թϱ��� Systems Europe.

Their feedback points to a clear theme: for many fast-growing biopharma companies, batch release remains too manual, too time-consuming, and too difficult to manage in real time. While every organization has its own operating model, product mix, and supply chain complexity, the responses are notably consistent.

Quality leaders are looking for ways to reduce manual effort, improve visibility, and coordinate batch release activity into a more connected digital process.

Batch release is still heavily manual

A found that “under-digitized and inconsistent” batch disposition processes damage efficiency and compliance in biopharma companies.

Feedback from �Թϱ��� Quality Executive Forum attendees aligns with this trend. When attendees were asked to describe the current state of batch release in their organizations, one word appears more than any other: manual.

This feedback reflects a broader challenge across the industry. Batch release often depends on information that lives across multiple functions and systems, including quality events, laboratory testing, supplier documentation, ERP data, regulatory information, market registrations, and product genealogy. When that information is not connected, teams are left to gather, check, and reconcile it manually.

For emerging biopharma companies, that may be manageable at an early stage. But as product portfolios expand, supply chains become more complex, and market footprints grow, manual release processes become harder to sustain.

The result is not just administrative friction. Manual batch release can slow decisions, increase dependency on tribal knowledge, and make it harder for teams to know whether a product is truly ready to release for a given market.

Leaders know what they want to improve

�Թϱ��� also asked attendees what they would fix first if they had a “magic wand” for batch release. There was another clear consensus on the answer: automation.

That priority is consistent with the challenges attendees identify. If manual effort and time are the biggest obstacles, automation becomes the natural area of focus. But feedback also shows that leaders are not looking for automation in isolation. They are looking for more connected, consistent, and visible batch release processes.

Top priorities include:

• Improving consistency and collaboration
• Moving away from paper-based processes
• Strengthening reporting
• Improving visibility into lot genealogy
• Connecting batch release with supplier documentation
• Integrating with ERP systems
• Automating jurisdictional release control

Taken together, these priorities point toward a more modern model for batch release: one where teams can bring together the data, documents, quality events, market requirements, and release status needed to make decisions faster, and with greater confidence.

The opportunity is not simply to digitize individual steps. It is to reduce the amount of manual coordination required across the full release process.

Benefiting from batch release best practice

This push for more automated and connected batch release processes isn’t new. The �Թϱ��� Batch Release product roadmap has been informed by a growing industry drive to move batch release activity into the fast lane by aggregating batch data to accelerate release decision-making.

By adopting a purpose-built solution that unifies real-time content and data streams from QMS, ERP, LIMS, and RIM activities, quality leaders can now automate manual and time-consuming processes for accelerated batch release.

Disposition owners and Qualified Persons (QPs) can access automatically centralized batch histories and lot genealogies at a glance, saving significant querying time and making market- ship decisions quicker, easier, and more accurate.

Automated monitoring of market registrations and changes facilitates jurisdictional control for companies shipping to multiple territories. Using collated batch data, real-time “traffic light” compliance statuses are displayed automatically on a market-by-market basis to indicate whether to move ahead with shipping.

The impact of automating ‘slow,’ ‘time-consuming’ legacy release processes can be considerable for biopharma operations:

• Reduced manual effort with automatic centralization of batch data from across the business, saving hundreds of hours of manual querying
• Minimized risk of non-compliant product release through proactive alerts and indicators
• Streamlined release processes that minimize carrying costs and deferred revenue

Looking ahead

The trends across all five Forum sessions are consistent. Leaders of emerging biopharma companies are dealing with batch release processes that often require disproportionate manual effort. They also know what needs to change: more automation, better visibility, stronger integration, and more consistent ways of working.

For organizations planning the next stage of growth, batch release modernization is not only about improving efficiency. It is about building a process that can scale with the business while giving quality teams the information they need to make timely, more accurate release decisions.

Looking to optimize your own batch release processes? Watch “Faster Batch Release: How to Get Started”.

The post Evolving Batch Release: Challenges and Priorities for Biopharma Leaders first appeared on �Թϱ��� Systems Europe.

As an industry we have not succeeded at true lasting change. Concepts like eSource and risk-based monitoring are not new to clinical research; many of these visions have existed for nearly 20 years and yet have not moved us towards our goal of more efficient and less costly clinical trials.

With operational pressures mounting, the focus must now shift from hype to delivery of innovation. By solving the most critical problems first with near-term realistic solutions, we can translate long-held visions into the scalable, standard practices that clinical trials actually need.

Innovating the whole ecosystem

While biopharma has proven that individual innovations can work in isolation, true transformation requires a connected ecosystem that functions across all stakeholders: sponsors, CROs, sites, and patients, and in compliance with regulatory authorities. We need to innovate in a way that allows for progress in one area to support the others – and we’re at a unique moment in time where technology and industry necessity have finally converged to make this possible.

�Թϱ��� continues to simplify and standardize clinical data while connecting stakeholders. Here is our vision to deliver value for sponsors, sites, and patients this year.

Innovating for sponsors and CROs: RBQM and RBDM

Industry research shows that data managers struggle with uniform cleaning across disconnected systems and workflows, spending too much time on inefficient manual tasks like data reconciliation, review, and cleaning. Data quality is at risk if inefficiencies aren’t addressed. Data management needs embedded automation and a commitment to risk-based initiatives.

According to ICH E6(R3) guidelines, risk-based quality management (RBQM) is governed by two principles: Quality-by-Design (QbD) and proportionality. QbD relates to the planning phase, and proportionality to the execution phase of a clinical trial. Risk-based data management (RBDM) is the application of these two RBQM pillars to the clinical data lifecycle.

RBQM should be employed to integrate clinical data with operational metrics, such as CTMS data, to orchestrate actions across functions. will align with ICH E6(R3) by automating according to three data catalog dimensions: value, risk, and burden. It will be the accounting record of all the data being processed, including what it is, why it’s valuable, and what we need to do to protect it.

To automate any process with precision, we need a record of which data point supports which endpoints. For example, when a primary endpoint collected in an EDC system is identified as critical, it usually undergoes source data verification (SDV). Automating review and SDV plans tells data managers what to focus on, and eliminates the need for SDV altogether. Having a workbench such as �Թϱ��� DQS to deliver key risk indicator (KRI) and quality tolerance limit (QTL) metrics drives RBQM and RBDM, by triggering signals for downstream systems.

This will enable clinical development teams to scale RBQM and RBDM with precision.

Innovating for sites: eSource

Manual data entry from paper to EDC systems is still common practice at sites. eSource holds promise to resolve associated challenges with paper-based processes, including: duplicative entry and errors, the need for lengthy SDV, delayed data, and considerable burden on site staff.

Historical attempts at EHR-EDC integration have resulted in one-off builds that are costly and lack repeatable connections – a considerable time and effort sink for sites. eSource as a solution is not new, but has so far failed to scale. To succeed, we need to empower sites to carry out digital data capture at the point of patient contact, with their own systems and applications.

will capture data once at the site where it flows directly into the sponsor’s environment, eliminating the need for transcription and associated verification. Sites will enter the three main data sources: EHR with FHIR mappings, direct data capture (through �Թϱ��� SiteVault), and, in time, document scanning using AI. Direct data capture will allow site staff to use the sponsor CRF for their site-specific data entry, and a FHIR-based approach enables precise, automated transfer of audit-ready data elements. This has a significant impact on eSource’s scalability.

EDC integration reduces the burden, cycle time, and cost of data collection by connecting directly with the sponsor system. Sites can take the initial protocol definition from EDC and augment it, without having to define the casebook from scratch. EHR integration means existing patient information can flow into eSource, so the site only needs to capture data that’s unique to the protocol. This all flows back to the EDC in real-time. Sites with existing eSource can work with their preferred tools and still connect to �Թϱ��� EDC, through an open API framework.

The industry has had proof of these concepts for years. Why will we now succeed with a scalable eSource? �Թϱ��� has the required specific infrastructure – SiteVault – and a wider connected ecosystem that brings together eISF, eConsent, CTMS, with the native interoperability between EHR and EDC.

Innovating for patients: Engagement and circle of care

Sponsors and sites both want to give patients the best study experience possible. But the complexity of trials, unreliable eCOA systems, and manual processes create considerable burden for patients. It’s important that we ease trial participation to improve engagement.

�Թϱ��� is focusing on not only giving patients more information, but easy access to timely and helpful information. We are expanding My�Թϱ��� for patients, building on the existing framework to extend beyond eCOA, adding study task view, assessment instructions, support material, site communications, and training. It’s a single point of information for all clinical trial activities with the vision to increase patient engagement.

The framework for delivering value at scale

Historically, concepts like eSource and RBQM have failed to embed and scale because the industry lacked foundational standards and focused on only one stakeholder at a time. According to a , nearly two thirds of organizations are either unsure, or do not have, the proper data management practices required for AI projects. Too often, the promise of a new flashy tool distracts from building or fixing the data foundation.

Think of deep-space travel. Before you can even think about your destination or autonomous navigation, you need a stable launchpad, a flawless fuel delivery system, and expert ground control staff. If your “ground data” is shaky or unorganized it doesn’t matter how advanced your engine is. Building the foundation isn’t boring prep work, it is crucial to the mission.

“If you abandon these big idea rollouts after two years because the money has dried up or the project is ‘done’, you don’t see it systematically embedded in the organization. I think at the end of the day that’s a disservice to our patients.”
Leianne Ebert
Head of Global Data Management, Alcon

We’ve built from the bottom up, tackling the most critical problems and building a solid data foundation first, so that IT teams can deliver reliable solutions repeatedly.

The change management challenge

Humans fear change. Over the years, vendors have developed technology to solve industry problems, but when clinical development teams don’t use the tech to its full potential, then people become the bottleneck.

There will always be new technology. AI will follow the innovation adoption curve along its course and something else will emerge for the early adopters to become excited about.

Crucially, must be initiated in parallel with the development of any new technology to ensure the industry can actually adapt to these new ways of working.

“I think we live in pilots entirely too long. We need to actually enforce the rollout systemically across the organization. We have a responsibility as leaders to come back and show that the objective measures of success have been achieved.”
Leianne Ebert
Head of Global Data Management, Alcon

The time is now

Success requires a functional tech ecosystem that is fully adopted by sponsors and CROs, sites, and patients. �Թϱ���’s unique advantage lies in having dedicated, separate teams for all three, which allows for a “triangulation” of expertise and support. By partnering with technology pioneers, we can take proven concepts and deliver them to the industry as mainstream standards, ensuring a controlled delivery of meaningful value.

�Թϱ��� can enable the industry with technology, and help organizations transition over time. Scalable innovation is a series of steps, rather than a single momentous leap. These calculated steps give change management the chance to keep up, but leaders must be prepared to lean into a more stable, connected future of clinical data.

Connect with industry leaders and explore how to put these strategies into practice at �Թϱ��� R&D and Quality Summit in Copenhagen or Boston.

The post Unlocking Clinical Data Value for Patients, Sites, and Sponsors first appeared on �Թϱ��� Systems Europe.

I recently sat down with leaders from Ipsen, Servier, and Bayer to discuss how they are building more efficient, data-driven KOL engagement strategies to ensure successful launches.

Validating and expanding the KOL landscape

All three leaders shared how they’ve moved away from traditional, subjective mapping in favor of objective data to deliver on business objectives across multiple functions and organizations. With Link Key People, they are challenging long-standing assumptions about who the “right” experts are and how to engage them.

Ipsen had an unmet need to have a unified approach to scientific expert identification and mapping, rather than using various data sources across departments within the business. “The MSLs had multiple data sources from which they were curating data, and [Link Key People] allowed us to bring that all together,” says Lucie Williams, global head of medical communications, capabilities and education. Link Key People also helped Ipsen validate and expand existing thought leader lists across all relevant therapy areas.

“MSLs can easily view HCP profiles, which helps them review and validate current thought leaders, and also identify emerging experts to consider.”
Lucie Williams,
Global Head of Medical Communications, Capabilities and Education, Ipsen

Bayer similarly found value in comparing subjective HCP lists, which had been pieced together over time through congresses and networking, to objective data. “Every time a new country started using objective data, they learned a great deal,” explains Daniel Jardanhazi-Kurutz, deputy director, customer engagement and digital initiatives. “Managers can pull a list from �Թϱ��� Link and ask their teams if they were visiting these stakeholders. And if not, why?’”

Yann Peoc’h, world operations transformation excellence director, says Servier also wanted to improve the way it engaged with HCPs in mature product areas by learning more about their recent publications and clinical trials. “We were able to challenge our assumptions: who we are targeting, and our knowledge about the HCPs we engage with,” he says. For new launches in oncology, where the patient journey involves many HCPs and stakeholders, access to deep KOL insights is also crucial. “We used this huge amount of data to map and better understand the HCPs that are engaged in these complex patient pathways.”

Accelerating congress planning and impact

Congresses help build scientific authority during a launch, but preparing for them is logistically complex. Using a centralized source of deep congress insights to identify experts and gather intelligence, Bayer and Ipsen have simplified congress planning.

“If you’ve ever tried to go through congress websites, it’s a catastrophe,” Jardanhazi-Kurutz says. “How do you find the people? How do you find the sessions? �Թϱ��� Link has all of that in a very structured manner.”

“You now have the confidence that every single piece of congress content — and we’re talking about hundreds and thousands of congresses — is in there, and it’s a very simple search.”
Daniel Jardanhazi-Kurutz
Deputy Director, Customer Engagement & Digital Initiatives, Bayer AG

Lucie Williams adds that staying up to date through real-time insights helps the Ipsen team maximize their time at congresses. “One of the real benefits of using Link is having the real-time data feed when a congress is happening. HCPs are actively contributing on social media which provides important insight into the scientific focus areas of interest at the congress. It’s a valuable tool for us as we prepare for congresses.”

Enabling commercial field teams to engage more effectively

As a product moves into the commercial phase of a launch, the relevance of each KOL interaction is essential to strengthening relationships. Bayer’s commercial team has embraced data-driven insights to ensure they walk into every meeting as credible partners.

“Our commercial team accesses KOL profiles via Vault CRM to facilitate warm introductions,” Jardanhazi-Kurutz explains. “When they meet a KOL, they know what that person has recently published, what conferences they’re participating in, and what clinical trials they do. People appreciate that we know about their scientific activities which results in a significantly better conversation.”

Ingesting deep KOL data to fuel AI and analytics

Deep, accurate data is the foundation for any successful AI strategy. As the industry enters a new era of AI-driven innovation, Servier and Bayer have ingested curated KOL data from Link directly into their internal systems to unlock new use cases.

“A key value is that we aren’t just buying access to a portal,” Peoc’h explains. “Thanks to data ingestion, we get analytics that go one step further. For example, being able to look at how another biopharma is engaging with HCPs that we might not even be visiting is where we can unlock the next best action.”

Jardanhazi-Kurutz highlights that local Bayer teams are taking the lead in innovating beyond segmentation and targeting. For example, local teams have used the data to power:

• AI-driven insights: One local team built a custom, ChatGPT-style chatbot that pulls data from Link Key People, allowing users to make queries and receive instant answers.
• Operational precision: Another local team leverages the data to validate the fair market value of HCPs, ensuring that incentivization is well-aligned.

Kurutz emphasizes that technology is only as effective as the people behind it. “My most important stakeholders in this regard are the data scientists who see opportunities in combining this data with other data sets,” Kurutz says. “You need the right people who ask questions and are passionate about data mining.”

Explore how other top biopharmas are using �Թϱ��� Link to drive enterprise value.

The post Ipsen, Servier, Bayer: Insights on Data-Driven KOL Engagement for Launch and Beyond first appeared on �Թϱ��� Systems Europe.

This new way of working is enabled by a new technology architecture: Models, Agents, and Applications, the MAAP Architecture™.

Models are frontier models like Anthropic, OpenAI, or Gemini. They can reason, generate, and interpret. They understand the capabilities of your core applications and use that context in their reasoning.

Agents are a new type of knowledge worker. They use models for reasoning and applications for execution. Dedicated agents will take on some tasks that previously required humans.

Applications are your systems of record. They codify your industry’s business rules and your company’s operating model, providing the structure and consistency needed to run the business. Core applications are becoming “dual mode,” controllable by both human users and agents. To enable this, applications must be easily understood by models and controllable by agents.

Your enterprise technology architecture is no longer just a technical decision. It reflects your operating model and your workforce. It’s about digital labor and the nature of work itself. CEOs need to take an active role and redefine how IT and the business work together in this new era.

The post The New Architecture of Work: MAAP^™ first appeared on �Թϱ��� Systems Europe.