Life sciences R&D is entering a new era, defined by exponential data growth, increasing experimental complexity, and rising expectations for speed, traceability, and predictability. As organizations pursue digital labs, AI driven science, and automation at scale, the limiting factor is no longer data generation, but the ability to contextualize data into insights that can act. In this presentation, the VP of Product and Strategy will share IDBS’ platform vision for enabling this shift. By unifying fragmented experimental data, embedding scientific context, and orchestrating programmatic and agentic AI driven workflows, IDBS is helping organizations move beyond static analytics toward closed loop automation.

Many organizations share the vision of an integrated database for bioprocess development to enable advanced data utilization. However, the primary barrier to realizing this vision is often not the digital technology itself, but the challenge of capturing high-quality, structured data at the source—the bench. Specifically, capturing not just assay results, but the full lineage and context of each sample is critical. To address this challenge, we adopted a pragmatic strategy: building the system in-house, led directly by scientists who understand the scientific needs, workflows, and culture. We utilized IDBS Spreadsheets as the core interface for data entry. By replacing traditional experiment records with structured Spreadsheets, we ensure consistent data structuring without adding a significant burden to the scientists. Our team of scientists, trained in data structure design and spreadsheet development, works in close proximity to end-users. This allows for rapid prototyping, immediate feedback loops, and agile adjustments—capabilities that are often difficult to achieve with external vendors. We have successfully designed a highly normalized data structure while keeping the user interface intuitive and natural for bench scientists. Currently, we have released initial Spreadsheets for routine use and are continuously expanding the scope. Furthermore, we have established an AWS-based data platform where data extracted from E-WorkBook is already powering automated analysis applications. In this presentation, we will demonstrate how our scientist-led approach effectively bridges the gap between conceptual data strategy and practical, daily lab operations.

A materials‑centric informatics architecture was developed to support modern laboratory workflows by making inventory data the backbone of the electronic laboratory notebook (ELN). Instead of relying on fixed templates or serving primarily as a file repository, the ELN is organized around the core elements of laboratory work, including materials, samples, and equipment. This structure allows workflows to naturally adapt to how experiments are performed at the bench while ensuring consistent documentation, traceability, and data integrity. Within the IDBS E-Workbook ecosystem, calculations, step dependencies, and workflow logic are handled automatically in the background, including the generation of instrument‑ready instructions. This reduces the need for manual data transcription and detailed upfront planning. As a result, the same workflow can be executed interchangeably by bench scientists or automated platforms without differences in documentation, calculations, or data structure. Barcode‑based tracking directly connects digital records to physical laboratory actions, improving accuracy, auditability, and operational consistency. Because the system is anchored in inventory, it supports scalable reagent registration workflows and enables structured, request‑driven coordination of multi‑step activities across teams and laboratories. Together, these capabilities transform the ELN from a passive record‑keeping tool into an adaptive operational system that supports experimental execution, automation, and cross‑team collaboration, which delivers organizational efficiency.

Research teams produce great science — but manual data entry, fragmented tools, and copy-paste workflows across databases, SharePoint, and Excel create invisible overhead that compounds across every experiment, every day. This session tells the story of how one research organization eliminated that overhead by building a unified, API-driven workflow around IDBS E-Workbook, Inventory, and the IDBS REST API. We'll walk through the pain points that drove the transformation and a real-world case study in formulation experiment automation that delivered over 1,600 automated runs — saving approximately three hours per experiment and reducing copy-paste errors to near zero. Whether you're a manager wanting to understand what modern research operations can look like, or a developer curious about what the IDBS REST API can do in practice, this session offers concrete takeaways you can bring back to your own team. Attendees will leave with: a clear picture of the before-and-after workflow, an understanding of how E-Workbook, Inventory, and the REST API work together, and a framework for identifying similar automation opportunities in their own environment.

Scientific teams are under pressure to accelerate discoveries without compromising compliance or data integrity. This session introduces how the IDBS Next Generation Platform speeds up the Lab Loop while preserving rigorous governance and trust. We’ll show how Polar Intelligence turns historical data into actionable hypotheses and experiment proposals, and how Modular Workflows convert those proposals into validated, reusable processes that replace scattered SOPs and spreadsheets. Attendees will see guided execution, real-time instrument and inventory data capture and AI-driven summarization that removes hours of manual effort; all built on responsible, auditable, GxP‑aligned AI where your data always remains your own. We’ll close with a look ahead to the safe, compliant evolution toward agentic, autonomous lab execution.

This presentation showcases an exciting digital transformation at Novartis GCP Bioanalysis Labs through the implementation of IDBS Polar ELN as a truly end-to-end GxP platform for regulated ligand binding assay (LBA) bioanalysis. In close collaboration with Novartis IT and IDBS subject matter experts, the team configured a novel, highly governed solution that brings controlled execution, seamless documentation, and full data lineage directly into everyday laboratory work. What began with core capabilities such as reagent and compound management, biobank tracking, standardized templates, controlled electronic documentation, and equipment integration rapidly expanded into fully traceable LBA experiment execution. The result is a powerful new way of working that strengthens compliance, inspection readiness, and data integrity while reducing manual effort and improving operational efficiency. Beyond immediate impact, the implementation creates a forward-looking digital foundation for advanced analytics, AI enabled insights, and expansion into additional regulated bioanalysis modalities, making it a compelling example of innovation in modern GxP laboratories.

As AI adoption accelerates across the life sciences, the success of large‑scale initiatives increasingly depends on the quality, provenance, and governance of the underlying data. This panel brings together leaders from pharmaceutical R&D, health informatics, regulatory affairs, and AI engineering to explore the tension between rapid AI deployment and the rigorous data standards demanded by regulated environments. Panelists will discuss the challenges of scaling AI across multimodal biological and clinical data, strategies for harmonizing fragmented datasets, and the impact of evolving regulatory frameworks such as FDA AI/ML guidance and the EU AI Act. Beyond technology, the conversation will address the organizational changes needed to build trust, transparency, and accountability — enabling organizations to scale AI responsibly without compromising scientific rigor or patient safety.

This presentation outlines how Novartis discovery bioanalytical labs, in partnership with Novartis IT and IDBS developers, used IDBS SmartBook and E-WorkBook to streamline our lab workflows—from integration and automation to study execution and FAIR data reporting. We will cover key milestones from ideation through implementation, discuss challenges and solutions, explain platform adaptations for specific needs, and highlight KPIs demonstrating enhanced lab performance. This presentation explains how these improvements are advancing our mission by reducing cycle times, improving integration across applications and driving faster and thus improved drug discovery decision making.

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Bayer Crop Science invests heavily in research, development and regulatory efforts. Capturing IP, data and regulatory studies. We are a global organization that requires system solutions that are secure, robust and accessible. IDBS E-workbook has been a great systems tool to collect our science and regulatory studies. This year we will migrate from the E-workbook solution to IDBS Polar. This move will update our lab practices and provide functionality to integrate product pipeline endeavors across our organization.