By Jonathan Gross, CPO Titian Software and Labguru

At SLAS 2025, I had the privilege of leading a session on one of the most pressing challenges in modern R&D: sample management in the era of multi-modal research. As scientific discovery continues to evolve beyond traditional small molecules, the ability to effectively manage diverse sample types—including biologics, cell therapies, nucleic acids, and antibody-drug conjugates—has become a critical bottleneck.

The Future of Drug Development is Multi-Modal

To set the stage, I shared projections from BCG's "Pharma of the Future" report, which estimates that new therapeutic modalities will account for $168 billion of the drug pipeline by 2029. This shift is already taking shape: a review of 2024 NIH-approved drugs reveals that 36% of new therapies originate from novel modalities, highlighting the urgent need for sample management infrastructures that can keep pace with scientific innovation.

Key Takeaways from Kristen Nailor (Genentech)

We were fortunate to hear from Kristen Nailor, Senior Principal Scientific Manager and Biologics Sample Management Team Leader at Genentech, who shared her experience in tackling the complexities of managing multi-modal samples. Some of the most notable insights included:

• Expanding the Scope of Sample Management Teams
  Traditional small-molecule management teams are now being asked to incorporate biologics, cell therapies, and nucleic acids - a fundamental shift in scope that requires new expertise and infrastructure
• Challenges in Centralisation
  Unlike small molecules, which have well-established centralised management systems, biological sample distribution remains fragmented. Some companies are integrating small- and large-molecule management from the outset, while others still operate separate teams
• Automation and Informatics Are Key Enablers
  In her experience, integrating automated liquid handling, barcoded sample tracking, and informatics platforms can help streamline multi-modal sample workflows. However, differences in sample regeneration, stability, and tracking make this more complex than traditional small-molecule libraries
• Balancing Standardisation and Flexibility
  Given the diversity of sample types, a one-size-fits-all approach is not feasible. Instead, organisations need modular and adaptable automation strategies that accommodate varying storage conditions, formats, and assay requirements

Roundtable Discussion: Industry Perspectives

Following Kristen’s presentation, we held a roundtable discussion with industry leaders to explore how technology and best practices are evolving to support multi-modal research.

Automation & Technology Innovations

John Fuller (Beckman Coulter Life Sciences) and Jason Meredith (Tecan) shared insights on how automation providers are adapting to multi-modal research:

• Storage & Liquid Handling Innovations
  New modalities often require different labware formats, sterility requirements, and cold storage solutions (-80°C, -196°C), posing significant design challenges for automation systems. Vendors are investing in more flexible, configurable storage and handling solutions to accommodate this.

• Emerging Technologies to Watch
  From AI-driven sample tracking to integrated robotics for large-molecule processing, automation companies are pushing the boundaries of what’s possible. The goal is to reduce the logistical burden on researchers so they can focus on discovery.

Software & Informatics for Multi-Modal Research

Arthur Yarwood (Titian Software) discussed how sample management software is evolving to support the increasing complexity of multi-modal workflows:

• Better Registration & Metadata Tracking
  Unlike small molecules, which are typically characterised by chemical structure, biomolecules require tracking of DNA sequences, expression conditions, and storage stability. Sample management platforms are evolving to capture richer metadata and ensure seamless tracking across research teams.

• Preparing for Future Modalities
  The panel agreed that new therapeutic modalities will continue to emerge, disrupting existing workflows. Organisations must future-proof their sample management systems by adopting flexible, scalable platforms that can adapt to new scientific advances.

Final Thoughts: Preparing for the Multi-Modal Future

As multi-modal research becomes the norm, companies must proactively address these sample management challenges to ensure they are not a bottleneck to innovation. Key strategies include:

• Investing in automation that accommodates diverse sample types
• Standardising informatics platforms to ensure sample traceability
• Centralising sample management where possible to improve efficiency
• Fostering collaboration between scientific, automation, and informatics teams

The Role of AI in Sample Management

One of the most exciting trends we discussed was how AI is transforming sample management workflows. Many companies are now investing in AI-powered solutions to optimise inventory tracking, predict sample needs, and automate quality control processes. Machine learning models can analyse sample usage patterns, suggest more efficient workflows, and even flag inconsistencies before they become problems. As AI-driven automation becomes more prevalent, it will further enhance efficiency, reduce human intervention, and allow scientists to focus on high-impact research rather than logistical challenges.

It was a fantastic discussion, and I am grateful to Kristen, John, Jason, and Arthur for sharing their expertise. The shift to multi-modal research presents challenges, but with the right strategies, it also opens new doors for scientific discovery.