Sapio Sciences Briefing Note
Axendia was recently briefed by Mike Hampton, Chief Commercial Officer, and Rob Brown, VP and Head of the Scientific Office, at Sapio Sciences. The focus of our discussion was the launch of Sapio’s AI Laboratory Notebook (AILN), branded Sapio ELaiN, which the company positions as the “first 3rd generation electronic lab notebook (ELN).” Sapio’s new release coincided with the Sapio Sessions tour kick-off and aims to reimagine the notebook as an AI-native “co-scientist” that can assist with experimental design, data retrieval and visualization.
Key takeaways from the Sapio Session in Boston, MA:
The ELN is a tool that has been available for decades, but the end-user’s needs have continued to evolve, making the traditional ELN concept outdated. Axendia has reported previously on the shifting expectations around functionality of digital tools for the lab, especially with the emphasis on digital transformation, knowledge transfer, product lifecycle management and overarching pushes for improved efficiency. In addition, while many life sciences organizations are experimenting with AI, only a fraction has taken full advantage of these capabilities to realize measurable business or scientific benefits.
From Large Language Models to Agentic AI
As customer needs continued to change, Brown explained that Sapio had come to recognize the limitations of generic large language models (LLMs) alone. While an LLM can be trained to command the software with natural language and to analyze text and numeric results within experiments, it cannot alone provide a full range of scientific analysis and design methods. Sapio’s latest release, ELaiN, incorporates agentic AI tuned to scientific contexts, which allows scientists to build complex experimental workflows through a handful of conversational prompts, saving a great deal of time.
For example, states Brown, “Sapio ELaiN can transform a document-based SOP into a structured experiment within minutes, which is a huge time savings compared to inputting the experimental design manually. It can retrieve relevant data across internal and external databases, and for medicinal chemists, suggest retrosynthesis routes and even recommend reagent sources.”
Brown continues, “The promise of AI-native lab notebooks is to let researchers “think about what data and outcomes they need from the experiment and spend less time toiling over the mechanics of the experiment.” Brown also emphasized that the system is not designed to replace scientific judgment. “The scientist remains the expert, just as a lead investigator would review and sign off on a technician’s work. ELaiN accelerates the process but keeps rigor and validation in place.”
With this new functionality, Kevin Cramer, CEO and CTO of Sapio Sciences, summed up the company’s perspective in their most recent press release announcing the AILN. “Almost one year ago, I predicted that AI would eat the ELN, and with Sapio ELaiN, traditional ELNs are now a thing of the past.”
Brown reinforces Cramer’s sentiment by elaborating on Sapio’s approach to product design and the incorporation of agentic AI functionality: “Rather than functioning as an add-on or bolt-on module, ELaiN was built as an AI-native environment. For workflows, it brings molecular docking, ad hoc analytics, codon optimization and small-molecule analysis directly into the lab notebook, with full provenance and compliance baked in. For organizations struggling to realize tangible returns from AI investments, this kind of embedded, workflow-aware intelligence represents a practical path forward.”
Understanding the Digital Landscape
Both R&D and Operations teams have long struggled with fragmented digital ecosystems such as multiple ELNs, LIMS and analytics tools that don’t talk to one another, and this is one reason behind the stalling of digital transformation efforts. A secondary issue is the difficulty in implementing new digital tools. Because of these roadblocks, Hampton stressed that Sapio is intent on making technology adoption as frictionless as possible. In addition, Hampton noted that Sapio’s foundation has always been “outside-in,” driven by industry trends and customer input. He states, “By unifying Sapio’s LIMS, ELN and Scientific Data Cloud, the company has continued to support multi-modal workflows, within the client’s digital ecosystem, while enabling GxP compliance across regulated and non-regulated environments.” Although the customer can bring on one module on its own, there is the potential for scalability with Sapio’s additional modules.
This is particularly relevant as organizations evaluate whether to build, buy or partner to stand up the right digital systems for their teams, inclusive of AI capabilities. Hampton observed that while the innovators in pharma have tended to build their own digital technologies early on, this can create a fragmented ecosystem later. Hampton estimates roughly two-thirds of companies have leaned towards a buy or hybrid strategy and also remarked that for the laggards, tools like ELaiN could actually help some of these companies leapfrog others because they’re not encumbered by legacy systems.
Overcoming Bottlenecks Across the Drug Development Lifecycle
One example of Sapio’s impact is at Schrödinger, a provider of advanced computational platforms for drug discovery and materials science. The company integrates physics-based modeling with machine learning to accelerate the design of novel therapeutics. The Sapio platform is helping Schrödinger’s scientists streamline research by embedding scientific context directly into workflows. Brown elaborates that “By reducing manual data entry and enabling more seamless integration across lab systems, Schrödinger’s teams are spending less time managing documentation and more time on the science. With AI embedded in ElaiN, the team can conduct queries and reach decisions much more quickly than before.” This shift reflects a broader industry move from experimental uses of AI toward practical tools that demonstrably improve productivity and accelerate R&D.
The same need for efficiency extends downstream into clinical development. It is a major milestone when a candidate therapy makes it to clinical trials, but this stage is also among the most resource-intensive. LabConnect, a global contract research organization (CRO), has launched LIMSConnect, built upon the Sapio Sciences platform, to help replace the manual, fragmented workflows associated with complex clinical research and to address the communication challenges around data exchange. “This is a great example of a customer looking to partner with Sapio in order to alleviate their clients’ pain points – with the goal of accelerating clinical trials,” notes Hampton. LabConnect has seen an improvement in study setup times, enhanced visibility into sample lifecycles, reduced transcription errors, and standardized processes across distributed sites. For situations like these, says Hampton, “Sapio is able to deliver a fully digital, AI-enabled environment that supports GxP compliance, maintains and improves data integrity, and ensure scalability across labs and multiple study sites.”
In Brief
The application of AI in the life sciences has so far been largely experimental, with limited measurable ROI. The next phase will focus on more utilitarian applications – tools that deliver consistent, practical value in real laboratory settings. Sapio’s AILN, described as the ELN that “thinks like a scientist,” reflects this shift by embedding scientific context into workflows and reducing the time spent on manual data entry and system management. Its multi-modal platform, which integrates LIMS, ELN, and the Scientific Data Cloud while supporting GxP compliance across regulated and non-regulated environments, helps to address long-standing challenges in knowledge transfer and product lifecycle management. This suggests the industry may be approaching an inflection point, with AI evolving from passive record-keeping to active support across the biopharma product lifecycle.
We will continue to provide updates on Sapio Sciences and AILN as they become available.
Related Content:
- Accelerating the Trajectory of Digital Sustainability in the Lab
- The State of Generative AI In Life Sciences: New Market Research
- Enabling Knowledge-Driven Manufacturing with an Agentic AI Platform
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The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.