Scaling Smarter: A New Era in Preclinical Animal Testing 

In preclinical research, there has long been an uncomfortable tension between the need for rigorous experimentation and the ethical considerations that come with it. The demand for high-quality data continues to grow, especially as the complexity of therapeutic candidates increases. Yet at the same time, societal and regulatory expectations are evolving, pushing the scientific community to rethink how that data is generated.

Recent developments have brought this issue into sharper focus. In late 2022, Congress passed the FDA Modernization Act 2.0, a significant shift in FDA animal testing policy that removed the long-standing requirement for animal-based preclinical trials. More recently, in 2025, the FDA outlined a roadmap to incorporate non-animal methods like organ-on-chip and AI-based toxicity models into preclinical evaluations, part of a long-term effort to make animal testing the exception, not the norm.

This shift is not limited to regulators. In a landmark announcement, the National Institutes of Health declared in May 2025 that it will no longer exclusively fund animal-based experiments, marking a major pivot toward human-relevant research models. Combined with public support for modern alternatives and the FDA’s evolving guidance, the momentum is clear: the future of preclinical research is being redefined.

For researchers, this raises a difficult question: Is it possible to scale research while reducing reliance on animal models?

The answer may lie in reimagining the tools that support both scientific rigor and more ethical approaches to preclinical animal testing.

Outdated Models: The Inefficiency of One-Candidate Workflows

Despite major strides in genomics and proteomics, the typical approach to preclinical animal testing has barely changed. Each therapeutic candidate is tested one-by-one in separate animal models. This linear workflow is not only slow, it’s expensive, labor-intensive, and consumes animal resources.

Consider this: a single study evaluating 24 drug candidates using one mouse per candidate would require 24 individual animals. At an average of $348 per mouse for study costs, that’s over $8,300 just for the animals alone. Add an estimated $21,600 in technician time, assuming 10 hours per study at $90/hour, and the total reaches nearly $30,000 for a single round of screening. For non-human primates (NHP), the costs are dramatically higher: with rhesus macaques now averaging up to $24,000 each due to supply shortages and care requirements, testing 24 candidates individually could surpass $576,000, even before factoring in housing or support staff. 

These inefficiencies create bottlenecks in drug development pipelines, delay candidate screening, and limit experimental scope. The model is overdue for disruption.

Introducing the 24-Plex Barcoding Kit: Triple the Data, Fewer Animals

Quantum-Si’s 24-Plex Barcoding Kit  is purpose-built for this disruption.

It’s the only commercially available system that allows researchers to test up to 24 protein candidates in a single model organism using direct protein detection and single-molecule sequencing. Built on the Platinum® platform, the kit offers:

• 50 fmol limit of detection (LOD)
• 10-fold dynamic range
• Less than 6 hours total workflow time, with only 1 hour of hands-on work

How Can 24 Candidates Be Tested in One Animal?   Each candidate is tagged with a unique protein barcode and introduced in multiplex within the same animal. After expression, samples are taken from various tissues to measure relative expression and localization, then Quantum-Si’s sequencing platform quantifies each candidate’s performance individually. This allows researchers to identify top candidates early, using fewer animals in preclinical animal testing, assessing protein expression directly while gaining deeper insights per experiment.

Real-World Impact

The implications of this upgrade are profound. In mouse studies, 24-plex barcoding can reduce animal use by up to 87%. In NHP models, the reduction climbs to 95%, resulting in substantial cost savings, faster timelines, and greater ethical alignment.

In practical terms, that means:

• Fewer study replicates
• Faster validation of promising targets
• Reduced variability across models
• Better use of limited biological material

It’s a more innovative, ethical approach to preclinical animal testing that scales with science and conscience.

Multiplexing Across the Pipeline: Use Cases and Applications

The 24-plex expansion isn’t just a quantitative upgrade; it unlocks entirely new experimental designs.

mRNA and LNP Therapeutics: Researchers can now test multiple lipid nanoparticle (LNP) formulations in one animal, accelerating delivery optimization across payloads, tissues, and conditions.

AAV and Gene Therapy: The increased throughput allows for simultaneous evaluation of 24 viral capsids or gene therapy constructs, streamlining selection and speeding up development cycles.[2] 

Protein Engineering and Drug Screening: Screening libraries of protein variants or therapeutic targets becomes significantly more efficient. Pooled functional screens can be run in vivo, delivering faster answers with fewer inputs.

Proteomics: By directly detecting protein expression and interaction, researchers can avoid the limitations of mass spectrometry and gain high-resolution readouts for pathway analysis.

This breadth of applications makes the 24-plex barcoding kit a core enabler of next-generation preclinical workflows.

Ultimately, 24-plex barcoding represents more than expanded throughput; it’s a practical solution to balancing scientific ambition with ethical responsibility. By enabling deeper insights per experiment and dramatically reducing animal use, Quantum-Si empowers researchers to rethink what’s possible in preclinical research, and to do it faster, smarter, and more humanely.

References:

Physicians Committee for Responsible Medicine. Landmark Shift: NIH Announces No More Exclusive Funding of Animal Experiments. May 15, 2025. Available at: https://www.pcrm.org/news/news-releases/landmark-shift-nih-announces-no-more-exclusive-funding-animal-experiments

American Veterinary Medical Association (AVMA). FDA Phasing Out Animal Testing in Preclinical Safety Studies. April 18, 2024. Available at: https://www.avma.org/news/fda-phasing-out-animal-testing-preclinical-safety-studies

U.S. Congress. S.5002 — FDA Modernization Act 2.0. 117th Congress (2021–2022). Available at:https://www.congress.gov/bill/117th-congress/senate-bill/5002

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David Sloan, PhD – VP, Market Development
Quantum-Si

David Sloan, PhD, joined Quantum-Si in July 2025 as the new VP of Market Development. He brings to the role more than 25 years of experience in the biotech industry, with a consistent focus on customer satisfaction, scientific engagement, and technology-driven application development.

Most recently, David served as Senior Vice President, Life Sciences at RedShift BioAnalytics, where he led Marketing, Product Management, Internal and External Scientific Applications, and Scientific Affairs / Collaborations. Prior to that, he held roles at ProteinSimple as a Sales Specialist and Applications Scientist, further deepening his expertise in supporting customers and advancing novel proteomic platforms.

David earned his PhD from Duke University Medical Center in the Department of Pharmacology and Cancer Biology, where he studied protein-protein interactions with the goal of identifying new therapeutic strategies. He also holds dual bachelor’s degrees in Biology and Chemistry from Cornell University.

David is passionate about exploring new research areas and building novel applications on cutting-edge technology platforms that solve real-world problems. We’re thrilled to have him on board as we continue to expand our impact.