Fostering Replicability in Preclinical Research: Envisioning Better Science Through the Digital Revolution
The Reproducibility Crisis in Preclinical Science
Preclinical research, the foundation of biomedical innovation, is facing a reproducibility crisis. A growing number of studies fail to replicate across laboratories, undermining the reliability of findings and their translation to human health.
This crisis stems from a range of preventable issues, including over-standardization, flawed or underpowered study designs, and environmental inconsistencies that are often overlooked.
Human involvement in experiments introduces additional variability, especially when studies are conducted during daytime hours, disrupting the natural rhythms of nocturnal animals like mice.
These challenges are not just technical—they are systemic. When preclinical findings cannot be reliably reproduced, the entire translational pipeline is compromised, delaying or derailing the development of effective therapies. By embracing new innovations, the scientific community can refine experimental design, reduce unnecessary replication, and ultimately replace outdated methodologies, ensuring the judicious use of animals, restoring confidence in preclinical research, and advancing 3Rs (reduction, replacement, refinement) impact.
A New Paradigm: Digital Home Cage Monitoring
To come up with more reliable and ethical research, scientists are turning to digital home cage monitoring, a transformative approach that enables continuous, non-invasive observation of animals in their natural environments. This method minimizes human interference, captures rich behavioral and physiological data, and enhances statistical power through automated, unbiased measurement.
One initiative helping to drive progress in this space is the Digital In Vivo Alliance (DIVA), a collaborative initiative led by The Jackson Laboratory. DIVA brings together pharmacologists, veterinarians, machine learning experts, and data scientists working together to clinically validate digital measures. These collaborative clinical validation studies aim to demonstrate biological relevance in specific contexts of use, addressing key challenges with broad adoption and improve the understanding how digital measures can be used to support basic research, key decisions in drug development, address research reproducibility and improve the translational relevance of preclinical models.
The JAX Envision™ platform, the enabling technology for this initiative, is an advanced digital in vivo monitoring system designed to assess mouse behavior and physiology in the home cage environment. It provides real-time, non-invasive tracking by leveraging computer vision and machine learning technologies. Envision provides scalable monitoring of individual animals in socially-housed environmental conditions and supports protocol harmonization, operator-independent assessments, and long-term data collection, all while reducing stress and providing improved assessment of research variability.
Researchers are committed to improving research reproducibility by applying frameworks such as the PREPARE (Planning Research and Experimental Procedures on Animals: Recommendations for Excellence) and ARRIVE (Animal Research: Reporting of In Vivo Experiments), guidelines to promote more rigor in experimental planning, design and reporting. Digital home cage monitoring technologies like Envision™ not only align with these frameworks, they operationalize them, offering researchers a practical and scalable way to meet the highest standards of scientific integrity and reproducibility and better inform human health research.
Case Study: Enhancing Replicability Across Sites
A recent initiative by DIVA’s Animal Health, Husbandry, and Welfare focus group provides a compelling example of how digital monitoring can improve reproducibility. The study, inspired by the seminal findings of Crabbe et al. (1999), which revealed significant inter-laboratory variability in mouse behavioral studies despite standardized protocols, aimed to assess sources of variability in rodent activity across three research sites.
Researchers hypothesized that combining continuous data collection with unbiased digital measures would enhance inter-site replication and allow for a more accurate understanding of variability.
The study involved both male and female mice from three genetic backgrounds (C57BL/6J, A/J, and J:ARC) housed and handled under standardized conditions across all sites. The 9-week replicability study produced 24,758 hours (2.82 years) of mouse video documenting 73,504 hours (8.39 years) of individual mouse behavior.
When data were aggregated over 24-hour periods, genotype emerged as the dominant factor, explaining over 80% of the variance. This is critical because researchers often compare wildtype to mutant and genotype is the main difference between groups. This shift highlights the power of long-duration (10+ days) monitoring to filter out noise and reveal meaningful biological signals (Saul et al., 2025).
Further analysis showed that the time of day significantly influenced replicability. Genetic effects were most detectable during early dark periods, times when animals are naturally active, but researchers are typically absent. In contrast, technical noise was more pronounced during standard work hours, during the periods when researchers are typically collecting data. This study provides insights into how continuous monitoring provides insights into improved model characterization and also confirmed that short-duration studies conducted during the day required far larger sample sizes to achieve replicable results. Most importantly, long-duration studies require significantly fewer animals to reach the same level of confidence (Saul et al., 2025) and therefore Envision™ directly addresses reduction of animal use, enabling 3Rs impact.
Looking Ahead: A More Reliable Future
This case study highlights that digital home cage monitoring technologies like Envision™, developed by The Jackson Laboratory, are more than just tools, they represent a fundamental shift in how we approach animal research. As a leading platform in digital home cage monitoring, Envision™ enables continuous, unbiased data collection in the animals’ home environment. This allows researchers to capture more accurate behavioral and physiological data while minimizing human interference and stress.
This case study also highlights that Envision™ aligns seamlessly with the goals of the PREPARE and ARRIVE guidelines, —two essential frameworks for improving the quality and reproducibility of animal studies.
Envision™ provides real-time, automated monitoring that helps identify and mitigate issues early in the study. This directly addresses PREPARE guidelines that emphasize comprehensive planning, including environmental control, welfare considerations, and contingency strategies.
Additionally, Envision™ generates structured, high-resolution datasets that document experimental conditions, such as microenvironmental parameters and timing of interventions, creating a comprehensive digital audit trail. This capability enhances the transparency and reproducibility of research protocols, directly supporting the ARRIVE guidelines’ emphasis on complete and accurate reporting to ensure findings can be reliability interpreted and replicated within and across laboratories.
The Digital In Vivo Alliance (DIVA) study mentioned above, conducted across three pharmaceutical companies, highlights the power of this approach. Using continuous digital home cage phenotyping, researchers found to their surprise that replication of activity differences driven by genotype was very robust across laboratories. Moreover, longer study durations (~10+ days) significantly reduced experimental noise, improved reproducibility, and lowered the number of animals needed to detect replicable effects. These findings underscore the potential of digital phenotyping to refine preclinical research practices by improving reproducibility and reducing variability, thereby contributing to more judicious use of animals with improved study designs.
Together, with these best-practice frameworks, Envision™ forms a powerful foundation for more reliable science. By integrating cutting-edge digital monitoring with rigorous planning and reporting standards, the research community can overcome systemic barriers to reproducibility. And most importantly, these innovations not only enhance the credibility of preclinical findings but also accelerate the translation of those findings into effective therapies for human health.
References
Saul, MC, Bratcher-Petersen, N, Ruidiaz, M, Oberhauser, P, Philip, V, Bolin, S, Gaskill BN, Madden, A, & TL Roberston (2025). Long-Duration Digital Home Cage Phenotyping Reduces Noise and Greatly Enhances Replication. [Manuscript in preparation].
Crabbe, J. C., Wahlsten, D., & Dudek, B. C. (1999). Genetics of mouse behavior: Interactions with laboratory environment. Science, 284(5420), 1670–1672.
https://doi.org/10.1126/science.284.5420.1670