Embracing variability in preclinical research: enhancing reproducibility and translation through digital behavioral measures

While clinical research increasingly embraces personalized biomarker-driven approaches, preclinical studies often prioritize minimizing variability. However, mastering research variability holds significant scientific value, potentially improving reproducibility and translational success. New strategies are needed to harness this variability rather than eliminate it. The objective of this study was to understand the interplay between individual variability and factors such as genotype, sex, temporal replicate, study duration and site, within a digital caging system. To that end, we adapted methods used in a classic study, Crabbe et al. (1999), and leveraged digital home cage measures to assess interlaboratory variation. We collected 21 days of digital home cage data for both sexes of three different mouse genotypes (A/J, C57BL/6J, and J:ARC(S)) at three different sites and over three different temporal replicates. We hypothesized that the data we collected would show the relative importance of these factors in explaining individual variation. The study produced 25,755 hours of video documenting 76,495 hours of mouse life. The video data were processed using novel machine vision algorithms of individual mouse behavior and cage-level activity was extracted from the datasets. We found that individual variation strongly manifested hour by hour. However, when analyzing the aggregated data, we found that averaging over extended durations of seven or more days reduced statistical noise, allowing us to detect large genotype effect sizes. Further, we found that noise varies depending upon the time of day mice are measured. By combining long duration with 24-hour capture, it is possible to greatly reduce sample sizes needed for replicable results. These results indicate that long duration studies, where data are collected continuously in the home cage, are able to capture and overcome individual variation. Our results also highlight how digital tools enable replication, enhance generalizability, and may ultimately improve translational outcomes in drug development. By integrating digital in vivo behavioral measures across domains such as neuromotor disease, epilepsy, and safety pharmacology, we can improve the fidelity of preclinical models to human conditions. This poster will share insights into the use of continuous home-cage monitoring over varying time scales to assess variability, sex differences, and genetic influences.

This work was funded by the Digital In Vivo Alliance

This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.