The failure of animal studies to translate to effective clinical therapeutics has driven efforts to identify underlying cause and develop solutions that improve the reproducibility and translatability of preclinical research. Common issues revolve around study design, analysis, and reporting as well as standardization between preclinical and clinical endpoints. To address these needs, recent advancements in digital technology, including biomonitoring of digital biomarkers, development of software systems and database technologies, as well as application of artificial intelligence to preclinical datasets can be used to increase the translational relevance of preclinical animal research. In this review, we will describe how a number of innovative digital technologies are being applied to overcome recurring challenges in study design, execution, and data sharing as well as improving scientific outcome measures. Examples of how these technologies are applied to specific therapeutic areas are provided. Digital technologies can enhance the quality of preclinical research and encourage scientific collaboration, thus accelerating the development of novel therapeutics.

A primary goal in preclinical animal research is respectful and responsible care aimed toward minimizing stress and discomfort while enhancing collection of accurate and reproducible scientific data. Researchers use hands-on clinical observations and measurements as part of routine husbandry procedures or study protocols to monitor animal welfare. Although frequent assessments ensure the timely identification of animals with declining health, increased handling can result in additional stress on the animal and increased study variability. We investigated whether automated alerting regarding changes in behavior and physiology can complement existing welfare assessments to improve the identification of animals in pain or distress. Using historical data collected from a diverse range of therapeutic models, we developed algorithms that detect changes in motion and breathing rate frequently associated with sick animals but rare in healthy controls. To avoid introducing selection bias, we evaluated the performance of these algorithms by using retrospective analysis of all studies occurring over a 31-d period in our vivarium. Analyses revealed that the majority of the automated alerts occurred prior to or simultaneously with technicians' observations of declining health in animals. Additional analyses performed across the entire duration of 2 studies (animal models of rapid aging and lung metastasis) demonstrated the sensitivity, accuracy, and utility of automated alerting for detecting unhealthy subjects and those eligible for humane endpoints. The percentage of alerts per total subject days ranged between 0% and 24%, depending on the animal model. Automated alerting effectively complements standard clinical observations to enhance animal welfare and promote responsible scientific advancement.

Despite several therapeutics showing promise in nonclinical studies, survival from ovarian cancer remains poor. New technologies are urgently needed to optimize the translation of nonclinical studies into clinical successes. While most nonclinical settings utilize subjective measures of physiological parameters, which can hamper the accuracy of the results, this study assessed the physical activity of mice in real time using an objective, non-invasive, cloud-based, digital vivarium monitoring platform. An initial range-finding study in which varying numbers of ovarian cancer cells were inoculated in mice was conducted to characterize disease progression using digital metrics such as motion and breathing rate. Data from the range-finding study were used to establish a motion threshold (MT) that might predict terminal endpoint. Using the MT, the efficacies of cisplatin and OS2966, an anti-CD29 antibody, were assessed. Results showed that MT predicted terminal endpoint significantly earlier than traditional parameters and correlated with therapeutic efficacy. Thus, continuous motion monitoring sensitively predicts terminal endpoint in nonclinical ovarian cancer models and could be applicable for drug efficacy testing.

Abstract Background: Successful translation of nonclinical data is reliant upon the validity of measures for assessing disease morbidity and endpoint in mouse oncology models. Conventional means of endpoint evaluation (i.e., tumor weight, volume of ascites, or clinical signs) can be subjective and unreliable. Here we demonstrate continuous automated measurement of motion reliably predicts endpoint; is correlative with efficacy in an ovarian cancer xenograft model with ascites; and can be used to improve adherence to the three Rs, the guiding principles utilized to ensure animal welfare when conducting nonclinical research. Methods: Six groups of athymic mice were intraperitoneally inoculated with increasing numbers of ES-2 ovarian cancer cells or control (PBS) and housed in a homecage on a continuous monitoring platform. Leveraging the platform’s videographic and electronic records, we retrospectively analyzed night-time motion data. Based on these analyses, we established a Motion Threshold for noticeable decline in the motion metric. Relative to the Motion Threshold, the following parameters were defined: motion loss post induction (MLPI) and motion loss from endpoint (MLFE). These were in turn analyzed for their ability to predict endpoint in comparison to conventional parameters. Upon validation, the Motion Threshold was applied to a subsequent study to analyze its ability to predict efficacy. Results: In the first study, MLPI showed significant correlation with endpoint when graphed on a linear regression plot (R2 = 0.87; p < 0.0001), while conventional metrics showed low correlations with endpoint: number of tumor nodules (R2 = 0.1334; p = 0.1133), volume of ascites (R2 = 0.2524; p = 0.0240), tumor weight (R2 = 0.1162; p = 0.1413). Additionally, we found the MLFE predicted endpoint earlier than the first onset of clinical signs (p < 0.0001). A subsequent study was performed utilizing the Motion Threshold to analyze the efficacy of cisplatin in combination with OS2966 versus vehicle or both therapies alone. OS2966 + cisplatin significantly extended normal activity of mice (according to the Motion Threshold) compared to vehicle (p = 0.003). The Motion Threshold was also shown to correlate with standard survival curve (R2 = 0.86; p < 0.0001) and was superior to conventional parameters for determining intergroup differences. Conclusion: The results demonstrate the high sensitivity, reproducibility, and objectivity of continuous collection of the motion metric. These results are relevant as they enhance study interpretation and reduce time to humane endpoint. The use of digital metrics also increases statistical power, which can be used to potentially reduce the number of animals required for cancer research, hence facilitating faithfulness to the three Rs. Citation Format: Chibueze D. Nwagwu, Erwin Defensor, Anne-Marie Carbonell, Shawn Carbonell. Digital vivarium cloud platform facilitates nonclinical endpoint assessment in an ovarian carcinoma xenograft model with ascites [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A21.

Many variables can influence animal behavior and physiology, potentially affecting scientific study outcomes. Laboratory and husbandry procedures-including handling, cage cleaning, injections, blood collection, and animal identification-may produce a multitude of effects. Previous studies have examined the effects of such procedures by making behavioral and physiologic measurements at specific time points; this approach can be disruptive and limits the frequency or duration of observations. Because these procedures can have both acute and long-term effects, the behavior and physiology of animals should be monitored continuously. We performed a retrospective data analysis on the effects of 2 routine procedures, animal identification and cage changing, on motion and breathing rates of mice continuously monitored in the home cage. Animal identification, specifically tail tattooing and ear tagging, as well as cage changing, produced distinct and reproducible postprocedural changes in spontaneous motion and breathing rate patterns. Behavioral and physiologic changes lasted approximately 2 d after tattooing or ear tagging and 2 to 4 d for cage changing. Furthermore, cage changes showed strain-, sex-, and time-of-day-dependent responses but not age-dependent differences. Finally, by reviewing data from a rodent model of multiple sclerosis as a retrospective case study, we documented that cage changing inadvertently affected experimental outcomes. In summary, we demonstrate how retrospective analysis of data collected continuously can provide high-throughput, meaningful, and longitudinal insights in to how animals respond to routine procedures.

e14690 Background: Translation of preclinical data is highly dependent on the validity of the metrics for assessing disease progression and endpoint in mouse oncology models. A digital cloud platform would provide an objective and non-invasive technology allowing real-time assessment of several parameters including motion, breathing, and activity. Here, we hypothesize digital metrics can serve to predict endpoint in an ovarian cancer xenograft model with ascites, thereby improving analysis and interpretation of therapeutic efficacy data and improved adherence to the Three Rs. Methods: Six groups of athymic mice were intraperitoneally inoculated with increasing numbers of ES-2 ovarian cancer cells or control and housed in the Vium Digital Vivarium. Leveraging Vium’s readily accessible videographic and electronic records we retrospectively analyzed night time motion data using the ROC curve on GraphPad Prism and R programming. The following parameters were defined: motion loss post induction (MLPI) and motion loss from endpoint (MLFE). These were in turn compared to the conventional parameters (ie, number of tumor nodules, tumor weight, volume of ascites, and clinical signs) in terms of predicting disease and endpoint. Results: MLPI showed significantly high correlation with endpoint when graphed on a linear regression plot (R 2 = 0.8671; p< 0.0001), while the conventional metrics showed low correlations with endpoint: number of tumor nodules (R 2 = 0.1334; p= 0.1133); volume of ascites (R 2 = 0.2524; p= 0.0240); tumor weight (R 2 = 0.1162; p= 0.1413). We also showed that MLFE was significantly higher than the days from onset of first clinical sign to endpoint. Analysis of additional digital metrics are underway. Conclusions: The results support the improved ability of a digital platform to predict endpoint over conventional metrics, and earlier than manually-recorded clinical signs. These results are important as they not only provide an objective parameter to enhance study interpretation, but they also reduce the time to humane endpoint. Additionally, the continuously recorded digital metrics increase statistical power, which can be leveraged to potentially reduce the number of animals required per study.

Abstract Spontaneous genetic models of systemic lupus erythematosus (SLE), such as MRL/lpr and NZB/W F1 mice, are commonly used to assess therapeutic efficacy. Therapies are evaluated against several reliable standard measures, including proteinuria, anti-dsDNA, skin lesions, splenomegaly, nephritis, and survival. We hypothesize that continuous monitoring of behavioral and physiologic parameters will provide additional meaningful data to assess disease and efficacy. Here, we treated MRL/lpr mice with two reference compounds, cyclophosphamide (CP: 25mg/kg, IP, weekly) and dexamethasone (DEX: 2mg/kg, IP, 3× week). Throughout the experiment, mice were housed in Vium’s Digital Vivarium, which provides continuous monitoring and assessment of behavioral and physiologic measures using a network of cameras and sensors. Digital metrics and standard measures were displayed and analyzed in near real-time, through an online interface. Group disease profiles were compared using automatically generated breathing rate and motion metrics, in addition to standard measures. Proteinuria, body weight, and spleen size showed disease establishment in MRL/lpr mice as early as 16 weeks of age, which was ameliorated by CP or DEX (p’s < 0.03). Breathing rate significantly increased in MRL/lpr vehicle-treated mice as early as 13 weeks of age, while nighttime motion significantly decreased in MRL/lpr vehicle-treated mice by 14 weeks. Changes in breathing rate and motion detected in MRL/lpr mice were rescued by administration of CP or DEX (p’s < 0.0001). Our results demonstrate that continuous measurement of breathing rate and motion are useful additional measures for evaluating disease severity and therapeutic efficacy in the MRL/lpr murine model of SLE.