Our vision
Enabling novel therapies through the adoption of digital measures in AI-enhanced in vivo research
Our mission
Working collaboratively to advance digital measures for preclinical in vivo research through their development, validation, adoption, and regulatory acceptance
What brings us together?
Digital In Vivo Alliance members work together to maximize the impact of in vivo research supporting the development of novel therapeutics and advancing our understanding of health and disease. The efforts of DIVA participants have a common goal: to ensure patients have access to life-changing therapies. The Digital In Vivo Alliance is developing a behavioral and physiologic measures that support various disease models and is actively collaborating to clinically validate these digital measures within specific contexts of use. The organizations work together to share data, algorithms, and best practices in the development, application, and validation of digital measures to speed life-saving therapies for patients. DIVA leverages the value of collaboration and engagement with health authorities to establish regulatory confidence and acceptance of these innovative capabilities.
Our STRATEGIC PILLARS
Nucleate and foster the formation of a community around digital in vivo technologies
Support and promote the discovery, development, and validation of digital measures
Identify & characterize specific contexts of use for digital in vivo technologies
Collaboratively developing digital in vivo data & algorithms
Promote validation, acceptance & best practices in the use of digital in vivo technologies
Membership
DIVA offers membership options for digital measure developers and researchers. Contact us today to learn more.
Digital In Vivo Alliance Management Team
Brian Berridge, DVM, Ph.D.
Digital In Vivo Alliance Chair
DIVA
Timothy Robertson, Ph.D.
Digital In Vivo Initiative Leader
DIVA
Michael Ellis, Ph.D.
Director of Innovation
DIVA
Vivek Kumar, Ph.D.
Strategic and Scientific Advisor
DIVA
Natalie A. Bratcher-Petersen
Digital In Vivo Alliance Manager
DIVAFrequently Asked Questions
What is DIVA?
The Digital In Vivo Alliance (DIVA) is a collection of organizations and scientists with a shared interest in leveraging the power of digital measures to maximize the therapeutic value or impact of in vivo research and advance our understanding of health and disease.
Why join DIVA?
DIVA members collaborate in a precompetitive environment where machine learning experts develop digital measures in a shared development environment and scientists work together to analytically and clinically validate these measures for specific research applications and contexts of use.
What are the requirements to join DIVA?
DIVA Developers contribute significant investments to developing and validating digital measures, including scientific expertise, data science, and engineering support. As DIVA continues to evolve and grow, we are exploring opportunities for academic engagement and sponsorship. If you have questions about how you can contribute, please reach out so that we can discuss participation opportunities.
Who can join DIVA?
Organizations interested in contributing to the discovery, development, and adoption of digital measures are invited to join DIVA.
What is a digital biomarker?
A digital biomarker is a digital measure, collected from a digital in vivo technology, that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions.
Why use digital measures in drug discovery?
Digital measures provide an unprecedented opportunity to monitor the temporal progression of therapeutic or biological events, enabling improved data richness and additional insights leading to heightened utility, accelerated throughput, and increased reproducibility of in vivo studies.
What digital measures are DIVA members developing and validating?
Digital In Vivo Alliance members have prioritized several digital measures for development and validation, including individual mouse and rat activity, respiration, seizure, and inferred sleep.
How can AI and ML improve in vivo studies?
With the advent of sophisticated AI and machine learning, an opportunity emerges to enhance preclinical studies with a dynamic, yet holistic approach to digital biomarker development. This technology promises accelerated throughput, heightened utility, and increased reproducibility of in vivo studies.
How are digital measures developed through the Digital In Vivo Alliance?
Developing a digital measures starts with defining a need or a problem to solve, which also defines the ultimate measure’s ‘context of use.’ Once a biological endpoint of interest is defined, studies are designed that will provide video data substrate for algorithm development. DIVA machine learning scientists use that video substrate to iteratively develop algorithms that can automatically detect and quantify the biological endpoint of interest.
How are novel digital measures verified and validated?
The Digital In Vivo Alliance has adapted the V3 Framework from the Digital Medicine Society. Verification and validation protocols are custom-designed and applied to each digital measure. Digital measures are validated for their analytical reliability and reproducibility, as well as their relevance in preclinical in vivo research using specific use cases.
How will digital measures add value in drug discovery and development?
Digital measures collected from animals continuously in their home cage environment provide an opportunity to monitor biological events throughout a daily circadian cycle and over the length of a study. This allows improved understanding of the temporal progression of disease, therapeutic, or toxic effects, detect effects earlier in their progression, avoid additional studies that often require special equipment or animal handling, and identify health effects before a moribundity or unexpected death. This also provides a unique opportunity to view and gain insights from animal behavior at night, during their wakeful cycle, whereas the majority of experiments typically occur during the day. Collectively, we expect these values to improve the translational relevance of animal studies for the benefit of patients.