Abstract
In drug discovery and development, traditional assessment of human patients and preclinical subjects occurs at limited time points in potentially stressful surroundings (i.e., the clinic or a test arena), which can impact data quality and welfare. However, recent advances in remote digital monitoring technologies enable the assessment of human patients and preclinical subjects across multiple time points in familiar surroundings. The ability to monitor a patient throughout disease progression provides an opportunity for more relevant and efficient diagnosis as well as improved assessment of drug efficacy and safety. In preclinical in vivo animal models, these digital technologies allow for continuous, longitudinal, and non-invasive monitoring in the home environment. This manuscript provides an overview of digital monitoring technologies for use in preclinical studies including their history and evolution, current engagement through use cases, and impact of digital biomarkers (DBs) on drug discovery and the 3Rs. We also discuss barriers to implementation and strategies to overcome them. Finally, we address data consistency and technology standards from the perspective of technology providers, end-users, and subject matter experts. Overall, this review establishes an improved understanding of the value and implementation of digital biomarker (DB) technologies in preclinical research.
Highlights
Drug discovery and development is under tremendous pressure to accelerate the production and delivery of novel, safe, and effective therapies to patients, which depends on collaboration among the pharmaceutical industry, academic collaborators, contract research organizations, technology providers, and regulatory agencies
As more knowledge is gained of biological systems, there are more possibilities to digitalize aspects of animal health, function, and physiology, and explore therapy guidance and disease progression
Recent advances in scalable digital biomarkers (DBs) technologies have the potential to improve assessment of safety and efficacy by reducing variability while increasing precision and sensitivity. This is because these assessments are objective, have high resolution, and realistic
Summary
Drug discovery and development is under tremendous pressure to accelerate the production and delivery of novel, safe, and effective therapies to patients, which depends on collaboration among the pharmaceutical industry, academic collaborators, contract research organizations, technology providers, and regulatory agencies. Automated systems have since been commonly used to quantify rodent behavior in a wide variety of test paradigms (Crawley, 2007; Carter and Shieh, 2015). These traditional behavioral measures are collected by removing animals from their home cage and placing them in temporary enclosures, which, along with even routine husbandry, may affect behavioral and physiological parameters (Saibaba et al, 1996; Balcombe et al, 2004; Schreuder et al, 2007; Meller et al, 2011; Gerdin et al, 2012). Removal from the home cage can cause stress, negatively impacting animal welfare and scientific data quality
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