Digitalization of toxicology: improving preclinical to clinical translation.

Though the portfolio of medicines that are extending and improving the lives of patients continues to grow, drug discovery and development remains a challenging business on its best day. Safety liabilities are a significant contributor to development attrition where the costliest liabilities to both drug developers and patients emerge in late development or post-marketing. Animal studies are an important and influential contributor to the current drug discovery and development paradigm intending to provide evidence that a novel drug candidate can be used safely and effectively in human volunteers and patients. However, translational gaps-such as toxicity in patients not predicted by animal studies-have prompted efforts to improve their effectiveness, especially in safety assessment. More holistic monitoring and "digitalization" of animal studies has the potential to enrich study outcomes leading to datasets that are more computationally accessible, translationally relevant, replicable, and technically efficient. Continuous monitoring of animal behavior and physiology enables longitudinal assessment of drug effects, detection of effects during the animal's sleep and wake cycles and the opportunity to detect health or welfare events earlier. Automated measures can also mitigate human biases and reduce subjectivity. Reinventing a conservative, standardized, and traditional paradigm like drug safety assessment requires the collaboration and contributions of a broad and multi-disciplinary stakeholder group. In this perspective, we review the current state of the field and discuss opportunities to improve current approaches by more fully leveraging the power of sensor technologies, artificial intelligence (AI), and animal behavior in a home cage environment.

Development of the Digital Arthritis Index, a Novel Metric to Measure Disease Parameters in a Rat Model of Rheumatoid Arthritis.

Despite a broad spectrum of anti-arthritic drugs currently on the market, there is a constant demand to develop improved therapeutic agents. Efficient compound screening and rapid evaluation of treatment efficacy in animal models of rheumatoid arthritis (RA) can accelerate the development of clinical candidates. Compound screening by evaluation of disease phenotypes in animal models facilitates preclinical research by enhancing understanding of human pathophysiology; however, there is still a continuous need to improve methods for evaluating disease. Current clinical assessment methods are challenged by the subjective nature of scoring-based methods, time-consuming longitudinal experiments, and the requirement for better functional readouts with relevance to human disease. To address these needs, we developed a low-touch, digital platform for phenotyping preclinical rodent models of disease. As a proof-of-concept, we utilized the rat collagen-induced arthritis (CIA) model of RA and developed the Digital Arthritis Index (DAI), an objective and automated behavioral metric that does not require human-animal interaction during the measurement and calculation of disease parameters. The DAI detected the development of arthritis similar to standard in vivo methods, including ankle joint measurements and arthritis scores, as well as demonstrated a positive correlation to ankle joint histopathology. The DAI also determined responses to multiple standard-of-care (SOC) treatments and nine repurposed compounds predicted by the SMarTRTM Engine to have varying degrees of impact on RA. The disease profiles generated by the DAI complemented those generated by standard methods. The DAI is a highly reproducible and automated approach that can be used in-conjunction with standard methods for detecting RA disease progression and conducting phenotypic drug screens.

An ingestible sensor for measuring medication adherence.

In this paper, we describe the design and performance of the first integrated-circuit microsensor developed for daily ingestion by patients. The ingestible sensor is a device that allows patients, families, and physicians to measure medication ingestion and adherence patterns in real time, relate pharmaceutical compliance to important physiologic metrics, and take appropriate action in response to a patient's adherence pattern and specific health metrics. The design and theory of operation of the device are presented, along with key in-vitro and in-vivo performance results. The chemical, toxicological, mechanical, and electrical safety tests performed to establish the device's safety profile are described in detail. Finally, aggregate results from multiple clinical trials involving 412 patients and 5656 days of system usage are presented to demonstrate the device's reliability and performance as part of an overall digital health feedback system.

Early clinical experience with networked system for promoting patient self-management.

OBJECTIVE: To gain early experience with a networked system designed to assess a patient's adherence to oral medication and physiologic metrics in an ambulatory, at-home setting. STUDY DESIGN: Prospective, observational studies. MATERIALS AND METHODS: This networked system for patient self-management consists of ingestible markers and a wearable, personal monitor. When a marker is ingested, it communicates to a monitor that time-stamps the ingestion and identifies the marker as unique. The monitor also records heart rate and activity. Data from third-party monitoring equipment (eg, sphygmomanometer, weight scale) can be integrated into the system. Collected data are summarized for patient and physician review. Directly observed ingestion (DOI) of placebo tablet markers was used to assess the system's technical performance. Markers were also coencapsulated with drugs to capture at-home adherence. A performance criterion of <95% was set as the objective for system performance. RESULTS: A total of 111 subjects ingested 7144 ingestible markers; 3298 were DOIs. The system's positive detection accuracy and negative detection accuracy in detecting ingested markers were 97.1% and 97.7%, respectively. It differentiated 100% of multiple drugs and doses taken simultaneously by type and by dose. Medication adherence was >85%. The most common adverse effect was mild skin rash from the monitor's electrodes. No definitive marker-related adverse effects were reported. CONCLUSION: The system appears to be safe and effective in capturing and integrating adherence and physiologic data. Efforts are under way to enhance system functionalities and refine user interfaces. By providing context-rich information, this system may enhance patient-provider collaboration.

Microholographic multilayer optical disk data storage.

Micrometer-sized reflection holograms can be written into a rapidly rotating homogeneous photopolymer disk at the focus of a high-numerical-aperture beam and its retroreflection to implement high-capacity multilayer digital data storage. This retroreflection is generated by an optical system with positive unity magnification to ensure passive alignment of the counterpropagating beam. Analysis reveals that the storage capacity and transfer rate of this bit-based holographic storage system compare favorably with traditional page-based systems but at a fraction of the system complexity and cost. The analysis is experimentally validated at 532 nm by writing and reading 12 layers of microholograms in a 125-microm photopolymer disk continuously rotating at 3600 rpm. The experimental results predict a capacity limit of 140 Gbytes in a millimeter-thick disk or over 1 Tbyte with the wavelength and numerical aperture of Blu-Ray.