Adapting FDA Guidance on Digital Measures for Preclinical Drug Development

In recent years, the integration of digital measures into clinical trials has revolutionized the landscape of drug development. These digital measures, encompassing wearable devices, mobile health technologies, and other digital tools, provide a wealth of real-time data that enhances our understanding of drug efficacy and patient response. The FDA has recognized this shift and issued multiple guidances to facilitate the use of digital measures in clinical trials. However, there remains a notable gap: there is no specific guidance for the use of digital measures in the preclinical stage of drug development.??

The Potential of Digital Measures in Preclinical Research??

Preclinical research is a critical phase in drug development, where potential drug candidates are tested in vitro and in vivo to assess their safety, efficacy, and biological activity before progressing to human trials. Integrating digital measures into this phase can offer numerous advantages:??

Enhanced Data Collection: Digital measures can provide continuous, real-time monitoring of physiological parameters, offering a more comprehensive data set than traditional methods.??

Early Detection of Drug Effects: Wearable devices and other digital tools can detect subtle changes in biological markers, enabling earlier identification of potential drug effects or adverse reactions.??

Improved Animal Welfare: Continuous monitoring can reduce the need for invasive procedures, thereby improving animal welfare and reducing stress-related confounding factors.??

Streamlined Data Management: Digital measures can facilitate automated data collection and analysis, increasing efficiency and reducing the potential for human error.??

Adapting FDA Clinical Guidance for Preclinical Use??

Although the FDA has not issued specific guidance for the use of digital measures in preclinical research, the principles outlined in their clinical guidance documents can be adapted to this earlier phase of drug development. Here are key points from relevant FDA guidances and how they can be applied to preclinical studies:??

FDA Guidance on Digital Health Technologies for Remote Data Acquisition in Clinical Investigations??

Examples of Application to Preclinical Research:??

• Data Integrity and Quality: Ensure that digital measures used in animal studies provide high-quality, accurate data. This involves selecting appropriate devices, validating their accuracy in the specific animal model, and implementing robust data management systems.??

• Study Design and Endpoint Selection: Tailor the use of digital measures to the specific objectives of the preclinical study. This includes identifying relevant physiological or behavioral endpoints that digital tools can monitor effectively.??

• Regulatory Considerations: Although preclinical studies are not subject to the same regulatory scrutiny as clinical trials, maintaining good laboratory practices (GLP) and ensuring data integrity is essential for regulatory submissions.??

FDA Guidance on Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims??

Examples of Application to Preclinical Research:??

• Outcome Measure Validation: Just as patient-reported outcomes need to be validated for clinical use, digital measures in preclinical research must be validated for accuracy and reliability in the specific animal model.??

• Data Interpretation: Understanding how digital measures correlate with traditional outcomes is crucial. For example, continuous respiratory rate monitoring in animals might need to be correlated with traditional respiratory?assessments.??

FDA Guidance on Real-World Data and Real-World Evidence??

Examples of Application to Preclinical Research:??

• Real-World Data Integration: Digital measures can generate “real-world data” (in home environment monitoring) in preclinical settings, providing insights into how a drug affects animals in least stressful environments. This can complement traditional controlled studies and provide a more comprehensive understanding of drug effects.??

• Data Quality and Usability: Ensure that “real-world data” collected from digital measures are of high quality and can be used to inform regulatory decisions. This involves rigorous data management and analysis protocols.??

So how do we move forward? To fully leverage the potential of digital measures in preclinical drug development, I believe following steps should be taken:??

• Develop Standardized Protocols: Establish standardized protocols for the use of digital measures in preclinical research, including device selection, validation procedures, and data management practices.??

• Collaborate with Regulatory Bodies: Engage with the FDA and other regulatory bodies to develop specific guidance for digital measures in preclinical research, drawing on the principles established for clinical trials.??

• Invest in Technology and Training: Invest in the necessary technology and training for researchers and facility and in vivo staff to effectively implement digital measures in preclinical studies.??

• Pilot Studies and Validation: Conduct pilot studies to validate the use of digital measures in various preclinical models and refine protocols based on the findings.??

I sincerely believe that adapting FDA guidance on digital measures from clinical trials to preclinical research offers a promising avenue to enhance drug development. By aligning preclinical research practices with these guidelines, we can fully harness the potential of digital measures. Leveraging the insights and principles outlined in existing guidance paves the way for more efficient, accurate, and humane preclinical studies. This proactive approach will ultimately contribute to the development of safer and more effective medicines, benefiting both the scientific community and public health.??

For more detailed information on the FDA guidances referenced, please visit the following links:??

FDA Guidance on Digital Health Technologies for Remote Data Acquisition in Clinical Investigations??

FDA Guidance on Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims??

FDA Guidance on Real-World Data and Real-World Evidence??

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