Biomonitoring and Digital Data Technology as an Opportunity for Enhancing Animal Study Translation

Area of Science:

• Preclinical research and drug development
• Translational science
• Digital health technologies

Background:

• Animal studies often fail to translate into effective clinical treatments.
• Issues in reproducibility and translatability stem from study design, analysis, reporting, and endpoint standardization.
• There is a critical need for solutions to improve preclinical research quality.

Purpose of the Study:

• To review how digital technologies can overcome challenges in preclinical research.
• To highlight the application of digital innovations in improving study design, execution, and data sharing.
• To demonstrate how these technologies enhance scientific outcome measures and accelerate therapeutic development.

Main Methods:

• Review of recent advancements in digital technology for preclinical research.
• Discussion of biomonitoring, digital biomarkers, software systems, database technologies, and artificial intelligence applications.
• Examination of case studies in specific therapeutic areas.

Main Results:

• Digital technologies offer solutions to improve reproducibility and translatability of animal studies.
• Innovations address common issues in study design, analysis, reporting, and endpoint standardization.
• Application of AI and digital tools enhances data quality and scientific collaboration.

Conclusions:

• Digital technologies are crucial for enhancing the quality and translational relevance of preclinical research.
• These tools can standardize methodologies and improve data sharing, fostering scientific collaboration.
• Implementing digital solutions accelerates the development of novel therapeutics by improving preclinical research outcomes.