Modernizing Preclinical Drug Development: The Role of New Approach Methodologies.

Area of Science:

• Drug Development
• Translational Science
• Computational Biology

Background:

• Over 90% of investigational drugs fail in clinical development due to poor preclinical to clinical translation.
• High costs and ethical concerns necessitate more reliable preclinical tools.

Purpose of the Study:

• To propose a structured workflow integrating New Approach Methodologies (NAMs) for predictive preclinical study design.
• To enhance translational accuracy and reduce reliance on animal studies in drug development.

Main Methods:

• Integration of advanced in vitro systems, in silico mechanistic models, and computational techniques (AI/ML).
• Development of an iterative "a priori in silico" workflow to guide preclinical study design.
• Application of physiologically based pharmacokinetic modeling and quantitative systems pharmacology.

Main Results:

• Case studies demonstrate NAMs' potential in improving translational accuracy and reducing animal use.
• Mechanistic modeling provided insights into drug-induced liver injury and tumor microenvironment.
• The proposed workflow enables in vivo predictions and model refinement for enhanced human predictions.

Conclusions:

• Integrating computational and in vitro NAMs optimizes preclinical drug development, improving translational accuracy.
• This paradigm shift is supported by regulatory initiatives like the FDA Modernization Act 2.0.
• NAMs are emerging as the standard for preclinical drug development, reducing clinical trial failures.