Quality by Computational Design: Harnessing AI to Advance Rational Drug Development

Area of Science:

• Pharmaceutical Sciences
• Computational Pharmaceutics
• Drug Development

Background:

• Traditional Quality by Design (QbD) predates AI and struggles with complex drug products.
• Conventional QbD relies on empirical data, limiting flexibility and advanced design capabilities.
• Computational pharmaceutics offers new methods for drug development.

Purpose of the Study:

• Introduce Quality by Computational Design (QbCD) as a novel paradigm.
• Integrate computational pharmaceutics into the QbD framework for mechanism-based and clinically guided formulation.
• Provide a practical guideline for QbCD implementation and regulatory compliance.

Main Methods:

• Defining QbCD concept, components, and implementation steps.
• Utilizing Artificial Intelligence (AI), physical modeling, and in vivo modeling.
• Presenting case studies for amorphous solid dispersions and long-acting injectables.

Main Results:

• Demonstrated QbCD's feasibility through virtual and real-world case studies.
• Showcased AI, physical, and in vivo modeling applications in drug development.
• Addressed regulatory considerations for QbCD implementation.

Conclusions:

• QbCD offers a more intelligent, efficient, and clinically aligned approach to drug development.
• Future work should focus on bridging data gaps and advancing methodological innovations.
• Fostering a supportive ecosystem is crucial for computational pharmaceutics adoption.