Functional Reclassification of Lipid-Based Drug Delivery Systems and Advances in Formulation Strategies and...

Area of Science:

• Pharmaceutical Sciences
• Biotechnology
• Materials Science

Background:

• Lipid-based drug delivery systems (LBDDS) have evolved into sophisticated platforms for diverse therapeutics, including mRNA vaccines and siRNA drugs.
• Despite successes, clinical translation is hindered by issues in classification, in vitro-in vivo correlation (IVIVC), scalability, and regulatory hurdles.

Purpose of the Study:

• To re-evaluate and reclassify LBDDS based on functional roles within biological systems.
• To address challenges in IVIVC, scale-up, and regulatory frameworks for advanced LBDDS.
• To propose strategies for developing next-generation lipid carriers using quality-by-design and digital approaches.

Main Methods:

• Literature review and critical analysis of existing LBDDS classification systems.
• Assessment of methods to enhance IVIVC for LBDDS.
• Exploration of quality-by-design (QbD) and quality-by-digital-design (QbD2) principles, mechanistic modeling, and machine learning for LBDDS development.

Main Results:

• A proposed functional reclassification system for LBDDS, linking formulation to biological performance and scale-up.
• Identification of strategies to improve IVIVC and overcome scale-up complexities.
• Highlighting the potential of QbD, QbD2, mechanistic modeling, and AI in creating robust lipid carriers.

Conclusions:

• A functional classification system is crucial for advancing LBDDS beyond current limitations.
• Integrating QbD, advanced modeling, and AI can facilitate the development of next-generation LBDDS.
• Addressing standardization, comparability, and regulatory gaps is essential for successful clinical translation of LBDDS.