How can machine learning and multiscale modeling benefit ocular drug development?

Area of Science:

• Ocular Drug Delivery
• Computational Pharmaceutics
• Biomedical Engineering

Background:

• Ocular drug delivery faces challenges due to the eye's complex physiology, limited space, and barriers.
• Traditional trial-and-error formulation methods are inefficient and invasive studies are ethically constrained.
• In silico modeling and simulation present non-invasive alternatives for ocular formulation development.

Purpose of the Study:

• To systematically review machine learning and multiscale simulation approaches for ocular drug development.
• To propose a computer-driven framework for rational pharmaceutical formulation design.
• To highlight integrated in silico methodologies for efficient, objective-oriented formulation design.

Main Methods:

• Review of theoretical underpinnings and applications of data-driven machine learning.
• Analysis of multiscale simulation approaches: molecular simulation, mathematical modeling, and PK/PD modeling.
• Proposal of a novel computer-driven framework for formulation design.

Main Results:

• Demonstrated the advantages of in silico methods in understanding drug delivery and facilitating formulation design.
• Proposed a framework integrating various computational techniques for rational ocular formulation.
• Identified key areas for advancement, including data challenges, model practicality, and interdisciplinary collaboration.

Conclusions:

• In silico modeling and simulation offer a paradigm shift for efficient ocular formulation development.
• An integrated computational framework can rationalize pharmaceutical design, overcoming traditional limitations.
• Further development in data, modeling, and collaboration is crucial for personalized and regulatory-accepted ocular drug design.