Machine Learning-Driven Advancements in Liposomal Formulations for Targeted Drug Delivery: A Narrative Literature...

Area of Science:

• Pharmaceutical Sciences
• Computational Chemistry
• Biotechnology

Background:

• Nanoliposomal formulations are advanced drug delivery systems using lipid bilayers for targeted therapeutic agent encapsulation.
• Machine learning (ML) is increasingly explored to optimize the complex processes involved in liposomal formulation.
• Understanding ML's role is crucial for advancing targeted drug delivery efficacy.

Purpose of the Study:

• To review the motivations and applications of integrating ML into nanoliposomal formulation development.
• To provide a nuanced understanding of ML's advantages in this field.
• To propose a conceptual model for effective ML incorporation in liposomal research.

Main Methods:

• Systematic review of current research on ML applications in liposomal formulations.
• Discussion of ML principles, techniques (ensemble learning, decision trees, neural networks), and feature selection relevant to liposomes.
• Analysis of common evaluation metrics and validation strategies in ML-driven formulation studies.

Main Results:

• ML techniques effectively optimize key formulation parameters: encapsulation efficiency, particle size, drug loading, and polydispersity index.
• Supervised learning models are highlighted for structured formulations requiring labeled data.
• Mean absolute error is a key metric, emphasizing consistency between predicted and actual values.

Conclusions:

• ML serves as a decision support system for optimizing nanoliposomal formulations.
• A structured framework involving experimentation, analysis, and iterative ML refinement is proposed for future studies.
• Seamless ML integration, emphasizing collaboration and validation, is advocated for robust advancements in liposomal drug delivery.