%0 Journal Article
%T Machine Learning-Driven Advancements in Liposomal Formulations for Targeted Drug Delivery: A Narrative Literature Review.
%A Hoseini B
%A Jaafari MR
%A Golabpour A
%A Rahmatinejad Z
%A Karimi M
%A Eslami S
%J Curr Drug Deliv
%V 0
%N 0
%D 2024 Jun 27
%M 38939987
%F 3.758
%R 10.2174/0115672018302321240620072039
%X Nanoliposomal formulations, utilizing lipid bilayers to encapsulate therapeutic agents, hold promise for targeted drug delivery. Recent studies have explored the application of machine learning (ML) techniques in this field. This study aims to elucidate the motivations behind integrating ML into liposomal formulations, providing a nuanced understanding of its applications and highlighting potential advantages. The review begins with an overview of liposomal formulations and their role in targeted drug delivery. It then systematically progresses through current research on ML in this area, discussing the principles guiding ML adaptation for liposomal preparation and characterization. Additionally, the review proposes a conceptual model for effective ML incorporation. The review explores popular ML techniques, including ensemble learning, decision trees, instance- based learning, and neural networks. It discusses feature extraction and selection, emphasizing the influence of dataset nature and ML method choice on technique relevance. The review underscores the importance of supervised learning models for structured liposomal formulations, where labeled data is essential. It acknowledges the merits of K-fold cross-validation but notes the prevalent use of single train/test splits in liposomal formulation studies. This practice facilitates the visualization of results through 3D plots for practical interpretation. While highlighting the mean absolute error as a crucial metric, the review emphasizes consistency between predicted and actual values. It clearly demonstrates ML techniques' effectiveness in optimizing critical formulation parameters such as encapsulation efficiency, particle size, drug loading efficiency, polydispersity index, and liposomal flux. In conclusion, the review navigates the nuances of various ML algorithms, illustrating ML's role as a decision support system for liposomal formulation development. It proposes a structured framework involving experimentation, physicochemical analysis, and iterative ML model refinement through human-centered evaluation, guiding future studies. Emphasizing meticulous experimentation, interdisciplinary collaboration, and continuous validation, the review advocates seamless ML integration into liposomal drug delivery research for robust advancements. Future endeavors are encouraged to uphold these principles.