Machine learning-enhanced nanofiber systems: A new frontier in controlled drug release

Area of Science:

• Biomaterials Science
• Nanotechnology
• Pharmacology

Background:

• Nanofiber-based drug delivery systems (N-DDS) offer significant advantages for controlled release due to their high surface area and tunable properties.
• Optimizing N-DDS involves numerous interdependent parameters, making traditional development methods time-consuming and inefficient.
• Electrospinning is a key fabrication technique for creating these advanced drug delivery platforms.

Purpose of the Study:

• To review the application of machine learning (ML) in accelerating the design and optimization of N-DDS.
• To highlight how ML replaces trial-and-error with predictive modeling for N-DDS development.
• To discuss the potential of ML-integrated N-DDS for future therapeutic applications.

Main Methods:

• Bibliometric analysis of literature on nanofibers and drug delivery systems (DDS), focusing on electrospinning.
• Review of ML applications in polymer selection, electrospinning process optimization, and encapsulation strategies.
• Analysis of ML model performance in predicting nanofiber morphology, encapsulation efficiency, and drug release kinetics.

Main Results:

• ML models demonstrate high predictive accuracy in tailoring N-DDS characteristics.
• Case studies show ML effectively optimizes parameters for desired drug release profiles.
• The review identifies key challenges for clinical translation, including data quality and scalability.

Conclusions:

• ML integration is crucial for advancing N-DDS development beyond traditional methods.
• ML-driven N-DDS hold promise for patient-specific, sustainable, and scalable therapeutic solutions.
• The synergy of ML and nanofiber engineering paves the way for precision medicine.