Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Area of Science:

• Biomedical Engineering
• Nanomedicine
• Drug Delivery Systems

Background:

• Cell-based drug delivery systems utilize cells or cell membranes as carriers for controlled therapeutic release.
• Significant challenges exist in developing these systems, necessitating predictive models for property assessment and risk reduction.
• Integrating artificial intelligence (AI) and nanotechnology offers innovative solutions for advanced nanomedicine.

Purpose of the Study:

• To explore how AI and machine learning (ML) can address challenges in developing cell-based drug delivery systems.
• To highlight the potential of predictive AI/ML models in designing safer and more effective nanocarriers.
• To review current cell-based delivery strategies and their associated hurdles.

Main Methods:

• Review of existing literature on cell-based drug delivery systems.
• Analysis of AI and ML applications in nanomedicine, focusing on predictive modeling.
• Discussion of AI types and their relevance to designing cell-derived nanocarriers.

Main Results:

• AI and ML can accelerate data mining and decision-making processes in nanomedicine.
• Predictive models offer a pathway to mitigate undesirable effects in cell-based delivery platforms.
• The review identifies key challenges and showcases AI/ML as a solution for developing advanced cell-based therapeutics.

Conclusions:

• AI and ML hold significant promise for overcoming developmental challenges in cell-based drug delivery.
• These technologies can enhance the design, safety, and efficacy of cell-derived nanocarriers.
• Future research should focus on leveraging AI/ML for precise control and prediction in nanomedicine applications.