Artificial Intelligence-enhanced Cannabidiol (CBD) Drug Delivery Systems: Integrating Machine Learning and...

Area of Science:

• Pharmacology and Drug Delivery
• Artificial Intelligence in Medicine
• Cannabinoid Research

Background:

• Cannabidiol (CBD) faces significant pharmacokinetic challenges, including poor solubility, high variability, and extensive metabolism.
• Current CBD formulations struggle with efficacy and consistent patient outcomes.
• Addressing these limitations is crucial for advancing CBD therapeutic applications.

Purpose of the Study:

• To critically analyze the theoretical potential of AI and machine learning in optimizing CBD drug delivery systems.
• To review current AI applications in CBD pharmacokinetic modeling, formulation design, and patient stratification.
• To identify limitations and future directions for AI-driven CBD delivery research.

Main Methods:

• Literature review of AI and machine learning applications in CBD research.
• Analysis of computational modeling and data-driven approaches for formulation screening and dose prediction.
• Evaluation of AI's role in pharmacokinetic modeling and nanoparticle engineering for CBD.

Main Results:

• AI demonstrates theoretical potential in optimizing CBD formulations and predicting doses, aiding in overcoming pharmacokinetic hurdles.
• Current AI applications are primarily based on preclinical data, simulations, and early-stage frameworks, not clinical validation.
• Machine learning shows promise in identifying key variables and optimizing parameters, but faces challenges in data quality and interpretability.

Conclusions:

• AI-driven CBD delivery systems represent an emerging research area with theoretical promise, not yet a clinically validated treatment.
• Further research requires standardized datasets, robust model development, and extensive experimental/clinical validation.
• AI holds potential for improving the efficacy, safety, and personalization of future CBD therapies.