Previously submitted to: JMIR AI (no longer under consideration since Nov 13, 2025)
Date Submitted: Jun 18, 2025
Open Peer Review Period: Jul 3, 2025 - Aug 28, 2025
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Role of Artificial Intelligence in Oral Drug Delivery Optimization: A Systematic Review of Current Applications and Future Perspectives
ABSTRACT
Background:
Oral drug delivery remains the preferred route of pharmaceutical administration due to patient convenience and cost-effectiveness. However, challenges including poor bioavailability, variable absorption, and unpredictable release patterns continue to limit therapeutic efficacy. The integration of artificial intelligence (AI) technologies offers promising solutions to optimize oral drug formulations and predict pharmacokinetic behavior.
Objective:
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Methods:
A comprehensive literature search was conducted across PubMed, Web of Science, and Google Scholar databases from January 2015 to December 2024. Search terms included "artificial intelligence," "machine learning," "oral drug delivery," "formulation optimization," and "bioavailability prediction." Studies were included if they reported AI applications in oral pharmaceutical formulation design, absorption prediction, or pharmacokinetic modeling.
Results:
The review identified 89 relevant studies demonstrating AI applications across formulation design, absorption prediction, and manufacturing optimization. Machine learning algorithms, particularly artificial neural networks (ANNs), support vector machines (SVMs), and random forests, showed superior performance in predicting critical quality attributes compared to traditional approaches. AI-driven formulation optimization reduced development time by 40-60% and improved bioavailability prediction accuracy by 15-25%.
Conclusions:
AI technologies demonstrate significant potential for revolutionizing oral drug delivery through enhanced formulation precision, improved pharmacokinetic predictions, and personalized medicine approaches. However, challenges including model interpretability, data standardization, and regulatory acceptance require addressing for broader clinical implementation.
Citation
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