Journal of Pharmaceutical Research
DOI: 10.18579/jopcr/v25.i4.175
Year: 2026, Volume: 25, Issue: 4, Pages: 229-238
Review Article
Puneet Joshi1, 2*, Abhijeet Ojha2, Rajeshwar Kamal Kant Arya1, Arun Kumar Singh2, Vikas Bhatt2, Saksham Verma1
1Department of Pharmaceutical Sciences, Sir J. C. Bose Technical Campus, Kumaun University, Bhimtal, Nainital, Uttarakhand, India
2Faculty of Pharmaceutical Sciences, Amrapali University, Haldwani, Uttarakhand, India
*Corresponding Author
Email: [email protected]
The integration of artificial intelligence, machine learning, and deep learning is transforming pharmaceutical drug delivery systems from empirical trial-and-error paradigms into rational, data-driven platforms. Conventional drug delivery modalities frequently suffer from suboptimal bioavailability, lack of anatomical target specificity, unpredictable burst release, and variable therapeutic outcomes. This critical narrative review provides an in-depth, evaluative examination of artificial intelligence methodologies specifically applied to pharmaceutical drug delivery. We establish clear delineations between computational drug discovery and drug delivery engineering while disambiguating the distinct operational roles of classical machine learning algorithms and deep neural architectures. A structured literature retrieval was conducted across major electronic databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar), covering peer-reviewed studies published from 2015 to 2024. Computational models demonstrate significant capacity to advance formulation optimization, predict nanocarrier critical quality attributes (hydrodynamic diameter, surface charge, and entrapment efficiency), model non-linear dissolution kinetics, and guide the three-dimensional printing of personalized dosage forms. Furthermore, the clinical translation of stimuli-responsive smart therapeutics and active micro-nanorobots is objectively appraised. Finally, we dissect the paramount translational roadblocks, encompassing pharmaceutical data scarcity, algorithmic interpretability, and regulatory harmonization under Good Machine Learning Practice and Predetermined Change Control Plans, providing a publication-standard roadmap for intelligent drug delivery.
Keywords: Drug delivery systems, Machine learning, Formulation optimization, Nanocarriers, Dissolution kinetics, Smart therapeutics
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© 2026 Published by Krupanidhi College of Pharmacy. This is an open-access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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