Artificial Intelligence Assisted Design and Optimization of Nanosponges for Targeted Drug Delivery
DOI:
https://doi.org/10.22270/ajprd.v14i3.1803Abstract
Pharmaceutical technology has been revolutionized by the new nanotechnology, with the emergence of novel nanocarrier systems for targeted drug delivery systems and controlled drug delivery. Nanoporous systems, such as nanosponges, have been shown to be promising candidates for nanoporous carriers due to their high surface area, tunable porosity, biocompatibility and potential to encapsulate hydrophilic and hydrophobic therapeutic agents. In biomedical applications nanosponges are used to increase the solubility, stability, bioavailaility and controlled release of drugs to improve their therapeutic efficacy. But the conventional method of preparing nanosponge (NS) is largely based on trial-and-error experimentation, which is time consuming, labor intensive and expensive. In recent years, Artificial Intelligence (AI) has proven to be a gamechanger in the field of pharmaceutical formulation, offering a range of predictive modeling, machine learning, deep learning, molecular simulations, and intelligent optimization techniques that can expedite the formulation development process. With the assistance of AI, NS engineering can be integrated to rationally select the polymer, optimize the crosslinking parameters, predict the interaction between the drug and the polymer, assess the toxicity, model the release rate of the drug, and design the personalized therapeutic treatment. AI driven NS systems can also facilitate targeted delivery, stimuli-responsive release, and precision medicine applications. This review highlights the structure of NS, AI methods, computational tools, engineering methods, therapeutic applications, existing challenges, and future prospects for smart NS systems for precision drug delivery.
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Copyright (c) 2026 Pranita Waghmare, Sameer Shafi, Shivlila Swami, Madhuri Damane, Ankita Gadhave
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