AI-Assisted Development of Thermosensitive In-Situ Gels for Ocular Drug Delivery in Dry Eye Disease: Recent Advances and Future Perspectives

Abstract

Dry eye disease (DED) is a common eye condition marked by unstable tear film, inflammation, high salt concentration, and sensory issues, leading to symptoms like dryness, irritation, discomfort, and blurred vision. Traditional eye treatments, especially eye drops, often have limited effectiveness due to rapid tear drainage, poor penetration into the cornea, short time on the eye surface, and low absorption. Thermosensitive in situ gels have emerged as a promising eye drug delivery method; they are applied as a liquid and turn into a gel at body temperature. This change helps keep the drug on the eye longer, improves retention, and allows for steady drug release. Artificial intelligence (AI) is changing how we develop pharmaceutical formulations by supporting faster, data-driven decisions. Tools like ChatGPT, Consensus, Semantic Scholar, Connected Papers, SciSpace, and Elicit help with literature analysis, combining evidence, and finding research gaps. Meanwhile, platforms such as PubChem, SwissADME, DeepChem, KNIME, Orange Data Mining, TensorFlow, and PyTorch aid in predicting molecular properties, virtual screening, improving formulations, and modelling. AI combined with Quality by Design (QbD) and Design of Experiments (DoE) makes the formulation process more efficient by reducing time, costs, and the need for trial-and- error. The research highlights recent progress in AI-supported development of thermos-sensitive in situ gels for treating dry eye disease, concentrating on formulation methods, characterisation techniques, optimisation strategies, current challenges, and future outlook. The combination of AI and advanced drug delivery technologies is expected to speed up pharmaceutical innovation and help create safe, effective, and patient-focused eye treatments.