NLP Analysis of Mannan-Based Drug Delivery Trends - Pratama, Reza; Cabellon-Semense, Daisy Jane; Sulastri, Lela; Arifka, Mia; Rizikiyan, Yayan (Sciences of Pharmacy, 2025)
Mannan, a polysaccharide derived from various sources, has gained attention for its biocompatibility and potential in targeted drug delivery. Since its initial use in 1911 as an ointment base, mannan has been applied in cancer therapy, vaccine development, and as an antimicrobial agent. However, research is still largely dominated by in vitro and preclinical studies, with few clinical trials conducted to date. This study aims to provide an overview of mannan's advancements, its uses in drug delivery, the mechanisms involved, the research gaps, and the underexplored areas with significant potential. This study analyzed 321 peer-reviewed articles selected from Scopus (2000-2024), employing natural language (NLP) and bibliometric mapping to identify key materials, application areas, and research trends. Mannan’s flexible molecular structure allows for copolymerization with polymers such as chitosan, alginate, polyacrylate, and polycaprolactione, enabling improved targeting, mucoadhesion, and controlled drug release. Chitosan emerged as the most frequently used co-polymer, particularly in nanogel formulations for cancer and inflammatory diseases. Keyword impact analysis also revealed growing interest in mannans role in post-COVID-19 cytokine storm mitigation and vaccine enhancement, despite limited representation in clinical pipelines. Optimization of polymer ratios, crosslinker use and formulation strategies, remains essential to improving translational outcomes. Future research should also focus on clinical trials to demonstrate its effectiveness. In conclusion, this study underscores mannans role as promising biomaterial for next-generation drug delivery systems, while identifying gaps in clinical validation, mechanistic insight, methodological consistency. To advance toward commercial and clinical applications, future research should integrate machine learning models for predicting formulation parameters and drug release profiles. This findings offer a roadmap for the design, standardization, and eventual commercialization of mannan-based delivery platforms.