Hydrogel-Based Dental Biomaterials and Artificial Neural Network (Ann) Based Modeling for Precision Head and Neck Immunotherapy: A Comprehensive Review of Solutions and Translational Advances Publisher

Summary: Can machine learning design better cancer-drug hydrogels? A modeling study suggests neural networks predict material behavior; clinical benefit is untested. #Hydrogels #CancerResearch

Alavi K ; Kadhim A O ; Omidi A ; Rezaei H ; Jangi A ; Mohammadi M ; Hemmati H ; Iranmanesh F ; Asefnejad A
Source: Progress in Biomaterials Published:2026

Abstract

Cancer immunotherapy has emerged as a transformative approach in oncology, harnessing the body’s immune system to target and eradicate malignant cells. However, clinical efficacy remains limited by systemic toxicity, inadequate drug availability, and poor tumor targeting. Hydrogels have gained significant attention as biomaterial platforms capable of addressing these limitations through localized and controlled delivery of immunotherapeutic agents, owing to their biocompatibility, biodegradability, and tunable properties. This article discusses recent advancements in hydrogel-based drug delivery systems for cancer immunotherapy, encompassing fundamental materials, fabrication methods, and applications in immune modulation, cell-based therapies, cancer vaccines, and tumor microenvironment remodeling. While preclinical studies demonstrate that hydrogels enhance localized delivery, improve immune responses, and reduce systemic side effects, challenges remain in large-scale production, immunogenicity concerns, and precise control over drug release kinetics. This study used an artificial neural network (ANN) to establish quantitative relationships between hydrogel formulation parameters and performance characteristics. A feedforward shallow neural network with one hidden layer containing 5 neurons was developed and trained using experimental data from five hydrogel samples, with polymer concentration (wt%) and swelling ratio (g/g) as inputs, and elastic modulus (kPa), burst release (%), and total release (%) as outputs. Predictive results revealed that increasing polymer concentration enhanced elastic modulus, while swelling ratio predominantly influenced release characteristics, with both parameters exhibiting antagonistic interactions when combined. Linear regression analysis confirmed exceptional prediction accuracy with error margins below 1%, validating the reliability of this computational approach. These results show that ANN serve as powerful tools for predicting hydrogel behavior, enabling rational design of delivery systems with optimized mechanical properties and controlled release kinetics. The integration of advanced biomaterials with computational modeling represents a promising pathway toward safer, more effective, and personalized cancer immunotherapies. © 2026 The Author(s). Published by the OICC Press.