Integrating machine learning for the optimization of polyacrylamide/alginate hydrogel

Artificial Intelligence tool for prediction of ECM mimics hydrogel formulations via click chemistry

A user-friendly machine learning (ML) predictive tool is reported for designing extracellular matrix (ECM)-mimetic hydrogels with tailored rheological properties. Developed for regenerative medicine and 3D bioprinting, the model leverages click chemistry crosslinking to fine-tune the mechanical behaviour of gelatin- and hyaluronic acid-based hydrogels. Using both experimental rheological data and synthetic datasets, our supervised ML approach accurately predicts hydrogel compositions, significantly reducing the cost and time associated with trial-and-error approach. Despite advancements in the field, existing models remain limited in their ability to mimic the ECM due to the use of non-natural polymers, reliance on a single type of biologically active macromolecule, and physical crosslinking reactions with limited tuneability. Additionally, their lack of generalizability confines them to specific formulations and demands extensive experimental data for training. This predictive platform represents a major advancement in biomaterial design, improving reproducibility, scalability, and efficiency. By integrating rational design, it accelerates tissue engineering research and expands access to customized ECM-mimetic hydrogels with tailored viscoelastic properties for biomedical applications, enabling both experts and non-experts in materials design.

Next-Generation Hydrogel Design: Computational Advances in Synthesis, Characterization, and Biomedical Applications

Hydrogels are pivotal in advanced materials, driving innovations in medical fields, such as targeted drug delivery, regenerative medicine, and skin repair. This systematic review explores the transformative impact of in-silico design on hydrogel development, leveraging computational tools such as molecular dynamics, finite element modeling, and artificial intelligence to optimize synthesis, characterization, and performance. We analyze cutting-edge strategies for tailoring the physicochemical properties of hydrogels, including their mechanical strength, biocompatibility, and stimulus responsiveness, to meet the needs of next-generation biomedical applications. By integrating machine learning and computational modeling with experimental validation, this review highlights how in silico approaches accelerate material innovation, addressing challenges and outlining future directions for scalable, personalized hydrogel solutions in regenerative medicine and beyond.

Current research and future potential of thermogels for sustained drug delivery

INTRODUCTION

Drug administration is ubiquitous in the healthcare field, and it is crucial to optimize drug delivery methods to improve drug efficacy, reduce systemic toxicity, and enhance patient compliance Thermogels have shown immense potential in drug delivery due to their injectability, biocompatibility, and ability to provide localized and sustained drug release.

AREA COVERED

This paper discusses the unique properties of thermogel in relation to drug kinetics and their suitability as a carrier. Different considerations and applications of thermogel drug delivery systems (DDS) were highlighted and their challenges to enter the market discussed. A comprehensive literature search was conducted using major databases such as PubMed, Scopus, and Web of Science. The search employed relevant keywords to identify studies on thermogel DDS. Clinicaltrials.gov was also utilized to determine the current state of clinical studies.

EXPERT OPINION

Nonetheless, thermogel holds great promise for the future in DDS with research achieving greater heights in terms of complexity and clinical pursuits. Their flexibility in fabrication and modularity manner makes it a great material to tailor to different drug delivery applications and to be integrated into various biomedical disciplinaries.