The Convergence of Biology and Material Science: Biomolecule-Driven Smart Drug Delivery Systems

Yaqin Hou, Xiaolei Yu · MDPI AG · 2025

Biomolecule-driven smart materials are revolutionizing drug delivery by enabling active, programmable responses to specific biological cues.

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Plain English summary

This review explores the emerging field of biomolecule-driven smart materials in drug delivery, highlighting their ability to respond to biological signals and external triggers. It categorizes these systems based on their stimuli-responsiveness and examines various biomolecular architectures, including DNA, peptides, and polysaccharides. The paper discusses key therapeutic applications in areas like oncology and gene therapy, emphasizing how these smart systems can enhance treatment effectiveness by overcoming biological barriers. It also addresses challenges in biocompatibility and manufacturing, while looking ahead to future developments in personalized medicine and AI-driven design.

Why this matters

This research is significant as it addresses the limitations of traditional drug delivery methods, which are often passive and less effective. By developing smart materials that can actively respond to specific cues, the potential for more effective and personalized treatments increases, which could lead to better patient outcomes in critical areas like cancer and inflammatory diseases.

Key findings

  • Biomolecule-driven systems transition drug delivery to active, programmable interventions.
  • Systems can respond to both endogenous and exogenous triggers.
  • Unique design spaces are offered by DNA nanotechnology, peptides, and polysaccharides.
  • Key applications include oncology, inflammatory diseases, and gene therapy.
  • Challenges include biocompatibility, scalability, and regulatory issues.

What's new

The review highlights the integration of biomolecules in smart materials for drug delivery, emphasizing their programmable nature and responsiveness to specific biological cues.

Limitations

The abstract does not provide specific experimental results or data, focusing instead on a review of existing literature and concepts.

Commercial context

The abstract does not mention any commercial applications or readiness.

Publication

Publisher
MDPI AG
Publication date
September 28, 2025
Research type
Paper
License
https://creativecommons.org/licenses/by/4.0/

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-4o-mini-2024-07-18