Boosting Drug Delivery: Protein–Polymer Nanoparticles with Enhanced Load Capacity and Stability

New drug delivery nanoparticles

In a significant advancement for drug delivery technologies, scientists at Xi'an Jiaotong-Liverpool University (XJTLU) and Nanjing University in China have developed a new class of protein–polymer nanoparticles that can carry greater drug payloads while offering improved long-term stability. Their findings, published in the journal ACS Applied Materials & Interfaces, promise to reshape how we treat diseases such as cancer by reducing side effects and increasing treatment efficiency.

This innovative approach combines PLGA (a widely used biodegradable polymer in medicine) with albumin (a natural blood protein that already plays a role in pharmaceutical formulations). When mixed, the two form stable, uniform nanoparticles that can carry an astonishing 40% by weight of the chemotherapy drug doxorubicin—a significant leap compared to existing drugs like Doxil, which typically contain around 11%.

Solving Longstanding Challenges

Current nanoparticle-based drug delivery systems face two main issues: low drug loading capacity and poor stability. Many systems tend to clump over time and degrade quickly, limiting their practical use. These newly developed nanoparticles overcome both hurdles simultaneously.

"We managed to solve two big problems at once," explains Dr. Gang Ruan, Senior Associate Professor at XJTLU. By using albumin—a protein that naturally helps transport molecules in the body—the nanoparticles achieve a biocompatible and stable structure that works harmoniously with PLGA to enhance drug delivery efficacy.

Innovative Drug Loading Techniques

The research team used a dual-loading strategy to incorporate drugs into the nanoparticles. The drug is introduced during particle formation and again afterward via passive diffusion. This two-step loading method ensures better retention and release control, critical for chronic diseases requiring sustained drug delivery.

Preclinical tests on cell cultures and animal models showed strong therapeutic performance with reduced toxicity to healthy tissues. Even after six months, the nanoparticles maintained their structure and effectiveness—a rare achievement in the world of nanomedicine.

Toward Scalable and Versatile Drug Delivery

The scalability of this approach is promising. The team successfully demonstrated that large-scale production is feasible without compromising the nanoparticles' quality. Moreover, they plan to adapt this platform to deliver various types of drugs, opening doors to customizable therapies for cancer and other chronic conditions.

This study underscores the powerful synergy between biology and polymer science and illustrates how hybrid materials can outperform traditional delivery methods. As the next step, researchers will likely explore clinical trials and further improvements in targeting capabilities.

For the full article, see: https://phys.org/news/2025-06-proteinpolymer-nanoparticles-higher-drug-stability.html

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📘 Download our latest company brochure to explore our software features, capabilities, and success stories: PWmat PDF Brochure

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

Comments

Post a Comment

Popular posts from this blog

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

MIT’s React-OT: The AI Model Revolutionizing Chemical Reaction Design

Image
MIT’s React-OT: The AI Model Revolutionizing Chemical Reaction Design In a major leap for materials science and computational chemistry, researchers at MIT have unveiled a new AI-driven model called React-OT , capable of predicting the “point of no return” in chemical reactions — also known as the transition state — with remarkable speed and accuracy. Published in Phys.org on April 23, 2025, this innovation could revolutionize how scientists design sustainable chemical processes and novel materials. Why it matters: Understanding and predicting transition states is vital for designing efficient reactions in pharmaceuticals, polymers, and fuels. Traditional methods based on quantum chemistry are computationally expensive — sometimes taking days for a single calculation. Enter React-OT — a machine-learning model trained on over 9,000 reactions that slashes prediction time to under a second. The model’s secret? It begins with a smart est...
Read more

How a Chatbot is Democratizing Quantum Chemistry for All

Image
Posted by Quantum Server Networks • April 9, 2025 Quantum chemistry has traditionally required advanced coding skills, expensive hardware, and deep theoretical expertise. But a new chatbot-powered platform developed at Emory University is flipping the script—empowering even undergraduate students to explore molecular dynamics in 3D with ease. The breakthrough platform, AutoSolvateWeb , is a free, cloud-based interface that simplifies the complex world of molecular simulations using an intuitive chatbot. By guiding users through the process in natural language, the platform makes quantum simulations accessible to nonexperts—no coding required. "It's a bit like a microscope, giving you an atomic-level view of molecules interacting in a solution." – Prof. Fang Liu, Emory University Instead of requiring hundreds of lines of code, AutoSolvateWeb lets users type in a molecule name—like caffeine—and select a solvent such as water...
Read more