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K-State Today

March 17, 2020

New research improves understanding of nanomedicine for cancer treatment

Submitted by Piper Brandt

Yi-Hsien Cheng, left, and Zhoumeng Lin, authors of the study, used a modeling and simulation approach to research the efficiency of nanomedicine for cancer treatment.

A new study from researchers with the Kansas State University College of Veterinary Medicine reports the current progress and limitations of using nanoparticle-based drug formulations to treat cancer.

The researchers' study, "Meta-analysis of nanoparticle delivery to tumors using a physiologically based pharmacokinetic modeling and simulation approach," was recently published in the journal ACS Nano. Lead author was Yi-Hsien Cheng, a postdoctoral researcher in the lab of Zhoumeng Lin, assistant professor of anatomy and physiology and faculty member of the Institute of Computational Comparative Medicine in the College of Veterinary Medicine; Lin; and former K-State researchers Chunla He, Jim Riviere and Nancy Monteiro-Riviere. Riviere and Monteiro-Riviere are university distinguished professors emeriti of the college.

Nanoparticles — tiny particles between 1 and 100 nanometers in size — can be used to deliver medication directly to cancer cells. This method may provide an alternative to traditional chemotherapy drugs, which can kill healthy cells in addition to cancer cells.

"Nanoparticles can be engineered to have different physicochemical and biological properties, such as different shapes, sizes, charges and surface coatings, to provide a multifunctional platform for diagnosis and targeting therapy," Cheng said. "Examples include, but are not limited to, self-assembled polymeric micelles and liposomes that can be encapsulated with anticancer drugs to enhance tumor targeting and on-site drug releasing."

In the last 15 years, a large amount of research has been devoted to the design of nanomedicines with different physicochemical properties that have higher cancer therapeutic indices. Many of these nanomedicines have been shown to be effective in reducing tumor size, but very few formulations have been approved for human use.

"To improve our understanding of cancer nanomedicine, we need to know the current progress of the delivery efficiency of nanoparticles to the tumor site, and the key factors that determine nanoparticle tumor delivery efficiency," Lin said.

Lin and his co-authors used a physiologically based pharmacokinetic, or PBPK, modeling and simulation approach to analyze 200 pharmacokinetic studies, which involve 376 datasets that cover a wide range of nanomedicines.

By using the PBPK modeling and simulation approach, Lin and his team found mean and median delivery efficiencies, at the last sampling time point, of only 2.23% and 0.76% of the injected dose, respectively. The mean and median delivery efficiencies were 2.24% and 0.76% of injected dose at 24 hours, and were decreased to 1.23% and 0.35% of injected dose at 168 hours, respectively, after intravenous administration.

These results show the efficiency of nanomedicine used for cancer treatment has room for improvement. If only a small percentage of the injected nanoparticles get delivered to the tumor, they cannot work as efficiently as they were designed to be.

"These surprisingly low tumor delivery and cancer cell targeting efficiencies suggest the importance of examining key physicochemical and pharmacokinetic determinants of nanoparticle disposition within the tumor microenvironment," said Riviere, the founding director of the Institute of Computational Comparative Medicine.

"While there is great potential of nanomedicine in treating cancer and there has been some progress in the past two decades, it is important to review the current progress and identify knowledge gaps to guide future studies," said Monteiro-Riviere, the founding director of the Nanotechnology Innovation Center of Kansas State.

"It is a challenge to systemically analyze hundreds of datasets from different studies with different study designs using different nanoparticles, but PBPK modeling makes this analysis possible because it is a mechanism-based modeling approach that considers the physiology of the organism and the physicochemical and pharmacokinetic properties of the nanoparticles in the analysis," Lin said. "PBPK models also have the advantage of robust extrapolation capability, across species, exposure doses, routes and duration."