Tag Archives: Virginia Tech

Better Treat Salmonella Than Face Cancer

An interdisciplinary team of three Virginia Tech faculty members affiliated with the Macromolecules Innovation Institute has created a drug delivery system that could radically expand cancer treatment options. The conventional cancer treatment method of injecting nanoparticle drugs into the bloodstream results in low efficacy. Due to the complexities of the human body, very few of those nanoparticles actually reach the cancer site, and once there, there’s limited delivery across the cancer tissue.

The new system created at Virginia Tech is known as Nanoscale Bacteria-Enabled Autonomous Drug Delivery System (NanoBEADS). Researchers have developed a process to chemically attach nanoparticles of anti-cancer drugs onto attenuated bacteria cells, which they have shown to be more effective than the passive delivery of injections at reaching cancer sites.

NanoBEADS has produced results in both in vitro (in tumor spheroids) and in vivo (in living mice) models showing up to 100-fold improvements in the distribution and retention of nanoparticles in cancerous tissues.This is a product of Bahareh Behkam, associate professor of mechanical engineering. Collaborators on this interdisciplinary team are Rick Davis, professor of chemical engineering, and Coy Allen, assistant professor of biomedical sciences and pathobiology in the Virginia-Maryland College of Veterinary Medicine.

You can make the most amazing drugs, but if you cannot deliver it where it needs to go, it cannot be very effective,” Behkam said. “By improving the delivery, you can enhance efficacy.

Humans have noticed, even as far back as Ancient Egypt, that cancer went into remission if the patient also contracted an infection like salmonella. Neither are ideal, but humans can treat salmonella infections more effectively than cancer.

In modern times, Allen said the idea of treating cancer with infections traces back to the late 1800s and has evolved into immunotherapy, in which doctors try to activate the immune system to attack cancerous cells. Of course, salmonella is harmful to humans, but a weakened version could in theory provide the benefits of immunotherapy without the harmful effects of salmonella infection. The concept is similar to humans receiving a weakened flu virus in a vaccine to build immunity.

The work, which combines expertise in mechanical engineering, biomedical engineering, chemical engineering, and veterinary medicine, was recently detailed in Advanced Science.

Source: https://vtnews.vt.edu/

New Materials For New Processors

Computers used to take up entire rooms. Today, a two-pound laptop can slide effortlessly into a backpack. But that wouldn’t have been possible without the creation of new, smaller processors — which are only possible with the innovation of new materials. But how do materials scientists actually invent new materials? Through experimentation, explains Sanket Deshmukh, an assistant professor in the chemical engineering department of Virginia Tech whose team’s recently published computational research might vastly improve the efficiency and costs savings of the material design process.

Deshmukh’s lab, the Computational Design of Hybrid Materials lab, is devoted to understanding and simulating the ways molecules move and interact — crucial to creating a new material. In recent years, materials scientists have employed machine learning, a powerful subset of artificial intelligence, to accelerate the discovery of new materials through computer simulations. Deshmukh and his team have recently published research in the Journal of Physical Chemistry Letters demonstrating a novel machine learning framework that trainson the fly,” meaning it instantaneously processes data and learns from it to accelerate the development of computational models. Traditionally the development of computational models are “carried out manually via trial-and-error approach, which is very expensive and inefficient, and is a labor-intensive task,” Deshmukh explained.

This novel framework not only uses the machine learning in a unique fashion for the first time,” Deshmukh said, “but it also dramatically accelerates the development of accurate computational models of materials.” “We train the machine learning model in a ‘reverse’ fashion by using the properties of a model obtained from molecular dynamics simulations as an input for the machine learning model, and using the input parameters used in molecular dynamics simulations as an output for the machine learning model,” said Karteek Bejagam, a post-doctoral researcher in Deshmukh’s lab and one of the lead authors of the study.

This new framework allows researchers to perform optimization of computational models, at unusually faster speed, until they reach the desired properties of a new material.

Source: https://vtnews.vt.edu/