Materials engineers at Stanford University have developed a novel hydrogel drug delivery system that transforms daily or weekly injections of diabetes and weight control drugs to just once every four months.

In a new study, published in Cell Reports Medicine, researchers believe that such a system would improve the management of both diabetes and weight, improve patient drug compliance and help those with Type 2 diabetes improve their long-term outcomes.

These drugs, Ozempic, Mounjaro, Trulicity, Victoza and others, all work by mimicking the hormone glucagon-like peptide 1 (GLP-1). But, as good as they are at helping people manage their diets and their weight, the typical daily or weekly injections are a burden for many patients.

“Adherence is one of the biggest challenges in Type 2 diabetes management,” said Eric Appel, associate professor of materials science and engineering at Stanford and principal investigator on the new hydrogel. “Needing only three shots a year would make it much easier for people with diabetes or obesity to stick with their drug regimens.”

Half a billion people worldwide suffer from Type 2 diabetes. Introduced only recently, the GLP-1 drugs have been described as “miracle drugs” with few side effects and profound control of energy intake by helping patients feel more satiated and less hungry, and by targeting other reward-related dietary effects.

The secret of the hydrogel is in the unique physical characteristics of the nanoparticles at its heart. Hydrogels are not new – many people today wear contact lenses made of hydrogels, for instance – but these are engineered to resist tearing and to hold their shape.

Appel’s hydrogel is instead engineered with polymers and nanoparticles that are weakly bound to one another, so as to hold together as a gel yet dissipate slowly over time. The hydrogel is formed from a mesh of polymer chains and nanoparticles that hold the drug molecules until the mesh dissolves away, releasing the drugs.

“Our hydrogel melts away over many months like a sugar cube dissolving in water, molecule by molecule,” Appel explained. “I often refer to the mesh being held together by a sort of molecular Velcro that sticks together quite well, but then can be easily pulled apart.”

The new hydrogel, technically known as a polymer-nanoparticle (PNP) hydrogel, has a Goldilocks “just right” quality of fluidlike flow that can be easily injected using off-the-shelf needles, yet a gel-like stability durable enough in the body to last the full four-month period. Molecules of the GLP-1 drugs are formulated into the hydrogel and are similarly doled out over time as the hydrogel slowly melts away.

The physician injects a small dollop of the drug-laden hydrogel under the skin in a convenient location such as under the arm. The key for the engineer is to design the hydrogel in such a way as to make this “depot” small enough to be comfortable and inconspicuous to the patient, yet large enough and durable enough to last the full four months. Appel believes his team has achieved that measure of control.

“We chose four months to match the cadence that people actually meet with their physician or endocrinologist and why we were so specific with the release period,” Appel said.

So far, the team has tested the new drug delivery system in laboratory rats with high success. In rats, a single injection of this hydrogel-based therapy improves the management of blood glucose and weight compared to daily injections of a leading commercial drug, Appel noted.

While this particular hydrogel was engineered specifically for the GLP-1 four-month checkup regimen, Appel said that the team has successfully tuned the release timeframes to anywhere from days to upward of six months. He adds that such systems have been used with other proteins, vaccines, and even therapeutic cells, and there is evidence that GLP-1 drugs can also reduce risk of cardiovascular disease.

All these signs point to the promising possibility that this drug delivery system can be applied to other drugs and other conditions.

“There’s even been really promising results with children with Type 1 diabetes,” Appel said of the promise ahead.

Next up will be tests in pigs, whose skin and endocrine systems are most similar to those in humans. If those trials go according to plan, Appel could see human clinical trials within a year and a half to two years.

“At the very least, we have laid a pathway for the prolonged release of therapeutic GLP-1–based anti-diabetic and anti-obesity treatments that could have beneficial impact on Type 2 diabetes management and, perhaps, other conditions as well,” Appel said.

New research has found that Mitochondria-targeting antibiotics could abrogate ageing and extend lifespan in C. elegans.

Ageing is a continuous degenerative process caused by a progressive decline of cell and tissue functions in an organism. It is induced by the accumulation of damage that affects normal cellular processes, ultimately leading to cell death. It has been speculated for many years that mitochondria play a key role in the ageing process.

In a new study, published in the US journal Aging, researchers from the University of Salford aimed to characterise the implications of mitochondria in ageing using Caenorhabditis elegans (C. elegans) as an organismal model. The C. elegans were treated with a panel of mitochondrial inhibitors and assessed for survival.

The researchers said in the paper: “Our ultimate goal is to find existing FDA-approved drugs and dietary supplements that can not only increase lifespan but also improve healthspan.”

“In our study, we assessed survival by evaluating worm lifespan, and we assessed ageing markers by evaluating the pharyngeal muscle contraction, the accumulation of lipofuscin pigment and ATP levels.”

Their results show that treatment of worms with either doxycycline, azithromycin – inhibitors of the small and the large mitochondrial ribosomes – or a combination of both, significantly extended the median lifespan of C. elegans. 

It also enhanced their pharyngeal pumping rate and reduced their lipofuscin content and their energy consumption (ATP levels), as compared to control untreated worms.

The results suggest an ageing-abrogating effect for these drugs. Similarly, DPI, an inhibitor of mitochondrial complex I and II, was capable of prolonging the median lifespan of treated worms. On the other hand, subjecting worms to vitamin C, a pro-oxidant, failed to extend C. elegans lifespan and upregulated its energy consumption.

The researchers concluded: We have identified some mitochondrial inhibitors for the extension of lifespan in the animal model of C. elegans. This supports the theory that mitochondria are heavily involved in the ageing process, although this remains a highly debated topic.

“Intriguingly, the compounds used in this study are for the most part repurposed agents for which preclinical and clinical studies have already been performed to establish their low toxicity.”

Researchers from The University of Texas claim to have developed a machine learning model that is 30 per cent more accurate than previous methods.

Machine learning (ML) techniques are becoming increasingly prevalent in medical settings as a way to predict survival rates and life expectancies among patients diagnosed with diseases such as cancer, heart disease, stroke and, more recently, Covid-19.

A professor and his doctoral student at The University of Texas at Arlington have published a new model of predicting survival from cancer that they say is 30 per cent more effective than previous models in predicting who will be cured of disease.

The model can help patients avoid treatments they don’t need while allowing treatment teams to focus instead on others who need additional interventions, the authors state.

“Previous studies modelling the probability of a cure, also called the cure rate, used a generalised linear model with a known parametric link function such as the logistic link function,” said principal investigator Suvra Pal, associate professor of statistics in the Department of Mathematics.

“However, this type of research doesn’t capture non-linear or complex relationships between the cure probability and important covariates, such as the age of the patient or the age of a bone marrow donor.

“Our research takes the previously tested promotion time cure model (PCM) and combines it with a supervised type of ML algorithm called a support vector machine (SVM) that is used to capture non-linear relationships between covariates and cure probability.”

Supported by a grant from the National Institute of General Medical Sciences, the new SVM-integrated PCM model (PCM-SVM) is developed in a way that builds upon a simple interpretation of covariables to predict which patients will be uncured at the end of their initial treatment and need additional medical interventions.

To test the technique, Pal and his student Wisdom Aselisewine took real survival data for patients with leukaemia, a type of blood cancer that is often treated with a bone marrow transplant.

The researchers chose the condition because it is caused by the rapid production of abnormal cancerous, white blood cells. Since this does not happen in healthy people, they were able to clearly see which patients in the historic data set were cured by treatments and which were not.

Both statistical models were tested and the newer PCM-SVM technique was found to be 30 per cent more effective at predicting who would be cured by the treatments compared to the previous technique.

“These findings clearly demonstrate the superiority of the proposed model,” Pal said.

“With our improved predictive accuracy of cure, patients with significantly high cure rates can be protected from the additional risks of high-intensity treatments. Similarly, patients with low cure rates can be recommended timely treatment so that the disease does not progress to an advanced stage for which therapeutic options are limited.

“The proposed model will play an important role in defining the optimal treatment strategy.”