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Newswise — A new paper in Cell Reports Medicine details the development of a nanoparticle-based system that delivers concentrated chemotherapy specifically to cancer cells and not normal cells, potentially allowing clinicians to administer higher, more effective doses of anti-cancer drugs while avoiding some of the well-known toxic side effects.

Glucose metabolism in cancer cells and healthy cells

Cancer cells are extremely difficult to distinguish from healthy cells — this is how they avoid detection by our bodies’ immune systems. It therefore remains a physiological challenge to kill cancer cells without damaging healthy cells in the process. To avoid toxic side effects, clinicians must administer treatments like chemotherapy and immunotherapy agents in limited doses, thereby restricting their effectiveness.

To address this problem, researchers at the University of Chicago Medicine Comprehensive Cancer Center sought to develop a drug delivery method that was released specifically near tumor cells. They achieved this by exploiting a well-known phenomenon called the “Warburg effect,” which involves a difference in the way cancer cells metabolize glucose compared to healthy cells. Instead of fully breaking down glucose to carbon dioxide and water to generate a lot of energy, cancer cells typically break down glucose only part way to a molecule called lactate, generating a smaller amount of energy.

“Depending on the cancer cell type, some solid tumors can accumulate more than 40-fold higher lactate concentration than normal,” said senior author Xiaoyang Wu, PhD, Associate Professor in the Ben May Department of Cancer Research at the University of Chicago. “So, the idea was to take advantage of this dramatic change in a specific metabolite and create a drug delivery system that specifically targets these lactate-rich environments.”

How does nanoparticle drug delivery work?

Wu and his colleagues used nanoparticles — specifically, microscopic silica particles with pores into which a variety of cancer drugs can be loaded. These particles, small enough to be injected into the bloodstream, have been used to improve drug delivery for decades, but only a few are currently approved for clinical use in cancer treatment.

The novelty of Wu’s nanoparticle is that it’s controlled by a lactate-specific switch. The switch has two parts: first is lactate oxidase, an enzyme that binds and breaks down lactate and produces hydrogen peroxide, and second is a hydrogen peroxide-sensitive molecule that caps the nanoparticle, preventing the drug from being released.

This way, when the nanoparticle is in lactate-poor environments, like healthy tissues in the body, the capping material remains intact, preventing the drug from causing any damage to these tissues. But in a lactate-rich environment like the area within and around a tumor, the lactate oxidase begins breaking down lactate, generating a high enough concentration of hydrogen peroxide to trigger the degradation of the capping material and release of the drug.

“I had been thinking about how to specifically target lactate for a long time, since it is so enriched in tumors,” said Wu. “But lactate itself is not a very reactive chemical, so it was difficult to create a system that chemically responded to lactate. The biggest innovation was designing a switch that translated this cancer-specific signal to a chemically active molecule: hydrogen peroxide.”

Using mice to model two different forms of cancer, Wu and his colleagues tested the nanoparticle’s ability to specifically release its cargo in tumors. As they expected, the drug was specifically released in the lactate-rich tumor environment, and not in healthy tissues. Compared to directly injecting the drug itself into the bloodstream — the typical method of administering chemo drugs — the nanoparticle was able to deliver a 10-fold higher concentration of the drug in the tumor. They also found that this delivery method enhanced outcomes like slowing tumor growth and increased survival relative to direct drug injection.

Another advantage of this method is that lactate concentration is already measured in cancer patients, since it’s a useful biomarker to indicate cancer progression.

“It’s very easy to quantify lactate in human patients using non-invasive imaging methods like MRI,” Wu said. “And since we can accurately quantify lactate in tumors, it would be a very good means of screening patients for clinical trials and predicting how they would respond to the treatment.”

Broad potential applications for lactate-gated nanoparticles

In initial tests of their nanoparticle platform, Wu and his colleagues focused largely on a common drug called doxorubicin, which is a primary therapy for various cancers like breast cancer, sarcoma, lymphoma and acute lymphocytic leukemia. However, they also showed that several other chemotherapy drugs and immunotherapy drugs can be successfully loaded onto the nanoparticles.

“By designing this specific switch that controls drug release based on a well-characterized change in the cancer microenvironment, we hope to improve the safety profile for many drugs and allow an increased dose to be administered in order to more effectively kill cancer cells,” he said.

Cancer is not the only disease associated with increased lactate concentration. Patients with arthritis, for example, may have higher levels of lactate in their joints due to chronic inflammation. Because anti-inflammatory medications also suppress the immune response for the whole body, they can also put the patient at higher risk for infections. The lactate-gated nanoparticle, with its specific targeting of lactate-rich environments, would help avoid this general adverse effect just as it does with toxic cancer drugs.

Toward clinical implementation and future research

Wu co-founded an oncology startup called Alnair Therapeutics through the Polsky Center for Entrepreneurship and Innovation to take this research to the next level.

“In the lab, you only need a tiny batch. For clinical trials, though, we need a 10-fold greater amount, because humans are so big! So, scaling up the manufacturing process is our current challenge,” Wu said. “The first goal is to make manufacturing work with Doxil [brand name for doxorubicin], since it’s so well-characterized. But we’re very interested in expanding the platform to other cancer therapy drugs, because high toxicity is a common problem.”

Wu is also interested in further researching the unique aspects of tumor metabolism.

“There are many more unknown differences between cancer cell metabolism and regular cell metabolism,” he said. “My personal interest is to figure out more about what’s changing in tumor cells and what kind of chemical signals we can use to target cancers, maybe not only through drug delivery, but through other approaches as well.”

The study, “Enabling tumor-specific drug delivery by targeting the Warburg effect of cancer,” was published in Cell Reports Medicine in January 2025. Additional authors include Jian Zhang, Tony Pan, Jimmy Lee, Sarah Ann King, Erting Tang, Yifei Hu, Lifeng Chen, Alex Hoover, and Jun Huang at the University of Chicago, Sanja Goldberg at Safra Children’s Hospital, Tel Aviv, Linyong Zhu at East China University of Science & Technology, Shanghai, Oliver S. King at the University of California, Irvine, Orange, CA, and Benjamin Dekel at Tel Aviv University, Tel Aviv.

This study was supported by National Institutes of Health grants R01OD023700, R21AR080761, R01DA047785, and R01AR78555, the Cancer Research Institute (CRI) Technology Impact Award, the Samuel Waxman Cancer Research Foundation, the Alan B. Slifka Foundation and Israel Cancer Fund for Pediatric Sarcoma Grant, the Rally Foundation Outside the Box Grant, the University of Chicago Comprehensive Cancer Center Duckworth Family Commercial Promise Award, the Cancer Immunotherapy Team Science Award, the Pancreatic Cancer SPORE grant, the UCHAP pilot award, and the Ullman Family Team Science Award (to X.W.) and National Institutes of Health New Innovator Award (to J.H.).

A brief illustration of the main cellular processes and biological pathways that contribute to the development of Pancreatic cancer, with emphasis on the genetic mutations and cellular alterations. Credit: Current Oncology (2022). Doi: https://doi.org/10

A brief illustration of how each diagnostic test contributes to cancer detection, progression assessment, as well as treatment decision-making, and how they complement each other in clinical settings to provide a thorough evaluation of pancreatic cancer.

AI models have the potential not only to spot pancreatic cancer at an early stage, but also to predict the deadly disease’s prognosis, say scientists

SHARJAH, EMIRATE OF SHARJAH, UNITED ARAB EMIRATES, June 30, 2025 /EINPresswire.com/ — Oncologists utilizing Artificial Intelligence (AI) in their tests to spot pancreatic cancer at an early stage can also gain an overall picture of how the deadly disease is bound to develop, scientists from the University of Sharjah have revealed in a new study.

Although still at its initial stage, the AI-enabled prognosis, the scientists say, has the potential to pave the way for the provision of individualized healthcare and treatment of pancreatic cancer patients.

The scientists, who describe their findings in Beni-Suef University Journal of Basic and Applied Sciences, arrived at the groundbreaking conclusion following a comprehensive review of pancreatic cancer-related scientific literature. https://doi.org/10.1186/s43088-025-00610-4

Due to its high mortality rate, pancreatic cancer poses a serious health concern, with 467,409 deaths reported worldwide in 2022 and 510,992 new cases. Researchers often refer to pancreatic cancer as the ‘king’ of all cancers due to the exceptional ability of its cancerous cells to quickly spread to other parts of the body if not detected at an early stage.

“Nevertheless, due to several factors, including the lack of distinct molecular markers and clinical symptoms, the disease tends to be detected at an advanced stage, rendering surgical interventions futile,” the authors warn. ”For this reason, early detection and precise stratification of pancreatic cancer stages are crucial for enhancing therapeutic outcomes.”

In their study, the scientists provide what they claim to be “a concise overview” of how AI is used in the diagnosis, prognosis, and treatment of pancreatic cancer.

“Utilizing AI for preliminary testing can significantly enhance the prognosis of those who have been diagnosed with pancreatic cancer,” they write. “Advances in AI-driven image analysis have the potential to transform computer-aided diagnostic systems, aiding doctors in establishing precise and reliable assessments.”

The researchers’ extensive review of the literature dwells on numerous aspects of AI and its multiple uses in handling cases of pancreatic cancer.

One important aspect for the scientists is multicomics, which requires the combination and analysis of different data types, along with professionals and scientists, to acquire a thorough and deep understanding of a complex and deadly disease like pancreatic cancer.

They note that “it is crucial to recognize the significance of AI in multiomics domains. The healthcare industry is at the forefront of a new era, driven by technological and scientific improvements, facilitated by the integration of AI in healthcare.

“This progress can only be made through the efforts of clinicians, scientists, data analysts, and technicians. Although computer systems have several limits, they are anticipated to contribute to substantial breakthroughs soon, owing to their amazing processing powers.”

The authors endow AI models with a high ability to spot pancreatic tumors at their earliest stages, helping doctors to correctly assess the risks for patients, and then provide the associated healthcare and draw plans for long-term treatment.

They call for a better grasp and control of these models, as they are not easy to operate and understand. They show that the plethora of AI-based solutions about pancreatic cancer detection, prognosis, and treatment have “made clinical use a bit sophisticated, whereby without understanding and clear interpretation, doctors cannot critically evaluate the output of these algorithms in terms of their applicability and reliability.”

Despite the sophistication associated with AI applications, the authors report that researchers have been exerting considerable efforts to develop a variety of approaches to make them accessible to healthcare professionals and at the same time gain the confidence of patients about their effectiveness.

They predict a promising future for upcoming AI tools, which they believe can be employed with much less sophistication due to the emergence of a new frontier in artificial intelligence called explainable AI that will make the tools easily accessible for clinical adoption.

They reveal that cancer researchers are “creating and applying explainable AI methods, including feature relevance ratings, infographics, and natural language explanations to interpret AI predictions.”

They highly commend the new Machine Learning models to identify pancreatic cancer at an early stage, with implications for a significant reduction in the morbidity and mortality rates.

The application of methods employing the Internet of Things have recently caught the attention of oncological researchers who, according to the authors, are expected to revolutionize pancreatic cancer detection, prognosis, and treatment.

“AI ought to help oncologists create personalized treatment regimens by combining patient-specific data. It is being utilized to predict how patients react to therapies like immunotherapy, chemotherapy, radiation therapy, and surgery,” they write.

In their recommendations, the authors call for more AI-based pancreatic cancer research to eventually build “semi-autonomous models that reduce clinician stress, boost productivity, or be fully autonomous.”

LEON BARKHO
University Of Sharjah
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The pursuit of a cure for Alzheimer’s disease is becoming an increasingly competitive and contentious quest with recent years witnessing several important controversies.

In July 2022, Science magazine reported that a key 2006 research paper, published in the prestigious journal Nature, which identified a subtype of brain protein called beta-amyloid as the cause of Alzheimer’s, may have been based on fabricated data.

One year earlier, in June 2021, the US Food and Drug Administration had approved aducanumab, an antibody-targeting beta-amyloid, as a treatment for Alzheimer’s, even though the data supporting its use were incomplete and contradictory.

Some physicians believe aducanumab never should have been approved, while others maintain it should be given a chance.

Related: New Link Connects Herpes to Alzheimer’s. Here’s What We Know.

With millions of people needing an effective treatment, why are researchers still fumbling in this quest for a cure for what is arguably one of the most important diseases confronting humankind?

Escaping the beta-amyloid rut

For years, scientists have been focused on trying to come up with new treatments for Alzheimer’s by preventing the formation of brain-damaging clumps of this mysterious protein called beta-amyloid.

In fact, we scientists have arguably got ourselves into a bit of an intellectual rut concentrating almost exclusively on this approach, often neglecting or even ignoring other possible explanations.

Regrettably, this dedication to studying the abnormal protein clumps has not translated into a useful drug or therapy. The need for a new “out-of-the-clump” way of thinking about Alzheimer’s is emerging as a top priority in brain science.

My laboratory at the Krembil Brain Institute, part of the University Health Network in Toronto, is devising a new theory of Alzheimer’s disease.

Based on our past 30 years of research, we no longer think of Alzheimer’s as primarily a disease of the brain. Rather, we believe that Alzheimer’s is principally a disorder of the immune system within the brain.

The immune system, found in every organ in the body, is a collection of cells and molecules that work in harmony to help repair injuries and protect from foreign invaders.

When a person trips and falls, the immune system helps to mend the damaged tissues. When someone experiences a viral or bacterial infection, the immune system helps in the fight against these microbial invaders.

The exact same processes are present in the brain. When there is head trauma, the brain’s immune system kicks into gear to help repair. When bacteria are present in the brain, the immune system is there to fight back.

Alzheimer’s as autoimmune disease

We believe that beta-amyloid is not an abnormally produced protein, but rather is a normally occurring molecule that is part of the brain’s immune system. It is supposed to be there.

When brain trauma occurs or when bacteria are present in the brain, beta-amyloid is a key contributor to the brain’s comprehensive immune response. And this is where the problem begins.

Because of striking similarities between the fat molecules that make up both the membranes of bacteria and the membranes of brain cells, beta-amyloid cannot tell the difference between invading bacteria and host brain cells, and mistakenly attacks the very brain cells it is supposed to be protecting.

This leads to a chronic, progressive loss of brain cell function, which ultimately culminates in dementia – all because our body’s immune system cannot differentiate between bacteria and brain cells.

When regarded as a misdirected attack by the brain’s immune system on the very organ it is supposed to be defending, Alzheimer’s disease emerges as an autoimmune disease.

There are many types of autoimmune diseases, such as rheumatoid arthritis, in which autoantibodies play a crucial role in the development of the disease, and for which steroid-based therapies can be effective. But these therapies will not work against Alzheimer’s disease.

The brain is a very special and distinctive organ, recognized as the most complex structure in the Universe.

In our model of Alzheimer’s, beta-amyloid helps to protect and bolster our immune system, but unfortunately, it also plays a central role in the autoimmune process that, we believe, may lead to the development of Alzheimer’s.

Though drugs conventionally used in the treatment of autoimmune diseases may not work against Alzheimer’s, we strongly believe that targeting other immune-regulating pathways in the brain will lead us to new and effective treatment approaches for the disease.

Other theories of the disease

In addition to this autoimmune theory of Alzheimer’s, many other new and varied theories are beginning to appear. For example, some scientists believe that Alzheimer’s is a disease of tiny cellular structures called mitochondria – the energy factories in every brain cell.

Mitochondria convert oxygen from the air we breathe and glucose from the food we eat into the energy required for remembering and thinking.

Some maintain that it is the end-result of a particular brain infection, with bacteria from the mouth often being suggested as the culprit. Still others suggest that the disease may arise from an abnormal handling of metals within the brain, possibly zinc, copper, or iron.

It is gratifying to see new thinking about this age-old disease. Dementia currently affects more than 50 million people worldwide, with a new diagnosis being made every three seconds.

Often, people living with Alzheimer’s disease are unable to recognize their own children or even their spouse of more than 50 years.

Alzheimer’s is a public health crisis in need of innovative ideas and fresh directions.

For the well-being of the people and families living with dementia, and for the socioeconomic impact on our already stressed health-care system coping with the ever-escalating costs and demands of dementia, we need a better understanding of Alzheimer’s, its causes, and what we can do to treat it and to help the people and families who are living with it.

Donald Weaver, Professor of Chemistry and Director of Krembil Research Institute, University Health Network, University of Toronto

This article is republished from The Conversation under a Creative Commons license. Read the original article.

An earlier version of this article was published in September 2022.