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Lawrence Tabak, D.D.S., Ph.D.

Wearable Sensor Promises More Efficient Early Cancer Drug Development

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A labeled sensor rests on the surface of the skin. Under the sensor, beneath the skin in a tumor. A graph shows the tumor's size over time.

Wearable electronic sensors hold tremendous promise for improving human health and wellness. That promise already runs the gamut from real-time monitoring of blood pressure and abnormal heart rhythms to measuring alcohol consumption and even administering vaccines.

Now a new study published in the journal Science Advances [1] demonstrates the promise of wearables also extends to the laboratory. A team of engineers has developed a flexible, adhesive strip that, at first glance, looks like a Band-Aid. But this “bandage” actually contains an ultra-sensitive, battery-operated sensor that’s activated when placed on the skin of mouse models used to study possible new cancer drugs.

This sensor is so sensitive that it can detect, in real time, changes in the size of a tumor down to one-hundredth of a millimeter. That’s about the thickness of the plastic cling wrap you likely have in your kitchen! The device beams those measures to a smartphone app, capturing changes in tumor growth minute by minute over time.

The goal is to determine much sooner—and with greater automation and precision—which potential drug candidates undergoing early testing in the lab best inhibit tumor growth and, consequently, should be studied further. In their studies in mouse models of cancer, researchers found the new sensor could detect differences between tumors treated with an active drug and those treated with a placebo within five hours. Those quick results also were validated using more traditional methods to confirm their accuracy.

The device is the work of a team led by Alex Abramson, a former post-doc with Zhenan Bao, Stanford University’s School of Engineering, Palo Alto, CA. Abramson has since launched his own lab at the Georgia Institute of Technology, Atlanta.

The Stanford team began looking for a technological solution after realizing the early testing of potential cancer drugs typically requires researchers to make tricky measurements using pincer-like calipers by hand. Not only is the process tedious and slow, it’s less than an ideal way to capture changes in soft tissues with the desired precision. The imprecision can also lead to false leads that won’t pan out further along in the drug development pipeline, at great time and expense to their developers.

To refine the process, the NIH-supported team turned to wearable technology and recent advances in flexible electronic materials. They developed a device dubbed FAST (short for Flexible Autonomous Sensor measuring Tumors). Its sensor, embedded in a skin patch, is composed of a flexible and stretchable, skin-like polymer with embedded gold circuitry.

Here’s how FAST works: Coated on top of the polymer skin patch is a layer of gold. When stretched, it forms small cracks that change the material’s electrical conductivity. As the material stretches, even slightly, the number of cracks increases, causing the electronic resistance in the sensor to increase as well. As the material contracts, any cracks come back together, and conductivity improves.

By picking up on those changes in conductivity, the device measures precisely the strain on the polymer membrane—an indication of whether the tumor underneath is stable, growing, or shrinking—and transmits that data to a smartphone. Based on that information, potential therapies that are linked to rapid tumor shrinkage can be fast-tracked for further study while those that allow a tumor to continue growing can be cast aside.

The researchers are continuing to test their sensor in more cancer models and with more therapies to extend these initial findings. Already, they have identified at least three significant advantages of their device in early cancer drug testing:

• FAST is non-invasive and captures precise measurements on its own.
• It can provide continuous monitoring, for weeks, months, or over the course of study.
• The flexible sensor fully surrounds the tumor and can therefore detect 3D changes in shape that would be hard to pick up otherwise in real-time with existing technologies.

By now, you are probably asking yourself: Could FAST also be applied as a wearable for cancer patients to monitor in real-time whether an approved chemotherapy regimen is working? It is too early to say. So far, FAST has not been tested in people. But, as highlighted in this paper, FAST is off to, well, a fast start and points to the vast potential of wearables in human health, wellness, and also in the lab.

Reference:

[1] A flexible electronic strain sensor for the real-time monitoring of tumor regression. Abramson A, Chan CT, Khan Y, Mermin-Bunnell A, Matsuhisa N, Fong R, Shad R, Hiesinger W, Mallick P, Gambhir SS, Bao Z. Sci Adv. 2022 Sep 16;8(37):eabn6550.

Links:

Stanford Wearable Electronics Initiative (Stanford University, Palo Alto, CA)

Bao Group (Stanford University)

Abramson Lab (Georgia Institute of Technology, Atlanta)

NIH Support: National Institute of Biomedical Imaging and Bioengineering


Time to Get Boosted

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Dr. Tabak receiving a vaccination in his shoulder
I got my COVID-19 bivalent vaccine booster last weekend. The Moderna and the Pfizer-BioNTech COVID-19 bivalent vaccine boosters should be now widely available in communities around the country. If it’s been two months since you completed your primary vaccination series or received a booster, you are eligible to receive the bivalent booster. I encourage all those eligible to get the updated vaccine booster, especially with winter on the way.

Dedicating Roy Blunt Center for Alzheimer’s Disease and Related Dementias

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Dr. Tabak speaks at a podium, four other people sit to his sides
On September 19, I welcomed everyone to the dedication of the brand-new Roy Blunt Center for Alzheimer’s Disease and Related Dementias, which is located on the main NIH campus, Bethesda, MD. Seated behind me (l-r) are former NIH director Francis Collins; Dawn Beraud, NIH’s National Institute on Aging; Senator Roy Blunt of Missouri; and Congressman Tom Cole of Oklahoma. The 24,000-square foot building, which includes 12,000 square feet of laboratory space, will house the NIH Intramural Research Program’s Center for Alzheimer’s and Related Dementias (CARD). The facility is named in honor of Senator Blunt, who will be retiring from Congress, to recognize his extraordinary leadership and unwavering commitment to speed medical progress in this important area that touches far too many lives and families. Credit: NIH

Celebrating NIH and Life-Saving Science During Rally for Medical Research

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Dr. Tabak at a podium with sign saying Rally for Medical Research, Capitol Hill Day
It was my pleasure to take part in a recent reception held on Capitol Hill to celebrate the lifesaving science that NIH supports. The well-attended evening reception was part of the Rally for Medical Research Capitol Hill Day, an annual event that is organized by the American Association for Cancer Research and supported by nearly 350 partnering organizations I spoke for several minutes about NIH’s role in furthering medical research and innovation, and how this wise national investment has led to more progress, more hope, and more lives saved. The reception was held in the Rayburn House Office Building on September 13. Credit: RallyforMedicalResearch.org

Using AI to Find New Antibiotics Still a Work in Progress

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Protein over a computer network

Each year, more than 2.8 million people in the United States develop bacterial infections that don’t respond to treatment and sometimes turn life-threatening [1]. Their infections are antibiotic-resistant, meaning the bacteria have changed in ways that allow them to withstand our current widely used arsenal of antibiotics. It’s a serious and growing health-care problem here and around the world. To fight back, doctors desperately need new antibiotics, including novel classes of drugs that bacteria haven’t seen and developed ways to resist.

Developing new antibiotics, however, involves much time, research, and expense. It’s also fraught with false leads. That’s why some researchers have turned to harnessing the predictive power of artificial intelligence (AI) in hopes of selecting the most promising leads faster and with greater precision.

It’s a potentially paradigm-shifting development in drug discovery, and a recent NIH-funded study, published in the journal Molecular Systems Biology, demonstrates AI’s potential to streamline the process of selecting future antibiotics [2]. The results are also a bit sobering. They highlight the current limitations of one promising AI approach, showing that further refinement will still be needed to maximize its predictive capabilities.

These findings come from the lab of James Collins, Massachusetts Institute of Technology (MIT), Cambridge, and his recently launched Antibiotics-AI Project. His audacious goal is to develop seven new classes of antibiotics to treat seven of the world’s deadliest bacterial pathogens in just seven years. What makes this project so bold is that only two new classes of antibiotics have reached the market in the last 50 years!

In the latest study, Collins and his team looked to an AI program called AlphaFold2 [3]. The name might ring a bell. AlphaFold’s AI-powered ability to predict protein structures was a finalist in Science Magazine’s 2020 Breakthrough of the Year. In fact, AlphaFold has been used already to predict the structures of more than 200 million proteins, or almost every known protein on the planet [4].

AlphaFold employs a deep learning approach that can predict most protein structures from their amino acid sequences about as well as more costly and time-consuming protein-mapping techniques.
In the deep learning models used to predict protein structure, computers are “trained” on existing data. As computers “learn” to understand complex relationships within the training material, they develop a model that can then be applied for making predictions of 3D protein structures from linear amino acid sequences without relying on new experiments in the lab.

Collins and his team hoped to combine AlphaFold with computer simulations commonly used in drug discovery as a way to predict interactions between essential bacterial proteins and antibacterial compounds. If it worked, researchers could then conduct virtual rapid screens of millions of new synthetic drug compounds targeting key bacterial proteins that existing antibiotics don’t. It would also enable the rapid development of antibiotics that work in novel ways, exactly what doctors need to treat antibiotic-resistant infections.

To test the strategy, Collins and his team focused first on the predicted structures of 296 essential proteins from the Escherichia coli bacterium as well as 218 antibacterial compounds. Their computer simulations then predicted how strongly any two molecules (essential protein and antibacterial) would bind together based on their shapes and physical properties.

It turned out that screening many antibacterial compounds against many potential targets in E. coli led to inaccurate predictions. For example, when comparing their computational predictions with actual interactions for 12 essential proteins measured in the lab, they found that their simulated model had about a 50:50 chance of being right. In other words, it couldn’t identify true interactions between drugs and proteins any better than random guessing.

They suspect one reason for their model’s poor performance is that the protein structures used to train the computer are fixed, not flexible and shifting physical configurations as happens in real life. To improve their success rate, they ran their predictions through additional machine-learning models that had been trained on data to help them “learn” how proteins and other molecules reconfigure themselves and interact. While this souped-up model got somewhat better results, the researchers report that they still aren’t good enough to identify promising new drugs and their protein targets.

What now? In future studies, the Collins lab will continue to incorporate and train the computers on even more biochemical and biophysical data to help with the predictive process. That’s why this study should be interpreted as an interim progress report on an area of science that will only get better with time.

But it’s also a sobering reminder that the quest to find new classes of antibiotics won’t be easy—even when aided by powerful AI approaches. We certainly aren’t there yet, but I’m confident that we will get there to give doctors new therapeutic weapons and turn back the rise in antibiotic-resistant infections.

References:

[1] 2019 Antibiotic resistance threats report. Centers for Disease Control and Prevention.

[2] Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. Wong F, Krishnan A, Zheng EJ, Stark H, Manson AL, Earl AM, Jaakkola T, Collins JJ. Molecular Systems Biology. 2022 Sept 6. 18: e11081.

[3] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Kavukcuoglu K, Kohli P, Hassabis D., et al. Nature. 2021 Aug;596(7873):583-589.

[4] ‘The entire protein universe’: AI predicts shape of nearly every known protein. Callaway E. Nature. 2022 Aug;608(7921):15-16.

Links:

Antimicrobial (Drug) Resistance (National Institute of Allergy and Infectious Diseases/NIH)

Collins Lab (Massachusetts Institute of Technology, Cambridge)

The Antibiotics-AI Project, The Audacious Project (TED)

AlphaFold (Deep Mind, London, United Kingdom)

NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences


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