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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


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


Finding HIV’s ‘Sweet Spot’

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One cell labeled "Healthy T-cell" and another cell that is surrounded by HIV, "Infected T-cell".

Each year, about 30,000 people in the United States contract the human immunodeficiency virus (HIV), the cause of AIDS [1]. Thankfully, most can control their HIV infections with antiretroviral therapy and will lead productive, high-quality lives. Many will even reach a point where they have no detectable levels of virus circulating in their blood. However, all must still worry that the undetectable latent virus hidden in their systems could one day reactivate and lead to a range of serious health complications.

Now, an NIH-funded team has found that patterns of sugars at the surface of our own human immune cells affect their vulnerability to HIV infection. These data suggest it may be possible to find the infected immune cells harboring the last vestiges of virus by reading the sugar profiles on their surfaces. If so, it would move us a step closer to eliminating latent HIV infection and ultimately finding a cure for this horrible virus.

These fascinating new findings come from a team led by Nadia Roan, Gladstone Institutes, San Francisco and Mohamed Abdel-Mohsen, The Wistar Institute, Philadelphia, PA. Among its many areas of study, the Roan lab is interested in why HIV favors infecting specific subsets of a special type of immune cell called memory CD4 T cells. These cells come in different varieties. They also play important roles in the immune system’s ability to recall past infections and launch a rapid response to an emerging repeat infection.

For years, her team and others have tried to understand the interplay between HIV and human immune cells primarily by studying the proteins present at the cell surface. But living cells and their proteins also are coated in sugars and, the presence or absence of these carbohydrates is essential to their biochemistry.

In the new study, published in the journal eLife, the researchers included for the first time the patterns of these sugars in their study of cell surface proteins [2]. They, like many labs, hadn’t done so previously for technical reasons: it’s much easier to track these proteins than sugars.

To overcome this technical hurdle, Roan’s team turned to an approach that it uses for quantifying levels of proteins on the surface of single cells. The method, called CyTOF, uses metal-studded antibodies that stick to proteins, uniquely marking precise patterns of selected proteins, in this case, on individual HIV-infected cells.

In collaboration with Abdel-Mohsen, a glycobiology expert, they adapted this method for cell surface sugars. They did it by adding molecules called lectins, which stick to sugar molecules with specific shapes and compositions.

With this innovation, Roan and team report that they learned to characterize and quantify levels of 34 different proteins on the cell surface simultaneously with five types of sugars. Their next questions were: Could those patterns of cell-surface sugars help them differentiate between different types of immune cells? If so, might those patterns help to define a cell’s susceptibility to HIV?

The answer appears to be yes to both questions. Their studies revealed tremendous diversity in the patterns of sugars at the cells surfaces. Those patterns varied depending on a cell’s tissue of origin—in this case, from blood, tonsil, or the reproductive tract. The patterns also varied depending on the immune cell type—memory CD4 T cells versus other T cells or antibody-producing B cells.

Those sugar and protein profiles offered important clues as to which cells HIV prefers to infect. More specifically, compared to uninfected memory CD4 T cells, the infected ones had higher surface levels of two sugars, known as fucose [3] and sialic acid [4]. What’s more, during HIV infection, levels of both sugars increased.

Scientists already knew that HIV changes the proteins that the infected memory CD4 T cell puts on its surface, a process known as viral remodeling. Now it appears that something similar happens with sugars, too. The new findings suggest the virus increases levels of sialic acid at the cell surface in ways that may help the virus to survive. That’s especially intriguing because sialic acid also is associated with a cell’s ability to avoid detection by the immune system.

The Roan and Abdel-Mohsen labs now plan to team up again to apply their new method to study latent infection. They want to find sugar-based patterns that define those lingering infected cells and see if it’s possible to target them and eliminate the lingering HIV.

What’s also cool is this study indicates that by performing single-cell analyses and sorting cells based on their sugar and protein profiles, it may be possible to discover distinct new classes of immune and other cells that have eluded earlier studies. As was the case with HIV, this broader protein-sugar profile could hold the key to gaining deeper insights into disease processes throughout the body.

References:

[1] Diagnoses of HIV infection in the United States and dependent areas, 2020. HIV Surveillance Report, May 2020; 33; Centers for Disease Control and Prevention.

[2] Single-cell glycomics analysis by CyTOF-Lec reveals glycan features defining cells differentially susceptible to HIV. Ma T, McGregor M, Giron L, Xie G, George AF, Abdel-Mohsen M, Roan NR.eLife 2022 July 5;11:e78870

[3] Biological functions of fucose in mammals. Schneider M, Al-Shareffi E, Haltiwanger RS. Glycobiology. 2016 Jun;26(6):543.

[4] Sialic acids and other nonulosonic acids. Lewis AL, Chen X, Schnaar RL, Varki A. In Essentials of Glycobiology [Internet]. 4th edition. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2022.

Links:

HIV/AIDS (National Institute of Allergy and Infectious Diseases/NIH)

Roan Lab (University of California, San Francisco)

Mohamed Abdel-Mohsen (The Wistar Institute, Philadelphia, PA)

NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Aging; National Institute of Neurological Disorders and Stroke


Small Study Suggests Approved Insomnia Drug Can Aid in Opioid Recovery

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inset of suvorexant blocking receptors for orexin, a sleeping woman

Opioid use disorders (OUD) now threaten the health and lives of far too many young and adult Americans. While getting treatment is a key first step to recovery, overcoming an opioid addiction often comes with brutal withdrawal symptoms, including bad bouts of insomnia that are often untreatable with traditional prescription sleep medications. These medications act as sedatives, making them unsafe for people in OUD recovery.

But now, researchers have found that an approved drug for insomnia that works differently than other sleep medications could offer some needed help for the sleeplessness that affects those overcoming an opioid addiction [1]. The drug, known as suvorexant (Belsomra ®), was provided in a study to people during and immediately after tapering off opioids, and it allowed them to sleep significantly more during this week-long period. Suvorexant also helped to reduce their opioid withdrawal and craving.

This study, which received support from NIH’s Helping to End Addiction Long-term (HEAL) Initiative certainly offers promising news. The Food and Drug Administration (FDA) approved suvorexant to treat insomnia in 2014, and it is available for off-label use to help people overcoming an OUD.

The good news, however, comes with a major caveat. This early clinical trial had relatively small enrollment numbers, and larger studies are definitely needed to follow up and confirm the initial results.

The latest findings, published in the journal Science Translational Medicine, come from a team at Johns Hopkins University School of Medicine, Baltimore, led by Andrew Huhn. He and colleagues recognized sleep disturbances as a severe problem during recovery. They wondered whether suvorexant might help.

Suvorexant doesn’t actively sedate people like other sleeping medications. Suvorexant works by targeting orexin, a biochemical made in the brain that helps keep you awake [2]. Interestingly, orexin signals also have been implicated in opioid withdrawal symptoms, sleep disturbances, and drug-seeking behaviors.

Thirty-eight people entered the Hopkins study, and 26 completed it. Their average age was about 40, with close to equal numbers of white and Black participants. Most were male, and all were undergoing supervised withdrawal treatment with buprenorphine/naloxone, which is used in combination as a medication-assisted treatment for OUD.

To find out if suvorexant helped, the researchers measured total sleep time nightly using wireless devices that recorded brain activity and movement in people taking either 20 milligrams or 40 milligrams of suvorexant versus a placebo. The researchers also used standard methods to assess symptoms of opioid withdrawal, along with suvorexant’s potential for abuse.

The data showed that people taking suvorexant over four days while tapering off opioids slept about 90 minutes longer per night on average. They also continued to sleep for an extra hour a night on average in the four days following the tapering period. The researchers note that these increases in sleep duration far exceed the American Academy of Sleep Medicine’s threshold for clinically meaningful improvement.

The researchers also didn’t see any differences in adverse events between those taking suvorexant versus a placebo. They also note that the main side effect of suvorexant in general is feeling sleepy the next day as the drug wears off slowly. There also wasn’t any evidence that suvorexant might come with a risk for drug abuse.

However, because the study was small, it lacked the needed statistical power to determine meaningful differences between the two doses of suvorexant. The study also didn’t include many women. But overall, the evidence that suvorexant or even other medications that target orexin could improve OUD treatment appears quite promising.

The NIH’s HEAL Initiative has launched over 600 research projects across the country. These studies cover a range of science and health care needs. But a common thread running through these projects is a desire to enhance the evidence base for lifesaving OUD interventions. Another is a commitment to discover better ways to help people recover from an OUD, and these latest data on suvorexant show this commitment in action.

References:

[1] Suvorexant ameliorated sleep disturbance, opioid withdrawal, and craving during a buprenorphine taper. Huhn AS, Finan PH, Gamaldo CE, Hammond AS, Umbricht A, Bergeria CL, Strain EC, Dunn KE. Sci Transl Med. 2022 Jun 22;14(650):eabn8238.

[2] The hypocretin/orexin system. Ebrahim IO, et al. J R Soc Med. 2002 May;95(5):227-30.

Links:

SAMHSA’s National Helpline (Substance Abuse and Mental Health Services Administration, Rockville, MD)

Opioids (National Institute on Drug Abuse/NIH)

Helping to End Addiction Long-term (HEAL) Initiative (NIH)

Andrew Huhn (Johns Hopkins School of Medicine, Baltimore)

NIH Support: National Institute on Drug Abuse


To Prevent a Stroke, Household Chores and Leisurely Strolls May Help

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An elderly man vacuums the floor while an elderly woman washes the windows
Credit: Shutterstock/Tartila

As we get older, unfortunately our chances of having a stroke rise. While there’s obviously no way to turn back the clock on our age, fortunately there are ways to lower our risk of a stroke and that includes staying physically active. Take walks, ride a bike, play a favorite sport. According to our current exercise guidelines for American adults, the goal is to get in at least two and a half hours each week of moderate-intensity physical activity as well as two days of muscle-strengthening activity [1].

But a new study, published in the journal JAMA Network Open, shows that reducing the chances of a stroke as we get older doesn’t necessarily require heavy aerobic exercise or a sweat suit [2]. For those who are less mobile or less interested in getting out to exercise, the researchers discovered that just spending time doing light-intensity physical activity—such as tending to household chores—“significantly” protects against stroke.

The study also found you don’t have to dedicate whole afternoons to tidying up around the house to protect your health. It helps to just get up out of your chair for five or 10 minutes at a time throughout the day to straighten up a room, sweep the floor, fold the laundry, step outside to water the garden, or just take a leisurely stroll.

That may sound simple, but consider that the average American adult now spends on average six and a half hours per day just sitting [3]. That comes to nearly two days per week on average, much to the detriment of our health and wellbeing. Indeed, the study found that middle-aged and older people who were sedentary for 13 hours or more hours per day had a 44 percent increased risk of stroke.

These latest findings come from Steven Hooker, San Diego State University, CA, and his colleagues on the NIH-supported Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. Launched in 2003, REGARDS continues to follow over time more than 30,000 Black and white participants aged 45 and older.

Hooker and colleagues wanted to know more about the amount and intensity of exercise required to prevent a stroke. Interestingly, the existing data were relatively weak, in part because prior studies looking at the associations between physical activity and stroke risk relied on self-reported data, which don’t allow for precise measures. What’s more, the relationship between time spent sitting and stroke risk also remained unknown.

To get answers, Hooker and team focused on 7,607 adults enrolled in the REGARDS study. Rather than relying on self-reported physical activity data, team members asked participants to wear a hip-mounted accelerometer—a device that records how fast people move—during waking hours for seven days between May 2009 and January 2013.

The average age of participants was 63. Men and women were represented about equally in the study, while about 70 percent of participants were white and 30 percent were Black.

Over the more than seven years of the study, 286 participants suffered a stroke. The researchers then analyzed all the accelerometer data, including the amount and intensity of their physical activity over the course of a normal week. They then related those data to their risk of having a stroke over the course of the study.

The researchers found, as anticipated, that adults who spent the most time doing moderate-to-vigorous intensity physical activity were less likely to have a stroke than those who spent the least time physically active. But those who spent the most time sitting also were at greater stroke risk, whether they got their weekly exercise in or not.

Those who regularly sat still for longer periods—17 minutes or more at a time—had a 54 percent increase in stroke risk compared to those who more often sat still for less than eight minutes. After adjusting for the time participants spent sitting, those who more often had shorter periods of moderate-to-vigorous activity—less than 10 minutes at a time—still had significantly lower stroke risk. But, once the amount of time spent sitting was taken into account, longer periods of more vigorous activity didn’t make a difference.

While high blood pressure, diabetes, and myriad other factors also contribute to a person’s cumulative risk of stroke, the highlighted paper does bring some good actionable news. For each hour spent doing light-intensity physical activity instead of sitting, a person can reduce his or her stroke risk.

The bad news, of course, is that each extra hour spent sitting per day comes with an increased risk for stroke. This bad news shouldn’t be taken lightly. In the U.S., almost 800,000 people have a stroke each year. That’s one person every 40 seconds with, on average, one death every four minutes. Globally, stroke is the second most common cause of death and third most common cause of disability in people, killing more than 6.5 million each year.

If you’re already meeting the current exercise guidelines for adults, keep up the good work. If not, this paper shows you can still do something to lower your stroke risk. Make a habit throughout the day of getting up out of your chair for a mere five or 10 minutes to straighten up a room, sweep the floor, fold the laundry, step outside to water the garden, or take a leisurely stroll. It could make a big difference to your health as you age.

References:

[1] How much physical activity do adults need? Centers for Disease Control and Prevention. June 2, 2022.

[2] Association of accelerometer-measured sedentary time and physical activity with risk of stroke among US adults. Hooker SP, Diaz KM, Blair SN, Colabianchi N, Hutto B, McDonnell MN, Vena JE, Howard VJ. JAMA Netw Open. 2022 Jun 1;5(6):e2215385.

[3] Trends in sedentary behavior among the US population, 2001-2016. Yang L, Cao C, Kantor ED, Nguyen LH, Zheng X, Park Y, Giovannucci EL, Matthews CE, Colditz GA, Cao Y. JAMA. 2019 Apr 23;321(16):1587-1597.

Links:

Stroke (National Institute of Neurological Disorders and Stroke/NIH)

REGARDS Study (University of Alabama at Birmingham)

NIH Support: National Institute of Neurological Disorders and Stroke; National Institute on Aging


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