All posts by Megan Scudellari

Can AI and Automation Deliver a COVID-19 Antiviral While It Still Matters?

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/artificial-intelligence/medical-ai/can-ai-and-automation-deliver-a-covid19-antiviral-while-it-still-matters

Within moments of meeting each other at a conference last year, Nathan Collins and Yann Gaston-Mathé began devising a plan to work together. Gaston-Mathé runs a startup that applies automated software to the design of new drug candidates. Collins leads a team that uses an automated chemistry platform to synthesize new drug candidates.

“There was an obvious synergy between their technology and ours,” recalls Gaston-Mathé, CEO and cofounder of Paris-based Iktos.

In late 2019, the pair launched a project to create a brand-new antiviral drug that would block a specific protein exploited by influenza viruses. Then the COVID-19 pandemic erupted across the world stage, and Gaston-Mathé and Collins learned that the viral culprit, SARS-CoV-2, relied on a protein that was 97 percent similar to their influenza protein. The partners pivoted.

Their companies are just two of hundreds of biotech firms eager to overhaul the drug-discovery process, often with the aid of artificial intelligence (AI) tools. The first set of antiviral drugs to treat COVID-19 will likely come from sifting through existing drugs. Remdesivir, for example, was originally developed to treat Ebola, and it has been shown to speed the recovery of hospitalized COVID-19 patients. But a drug made for one condition often has side effects and limited potency when applied to another. If researchers can produce an ­antiviral that specifically targets SARS-CoV-2, the drug would likely be safer and more effective than a repurposed drug.

There’s one big problem: Traditional drug discovery is far too slow to react to a pandemic. Designing a drug from scratch typically takes three to five years—and that’s before human clinical trials. “Our goal, with the combination of AI and automation, is to reduce that down to six months or less,” says Collins, who is chief strategy officer at SRI Biosciences, a division of the Silicon Valley research nonprofit SRI International. “We want to get this to be very, very fast.”

That sentiment is shared by small biotech firms and big pharmaceutical companies alike, many of which are now ramping up automated technologies backed by supercomputing power to predict, design, and test new antivirals—for this pandemic as well as the next—with unprecedented speed and scope.

“The entire industry is embracing these tools,” says Kara Carter, president of the International Society for Antiviral Research and executive vice president of infectious disease at Evotec, a drug-discovery company in Hamburg. “Not only do we need [new antivirals] to treat the SARS-CoV-2 infection in the population, which is probably here to stay, but we’ll also need them to treat future agents that arrive.”

There are currently about 200 known viruses that infect humans. Although viruses represent less than 14 percent of all known human pathogens, they make up two-thirds of all new human pathogens discovered since 1980.

Antiviral drugs are fundamentally different from vaccines, which teach a person’s immune system to mount a defense against a viral invader, and antibody treatments, which enhance the body’s immune response. By contrast, anti­virals are chemical compounds that directly block a virus after a person has become infected. They do this by binding to specific proteins and preventing them from functioning, so that the virus cannot copy itself or enter or exit a cell.

The SARS-CoV-2 virus has an estimated 25 to 29 proteins, but not all of them are suitable drug targets. Researchers are investigating, among other targets, the virus’s exterior spike protein, which binds to a receptor on a human cell; two scissorlike enzymes, called proteases, that cut up long strings of viral proteins into functional pieces inside the cell; and a polymerase complex that makes the cell churn out copies of the virus’s genetic material, in the form of single-stranded RNA.

But it’s not enough for a drug candidate to simply attach to a target protein. Chemists also consider how tightly the compound binds to its target, whether it binds to other things as well, how quickly it metabolizes in the body, and so on. A drug candidate may have 10 to 20 such objectives. “Very often those objectives can appear to be anticorrelated or contradictory with each other,” says Gaston-Mathé.

Compared with antibiotics, antiviral drug discovery has proceeded at a snail’s pace. Scientists advanced from isolating the first antibacterial molecules in 1910 to developing an arsenal of powerful antibiotics by 1944. By contrast, it took until 1951 for researchers to be able to routinely grow large amounts of virus particles in cells in a dish, a breakthrough that earned the inventors a Nobel Prize in Medicine in 1954.

And the lag between the discovery of a virus and the creation of a treatment can be heartbreaking. According to the World Health Organization, 71 million people worldwide have chronic hepatitis C, a major cause of liver cancer. The virus that causes the infection was discovered in 1989, but effective antiviral drugs didn’t hit the market until 2014.

While many antibiotics work on a range of microbes, most antivirals are highly specific to a single virus—what those in the business call “one bug, one drug.” It takes a detailed understanding of a virus to develop an antiviral against it, says Che Colpitts, a virologist at Queen’s University, in Canada, who works on antivirals against RNA viruses. “When a new virus emerges, like SARS-CoV-2, we’re at a big disadvantage.”

Making drugs to stop viruses is hard for three main reasons. First, viruses are the Spartans of the pathogen world: They’re frugal, brutal, and expert at evading the human immune system. About 20 to 250 nanometers in diameter, viruses rely on just a few parts to operate, hijacking host cells to reproduce and often destroying those cells upon departure. They employ tricks to camouflage their presence from the host’s immune system, including preventing infected cells from sending out molecular distress beacons. “Viruses are really small, so they only have a few components, so there’s not that many drug targets available to start with,” says Colpitts.

Second, viruses replicate quickly, typically doubling in number in hours or days. This constant copying of their genetic material enables viruses to evolve quickly, producing mutations able to sidestep drug effects. The virus that causes AIDS soon develops resistance when exposed to a single drug. That’s why a cocktail of antiviral drugs is used to treat HIV infection.

Finally, unlike bacteria, which can exist independently outside human cells, viruses invade human cells to propagate, so any drug designed to eliminate a virus needs to spare the host cell. A drug that fails to distinguish between a virus and a cell can cause serious side effects. “Discriminating between the two is really quite difficult,” says Evotec’s Carter, who has worked in antiviral drug discovery for over three decades.

And then there’s the money barrier. Developing antivirals is rarely profitable. Health-policy researchers at the London School of Economics recently estimated that the average cost of developing a new drug is US $1 billion, and up to $2.8 billion for cancer and other specialty drugs. Because antivirals are usually taken for only short periods of time or during short outbreaks of disease, companies rarely recoup what they spent developing the drug, much less turn a profit, says Carter.

To change the status quo, drug discovery needs fresh approaches that leverage new technologies, rather than incremental improvements, says Christian Tidona, managing director of BioMed X, an independent research institute in Heidelberg, Germany. “We need breakthroughs.”

Iktos’s AI platform was created by a medicinal chemist and an AI expert. To tackle SARS-CoV-2, the company used generative models—deep-learning algorithms that generate new data—to “imagine” molecular structures with a good chance of disabling a key coronavirus protein.

For a new drug target, the software proposes and evaluates roughly 1 million compounds, says Gaston-Mathé. It’s an iterative process: At each step, the system generates 100 virtual compounds, which are tested in silico with predictive models to see how closely they meet the objectives. The test results are then used to design the next batch of compounds. “It’s like we have a very, very fast chemist who is designing compounds, testing compounds, getting back the data, then designing another batch of compounds,” he says.

The computer isn’t as smart as a human chemist, Gaston-Mathé notes, but it’s much faster, so it can explore far more of what people in the field call “chemical space”—the set of all possible organic compounds. Unexplored chemical space is huge: Biochemists estimate that there are at least 1063 possible druglike molecules, and that 99.9 percent of all possible small molecules or compounds have never been synthesized.

Still, designing a chemical compound isn’t the hardest part of creating a new drug. After a drug candidate is designed, it must be synthesized, and the highly manual process for synthesizing a new chemical hasn’t changed much in 200 years. It can take days to plan a synthesis process and then months to years to optimize it for manufacture.

That’s why Gaston-Mathé was eager to send Iktos’s AI-generated designs to Collins’s team at SRI Biosciences. With $13.8 million from the Defense Advanced Research Projects Agency, SRI Biosciences spent the last four years automating the synthesis process. The company’s automated suite of three technologies, called SynFini, can produce new chemical compounds in just hours or days, says Collins.

First, machine-learning software devises possible routes for making a desired molecule. Next, an inkjet printer platform tests the routes by printing out and mixing tiny quantities of chemical ingredients to see how they react with one another; if the right compound is produced, the platform runs tests on it. Finally, a tabletop chemical plant synthesizes milligrams to grams of the desired compound.

Less than four months after Iktos and SRI Biosciences announced their collaboration, they had designed and synthesized a first round of antiviral candidates for SARS-CoV-2. Now they’re testing how well the compounds work on actual samples of the virus.

Theirs isn’t the only collaboration applying new tools to drug discovery. In late March, Alex Zhavoronkov, CEO of Hong Kong–based Insilico Medicine, came across a YouTube video showing three virtual-reality avatars positioning colorful, sticklike fragments in the side of a bulbous blue protein. The three researchers were using VR to explore how compounds might bind to a SARS-CoV-2 enzyme. Zhavoronkov contacted the startup that created the simulation—Nanome, in San Diego—and invited it to examine Insilico’s ­AI-generated molecules in virtual reality.

Insilico runs an AI platform that uses biological data to train deep-learning algorithms, then uses those algorithms to identify molecules with druglike features that will likely bind to a protein target. A four-day training sprint in late January yielded 100 molecules that appear to bind to an important SARS-CoV-2 protease. The company recently began synthesizing some of those molecules for laboratory testing.

Nanome’s VR software, meanwhile, allows researchers to import a molecular structure, then view and manipulate it on the scale of individual atoms. Like human chess players who use computer programs to explore potential moves, chemists can use VR to predict how to make molecules more druglike, says Nanome CEO Steve McCloskey. “The tighter the interface between the human and the computer, the more information goes both ways,” he says.

Zhavoronkov sent data about several of Insilico’s compounds to Nanome, which re-created them in VR. Nanome’s chemist demonstrated chemical tweaks to potentially improve each compound. “It was a very good experience,” says Zhavoronkov.

Meanwhile, in March, Takeda Pharmaceutical Co., of Japan, invited Schrödinger, a New York–based company that develops chemical-simulation software, to join an alliance working on antivirals. Schrödinger’s AI focuses on the physics of how proteins interact with small molecules and one another.

The software sifts through billions of molecules per week to predict a compound’s properties, and it optimizes for multiple desired properties simultaneously, says Karen Akinsanya, chief biomedical scientist and head of discovery R&D at Schrödinger. “There’s a huge sense of urgency here to come up with a potent molecule, but also to come up with molecules that are going to be well tolerated” by the body, she says. Drug developers are seeking compounds that can be broadly used and easily administered, such as an oral drug rather than an intravenous drug, she adds.

Schrödinger evaluated four protein targets and performed virtual screens for two of them, a computing-intensive process. In June, Google Cloud donated the equivalent of 16 million hours of Nvidia GPU time for the company’s calculations. Next, the alliance’s drug companies will synthesize and test the most promising compounds identified by the virtual screens.

Other companies, including Amazon Web Services, IBM, and Intel, as well as several U.S. national labs are also donating time and resources to the Covid-19 High Performance Computing Consortium. The consortium is supporting 87 projects, which now have access to 6.8 million CPU cores, 50,000 GPUs, and 600 petaflops of computational resources.

While advanced technologies could transform early drug discovery, any new drug candidate still has a long road after that. It must be tested in animals, manufactured in large batches for clinical trials, then tested in a series of trials that, for antivirals, lasts an average of seven years.

In May, the BioMed X Institute in Germany launched a five-year project to build a Rapid Antiviral Response Platform, which would speed drug discovery all the way through manufacturing for clinical trials. The €40 million ($47 million) project, backed by drug companies, will identify ­outside-the-box proposals from young scientists, then provide space and funding to develop their ideas.

“We’ll focus on technologies that allow us to go from identification of a new virus to 10,000 doses of a novel potential therapeutic ready for trials in less than six months,” says BioMed X’s Tidona, who leads the project.

While a vaccine will likely arrive long before a bespoke antiviral does, experts expect COVID-19 to be with us for a long time, so the effort to develop a direct-acting, potent antiviral continues. Plus, having new antivirals—and tools to rapidly create more—can only help us prepare for the next pandemic, whether it comes next month or in another 102 years.

“We’ve got to start thinking differently about how to be more responsive to these kinds of threats,” says Collins. “It’s pushing us out of our comfort zones.”

This article appears in the October 2020 print issue as “Automating Antivirals.”

AI Tool to Diagnose Autism Could Give Concerned Parents a Fast Diagnosis

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/cognoa-ai-autism-diagnostic-seeks-fda-clearance

This week, a California-based company announced it will seek FDA clearance for a first-of-its-kind autism spectrum disorder (ASD) diagnostic tool. Cognoa’s technology uses artificial intelligence to make an ASD diagnosis within weeks of signs of concern—far faster than the current standard of care. If cleared by the FDA, it would be the first tool enabling primary care pediatricians to diagnose autism.

The approach is “innovative,” says Robin Goin-Kochel, a clinical autism researcher at Baylor College of Medicine and associate director for research at Texas Children’s Hospital’s Autism Center, who is not affiliated with Cognoa. The field absolutely needs a way to “minimize the time between first concerns about development or behavior and eventual ASD diagnosis,” she adds.

Cognoa’s tool is the latest application of AI to healthcare, a fast-moving field we’ve been tracking at IEEE Spectrum. In many situations, AI tools seek to replace doctors in the prediction or diagnosis of a condition. In this case, however, the application of AI could enable more doctors to make a diagnosis of autism, thereby opening a critical bottleneck in children’s healthcare.

N95 Masks’ Efficiency Can Be Restored With Electricity: Smart Masks May Follow

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/one-day-medical-workers-might-plug-in-their-smart-masks

Throughout the COVID-19 pandemic, now entering its seventh month, a simple piece of personal protective equipment has been in short supply: N95 masks.

N95 and other medical-grade masks rely on two filtration methods: mechanical filtering by mask fibers, and electrostatic filtering, in which stationary electric charges attract and ensnare tiny 0.3-micron particles such fluid droplets containing viruses. The masks are specified for single-use only because even after a day, the electrostatic charges in the mask leak out into the air and the mask becomes less effective at filtering out particles. That gradual loss of efficiency is even worse in countries like India where high humidity speeds the loss of static charge to the air.

The problem is exacerbated when healthcare workers turn to procedures to decontaminate and reuse masks, such as baking or boiling, UV light towers, even large fumigation machines, all of which can extinguish a mask’s electrostatic charge.

“We wondered, why can’t we recharge it?” says Dov Levine, a professor of biophysics at Technion-IIT in Haifa, Israel. “Well, it turns out you can.”

First Human Trial for COVID-19 Antibody Drug Begins

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/first-human-trial-for-covid19-antibody-drug-begins

Just three months after the start of the pandemic, drugmaker Eli Lilly has announced the first human test of an antibody treatment designed to fight the novel coronavirus.

The potential drug, developed by Lilly, Vancouver-based biotech company AbCellera, and the Vaccine Research Center at the U.S. National Institute of Allergy and Infectious Disease, was identified by screening over 5 million immune cells in the blood of one of the first people in North America to recover after having contracted COVID-19.

The drug candidate is being tested in a randomized, placebo-controlled safety trial with 32 patients hospitalized for COVID-19 at major medical centers in the U.S.  

Using Weak Electric Fields to Make Virus-Killing Face Masks

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/using-weak-electric-fields-to-make-viruskilling-face-masks

Face masks help limit the spread of COVID-19 and are currently recommended by governments worldwide. 

Now, engineers at Indiana University demonstrate for the first time that a fabric generating a weak electric field can inactivate coronaviruses. The electroceutical fabric, described in a ChemRxiv preprint that has not yet been peer-reviewed, could be used to make face masks and other personal protective equipment (PPE), the authors say.

The fabric was tested against a pig respiratory coronavirus and a human coronavirus that causes the common cold. It has not yet been tested against SARS-CoV-2, the virus that causes COVID-19.

“The work is of interest for the scientific community; it will open new [areas to] search to provide smart solutions to overcome the COVID-19 pandemic,” says Mahmoud Al Ahmad, an electrical engineer at the University of United Arab Emirates, who was not involved in the research. While the concept will require more development before being applied to PPE, he says, “it is an excellent start in this direction.”

Beyond masks, the findings raise the possibility of using weak electrical fields to curb the spread of viruses in many ways, such as purifying air in common spaces or disinfecting operating room surfaces, says study author Chandan Sen, director of the Indiana Center for Regenerative Medicine and Engineering at Indiana University School of Medicine. “Coronavirus is not the first or last virus that is going to disrupt our lives,” he says. “We’re thinking about bigger and broader approaches to utilize weak electric fields against virus infectivity.”

Sen’s lab has been co-developing the electroceutical fabric technology, under the proprietary name V.Dox Technology, with Arizona-based company Vomaris for the past six years. Sen retains a financial stake in the company.

The technology consists of a matrix pattern of silver and zinc dots printed onto a material, such as polyester or cotton. The dots form a battery generating a weak electric field: When exposed to a conductive medium, like gel or sweat, electrons transfer from the zinc to the silver in a REDOX reaction, generating a potential difference of 0.5 volts. The technology is FDA-cleared and commercialized for wound care, where it has been shown to treat bacterial biofilm infections.

To be used in masks, moisture will need to be applied in some fashion. According to Sen, approaches could include embedding a hydrogel so it activates the dots or inserting liquid-filled piping on periphery of the mask. Moisture from exhaled air will continue to keep the fabric moist.

When the COVID-19 pandemic began, Sen and his team began to wonder if the technology might affect viruses as well as bacteria. Past work in the literature suggested coronaviruses rely on electrostatic forces for attachment and genome assembly, and Sen hoped an electric field would disrupt those forces and therefore kill the virus.

In collaboration with IU geneticist Kenneth Cornetta, who performed some of the initial virus experiments in his laboratory, the team exposed a pig respiratory coronavirus to the electroceutical fabric for 1 or 5 minutes. After one minute, they found evidence that the virus particles had begun to destabilize and aggregate, becoming larger than before exposure. That suggests the weak electric field was causing “damaging structural alterations to the virions,” the authors write.

Next, the team tested the virus particles exposed to the fabric against cells in a dish. “The infectivity was gone,” says Sen.

The results indicate “promise for this strategy,” says Murugappan Muthukumar, a professor of polymer science and engineering at the University of Massachusetts, Amherst, who was not involved in the study. “The authors’ hypothesis that the electrostatic forces within the virus particles and between the virus particles and the fabric are important is correct and is a very good idea.”

Still, Muthukumar notes, it is difficult to extrapolate how the electric field affects the viral genome, and more work needs to be done to investigate the effects observed in the paper.

Since publishing the preprint, the team also tested the fabric against human coronavirus 229E, a cause of upper respiratory tract infections, and gotten similar results, adds Sen.

The team has submitted the data to the FDA in the hopes of receiving Emergency Use Authorization to use the fabric in face masks. The technology could even be incorporated into the manufacturing of N95 masks or as an insert, says Sen.

currently sells their wound-dressing kits for between $38 and $69 online. Sen says the technology is inexpensive to manufacture and could be used in PPE at a modest cost.

Independent of Vomaris, Sen’s laboratory is developing a tunable electroceutical called patterned electroceutical dressing, in which the field strength can be altered depending on need. The dressing has shown to be safe for patients with wounds, says Sen, and is currently in clinical testing. 

Six Feet Is Not Always Enough: How Saliva Droplets Spread Through the Air

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/imaging/six-feet-is-not-always-enough-how-saliva-droplets-spread-through-the-air

IEEE COVID-19 coverage logo, link to landing page

In Maryland, restaurant patrons stand inside bumper-style tables to keep six feet apart. In New York, sunbathers maintain distance by lounging in white chalk circles painted on a grassy field.

As the United States slowly begins opening public spaces, organizations are getting creative about how to encourage social distancing. But two new studies on the airborne spread of saliva droplets, which can harbor virus particles from respiratory diseases like COVID-19, suggest those six feet alone are not always enough.

COVID-19 Digital Contact Tracing: Apple and Google Work Together as MIT Tests Validity

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/covid19-digital-contact-tracing-apple-google-mit-tests-validity

IEEE COVID-19 coverage logo, link to landing page

In a rare act of cooperation, Google and Apple this month released specifications for software developers to build digital contact tracing apps for Apple and Google mobile operating systems, which jointly encompass the majority of smartphones around the world.

Digital contact tracing, which can automatically notify an individual if they’ve crossed paths with someone who tested positive for COVID-19, has been proposed as a way to augment manual contact tracing, which requires the painstaking work of thousands of trained workers per state to identify, track, and assist individuals exposed to the virus.

As digital contact tracing technologies advance, two questions rise to the surface: Will state health officials and individuals opt to use the technology? And, if so, how well will it work?

COVID Moonshot: Can AI Algorithms and Volunteer Chemists Design a Knockout Antiviral?

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/artificial-intelligence/medical-ai/covid-moonshot-can-ai-algorithms-and-volunteer-chemists-design-a-knockout-antiviral

IEEE COVID-19 coverage logo, link to landing page

It started with a tweet. Alpha Lee, co-founder and chief scientific officer of machine-learning company PostEra, read on Twitter that Diamond Light Source, the UK’s national synchrotron facility, had identified a set of chemical fragments that attach to an important coronavirus protein.

Lee wondered if his company, formed just six months earlier, could help connect the dots from fragments to viable drugs to fight COVID-19. PostEra uses AI algorithms to map routes for drug synthesis to speed the drug discovery process. But to do so, they would need some design ideas. So Lee asked the Internet.

On 17 March, in collaboration with Diamond, the PostEra team launched the COVID Moonshot to crowdsource drug designs from medicinal chemists. Then PostEra applied their technology, pro-bono, to determine if and how those designs could be made.

COVID-19 AI Challenge: How Are Lockdowns Affecting the Most Vulnerable?

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/ethics/covid19-ai-challenge-how-are-lockdowns-affecting-the-most-vulnerable

In reaction to the rapid spread of COVID-19, over 100 countries worldwide have instituted lockdowns, restricting movement for billions of people. Those restrictions are having major effects, such as a global food crisis, on economically vulnerable populations.

Now, a group of 73 volunteer engineers, students, and policy experts are working to identify and quantify unintended consequences of the COVID-19 lockdown on vulnerable populations.

For two months, the group, organized by Palo Alto-based Omdena, will scour publicly available data sources and apply data visualization and AI tools to investigate how government policies are impacting four effects of the pandemic lockdowns: reduced access to healthcare, wage loss, employment loss, and domestic abuse.

“All over the world, from the media to state leaders, we are hearing a lot of noise and a lot of propaganda, amidst a lot of facts,” says Baidurja Ray, a computational scientist and engineer based in Texas who is participating in the effort. “I hope this project provides a purely factual and data-driven look into the effect of government policies on their citizens.”

Hospitals Deploy AI Tools to Detect COVID-19 on Chest Scans

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/imaging/hospitals-deploy-ai-tools-detect-covid19-chest-scans

Over the past week, companies around the world announced a flurry of AI-based systems to detect COVID-19 on chest CT or X-ray scans. Already, these deep learning tools are being used in hospitals to screen mild cases, triage new infections, and monitor advancing disease.

AI-powered analysis of chest scans has the potential to alleviate the growing burden on radiologists, who must review and prioritize a rising number of patient chest scans each day, experts say. And in the future, the technology might help predict which patients are most likely to need a ventilator or medication, and which can be sent home.

“That’s the brass ring,” says Matthew Lungren, a pediatric radiologist at Stanford University Medical Center and co-director of the Stanford Center for Artificial Intelligence in Medicine and Imaging. “That would be the killer app for this.”

To Answer Dire Shortages, This Healthcare Team Designed, 3D-Printed, and Tested Their Own COVID-19 Swabs in One Week

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/healthcare-team-designed-3dprinted-tested-covid19-swabs-one-week

Last Wednesday, Todd Goldstein was working on other projects. Then physicians in the New York-based hospital system where he works, hard hit by a surge in COVID-19 cases, told him they were worried about running out of supplies.

Specifically, they needed more nasal test swabs. A nasopharyngeal swab for COVID-19 is no ordinary Q-tip. These specialty swabs cannot be made of cotton, nor have wood handles. They must be long and skinny to fit up behind the nose into the upper part of the throat.

Goldstein, director of 3D Design and Innovation at Northwell Health, a network of 23 hospitals and 800 outpatient facilities, thought, “Well, we can make that.” He quickly organized a collaboration with Summer Decker and Jonathan Ford of the University of South Florida, and 3D-printing manufacturer Formlabs. In one week, the group designed, made, tested, and are now distributing 3D-printed COVID-19 test swabs.

Five Companies Using AI to Fight Coronavirus

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/artificial-intelligence/medical-ai/companies-ai-coronavirus

As of Thursday afternoon, there are 10,985 confirmed cases of COVID-19 in the United States and zero FDA-approved drugs to treat the infection.

While DARPA works on short-term “firebreak” countermeasures and computational scientists track sources of new cases of the virus, a host of drug discovery companies are putting their AI technologies to work predicting which existing drugs, or brand-new drug-like molecules, could treat the virus.

Drug development typically takes at least decade to move from idea to market, with failure rates of over 90% and a price tag between $2 and $3 billion. “We can substantially accelerate this process using AI and make it much cheaper, faster, and more likely to succeed,” says Alex Zhavoronkov, CEO of Insilico Medicine, an AI company focused on drug discovery.

Here’s an update on five AI-centered companies targeting coronavirus:

Deargen

In early February, scientists at South Korea-based Deargen published a preprint paper (a paper that has not yet been peer-reviewed by other scientists) with the results from a deep learning-based model called MT-DTI. This model uses simplified chemical sequences, rather than 2D or 3D molecular structures, to predict how strongly a molecule of interest will bind to a target protein.

The model predicted that of available FDA-approved antiviral drugs, the HIV medication atazanavir is the most likely to bind and block a prominent protein on the outside of SARS-CoV-2, the virus that causes COVID-19. It also identified three other antivirals that might bind the virus.

While the company is unaware of any official organization following up on their recommendations, their model also predicted several not-yet-approved drugs, such as the antiviral remdesivir, that are now being tested in patients, according to Sungsoo Park, co-founder and CTO of Deargen.

Deargen is now using their deep learning technology to generate new antivirals, but they need partners to help them develop the molecules, says Park. “We currently do not have a facility to test these drug candidates,” he notes. “If there are pharmaceutical companies or research institutes that want to test these drug candidates for SARS-CoV-2, [they would] always be welcome.”

Insilico Medicine

Hong Kong-based Insilico Medicine similarly jumped into the field in early February with a pre-print paper. Instead of seeking to re-purpose available drugs, the team used an AI-based drug discovery platform to generate tens of thousands of novel molecules with the potential to bind a specific SARS-CoV-2 protein and block the virus’s ability to replicate. A deep learning filtering system narrowed down the list.

“We published the original 100 molecules after a 4-day AI sprint,” says Insilico CEO Alex Zhavoronkov. The group next planned to make and test seven of the molecules, but the pandemic interrupted: Over 20 of their contract chemists were quarantined in Wuhan.

Since then, Insilico has synthesized two of the seven molecules and, with a pharmaceutical partner, plans to put them to the test in the next two weeks, Zhavoronkov tells IEEE. The company is also in the process of licensing their AI platform to two large pharmaceutical companies.

Insilico is also actively investigating drugs that might improve the immune systems of the elderly—so an older individual might respond to SARS-CoV-2 infection as a younger person does, with milder symptoms and faster recovery—and drugs to help restore lung function after infection. They hope to publish additional results soon.

SRI Biosciences and Iktos

On March 4, Menlo Park-based research center SRI International and AI company Iktos in Paris announced a collaboration to discover and develop new anti-viral therapies. Iktos’s deep learning model designs virtual novel molecules while SRI’s SynFini automated synthetic chemistry platform figures out the best way to make a molecule, then makes it.

With their powers combined, the systems can design, make and test new drug-like molecules in 1 to 2 weeks, says Iktos CEO Yann Gaston-Mathé. AI-based generation of drug candidates is currently in progress, and “the first round of target compounds will be handed to SRI’s SynFini automated synthesis platform shortly,” he tells IEEE.

Iktos also recently released two AI-based software platforms to accelerate drug discovery: one for new drug design, and another, with a free online beta version, to help synthetic chemists deconstruct how to better build a compound. “We are eager to attract as many users as possible on this free platform and to get their feedback to help us improve this young technology,” says Gaston-Mathé.

Benevolent AI

In February, British AI-startup Benevolent AI published two articles, one in The Lancet and one in The Lancet Infectious Diseases, identifying approved drugs that might block the viral replication process of SARS-CoV-2.

Using a large repository of medical information, including data extracted from the scientific literature by machine learning, the company’s AI system identified 6 compounds that effectively block a cellular pathway that appears to allow the virus into cells to make more virus particles.

One of those six, baricitinib, a once-daily pill approved to treat rheumatoid arthritis, looks to be the best of the group for both safety and efficacy against SARS-CoV-2, the authors wrote. Benevolent’s co-founder, Ivan Griffin, told Recode that Benevolent has reached out to drug manufacturers who make the drug about testing it as a potential treatment.

Currently, ruxolitinib, a drug that works by a similar mechanism, is in clinical trials for COVID-19.

Wanted: Rapid, Portable Tests for Coronavirus

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/rapid-portable-tests-coronavirus-news

At Brigham and Women’s Hospital in Boston, a potential COVID-19 patient can now drive in to the ambulance bay, roll down their window, and ask staff to swab their nose and throat.

Those swabs will be sent to a state lab for a real-time PCR test, which amplifies any viral genetic material so it can be compared to the new coronavirus, SARS-CoV-2. But this standard test must be carried out in a certified laboratory with trained technicians, takes 3 to 4 days to deliver results, and produces some false negatives.

Skin Monitoring Apps Fail to Detect Melanomas

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/skin-monitoring-apps-fail-detect-melanomas

Artificial intelligence-based health technologies are rapidly moving from the lab—where AI has routinely out-performed doctors—into the hands of consumers.

Publicly available skin cancer detection apps, such as SkinVision, use AI-based analysis to determine if a new or changing mole is a source of concern or nothing to worry about. Yet according to a new analysis of the scientific evidence behind those apps, there’s a lot to worry about.

Researchers Can Now Interrogate Body-on-Chip

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/bodyonchip-darpa-challenge

Scientists have just announced the completion of a lofty DARPA challenge to integrate 10 human organs-on-chips in an automated system to study how drugs work in the body. The technology provides an alternative to testing drugs on humans or other animals.

Referred to as the “Interrogator” by its developers, the system links up to ten different human organ chips—miniaturized microfluidic devices containing living cells that mimic the function of the organs they represent, such as intestine, heart or lung—and maintains their function for up to three weeks. In two experiments, the system successfully predicted how a human body metabolizes specific drugs.

The technology, described in two papers published this week in the journal Nature Biomedical Engineering, was developed by Donald Ingber and colleagues at Harvard’s Wyss Institute for Biologically Inspired Engineering.

“This is a wonderful technology for the field of organ-on-a-chip,” says Yu Shrike Zhang, a bioengineer at Harvard University Medical School and Brigham and Women’s Hospital in Boston, who was not involved in the research. A platform that automates the culturing, linking, and maintenance of multiple human organ-on-chips, all while inside a sterile incubator, “represents a great technological advancement,” says Zhang, who last year wrote about the promises and challenges of organ-on-a-chip systems for IEEE Spectrum.

In 2010, Ingber and colleagues reported the first human organ-on-a-chip, a human lung. Each chip, roughly the size of a computer memory stick, is composed of a clear polymer containing hollow channels: one channel is lined with endothelial cellsthe same cells that line human blood vessels, and another hosts organ-specific cells, such as liver or kidney cells.

After creating numerous individual organ chips, Ingber received a 2012 DARPA grant to try to integrate 10 organs-on-chips and use them to study how drugs are absorbed and metabolized in the body.

Eight years and three prototypes later, the team succeeded. The most recent version of the platform took four years to develop, says Richard Novak, a senior staff engineer at the Wyss Institute who built the machine. Within that time, a whole year was needed to develop a user interface that biologists with no programming experience could easily operate.

“It enables a really complex experiment to be set up in two minutes,” says Novak.

The “Interrogator,” as Novak fondly calls it, consists of a robotic system that pipettes liquids—such as a blood substitute and/or a drug of choice—into the channels; a peristaltic pump to move those liquids through the microfluidic chips; custom software with an easy drag-n-drop interface; and a mobile microscope to monitor the chips and their connections without having to manually reach in and take out each chip for examination, as was done with older systems.

Best of all, says Novak, the whole machine fits into a standard laboratory incubator, which maintains living cells at constant temperature and light conditions.

Finally, the team was ready to interrogate the Interrogator. Could the system truly mimic the human body in a drug test? To find out, the scientists connected a human gut chip, liver chip, and kidney chip, then added nicotine to the gut chip to simulate a person orally swallowing the drug (such as if a person were chewing nicotine gum). The time it took the nicotine to reach each tissue, and the maximum nicotine concentrations in each tissue, closely matched levels previously measured in patients.

In a second test, the researchers linked liver, kidney, and bone marrow chips and administered cisplatin, a common chemotherapy drug. Once again, the drug was metabolized and cleared by the kidney and liver at levels that closely matched those measured in patients. Cells in the kidney chip even expressed the same biological markers of injury as a living kidney does during chemotherapy treatment.

“Compared against clinical studies, they matched up really nicely,” says Novak. The team is now using their linked organ chips to study the gut microbiome and influenza transmission. The Interrogator technology IP has been licensed by a Wyss Institute spin-off company, Boston-based Emulate, which Ingber founded.

AI-Designed ‘Living Robots’ Crawl, Heal Themselves

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/aidesigned-living-robots-crawl-heal-themselves

Biological organisms have certain useful attributes that synthetic robots do not, such as the abilities to heal, adapt to new situations, and reproduce. Yet molding biological tissues into robots or tools has been exceptionally difficult to do: Experimental techniques, such as altering a genome to make a microbe perform a specific task, are hard to control and not scalable.

Now, a team of scientists at the University of Vermont and Tufts University in Massachusetts has used a supercomputer to design novel lifeforms with specific functions, then built those organisms out of frog cells.

The new, AI-designed biological bots crawl around a petri dish and heal themselves. Surprisingly, the biobots also spontaneously self-organize and clear their dish of small trash pellets.

Quantum Dots Encode Vaccine History in the Skin

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/imaging/quantum-dots-encode-vaccine-history-in-skin

I remember a faded yellow booklet, about the size of a wallet, that my mother used to pull out once a year at the doctor’s office to record my vaccines. Today, nurses document my children’s vaccination history in electronic health records that will likely follow them to adulthood.

To eradicate a disease—such as polio or measles—healthcare workers need to know who was vaccinated and when. Yet in developing countries, vaccination records are sparse and, in some cases, non-existent. For example, during a rural vaccination campaign, a healthcare worker may mark a child’s fingernail with a Sharpie, which can wash or scrape off within days.

Now, a team of MIT bioengineers has developed a way to keep invisible vaccine records under the skin. Delivered through a microneedle patch, biocompatible quantum dots embed in the skin and fluoresce under near-infrared light—creating a glowing trace that can be detected at least five years after vaccination. The work is described today in the journal Science Translational Medicine.  

Liquid Electrodes Morph Into Flexible Wires for Neural Stimulation

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/liquid-electrodes-form-malleable-wires-inside-the-body

Our nervous system is specialized to produce and conduct electrical currents, so it’s no surprise that gentle electric stimulation has healing powers. Neural stimulation—also known as neuromodulation, bioelectronic medicine, or electroceuticals—is currently used to treat pain, epilepsy, and migraines, and is being explored as a way to combat paralysis, inflammation, and even hair loss. Muscle stimulation can also bestow superhuman reflexes and improve short-term memory.

But to reach critical areas of the body, such as the brain or the spine, many treatments require surgically implanted devices, such as a cuff that wraps around the spinal cord. Implanting such a device can involve cutting through muscle and nerves (and may require changing a battery every few years).

Now, a team of biomedical engineers has created a type of electrode that can be injected into the body as a liquid, then harden into a stretchy, taffy-like substance. In a paper in the journal Advanced Healthcare Materials, the multi-institutional team used their “injectrodes” to stimulate the nervous systems of rats and pigs, with comparable results to existing implant technologies.

Wireless E-Skin Patch Conveys a Gentle Touch

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/devices/wireless-eskin-patch-conveys-a-gentle-touch

A mother smiles at her toddler via a live video feed, then runs her fingers along the computer screen. Miles away, the boy feels the strokes of her hand on his back.

A man with a lower-arm amputation picks up a beer can with his prosthetic hand and feels the artificial fingers make contact with the can.

A gamer’s animated character is struck on the arm and shoulder by an opponent, and the gamer feels pressure on her corresponding body parts.

These are real-life applications of a new electronic skin technology from the lab of John Rogers and his colleagues at Northwestern University, detailed in a paper published today in the journal Nature. The soft, lightweight sheet of electronics is wireless, battery-free, sticks right to the skin, and re-creates a sensation of touch.

Can Big Data Help Prevent Alzheimer’s Disease?

Post Syndicated from Megan Scudellari original https://spectrum.ieee.org/the-human-os/biomedical/diagnostics/can-big-data-solve-alzheimers

During the Framingham Heart Study, a long-term research study initiated in 1948 that collected health data from thousands of people, researchers discovered that high cholesterol and elevated blood pressure increase one’s risk of heart disease. Thanks to that insight, at-risk individuals can reduce their chances of developing the condition by taking drugs to lower cholesterol and blood pressure.

Could the same be done for Alzheimer’s disease, a notoriously opaque and complex progressive brain disease?