cryo-EM
Artificial Intelligence Accurately Predicts Protein Folding
Posted on by Dr. Francis Collins
Proteins are the workhorses of the cell. Mapping the precise shapes of the most important of these workhorses helps to unlock their life-supporting functions or, in the case of disease, potential for dysfunction. While the amino acid sequence of a protein provides the basis for its 3D structure, deducing the atom-by-atom map from principles of quantum mechanics has been beyond the ability of computer programs—until now.
In a recent study in the journal Science, researchers reported they have developed artificial intelligence approaches for predicting the three-dimensional structure of proteins in record time, based solely on their one-dimensional amino acid sequences [1]. This groundbreaking approach will not only aid researchers in the lab, but guide drug developers in coming up with safer and more effective ways to treat and prevent disease.
This new NIH-supported advance is now freely available to scientists around the world. In fact, it has already helped to solve especially challenging protein structures in cases where experimental data were lacking and other modeling methods hadn’t been enough to get a final answer. It also can now provide key structural information about proteins for which more time-consuming and costly imaging data are not yet available.
The new work comes from a group led by David Baker and Minkyung Baek, University of Washington, Seattle, Institute for Protein Design. Over the course of the pandemic, Baker’s team has been working hard to design promising COVID-19 therapeutics. They’ve also been working to design proteins that might offer promising new ways to treat cancer and other conditions. As part of this effort, they’ve developed new computational approaches for determining precisely how a chain of amino acids, which are the building blocks of proteins, will fold up in space to form a finished protein.
But the ability to predict a protein’s precise structure or shape from its sequence alone had proven to be a difficult problem to solve despite decades of effort. In search of a solution, research teams from around the world have come together every two years since 1994 at the Critical Assessment of Structure Prediction (CASP) meetings. At these gatherings, teams compete against each other with the goal of developing computational methods and software capable of predicting any of nature’s 200 million or more protein structures from sequences alone with the greatest accuracy.
Last year, a London-based company called DeepMind shook up the structural biology world with their entry into CASP called AlphaFold. (AlphaFold was one of Science’s 2020 Breakthroughs of the Year.) They showed that their artificial intelligence approach—which took advantage of the 170,000 proteins with known structures in a reiterative process called deep learning—could predict protein structure with amazing accuracy. In fact, it could predict most protein structures almost as accurately as other high-resolution protein mapping techniques, including today’s go-to strategies of X-ray crystallography and cryo-EM.
The DeepMind performance showed what was possible, but because the advances were made by a world-leading deep learning company, the details on how it worked weren’t made publicly available at the time. The findings left Baker, Baek, and others eager to learn more and to see if they could replicate the impressive predictive ability of AlphaFold outside of such a well-resourced company.
In the new work, Baker and Baek’s team has made stunning progress—using only a fraction of the computational processing power and time required by AlphaFold. The new software, called RoseTTAFold, also relies on a deep learning approach. In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result—in this case, a protein structure.
Given the complexity of the problem, instead of using a single neural network, RoseTTAFold relies on three. The three-track neural network integrates and simultaneously processes one-dimensional protein sequence information, two-dimensional information about the distance between amino acids, and three-dimensional atomic structure all at once. Information from these separate tracks flows back and forth to generate accurate models of proteins rapidly from sequence information alone, including structures in complex with other proteins.
As soon as the researchers had what they thought was a reasonable working approach to solve protein structures, they began sharing it with their structural biologist colleagues. In many cases, it became immediately clear that RoseTTAFold worked remarkably well. What’s more, it has been put to work to solve challenging structural biology problems that had vexed scientists for many years with earlier methods.
RoseTTAFold already has solved hundreds of new protein structures, many of which represent poorly understood human proteins. The 3D rendering of a complex showing a human protein called interleukin-12 in complex with its receptor (above image) is just one example. The researchers have generated other structures directly relevant to human health, including some that are related to lipid metabolism, inflammatory conditions, and cancer. The program is now available on the web and has been downloaded by dozens of research teams around the world.
Cryo-EM and other experimental mapping methods will remain essential to solve protein structures in the lab. But with the artificial intelligence advances demonstrated by RoseTTAFold and AlphaFold, which has now also been released in an open-source version and reported in the journal Nature [2], researchers now can make the critical protein structure predictions at their desktops. This newfound ability will be a boon to basic science studies and has great potential to speed life-saving therapeutic advances.
References:
[1] Accurate prediction of protein structures and interactions using a three-track neural network. Baek M, DiMaio F, Anishchenko I, Dauparas J, Grishin NV, Adams PD, Read RJ, Baker D., et al. Science. 2021 Jul 15:eabj8754.
[2] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Green T, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. et al. Nature. 2021 Jul 15.
Links:
Structural Biology (National Institute of General Medical Sciences/NIH)
The Structures of Life (NIGMS)
Baker Lab (University of Washington, Seattle)
CASP 14 (University of California, Davis)
NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences
What A Year It Was for Science Advances!
Posted on by Dr. Francis Collins
At the close of every year, editors and writers at the journal Science review the progress that’s been made in all fields of science—from anthropology to zoology—to select the biggest advance of the past 12 months. In most cases, this Breakthrough of the Year is as tough to predict as the Oscar for Best Picture. Not in 2020. In a year filled with a multitude of challenges posed by the emergence of the deadly coronavirus disease 2019 (COVID-2019), the breakthrough was the development of the first vaccines to protect against this pandemic that’s already claimed the lives of more than 360,000 Americans.
In keeping with its annual tradition, Science also selected nine runner-up breakthroughs. This impressive list includes at least three areas that involved efforts supported by NIH: therapeutic applications of gene editing, basic research understanding HIV, and scientists speaking up for diversity. Here’s a quick rundown of all the pioneering advances in biomedical research, both NIH and non-NIH funded:
Shots of Hope. A lot of things happened in 2020 that were unprecedented. At the top of the list was the rapid development of COVID-19 vaccines. Public and private researchers accomplished in 10 months what normally takes about 8 years to produce two vaccines for public use, with more on the way in 2021. In my more than 25 years at NIH, I’ve never encountered such a willingness among researchers to set aside their other concerns and gather around the same table to get the job done fast, safely, and efficiently for the world.
It’s also pretty amazing that the first two conditionally approved vaccines from Pfizer and Moderna were found to be more than 90 percent effective at protecting people from infection with SARS-CoV-2, the coronavirus that causes COVID-19. Both are innovative messenger RNA (mRNA) vaccines, a new approach to vaccination.
For this type of vaccine, the centerpiece is a small, non-infectious snippet of mRNA that encodes the instructions to make the spike protein that crowns the outer surface of SARS-CoV-2. When the mRNA is injected into a shoulder muscle, cells there will follow the encoded instructions and temporarily make copies of this signature viral protein. As the immune system detects these copies, it spurs the production of antibodies and helps the body remember how to fend off SARS-CoV-2 should the real thing be encountered.
It also can’t be understated that both mRNA vaccines—one developed by Pfizer and the other by Moderna in conjunction with NIH’s National Institute of Allergy and Infectious Diseases—were rigorously evaluated in clinical trials. Detailed data were posted online and discussed in all-day meetings of an FDA Advisory Committee, open to the public. In fact, given the high stakes, the level of review probably was more scientifically rigorous than ever.
First CRISPR Cures: One of the most promising areas of research now underway involves gene editing. These tools, still relatively new, hold the potential to fix gene misspellings—and potentially cure—a wide range of genetic diseases that were once to be out of reach. Much of the research focus has centered on CRISPR/Cas9. This highly precise gene-editing system relies on guide RNA molecules to direct a scissor-like Cas9 enzyme to just the right spot in the genome to cut out or correct a disease-causing misspelling.
In late 2020, a team of researchers in the United States and Europe succeeded for the first time in using CRISPR to treat 10 people with sickle cell disease and transfusion-dependent beta thalassemia. As published in the New England Journal of Medicine, several months after this non-heritable treatment, all patients no longer needed frequent blood transfusions and are living pain free [1].
The researchers tested a one-time treatment in which they removed bone marrow from each patient, modified the blood-forming hematopoietic stem cells outside the body using CRISPR, and then reinfused them into the body. To prepare for receiving the corrected cells, patients were given toxic bone marrow ablation therapy, in order to make room for the corrected cells. The result: the modified stem cells were reprogrammed to switch back to making ample amounts of a healthy form of hemoglobin that their bodies produced in the womb. While the treatment is still risky, complex, and prohibitively expensive, this work is an impressive start for more breakthroughs to come using gene editing technologies. NIH, including its Somatic Cell Genome Editing program, continues to push the technology to accelerate progress and make gene editing cures for many disorders simpler and less toxic.
Scientists Speak Up for Diversity: The year 2020 will be remembered not only for COVID-19, but also for the very public and inescapable evidence of the persistence of racial discrimination in the United States. Triggered by the killing of George Floyd and other similar events, Americans were forced to come to grips with the fact that our society does not provide equal opportunity and justice for all. And that applies to the scientific community as well.
Science thrives in safe, diverse, and inclusive research environments. It suffers when racism and bigotry find a home to stifle diversity—and community for all—in the sciences. For the nation’s leading science institutions, there is a place and a calling to encourage diversity in the scientific workplace and provide the resources to let it flourish to everyone’s benefit.
For those of us at NIH, last year’s peaceful protests and hashtags were noticed and taken to heart. That’s one of the many reasons why we will continue to strengthen our commitment to building a culturally diverse, inclusive workplace. For example, we have established the NIH Equity Committee. It allows for the systematic tracking and evaluation of diversity and inclusion metrics for the intramural research program for each NIH institute and center. There is also the recently founded Distinguished Scholars Program, which aims to increase the diversity of tenure track investigators at NIH. Recently, NIH also announced that it will provide support to institutions to recruit diverse groups or “cohorts” of early-stage research faculty and prepare them to thrive as NIH-funded researchers.
AI Disentangles Protein Folding: Proteins, which are the workhorses of the cell, are made up of long, interconnected strings of amino acids that fold into a wide variety of 3D shapes. Understanding the precise shape of a protein facilitates efforts to figure out its function, its potential role in a disease, and even how to target it with therapies. To gain such understanding, researchers often try to predict a protein’s precise 3D chemical structure using basic principles of physics—including quantum mechanics. But while nature does this in real time zillions of times a day, computational approaches have not been able to do this—until now.
Of the roughly 170,000 proteins mapped so far, most have had their structures deciphered using powerful imaging techniques such as x-ray crystallography and cryo–electron microscopy (cryo-EM). But researchers estimate that there are at least 200 million proteins in nature, and, as amazing as these imaging techniques are, they are laborious, and it can take many months or years to solve 3D structure of a single protein. So, a breakthrough certainly was needed!
In 2020, researchers with the company Deep Mind, London, developed an artificial intelligence (AI) program that rapidly predicts most protein structures as accurately as x-ray crystallography and cryo-EM can map them [2]. The AI program, called AlphaFold, predicts a protein’s structure by computationally modeling the amino acid interactions that govern its 3D shape.
Getting there wasn’t easy. While a complete de novo calculation of protein structure still seemed out of reach, investigators reasoned that they could kick start the modeling if known structures were provided as a training set to the AI program. Utilizing a computer network built around 128 machine learning processors, the AlphaFold system was created by first focusing on the 170,000 proteins with known structures in a reiterative process called deep learning. The process, which is inspired by the way neural networks in the human brain process information, enables computers to look for patterns in large collections of data. In this case, AlphaFold learned to predict the underlying physical structure of a protein within a matter of days. This breakthrough has the potential to accelerate the fields of structural biology and protein research, fueling progress throughout the sciences.
How Elite Controllers Keep HIV at Bay: The term “elite controller” might make some people think of video game whizzes. But here, it refers to the less than 1 percent of people living with human immunodeficiency virus (HIV) who’ve somehow stayed healthy for years without taking antiretroviral drugs. In 2020, a team of NIH-supported researchers figured out why this is so.
In a study of 64 elite controllers, published in the journal Nature, the team discovered a link between their good health and where the virus has inserted itself in their genomes [3]. When a cell transcribes a gene where HIV has settled, this so-called “provirus,” can produce more virus to infect other cells. But if it settles in a part of a chromosome that rarely gets transcribed, sometimes called a gene desert, the provirus is stuck with no way to replicate. Although this discovery won’t cure HIV/AIDS, it points to a new direction for developing better treatment strategies.
In closing, 2020 presented more than its share of personal and social challenges. Among those challenges was a flood of misinformation about COVID-19 that confused and divided many communities and even families. That’s why the editors and writers at Science singled out “a second pandemic of misinformation” as its Breakdown of the Year. This divisiveness should concern all of us greatly, as COVID-19 cases continue to soar around the country and our healthcare gets stretched to the breaking point. I hope and pray that we will all find a way to come together, both in science and in society, as we move forward in 2021.
References:
[1] CRISPR-Cas9 gene editing for sickle cell disease and β-thalassemia. Frangoul H et al. N Engl J Med. 2020 Dec 5.
[2] ‘The game has changed.’ AI triumphs at protein folding. Service RF. Science. 04 Dec 2020.
[3] Distinct viral reservoirs in individuals with spontaneous control of HIV-1. Jiang C et al. Nature. 2020 Sep;585(7824):261-267.
Links:
COVID-19 Research (NIH)
2020 Science Breakthrough of the Year (American Association for the Advancement of Science, Washington, D.C)
Caught on Camera: Neutralizing Antibodies Interacting with SARS-CoV-2
Posted on by Dr. Francis Collins
As this long year enters its final month, there is good reason to look ahead to 2021 with optimism that the COVID-19 pandemic will finally be contained. The Food and Drug Administration is now reviewing the clinical trial data of the Pfizer and Moderna vaccines to ensure their safety and efficacy. If all goes well, emergency use authorization could come very soon, allowing immunizations to begin.
Work also continues on developing better therapeutics against SARS-CoV-2, the novel coronavirus that causes COVID-19. Though we’ve learned a great deal about this coronavirus in a short time, structural biologists continue to produce more detailed images that reveal more precisely where and how to target SARS-CoV-2. This research often involves neutralizing antibodies that circulate in the blood of most people who’ve recovered from COVID-19. The study of such antibodies and how they interact with SARS-CoV-2 offers critical biological clues into how to treat and prevent COVID-19.
A recent study in the journal Nature brings more progress, providing the most in-depth analysis yet of how human neutralizing antibodies physically grip SARS-CoV-2 to block it from binding to our cells [1]. To conduct this analysis, a team of NIH-supported structural biologists, led by postdoc Christopher Barnes and Pamela Björkman, California Institute of Technology, Pasadena, used the power of cryo-electron microscopy (cryo-EM) to capture complex molecular interactions at near-atomic scale.
People infected with SARS-CoV-2 (or any foreign substance, for that matter) generate thousands of different versions of attack antibodies. Some of these antibodies are very good at sticking to the coronavirus, while others attach only loosely. Barnes used cryo-EM to capture highly intricate pictures of eight different human neutralizing antibodies bound tightly to SARS-CoV-2. Each of these antibodies, which had been isolated from patients a few weeks after they developed symptoms of COVID-19, had been shown in lab tests to be highly effective at blocking infection.
The researchers mapped all physical interactions between several human neutralizing antibodies and SARS-CoV-2’s spike protein that stud its surface. The virus uses these spiky extensions to infect a human cell by grabbing on to the angiotensin-converting enzyme 2 (ACE2) receptor. The molecular encounter between the coronavirus and ACE2 takes place via one or more of a trio of three protein domains, called receptor-binding domains (RBDs), that jut out from its spikes. RBDs flap up and down in the fluid surrounding cells, “reaching up” to touch and enter, or “laying down” to hide from an infected person’s antibodies and immune cells. Only an “up” RBD can attach to ACE2 and get into a cell.
Taken together with other structural information known about SARS-CoV-2, Barnes’ cryo-EM snapshots revealed four different types of shapes, or classes, of antibody-spike combinations. These high-resolution molecular views show that human neutralizing antibodies interact in many different ways with SARS-CoV-2: blocking access to either one or more RBDs in their “up” or “down” positions.
These results tell us a number of things, including underscoring why strategies that combine multiple types of antibodies in an “antibody cocktail” might likely offer broader protection against infection than using just a single type of antibody. Indeed, that approach is currently being tested in patients with COVID-19.
The findings also provide a molecular guide for custom-designing synthetic antibodies in the lab to foil SARS-CoV-2. As one example, Barnes and his team observed that one antibody completely locked all three RBDs into closed (“down”) positions. As you might imagine, scientists might want to copy that antibody type when designing an antibody-based drug or vaccine.
It is tragic that hundreds of thousands of people have died from this terrible new disease. Yet the immune system helps most to recover. Learning as much as we possibly can from those individuals who’ve been infected and returned to health should help us understand how to heal others who develop COVID-19, as well as inform precision design of additional vaccines that are molecularly targeted to this new foe.
While we look forward to the arrival of COVID-19 vaccines and their broad distribution in 2021, each of us needs to remember to practice the three W’s: Wear a mask. Watch your distance (stay 6 feet apart). Wash your hands often. In parallel with everyone adopting these critical public health measures, the scientific community is working harder than ever to meet this moment, doing everything possible to develop safe and effective ways of treating and preventing COVID-19.
Reference:
[1] SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies. Barnes CO, Jette CA, Abernathy ME, et al. Nature. 2020 Oct 12. [Epub ahead of print].
Links:
Coronavirus (COVID-19) (NIH)
Combat COVID (U.S. Department of Health and Human Services, Washington, D.C.)
Freezing a Moment in Time: Snapshots of Cryo-EM Research (National Institute of General Medical Sciences/NIH)
Björkman Lab (California Institute of Technology, Pasadena)
NIH Support: National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases
Protein Mapping Study Reveals Valuable Clues for COVID-19 Drug Development
Posted on by Dr. Francis Collins
One way to fight COVID-19 is with drugs that directly target SARS-CoV-2, the novel coronavirus that causes the disease. That’s the strategy employed by remdesivir, the only antiviral drug currently authorized by the U.S. Food and Drug Administration to treat COVID-19. Another promising strategy is drugs that target the proteins within human cells that the virus needs to infect, multiply, and spread.
With the aim of developing such protein-targeted antiviral drugs, a large, international team of researchers, funded in part by the NIH, has precisely and exhaustively mapped all of the interactions that take place between SARS-CoV-2 proteins and the human proteins found within infected host cells. They did the same for the related coronaviruses: SARS-CoV-1, the virus responsible for outbreaks of Severe Acute Respiratory Syndrome (SARS), which ended in 2004; and MERS-CoV, the virus that causes the now-rare Middle East Respiratory Syndrome (MERS).
The goal, as reported in the journal Science, was to use these protein “interactomes” to uncover vulnerabilities shared by all three coronaviruses. The hope is that the newfound knowledge about these shared proteins—and the pathways to which they belong—will inform efforts to develop new kinds of broad-spectrum antiviral therapeutics for use in the current and future coronavirus outbreaks.
Facilitated by the Quantitative Biosciences Institute Research Group, the team, which included David E. Gordon and Nevan Krogan, University of California, San Francisco, and hundreds of other scientists from around the world, successfully mapped nearly 400 protein-protein interactions between SARS-CoV-2 and human proteins.
You can see one of these interactions in the video above. The video starts out with an image of the Orf9b protein of SARS-CoV-2, which normally consists of two linked molecules (blue and orange). But researchers discovered that Orf9b dissociates into a single molecule (orange) when it interacts with the human protein TOM70 (teal). Through detailed structural analysis using cryo-electron microscopy (cryo-EM), the team went on to predict that this interaction may disrupt a key interaction between TOM70 and another human protein called HSP90.
While further study is needed to understand all the details and their implications, it suggests that this interaction may alter important aspects of the human immune response, including blocking interferon signals that are crucial for sounding the alarm to prevent serious illness. While there is no drug immediately available to target Orf9b or TOM70, the findings point to this interaction as a potentially valuable target for treating COVID-19 and other diseases caused by coronaviruses.
This is just one intriguing example out of 389 interactions between SARS-CoV-2 and human proteins uncovered in the new study. The researchers also identified 366 interactions between human and SARS-CoV-1 proteins and 296 for MERS-CoV. They were especially interested in shared interactions that take place between certain human proteins and the corresponding proteins in all three coronaviruses.
To learn more about the significance of these protein-protein interactions, the researchers conducted a series of studies to find out how disrupting each of the human proteins influences SARS-CoV-2’s ability to infect human cells. These studies narrowed the list to 73 human proteins that the virus depends on to replicate.
Among them were the receptor for an inflammatory signaling molecule called IL-17, which has been suggested as an indicator of COVID-19 severity. Two other human proteins—PGES-2 and SIGMAR1—were of particular interest because they are targets of existing drugs, including the anti-inflammatory indomethacin for PGES-2 and antipsychotics like haloperidol for SIGMAR1.
To connect the molecular-level data to existing clinical information for people with COVID-19, the researchers looked to medical billing data for nearly 740,000 Americans treated for COVID-19. They then zeroed in on those individuals who also happened to have been treated with drugs targeting PGES-2 or SIGMAR1. And the results were quite striking.
They found that COVID-19 patients taking indomethacin were less likely than those taking an anti-inflammatory that doesn’t target PGES-2 to require treatment at a hospital. Similarly, COVID-19 patients taking antipsychotic drugs like haloperidol that target SIGMAR1 were half as likely as those taking other types of antipsychotic drugs to require mechanical ventilation.
More research is needed before we can think of testing these or similar drugs against COVID-19 in human clinical trials. Yet these findings provide a remarkable demonstration of how basic molecular and structural biological findings can be combined with clinical data to yield valuable new clues for treating COVID-19 and other viral illnesses, perhaps by repurposing existing drugs. Not only is NIH-supported basic science essential for addressing the challenges of the current pandemic, it is building a strong foundation of fundamental knowledge that will make us better prepared to deal with infectious disease threats in the future.
Reference:
[1] Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Gordon DE et al. Science. 2020 Oct 15:eabe9403.
Links:
Coronavirus (COVID-19) (NIH)
Krogan Lab (University of California, San Francisco)
NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences
Pursuing Safe and Effective Anti-Viral Drugs for COVID-19
Posted on by Dr. Francis Collins
Right now, the world is utterly focused on the coronavirus outbreak known as COVID-19. That’s certainly true for those of us at NIH. Though I am working from home to adhere rigorously to physical distancing, I can’t remember ever working harder, trying to do everything I can to assist in the development of safe and effective treatments and vaccines.
Over the past several weeks, a mind-boggling array of possible therapies have been considered. None have yet been proven to be effective in rigorously controlled trials, but for one of them, it’s been a busy week. So let’s focus on an experimental anti-viral drug, called remdesivir, that was originally developed for the deadly Ebola virus. Though remdesivir failed to help people with Ebola virus disease, encouraging results from studies of coronavirus-infected animals have prompted the launch of human clinical trials to see if this drug might fight SARS-CoV-2, the novel coronavirus that causes COVID-19.
You may wonder how a drug could possibly work for Ebola and SARS-CoV-2, since they are very different viruses that produce dramatically different symptoms in humans. The commonality is that both viruses have genomes made of ribonucleic acid (RNA), which must be copied by an enzyme called RNA-dependent RNA polymerase for the virus to replicate.
Remdesivir has an affinity for attaching to this kind of polymerase because its structure is very similar to one of the RNA letters that make up the viral genome [1]. Due to this similarity, when an RNA virus attempts to replicate, its polymerase is tricked into incorporating remdesivir into its genome as a foreign nucleotide, or anomalous letter. That undecipherable, extra letter brings the replication process to a crashing halt—and, without the ability to replicate, viruses can’t infect human cells.
Would this work on a SARS-CoV-2 infection in a living organism? An important step was just posted as a preprint yesterday—a small study showed infusion of remdesivir was effective in limiting the severity of lung disease in rhesus macaques [2]. That’s encouraging news. But the only sure way to find out if remdesivir will actually help humans who are infected with SARS-CoV-2 is to conduct a randomized, controlled clinical trial.
In late February, NIH’s National Institute of Allergy and Infectious Diseases (NIAID) did just that, when it launched a randomized, controlled clinical trial to test remdesivir in people with COVID-19. The study, led by NIAID’s Division of Microbiology and Infectious Diseases, has already enrolled 805 patients at 67 testing sites. Most sites are in the United States, but there are also some in Singapore, Japan, South Korea, Mexico, Spain, the United Kingdom, Denmark, Greece, and Germany.
All trial participants must have laboratory-confirmed COVID-19 infections and evidence of lung involvement, such as abnormal chest X-rays, rattling sounds when breathing (rales) with a need for supplemental oxygen, or a need for mechanical ventilation. They are randomly assigned to receive either a round of treatment with remdesivir or a harmless placebo with no therapeutic effect. To avoid bias from creeping into patient care, the study is double-blind, meaning neither the medical staff nor the participants know who is receiving remdesivir.
There is also an early hint from another publication that remdesivir may benefit some people with COVID-19. Since the end of January 2020, Gilead Sciences, Foster City, CA, which makes remdesivir, has provided daily, intravenous infusions of the drug on a compassionate basis to more than 1,800 people hospitalized with advanced COVID-19 around the world. In a study of a subgroup of 53 compassionate-use patients with advanced complications of COVID-19, nearly two-thirds improved when given remdesivir for up to 10 days [3]. Most of the participants were men over age 60 with preexisting conditions that included hypertension, diabetes, high cholesterol, and asthma.
This may sound exciting, but these preliminary results, published in the New England Journal of Medicine, come with major caveats. There were no controls, participants were not randomized, and the study lacked other key features of the more rigorously designed NIH clinical trial. We can all look forward to the results from the NIH trial, which are are expected within a matter of weeks. Hopefully these will provide much-needed scientific evidence on remdesivir’s safety and efficacy in people with COVID-19.
In the meantime, basic researchers continue to learn more about remdesivir and its interaction with the novel coronavirus that causes COVID-19. In a recent study in the journal Science, a research team, led by Quan Wang, Shanghai Tech University, China, mapped the 3D atomic structure of the novel coronavirus’s polymerase while it was complexed with two other vital parts of the viral replication machinery [4]. This was accomplished using a high-resolution imaging approach called cryo-electron microscopy (cryo-EM), which involves flash-freezing molecules in liquid nitrogen and bombarding them with electrons to capture their images with a special camera.
With these atomic structures in hand, the researchers then modeled exactly how remdesivir binds to the polymerase of the novel coronavirus. The model will help inform future efforts to tweak the structure of the drug and optimize its ability to disrupt viral replication. Such detailed biochemical information will be vital in the weeks ahead, especially if data generated by the NIH clinical trial indicate that remdesivir is a worthwhile lead to pursue in our ongoing search for anti-viral drugs to combat the global COVID-19 pandemic.
References:
[1] Nucleoside analogues for the treatment of coronavirus infections. Pruijssers AJ, Denison MR. Curr Opin Virol. 2019 Apr;35:57-62.
[2] Clinical benefit of remdesivir in rhesus macaques infected with SARS-CoV-2. Williamson B, Feldmann F, Schwarz B, Scott D, Munster V, de Wit E et. al. BioRxiv. Preprint posted 15 April 2020.
[3] Compassionate use of remdesivir for patients with severe Covid-19. Grein J, Ohmagari N, Shin D, Brainard DM, Childs R, Flanigan T. et. al. N Engl J Med. 2020 Apr 10. [Epub ahead of publication]
[4] Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Gao Y, Yan L, Liu F, Wang Q, Lou Z, Rao A, et al. Science. 10 April 2020. [Epub ahead of publication]
Links:
Coronavirus (COVID-19) (NIH)
Accelerating COVID-19 Therapeutic Interventions and Vaccines (NIH)
NIH Clinical Trial of Remdesivir to Treat COVID-19 Begins (National Institute of Allergy and Infectious Diseases/NIH)
Developing Therapeutics and Vaccines for Coronaviruses (NIAID)
COVID-19, MERS & SARS (NIAID)
NIH Support: National Institute of Allergy and Infectious Diseases
Next Page
