The recent success of Ensitrelvir at preventing COVID-19 is a milestone in a drug quest that began with a singular development at the start of the pandemic.

In January 2020, the full-length genome sequence of a strain of the SARS-CoV-2 virus was posted on an online discussion forum for virologists. The sequence revealed that SARS-CoV-2, like its relative SARS-CoV-1, keeps most of its proteins as one long chain. The individual proteins are then cut from this chain and released by enzymes called  proteases.

Inconvenient guests

SARS-Cov-2 has two proteases called the main protease (M^pro), which does most of the cutting, and the Papain-like protease (PL^pro) which cuts the three sites at one end of the chain that the M^Pro does not process. Almost immediately after the genome sequence was published, scientists knew that both these proteases were attractive drug targets.

Viruses are made of the same material as their host cells. The viral membrane is derived from the host’s cell-membranes, and the viral proteins are made by the host cell itself. This makes antiviral drug development more challenging than developing drugs against, say, bacteria.

Most other pathogens are living cells with several features that humans do not have. For instance, bacteria have a rigid cell wall. Antibiotics can be made that block the synthesis of this cell wall, and those antibiotics will affect only the bacteria without affecting humans much. Also, that antibiotic will work against most classes of bacteria because most of them have a cell wall.

Viruses, however, do not offer such conveniences. Since they rely heavily on the host cell’s machinery, drugs that attack the virus can also risk damaging healthy cells. As a result, there is no broad-spectrum antiviral drug. Instead, scientists usually need to design drugs specifically for a particular virus, or if they are lucky, for a group of closely related viruses that share similar proteins.

A drug shelved

This is why viral proteins that are essential for the virus while being significantly different from human proteins are extremely valuable drug targets. Both SARS-CoV-2 proteases fit this description perfectly. The virus cannot replicate without them and human cells do not have identical versions of these enzymes. Among them, M^pro was the first choice for most researchers.

Also read: Explained | Molnupiravir, Merck’s new drug to treat COVID-19

When scientists sequenced the SARS-CoV-2 genome, those at Pfizer realised its M^pro protease was very similar to that of SARS-CoV-1. It so happened that a few years ago, they had developed an intravenous drug called PF-00835231 targeting the M^pro of SARS-CoV-1. However, when the virus vanished in early 2004, they shelved the compound thinking they would not need it again.

In 2020, knowing that the two M^pro proteins^{are similar, they set out to re-engineer PF-00835231 because an intravenous drug would be useless at controlling a pandemic the size of COVID-19, where most people would not get admitted to a hospital. After a few months, they developed Nirmatrelvir. By the time they finished clinical trials, it was already late 2021.}

Different approach

However, while Nirmatrelvir was very effective as an oral drug, it had major problems. First, it was metabolised very quickly by the liver. To keep it in the bloodstream for longer, scientists had to add a second drug, Ritonavir, to the regimen to slow down the liver’s ability to metabolise Nirmatrelvir. The problem? Ritonavir had dangerous interactions with common heart and blood pressure medications. And then there was the added side effect of a bitter aftertaste.

Around the same time that Pfizer was engineering Nirmatrelvir, a Japanese pharmaceutical company named Shionogi took a different approach to targeting M^pro. Instead of making and testing millions of potential drug candidates, they decided in the interest of time that they would use computational chemistry to ‘virtually’ screen compounds.

Using the structure of the M^pro protease, they simulated thousands of molecules that would bind the protease and inactivate it. This approach quickly identified a promising molecule that they could then test further. Then they partnered with the International Institute for Zoonosis Control at Hokkaido University for testing. With a few tweaks, they came up with a molecule that stayed in the blood for a long time without the need for additional drugs and did not have the bitter after-taste. They called this molecule Ensitrelvir.

SCORPIO-PEP trial

Ensitrelvir was granted emergency approval in Japan for use in November 2022 to treat mild-to-moderate COVID-19. In early 2024, it was granted full authorisation for regular use. Now, a new paper in the New England Journal of Medicine has reported that it can also be used for postexposure prophylaxis — to prevent infection after exposure to the virus.

The international study, called the SCORPIO-PEP trial, involved 2,387 participants, all of whom were exposed to an infected household member. They were assigned to a test or a placebo group at random. Participants in the test group received Ensitrelvir and the control group received the placebo. 

The results revealed that Ensitrelvir lowered the incidence of symptomatic COVID-19 from 9% in the control group to 2.9% in the group that took the drug — a drop of 67%. The trial scientists showed that irrespective of whether symptoms developed, the presence of SARS-CoV-2 in the contacts was reduced from 21.5% to 14%. Importantly, the drug maintained a good safety profile in the study. The authors concluded that giving Ensitrelvir to contacts within 72 hours of a primary patient showing symptoms could effectively prevent COVID-19.

SARS-CoV-2 today does not pose the threat it once did but the importance of Ensitrelvir extends well beyond COVID-19. Betacoronaviruses have now caused three major outbreaks in just over two decades: SARS-CoV-1, MERS-CoV, and SARS-CoV-2. Drugs like Ensitrelvir provide scientists with a valuable starting point against this entire sub-family of viruses, possibly allowing humankind to respond more quickly when the next virus will inevitably emerge.

Arun Panchapakesan is an assistant professor at the Y.R. Gaitonde Centre for AIDS Research and Education, Chennai.

Published – May 25, 2026 07:30 am IST

“It’s still difficult to conclusively say what really happened with many things, scientifically”
| Photo Credit: Getty Images/iStockphoto

Many of us want to know how the SARS-CoV-2 virus originated. To do that, right now we need to unravel its evolution from its bat coronavirus ancestor by sequencing the genomes of animals and viruses near the outbreak site and we need to effect China’s cooperation to check whether SARS-CoV-2 could have ‘leaked’ from a lab. Where the virus came from was once singularly important because the answer could have pointed the way to avoiding similar outbreaks in future. But today, there is good reason for this question to take the back seat.

We don’t know where or how the virus originated. If it did in a lab, we would have to re-examine how we regulate research facilities and their safeguards and the manner of political oversight that won’t curtail research freedom. If the virus is au naturel, we would have to institute and/or expand pathogen surveillance, eliminate wildlife trafficking, and improve social security measures to ensure populations can withstand outbreaks without becoming distressed. But even as these possibilities aren’t equally likely (according to scientists I trust), the origin of SARS-CoV-2 is less important than it once was because the COVID-19 pandemic caused us to implement all these outcomes to varying degrees.

SARS-CoV-2 isn’t special of course: it’s still difficult to conclusively say what really happened with many things, scientifically. In 1977, a telescope in the U.S. recorded a signal from outer space that remains strange to this day. We don’t have a physical explanation for the “spooky” result of an experiment Anton Zeilinger and co. conducted in 1998. We lack a complete understanding of how general anaesthesia works its magic on the brain. Not even their makers fully know how powerful AI models work the way they do. No existing theory of nature can say what happens in intervals shorter than 10^(-43) seconds.

In fact, not knowing is ubiquitous. To quote philosopher Nicholas Rescher, “no one can say in advance what questions natural science can and cannot answer.” But science communication has taught me not all of us can know everything unless we invest considerable, perhaps even impossible, resources. Years ago, the philosopher Daniel Sarewitz wrote an article that changed my relationship with science. He argued that while we may know about the Higgs boson particle and that it gives other elementary particles their masses, we can’t truly know any of this until we learn the complicated mathematics required to make sense of it. Until then, we just have faith in the physicists who know. This relationship goes for most technical information in our lives.

Science journalists like me communicate science by providing for scientists’ claims, to quote Rescher, “the backing of a rationale that renders [their] correctness evident”, but I still demand a considerable amount of faith from readers. At some point faith also becomes trust but trust still isn’t understanding. (This said, the system of sanctions should they err provides a reasonable backstop for trust in scientists’ and journalists’ work.) The general idea here is that you pick someone you trust and you believe what they say to be true. Let’s call this the social character of scientific knowledge.

When people encounter a weighty concept scientists aren’t able to explain fully, the social character becomes more apparent than it normally is. Some people trust impassioned scientists unwilling to consider extra-scientific possibilities. Some lean towards authority figures who don’t trust science to provide the answer. Historically, people have turned to faith in the face of the unknown. The problems arise when we don’t know, or choose to overlook, where science ends and faith/trust begins. Then we fixate on answers that may never matter at the expense of answers that are already useful.

mukunth.v@thehindu.co.in

While it’s one thing to acknowledge the limits to our knowledge of the microbial soup we live in, more recent attention has focused on how we might systematically approach future pandemic risks.

Former US Secretary of Defense Donald Rumsfeld famously talked about “known knowns” (things we know we know), “known unknowns” (things we know we don’t know), and “unknown unknowns” (the things we don’t know we don’t know).

Although this may have been controversial in its original context of weapons of mass destruction, it provides a way to think about how we might approach future pandemic threats.

Influenza: a ‘known known’

Influenza is largely a known entity; we essentially have a minor pandemic every winter with small changes in the virus each year. But more major changes can also occur, resulting in spread through populations with little pre-existing immunity. We saw this most recently in 2009 with the swine flu pandemic.

However, there’s a lot we don’t understand about what drives influenza mutations, how these interact with population-level immunity, and how best to make predictions about transmission, severity and impact each year.

The current H5N1 subtype of avian influenza (“bird flu”) has spread widely around the world. It has led to the deaths of many millions of birds and spread to several mammalian species including cows in the United States and marine mammals in South America.

Human cases have been reported in people who have had close contact with infected animals, but fortunately there’s currently no sustained spread between people.

While detecting influenza in animals is a huge task in a large country such as Australia, there are systems in place to detect and respond to bird flu in wildlife and production animals.

It’s inevitable there will be more influenza pandemics in the future. But it isn’t always the one we are worried about.

Attention has been focused on avian influenza since 1997 when an outbreak in birds in Hong Kong caused severe disease in humans. However the subsequent pandemic in 2009 originated in pigs in central Mexico.

Coronaviruses: an ‘unknown known’

Although Rumsfeld didn’t talk about “unknown knowns”, coronaviruses would be appropriate for this category. We knew more about coronaviruses than most people might have thought before the COVID pandemic.  

We’d had experience with severe acute respiratory syndrome (SARS) and Middle Eastern respiratory syndrome (MERS) causing large outbreaks. Both are caused by viruses closely related to SARS-CoV-2, the coronavirus that causes COVID. While these might have faded from public consciousness before COVID, coronaviruses were listed in the 2015 WHO list of diseases with pandemic potential.

Previous research into the earlier coronaviruses proved vital in allowing COVID-19 vaccines to be developed rapidly. For example, the Oxford group’s initial work on a MERS vaccine was key to the development of AstraZeneca’s COVID-19 vaccine.

Similarly, previous research into the structure of the spike protein – a protein on the surface of coronaviruses that allows it to attach to our cells – was helpful in developing mRNA vaccines for COVID.

It would seem likely there will be further coronavirus pandemics in the future. And even if they don’t occur at the scale of COVID, the impacts can be significant. For example, when MERS spread to South Korea in 2015, it only caused 186 cases over two months, but the cost of controlling it was estimated at US$8 billion (A$11.6 billion).

The 25 viral families: an approach to ‘known unknowns’

Attention has now turned to the known unknowns. There are about 120 viruses from 25 families that are known to cause human disease. Members of each viral family share common properties and our immune systems respond to them in similar ways.

An example is the flavivirus family, of which the best-known members are yellow fever virus and dengue fever virus. This family also includes several other important viruses, such as Zika virus (which can cause birth defects when pregnant women are infected) and West Nile virus (which causes encephalitis, or inflammation of the brain).

The WHO’s blueprint for epidemics aims to consider threats from different classes of viruses and bacteria. It looks at individual pathogens as examples from each category to expand our understanding systematically.

The US National Institute of Allergy and Infectious Diseases has taken this a step further, preparing vaccines and therapies for a list of prototype pathogens from key virus families. The goal is to be able to adapt this knowledge to new vaccines and treatments if a pandemic were to arise from a closely related virus.

Pathogen X, the ‘unknown unknown’

There are also the unknown unknowns, or “disease X” – an unknown pathogen with the potential to trigger a severe global epidemic. To prepare for this, we need to adopt new forms of surveillance specifically looking at where new pathogens could emerge.

In recent years, there’s been an increasing recognition that we need to take a broader view of health beyond only thinking about human health, but also animals and the environment. This concept is known as “One Health” and considers issues such as climate change, intensive agricultural practices, trade in exotic animals, increased human encroachment into wildlife habitats, changing international travel, and urbanisation.

This has implications not only for where to look for new infectious diseases but also for how we can reduce the risk of “spillover” from animals to humans. This might include targeted testing of animals and people who work closely with animals. Currently, testing is mainly directed towards known viruses, but new technologies can look for as yet unknown viruses in patients with symptoms consistent with new infections.

We live in a vast world of potential microbiological threats. While influenza and coronaviruses have a track record of causing past pandemics, a longer list of new pathogens could still cause outbreaks with significant consequences.

Continued surveillance for new pathogens, improving our understanding of important virus families, and developing policies to reduce the risk of spillover will all be important for reducing the risk of future pandemics.

This article is part of a series on the next pandemic.

Allen Cheng, Professor of Infectious Diseases, Monash University 

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

File photo used for representational purpose only.

Children infected with the Omicron variant remain infectious for three days on average after testing positive for the SARS-CoV-2 virus, according to a study.

Researchers at the University of Southern California (USC) and Stanford University in the U.S. noted that school policies that require students with COVID-19 to stay out of the classroom for five days are more than sufficient.

“We are basically saying five days is more than sufficient; public health and education leaders may consider shorter durations,” said study co-author Neeraj Sood, Director of the COVID-19 Initiative and a senior fellow at the USC Schaeffer Center.

The study, published in the journal JAMA Pediatrics, found that the median time of infectivity was three days, with 18.4% and 3.9% of children still infectious on day five and day 10, respectively.

The researchers also found no association between how long children were infectious and whether they were vaccinated, suggesting return-to-school policies may not need discriminate by vaccine or booster status.

The study seeks to inform policymakers who grapple with how long children must isolate when they contract COVID-19. Such self-isolation policies, aimed at halting the spread of the virus, can negatively interrupt children’s education.

“We want to protect other children in the school who could potentially get infected, but at the same time, we don’t want to disrupt education for the child who is infected, given the amount of disruption that’s already happened,” said Mr. Sood.

“The duration of infectivity is an important parametre into figuring out what the optimal duration of self-isolation should be,” he added.

The researchers partnered with a virus testing company and examined nasal swabs from 76 children in Los Angeles County who were between the ages of seven and 18 and tested positive for COVID-19.

Survey participants provided samples during five home visits over a 10-day period and samples were examined in a lab to find evidence of cell death, a sign of infectivity. All participants were infected with the Omicron variant of COVID-19.

“We wanted to capture how infectivity changed over the 10-day window,” said study lead author Nikhilesh Kumar, a Doctor of Medicine student at the USC Keck School of Medicine.

The findings are consistent with previous research on adults who contracted the Omicron variant, which showed no association between vaccination status and time of infectivity.

“That research, published in the New England Journal of Medicine, showed adults with Omicron were infected for a slightly longer duration, with a median time of five days,” the authors noted.

The team called for further research so that policymakers can consider adjusting the time students must stay out of the classroom.