Sending the messengers

If mRNA vaccines for Covid-19 prove themselves, manufacturers may ramp up production surprisingly quickly.

jigsaw2Speculation time: Let’s imagine that Moderna’s messenger RNA vaccine for Covid-19, already in clinical trials, is effective enough for approval. And/or the mRNA candidate from BioNTech, which might begin trials this month. And/or one of the candidates from CureVac or Translate Bio or many other groups feverishly working on mRNA vaccines.

True, no mRNA vaccine candidate has ever been generated in large numbers. So how could we scale up to the billions of doses that the world needs yesterday?

CureVac says it can manufacture millions of doses by this summer. Moderna has built an enormous, fully digital, fully operational, very impressive plant in Norwood, Massachusetts.* The other players are making suitably serious plans.

But beyond that, let’s remember that mRNA medical technology is radically different and one key difference is that it lends itself to extremely fast and flexible manufacturing. It’s built around synthesizing DNA and RNA rather than growing the infinitely idiosyncratic cells in traditional biotech factories. The bioreactor that generates the actual antiviral response is the patient’s body, so the amounts of active ingredient in an mRNA vaccine are almost unimaginably tiny.

And unlike traditional biotech factories, mRNA facilities are designed to rapidly switch between multiple products.

So: When and if one of these mRNA vaccines proves itself, is there any technical reason that all of these companies could not switch their production lines to churn out that one?

* On April 16th, the Biomedical Advanced Research and Development Authority (BARDA) announced funding up to $483 million for Moderna to ramp up. “Plans now call for producing millions of doses in the fall, tens of millions next year.”

Moderna’s vaccine dream machine

How a messenger RNA drug became the first novel candidate to take on the COVID-19 pandemic.

In the world of vaccines, this was crazy fast.

The SARS-CoV2 coronavirus was sequenced on January 7. Scientists at Moderna and the National Institute of Allergy and Infectious Diseases (NIAID) selected a vaccine target on January 13. By February 7, Moderna had a vaccine candidate. After some safety testing, on February 23 the company shipped this “mRNA-1273” vaccine to the NIAID, which is now recruiting for a first clinical trial.*

Moderna is a pioneer of messenger RNA (mRNA) drugs, designed to generate exquisitely tailored therapeutic proteins within each patient’s body.

As you recall, the central dogma of molecular biology is that DNA makes RNA that makes protein. The mRNA drug candidates created by Moderna and a few other biotechs tap into those natural steps.

Once researchers design the desired therapeutic proteins, manufacturing for an mRNA drug starts with a DNA template for those proteins. The DNA is treated with a suite of biological players to generate mRNA. Next, the mRNA is purified and put inside a lipid nanoparticle built slip inside cells. After quality controls, you get an injectable drug. Once it’s injected, the patient’s own cells churn out proteins that attack the disease—in the case of Covid-19, by mimicking the deadly disease to alert the immune system.

No mRNA drugs have been approved, but there’s a real chance they will become that rare beast, a genuine revolution in medicine. That’s what Juan Andres, chief technical operations and quality officer, told me last summer when I visited the company’s giant facility in Norwood, Massachusetts for a Nature story on flu vaccines.

Traditional biotech drugs go after extracellular proteins, because the drugs can’t enter cells. But mRNA drugs open the possibility of producing proteins inside the cell. Moreover, unlike gene therapies, “we are not touching the DNA,” Andres said. “We are not touching the hardware of the body.”

mRNA drug manufacture and delivery are also stunningly efficient, at least in theory. You’re not using cells at all, so you don’t need the giant bioreactor vats in traditional biotech factories. In fact, the main bioreactor in the Norwood plant was roughly the size of my home’s hot-water heater.

Unusually for a biotech startup, even one as well-heeled as Moderna, the company invested heavily to achieve fully integrated production, starting with raw materials, at  Norwood. “We produce the active product ingredient, which is basically mRNA itself,” Andres said. “We formulate it, we fill into vials, we finish it, we do the quality control and we ship it into clinical sites.”

So one of Moderna’s potential manufacturing advantages is speed—especially crucial for vaccines for seasonal flu or for epidemics, where the clock is always ticking (or rather, alarms are ringing loudly). The technology also may offer other major benefits, eventually, in product quality, scalability and cost.

And mRNA drugs just may end up addressing a broad spectrum of medical need. Moderna is examining many opportunities for treatment, among them personalized cancer vaccines and localized regenerative medicine.

But the company’s most advanced programs are in preventive vaccines. So far it has chalked up positive results in phase 1 clinical trials for six vaccines, among them a cytomegalovirus vaccine combining no fewer than six mRNAs that has moved into a phase II trial.

Good safety signals from these early vaccine studies encouraged NIAID to launch the mRNA-1273 trial even before the vaccine was tested in animal studies.

It will be many months before trials can prove safety and efficacy for mRNA-1273, if indeed they do.

The desperate global need drove both quick funding for the novel vaccine from the Coalition for Epidemic Preparedness Innovations (yes, not the CDC) and the supercharged development. “There was a huge amount of motivation,” Andres commented at a Moderna online forum last week. “I have not seen this kind of energy anywhere before.”

* Volunteers received first injections on March 16.

Images courtesy Moderna.

Object lessons

What do museums save of our pasts and which of those treasures do they surface in public?

In a giant two-year-old warehouse in South Greenwich, the Prince Phillip Maritime Collection holds… well, almost anything you can imagine that connects to Britain and the sea. Huge rooms filled with racks of paintings. Calfskin charts of the Mediterranean that predate lines of latitude and longitude, 400-year-old astronomical globes of remarkable precision and beauty, a 4,000-year-old Egyptian boat model… altogether, something like two million objects. Last week it was my privilege to take a behind-the-scenes tour of this motherlode, led by friendly staff.

I also wandered the Museum of London, just north of Saint Paul’s Cathedral, and its beautifully designed galleries with artifacts along a timeline from the prehistorical to the present day. This is not at all a small museum but a vastly larger version is well underway, created in part to display more of the  collection of some seven million items. And I gave a wave to HMS Belfast, the largest survivor of the British WW2 navy.

I’m a huge fan of digital humanities efforts, notably the European Time Machine megaproject, that seek to digitally preserve and present a wealth of historical material that otherwise could never appear in public. But we also hunger to see the original beasts—to come (almost) close enough to touch the actual paintings or charts or globes. How do museum curators choose?

HMS Belfast

To be or not to be virtual

In ‘Hamlet 360’, the play is the thing wherein to catch the concept of virtual reality drama.

How well can you present a serious theatrical performance with virtual reality (VR) tech?

That’s the challenge taken on by “Hamlet 360: Thy Father’s Spirit”, an hour-long condensation of the play created by Boston’s beloved Commonwealth Shakespeare Company and Google. “Hamlet 360” can be seen online but is best delivered to small groups of people, each with their own VR headset and swivel chair. We were lucky enough to see it in its short Boston run, presented by Commonwealth founding artistic director Steven Maler.

Given Commonwealth’s professional skills, it was no surprise that the play was beautifully acted and staged. We quickly adjusted to the VR setup. The headsets and earphones were comfortable and easily removed. The visual resolution and color quality were disappointing, like a 360-degree version of old broadcast TV. Some of us particularly liked being held within our own hermetically sealed VR environment, some of us not so much.  I wondered how the experience would strike people in their teens and twenties, who grew up with sophisticated computer games and primitive VR platforms such as Nintendo’s Wii.

We were soon turning in our chairs to follow the action, which might be anywhere around us since we were in the middle of each scene. (Yes, the inverse of theater-in-the round.)  Since that viewpoint is fixed during the scene, the director and performers may need to fundamentally reconsider their stage directions. But the action scenes were convincing, especially the fatal duel between Hamlet and Laertes. And there’s a twist at the end of the play that has nothing to do with tech and that I found moving.

Cartography blanche

Guerrilla Cartography highlights the power of maps to inform, persuade and inspire.

“Everyone believes a map. No other narrative device—not story or song or historical treatise—is so readily accepted as true. We have come to accept the map as fact.”

So writes Darin Jensen in Water: An Atlas, a remarkable collection of maps with many ways to view water that was released in 2017.

The maps for this crowd-funded publication were contributed by cartographers and other researchers around the world via Guerrilla Cartography, an open collaboration Jensen founded to “widely promote the cartographic arts.” The group’s first project was Food: An Atlas, a milestone accomplishment in 2012.

Its latest production is Atlas in a Day: Migration, a stunning response to a challenge to research and design an atlas about migration in one day last October. No fewer than 43 maps “interpret the theme of migration in diverse ways, considering the movements of people, animals, climates, physical materials and cultural artifacts over time and space. Some of them represent the culmination of years of research on a critical topic; others are quick sketches inspired by current events and concerns.”

All three atlases can be downloaded free, and Food and Water can be bought as books.

Maps retain plenty of power in print, Jensen points out.

“Guerrilla Cartography is about letting story emerge from data and illustrating the story through the art of cartographic design,” he says. “We give voice to the talents of mapmakers who may have no other platform for a wide and printed distribution of their work and ideas.”

Inflammatory statements

Maybe the culprit in type 1 diabetes isn’t T cells gone bad.

8ee03-beta-cell3_Itkin-Ansari

Follow diabetes research and you start obsessing about beta cells—maybe a gram of cells buried across the pancreas that produce the insulin we need to live. Or stop producing it, in the case of type 1 diabetes.

These cells are heroic microbeasts. “The beta cell is a wonder of nature,” Bart Roep of the City of Hope National Medical Center told me during an interview for a Knowable story. “It’s the hardest-working cell in our body. Every second, each beta cell can make two thousand molecules of insulin; that’s daunting. It also has to be able to release insulin when it’s needed and only when it’s needed.”

In type 1 diabetes, some mix of fairly well understood genetics and not very well understood environmental factors goes wrong. T cells go haywire and begin to wipe out beta cells. So type 1 is described as an autoimmune disease in tens of thousands of research papers.

But maybe things work the other way around: Beta cells stress out and misbehave, and the immune system is just doing its job.

“I actually think that type 1 diabetes is not an immune problem, it is a beta cell problem,” said Roep.

He and his colleagues laid out some evidence in a 2017 Nature Medicine article. “We showed that if beta cells get stressed, which they do very quickly, they produce new antigens like those that expose cancers and infections to the immune system,” he noted.

“I now contend that the immune system is not making a mistake,” Roep said. “It’s the beta cell, and the immune system is actually responding with the best intentions, namely to target stressed tissue… The immune system is not interested in happy tissue.”

Roep is not the only prominent scientist who questions the T-cells-gone-wrong framework for type 1. At the Joslin International Symposium last month in Boston, Olle Korsgren of Uppsala University made another case, skimming through decades of studies on human pancreatic tissue samples analyzed by many researchers.

Among his points, Korsgren cited data suggesting that the T cell attack is surprisingly weak, this attack goes after the whole pancreas rather than just beta cells, and there are frequent signs of beta cell stress such as bleeding. “Could bleeding cells attract the immune system?” he asked.

His hypothesis: Type 1 is not an autoimmune disease that targets beta cells. Rather, it’s an inflammatory disease affecting the entire pancreas. Moreover, the inflammation might be driven by gut microbes invading the pancreas next door.

And Korsgren’s theory just might dovetail very nicely with recent research on the role of the gut microbiome in type 1, now well documented in large epidemiological studies and explored in many labs.

Image: Pamela Itkin-Ansari lab

Turing the world

AI will bring us many new understandings. And confusions, as Joseph Weizenbaum warned.

As we think our electronic world is becoming more human, often it’s becoming less.

In the past few years, Siri and Alexa and their kin have shot past the Turing test, proposed by British mathematician Alan Turing in 1950. We can’t always tell if there’s a human or machine on the other side of conversation. The raw power of the underlying artificial intelligence keeps accelerating, especially for the type of AI known as deep learning, built on connections between layers of neural networks. Deep learning systems already can beat humans at making predictions from, say, medical images. And they can make findings that humans wouldn’t attempt—for instance, tapping ECG data to predict patient sex and age.

Although some of these models try valiantly to explain their decisions, more often than not it’s a mistake to think we understand what’s going on under the covers. “A full explanation might require looking at thousands or tens of thousands of variables and complex probabilistic relationships that connects things where we don’t see any connections,” says David Weinberger, author of Everyday Chaos. “You have to look at all of that, and in many instances we just can’t.”

It’s also a mistake to believe deep learning and other AI technologies actually understand our world. They don’t see a kitten or a tumor or your favorite Calvin and Hobbes collection. All they see are patterns of swirls in their oceans of data.

When chatting with Siri and Alexa and our other semi-loyal cloud servants, though, we tend to anthropomorphize these beasts. Seeing the world as human-like has been a common human trait for longer than we can track. We imagined supernatural beings based on the worst human patriarchs; now we teach our children that dolphins are happy to be enslaved so that they can entertain us. Back in the 1980s as he introduced a crude personal robot, Nolan Bushnell remarked that the robot’s bugs were what gave it personality. We’re still there, looking for personality as we try to tease Siri.

So it’s good to think carefully about the right roles for the strange computing power lurking so many places. In medicine, better ways to figure the around-the-clock insulin dosing for people with type 1 diabetes would be great. Ditto a tool to predict if someone in the ICU will go into cardiac arrest shortly. But forget any chatbot “therapist” that claims to understands us.

Back in 1966 Joseph Weizenbaum wrote the first chatbot, Eliza, with one variant called Doctor modeled on simple psychotherapy. Weizenbaum was horrified when his secretary didn’t want him to see her conversation with the Doctor and then when other computer scientists suggested building clinical versions.

“What I had not realized is that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people,” he wrote in Computer Power and Human Reason, published in 1976. “Computers and men are not species of the same genus… However much intelligence computers may attain, now or in the future, theirs must always be an intelligence alien to genuine human problems and concerns.”

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