A rendering of emerging medical technology.
Ethics needs context. So does science – specifically, science that aims to create bioengineered models of early human embryo development in a dish (hereafter synthetic embryos). Even the term "synthetic embryos" begs for an explanation. What are these? And why would anyone want to create them?
"This knowledge may help scientists understand how certain birth defects are formed and why miscarriages often occur."
First the research context. Synthetic embryos are stem cell-derived simulations of human post-implantation embryos that are designed to mimic a stage of early development called gastrulation. That's the stage—around 14-15 days after fertilization – when embryos begin to form a very primitive body plan (basic dorsal-ventral and anterior-posterior axes, and distinct cell lineages). Researchers are starting to create synthetic embryos in the lab – albeit imperfect and incomplete versions – to learn how gastrulation might unfold in real human embryos embedded unseen in the womb. This knowledge may help scientists understand how certain birth defects are formed and why miscarriages often occur soon after implantation. As such, synthetic embryos are meant to be models of human embryo development, not themselves actually embryos. But will synthetic embryos ever get to the point where they are practically the same thing as "natural" human embryos? That is my concern and why I think researchers should avoid creating synthetic embryos capable of doing everything natural embryos can do.
It may not be too difficult to prevent this slide from synthetic to real. Synthetic embryos must be created using sophisticated 3D culture systems that mimic the complex architecture of human embryos. These complex culture systems also have to incorporate precise microinjection systems to chemically trigger the symmetry-breaking events involved in early body plan formation. In short, synthetic embryos need a heavy dose of engineering to get their biological processes going and to help keep them going. And like most engineered entities, designs can be built into the system early to serve well-considered goals – in our case, the goal of not wanting to create synthetic embryos that are too realistic.
"If one wants to study how car engines work, one can model an engine without also modeling the wheels, transmission, and every other car part together."
A good example of this point is found a report published in Nature Communications where scientists created a human stem cell-based 3D model that faithfully recapitulates the biological events around post-implantation amniotic sac development. Importantly, however, the embryo model they developed lacked several key structures and therefore – despite its partial resemblance to an early human embryo – did not have complete human form and potential. While fulfilling their model's aim of revealing a previously inaccessible early developmental event, the team intentionally did not recreate the entire post-implantation human embryo because they did not want to provoke any ethical concerns, as the lead author told me personally. Besides, creating a complete synthetic embryo was not necessary or scientifically justified for the research question they were pursuing. This example goes to show that researchers can create a synthetic embryo to model specific developmental events they want to study without modeling every aspect of a developing embryo. Likewise – to use a somewhat imprecise but instructive analogy – if one wants to study how car engines work, one can model an engine without also modeling the wheels, transmission, and every other car part together.
A representative "synthetic embryo," which in some ways resembles a post-implantation embryo around 14 days after fertilization.
(Courtesy of Yue Shao)
But why should researchers resist creating complete synthetic embryos? To answer this, we need some policy context. Currently there is an embryo research rule in place – a law in many nations, in others a culturally accepted agreement – that intact human embryos must not be grown for research in the lab for longer than 14 consecutive days after fertilization or the formation of the primitive streak (a faint embryonic band that signals the start of gastrulation). This is commonly referred to as the 14-day rule. It was established in the UK decades ago to carve out a space for meritorious human embryo research while simultaneously assuring the public that researchers won't go too far in cultivating embryos to later developmental stages before destroying them at the end of their studies. Many citizens accepting of pre-implantation stage human embryo research would not have tolerated post-implantation stage embryo use. The 14-day rule was a line in the sand, drawn to protect the advancement of embryo research, which otherwise might have been stifled without this clear stopping point. To date, the 14-day rule has not been revoked anywhere in the world, although new research in extended natural embryo cultivation is starting to put some pressure on it.
"Perhaps the day will come when scientists don't have to apply for research funding under such a dark cloud of anti-science sentiment."
Why does this policy context matter? The creation of complete synthetic embryos could raise serious questions (some of them legal) about whether the 14-day rule applies to these lab entities. Although they can be constructed in far fewer than 14 days, they would, at least in theory, be capable of recapitulating all of a natural embryo's developmental events at the gastrulation stage, thus possibly violating the spirit of the 14-day rule. Embryo research laws and policies worldwide are not ready yet to tackle this issue. Furthermore, professional guidelines issued by the International Society for Stem Cell Research prohibit the culture of any "organized embryo-like cellular structures with human organismal potential" to be cultured past the formation of the primitive streak. Thus, researchers should wait until there is greater clarity on this point, or until the 14-day rule is revised through proper policy-making channels to explicitly exclude complete synthetic embryos from its reach.
I should be clear that I am not basing my recommendations on any anti-embryo-research position per se, or on any metaphysical position regarding the positive moral status of synthetic embryos. Rather, I am concerned about the potential backlash that research on complete synthetic embryos might bring to embryo research in general. I began this essay by saying that ethics needs context. The ethics of synthetic embryo research needs to be considered within the context of today's fraught political environment. Perhaps the day will come when scientists don't have to apply for research funding under such a dark cloud of anti-science sentiment. Until then, however, it is my hope that scientists can fulfill their research aims by working on an array of different but each purposefully incomplete synthetic embryo models to generate, in the aggregate of their published work, a unified portrait of human development such that biologically complete synthetic embryo models will not be necessary.
Editor's Note: Read a different viewpoint here written by a leading New York fertility doctor/researcher.
AI was integral to creating Moderna's mRNA vaccine against COVID.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
