Category: Science & Tech

All told, the law sparked a national innovation renaissance that continues to this day. In 2002, the Economist dubbed it “possibly the most inspired piece of legislation to be enacted in America over the past half-century.” I consider it so vital that after I retired, I joined the advisory council of an organization devoted to celebrating and protecting it. 

But the efficacy of the Bayh-Dole Act is now under serious threat from a draft framework the Biden administration is currently in the process of finalizing after a months-long public comment period that concluded on February 6.

In an attempt to control drug prices in the US, the administration’s proposal relies on an obscure provision of Bayh-Dole that allows the government to “march in” and relicense patents. In other words, it can take the exclusively licensed patent right from one company and grant a license to a competing firm. 

The provision is designed to allow the government to step in if a company fails to commercialize a federally funded discovery and make it available to the public in a reasonable time frame. But the White House is now proposing that the provision be used to control the ever-rising costs of pharmaceuticals by relicensing brand-name drug patents if they are not offered at a “reasonable” price. 

On the surface, this might sound like a good idea—the US has some of the highest drug prices in the world, and many life-saving drugs are unavailable to patients who cannot afford them. But trying to control drug prices through the march-in provision will be largely ineffective. Many drugs are separately protected by other private patents filed by biotech and pharma companies later in the development process, so relicensing just an early-stage patent will do little to help generate generic alternatives. At the same time, this policy could have an enormous chilling effect on the very beginning of the drug development process, when companies license the initial innovative patent from the universities and research institutions.

If the Biden administration finalizes the draft march-in framework as currently written, it will allow the federal government to ignore licensing agreements between universities and private companies whenever it chooses and on the basis of currently unknown and potentially subjective criteria, such as what constitutes a “reasonable” price. This would make developing new technologies far riskier. Large companies would have ample reason to walk away, and investors in startup companies—which are major players in bringing innovative university technology to market—would be equally reluctant to invest in those firms.

Any patent associated with federal dollars would likely become toxic overnight, since even one cent of taxpayer funding would make the resulting consumer product eligible for march-in on the basis of price. 

What’s more, while the draft framework has been billed as a “drug pricing” policy, it makes no distinction between university discoveries in life sciences and those in any other high-tech field. As a result, investment in IP-driven industries from biotech to aerospace to alternative energy would plummet. Technological progress would stall. And the system of technology transfer established by the Bayh-Dole Act would quickly break down.

Unless the administration withdraws its proposal, the United States will return to the days when the most promising federally backed discoveries never left university labs. Far fewer inventions based on advanced research will be patented, and innovation hubs like the one I watched grow will have no chance to take root.

Lita Nelsen joined the Technology Licensing Office of the Massachusetts Institute of Technology in 1986 and was director from 1992 to 2016. She is a member of the advisory council of the Bayh-Dole Coalition, a group of organizations and individuals committed to celebrating and protecting the Bayh-Dole Act, as well as informing policymakers and the public of its benefits.

An ultrasound, for example, might reveal that a fetus’s kidneys are smaller than they should be, but absent a glaring genetic defect, doctors can’t say why they’re small or figure out a fix. But if they can take a small sample of amniotic fluid and grow a kidney organoid, the problem might become evident, and so might a potential solution.  

Exciting, right? But organoids can do so much more!

Let’s do a roundup of some of the weird, wild, wonderful, and downright unsettling uses that researchers have come up with for organoids.

Organoids could help speed drug development. By some estimates, 90% of drug candidates fail during human trials. That’s because the preclinical testing happens largely in cells and rodents. Neither is a perfect model. Cells lack complexity. And mice, as we all know, are not humans.

Organoids aren’t humans either, but they come from humans. And they have the advantage of having more complexity than a layer of cells in a dish. That makes them a good model for screening drug candidates. When I wrote about organoids in 2015, one cancer researcher told me that studying cells to understand how an organ functions is like studying a pile of bricks to understand the function of a house. Why not just study the house?

Big Pharma appears to agree. In 2022, Roche hired organoid pioneer Hans Clevers to head its Pharma Research and Early Development division. “My belief is that human organoids will eventually complement everything we are currently doing. I’m convinced, now that I’ve seen how the whole drug development process runs, that one can implement human organoids at every step of the way,” Clevers told Nature.

Organoids are trickier to grow than cell lines, but some companies are working to make the process automated. The Philadelphia-based biotech Vivodyne has developed a robotic system that combines organoids with organ-on-a-chip technology. The system grows 20 kinds of human tissue, each containing 200,000 to 500,000 cells, and then doses them with drugs. These “lab-grown human test subjects” provide “huge amounts of complex human data—larger than you could get from any clinical trial,” said Andrei Georgescu, CEO and cofounder of Vivodyne, in a press release.

According to Viodyne’s website, the proprietary machines can test 10,000 independent human tissues at a time, “yielding vivarium-scale output.” Vivarium-scale output. I had to roll this phrase around my brain quite a few times before I understood what they meant: the robot provides the same amount of data as a building full of lab mice.

Note: PLOS is delighted to once again partner with the Einstein Foundation Award for Promoting Quality in Research. The awards program honors researchers who reflect rigor, reliability, robustness, and transparency in their work. The Einstein Foundation received dozens of stellar submissions. We asked this year’s finalists to write about their research in the run up to the ceremony on March 14^th in Berlin. This is the fourth blog in our 5-part series.  

Author: Dr. David Blumenthal, Friedrich-Alexander-Universität, Faculty of Engineering, Department Artificial Intelligence in Biomedical Engineering

Disease association databases (DisGeNET [1], OMIM [2], DrugBank [3], etc.) containing disease-gene, disease-symptom, or disease-drug links are used extensively in various subfields of data-centric biomedicine. However, in such databases, diseases are annotated using our current mainly organ- or symptom-based disease definitions (UMLS concept unique identifiers (CUIs), MONDO identifiers, ICD-10 codes, etc.), which are often umbrella terms for several yet unknown causal molecular mechanisms with similar phenotypic effects [4]. Consequently, widely used large-scale disease association databases are systematically biased towards reproducing our current disease ontologies and are hence of limited use for data-driven precision medicine approaches which aim at a more fine-grained understanding of disease subtypes [5].

In view of this bias, one option would be to simply not use large-scale disease association databases for data-centric precision medicine. However, given the vast amount of aggregated knowledge contained therein, this is very unsatisfactory. In the proposed project, we will hence aim at salvaging large-scale disease association databases for precision medicine by disentangling the associations for ill-defined umbrella disease terms into subsets of associations for which there is solid evidence that they indeed describe endotypes corresponding to disjoint molecular mechanisms.

Consider the example of DisGeNET – an extremely widely used database containing associations between diseases and genes and disease and genetic variants. In DisGeNET, diseases are annotated using UMLS CUIs and disease links are defined mainly via aggregation of the results of genome-wide association studies (GWAS). Yet, for mechanistically ill-defined “umbrella” CUIs that do not correspond to distinct molecular mechanisms, the underlying aggregated GWAS were most likely investigating patients suffering from distinct unknown disease subtypes. In DisGeNET, genetic associations for all of these different subtypes are merged under the set of associations for the umbrella CUI.

In the proposed project, our aim is to develop a network-based computational approach to reverse such unspecific aggregations, which affect virtually any large-scale disease association database. As input, the envisaged algorithm will accept a set of (at least two) disease association databases containing different types of association data (e.g., disease-gene and disease-symptom associations) and a potentially ill-defined disease term D contained in all input databases. As output, it will return subsets of the term’s associations in the input databases and a P-value which quantifies the evidence that these subsets indeed contain associations corresponding to distinct (possibly unknown) subtypes of the input disease D. Instead of starting with the unspecific associations for disease D, downstream precision medicine approaches can then make use of the uncovered subsets, potentially leading to more specific and promising hypotheses about molecular mechanisms driving complex diseases.

[1] J. Piñero et al., Nucleic Acids Res., vol. 48, no. D1, pp. D845–D855, 2020, doi: 10.1093/nar/gkz1021.

[2] J. S. Amberger et al., Nucleic Acids Res., vol. 47, no. D1, pp. D1038–D1043, 2019, doi: 10.1093/nar/gky1151.

[3] D. S. Wishart et al., Nucleic Acids Res., vol. 46, no. D1, pp. D1074–D1082, 2018, doi: 10.1093/nar/gkx1037.

[4] C. Nogales et al., Trends Pharmacol. Sci., vol. 43, no. 2, pp. 136–150, 2022, doi: 10.1016/j.tips.2021.11.004.

[5] S. Sadegh et al., Nat. Commun., vol. 14, no. 1, p. 1662, 2023, doi: 10.1038/s41467-023-37349-4.

But last year, weather conditions caused hydropower to fall short in a major way, with generation dropping by a record amount. In fact, the decrease was significant enough to have a measurable effect on global emissions. Total energy-related emissions rose by about 1.1% in 2023, and a shortfall of hydroelectric power accounts for 40% of that rise, according to a new report from the International Energy Agency.

Between year-to-year weather variability and climate change, there could be rocky times ahead for hydropower. Here’s what we can expect from the power source and what it might mean for climate goals. 

Drying up

Hydroelectric power plants use moving water to generate electricity. The majority of plants today use dams to hold back water, creating reservoirs. Operators can allow water to flow through the power plant as needed, creating an energy source that can be turned on and off on demand. 

This dispatchability is a godsend for the grid, especially because some renewables, like wind and solar, aren’t quite so easy to control. (If anyone figures out how to send more sunshine my way, please let me know—I could use more of it.) 

But while most hydroelectric plants do have some level of dispatchability, the power source is still reliant on the weather, since rain and snow are generally what fills up reservoirs. That’s been a problem for the past few years, when many regions around the world have faced major droughts. 

The world actually added about 20 gigawatts of hydropower capacity in 2023, but because of weather conditions, the amount of electricity generated from hydropower fell overall.

The shortfall was especially bad in China, with generation falling by 4.9% there. North America also faced droughts that contributed to hydro’s troubles, partly because El Niño brought warmer and drier conditions. Europe was one of the few places where conditions improved in 2023—mostly because 2022 was an even worse year for drought on the continent.

As hydroelectric plants fell short, fossil fuels like coal and natural gas stepped in to fill the gap, contributing to a rise in global emissions. In total, changes in hydropower output had more of an effect on global emissions than the post-pandemic aviation industry’s growth from 2022 to 2023. 

LORE MACHINE / WILL DOUGLAS HEAVEN

After more than a year in development, Lore Machine is now available to the public for the first time. For $10 a month, you can upload 100,000 words of text (up to 30,000 words at a time) and generate 80 images for short stories, scripts, podcast transcripts and more. There are price points for power users too, including an enterprise plan costing $160 a month that covers 2.24 million words and 1792 images. The illustrations come in a range of preset styles, from manga to watercolour to pulp 80s TV show.

Zac Ryder, founder of creative agency Modern Arts, has been using an early-access version of the tool since Lore Machine founder Thobey Campion first showed him what it could do. Ryder sent over a script for a short film and Campion used Lore Machine to turn it into a 16-page graphic novel overnight.

“I remember Thobey sharing his screen. All of us were just completely floored,” says Ryder. “It wasn’t so much the image generation aspect of it. It was the level of the storytelling. From the flow of the narrative to the emotion of the characters, it was spot on right out of the gate.”

Modern Arts is now using Lore Machine to develop a fictional universe for a manga series based on text written by the creator of Netflix’s Love, Death and Robots.

LORE MACHINE / WILL DOUGLAS HEAVEN

Under the hood, Lore Machine is built from familiar parts. A large language model scans your text, identifying descriptions of people and places as well as its overall sentiment. A version of Stable Diffusion generates the images. What sets it apart is how easy it is to use. Between uploading my story and downloading its storyboard, I clicked maybe half a dozen times.

That makes it one of a new wave of user-friendly tools that hide the stunning power of generative models behind a one-click web interface. “It’s a lot of work to stay current with new AI tools, and the interface and workflow for each tool is different,” says Ben Palmer, CEO of The New Computer Corporation, a content creation firm. “Using a mega-tool with one consistent UI is very compelling. I feel like this is where the industry will land.”

Look! No prompts

Campion set up the company behind Lore Machine two years ago to work on a blockchain version of Wikipedia. But when he saw how people took to generative models he switched direction. Campion used the free-to-use text-to-image model Midjourney to make a comic-book version of Samuel Coleridge’s “The Rhyme of the Ancient Mariner”. It went viral, he says, but it was no fun to make.

LORE MACHINE / WILL DOUGLAS HEAVEN

“My wife hated that project,” he says. “I was up to four in the morning, every night, just hammering away, trying to get these images right.” The problem was that text-to-image models like Midjourney generate images one by one. That makes it hard to maintain consistency between different images of the same characters. Even locking in a specific style across multiple images can be hard. “I ended up veering towards a trippier, abstract expression,” says Campion.