Titouan Bernicot founded Coral Gardeners, a nonprofit driving coral reef restoration, at just 18 years old. He was born on a pearl farm on a small atoll, a ring-shaped coral island, in French Polynesia. Instead of playing in the dirt with neighborhood friends after school, Bernicot, the only kid on the little atoll, spent his childhood in the water, immersed in the colorful and flourishing life around him (1). With no shops or markets, the community sustained themselves only with what the island had to offer, therefore relying on a thriving local ecosystem. “A really connected-to-nature way of living” says Bernicot (2). Largely disconnected from the rest of the human world but uniquely integrated with the natural one.
Today’s blog is guest-written by Jana Krietsch (University of Zurich), edited by Nour Mozaffari (Promega)
Each time a cell divides, it must make an accurate copy of its entire genome—in human cells, this means roughly 6.2 billion individual DNA building blocks to duplicate. This enormous molecular task takes place inside the nucleus, a crowded, highly organized, yet remarkably dynamic environment. As the DNA-copying machinery moves along the genome, it may encounter roadblocks such as damaged DNA, sequences that are difficult to copy, tightly packed chromatin, or other molecular processes using the same DNA at the same time. These obstacles can slow or stall replication, a phenomenon known as DNA replication stress.
A Hidden Vulnerability of Cancer Cells
Cells are well equipped to deal with replication stress. Depending on the type and severity of the problem, they activate specialized signaling and repair mechanisms that protect replicating DNA. Failure of these responses can jeopardize genome integrity and result in permanent genetic changes that contribute to disease development.
In cancer, oncogenic changes and rapid proliferation places tumor cells under persistent replication stress. This promotes genome instability, a cancer hallmark, and drives tumor evolution. At the same time, it makes cancer cells hyper-dependent on replication stress response mechanisms to survive, creating a hidden vulnerability. Several treatments exploit this Achilles’ heel by increasing replication stress levels beyond what cancer cells can tolerate.
From Sequential Model to Dynamic Choreography
Many stress response pathways have been successfully reconstituted in the test tube. They are typically described as linear sequence of events triggered when an active replication site, termed replication fork, encounters a roadblock: the fork encounters an obstacle, its structure changes, signaling proteins are activated, repair factors are recruited, and DNA synthesis eventually resumes.
You’ve had food stuck in your teeth at some point during a conversation you thought was going well. Or toilet paper trailing from the back of your shoe on a day you felt particularly put together. Maybe you’ve even forgotten to color in your very blonde eyebrows and spent the rest of the day looking like someone erased the top half of your face. But you had no idea. You’re walking around with the quiet, complete confidence of someone operating on incomplete information.
You weren’t wrong about anything you could see. You just couldn’t see everything, which is a different problem than getting a bad result. A bad result tells you something is wrong, but this doesn’t. This happens in the lab too. The number is clean, the program is advancing, and somewhere in the data, something is happening that your readout has no way to show you.
Antibody-drug conjugate (ADC) development has a version of this problem. The cytotoxicity readout is real, reliable, and correct. It’s also an aggregate, and an aggregate compresses everything that happened into a single number. That number can’t tell you which mechanisms produced it, which ones are underperforming, or what to change if the program stops working. It just tells you cells died, or they didn’t. You’re walking around with the quiet, complete confidence of someone operating on incomplete information.
Cytotoxicity: Where Every Program Begins
Cell viability assays were built to answer one question: did the cells die? Increase the dose, more cells die. Decrease it, fewer do. The curve is clean, the data is reliable, and the payload is doing what it was designed to do. For cytotoxicity, it’s the right question to ask.
That question has an established platform with consistent, reproducible data: our CellTiter-Glo® and RealTime-Glo™ Assays.
For most ADC programs, this is where the measurement work starts and stops, but it should only be where it starts. The question they answer well is only one of several your ADC is raising. The number means what it says, but the question it answers has a boundary, and the boundary leaves you with an incomplete picture.
Antibiotic resistance has an obvious suspect: antibiotics. The more we use them across human and animal medicine, the more we select for the bacteria that survive them (1). But that pressure doesn’t only come from the drugs we designed to kill bacteria. A growing line of research points to the fields, soils and waterways where our food is grown, and to chemicals that were never meant to be antibiotics at all (2,3). It is a reminder of what researchers mean by One Health: human, animal and environmental health are one connected system, not three separate problems, and resistance travels the connections between them (1,3).
In elementary school, I spent an art class layering greens. Jungle green fabric with a subtle coordinating stripe, delicate spring green tissue paper so translucent you could see through it, pieces of chunky emerald felt cut into the shape of leaves, sparkling seafoam and teal glitter, all of it pressed and glued onto a piece of tagboard in what my teacher called a monochromatic collage. All shades of one color. Mono: one. Chroma: color. I didn’t think about the word at the time because I was too busy glittering.
That same week, I took a science quiz on monocots and dicots and got the answer wrong. I couldn’t remember which plants had one seed leaf and which had two. When I got the test back and saw my error, it clicked. Of course a monocot has one seed leaf. I’d been using mono in art class while missing it on a science quiz down the hall. The root moved between rooms, but I hadn’t learned yet that it could.
That stuck with me. Although my formal education is in English and linguistics, my mind naturally sought out scientific connections. Greek and Latin roots were the place where those two interests didn’t have to compete. They had the precision of formulas, a combinatorial logic where twenty roots could unlock hundreds of words, and they worked on both sides of the hallway.
I write about science now, and the roots have remained my constant companion. A few weeks ago I was researching an organism called Rapaza viridis for a blog post on endosymbiosis. I’d never heard of it, but since viridis is Latin for green (the same root that gives us “verdant”), I already guessed this creature had something to do with photosynthesis. It does. R. viridis is a single-celled predator that steals chloroplasts from the algae it hunts and uses them to photosynthesize.
Learning more about the organism, I came across the term “transient chimerism.” Also new to me, but also immediately legible. The trans in “transient” is the same as in “transparent” and “transport”: ‘across,’ ‘through.’ Something passing through. Chimera: the Greek monster stitched together from a lion, a goat, and a serpent. Put together: a temporary state of being made from parts of more than one organism. The roots came through for me once again.
This keeps happening. I’ll hit a term I’ve never seen, and its pieces already feel familiar. I could spend this entire blog walking through terms and showing you their roots, but definitions only stay interesting for so long (and my family already compares me to the father in My Big Fat Greek Wedding). What I’ve become more interested in is something the roots do beyond defining. The roots that end up in a name tend to carry more than a definition. They carry an interpretation, an argument about the thing itself.
For years, I wondered about apoptosis. I knew apo meant ‘away from.’ I could see ptosis maybe shared something with “asymptote,” but I couldn’t figure out what cell death had to do with calculus. In 1972, John Kerr, an Australian pathologist, had been studying a form of cell death that looked nothing like anything he’d seen before. For most of pathology’s history, the only cell death anyone studied was the kind that showed up when something had gone wrong: a cell damaged by injury or infection or toxins, dying violently. That kind of death had a name: necrosis, from the Greek nekros (‘corpse’). The cell swells, ruptures, spills its contents, and triggers inflammation. Studying liver tissue, Kerr noticed a second kind of death happening quietly alongside it, in cells that hadn’t been damaged at all. The cell death he was watching was the opposite. The cell shrank, and its contents condensed. It broke apart into tidy packages that neighboring cells quietly absorbed. No mess. The body had planned this.
FDA Regulation changes: The Problem is still Validation
In March 2026, the FDA published draft guidance that fundamentally changed how NAMs (New Approach Methodologies) are evaluated in drug development. If you’re designing NAMs, whether that be spheroids, organoids, or organs-on-a-chip, your model now must meet specific validation criteria set by regulators. This draft document, titled ‘General Considerations for the Use of New Approach Methodologies in Drug Development’ provides requirements on the use of NAMs, including in vitro, in silico and in chemico methods (FDA & CDER, 2026). This guidance is a big shift from aspirational recommendations towards clearer regulatory recommendations.
It comes on the coattails of consistent feedback and challenges the market has seen over the past few years as they attempt to transition and optimize away from animal models. Mainly being:
“How can I be sure the data I get from this non-animal model is reliable, trustworthy and relevant?”
This regulatory mandate from the FDA requires researchers to use models and assays that meet four validation criteria:
Context of use
Human biological relevance
Technical characterization
Fit-for-purpose
This guidance initially applies to antibody development, biologics, and will eventually be relevant for small molecules. The momentum in the market is sound and indicates that there is a real need for assays that meet these new requirements.
The FIFA World Cup has drawn fans from dozens of countries around the world to the United States, making it one of the largest international sporting gatherings in history. With thousands of people gathering together, it’s important for scientists and public health professionals to track contagious diseases. These pathogens spread easily, which is why early detection matters.
Testing everyone who comes into an event is impractical, and if you wait too long, the disease may have already spread. How then do scientists preemptively use wastewater to check for disease spread without testing every individual?
How do scientists test for wastewater?
Wastewater surveillance, or wastewater-based epidemiology (WBE), is a rapidly growing field that has recently proved effective in tracking the spread of diseases in communities around the world. WBE refers to the process of analyzing the wastewater output from a population to detect the presence of certain compounds or pathogens. Though its use became widespread during the pandemic, it continues to show utility in monitoring other infectious diseases as well, including polio, influenza and monkeypox, among others.
Samples collected from wastewater treatment plants provide a wealth of information, but the output from individual buildings can sometimes offer more specificity as to where exactly a pathogen is circulating. Occasionally, viruses that go undetected in samples from a treatment plant are still found on a micro level in sewage from facilities like hospitals or schools.
Most cancer-related deaths from solid tumors aren’t caused by the primary tumor itself, but are the result of metastasis. Metastasis is the process by which cancer cells break away from the primary tumor, travel through the bloodstream and establish new tumors in distant tissues. This is a complex, multi-step process and cancer researchers have spent decades trying to understand and disrupt the metastatic cascade. What is known: platelets and calcium play important roles in metastasis. What is unknown: How do these two factors interact?
Here, we explore a recent study published in Scientific Reports by researchers at George Washington University1. They investigate how calcium levels influence platelet-cancer cell interactions and what happens when both factors converge.
Did you see the movie where Spider-Man files his taxes? Or the one where Wonder Woman sits on hold with her insurance company while her pasta water boils over? Or where Captain America finds blight on his tomato plants and drives to the county extension office where he spends fifty minutes with a seventy-four-year-old master gardener named Marlys then leaves with a handwritten note covering his soil composition, his watering schedule, and what Marlys calls “the mulching situation”?
No. Because the ordinary day-to-day doesn’t stand a chance next to the saving of the world.
We spend most of our lives in the ordinary. Not because we’re failing to reach the extraordinary, but because the ordinary is what holds everything together while we get there. It’s not the backdrop but the foundation. It’s what the story depends on, whether or not it gets any credit.
Drug Discovery Has a Storytelling Problem
Drug discovery runs almost entirely on ordinary days, punctuated by the moments that make the news: a new target gets identified, a compound shows promise, a trial produces results. Those moments get the headlines, press releases and keynote slots. What doesn’t get the same attention is the years of work behind those moments: the assays, the failed experiments, the redesigns, the slow accumulation of evidence that either holds up or doesn’t. That work has always been the majority of drug discovery.
Some of the most important work in drug discovery ends in a result nobody publishes, but a dead end isn’t a failure of the program. It’s the program working. The researcher who rules something out has learned something true. That knowledge travels forward even when it doesn’t make the headline because it can redirect the next hypothesis, narrow the next experiment or just quietly move things along. That work moves research forward without anyone announcing it.
The Shiniest Thing in the Room
Artificial intelligence is drug discovery’s latest extraordinary announcement, and the fanfare is legitimate. Most of the druggable proteome has never been touched. Of approximately 4,500 human proteins considered druggable, all approved drugs to date work through only 716 distinct targets. Drug hunters knew there was more biology to address but lacked a way to find and prioritize candidates at scale. AI is changing that. By scanning genetic evidence, biological networks and scientific literature at a scale no human team can match, AI is surfacing targets that were previously out of reach and ranking them by the strength of the evidence behind them.
Agricultural soils in floodplain areas face contamination from waterborne pathogens during flooding events, yet characterization of these microbial communities remains limited. Furtak and Marzec-Grządziel investigated potentially pathogenic microorganisms in cultivated soils from the Vistula River valley in Poland, comparing soil samples collected before and during simulated flooding conditions.
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