Detecting and isolating proteins is challenging when they’re expressed at low native abundance, lack good antibodies, or localize to complex subcellular compartments. Promega developed HaloTag® technology to address these challenges. The system is quite simple. A HaloTag® protein fusion tag is added to your protein of interest, which can be paired with one of many interchangeable ligands to form a highly specific covalent bond. The range of synthetic ligands includes chemical probes, affinity handles, and fluorescent dyes.
Beyond the ligand binding, HaloTag itself functions as a powerful foothold for protein isolation studies. The Magne® HaloTag® Beads enable magnetic capture and isolation of a protein of interest with minimal non-specific binding, supporting clean downstream pull-downs and purification. Below I highlight an exciting publication that demonstrates the power of HaloTag in enriching phosphotyrosine peptides, a classically challenging area of proteomics.
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.
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.
In late May 2026, a clinical trial result landed in the New England Journal of Medicine and immediately rewrote what oncologists believed was possible for patients with metastatic pancreatic cancer. Before the paper was published, people in the field were already calling it “transformative.” The data, when it came, agreed. In a disease where most second-line treatments offer months at best, a drug called daraxonrasib nearly doubled how long patients lived compared to those who received chemotherapy.1 It was approved by the FDA in August 2026 as RASONQUE, six months ahead of schedule.2
RAS proteins, which regulate cell growth and are mutated in more than a third of all human cancers,3 had spent forty years resisting every attempt to drug them. The protein’s surface offered no obvious foothold for a small molecule. Once the word “undruggable” attached itself to the RAS protein family, most of the field moved on to more cooperative targets.
Some researchers stayed. And Promega stayed committed to the question that never goes away: does this new compound work inside a living cell? When the next chapter of the RAS story arrived, the tools were ready. Daraxonrasib is one culmination of a much longer story, one that matters for every researcher pursuing a target the field has written off.
The First Answer
To understand what daraxonrasib represents, it helps to see how an earlier chapter of the RAS story faced the same fundamental measurement challenge.
Kierkegaard observed that one of humanity’s enduring tensions is that while life can only be understood backwards, it must be lived forwards. It’s a truth medicine knows intimately: in the treatment that worked until it didn’t, the resistance that arrived without warning, the moment a doctor has to tell a patient that the drug that was helping has stopped. Not because anyone made a mistake, but because the critical knowledge that would have mattered arrived too late, if at all.
A recent paper from the National Cancer Institute is, in a small but meaningful way, science’s pursuit of that elusive foresight: an understanding that emerges early enough, for once, to change what happens next.
The Elegant Idea
For decades, chemotherapy has worked by brute force, flooding the body with toxins designed to kill rapidly dividing cells. The problem is that rapid division isn’t unique to cancer. Hair follicle cells, gut lining cells and immune cells also divide rapidly, which is why patients lose hair, lose energy and become susceptible to infection. Chemotherapy targets a behavior, but the drug has no way to tell a healthy cell from a cancerous one.
Antibody-drug conjugates (ADCs) change that. Instead of targeting what cancer cells do, they target what cancer cells are. Cancer cells tend to display certain proteins on their surface in far greater numbers than healthy cells do. The antibody is engineered to seek out those proteins specifically. It navigates to its target, binds and waits for the cell to do what cells routinely do: pull it inside. Once there, the cell’s own digestive machinery (the lysosome) breaks down the chemical tether holding the toxin to the antibody, releasing the toxin to kill the cell from within. More than a dozen ADCs have received FDA approval in recent years, and the field is evolving fast.
What the Cell Does Next
But cancer cells don’t simply accept their fate. Even when an ADC delivers its payload perfectly—the antibody finds its target, the cell pulls it inside, the lysosome cuts the tether—a pump embedded in the cell membrane can grab the released toxin and throw it back out before it causes damage.
The delivery worked. The package got ejected anyway.
These pumps—ATP-binding cassette transporters, or more plainly, efflux pumps—are a normal feature of cell biology. Their job is cellular housekeeping, clearing out unwanted or toxic substances before they cause damage. Under the pressure of drug treatment, cancer cells do what life has always done under pressure: the ones best equipped to survive do. The same mechanism that has shaped living things for billions of years now works against the treatment. Not all cancer cells are identical, and the ones that happen to produce more pumps survive while others don’t, gradually shifting the tumor toward resistance.
The brain is one of the most complex and fascinating parts of biology. Thankfully, it’s also remarkably good at protecting itself. When exposed to a pathogen, an injury or even misfolded proteins, microglia and astrocytes function as the central nervous system’s (CNS) primary immune defenders. They mount an inflammatory response by releasing cytokines and working to contain the damage. Yet this same system can malfunction or not resolve, which manifests as devastating consequences.
Chronic neuroinflammation is now recognized as a shared characteristic across some of the most common and difficult-to-treat neurological conditions. A 2023 review in Signal Transduction and Targeted Therapy highlighted the dualistic nature of neuroinflammation: while acute responses serve a protective role, chronic or dysregulated inflammatory signaling can initiate and accelerate neurodegeneration, identifying these pathways as priority targets for therapeutic intervention (Zhang et al., 2023). A 2025 review in Science reinforced this view, noting that within Multiple Sclerosis, disease-modifying therapies targeting neuroinflammation have seen the most clinical success (Shi & Yong, 2025). This could suggest applications within neurological conditions where the same inflammatory mechanisms are at work.
Understanding how and where these inflammatory signals originate in the CNS is an active area of preclinical research. One cytokine being actively studied is IL-6. IL-6 is produced by several cell types, including astrocytes and microglia in the CNS. As a key mediator of inflammatory responses, it mediates pro-inflammatory effects through its trans-signaling, which occurs via soluble IL-6 receptors. Dysregulation of this mechanism may contribute to the chronic neuroinflammation seen in several neurological conditions. Characterizing how and when IL-6 is secreted from CNS cells is an important step toward understanding the neuroinflammatory processes underlying these disorders.
Adoptive T-cell therapies rely on generating metabolically fit, functional cells during ex vivo expansion—but this process often pushes T cells toward highly glycolytic, terminally differentiated states that limit their persistence and therapeutic potential. These metabolic programs begin shifting within hours of activation, therefore understanding early metabolic remodeling is essential for designing culture conditions that support durable, cytotoxic, and memory-enriched T-cell populations.
Researchers at Promega set out to address this challenge by systematically mapping how media composition and activation strength shape T-cell metabolism during the first 72 hours after stimulation. Using a suite of bioluminescent assays, they profiled intracellular energy cofactors, redox balance, and extracellular metabolites across several conditions. This approach revealed distinct, media-driven metabolic states that not only emerged early but also predicted downstream expansion, proliferation, and cytotoxic function.
Their work demonstrates how integrating metabolic profiling into in vitro expansion workflows can provide a more informed framework for optimizing T-cell manufacturing strategies.
Liver disease is a global health challenge, affecting millions each year. The liver has a remarkable ability to regenerate; however, chronic damage arising from obesity, alcohol, or metabolic dysfunction can lead to irreversible failure. At the University of Edinburgh’s Centre for Regenerative Medicine, Professor David Hay’s lab is developing innovative ways to study liver function and disease using a lab-grown mini-organ. In this blog, we highlight how Dr. Hay’s lab is redefining liver disease research through 3D models that reveal how hormones influence metabolic health.
This blog was written by guest contributor Tian Yang, Associate Product Manager, Promega, in collaboration with Kristin Huwiler, Manager, Small Molecule Drug Discovery, Promega.
Determining the selectivity of a compound is critical during chemical probe or drug development. In the case of chemical probes, having a clearly defined mechanism of action and specific on-target activity are needed for a chemical probe to be useful in delineating the function of a biological target of interest in cells. Similarly, optimizing a drug candidate for on-target potency and reducing off-target interactions is important in the drug development process (1,2). A thorough understanding of the selectivity profile of a drug can facilitate drug repurposing, by enabling approved therapeutics to be applied to new indications (3). Interestingly, small molecule drugs do not necessarily require the same selectivity as a chemical probe, since some drugs may benefit from polypharmacology to achieve their desired clinical outcome.
Selectivity profiling panels based on biochemical methods have commonly been used to assess compound specificity for established target classes in drug discovery and chemical probe development. Biochemical assays are target-specific and often quantitative, enabling direct measurements of compound affinities for targets of interest and facilitate comparison of compound engagement to a panel of targets. As an example, several providers offer kinase selectivity profiling services using different assay formats and kinase panels comprised of 100 to 400 kinases (4). However, just as biochemical target engagement does not always translate to cellular activity, selectivity profiles based on biochemical platforms may not reflect compound selectivity in live cells (5).
In targeted protein degradation (TPD), timing is everything. Understanding not just whether a degrader works—but how fast, how thoroughly and how sustainably—can dramatically influence early discovery decisions. Dr. Kristin Riching (Promega) dove into the real-time world of degradation kinetics in the webinar: Degradation in Motion: How Live-Cell Kinetics Drive Degrader Optimization, sharing how dynamic data provides a clearer view of degrader performance than traditional endpoint assays.
Whether you’re exploring your first PROTAC or optimizing a molecular glue series, the expertise offered in Dr. Riching’s presentation gives you actionable insights that will help you connect kinetic data to better therapeutic design.
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