Cell Based Assays

AI Identifies the Target. Someone Still Has to Validate It.

Posted on by Elise Johnson

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.

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Another Crack in the RAS Code: The Measurement Story Behind Daraxonrasib

Posted on by Elise Johnson

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

RAS proteins, which regulate cell growth and are mutated in more than a third of all human cancers,² 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.

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Before the First Dose

Posted on by Elise Johnson

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.

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Detecting Neuroinflammation in Microglia and Astrocytes

Posted on by Simon Moe

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.

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Your Media Choice Might Be Designing Your T-Cell Fate

Posted on by Simon Moe

Why Metabolism Matters in T-Cell Expansion

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.

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Insights from 3D Liver Models: Rethinking Fatty Liver Disease with Hormone Correction

Posted on by Simon Moe

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.

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Cellular Selectivity Profiling: Unveiling Novel Interactions and More Accurate Compound Specificity

Posted on by Promega

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).

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Unlocking the Power of Live-Cell Kinetics in Degrader Development

Posted on by Kelly Grooms

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.

3D visualization of a protein structure within a live-cell environment, highlighting the interaction site relevant to targeted protein degradation, set against a dark cellular background to emphasize kinetic dynamics.
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Exploring the Relationship Between IC50 and Kd in Pharmacology

Posted on by Promega

This guest blog post is written by Tian Yang, Associate Product Manager at Promega.

In the realm of chemical probe development and drug discovery, understanding the interactions between drugs/compounds and their targets is crucial. Two frequently used metrics to characterize these interactions are IC50 and Kd, which guide researchers in evaluating the potential of compounds in effecting changes in target function. IC50 offers insights into a compound’s potency by quantifying its ability to inhibit a specific biological activity. Kd provides a measure of the affinity between a ligand and its receptor, reflecting how tightly a compound binds to its target (1). Together, these parameters are instrumental in the early stages of drug development, helping to identify promising candidates by assessing a compounds’s binding characteristics and its observed efficacy.

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Drug Target Confirmed? Tivantinib’s Lesson on the Importance of Cellular Target Engagement

Posted on by Promega

This guest blog post is written by Tian Yang, Associate Product Manager at Promega.

There are often challenges with translating results from a test tube into a living system, demanding more physiologically relevant assays. In drug discovery, demonstrating a compound’s ability to modulate its target protein in live cells is a critical step in the hit-to-lead workflow. A variety of cell-based assays can be used to assess a compound’s activity in live cells. Take kinase inhibitors as an example, these assays can range from substrate phosphorylation assays that more directly report on the activity of target kinases, to genetic reporter assays or cell viability assays that assess the downstream effects of target modulation.

In the case of Tivantinib, several pieces of data from its development were used to establish its role as an inhibitor of MET kinase. MET Kinase is a prominent target for anti-cancer therapeutics due to frequent MET dysregulation in a wide range of tumors. For example, over-activation of MET drives cancer proliferation and metastasis. In the initial report on Tivantinib, in addition to biochemical activity assays performed with purified MET, the activity of Tivantinib in cells was verified by several methods, including: 1) inhibition of phosphorylation of MET and downstream signaling pathways, 2) cytotoxicity in cancer cell lines expressing MET, and 3) antitumor activity in xenograft mouse models (1). Additionally, a co-crystal structure of the MET-Tivantinib complex was solved, seemingly confirming that Tivantinib is a bona fide MET inhibitor capable of engaging MET in live cells (2). Based on these observations and other pre-clinical data, Tivantinib appeared to be a promising drug candidate and was taken through phase 3 clinical trials targeting cancers with MET overexpression. However, Tivantinib ultimately was not approved as a new therapeutic, failing to show efficacy in these phase 3 clinical trials (3,4).  

Within three years of the initial publication on Tivantinib, two separate articles challenged the mechanism of action in Tivantinib-induced cytotoxicity of tumor cells (5,6). Authors for both articles showed that Tivantinib can kill both MET-addicted and nonaddicted cells with similar potency. Both articles also concluded that perturbation of microtubule dynamics, instead of MET inhibition, is likely responsible for the cytotoxicity observed with Tivantinib. Considering the failed clinical trials and uncertainties regarding the mechanism of action, one may wonder if the original pre-clinical work adequately determined if Tivantinib effectively binds and inhibits MET in cells? If Tivantinib’s cellular engagement to MET was assessed directly rather than by MET phosphorylation analysis, would a different pre-clinical recommendation have been made?

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