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State of the Diagnostic Industry: Recombinants on the Rise

Discovery of a fragile foundation

Four years ago in GEN, Scripps Laboratories predicted that the clinical diagnostic industry was on the verge of a recombinant protein revolution. At the time, in vitro diagnostic (IVD) assay developers were opposed to using recombinant proteins as replacements for proteins derived from human or animal tissues, glands, organs, and fluids, so-called “native” proteins. The pushback was vigorous, even palpable.

David A. George
David A. George
Director, Product Research
Scripps Laboratories

Today, the transition to recombinants is underway, as they are being approved and adopted in IVD assays around the globe. I witnessed firsthand the shortage of native starting materials and helped drive this shift by developing recombinants suitable for the IVD industry. Recombinants are now the most responsible option in many diagnostic areas for laboratories that care about long‑term risk management, supply chain resilience, sustainable sourcing, and price stability.

The IVD industry relied far too long on a surprisingly fragile supply network. Many of the proteins used in diagnostic assays are purified from starting materials obtained from human donors, or from abattoirs in the case of animal-sourced materials. For decades, this system appeared satisfactory: native materials were available, performance was good, and IVD assays were being produced to meet global demand. The system appeared sustainable, and there was no visible reason to change; that is, until there was.

Native sourcing becomes unsustainable

The erosion of the native starting material supply chain was not a single, isolated event. It occurred over many years, even decades. Today, native raw materials for critical proteins in several diagnostic areas are unavailable in the quantities needed to support the growing IVD industry.

Going back 10 to 15 years, human hearts and livers were becoming increasingly expensive and difficult to obtain. In addition, the quality of the donor organs made available to material manufacturing companies was deteriorating severely. Many organs were either resected or visibly diseased. The poor-quality hearts yielded less and less of the cardiac biomarkers creatine kinase MB (CK-MB), troponin I (TnI), and troponin T (TnT). Similarly, yields of the iron-storage protein ferritin from human livers decreased precipitously.

Pituitary glands have a similar story of declining availability and spiking costs. Pituitaries are the source of follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin (PRL), and thyroid-stimulating hormone (TSH). These hormones are essential to testing in reproductive medicine (FSH, LH, PRL) and thyroid disease (TSH). The pituitary gland is small, the size of a pea, and each human has only one. Several thousand glands are needed, from several thousand donors, for a single pituitary gland-extraction batch. Given the growing size of the reproductive and thyroid testing markets, such large-scale consumption of this limited resource was not sustainable.

Animal-derived proteins are not immune to such supply chain disruptions. Changes in how porcine stomachs are processed at abattoirs around the world significantly reduced the intrinsic factor content available for purification. Porcine intrinsic factor has a high affinity for vitamin B12 and has been used for decades in metabolic diagnostics as the binding reagent in B12 assays. With the new stomach excision process resulting in lower yields, producing native intrinsic factor has become more challenging and expensive.

One telling indicator that some areas of the native protein model are under strain is the behavior of IVD assay manufacturers themselves. Many long-standing hormone customers have implemented a “last time buy” strategy, purchasing native hormones in quantities to last three to five years. This tactic may bridge a short-term gap, but it signals a deeper, industry-wide revelation: continuing to build assay portfolios on such vulnerable raw materials is not aligned with long-term risk management.

From skepticism to necessity

When companies began presenting recombinant alternatives to the IVD industry, the reception was cool. Many companies would not entertain a discussion about recombinants, let alone consider evaluating them. The conventional wisdom was that native proteins were inherently superior in immunoassays, particularly for structurally complex proteins, like the 24-subunit ferritin molecule, or for glycosylated, two-subunit proteins, like FSH, LH, and TSH. To be fair, the recombinants available 10 or 20 years ago were not produced with the IVD industry in mind and did not perform up to industry standards.

In only a few years, the IVD industry’s attitude toward recombinants has shifted dramatically. A willingness to evaluate them as replacements for native proteins has spread across the globe. The same diagnostic laboratories that refused to have a conversation about recombinants four years ago are now proactively soliciting their suppliers for recombinant alternatives to native proteins. Many global IVD leaders have implemented a mandate to switch to recombinant proteins wherever a native protein may be considered at risk of a raw material shortage. Furthermore, when a new assay is being developed, a “recombinant-first” approach is now the norm.

I have also witnessed a cultural element to the shift. Some of the larger IVD companies have said that their scientific staff was reluctant to switch away from native proteins, but that the transition to recombinants is happening, regardless. This, too, demonstrates a broader understanding in the industry of the fallibility of the old native model.

Recombinants taking over

The most swift and dramatic transition to recombinant hormones is occurring in the fields of reproductive biology (FSH, LH, PRL) and thyroid disease (TSH). Historically, recombinant forms of these hormones performed poorly, so the resistance to evaluating recombinants was strong. As the supply of pituitary glands contracted, however, assay manufacturers were forced to confront the vulnerability of their supply chain. Fortunately, having inside knowledge of the pituitary supply constraints, our laboratory set out early to develop recombinant forms of these hormones. By the time the supply crisis hit, we were prepared with a full line of IVD-assay-tested recombinant hormones.

Structure of recombinant bovine chymosin
Credit: vdvornyk/ iStock / Getty Images Plus

The response in the industry has been decisive and far-reaching. Most customers for native hormones have now tested, approved, and switched to recombinant versions. This change did not occur because native hormones suddenly became unusable, but because their supply became incompatible with the magnitude, reliability, and planning requirements of the industry. By contrast, recombinant hormones can be produced at scale in controlled systems with consistent quality and predictable availability.

Cardiovascular diagnostics are following a similar path. Recombinant TnI, TnT, CK-MB, and myoglobin are being adopted quickly as replacements for the native forms derived from human hearts. The supply of suitable organs cannot keep pace with industry demand, as cardiovascular disease is on the rise globally and the growth of point-of-care testing continues. Recombinant cardiac markers offer a solution to organ supply shortages, meeting the industry’s high demand for these proteins, while maintaining the performance characteristics IVD laboratories expect.

In anemia and metabolic diagnostics, the switch has not been immediate, but it is underway. Recombinant apoferritin (ferritin without iron) and recombinant human intrinsic factor are available to replace the native proteins, and they are being evaluated and approved. The global supply of native ferritin and intrinsic factor is diminishing, but the situation is not as dire as with heart- and pituitary-derived proteins. Thus, the transition is progressing, but is not as far along.

Keys to producing recombinants

To justify switching to a recombinant protein, the recombinant must perform comparably to the native protein it is intended to replace. Early recombinants did not perform well, resulting in the skepticism seen initially. In antibody-based assays, even subtle structural differences can translate into poor recognition, reduced sensitivity, or altered calibrator performance. Overcoming these issues requires more than simply expressing a protein in a convenient host; it requires a project development and testing strategy tailored to the nuances of IVD assay development.

At Scripps, our intention was to devise and implement a strategy that would produce recombinants suitable for the IVD industry. The process involves appropriate gene, expression vector, and host cell line selection; tagless protein expression; early and extensive testing in antibody-based systems, including clinical analyzers; and a willingness to revisit any or all of these elements if the desired recombinant is not produced.

This development strategy addresses the concern about recombinant protein performance in the IVD industry. When a recombinant biomarker performs well and can be supplied consistently, without relying on the unstable supply framework of donor materials, the recombinant becomes not just an acceptable option, but the preferred one.

Looking ahead

The IVD industry is at an inflection point, bending toward global acceptance of recombinant biomarkers. The constraints on native tissue supply and quality will not ease; in fact, they will likely intensify. Simultaneously, industry expectations surrounding ethical sourcing, supply chain stability, risk mitigation, and long-term cost control will become more stringent. Given this environment, continued reliance on donor materials is difficult to justify and is perhaps foolish.

Recombinant proteins offer a way forward that unites consistent assay performance with sound business judgement. Disconnected from unreliable tissue supply networks, recombinants support sustainable and ethical sourcing practices, providing IVD assay manufacturers with a stable foundation for planning and growth. The experience of recent years—in reproductive biology, cardiology, and thyroid disease in particular—has shown that when recombinants are developed with clinical assay performance in mind, they can match or even exceed the standards set by native proteins.

I have seen the industry’s view of recombinants evolve from skepticism to necessity. Focusing on tagless expression and rigorous early testing, recombinants can be produced not as lesser-quality replacements, but as robust solutions. As assay developers and IVD executives look ahead to the next decade of innovation, recombinants are no longer a speculative option. They are the most responsible path toward assuring the continued availability of the tests that patients and clinicians rely on daily.

 

David A. George is director of product research at Scripps Laboratories.

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Next Generation Biopharma Innovation

Researchers are digging deeper into biology’s complexity. In preclinical research, the traditional in vivo models are simply not enough to fuel the engine with the relevant translational data needed to progress successfully to the clinic.

As research needs evolve in immunology and immune-oncology—as focus on neuroscience increases and metabolic drugs such as GLP-1-based therapeutics become more prevalent—in vivo model suppliers are being requested to up the game on new platforms. In response, these suppliers are expanding their humanization platforms while developing advanced models that can be used to study complex and overlapping disease biology.

Regulatory factors also affect this market. The continued focus on the reduction of the use of animals by U.S. and European regulatory authorities has further opened the door to new approach methodologies (NAMs). NAMs are not new. Organ-on-chip or microphysiological systems, organoids, and iPSCs have been available for years. Finally, these systems are entering the limelight. Although the NAM market still requires more standardization across platforms, these systems are starting to impact preclinical research.

Building translational engines

The Jackson Laboratory (JAX) recently launched its latest humanized model, the NSG®-SGM3-IL15-MHC I/II DKO (S15-DKO). The S15-DKO represents their latest advancement in generating PBMC-humanized mice, supporting broad engraftment of immune cell subtypes such as CD4+ and CD8+ T cells, CD33+ myeloid cells, and CD16+/CD56+ natural killer (NK) cells. The knockout of the murine MHC Class I/II receptors delays the onset of Graft vs. Host Disease (GvHD).

Profile of the NSG-SGM3-IL15-DKO
S15-DKO model is JAX’s latest advancement in generating PBMC-humanized mice, supporting broad engraftment of immune cell subtypes. The knockout of the murine MHC Class I/II receptors delays the onset of Graft vs. Host Disease (GvHD). [The Jackson Laboratory]

The model also supports the engraftment of rare immune cell subsets, including gd T cells and CD19+/CD38+ B cells that retain the memory state of the donor PBMCs.

Another advanced model for CD34+ hematopoietic stem cell (HSC) humanization, the NSG-FLT3-IL15 mouse generates a cellular-diverse human immune system encompassing myeloid cells, mature NK cells, functional dendritic cells, and T cells.

Both models are available in naïve strains, or off-the-shelf pre-characterized PBMC- and HSC-engraftment, along with full preclinical services tailored to immuno-oncology and autoimmune drug discovery.

“With the FDA’s renewed focus on reducing reliance on non-human primates in biologic development, demand for validated, translational preclinical models has never been higher,” said Luke Dimasi, senior director, JAX.

The genetically humanized FcRn platform and the newly expanded Tg32 hALB mouse address this need. Lacking murine Fcgrt and albumin while expressing their human counterparts, the Tg32 hALB is the first model for studying the pharmacokinetics and pharmacodynamics of human albumin therapeutics, as well as human IgG and Fc-domain-based biologics. Preclinical mAb testing services are available.

“Our offering extends beyond the vivarium,” Dimasi emphasized. JAX’s iPSC repository continues to grow with engineered lines carrying disease-relevant mutations linked to Alzheimer’s, Parkinson’s, ALS, and frontotemporal dementia. In 2025, JAX added HALO-tagged and TET-inducible lines to the collection. The acquisition and integration of the New York Stem Cell Foundation (NYSCF) brings complementary patient-derived iPSCs to the portfolio.

“As the field moves towards new approach methodologies (NAMs), we are evolving alongside it,” Dimasi pointed out. “Our in vivo mouse capabilities give us decades of deeply validated biological context. We are now layering human iPSCs and AI-computational phenotyping on top of that foundation to build a convergent translational engine that no single approach could deliver alone.”

Developing relevant models

According to Jason Rashkow, PhD, product manager for research models, Charles River Laboratories, the company’s comprehensive collection of spontaneously developing rat models spans metabolic disease, diabetes, hypertension, and heart failure, providing strong translational relevance across cardiometabolic indications.

Custom diet preconditioning services allow researchers to tailor disease progression to specific study objectives through strategic model selection and diet design. Standardized preconditioning offerings are planned. “This approach will accelerate study initiation, giving researchers faster access to these metabolic disease models,” said Rashkow.

The increasing prevalence of GLP-1-based therapeutics and next-generation incretin and poly-agonist therapies expanding into cardiometabolic indications such as heart failure with preserved ejection fraction (HFpEF) is accelerating demand for advanced disease models. The combination of established disease models, standardized preconditioning approaches, and custom solutions reflects the complexity of modern metabolic drug development.

In addition, optimization of the generation of CD34+ HSC-humanized mice continues. These models, developed on the severely immunodeficient NCG strain, support research in immuno-oncology, autoimmune disease, vaccine research, and related fields.

As immuno-oncology research needs shift, so does the need for models that enable the study of NK cell-based therapies, tumor microenvironment reprogramming, and cancer vaccines. “Although variant NCG models expressing human cytokines or HLA transgenes begin to meet these needs, transgenes can influence humanization requirements,” Rashkow noted.

To counteract this, the company expanded access to a peripheral blood mononuclear cell (PBMC) engrafted NCG variant strain carrying a double knockout for murine MHC class I and class II, which significantly delays the onset of GvHD, allowing for longer-term studies in the context of mature T cells.

To better support researchers studying HLA-A2-restricted immune responses in vivo, humanization optimization of a NCG variant expressing human HLA-A*02:01 was completed. Further development of the humanization protocols for other variant strains will support next-generation immunotherapy discovery and translational research.

Lastly, the expanded aged C57BL/6 mouse offerings support researchers investigating age-related disease. As a licensed distributor of JAX® Mice to researchers in Europe and Asia, Charles River Europe can now provide aged C57BL/6J mice up to 90 weeks of age. In North America, Charles River offers aged C57BL/6N mice up to 77+ weeks of age.

Improving translational fidelity

“Improved translational fidelity, increased demand for study-ready systems that better align with clinical endpoints, and the need to model complex and overlapping disease biology are driving model development,” related Michael Seiler, PhD, vice president of portfolio management, Taconic Biosciences.

Complex modalities such as checkpoint inhibitors and engineered cell therapies require more complete immune system function and deeper phenotyping. Expansion of the FcResolv® NOG portfolio and huSelect™ services reduces murine immune interference and donor variability. Advanced flow cytometry panels support deeper, standardized

immune profiling.

Animals Alternatives
With the goal of improving translation relevance, Taconic develops in vivo models that reflect complex and overlapping disease biology in immunology, immuno-oncology, neurobiology, and cardiometabolic indications. [Taconic Biosciences]

Planned launches include platforms and models designed to support immuno-oncology, biologics, engineered cell therapies, infectious disease, and autoimmune research, with a focus on more complete and functional human immune system biology. Gene and protein analysis services are available.

In neuroscience, the shift is toward better alignment with clinical disease biology, particularly in Alzheimer’s disease and neuroinflammation, along with increased focus on blood-brain barrier (BBB) biology and CNS delivery. Parkinson’s disease model offerings include aSyn KI/KO, PINK1 KO, and LRRK2 KO rat models.

Future models include BBB-focused platforms such as TFRC and CD98, ARTE10 crosses with BBB models, and neuroimmunology-focused NOG variants, including IL-34 and TREM2-related models.

The rapid growth of obesity therapeutics, including GLP-1 and next-generation incretin approaches, is accelerating demand for more predictive metabolic and liver models in cardiometabolic disease. A range of models are aimed at obesity, MASH, cardiovascular disease, and DMPK applications.

Taconic is expanding its capabilities in transgene characterization, CRISPR off-target analysis, and tiered Custom Model Generation Solutions. The acquisition of TransCure bioServices significantly bolsters support of integrated in vivo study services, particularly in humanized immune system and immuno-oncology research. “We now offer a more seamless, end-to-end solution from model selection through study execution and data generation,” said Seiler.

“We continue to evolve toward integrated solutions rather than standalone models. This includes expanded CMS and CMGS capabilities, humanization-as-a-service, deeper phenotyping and multiomic analysis, and partner-enabled data generation,” Seiler added.

Importantly, the move toward integrating in vivo models with complementary technologies such as organoids, iPSCs, and AI-enabled analysis will influence how models are developed and deployed within research workflows.

Standardizing NAMs

The field is clearly shifting toward ready-to-use biology, producing a strong demand for standardized NAM platforms and services that deliver consistent, high-quality results. To facilitate scientists, MIMETAS continues to develop robust OrganoReady® models and advanced services, including immune-competent and vascularized systems across multiple organs.

“Last year, we strengthened our fee-for-service capabilities and advanced several models to deliver high-quality biology in a consistent, scalable way,” said Paul Vulto, PhD, co-CEO and co-founder, MIMETAS. “We made strong progress in our kidney tubuloid research program, CAR T-related applications, and a BBB model under unidirectional flow.”

The novel human distal nephron-on-chip model in the OrganoPlate® replicates physiologic sodium and water transport using primary human kidney cells. This three-dimensional microfluidic platform, as detailed in Kidney360, serves as a high-throughput tool for functional drug screening and investigating distal nephron physiology and disease.1

A polarized kidney tubuloid in an OrganoPlate chip
A polarized kidney tubuloid in an OrganoPlate chip showcases apical and basolateral access. Immunofluorescence 3D reconstruction demonstrates tubule polarization and barrier formation: blue, DNA; red, acetylated tubulin; and green, Na /KATPase. [MIMETAS]

In addition, a three-dimensional BBB microvasculature model developed on the OrganoPlate Graft 48 UniFlow was evaluated in a recent Fluids Barriers CNS publication. Tri-cultures of endothelial cells, pericytes, and astrocytes were used to demonstrate that this pump-free, unidirectional perfused, three-dimensional BBB model outperformed simpler systems on vascular architecture and barrier function. Its high-throughput nature renders the model suitable for studies of BBB function in health, disease, and therapeutic development.2

This year, the company’s UniFlow technology will be offered for in-lab use, enabling customers to create a stable, perfusable vascularized bed for endothelial tissues. New OrganoServices for gastrointestinal toxicity (GI tox) and drug-induced vascular injury (DIVI), alongside a multi-donor expansion of the OrganoReady Colon Organoid product, are also planned.

A major trend in NAMs is the increased need for standardization and regulatory alignment across the field. With initiatives like IAMPS (Industry Alliance for MicroPhysiological Systems), of which MIMETAS is a founding member, industry innovators will work together to advance regulatory acceptance.

The space is evolving quickly, but Vulto emphasized that their focus remains unchanged: building robust human models that help researchers make better decisions.

Improving organoid access

“Organoids are part of a broader innovation focus to help researchers work with more predictive models, more advanced tools, and more connected workflows across the path from discovery to development,” commented Heather Hargett, PhD, head of cell biology reagents franchise at MilliporeSigma, the U.S. and Canada Life Science business of Merck KGaA, Darmstadt, Germany.

The regulatory landscape is becoming increasingly favorable to NAMs. In March 2026, the FDA issued a draft guidance to establish clear validation principles for NAMs, including organoids and in silico (or AI) models, when submitted in support of drug applications.

Phasing out animal use for research and regulatory purposes is also supported by the European Commission’s Roadmap Towards Phasing Out Animal Testing for Chemical Safety Assessments.

Patient-derived organoids
Patient-derived organoids (PDOs) retain individual genetic and phenotypic characteristics, enabling drug response testing across diverse patient backgrounds and disease subtypes. The image shows immunocytochemical (ICC) characterization of human colon PDOs that are positive for the colon-specific marker CA II (green), nuclei (blue) and actin (red). [MilliporeSigma, the U.S. and Canada Life Science business of Merck KGaA, Darmstadt, Germany

HUB’s advanced organoid capabilities are now being combined with the company’s cell culture expertise, manufacturing scale, global commercial reach, and broad life science portfolio to make organoids a more practical and scalable tool in drug discovery and translational research.

Key priorities include expanding the validated organoid biobank across additional therapeutic areas, tissues, disease states, and patient backgrounds. “Last October, we announced a strategic partnership with Promega Corporation,” said Hargett. “By combining our organoid expertise with Promega’s advanced reporter technology, we aim to enable high-throughput screening that helps researchers identify safer and more effective drug candidates.”

The case of petosemtamab, developed by Merus, is a notable example of the real-world impact of organoid technology. Petosemtamab’s efficacy was tested using HUB organoids. The EGFR x LGR5 bispecific antibody has received FDA Breakthrough Therapy Designation for use in combination with pembrolizumab for first-line treatment of PD-L1-positive recurrent/metastatic head and neck squamous cell carcinoma (HNSCC). A global Phase III trial is ongoing. Recently, Genmab acquired Merus for approximately $8 billion USD.

Adopting organoid technology is a capital efficiency strategy, according to Hargett. Patient-derived organoids retain individual genetic and phenotypic characteristics, enabling drug response testing across diverse patient backgrounds and disease subtypes. Organoids support a “fail fast” approach by identifying non-viable candidates earlier, reducing costly late-stage clinical trial failures, and allowing companies to redirect resources toward the most promising programs.

 

References

  1. Bernardi MDL, Dilmen E, Kurek D et al. A Novel Human Distal Tubuloid-on-a-Chip Model for Investigating Sodium and Water Transport Mechanisms. Kidney360. 2025 Nov 1;6(11):1981-1993. doi: 10.34067/KID.0000000992.
  2. Admiraal J, Emeh PO, Bokkers M et al. Building the blood-brain barrier: a scalable self-assembling 3D model of the brain microvasculature under unidirectional flow. Fluids Barriers CNS. 2026 Jan 23;23(1):29. doi: 10.1186/s12987-026-00765-x.

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The Confidence Gap: Why Drug Discovery’s Data Explosion Hasn’t Solved Its Billion-Dollar Decision Problem

Laurence Arnold
Laurence Arnold, PhD
Head of R&D
Pelago Bioscience

We’ve never had more data in drug discovery. Yet despite this explosion in capability, our industry’s most fundamental challenge remains stubbornly intact: making confident early decisions about which drug programs deserve billion-dollar investments, and which should be shelved.

It costs two to three billion dollars to bring a drug to market, with a 90% failure rate, often higher. These numbers mask something more troubling. We’re not just failing because biology is hard; we’re failing because the mountains of data we’re generating aren’t giving us what we actually need at decision points that matter.

In my view, we don’t have a data volume problem—we have a data relevance problem.

Biological activity is not relevance

Traditional drug discovery relies on a “dissect and build” approach: isolate one variable, measure it in a controlled environment, then extrapolate. It’s disciplined. It’s reproducible. And it has delivered important medicines.

But the persistently high failure rate in drug development tells us we’re reaching the limits of this approach. In reality, biology operates through cascading networks, feedback loops, and context-dependent equilibria. These are dynamic biological systems where cause and effect rarely follow straight lines.

We’ve successfully drugged only about 650 of 20,000 potentially druggable proteins. Not because scientists lack talent, but because for most targets, we don’t have robust ways to measure what matters—the initiating molecular event in a biologically relevant context.

We’re good at measuring activity. What we struggle with is measuring relevance.

An assay telling you a compound binds to your target protein is useful, but does it bind in living cells? In the disease context that matters? With the pharmacokinetics to reach patients? A compound brilliant in a purified enzyme assay might never reach its target in cells, or it might hit off-targets producing effects through entirely different mechanisms.

The result? Ever-expanding data sets that still don’t answer the critical question in modern drug discovery: Are we making the right decision?

The cost of borrowed confidence

There’s a human dimension here that rarely makes it into industry discussions. Despite what is often repeated in drug discovery circles, scientists in R&D are rewarded for being right, not for being bold.

Most scientists think in terms of “future hindsight”: will we look back and realize we missed something obvious? The responsibility isn’t to push programs forward at all costs. It’s to execute each step well, knowing that most will fail. Success stories often appear bold in retrospect. In practice, they are usually built on careful, incremental decisions that gradually improve the odds.

So, teams do their jobs with discipline and rigor. They hit milestones, generate data, and advance programs. Everyone knows 90% of projects will fail, but this one has shown activity in the assay, has a plausible mechanism, and has momentum. The data might not be perfect, but it’s good enough to keep going.

Until it isn’t. And the failure comes late, after years of effort and hundreds of millions spent.

Of course, failure is how science advances. But many of these failures were avoidable earlier. Hard-working teams just didn’t have data that would let them make the call with confidence when it mattered most, before massive resources were committed.

What decision-ready evidence looks like

The best experiment isn’t always the one that moves your program forward—it’s the one that tells you when to stop.

Think of it as taking a stepladder to look over a thick hedge rather than hacking through it with an axe. You might not learn everything about what’s inside it, but you’ll know much faster whether there’s anything worth pursuing on the other side.

The pharmaceutical industry has been built on a model of going through the hedge, but the resource cost and timelines are increasingly untenable. So, what would an alternative, evidence-driven discovery model look like?

Evidence-driven discovery requires a hierarchy of questions. Before optimizing potency or selectivity, can you prove that engaging this target in this context produces therapeutically relevant effects? Not in an abstract system, but in actual disease biology.

This is about front-loading proof of concept before investing in optimization. Measure the initiating molecular interaction early, free from tags or unnatural expression control, in cells and tissues that approximate disease.

It also requires new frameworks for proof of target engagement. We’re seeing this with technologies that measure binding in native cellular contexts, patient-derived models, and translational designs that test hypotheses much earlier in preclinical development. The goal isn’t replacing traditional assays, but knowing which programs deserve that investment.

Ultimately, the win comes from making the right decision at each stage, even when that decision is to stop.

The path forward

Successful programs will establish coherent lines of evidence from initial target engagement through preclinical models to human proof of concept—and they will do it fast enough to fail early when evidence doesn’t align.

This means rigorously testing hypotheses in the real biological context of disease before perfecting molecules or committing billions of dollars.

Some will argue this is unrealistic—that you need optimized compounds, and that shortcuts lead to false negatives killing promising programs. These concerns aren’t wrong; they’re just insufficient when the old model demonstrably isn’t working.

The real question is whether the risk of earlier translational testing exceeds spending nine years and a billion dollars on a target that was never going to work.

Making the call with confidence

Here’s what I tell my team: Your job isn’t to get a drug to the clinic. Your job is to do each step exceptionally well, building evidence you can defend. Because if we’re systematic about gathering the right evidence early, and if we’re honest about what the data is—and isn’t—telling us, the statistics start working in our favor.

The industry is moving toward evidence-first approaches—technologies validating targets in relevant contexts, translational frameworks testing hypotheses earlier, and computational tools trained on quality data.

But all this data is just noise until it answers the question keeping many of us up at night: Can I make this call with confidence, or am I crossing my fingers and hoping?

We won’t solve the 90% failure rate entirely. Biology is too complex. But we can close the confidence gap by using the right data, at the right time, to answer the key question: Should we keep going?

And sometimes—often, even—the most valuable answer will be no.

 

Laurence Arnold, PhD, is the Head of R&D at Pelago Bioscience.

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Novel Therapeutic Modalities Target the Undruggable

From small molecules and protein therapeutics to gene therapies, biotech industry players have placed their bets on a wide range of modalities that push the limits of what was once considered “druggable.”

AI biologics company, Absci, focuses on rational antibody design to bypass labor-intensive experimental screens. The ability to computationally design antibodies from scratch, or de novo, without reference to a known binder, could transform an antibody drug market projected to reach $445 billion within the next five years.

Unveiled in January, the company’s latest protein design model, Origin-1, generated developability-optimized antibodies that achieved nanomolar binding affinity and functional inhibition of IL36RA, a therapeutic target for squamous cell carcinomas. By simulating the delivery of pro-inflammatory cytokine, IL-36, the AI-designed drug candidate boosts intratumor immune response for cancer control.

Origin-1 generates de novo antibodies for “zero-prior” epitopes, or target sites that lack structural data from known protein-protein complexes. Sean McClain, CEO of Absci, emphasizes the approach as a “more expansive” version of de novo design that requires only a monomeric structure as input to generate viable candidates.

Nathaniel Bennett, PhD, co-founder at Xaira Therapeutics, highlights that Absci’s atomic-level experimental validation contributes to the field’s understanding of how AI will play a major role in therapeutic development, particularly for expanding the range of tractable drug targets.

“This is a solid piece of work that shows how AI-driven antibody design continues to mature,” says Bennett, “particularly in settings with limited prior structural information.”

Janani Iyer, PhD, head of AI/ML product at Absci, emphasizes that the targets that most often strike interest from pharma partners are typically less studied and lack epitope structure in the public domain. “We’re focused on building an AI platform technology that unlocks really unmet needs,” she said.

Permanently bound

While highly precise therapeutics, biologics, such as antibodies, are typically constrained to intravenous delivery. A growing number of biotech companies are expanding the capabilities of small molecules, which offer the advantage of convenient oral administration.

Unveiled from stealth last October, Expedition Medicines leverages generative AI to design small-molecule drugs that target shallow pockets using covalent chemistry. The Flagship Pioneering spinout targets a range of traditionally undruggable sensors, regulators, and transcription factors, where disease is driven by interactions across protein surfaces. These small molecules remain inert inside the body until activated by the appropriate protein catalyst.

“Small molecules have historically been more challenging for generative AI, but I think we are at an inflection point, with the right chemistry insights, data, algorithms, and compute finally coming together,” said Molly Gibson, PhD, CEO of Expedition.

small-molecule
Expedition Medicines leverages generative AI to design small-molecule drugs that hit shallow pockets using covalent chemistry. The approach targets a wealth of traditionally undruggable sensors, regulators, and transcription factors, where interactions across surfaces drive disease.
[Expedition Medicines]

She notes that Expedition’s technology contrasts with many of today’s molecular design efforts, which use 3D atomic positions to model reversible interactions in deep pockets.

The company’s tech stack trains AI models on high-throughput mass spectrometry data that measures the potency of each small molecule against 20,000 sites in the proteome. These fit-for-purpose datasets are advantageous over DNA-encoded libraries (DELs), which are burdened by substantial noise that can limit predictive power.

Expedition is focusing on demonstrating clinical proof points. In a partnership with Pfizer, the startup is identifying target molecules correlated with prostate cancer disease progression and treatment resistance. As a long-term goal, the team plans to expand the proteomics platform to additional modalities, such as proximity events that drive protein degradation or stability.

Biologic in a pill

AI drug developer, 1910 Genetics, has recently tackled macrocyclic peptides, a class that aims to combine the oral convenience of small molecules with the high specificity of biologics. Historically, these compounds have struggled to balance cell-membrane permeability with key therapeutic properties such as potency and solubility.

To address this gap, 1910’s AI model, PEGASUS, is trained on a multi-modal dataset that generates billions of cyclic peptides separated by permeability-related characteristics and solvent-dependent computational simulations. PEGASUS was able to demonstrate the first cyclic peptides with more than two polar or ionizable fragments to achieve in vitro cell-membrane permeability.

Jen Asher, PhD, founder and CEO of 1910, describes the model as a “versatile tool” that accelerates the design-make-test cycle by triaging compounds for synthesis, supporting lead optimization, and designing new starting peptides with desired properties.

With a company name that references the year that the first patient was diagnosed with sickle cell disease in the United States, the first condition for which the field identified a molecular basis, 1910 is committed to multi-modality drug discovery. The company’s platform also houses CANDID-CNS, an AI model that predicts small molecule blood-brain barrier (BBB) penetration within Beyond-Rule-of-5 (bRo5) chemical space to advance therapies for neurological disease.

With only about two percent of small-molecule drugs able to cross the BBB, accurate penetration prediction can identify promising candidates that are more likely to succeed in the clinic. The model achieved an 87% success rate for predicting bRo5 small molecule brain penetration and distribution, outperforming a 56% success rate for the industry standard, Pfizer’s CNS Multiparameter Optimization (CNS-MPO) score.

Encrypted message

Jacob Becraft, PhD, CEO at Strand Therapeutics, is placing his bet on programmable mRNA therapeutics for cancers and autoimmune diseases. Strand is among a vibrant genetic medicine ecosystem, where engineered vehicles, such as adeno-associated vectors (AAVs) and lipid nanoparticles (LNPs), deliver therapeutic genetic material into patient cells to produce therapeutic proteins. These medicines must achieve therapeutic potency in the right tissues while avoiding off-target effects. Yet, targeted delivery beyond the liver remains a challenge.

STX-005 illustration
STX-005 extends the same programmable mRNA platform behind STX-001 to in vivo CAR T therapy, using circular RNA and targeted systemic delivery to generate CAR T cells directly inside the body. The approach is designed to produce long-term, cell-specific expression without the ex vivo manufacturing required by conventional CAR T. The program extends the company’s work in targeted, safe, and effective systemic delivery and has potential applications to autoimmune diseases and blood cancers. [Strand Therapeutics]

Strand’s technology addresses this gap by enabling selective mRNA expression within cancer cells while sparing healthy tissue. This approach allows mRNA to be delivered broadly while targeting expression to the intended tumor cells.

“It’s like an encrypted message. It doesn’t matter who picks up my message because they can’t read it,” Becraft said. “If the protein doesn’t get created, then it’s not off-target.” The tech stack challenges the “old school mentality” that mRNA biodistribution is the key metric that defines off-target effects.

Strand’s technology leverages a machine learning–driven approach that applies molecular sensors to detect microRNA expression signatures distinguishing tumor cells from healthy cell types. As an example, liver-specific microRNAs bind to target sites in the 3¢ UTR of the delivered mRNA to suppress its expression in healthy hepatocytes and prevent off-target effects.

Last May, Strand announced the Phase I dose-escalation trial for STX001, a programmable, self-replicating mRNA therapy designed to treat advanced solid tumors by producing IL-12 directly in the tumor microenvironment. Notably, STX001 demonstrated an abscopal response, in which localized treatment of a single tumor led to a systemic immune response that reduced distant tumor sites. The company looks to advance the candidate to Phase II trials.

As the therapeutic toolbox continues to expand, the field is working to close the “undruggable” gap.

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Mass Spectrometry’s Discovery Revolution

Mass spectrometry (MS) has quietly undergone one of the most consequential evolutions in modern drug discovery. Once viewed primarily as a confirmatory analytical tool, it is now reshaping how researchers identify, validate, and optimize therapeutic candidates. Across chemoproteomics, metabolomics, immunopeptidomics, and beyond, MS is increasingly positioned not at the end of the pipeline—but at its beginning, where the most crucial decisions are made.

“Mass spectrometry is no longer just a downstream analytical checkpoint,” says Aaron Robitaille, PhD, the senior director of product & vertical marketing of mass spectrometry at Thermo Fisher Scientific. “It is increasingly serving as a discovery engine.”

This shift reflects a broader transformation across the pharmaceutical industry: from hypothesis-driven experimentation toward data-rich, systems-level interrogation of biology.

Seeing biology more clearly

Drug discovery has always struggled with a fundamental problem: Biology is complex, noisy, and often opaque. Many of the molecules that determine therapeutic success are low in abundance, transient, or entirely unknown. MS addresses this challenge by enabling researchers to observe biological systems with unprecedented depth and specificity.

According to Robitaille, MS now supports nearly every stage of early discovery—from target identification and engagement to pharmacokinetics and mechanism-of-action studies. One of its most transformative applications is chemoproteomics, where researchers can directly measure drug-protein interactions within living cells. This enables scientists to evaluate not just whether a compound binds its intended target, but also whether it interacts with unintended ones.

Crucially, MS is moving upstream in the discovery pipeline. “What makes that important is not merely breadth. It is timing,” Robitaille notes. By enabling high-throughput screening with detailed molecular readouts, MS helps eliminate poor candidates earlier—saving time, cost, and effort.

Technological advances are driving this shift. Historically, researchers faced trade-offs between speed and sensitivity, or between targeted and untargeted analyses. Newer platforms are collapsing these compromises. Hybrid acquisition methods, for example, allow targeted and untargeted data to be collected simultaneously in a single experiment, enabling both hypothesis testing and discovery.

The Thermo Scientific Orbitrap Astral Zoom MS exemplifies this convergence. Built around parallelized acquisition and enhanced ion handling, the system delivers high throughput, deep proteome coverage, and precise quantitation—all in one platform. Its ability to process hundreds of samples per day while quantifying thousands of proteins illustrates how MS is becoming both scalable and decision-ready.

Interrogating biology at scale

For Mike Knierman, biopharma workflow manager at Agilent, the expanding role of MS reflects the growing complexity of new therapies. “Drug discovery today spans multiple therapeutic modalities, including small molecules, monoclonal antibodies, oligonucleotides, and cell-based therapies,” he explains. MS provides a unifying analytical backbone across this diversity.

One of the most significant recent developments, Knierman emphasizes, is MS’s ability to interrogate biology at scale. Techniques such as proteomics, metabolomics (See Sidebar), and lipidomics allow researchers to observe how candidate drugs perturb entire cellular systems, rather than isolated targets. This systems-level insight is essential for understanding the mechanism of action and identifying off-target effects early in development.

Emerging measurements—such as protein turnover—are also enabling new therapeutic strategies. These include targeted protein degradation approaches, which require a detailed understanding of dynamic protein lifecycles rather than static abundance.

Agilent’s Revident LC/Q-TOF platform reflects this trend toward intelligent, high-resolution analysis. Designed for accurate-mass performance with built-in diagnostics, the system incorporates features that automate quality control and maintain data consistency. Its ultra-fast detector supports a wide dynamic range without sacrificing resolution, enabling confident identification and quantitation in complex biological samples.

Equally important are workflow innovations. The platform’s Intelligent Reflex capabilities automate routine checks—such as calibration verification and carryover detection—reducing manual intervention and ensuring consistent performance. In drug discovery environments where throughput and reproducibility are crucial, these features help maintain data integrity while accelerating timelines.

Ultimately, Knierman highlights MS as a driver of “biology-driven discovery,” where decisions are guided by comprehensive molecular data rather than limited readouts.

A shift in discovery models

Todd Stawicki, senior global market development manager for pharma, SCIEX, places MS within a broader transformation of drug discovery itself. The industry is moving away from traditional in vivo models toward more complex in vitro systems—such as organoids and tissue-based assays—in an effort to reduce impacts to laboratory animals and rising global regulatory efforts.

This shift dramatically increases the number and complexity of experimental endpoints. “Many or most of these endpoints are best served by mass spectrometry,” Stawicki notes. As a result, MS is becoming indispensable for analyzing the rich datasets generated by these models.

MS is also deeply embedded throughout the discovery lifecycle. In the early stages, it supports proteomics and complements genomic studies. It plays a central role in hit identification and lead optimization, and remains crucial in ADME (absorption, distribution, metabolism, and excretion) and DMPK (drug metabolism and pharmacokinetics) studies.

Analysis of a system suitability test and rat plasma matrix
Analysis of a system suitability test (SST, top) and rat plasma matrix (bottom) injections on the SCIEX 7500+ system for three drug compounds shows coefficients of variation (%CV) of three to five percent across more than 10,000 injections of rat plasma. [SCIEX]

Technological innovation continues to expand MS’s capabilities. Acoustic ejection-based MS, for example, enables rapid, label-free screening, while advanced systems—like the SCIEX 7500+ system—address one of the field’s most persistent challenges: balancing sensitivity with dynamic range.

As new drug modalities become more potent and targeted, they often exist at extremely low concentrations in complex biological matrices. This creates a dual requirement for high sensitivity and a broad quantitation range. The SCIEX 7500+ system meets this need, enabling accurate measurement across diverse tissues and concentration levels.

Robustness is another key consideration. SCIEX Mass Guard technology, for instance, enhances system uptime, ensuring that high-throughput workflows can run reliably over extended periods. In an environment where delays can be costly, this operational stability is as important as analytical performance.

Balancing throughput and insight

Shimadzu’s perspective underscores the importance of versatility in modern MS workflows. “Mass spectrometry has become one of the most versatile analytical tools in drug discovery,” says Lihini Mendis, PhD, LCMS product specialist at Shimadzu Scientific Instruments, noting that it now supports everything from early screening to preclinical development.

Triple-quadrupole MS systems
Triple-quadrupole MS systems can be used in drug discovery for bioanalysis and studies of drug metabolism and pharmacokinetics. [Shimadzu Scientific Instruments]

A major recent trend is the push toward higher throughput without compromising data quality. Rapid LC-MS methods and triple quadrupole systems are increasingly used to process large sample volumes efficiently, particularly in quantitative workflows such as bioanalysis and DMPK studies.

At the same time, qualitative MS capabilities are expanding. High-resolution instruments, combined with advanced fragmentation techniques, allow researchers to gain deeper structural insights into complex molecules such as lipids and metabolites. This dual capability—quantitative precision and qualitative depth—enables scientists to answer both “how much” and “what exactly” within the same experiment, Mendis explains.

Shimadzu’s portfolio reflects this balance. Single-quadrupole systems provide accessible, high-throughput screening, while triple-quadrupole platforms emphasize stability and reproducibility for quantitative analysis. High-resolution instruments extend capabilities into accurate-mass analysis and structural elucidation, all while maintaining user-friendly operation.

The overarching goal is not complexity for its own sake, but meaningful data that supports confident decision-making. By focusing on workflow efficiency and reliability, Shimadzu aims to streamline the path from data acquisition to actionable insight.

A proteoform-centric vision

While incremental improvements in speed and sensitivity have driven much of MS innovation, Bruker’s recently introduced timsOmni system points toward a more fundamental shift: a move toward protein-centric analysis at the level of intact proteoforms—structurally distinct variants of proteins that arise from genetic mutations, alternative splicing, or post-translational modifications.

The platform introduces a multimodal trapping approach that enables precise control over ion reactions, supporting a wide range of fragmentation techniques. This flexibility allows researchers to tailor experiments to extract detailed structural information from complex biomolecules.

Rather than focusing solely on peptides or simplified representations of proteins, the system emphasizes intact protein analysis. This is particularly important for identifying proteoforms. These variants often play critical roles in disease but are difficult to detect using conventional approaches.

The timsOmni platform enables detailed mapping of such variations, including modifications, such as acetylation and glycosylation, that influence protein function and cellular signaling. By combining high sensitivity with advanced fragmentation methods, it allows researchers to generate comprehensive sequence information and localize modifications with precision.

Importantly, this capability extends beyond discovery into biopharma development and quality control. The ability to characterize therapeutic antibodies and other biologics at the proteoform level has significant implications for both efficacy and safety.

Supporting software further enhances this capability by translating complex spectral data into actionable insights. Advanced algorithms enable de novo sequencing, charge state assignment, and modification identification, making it easier for researchers to navigate the complexity of proteoform analysis.

Accelerating insights

As therapeutic modalities become more complex, the need for faster, more precise characterization tools has never been greater. David Curtin, vice president and general manager, biologics business, Waters Analytical Sciences, Waters Corporation, highlights how emerging platforms are enabling researchers to generate deeper insights earlier in the development cycle—when those insights can have the greatest impact.

As one example, Curtin describes the Xevo CDMS platform as a breakthrough in capability and accessibility. As the first dedicated benchtop charge-detection mass spectrometry system, it enables measurement across a wide spectrum of mega-mass biomolecules. Crucially, it supports “characterization in process development when decisions matter most,” Curtin says, allowing teams to act on high-quality data in real time.

Speed is one of its most transformative advantages. “Xevo CDMS delivers accurate analysis in less than 10 minutes,” Curtin explains. This represents a dramatic improvement over traditional workflows that could take hours, days, or even weeks when outsourced. The result is a shift to same-day decision-making, fundamentally changing how process development is executed and optimized.

Efficiency is another key differentiator. Curtin notes that “the system requires up to 100 times less sample than current methods,” addressing a long-standing limitation in biopharma research. With reduced sample demands, scientists can run more experiments per batch, leading to “lower cost, higher yields, fewer impurities, and faster time to market,” he says.

Beyond operational improvements, the platform unlocks new scientific possibilities. Curtin emphasizes that it delivers direct mass and charge measurements for individual 100-kilodalton to 150-megadalton molecules, including complex structures such as glycosylated proteins, viral vectors like AAV, and lipid nanoparticles. In many of these cases, “CDMS isn’t just a better option; it’s the only option,” Curtin says.

Ultimately, Curtin underscores the broader impact: researchers are now generating “fast, accurate orthogonal data” that validates existing approaches while opening entirely new lines of inquiry. Scientists, he says, are “asking and answering questions they couldn’t tackle before”—a powerful indicator of how this technology is advancing the development of therapies for diseases including cancer, heart disease, and Alzheimer’s.

From data to decisions

Across all these perspectives, a common theme emerges: MS is no longer defined by its ability to generate data, but by its ability to inform decisions. This clarity is transforming drug discovery. By revealing off-target effects, validating mechanisms of action, and identifying biomarkers at early stages, MS helps reduce uncertainty and improve success rates. It allows researchers to prioritize the most promising candidates and eliminate those unlikely to succeed.

As Robitaille puts it, the ultimate value of modern MS lies in “the ability to see meaningful biology early enough to act on it.” In an industry where time, cost, and complexity are ever-increasing, that capability might prove to be one of the most important advances of all.

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Macrocyclic Peptide Drugs Unlocked, Membrane Permeability Screened at Scale

Macrocyclic peptides are a promising drug modality that combine the oral convenience of small molecules with the high specificity of large biologics. Yet, they struggle with cell membrane permeability, limiting their ability to target disease interactions within cells.  

In a new study published in Nature Chemical Biology titled, “Generation of membrane-permeable cyclic peptides inhibiting protein–protein interaction”, researchers from École Polytechnique Fédérale de Lausanne (EPFL) have developed a new method to generate and screen large libraries of synthetic cyclic peptides to identify compounds that can enter cells for therapeutic effect. 

“We focused on small, less than 1000-Dalton, non-polar cyclic peptides that can enter cells by rapidly crossing the hydrophobic inner region of cell membranes,” says Christian Heinis, PhD, associate professor at EPFL. “The challenge was then to develop cyclic peptides with suitable shapes so that they can bind to targets of interest.” 

The authors focused on protein interactions linked to inflammation, oxidative stress, and neurodegeneration, and cancer. The study synthesized and screened a library of 15,360 fully random cyclic peptides, all designed to be small, compact, and relatively nonpolar to support membrane permeability. The screen identified several compounds capable of disrupting the disease-associated Keap1–Nrf2 interaction. 

The team optimized a cyclic peptide candidate, termed peptide 30, which combined strong target binding with membrane permeability. Peptide 30 inhibited the Keap1–Nrf2 interaction inside living cells in a dose-dependent assay. Compared with the natural Nrf2 sequence, peptide 30 had no electrical charge, fewer hydrogen bond donors, and lower polar surface area to support membrane permeability.  

The study demonstrated that membrane-permeable cyclic peptides can be developed without starting from known ligands, natural products, or binding motifs, broadening access to intracellular targets previously considered difficult to drug. 

“Our lab is now further advancing the technology to synthesize and screen even larger libraries of small, membrane-permeable cyclic peptides,” says Heinis. “And we are applying the technology to some of the most challenging protein–protein interaction targets, including big cancer targets like KRAS, b-catenin and c-Myc.” 

Heinis’s group has patented the method and founded the spin-off company Orbis Medicines, which recently raised more than €90 million in Series A funding to further develop and apply the technology for drug discovery. 

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Multiomics Mass Spec Workflows in Drug Discovery

Advances in end-to-end multiomics platforms and the underlying scientific knowledge now enable faster and more precise biomarker discovery, mechanistic insight generation, and therapeutic design—core drivers of modern drug discovery programs. Within this integrated ecosystem, mass spectrometry-based metabolomics serves as a central analytical modality, offering the ability to quantify large numbers of metabolites from a single sample with high sensitivity and rapid turnaround.

Metabolomics supports biochemical pathway-level interpretation, where a primary biomarker can be contextualized alongside upstream and downstream metabolites to inform target identification, pathway modulation, and pharmacodynamic response assessment. Rather than focusing solely on the discovery of novel metabolites, emerging approaches emphasize the identification of characteristic metabolic signatures that differentiate disease states, therapeutic responses, or mechanistic subtypes.

Realizing this potential requires the development and deployment of AI enabled data analysis workflows that can reduce interpretation time, expand the breadth of detectable targets, and uncover complex patterns of metabolite perturbation. These capabilities ultimately enhance the precision and effectiveness of targeted therapeutic development.

 

Taraka Donti, PhD, is director of lab services at Revvity Omics.

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Personalized Cancer Vaccine Reduces Melanoma Recurrence by 49%

Results from the Phase IIb KEYNOTE-942 trial reveal that adding a personalized mRNA vaccine to standard immunotherapy after surgery can reduce the five-year risk of recurrence and death by 49% among patients with advanced melanoma. These findings were presented today at the annual meeting of the American Society of Clinical Oncology and published in the Journal of Clinical Oncology.

“Our study offers strong evidence to melanoma patients that intismeran vaccine therapy, when used in combination with immunotherapy, can demonstrably reduce their risk of  having their cancer return and improve clinical outcomes,” said Janice Mehnert, MD, professor in the department of medicine at NYU Grossman School of Medicine and associate director of clinical research at Perlmutter Cancer Center.

Melanoma is one of the most common forms of cancer. Although immunotherapies like pembrolizumab (Keytruda) have significantly improved outcomes for melanoma patients, this cancer is still known for its ability to evade the immune system and become resistant to treatment. 

Developed by Moderna, intismeran autogene is an mRNA cancer vaccine made specifically for each patient. The bespoke therapy is created by screening tumor samples for 34 neoantigens that can be leveraged to strengthen the immune system’s response against tumor cells and enhance the efficacy of immunotherapy. 

The KEYNOTE-942 trial recruited 157 patients with advanced stages of melanoma and at high risk of recurrence who were randomized to either receive standard pembrolizumab immunotherapy, or a combination of pembrolizumab and intismeran. Since the treatment was administered after surgery, intismeran was individually manufactured for each patient based on an analysis of their resected tumor. 

After five years, 68.8% of patients treated with the combination therapy remained alive and cancer-free, compared to 49.1% for those who received standard treatment. The addition of personalized vaccines also reduced the risk of developing a distant metastasis by 59%. 

“Now with five years of follow-up data, today’s results highlight the potential of a prolonged benefit of the intismeran autogene and Keytruda combination in patients with resected high-risk melanoma,” said Kyle Holen, MD, senior vice president and head of development, oncology and therapeutics at Moderna. “We continue to invest in our platform in oncology because of encouraging outcomes like these, which illustrate mRNA’s potential in cancer care.” 

A Phase III clinical trial is already underway to confirm the efficacy of intismeran as a first line therapy for melanoma in combination with pembrolizumab. Additional studies are looking into the effects of the cancer vaccine in other types of cancer, including non-small cell lung cancer, bladder cancer, and renal cell carcinoma. 

“Our findings also serve as encouragement to cancer researchers globally that mRNA vaccines like intismeran could work well in combination with immunotherapy for other cancers whose high rates of mutations have proven difficult to target,” said Mehnert.

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