A recent Analytical Chemistry article by scientists from Pfizer and Megadalton Solutions demonstrates how charge detection mass spectrometry (CDMS) helped to close this gap. The researchers directly measured intact mRNA constructs up to 4522 nucleotides, while simultaneously assessing sequence integrity, nucleotide modifications, poly(A) tail length, and the impact of lipid nanoparticle (LNP) formulation.

Why does this matter

As mRNA therapeutics expand beyond vaccines into gene editing, oncology, and other advanced modalities, developers need analytical tools that provide a more complete picture of the molecule itself.

This study shows that CDMS can bridge a long-standing gap by offering direct measurements of intact mRNA while simultaneously providing information on several critical quality attributes (CQAs). Rather than replacing established methods, CDMS complements them by providing something they cannot: a direct view of the whole therapeutic molecule.

The analytical challenge

Standard workflows digest mRNA into small fragments before MS analysis—useful for confirming sequence composition, but blind to whether the full-length transcript survived intact. That blind spot grows with size: many therapeutic mRNAs exceed 4000 nucleotides, and their highly charged phosphate backbones attract counterions that broaden signals and collapse the charge-state resolution that conventional MS depends on.

Native MS has been pushed to mRNAs around 800 nt, and single-ion Orbitrap methods to roughly 2100 nt, but overlapping charge states and adduct formation still obscure the low-abundance truncated species that quality assessment depends on finding.

What did CDMS find

CDMS overcomes these limitations by measuring both mass and charge for individual ions, eliminating the need for conventional charge-state resolution.  With this approach applied to four constructs, eGFP, luciferase, β-galactosidase, and Cas9 transcripts, the researchers showed that CDMS delivered:

• Confirm intact mRNA length and integrity, measuring constructs from 997 to 4522 nucleotides with approximately 0.5% uncertainty, while measured mass tracked nucleotide count with an R² of 0.99998.
• Verify chemical modifications, accurately detecting the mass shifts associated with methoxyuridine incorporation. For luciferase mRNA, an 8.3 kDa shift was observed, closely matching the 8.2 kDa theoretical value.
• Differentiate poly(A) tail lengths, resolving hemagglutinin mRNA constructs containing tails from 0 to 74 nucleotides with an uncertainty of approximately ±8 nucleotides, supported by orthogonal CE and LC-MS measurements.
• Assess formulation integrity, showing that mRNA extracted from LNPs was statistically indistinguishable from the original material, indicating that encapsulation did not measurably alter the mRNA cargo

A practical insight

The study also identified conditions that improved performance. Ammonium-based buffers consistently produced more accurate mass measurements than water alone, while higher-charge mRNA populations delivered the most reliable and highest-resolution results. These insights provide useful guidance for future nucleic acid analyses.

The key takeaway

A single CDMS workflow can confirm intact mRNA length, assess sequence integrity, verify nucleotide modifications, differentiate poly(A) tail lengths, and evaluate the impact of LNP formulation, all through direct measurement of the intact molecule.  

Read the full study, “Advancing Charge Detection Mass Spectrometry for Characterizing Large, Intact mRNA Constructs”, to discover how, with mRNA therapeutics continuing to grow in complexity, CDMS is emerging as a powerful addition to your analytical toolbox.

Bringing AI closer to the lab

Lab software supports highly specialized work, but routine tasks can still involve moving through multiple applications, menus, and setup steps. Creating acquisition methods, building sample lists, assigning instruments, submitting work, and retrieving results all require time and attention. For experienced users, that can mean repetitive administration, whereas for newer analysts it can create a steep learning curve and greater dependence on expert colleagues.

Waters AI Link introduces a more intuitive way to engage with these capabilities. Instead of requiring users to navigate every step manually, it makes supported waters_connect Software functions available through natural language. A scientist or analyst can describe what they want to accomplish, while the underlying tools translate that intent into defined actions within the platform.

What is Waters AI Link?

Waters AI Link is a Model Context Protocol (MCP) server that converts waters_connect Software  capabilities into governed AI tools. MCP is an open standard that enables AI assistants to connect with software capabilities in a consistent way. For Waters customers, this creates the foundation for conversational interaction with laboratory workflows without replacing the scientific applications, validated assets, or domain expertise on which laboratories rely.

Waters AI Link currently brings together capabilities supporting quantitative acquisition and processing workflows. These tools enable activities such as creating and editing sample lists, working with acquisition methods, assigning instruments, accessing analytical results, and generating reports. As coverage expands, the goal is a connected, end-to-end experience from method development through acquisition, processing, and result retrieval.

Designed to make everyday work simpler

The value of conversational AI is most tangible when it removes friction from real tasks. With Waters AI Link, a request that once necessitated a series of manual setup steps can now be expressed in a single conversation. Teams can more readily reuse existing methods and sample structures, helping them build on their validated work rather than starting again. Results and supporting information can also be presented more directly, reducing the time spent moving between screens to find the right data.

• For analysts: A simpler route through routine workflows.
• For scientists and method developers: Less time on repetitive setup and more time for scientific judgement.
• For laboratory managers: More consistent execution and reduced reliance on a small number of experienced users.

The result is not AI for its own sake, but a practical way to help laboratories work more efficiently and make established capabilities easier to access.

Governed AI for a scientific environment

In laboratory informatics, ease of use cannot come at the expense of control. Waters AI Link uses a governed approach:

• It does not give an AI model unrestricted access to the waters_connect Software.
• Each tool is explicitly defined and constrained.
• Every action remains subject to the platform’s authentication, permissions, and access controls.
• Users can only perform actions through AI that they are authorized to perform in waters_connect Software.

This governed approach distinguishes Waters AI Link from an uncontrolled agent. Natural language becomes an additional interface to trusted platform capabilities—not a shortcut around product ownership, validation, or laboratory policy. The domain applications remain in the authoritative environment for deep scientific work, while Waters AI Link provides a more accessible conversational entrance.

An open foundation that grows with the laboratory

By building on the open MCP standard, Waters is creating an extensible foundation rather than a point solution tied to a single AI client. MCP-compatible assistants can access published tools without a separate custom integration for every use case. A plugin model will allow additional waters_connect Software capabilities to be made available through the same governed mechanism, extending the potential reach of conversational AI across laboratory workflows over time.

Waters brings together analytical instruments, scientific data, and a connected informatics platform. Waters AI Link provides an additional layer that allows AI to interact with those capabilities in a controlled and useful way. By combining natural-language access, open architecture, and platform-level governance, Waters is helping lead the introduction of AI into lab software on terms that reflect the needs of modern science.

Waters AI Link is an important step towards the AI-enabled lab: one where routine complexity is reduced, trusted scientific assets are easier to reuse, and people can focus more of their time on insight and decision-making. As its tool library and workflow coverage continue to grow, the same foundation can support an expanding range of laboratory experiences, making AI a practical, governed part of everyday work.

Explore how waters_connect Software advances your science through connected and accessible data.

Introduction

Grain, feed ingredients, and finished feed are routinely tested for mycotoxins to support quality, compliance, and risk management decisions. Recent peer-reviewed research suggests that the question may no longer be whether mycotoxins are present, but whether testing programs capture the full range of contaminants present in a sample.

Many monitoring programs have historically focused on individual contaminants, such as aflatoxin, deoxynivalenol (DON), fumonisins, or zearalenone. While these toxins remain important, a growing body of literature indicates that frequently multiple mycotoxins occur together. Testing programs only report the mycotoxins measured, which may not provide the full picture. In other words, a result can be accurate and still be incomplete.

Across both grain and feed systems, multiple mycotoxins are increasingly understood to be the norm rather than the exception.

This has practical implications across the supply chain, where materials are evaluated at multiple points as they move from grain to finished feed. Decisions at intake, in the laboratory, and in formulation are based on the data available at that moment. When that data is limited in scope, the picture guiding those decisions may not always be complete.

Co-occurrence begins in grain

Large-scale survey data indicate that co-occurrence is common at the earliest stages of the supply chain. In a seven-year study of corn grain and corn silage in the United States, Weaver et al. (2021) reported that 90.2% of corn grain samples and 96.8% of corn silage samples contained two or more mycotoxins, with an average of 4.8 and 5.2 mycotoxins per sample, respectively.

Regional data reinforces these findings. Fusilier et al. (2022), analyzing maize grain harvested in Michigan, reported that every sample contained multiple mycotoxins, with at least four detected in one year and six in another, with several toxins frequently occurring together rather than in isolation.

These results indicate that co-occurrence is not an isolated condition. It is a consistent feature of grain entering feed and food supply chains. Identifying a single mycotoxin therefore does not necessarily characterize the contaminant profile of the sample.

Co-occurrence extends beyond harvest

Co-occurrence does not end at grain intake. It continues through feed ingredients and finished feed, where contamination may reflect a combination of field conditions, storage environments, and ingredient blending.

Pokoo-Aikins et al. (2026) evaluated poultry feed and feed ingredients across more than 570 samples and reported fumonisins in 86% of samples, DON in 45%, zearalenone in 36%, and aflatoxin in 10%. All corn and finished feed samples contained at least one mycotoxin, and more than half contained multiple contaminants.

Importantly, the study found that co-contamination patterns were consistent in both corn and finished feed. This indicates that mycotoxin risk is not confined to a single stage of the supply chain but persists throughout it.

When incomplete screening becomes visible

The limitations of single-analyte testing are not always apparent at the point of initial screening. In many cases, they become visible when materials are evaluated again in a different context.

A shipment may meet criteria for one mycotoxin while other contaminants remain unexamined, or an ingredient may be accepted for use based on partial information. In some cases, additional testing is triggered later or materials already in process are reconsidered as new information becomes available.

In practice, this often means that a result initially considered acceptable may need to be revisited when additional information becomes available.

In these situations, testing has not failed. It has provided a view aligned to the scope of the assay performed. For operations responsible for grain quality, formulation, and production, that distinction becomes operationally significant.

Operational impact and economic context

At the operational level, the effects of mycotoxin contamination are not abstract. They can appear as discounts, rejected loads, additional testing requirements, and adjustments to ingredient use or production plans.

Recent studies continue to demonstrate how frequently these decisions occur under real market conditions. For example, Postma et al. (2023) reported that only about 41% of harvested maize fell below the 20 μg/kg threshold, while 38% fell between 20 and 300 μg/kg—a range that may trigger discounting or rejection at the grain elevator.

The implication is straightforward. Incomplete visibility does not eliminate risk. It can shift when and where that risk becomes visible, often at the point where a decision must be confirmed, reconsidered, or delayed.

Expanding insight within operational constraints

The prevalence of co-occurrence is well established. The challenge for many organizations is how to respond without introducing additional operational burden.

Expanding monitoring scope can require additional extractions, increased testing time, and greater laboratory workload. For facilities managing high sample volumes, those constraints are significant.

As a result, the priority is not simply broader testing. It is broader understanding that remains practical within existing workflows. Approaches that allow multiple mycotoxins to be evaluated from a single extraction can expand visibility without proportionally increasing complexity.

A practical path forward

The question facing feed and grain operations is no longer whether multiple mycotoxins are present. The question is whether current testing approaches provide a complete enough picture to support confident decisions.

A result may be accurate for the analyte tested, but still incomplete in the context of co-occurrence. Bridging that gap requires approaches that align analytical capability with operational reality.

Expanding mycotoxin monitoring with Waters VICAM Solutions

In a supply chain where multiple mycotoxins are common, the most important risk may still be the one that hasn’t been measured. The evidence is clear: multiple mycotoxins are frequently present within the same sample. The remaining challenge is not awareness, but how to expand visibility without adding time, complexity, or additional testing steps.

Waters VICAM Myco 5-in-1 PLUS workflow, used with the Vertu Touch platform, enables the analysis of six mycotoxins from a single extraction. By consolidating multiple assays into one workflow, food safety and quality teams can reduce total testing time while maintaining precise, quantitative results.

Results are delivered in less than 10 minutes per assay, excluding sample preparation, allowing decisions to be made within the same workflow rather than across multiple testing steps.

The ability to evaluate multiple mycotoxins in a single method reflects a shift toward testing strategies that better align with the reality of co-occurrence.

Take the next step

Expanding the scope of mycotoxin testing does not require expanding complexity.

With Waters VICAM, one extraction enables a more complete mycotoxin profile without adding additional steps, time, or complexity.

• One extraction, six mycotoxins

• Reduced testing time by eliminating separate assays

• Quantitative results delivered rapidly within a single workflow

Multi-mycotoxin monitoring enables a more complete view of contamination risk while preserving the speed and efficiency required for everyday operations.

Visit VICAM.com or connect with a VICAM representative to learn more about Myco 5-in-1 PLUS.

Request a quote

References

1. Weaver, A.C., Weaver, D.M., Adams, N., & Yiannikouris, A. (2021). Co-Occurrence of 35 Mycotoxins: A Seven-Year Survey of Corn Grain and Corn Silage in the United States. Toxins, 13(8), 516.

2. Fusilier, K., Chilvers, M.I., Limay-Rios, V., & Singh, M.P. (2022). Mycotoxin Co-Occurrence in Michigan Harvested Maize Grain. Toxins, 14(7), 431.

3. Pokoo-Aikins, A., Mitchell, T.R., Read, Q.D., Shanmugasundaram, R., & Glenn, A.E. (2026). Mycotoxin Occurrence in Poultry Feed and Feed Ingredients. Journal of Applied Poultry Research.

4. Postma, K., et al. (2023). Economic benefit of co-regulation to manage aflatoxin risk in maize. Heliyon.

PROTACs combine two binding elements with a linker to selectively target proteins for degradation. Their larger size, flexibility, and multiple functional groups can create new analytical considerations compared to more conventional small molecules.

When developing methods, the question isn’t whether liquid chromatography still works. It does. The question you should ask is how to approach method development when the molecule itself is more complex.

Complexity changes the method development challenge

A PROTAC can behave differently from a traditional small molecule in ways that are not always predictable from molecular weight alone. Retention, selectivity, peak shape, recovery, and reproducibility become even more critical to monitor.

This is particularly relevant as the pharmaceutical industry explores new therapeutic modalities and is working to establish reliable analytical workflows for development, stability testing, and quality control.

For analytical scientists, that can mean moving beyond incremental changes to a single chromatographic condition.

A systematic approach can help identify what works rather than relying on what you expect to work.

A case study in systematic method development

Our recently published application note on PROTAC ARV-825 provides an example with the method developed in just over two days.

ARV-825 is a heterobifunctional molecule containing pomalidomide and birabresib connected by a polyethylene glycol linker. To evaluate its stability, the compound was subjected to forced degradation conditions, generating additional components that needed to be separated and characterized.

Rather than optimizing one chromatographic condition through trial and error, the study applied a systematic approach.

The process began by evaluating retention at different pH conditions. Low-pH conditions provided better retention of lower-level degradants, while the primary ARV-825 peak was relatively unaffected. This information dictated the mobile-phase conditions for the next stage of screening, low-pH mobile phase additives.

The next step was to deliberately introduce chromatographic selectivity by screening multiple stationary phases and mobile-phase combinations.

This distinction is important. Changing the gradient changes how compounds move through the column. Changing the stationary phase can change how they interact with the column in the first place.

For complex molecules and unknown degradation products, that difference can be critical.

Look beyond the chromatogram

The ARV-825 work also highlights why LC-MS can be valuable during method development.

When degradation products are not available as individual standards, UV detection alone may not provide enough information to confidently track what is being separated. In the study, mass detection was used to help identify and track components by their mass, providing additional information during the separation process.

The study also evaluated against standard stainless-steel hardware using the same stationary phase. Differences in retention and selectivity were observed for some degradants, demonstrating that interactions with metal surfaces can become another variable in the chromatographic equation. The MaxPeak Premier Columns also demonstrated improved peak-area reproducibility in the study.

For complex molecules, controlling variables early in method development can help reduce the risk of having to revisit the method later.

A more deliberate path to robust methods

The ARV-825 example demonstrates a broader principle for analytical scientists working with emerging drug modalities.

When the molecule is different, the method-development strategy may need to be different too.

A systematic workflow can provide a logical path from retention and pH screening to stationary-phase selectivity to optimization and detection, while creating a more traceable basis for the final method.

In the ARV-825 study, the combination of systematic screening and MaxPeak Premier Columns produced baseline resolution of the detected degradants and the main ARV-825 peak, with the method developed in just over two days.

As drug discovery continues to expand into more complex molecular architectures, analytical workflows will need to evolve alongside them. The goal is not to make chromatography more complicated. It is to make method development more deliberate, reproducible, and efficient.

For scientists characterizing PROTACs and other emerging small-molecule modalities, the right combination of systematic screening, chromatographic selectivity, surface control, and complementary detection can help turn molecular complexity into a manageable analytical challenge.

Ultimately, better analytical methods help provide the reliable information needed to advance new therapies and, in turn, accelerate the benefits of pioneering science to improve human health.

Learn more about how MaxPeak Premier Solutions can enhance your PROTACs analysis.

Why does this matter

CDMS is uniquely suited for analyzing very large and heterogeneous biomolecules because it measures both the mass-to-charge ratio (m/z) and the charge (z) of individual ions. Until now, however, high-performance ELIT-CDMS systems have largely been limited to specialized academic laboratories. This new benchtop platform makes high-resolution CDMS more accessible by combining advanced performance with a compact, user-friendly design.

The challenge with large and heterogeneous biomolecules

Traditional native MS works well for many proteins, but it becomes increasingly difficult to analyze very large and heterogeneous particles.

As molecular size increases, charge-state distributions overlap, making accurate mass determination challenging. This challenge is especially important for AAV gene therapy vectors, virus-like particles (VLPs), lipid nanoparticles (LNPs), vaccines, heavily glycosylated proteins, and large protein assemblies.

Alternative techniques, such as analytical ultracentrifugation (AUC), size-exclusion chromatography with multi-angle light scattering (SEC-MALS), and mass photometry, can provide useful information, but often lack the resolution, throughput, or particle-level detail needed for these complex samples.

How CDMS makes a difference

Unlike conventional mass spectrometry, CDMS directly measures individual ions and determines both their charge and m/z value. This allows the mass of each particle to be calculated directly, overcoming the limitations caused by overlapping charge states.

The new ELIT-CDMS platform offers: Ion optics optimized to cover a broad m/z range (up to m/z 250,000), detection of ions carrying up to ~3000 charges, mass measurements extending into the hundreds of megadaltons, real-time data processing and visualization, and high-resolution measurements enabled by charge quantization.

Improved charge precision delivers higher resolution

A key innovation highlighted in the study is the instrument’s ability to achieve highly precise charge measurements. By improving charge precision, the system can accurately assign individual charge states, significantly increasing mass resolution. The researchers demonstrated mass resolutions of up to approximately 160, representing the highest mass resolution reported for a commercially available CDMS platform.

Strong performance for AAV characterization

A major focus of the study was AAV analysis, a critical application in gene therapy development. The platform successfully distinguished empty, full, and overfilled AAV capsids, quantified empty-to-full ratios, resolved partially filled particle populations, analyzed multiple AAV serotypes, and delivered highly reproducible results across repeated measurements

The instrument also showed strong quantitative performance and demonstrated approximately 20-fold higher sensitivity when operating in Target Enhancement Mode (TEM), making it suitable for lower-concentration samples.

Beyond gene therapy: Vaccines and VLPs

The authors also demonstrated the platform’s capabilities using chikungunya and dengue virus-like particles. CDMS revealed intact particles, degradation products, and aggregates across a wide mass range. For chikungunya VLPs, particles with masses up to approximately 350 MDa were observed.

For dengue VLPs, researchers used controlled heating experiments to investigate particle dissociation and stability, gaining structural insights that would be difficult to obtain using conventional approaches.

Looking ahead

As advanced therapeutics continue to grow in size and complexity, analytical technologies must evolve as well. This study demonstrates how commercial benchtop ELIT-CDMS can provide:

• Direct measurement of heterogeneous megadalton assemblies
• High-confidence AAV characterization
• Structural insights for VLPs and vaccines
• Broad mass range coverage
• Ease of use for both industry and academic laboratories

The introduction of a commercial benchtop CDMS platform represents an important milestone for the field. By making high-resolution analysis of very large biomolecules more accessible, CDMS is poised to become an increasingly valuable tool for gene therapy, vaccine development, nanoparticle characterization, and other emerging biopharmaceutical applications.

Read the full paper, “A Charge Detection Mass Spectrometer for the Analysis for Megadalton-sized Molecules”, and see how a new benchtop ELIT-CDMS platform is helping researchers overcome the challenges of characterizing the world’s largest and most heterogeneous biomolecular assemblies.

A recent bioRxiv paper by Dr. Charlotte Uetrecht and colleagues at the Centre for Structural Systems Biology (CSSB) in Hamburg, Germany, in collaboration with Waters scientists, demonstrates how charge detection mass spectrometry (CDMS) is pivotal for uncovering a non-classical capsid assembly invisible to any other method.

In the study, CDMS was used to analyze two human norovirus strains, with cryo-EM and bottom-up proteomics providing orthogonal validation. 

The core problem: Heterogeneity defeats conventional MS

Norovirus VLPs can form T=1, T=3, T=4, and other geometries. Combined with N-terminal processing of the VP1 capsid protein, this creates overlapping mass distributions that conventional native MS cannot resolve—coexisting particle populations, including non-icosahedral species, are entirely obscured.

How CDMS solves the problem

CDMS determined the mass and charge of individual ions simultaneously, by passing the need for charge state resolution.  The authors compared results obtained from the electrostatic linear ion trip (ELIT)-based Xevo CDMS and the Orbitrap-based Direct Mass Technology (DMT) and reported that:

• Both platforms identified the same major assemblies in GI.1 Norwalk and GII.17 Kawasaki VLPs
• Waters Xevo CDMS produced masses closer to theoretical values, attributed to its dual m/z and charge calibration that accounts for adduct mass
• DMT combined with UniDec deconvolution (UCD) achieved higher apparent mass resolution, at the cost of greater workflow complexity and risk of deconvolution artifacts

CDMS and cryo-EM jointly reveal a Novel GII.17 capsid

Combining CDMS and cryo-EM enabled discovery and structural validation of a novel viral assembly state that would have been difficult to identify with either technique alone.

• CDMS detected three distinct capsid populations in GII.17 Kawasaki: classical T=3 (~10.65 MDa), T=4 (~14.25 MDa), and an unexpected intermediate species (~12.58 MDa).
• Mass analysis estimated ~212 VP1 subunits for the intermediate particle, suggesting a non-icosahedral assembly inconsistent with traditional Caspar-Klug symmetry.
• cryo-EM confirmed the structural identity of this species as a prolate (oval-shaped) capsid and yielded the first images of GII.17 Kawasaki T=3, T=4, and prolate assemblies.

Instrument comparison guides future strategy

Stepwise inlet heating in the Xevo CDMS reduced measured mass toward theoretical values—a practical tool for separating true particle mass from adduct contributions.

Key takeaways:

• Xevo CDMS offers a streamlined workflow with live data visualization and mass accuracy within ±1%
• DMT plus UCD deconvolution reduces peak width by ~50%, but requires careful application to avoid artifacts
• Analyte-matched calibration standards are critical—both platforms showed mass deviations linked to calibrant size mismatch

Read the full paper, “Applying distinct CDMS strategies to observe non-classical virus capsid assembly”, to see how two CDMS platforms together reveal the full complexity of norovirus VLP populations.

In a research environment, that compromise may be tolerable. In the clinical laboratory, it’s not.

Today’s diagnostic laboratories face relentless pressure: increasing sample volumes, faster turnaround times, strict regulatory oversight, and uncompromising expectations of analytical accuracy. In this context, the question is no longer whether high analytical sensitivity can be achieved, but whether it can be delivered consistently, across thousands of patient samples, by multiple users, day after day.

This is where platforms like the Xevo TQ Absolute XR IVD Mass Spectrometer redefine expectations.

Analytical Sensitivity is only useful if it lasts

Clinical LC–MS/MS has rapidly expanded over the last decade. It is now embedded in routine diagnostic laboratories, supporting endocrine testing, therapeutic drug monitoring, toxicology, and vitamin analysis. Sensitivity underpins these applications, enabling confident quantification at clinically relevant concentrations, often near   decision points, and supporting increasingly small sample volumes.

In routine diagnostic laboratories, downtime is one of the costliest failures of an MS system. Whether it manifests as falling analytical sensitivity, failed QC, or unscheduled maintenance, the root cause of the issue is often the same: contamination of critical ion optics by real clinical samples. Clinical laboratories therefore need more than impressive detection limits; they need sustained analytical sensitivity that survives real‑world operating conditions.

The Xevo TQ Absolute XR IVD Mass Spectrometer has been designed with this requirement in mind. Its long‑term robustness and signal stability take priority alongside analytical performance.

Capturing ions, not contamination

At the heart of this balance between analytical sensitivity and robustness is the StepWave XR Ion Guide.

Clinical samples are complex by nature. Even with good sample preparation, they contain large amounts of:

• Neutral species (salts, solvents, or matrix components)
• Phospholipids and other endogenous contaminants
• Chemical noise with no analytical value

In conventional ion guides, both ions of interest and neutral species are transmitted toward the mass analyzer. Over time, the neutrals deposit on lenses, quadrupoles, and detectors, leading to gradual signal loss, increased chemical noise, more frequent cleaning and recalibration, and, ultimately, unplanned instrument downtime.

By using a dual‑stage ion guide design, the StepWave XR Ion Guide selectively captures and focuses analyte ions while efficiently diverting neutral species away from the mass analyzer.  This results in up to six times more robustness,¹ as fewer contaminants reach critical components.

The result is not just strong initial performance, but analytical sensitivity that remains stable over long analytical sequences and extended operational periods.

Robustness and reliability: The metrics that matter in clinical diagnostics

In clinical laboratories, robustness and reliability are essential factors in delivering on‑time results, reduced troubleshooting, and predictable workflows.

The combination of the StepWave XR Ion Guide with the compact, integrated design of the Xevo TQ Absolute XR IVD Mass Spectrometer directly addresses these demands. Because fewer neutral contaminants reach the ion optics, laboratories can benefit from:

• Longer intervals between source and ion optics cleaning
• Reliable quantitative performance over time
• Reduced frequency of performance troubleshooting

Predictable performance reduces method drift, simplifies quality assurance, and increases confidence in reported results.

By maintaining stable sensitivity with minimal operator intervention, laboratories can focus less on instrument management and more on clinical outcomes. This is particularly important as MS continues to expand beyond specialist centers into broader diagnostic networks.

Designed for real clinical workflows

Clinical laboratories rarely operate under ideal conditions. Sample quality varies, workloads fluctuate, and staff experience levels differ. Systems designed primarily for expert users or tightly controlled research workflows often struggle when exposed to this reality.

The Xevo TQ Absolute XR IVD Mass Spectrometer is designed for routine clinical use. Features, such as the StepWave XR Ion Guide, are not about peak performance in isolation, but about ensuring consistent, dependable results across the full spectrum of everyday laboratory challenges.

Analytical sensitivity you can rely on

In routine diagnostics, the most valuable instrument is not the one that delivers the strongest signal on day one—it is the one that delivers the right result, repeatedly, over months and years of operation.

The Xevo TQ Absolute XR IVD Mass Spectrometer, powered by the StepWave XR Ion Guide, reflects a shift in how clinical MS is engineered, moving away from short‑term optimization toward sustained, reliable performance. It shows that high sensitivity and robustness are no longer competing priorities, but complementary ones.

So, is high sensitivity and robustness in mass spectrometry a myth?

The reality for modern clinical diagnostics is that you can now have both.

Learn more about the Xevo TQ Absolute XR IVD Mass Spectrometer.

References

1. Robustness in Regulated Bioanalysis: A 30,000 Injection Study of Naltrexone in Human Plasma using the Xevo TQ Absolute XR Mass Spectrometer

Written by Tracy Hibbs and Tony Sacchetti, Waters Corporation

Artificial intelligence (AI) is poised to take routine work and shift it to fully automated workflows. Currently, 80% of AI projects fail.¹ As with most paradigm-shifting technology, it will require time, patience, and strong data governance to realize the potential gains. It requires deep expertise to ensure the AI performs as expected for your intended use and vigilant, continuous oversight to reduce the potential for risks to be realized.

Failure is not an option

With approximately 95% of pharmaceutical companies investing in AI,² it is clear hopes are high that it will accelerate drug development, secure supply chains, increase operational resilience, streamline audit predictability, and reduce time and cost to patients. At the same time, regulatory agencies are investing to increase the speed of filing response, identify potential supply chain issues and trends, and pinpoint where to focus inspections. Innovation and regulation are happening in parallel, increasing the criticality of getting it right, or risk falling behind.

For a closer look at the regulatory landscape and at inspection readiness specifically, see our recent post, “Is Your Organization Ready for AI Inspection?”

“In pharma, 75 to 85 percent of workflows contain tasks that could be enhanced or automated by agents, potentially freeing up 25 to 40 percent of an organization’s capacity.” ³

Considering the extensive investments that have been made, organizations are still experiencing a lack of ROI. Whether it is efforts to build homegrown AI with existing resources, gaps in needed expertise, siloed and disparate activities, or data governance issues impacting success is to be determined, and in many cases, it is likely combinations of challenging dynamics.

From a practical standpoint, there are critical areas, such as AI validation and GMP compliance, where things can break down:

You cannot AI your way out of bad data

Data integrity is not optional and ALCOA++ is essentially the regulatory floor. Any potential data integrity issues that exist in your input data will persist in your AI—it inherits and amplifies your data. You may miss issues if your approach is black box and you lack a clear understanding of how the model you use works and where it fails. Your data governance and validation practices must be in a continuous state, especially if you are attempting to adopt adaptive AI.

These fundamentals must be working in concert to truly realize the potential of AI.

If you consider agentic AI, for example, it is not just AI. It is AI that plans, reasons, and executes multi-step actions toward a goal—language that mirrors the FDA’s own working definition of agentic AI.⁴ It is not generating one single, simple prediction, it is making a sequence of decisions, often with autonomous correction. Think of an AI that does more than just flag an OOS result, it autonomously launches the investigation, retrieves prior batch records, drafts the deviation report, and routes it for approval.

Your AI vendor choice becomes your AI governance choice

How will you ensure your AI consistently produces reliable outcomes? If you are building solutions for your organization, your custom code carries a high validation burden compared to partnering with an organization that has intentionally designed and built solutions for high-value use cases. You also gain the burden of building and maintaining all infrastructure required to sustain the custom code you developed.

AI accelerates transformation at warp speed, but only mature organizations can safely control and sustain that speed. As your organization looks to accelerate, your vendor selection becomes a critical piece that is essentially a regulatory decision.

Things to consider:

• Choose vendors for governance posture, not merely feature breadth.
• Look to get it right the first time over getting there first.
• Execution that increases confidence is as critical as innovation.

The right vendor will ensure there is AI transparency, with documented inputs, outputs, decision boundaries, and limitations. Alongside this, vendor assessments against regulations, data flow and process flow maps, and risk assessments are imperative to streamline your compliance efforts and support your quality risk management. It is critical to have a clear understanding for your quality unit (QU) and frameworks that provide explicit information for human oversight, locations of where the human can be put into the loop, and authorization.

Getting it right means continuous, embedded validation with performance monitoring, drift detection, and change control embedded in the same platform of capabilities versus an external bolt-on or afterthought.

Is your organization ready to leap ahead?

Connect with a Waters expert. Bring your gaps and we will bring the compliance expertise needed. Together, we will keep your data inspection ready.

References

1. Rand^®, Gartner^®, Stephen Ferrell – CPO Valkit.ai, ISPE^® GAMP^®
2. Mordor Intelligence (2025)
3. Reimagining life science enterprises with agentic AI, McKinsey (2025)
4. FDA Expands Artificial Intelligence Capabilities with Agentic AI Deployment (December 2025)

Written by Tracy Hibbs and Tony Sacchetti, Waters Corporation

Exploring artificial intelligence (AI) and machine learning (ML) in regulated laboratories governed by Current Good Manufacturing Practices (CGMP) may be new for your organization. Inspecting AI is not new to regulators. If we look at FDA, for example, use of AI/ML-enabled medical device authorizations have grown sharply since 2018, as shown in the chart below.^1,2

Regulatory expectations: The fundamentals remain

As an industry, we have discussed AI regulation for years, while regulators have moved quickly to address rapid technological change. The fundamentals remain unchanged: data integrity, data governance, and risk management continue to underpin AI oversight, and inspectors are placing even greater scrutiny on these core principles.

The foundational guidance used daily and incorporated into your Pharmaceutical Quality System (PQS) remains unchanged. ICH Q9(R1) Quality Risk Management³ and PIC/S Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments⁴ continue to anchor responsible use. Newer papers and draft guidance extend that foundation specifically to AI, including EMA Annex 22⁵ and the FDA and EMA jointly issued Guiding Principles of Good AI Practice in Drug Development.⁶

Between them, there is more than enough guidance to inform a responsible AI adoption strategy. For a wider view of the regulatory landscape, read our blog, “AI in GMP: Navigating Evolving Expectations for Regulated Labs.”

Enforcement is not theoretical

Inspections and authorizations to date have leveraged existing regulations and finalized guidance. Regulatory actions have leveraged the same. The FDA published multiple warning letters referencing AI from 2023 to 2026, and an April 2026 CGMP warning letter was the first to cite inappropriate use of AI as a stand-alone CGMP deficiency.⁷

Citing 21 CFR 211.22(c) ⁸ and the responsibilities of the quality control unit, the warning letter message is direct: if AI is used to support CGMP activities, recommendations and outputs from AI agents must be reviewed and cleared by an authorized human representative of your quality unit (QU).

Quality unit responsibility is not reduced when AI is involved

Accountability remains unchanged. Leveraging AI assistance does not reduce the obligation of your qualified personnel. It is critical that your QU knows where AI is leveraged across your organization. If this visibility does not exist today, building it should be an immediate priority.

Your supply chain partners are also in scope. Contract development and manufacturing organizations (CDMOs) and contract testing labs operate as extensions of the manufacturer, and inspectors continue to treat them that way. If a contract organization uses AI to draft batch records, specifications, or SOPs without governance, the risk transfers to the sponsor. Quality agreements and supplier audits now need to address AI use explicitly: permitted applications, human oversight requirements, audit rights, and evidence expectations.

Your AI is rated whether you have formally rated it or not

The draft FDA⁹ framework provides a practical way to assess regulatory burden. The complexity of the AI system and its impact on product quality define the category, the intersection signals the oversight required. This principle is consistent across guidance: oversight must be commensurate with risk to product quality and patient safety.

Looking at risk categories, expectations vary:

An unclassified AI system is not an unregulated one. Inspectors will assign a category whether your organization has or not, and they will expect controls and evidence to match.

There are expectations in your inspections

If an inspector were to audit you tomorrow, could you answer each of these five questions with confidence and documented evidence?

1. Where is AI used in your CGMP/GxP workflows today, and at what risk tier?
2. Who reviews and approves AI-generated outputs, and what is the documented evidence?
3. How is your training data separated from your validation and test data?
4. How do you detect data drift and model performance degradation in production?
5. What is your AI literacy plan and your agentic AI governance for staff?

Where does a state of control break down?

Demonstrating a state of control is essential in regulated labs. With AI in use, the demonstration could potentially falter in several predictable ways. Each of the patterns below maps to one of the five inspector questions above:

The time to be ready is now

Now is the moment to assess your readiness for AI adoption under evolving global regulations. Whether you are exploring AI for the first time or scaling existing capabilities, alignment with regulatory expectations unlocks AI’s full potential, without compromising product quality or patient safety.

Connect with one of our experts to help assess your readiness for AI and for AI inspections.

References

1. Innolitics 510(k) Year-in-Review (2025)
2. Imaging Wire · FDA AI/ML Device list (December 2025)
3. ICH Q9(R1) Quality Risk Management
4. PIC/S PI 041-1, Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments
5. EMA Annex 22 (Draft 2025)
6. FDA and EMA Guiding Principles of Good AI Practice in Drug Development (January 2026)
7. FDA Warning Letter 722591 (April 2026)
8. United States Code of Federal Regulations 21 CFR 211.22
9. FDA Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, AI/ML Risk Classification Framework (Draft January 2025)

Additional Resources

• ISPE^® GAMP^® Guide: Artificial Intelligence (July 2025)
• European Union, AI Act (Regulation (EU) 2024/1689), with Article 4 AI literacy obligation effective August 2, 2026

Written by Tracy Hibbs and Tony Sacchetti, Waters Corporation

Regulators are already inspecting AI use, how prepared is your organization?

For years, the pharmaceutical industry has anticipated regulatory clarity on the use of artificial intelligence (AI) and machine learning (ML) in regulated labs. This clarity has emerged, not only through guidance, but also through enforcement. For quality control (QC) labs, this moment signals both opportunity and accountability. AI can deliver speed, consistency, and efficiency, but only with strong AI governance and when deployed under conditions that preserve transparency, traceability, and scientific integrity.

Foundations: The regulatory path that got us here

In 2021, the Danish Medicines Agency (DKMA) became one of the first European regulators to address AI in pharmaceutical quality systems when they took a bold step toward defining the role of AI in regulated environments by publishing a draft guidance entitled “Suggested Criteria for Using AI/ML Algorithms in GxP.”¹

Their work informed reflection papers and draft guidance from the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA), all formally recognizing AI and ML as viable tools in regulated pharmaceutical operations, including QC. These documents do more than permit AI—they define the conditions under which it can be trusted.

2021 DKMA Suggested Criteria for Using AI/ML Algorithms in GxP emphasizes:

• Use of static, supervised models for critical functions
• Independent test data and bias mitigation
• Strong data integrity and validation practices

2023 EMA Reflection Paper on the Use of Artificial Intelligence (AI) in the Medicinal Product Lifecycle² emphasizes:

• Transparency and explainability
• Risk-based validation
• Human oversight

2025 EMA with Pharmaceutical Inspection Convention and Pharmaceutical Inspection Co-operation Scheme (PIC/S) Draft Guidance Annex 22: Artificial Intelligence³ explicitly excludes adaptive or generative AI from critical GMP applications and mandates:

• Static, deterministic models only
• Locked training data and independent test sets
• Explainability tools (e.g., SHAP, LIME) and confidence scoring
• Defined operator roles in human-in-the-loop (HITL) systems

2025 FDA Draft Guidance Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products⁴ introduces a risk-based credibility assessment framework, focused on:

• Model transparency and traceability
• Data governance and independence of training/test sets
• Lifecycle management of AI models
• Context-of-use validation

Ten principles, two regulators, and one path

The FDA and EMA jointly published Guiding Principles for Good AI Practice in Drug Development.⁵ The synchronized framework is significant, indicating a renewed alignment between two influential regulatory agencies and signaling that organizations operating across jurisdictions can plan against a stable target.

The joint announcement highlights 10 critical areas:

Apply critical thinking

Core skills developed across your organization, from risk management to data governance and vigilant monitoring, become more critical when leveraging AI. Your organization remains accountable for your quality.

• AI reliability: Data quality is foundational, requiring strict data segregation, traceability, and version control. Lifecycle management is a cornerstone, as are data integrity and bias mitigation.
• AI successful implementation: Collaboration is an imperative requiring cross-functional teams including your analysts, QA, IT, AI ethics team, data integrity officer, and regulatory affairs team.
• AI decision impact: Understanding the criticality of the decision is not optional. Risk assessment good practices and data flow and workflow maps identifying your intended use underpin your implementation.
• AI oversight: Knowing the number of decisions being made and applying vigilant monitoring at each decision point are fundamental.

Why this matters: The risk to product quality and patient safety

The stakes in pharmaceutical QC are uniquely high. Every decision in QC labs has downstream implications for product quality, compliance, and, ultimately, patient safety. AI systems that are opaque, unvalidated, or poorly governed have the potential to introduce detrimental risks, including:

• Undetected anomalies in chromatographic data could allow substandard or contaminated products to reach patients.
• Adaptive models that change behavior over time may produce inconsistent results, undermining batch release decisions.
• Lack of explainability can obscure the rationale behind critical decisions, making root cause analysis and regulatory inspections more difficult.
• Insufficient validation or biased training data can lead to systematic errors, disproportionately affecting certain product types or patient populations.

This is why regulators are drawing a clear line. AI must not compromise the scientific rigor, traceability, or reproducibility that underpin GMP. Instead, it must enhance them.

What does this mean for QC labs?

For QC labs, this is a pivotal moment. AI/ML can now be deployed to:

But adoption must be deliberate. Labs and quality teams must ensure:

• Models are locked and validated before use.
• Outputs are interpretable and reviewable.
• Human reviewers remain accountable and trained.
• Strong quality oversight persists.

This is not about replacing analysts. It’s about amplifying your deep expertise and focusing attention where it matters most.

Looking ahead: The message is straightforward

Regulatory direction is clear: AI use in GMP areas is permitted, provided it operates within a framework of transparency, reproducibility, and oversight. As regulators continue to refine expectations, particularly around validation standards, model updates, and governance of agentic systems along with real-time monitoring, QC labs that invest now in compliance-enabled AI will be better positioned for the future.

Is your lab ready for the AI era?

Connect with our experts to explore how Waters is enabling labs to take the next step toward intelligent, always audit-ready systems.

References

1. Danish Medicines Agency (DKMA), Suggested Criteria for Using AI/ML Algorithms in GxP (2021).
2. EMA Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle (Draft 2023, Final 2024).
3. EMA GMP Annex 22 (Draft 2025).
4. FDA Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (Draft January 2025).
5. FDA and EMA Guiding Principles of Good AI Practice in Drug Development (January 2026).

Additional Resources

• ISPE® GAMP® Guide: Artificial Intelligence (July 2025)
• FDA Computer Software Assurance (CSA) Guidance (February 2026)
• ICH Q9(R1) Quality Risk Management
• PIC/S PI 041-1, Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments