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

While investment decisions often focus on mass spectrometry (MS) detection, experience across clinical laboratories shows that chromatography is frequently the limiting factor, both scientifically and financially.

When chromatographic performance is unstable, the cost is not confined to poorer data quality. It appears as lost productivity, reanalysis, delayed reporting, and increased operational risk. In this environment, Ultra High Performance Liquid Chromatography (UHPLC) technology is essential for protecting return on investment (ROI) in clinical LC‑MS workflows.

The hidden financial cost of unstable chromatography

In routine clinical laboratories, chromatography must perform reliably across:

• Hundreds to thousands of samples per day
• Complex and variable biological matrices
• Multiple operators and long assay lifetimes
• Regulated and audited workflows

When it does not, the consequences are immediate and costly. Retention time instability and poor chromatographic reproducibility lead to:

• Wider MS acquisition windows
• Reduced dwell time and fewer data points per peak
• Increased risk of missed detections in large analyte panels
• Higher rates of manual data review, reruns, and batch failures

Each of these outcomes consumes analyst time, instrument capacity, reagents, and consumables; eroding throughput and increasing cost per result. Over time, these inefficiencies quietly undermine the financial viability of LC‑MS testing.

Retention time stability drives both performance and productivity

Retention time stability is often discussed as a technical metric, but in clinical LC‑MS it has direct economic implications. Stable retention times allow laboratories to:

• Use narrow, confident MS acquisition windows
• Increase dwell time per transition
• Generate more robust quantitative data per injection
• Reduce uncertainty that forces manual review

When retention times drift, acquisition windows are often widened “just in case.” This precaution reduces MS efficiency and sensitivity, especially for low‑abundance analytes, increasing the likelihood of reanalysis or inconclusive results.

In high‑throughput environments, retention time variability is one of the fastest ways to increase your cost per sample while reducing throughput.

UHPLC as a cost‑containment and risk‑reduction strategy

For clinical laboratories, UHPLC is not simply about achieving better separation—it is about controlling operational and financial risk. Robust, stable UHPLC reduces:

• Missed detections that require repeat analysis
• Batch failures triggered by chromatographic drift
• Analyst time spent on troubleshooting and manual review
• Variability that complicates trending and audit investigations

By stabilizing chromatography, laboratories stabilize workflow efficiency, instrument utilization, and reporting timelines—directly contributing to improved ROI over the assay lifecycle.

ACQUITY UPLC I‑Class PLUS System: Designed for long‑term ROI in clinical labs

The Waters ACQUITY UPLC I‑Class PLUS System was developed with decades of expertise. Rather than prioritizing maximum pressure or speed alone, the system is engineered to deliver:

• Highly stable solvent delivery for consistent retention times
• Precision injection supporting reproducible LC‑MS acquisition
• Robust operation suitable for continuous, high‑throughput use
• Design principles aligned with regulated clinical environments

The result is not simply higher performance, but fewer workflow interruptions, lower rework rates, and more predictable assay behaviour over time.

The takeaway

As clinical LC‑MS testing expands, laboratories must evaluate technology choices not only on analytical capability, but on total operational impact.

Unstable chromatography increases cost per sample, consumes valuable staff time, and introduces unnecessary risk. Stable, clinically optimized UPLC enables higher throughput, consistent performance, and predictable outcomes across the life of the assay.

With systems such as the ACQUITY UPLC I‑Class PLUS System, UHPLC helps ensure that chromatography strengthens the return on investment in modern clinical LC‑MS.

Explore how UPLC can reduce risk and increase efficiency in your clinical lab.

Chromatography is often the root cause of the variability observed at the mass spectrometer.

Today’s MS platforms are technologically mature, electrically stable, and highly reproducible over time. When signal changes occur, the detector is usually doing exactly what it was designed to do:  reporting the conditions under which analytes entered the ion source.

Those conditions are set upstream by chromatography.

Chromatography determines what is delivered to the MS

Before an analyte ever reaches the mass spectrometer, chromatography determines when it arrives, how concentrated it is at any given moment, and which other components arrive alongside it. Retention time, peak width, peak shape, and co‑elution patterns all originate in the LC system.

Any variability in these parameters is immediately translated into variability at the MS. From the detector’s perspective, changing chromatography appears as changing signal response, even if the MS itself is perfectly stable. This is why apparent “MS variability” so often persists despite tuning, cleaning, or servicing the mass spectrometer.

How chromatographic instability manifests as MS variability

One of the most common contributors is retention time instability. When retention times shift, acquisition windows must be widened to avoid missed detections. Wider windows reduce dwell time per transition, decrease the number of data points across the peak, and increase exposure to interfering signals. The result is poorer precision and less consistent quantitation.

Peak broadening, often caused by extra‑column dispersion, has a similar impact. When an analyte band is spread out after leaving the column, the same amount of analyte is delivered to the ion source over a longer period. Peak height drops, signal‑to‑noise suffers, and integration becomes less consistent. Again, the MS registers variability that originated from the chromatography.

Matrix co‑elution further compounds the problem. Inadequate chromatographic separation allows more background components into the ion source, increasing ion suppression or enhancement. These matrix‑driven effects are highly variable and can make MS response appear unstable, even though the underlying cause is insufficient chromatographic control.

Why does this matter more in clinical laboratories

Clinical LC‑MS workflows amplify the impact of chromatographic variability. High‑throughput operation, large multi‑analyte panels, complex biological matrices, and long assay lifetimes leave little tolerance for drift or inconsistency.

Small chromatographic changes that might be manageable in research settings become serious operational issues in clinical labs. Over time, they lead to increased manual data review, more frequent reruns or failed batches, difficulty trending system suitability metrics, and reduced confidence during audits and inspections.

Crucially, the mass spectrometer cannot correct for unstable chromatography. It can only report the consequences.

Stabilizing chromatography stabilizes the MS

When chromatography is stable, MS analytical variability decreases.

Stable retention times allow narrow, confident acquisition windows. Consistent peak shapes ensure reproducible ionization conditions. Reduced dispersion preserves peak height and signal quality. Together, these factors lead to more reproducible MS signals, improved quantitative precision, and more reliable detection of low‑level analytes.

In many cases, improving chromatographic stability is the most effective and sustainable way to improve overall LC‑MS performance.

The Role of UHPLC and Low‑Dispersion System Design

Modern UHPLC systems designed for clinical use address key chromatographic drivers of MS variability by delivering high efficiency alongside long‑term reproducibility.

Low‑dispersion system designs are particularly important, as they preserve the narrow peaks generated by UHPLC columns and maintain predictable retention time behavior.

By minimizing variability in analyte delivery to the ion source, these systems reduce apparent MS variability at its source.

The Waters ACQUITY UPLC I‑Class PLUS IVD System reflect this philosophy. The system design prioritizes stable solvent delivery, precision injection, ultra‑low dispersion, and consistent chromatographic behaviour over extended routine operation—attributes that directly translate into more reproducible MS data in clinical workflows.

The takeaway

When LC‑MS variability appears, it is tempting to look downstream. But in clinical workflows, the mass spectrometer is often responding correctly to unstable conditions created upstream.

Chromatography determines when, how, and under what conditions analytes reach the MS. If chromatography varies, then MS data will vary with it.

For clinical laboratories seeking reliable, scalable, and defensible LC‑MS, the most effective place to reduce variability is not the detector, but the chromatography that feeds it.

Learn more about the impact of UPLC on clinical analysis.

In pharmaceutical quality control (QC), consistency should mean more than an analyst ending a shift relieved that the results were repeatable and there were no obvious failures. It should mean having enough confidence in the process, the controls, and the data to know that those results are genuinely reliable, explainable, and sustainable over time. This is where Stage 3 process verification becomes so important.^1,2

Stage 3 is the ongoing phase of the analytical procedure lifecycle, where the laboratory verifies during routine use that the procedure remains in a state of control.^1,2 It shifts the focus away from a purely reactive model, where laboratories respond only after an out-of-specification (OOS), out-of-trend (OOT), or investigation appears, and toward a more mature way of working—one built on vigilant monitoring, traceability, and evidence-based oversight.^2,4

Why can validation alone no longer build confidence?

That shift matters because the regulatory expectation has changed. A validation package on its own is no longer enough to create confidence that a laboratory process remains fit for purpose. What matters now is whether the laboratory can show that its procedures continue to perform reliably in routine use, that sources of variability are being watched, and that decisions are supported by data rather than assumption.^1,2

As regulatory expert Peter Baker (Live Oak Quality Assurance LLC) noted in a recent webinar on Managing Method Variability: A Foundation for Risk-Based Change, “We really have to change the way we define validation.”⁴ That is a strong message for QC teams, because it shifts the conversation from validation as a completed exercise to control as an ongoing expectation. The question is no longer only whether a method was validated once. It’s now whether the laboratory can show that the method remains under control throughout its entire lifetime of use.^1,2

Vigilant monitoring: Detecting drift before it escalates

Ongoing verification matters because it gives QC teams a structured way to see developing signals earlier, understand whether procedure performance is starting to drift, and respond before variability becomes disruptive.^2,4 In a different webinar, Managing Process Performance in Stage 3 Process Validation: Regulatory Expectations for Continued Process Verification, Peter stressed the importance of laboratories understanding where variability exists within methods and procedures, and the need for monitoring programs to be implemented before those issues turn into investigations.⁴ This is the real value of Stage 3 verification. Continuous control starts with knowing what to watch, and then watching it consistently.^2,3

This broader view of monitoring is also why simply trending final results is not enough. Final results matter, of course, but on their own they do not always tell the laboratory where the problem is coming from. Peter made that point directly in the webinar when he explained that, if you are only trending the end result, “you don’t know whether the high or low result is due to the product or the method.”⁴

For QC laboratories, this is a critical distinction. When an investigation starts, time is already against you. If monitoring has been limited to final results only, the lab may not have enough visibility to determine whether the underlying signal points to the procedure, the sample, the instrument, or the process. Vigilant monitoring is what reduces that ambiguity.^2,4

Traceability: Turning data into defensible evidence

This is where traceability becomes a practical requirement, not just a compliance concept. If the laboratory can see what happened, who did what, what changed, and how results were reviewed, then data becomes easier to trust and easier to defend.^2,4 Such traceability is especially important in pharma QC, where data is used to support batch decisions, trend stability, justify investigations, and demonstrate control during inspection. A laboratory that can show its reviewers, auditors, and regulators a clear line from execution to review to conclusion is in a much stronger position than one that has to reconstruct the story after the fact. Consistent operations and compliant decision making both depend on that visibility.^2,4

This also changes how confidence is built in QC. Confidence should not come from the absence of obvious failures alone. It should come from evidence that the process is behaving as expected and that any emerging signs of drift will be seen and understood early enough to act. Peter made that point clearly in the webinar when he said that “accuracy and completeness are the two factors that FDA is really looking for.”⁴

For QC laboratories, that is the heart of the issue. Audit trails, review practices, process knowledge, trend analysis, and procedural controls matter because they support those two outcomes.^2,4 Once Stage 3 verification is viewed through that lens, its purpose becomes much clearer. It is not about creating more review for the sake of it. It is about maintaining confidence that the system remains in control and that the data can support critical product quality decisions.^2,3

From audit readiness to operational advantage

This is where Stage 3 verification moves beyond simple compliance and becomes a real operational advantage. A laboratory that monitors the right indicators is better able to detect drift early, identify repeat issues before they escalate, and distinguish isolated noise from a genuine performance signal.^2,3 That makes investigations more focused, root cause work more effective, and day-to-day operations more resilient. It also reduces the amount of firefighting that so often pulls QC teams away from productive work. Continuous control is not about adding more oversight for the sake of it; it’s about making sure the laboratory can stay in a state of control with fewer surprises and fewer decisions based on incomplete information. Continuous control means continuous confidence.^2,4

For pharma QC, that kind of confidence matters at every level: analysts need confidence that procedures are behaving as expected, supervisors need confidence that trending and review processes will highlight emerging issues in time, and quality leaders need confidence that the laboratory can explain its performance clearly and defend its decisions during inspection. That’s what a mature quality system looks like in practice. It is controlled, confident, and compliant, not because it says so, but because the evidence is there in the data, the review process, and the operational discipline that supports them.^2,4 The next step is making that evidence easier to generate, review, and defend consistently—day after day, method after method.

This is where Waters can help, enabling laboratories to generate, review, and defend that evidence through integrated systems, reproducible chromatographic technologies, traceable workflows, data intelligence tools, and expert services. The Waters approach combines purposefully designed instrumentation, scalable chemistries, informatics, and professional support to strengthen control from method design through routine monitoring.⁵

Empower Software supports audit‑ready operations by creating traceable records through audit trails and compliance‑enabled workflows, and Empower Subscriptions can make it easier to stay current with updates and related cloud‑based applications, reducing upgrade burden while maintaining compatibility and security as requirements evolve. In parallel, Waters Data Intelligence Software is designed to trend key measures, visualize risk, and support audit-readiness through data‑driven oversight.⁷

Waters Professional Services, Software Compliance Services, Instrument Qualification Services, and Empower Software‑based system services can then help laboratories deploy, qualify, validate, and maintain these approaches to support consistent operations and inspection readiness across the lifecycle.⁵

The message from regulators is clear. In modern pharma QC, confidence doesn’t come from a validation report alone. It comes from continuous evidence that the operation remains in control.^1,2 Stage 3 process verification is what helps laboratories create that evidence. It allows them to monitor what matters, reduce ambiguity, strengthen traceability, and build decisions on data rather than assumption.^2-4

That’s the foundation of consistent operations. It’s also the basis for a laboratory that is ready, every day, to show that its processes are controlled, its data is trustworthy, and its decisions can stand up to scrutiny. Built on control and backed by data.^2,4

Special thanks to Peter Baker, President, Live Oak Quality Assurance LLC, for generously sharing the regulatory insights and practical perspectives from the webinar series that informed this article.

To hear Peter Baker’s perspectives on Regulatory Expectations for Continued Process Verification in full, watch the webinar.

References

1. International Council for Harmonisation. (2023). ICH harmonised guideline Q14: Analytical procedure development. Adopted 1 November 2023.

2. United States Pharmacopeia. (2021). <1220> Analytical Procedure Life Cycle. USP–NF. Rockville, MD: United States Pharmacopeial Convention.

3. International Council for Harmonisation. (2023). ICH harmonised guideline Q9(R1): Quality risk management. Adopted 18 January 2023.

4. Baker, P. (2026). Webinar Managing Process Performance in Stage 3 Process Validation: Regulatory Expectations for Continued Process Verification.

5. Audit Ready Labs with Risk-Based Quality Control.

As clinical laboratories increasingly rely on LC‑MS for high‑throughput, multi‑analyte testing, retention time stability has become a defining factor in assay robustness, data quality, and operational confidence.

Why does retention time stability matter in clinical workflows

In research settings, small shifts in retention time may be tolerable. In clinical laboratories, they are not. Clinical LC‑MS assays are expected to deliver:

• Consistent results across long assay lifetimes
• High throughput with minimal manual intervention
• Defensible data in regulated and audited environments

When retention times drift, laboratories are forced to widen MS acquisition windows to avoid missing analytes. This creates a cascade of downstream effects, including:

• Reduced dwell time per transition
• Fewer data points across each chromatographic peak
• Increased susceptibility to interference
• Compromised quantitative precision, particularly for low‑level analytes

In large panels, even modest variability can lead to missed detections, increased manual review, and higher rates of reruns or batch failures. In practice, retention time stability is a prerequisite for scalable, high‑confidence clinical LC‑MS.

The link between retention time stability and MS performance

MModern tandem mass spectrometers are capable of extremely fast acquisition, but their performance is fundamentally constrained by chromatographic predictability.

Stable retention times enable laboratories to:

• Use narrow, well‑defined acquisition windows
• Optimize dwell time for each transition
• Increase the number of data points across peaks
• Maintain consistent peak integration and quantitation

When chromatography is unstable, mass spectrometry efficiency is lost, not because of the detector, but because the acquisition strategy must compensate for uncertainty upstream.

In this way, chromatography does not merely precede detection. It defines how effectively MS technology can be used in routine clinical workflows.

Why is UHPLC critical for retention time stability

Ultra-High Performance Liquid Chromatography (UHPLC) is often associated with sharper peaks and faster separations. In clinical LC‑MS, however, its most important contribution is reproducible chromatographic behavior over time. UHPLC systems designed for clinical use offer:

• Improved control of mass transfer and dispersion
• Greater consistency across injections and batches
• Reduced sensitivity to small changes in system conditions

These attributes translate directly into more stable retention times, which is particularly important when operating large panels in complex biological matrices. However, achieving retention time stability in a clinical environment requires more than high pressure alone. It requires systems engineered explicitly for robust, routine operation.

Retention time stability as a risk‑reduction strategy

From a clinical perspective, retention time stability is not just an analytical parameter, it is a risk‑reduction mechanism. Stable chromatography supports:

• Lower rates of assay failure and reanalysis
• Reduced manual data review
• Cleaner trending of system suitability metrics
• Greater confidence during audits and inspections

Conversely, unstable retention times can drive deviation investigations, complicate method maintenance, and erode trust in results over time.

For laboratories managing regulated workflows, chromatographic stability directly supports quality and compliance objectives.

Waters ACQUITY UPLC I‑Class PLUS IVD System: Designed for retention time confidence

The ACQUITY UPLC I‑Class PLUS IVD System has been engineered to deliver the level of retention time stability required for routine clinical diagnostics. This design philosophy reflects the realities of clinical laboratories, where consistency across shifts, operators, and time is paramount.

By providing highly reproducible retention times, the ACQUITY UPLC I‑Class PLUS IVD System enables:

• Narrow acquisition windows
• Efficient use of MS dwell time
• Reliable quantitation across high‑volume sample sets
• Sustained performance throughout the assay lifecycle

Chromatography expertise matters in clinical implementation

Retention time stability is not achieved by hardware alone. It reflects decades of chromatographic understanding applied to system design and clinical use cases.

Waters leadership in UHPLC technology, and its long experience supporting regulated laboratories has shaped how systems, like the ACQUITY UPLC I‑Class PLUS IVD System, are optimized for real‑world clinical operation.

In today’s clinical laboratory, retention time stability is not just a technical detail. It is the foundation of trust in LC‑MS results.

Discover how UPLC can elevate your clinical analysis.