In this Q&A interview with the PharmTech Group, 

, PhD, vice president of regulatory strategy at Abzena, and 

, MS, director of regulatory affairs at Cellares, explore the implementation of risk-based strategies to navigate 

, adaptive clinical trial designs, and data integrity are transforming the drug development lifecycle. They note how, by embedding quality risk management into initial process designs and leveraging prior knowledge from contract development and manufacturing organizations (CDMOs), the industry can achieve greater manufacturing efficiency, faster innovation, and improved patient outcomes.

: How are evolving global regulations shaping biopharma quality standards?

Mocny (Abzena): There is a shift toward risk-based thinking and leveraging prior knowledge as often as possible. As drug developers, we need to understand the products we manufacture and maintain control over the manufacturing process, materials, and analytical aspects to demonstrate the molecule meets requirements. As regulators adopt this risk-based thinking, leveraging the prior knowledge held by CDMOs offers opportunities to use existing tools in new ways to allow for faster innovation. This reduces development time and allows developers, sponsors, and CDMOs to focus on better patient outcomes and faster-to-market strategies. 

Mocny (Abzena): Safety and risk-based thinking are at the core of these trial models. Trial designs have evolved to improve efficiency and ethical standards by enabling real-time data and informed decision-making. This allows us to leverage prior knowledge and data from other sources to foster faster development and reach go/no-go decisions more quickly. Because clinical trial activities, monitoring, and safety drive the largest portion of drug development costs, economized thinking in these trials is key. Incorporating real-world evidence makes trials stronger and tells a more complete story regarding safety, efficacy, and mechanism of action. This allows products to move through trials quickly based on milestones and an understanding of how the product works.

Mocny (Abzena): Recent FDA guidance and changes in thinking will change the landscape. Placebo-controlled mandates are beneficial for understanding drug performance, and returning to these clinical trial basics is helpful. Reduced animal testing and the incorporation of real-world evidence offer better chances for novel strategies to reach the market and affect positive health outcomes. From a CDMO perspective, this introduces aspects of supply chain and strategic compliance, such as the BIOSECURE Act.

 This has caused intensified oversight of local US-based manufacturing operations, which benefits the market by driving the development of high-quality, patient-centric medicines. It also offers CDMOs an opportunity to modernize approaches, be more competitive on the global market, and offer higher-quality materials at lower prices.

What strategies can biopharma companies use to implement risk-based quality frameworks?

McMahon (Cellares): Implementing a robust risk-based quality framework requires integrating risk management into design, not just operations. A common mistake is treating quality risk management as something that kicks in during manufacturing or post approval; instead, it should be embedded into process design and development from the start. For example, automated cell therapy manufacturing platforms build risk considerations into the engineering requirements of the system to reduce operator intervention and standardize workflows. It is also essential to foster a risk-aware quality culture where personnel are trained on why procedures exist and are empowered to escalate emerging risks. Practically, this means building cross-functional quality review boards where manufacturing, regulatory, and clinical teams collaborate.

Mocny (Abzena): CDMOs have the luxury of leveraging prior knowledge gained from developing multiple products over many years. For instance, IgG1 [immunoglobulin G1] manufacturing platforms have been used since the 1980s and are well understood, allowing for predictable and fast-moving strategies. Risk-based thinking involves carefully considering whether a platform strategy is best for phase 1 clinical trials while understanding critical quality attributes and manufacturing design considerations. Implementing these frameworks relies on data integrity and control over the processes within the facility. While the expectation of data integrity and control is always present, the level of detail and understanding of the product evolves at each phase of development.

How are regulatory shifts influencing manufacturing and clinical development workflows?

Mocny (Abzena): ICH [International Council for Harmonisation] Q14 and Q2(R2) have changed the approach to analytical methods, moving away from rigid thinking toward a risk-based understanding of method design and function. Analytical development is now viewed as a continuum that evolves with the product, allowing developers to leverage prior knowledge to streamline validation. Additionally, the FDA has moved away from the need for clinical trials for biosimilars, emphasizing robust analytical development programs to demonstrate biosimilarity. This [move] reduces financial barriers and allows products to move to market more quickly.

McMahon (Cellares): FDA-accelerated approval modernization and the FDORA [Food and Drug Omnibus Reform Act]

 provisions strengthen the authority to require postapproval confirmatory trials. This leads companies to build commercial-scale manufacturing readiness in parallel with development, rather than sequentially. The agency has also announced more flexible development strategies for cell and gene therapies in areas like clinical development, commercial specifications, and process validation. In Europe, the EU HTA [European Union Health Technology Assessment]

 regulation requires evidence that goes beyond traditional end points like overall survival. This means phase 2 protocols must be designed with joint clinical assessment requirements in mind from the outset, requiring early collaboration between regulatory, medical affairs, and market access functions.

What challenges do companies face when aligning evidence generation with modern FDA guidances?

Mocny (Abzena): A primary challenge is data integrity, as evidence is presented based on collected data. There is a shift toward structured data submissions to regulators, moving away from the written word toward data and numbers. This change offers an opportunity to judge a product’s analytical performance against the field and understand how manufacturing fits with clinical activities. Establishing a clear link between manufacturing unit operations and clinical performance demonstrates an understanding of the molecule and consistent manufacturing. As regulators modernize, the shift toward structured data elements and understanding interrelationships will become more prevalent. While AI tools are being developed to help manage these data, the FDA still emphasizes the importance of having a human in the mix.

How can quality and compliance strategies improve biopharma development efficiency?

Mocny (Abzena): Quality is a scientist’s best friend. Scientists must understand why they follow specific [guidelines] and how those actions affect the product and the patients. A renewed focus on the purpose behind development activities leads to enhanced efficiency. When an organization understands that everything is done with purpose, it is easier to translate the “why” and “how” of meeting regulatory requirements. Compliance is one thing, but making material with a purpose and maintaining that cultural mindset within an organization is the essence of a successful quality strategy.

BIOSECURE Act of 2025, S 3469, 119th Cong (2025). Accessed May 11, 2026. 

https://www.congress.gov/bill/119th-congress/senate-bill/3469

Food and Drug Omnibus Reform Act (FDORA) of 2022. FDA. December 5, 2025. Accessed May 11, 2026. 

https://www.fda.gov/regulatory-information/selected-amendments-fdc-act/food-and-drug-omnibus-reform-act-fdora-2022

New EU rules on health technology assessment open up a new era for patient access to innovation. News release. European Commission. January 9, 2025. Accessed May 11, 2026. 

https://ec.europa.eu/commission/presscorner/detail/en/ip_25_226

Articles

Jeffrey C. Mocny, PhD, of Abzena, and Anna McMahon, MS, of Cellares, discuss how biopharmaceutical risk-based standards accelerate innovation and speed-to-market by leveraging data for better patient outcomes and safety.

Using Risk-Based Quality Frameworks to Fast-Track Biopharma Development: A Q&A with Abzena’s Jeffrey C. Mocny and Cellares’ Anna McMahon

The FDA’s April 2026 warning letter to Purolea Cosmetics Lab provides a concrete enforcement example involving artificial intelligence (AI) in current good manufacturing practice (CGMP) documentation.

 During the inspection, the firm told the FDA investigators that it had used AI agents to create drug product specifications, procedures, and master production or control records to help comply with the FDA requirements. The FDA’s concern was direct: Firms using AI to assist document creation must review the resulting documents for accuracy and compliance with CGMP before use. The FDA cited the failure to do so as a violation of 21 

 (CFR) 211.22(c). The letter also describes a process-validation gap under 21 CFR 211.100 after the firm stated that the AI agent had not identified the requirement. AI-assisted drafting can support documentation work only when the resulting document is subject to quality-system controls. That requires controlled source material, qualified review, quality unit approval, version history, and retained evidence that the final record was checked before use.

The Purolea letter is notable because the FDA explicitly addressed AI in the body of a CGMP warning letter and linked its use to established quality-system responsibilities. The firm had used AI agents to create specifications, procedures, and master production or control records. These are controlled documents that can shape manufacturing, testing, batch documentation, and product disposition.

The FDA did not need an AI-specific rule to make this finding. The letter states that, when AI is used to assist document creation, the firm must review the resulting documents to confirm their accuracy and compliance with CGMP. The FDA cited the failure to do so under 21 CFR 211.22(c), which assigns the quality control unit responsibility for approving or rejecting procedures or specifications that affect identity, strength, quality, and purity.

The letter also describes a process-validation failure. The FDA investigators found that process validation had not been conducted before distribution of drug products, as required under 21 CFR 211.100. The firm stated that its AI agent had not identified the requirement. That explanation does not shift accountability: Software can assist a task, but it cannot substitute for the firm’s responsibility for ensuring that required GMP activities are performed.

Where Does the Quality Unit’s Responsibility Begin?

The quality unit is not merely a final reviewer. Under 21 CFR 211.22, it has authority over components, in-process materials, packaging, labeling, drug products, and production records. It is also responsible for approving or rejecting procedures or specifications that affect the identity, strength, quality, and purity of the drug product. The unit’s responsibilities and procedures must be in writing and followed.

That matters for AI-assisted drafting because the relevant risk is not the tool used to prepare the first draft. The risk is that a procedure or specification enters use without a controlled basis, documented review, or a quality unit decision. The Purolea finding applies an established framework to a new drafting route: A GMP document still requires defined sources, qualified review, and quality unit approval before use.

Section 211.100 makes the same point from a different angle. Written procedures for production and process control must be designed to assure identity, strength, quality, and purity. They must be drafted, reviewed, and approved by appropriate organizational units and reviewed and approved by the quality control unit. They must also be followed during production, and deviations must be recorded and justified.

What Makes AI-Assisted GMP Documentation Fragile?

The central issue is not simply that AI-generated text can be wrong, a risk common to any draft. AI output can appear complete while obscuring missing source checks, unsupported assumptions, or weak linkage to the firm’s approved process. Those gaps create a documentation risk before they become technical risks.

A specification generated from the wrong source can look polished. A procedure can describe a generic manufacturing step that does not match the site’s equipment, validated parameters, sampling plan, or cleaning controls. A master production or control record can omit a required verification step and still read fluently. In a GMP setting, fluency has no evidentiary value unless the record can be traced back to approved sources and accountable review.

This is why the Purolea letter is better understood as a quality-system signal than as a general warning against AI. The firm needed a process that made each AI-assisted document reviewable: what source material was used, who checked the output, what changes were made, who approved the final document, and where the supporting evidence was retained.

How Should AI-Assisted Drafting Be Controlled?

A workable control model does not require every prompt to be treated as a regulated system event. It should, however, distinguish informal drafting from AI use that creates or revises a GMP-controlled record. When AI is used to prepare or revise a specification, procedure, batch record, validation document, investigation response, or other controlled record, the output should enter a defined review path.

The first step is to define intended use. Teams should know which tasks are permitted, which are prohibited, and which require prior quality unit approval. The second step is to assemble a source package before drafting. AI should not be used to establish the factual or procedural basis for a GMP document. The approved procedure, product specification, validation report, batch record template, change control, regulatory commitment, or supplier document should be identified before the output is reviewed.

The third step is technical review. A subject-matter expert should check the output against the approved source package and the process used at the site. The fourth step is quality unit approval. The final document should follow the same document-control process as if the draft had been prepared without AI. The fifth step is retention. The firm should be able to produce the final approved record, the key source material, the review evidence, and the approval history.

 summarizes these controls and the evidence a firm should retain to make the process inspectable.

Part 11 should be handled carefully. The FDA’s scope guidance states that Part 11 applies when records required by predicate rules are maintained in electronic format instead of paper, or when electronic records are relied upon to perform regulated activities. A firm should therefore document whether the AI workspace, the document-management system, or the approval workflow is part of the electronic record relied upon for GMP activity.

That does not mean that every AI draft becomes a Part 11 record. If the regulated record is a controlled final procedure in the document-management system, the Part 11 assessment may focus on that system and its approval workflow. If the AI workspace stores source material, review history, or approvals that the firm relies on to defend the record, the compliance analysis changes. The firm should document that decision prospectively rather than address it only during an inspection.

Data integrity has a narrower but useful role here. The FDA’s data integrity guidance expects data to be reliable and accurate and allows for risk-based strategies to prevent and detect data integrity lapses. For AI-assisted GMP documentation, that means the firm should be able to reconstruct how the final record was created, checked, approved, and retained. It also means avoiding uncontrolled copy-paste routes that leave no review trail.

How Should Firms Apply Risk-Based Review?

A risk-based approach is appropriate, but it cannot be used to bypass CGMP requirements. The International Council for Harmonisation’s Q9(R1) states that the level of effort, formality, and documentation should be commensurate with the level of risk. It also makes clear that quality risk management does not replace the obligation to comply with regulatory requirements. This distinction is useful when defining AI controls.

A draft internal training outline may need a lighter review than a master production record. A revised product specification, process-control procedure, validation protocol, or batch-record instruction should receive a much higher level of scrutiny. The review burden should follow the possible effect on product quality, patient risk, regulatory commitments, and batch disposition.

The FDA’s January 2025 draft guidance on AI for regulatory decision-making is not a GMP documentation guidance and is not for implementation. Its context-of-use and credibility framework is nevertheless useful. Within a quality system, the same logic supports practical questions: What decision does this AI output support, how could it affect quality, and what evidence is needed before the output is relied upon?

The immediate work is to identify where AI is already being used to draft or revise GMP content. Firms should classify those uses by risk, assign an accountable owner, and determine which outputs must follow a defined source-review-approval path. The review should involve product and process experts, not only document-control staff.

Quality units should also define the evidence that will be retained. The retained record does not necessarily need to preserve every informal prompt used during early drafting. The firm should define, through a risk-based procedure, the inputs and review artifacts needed to defend the final GMP decision: approved source documents, key reviewer checks, changes made before approval, quality unit clearance, effective date, and version history.

Supplier and contractor arrangements require the same discipline. The FDA reminded Purolea that a firm remains responsible for the quality of its drugs regardless of agreements with contractors. If a contractor, consultant, or software vendor supports AI-assisted GMP documentation, the firm still needs enough control to defend the final record and the process that produced it.

The Purolea warning letter does not establish a separate AI regulatory framework. It applies existing CGMP accountability to AI-assisted drafting. In practice, AI may help prepare GMP content, but the firm must establish and retain the controls needed before that content is used in manufacturing or quality operations.

For quality and CMC leaders, the practical response is to integrate AI-assisted drafting into existing document-control and quality-system processes. Define permitted uses, control source material, verify the output, approve the final record, and retain evidence of the review.

1. Warning letter: Purolea Cosmetics Lab, 320-26-58, MARCS-CMS 722591. FDA. April 2, 2026. Accessed May 26, 2026. https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/purolea-cosmetics-lab-722591-04022026

2. Responsibilities of Quality Control Unit. 21 CFR §211.22 (2026). Accessed May 26, 2026. https://www.ecfr.gov/current/title-21/chapter-I/subchapter-C/part-211/subpart-B/section-211.22

3. Written Procedures; Deviations. 21 CFR §211.100 (2026). Accessed May 26, 2026. https://www.ecfr.gov/current/title-21/chapter-I/subchapter-C/part-211/subpart-F/section-211.100

Guidance for Industry: Quality Systems Approach to Pharmaceutical CGMP Regulations. 

FDA; September 2006. Accessed May 26, 2026. https://www.fda.gov/media/71023/download

Data Integrity and Compliance With Drug CGMP: Questions and Answers: Guidance for Industry.

 FDA; December 2018. Accessed May 26, 2026. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/data-integrity-and-compliance-drug-cgmp-questions-and-answers

Guidance for Industry: Part 11, Electronic Records; Electronic Signatures —  Scope and Application. 

FDA; August 2003. Accessed May 26, 2026. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/part-11-electronic-records-electronic-signatures-scope-and-application

. International Council for Harmonisation; January 18, 2023. Accessed May 26, 2026. https://database.ich.org/sites/default/files/ICH_Q9%28R1%29_Guideline_Step4_2022_1219.pdf

Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. Draft Guidance for Industry and Other Interested Parties

. FDA; January 2025. Accessed May 26, 2026. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological

Gourav Pandey is pharmaceutical science quality lead, Takeda. Svyatoslav Borshchenko is CEO and cofounder, GxPilot.

FDA’s April 2026 warning letter to Purolea Cosmetics Lab operationalizes how existing CGMP expectations apply when AI agents draft specifications, procedures, and master production/control records. The central deficiency was deploying AI-generated controlled documents without documented accuracy and CGMP compliance review by qualified personnel and the quality control unit, triggering 21 CFR 211.22(c) findings. A separate gap involved missing process validation under 21 CFR 211.100, with reliance on AI’s failure to flag requirements deemed non-exculpatory. Practical controls include defined source packages, SME review, QU approval, versioning, and retained review evidence.

An FDA warning letter to Purolea Cosmetics Lab shows that AI-assisted drafting can support documentation work only when the resulting document is subject to quality-system controls.

What FDA's AI Warning Letter Tells Us About GMP Accountability

Laboratory scientists, regulatory reviewers, and other professionals responsible for ensuring the quality of medicines are increasingly moving toward digitized workflows. Digital-first tools promise greater efficiency, stronger data integrity, and faster decision-making, yet in reality, their use remains fragmented in many quality environments. Manual steps, such as retyping quality testing instructions, transferring data between systems, and translating standards into formats digital tools can use, continue to slow progress and introduce risk.

As the pharmaceutical industry and quality control labs move toward more connected, data-driven operations, the challenge is not whether to adopt digital technologies but how to do so while upholding the rigor and confidence that patient safety demands. 

 offer a path forward by embedding established quality expectations for medicines directly into digital environments, helping quality professionals and regulators work more efficiently without compromising control or traceability.

At its core, digitizing quality workflows is about enabling experts to focus on science rather than systems. Making it faster and easier for quality control laboratories and regulators to adopt tools that help ensure the quality of medicine as workflows evolve is essential. Digital standards are poised to play a critical role by complementing and aligning traditional compendial expectations and modern laboratory technologies to strengthen confidence as more data, methods, and decisions become digital.

 are digitally structured, machine-readable quality standards. They are designed to maintain the same science-based rigor as traditional standards, in formats that integrate seamlessly into digital workflows.

For experts in quality control labs, this means fewer manual handoffs, less rework, faster method deployment, and clearer traceability across the lifecycle of a method or data set. For scientists, as well as regulators and reviewers, digitally structured standards can support more consistent standards interpretation and reduced ambiguity on standards implementation to reinforce confidence as regulatory submissions and inspections rely increasingly on digital data.

In quality control labs, the practical impact of digital standards can be significant. When standards are integrated into digital workflows, teams spend less time translating text into systems and more time applying scientific judgment. Risk mitigation and traceability are enhanced through electronic capture of steps, version control, and clearer references. Technology transfer can be accelerated through standardized digital workflows and “digital twin” reference materials that help reduce variability across sites and organizations. Automation and advanced analytics become more accessible, freeing resources and supporting continuous improvement. Digital reference standards also avoid some of the inherent limitations of physical materials, such as degradation, logistical and shipping constraints, and environmental impacts associated with solvents or hazardous substances.

From Technology Adoption to Quality Enablement

As quality specialists know, technology alone is not the solution to laboratory digitization. As digital tools evolve rapidly, quality still depends on consistent, trusted standards that can be applied across platforms and organizations. This is where collaboration becomes essential. Enabling adoption of new technologies while upholding quality requires close alignment among method developers, manufacturers, technology providers, and regulators. Digital standards can act as shared, trusted building blocks, helping organizations scale digital workflows with confidence while keeping quality and traceability front and center—from early development of new medicines through commercial manufacturing and delivery.

Progress in developing and implementing digital standards that can make the biggest difference depends on engaging with real-world use cases and learning how new digital technologies perform in operational laboratories. Collaborative development helps ensure that digital standards are practical, fit for purpose, and aligned with regulatory expectations. When industry and regulators contribute early input to these emerging standards, uncertainty may be reduced and adoption may become more feasible, even for organizations with limited resources navigating complex digital transformations.

Quality as a Shared Responsibility in a Digital Era

 (USP), quality is the foundation of a range of solutions being developed to facilitate digitization of quality control labs. USP is advancing 

 and related tools to support innovation while preserving the trust placed in compendial standards worldwide.

 By evolving reference standards and documentary methods into validated, digital-first, machine-readable formats, USP is working to integrate quality expectations directly into digitized workflows and to build clear compendial pathways for adopting and scaling emerging technologies—from advanced laboratory informatics to nuclear magnetic resonance analysis software, artificial intelligence, and advanced analytics.

quality standards framework formally acknowledge that reference standards may be provided as either physical materials or digital data, which is an important step toward reducing uncertainty and strengthening regulatory confidence as digital approaches mature.

 Complementary tools, such as automated software solutions that embed compendial references into analytical workflows, illustrate how digital standards can be applied in practice.

Ultimately, ensuring the quality of medicines in a digital era is a shared responsibility. Digital standards will succeed not because they are new but because they are trusted, science based, and developed in collaboration with the community they serve to maximize the value they bring to that community. By working together to align innovation with quality, industry, laboratory scientists, regulators, and other professionals can harness the efficiencies of digital transformation while ensuring quality remains the shared foundation connecting innovation, regulation, and patient care.

Emerging standards platform. US Pharmacopeia. Accessed June 9, 2026. 

https://www.usp.org/get-involved/emerging-standards-platform

Standards: supporting the people who protect the quality of medicines in a digital world. US Pharmacopeia. March 24, 2026. 

https://qualitymatters.usp.org/digital-standards-supporting-people-who-protect-quality-medicines-digital-world

Confidence in every step of your digital transformation. US Pharmacopeia. Accessed June 9, 2026. https://www.usp.org/digital-standards

United States Pharmacopeia-National Formulary.

 (USP) is a private, nonprofit, scientific organization that develops standards and solutions that help build trust in the supply of quality medicines. Through its rigorous science, USP sets public quality standards that help protect patient safety and improve the health of people around the world. Engage with USP on digital standards by visiting USP’s digital standards 

Quality organizations are shifting to digital-first laboratory and regulatory workflows, yet manual transcription, data transfers, and format translation still fragment operations and elevate risk. Machine-readable digital quality standards can embed compendial expectations into informatics systems, improving traceability, version control, and consistency of interpretation while accelerating method deployment and technology transfer. Digital reference standards and “digital twin” materials may reduce variability and limitations of physical standards. Broad adoption hinges on collaboration among developers, manufacturers, vendors, and regulators to ensure fit-for-purpose implementation and sustained regulatory confidence.

As quality control labs move toward more connected, data-driven operations, digital technologies must be adopted in a way that upholds patient safety.

Digital Standards Aim to Modernize Quality Assurance Without Compromising Confidence

The 2024 implementation of the International Council for Harmonisation’s (ICH) Q5A(R2)

guideline marks a substantive evolution in viral safety for biotechnology products since the last iteration was published in 1997. This guidance supports more risk‑based, technology‑enabled controls that keep pace with modern modalities, manufacturing models, and technologies.

The revision formalizes the role of molecular methods, notably next-generation sequencing (NGS), for broad adventitious agent detection and encourages judicious replacement of legacy in vivo assays. It also addresses continuous manufacturing, platform prior‑knowledge, and new product modalities where viral clearance is demonstrably feasible.

This article explores practical actions for manufacturing and chemistry, manufacturing, and controls (CMC) teams to align with Q5A(R2)’s updated guidance and stay ahead of the rapidly changing viral safety environment. Implemented well, these actions can shorten testing timelines and strengthen assurance of participant safety.

The Q5A(R2) revisions that CMC teams should take specific notice of are the following:

Molecular methods mainstreamed: Q5A(R2) formalizes the role of nucleic‑acid methods, especially NGS, for broad adventitious agent detection in cell banks and unprocessed bulk, with a risk‑based path to replace certain legacy in vivo tests.

Continuous manufacturing (CM): A new section details how to evidence viral clearance when operations run for extended durations with pooling and diversion.

Prior knowledge and new modalities: The guideline clarifies when platform experience can reduce study scope and explicitly covers modalities amenable to clearance (eg, adeno-associated virus produced in cell lines, baculovirus‑expressed virus-like particles).

Q5A(R2) expects sponsors to build and justify a risk-based viral safety from overlapping controls: what you prevent from entering the process, what the process removes or inactivates, and what you verify through testing at defined steps.

Start with a simple risk narrative that spans cell substrate → raw materials → upstream → downstream → drug substance. For each link, decide whether control comes from testing, from clearance, or both. Maintain the classic triad of cell line and materials control, process clearance, and testing at appropriate steps, but tie claims to critical quality attributes (CQAs), critical process parameters (CPPs), and a representative small‑scale model.

Q5A(R2) supports reduced product-specific work when prior knowledge is robust, and the rationale is evidence-backed. Define clear platform boundaries (process architecture, materials, operating ranges), explain mechanisms (inactivation vs removal), and bridge with historical data under worst-case CPPs. The revised guidance offers worked examples (low pH, solvent/detergent, filtration) that sponsors can use as a template for required structure and depth. When properly supplemented with prior knowledge dossiers, testing timelines may be reduced. 

NGS in Practice: Where It Replaces vs Supplements

NGS is a vital part of an integrated control strategy alongside traditional in vivo and in vitro assays and clearance validation. It offers broad, agnostic detection even for variants and potential novel viruses, making it a credible alternative to some targeted assays. However, NGS detects nucleic acid, not infectivity. As such, dossiers need an orthogonal confirmation plan and a validation package that matches the claim, whether that’s breadth vs sensitivity, targeted vs non‑targeted workflows, database governance, bioinformatics controls, etc.

A pragmatic path forward with NGS is first to supplement then replace. Piloting NGS alongside established assays and demonstrating concordance builds evidence to justify replacement where risk and data allow. Industry feedback during the Q5A(R2) consultation and subsequent implementation guidance emphasize the need to define intended use, detection limits in relevant manufacturing matrices, and the application of expert curation of bioinformatic results.

Demonstrable Clearance in Continuous Manufacturing

In CM, viral clearance shifts from one-time proof to demonstrating its reliability across a continuously operating system so that viral risk remains controlled across time, variability, and scale.

Demonstrable clearance requires sponsors to position low-pH inactivation, dedicated virus inactivation steps, chemistry where applicable, and nanofiltration so they bound risk; define material segmentation, diversion, and pooling rules; and prove the scale‑down model remains representative over the intended run duration to support viral clearance claims. Sampling plans should align with segmentation, not just hours of operation, to ensure samples represent a single, clearly defined lot or batch.

The following plant-ready viral safety 2.0 checklist can be used to confirm that viral safety controls are clearly defined, evidence-based, and ready for routine manufacturing use:

Map controls end‑to‑end and keep the map in change control (cell banks, raw materials, holds, unit ops, sampling points).

Define plain‑language clearance claims and link each to CQAs/CPPs, scale‑down evidence, and routine monitoring.

Harden raw‑material governance: origin, treatments (eg, irradiation/filtration), and risk‑ranked incoming controls.

If deploying NGS, lock down sample handling, sequencing depth, database curation, and a bioinformatics validation plan—with expert review and an orthogonal confirmation playbook.

For CM, predefine segmentation/pooling logic and time‑at‑risk bounds; align sampling to segments.

Build a modular prior‑knowledge library per unit operation and maintain applicability limits and data currency.

Q5A(R2) makes viral safety transparent and risk‑based, supported by modern analytics and demonstrable clearance. Understanding the guidance’s revisions and how to implement them creates opportunities for accelerated testing timelines, stronger safety assurances, and the integration of next-generation innovations.

Q5A(R2) Guideline on viral safety evaluation of biotechnology products derived from cell lines of human or animal origin: scientific guideline—Step 5

. EMA/CHMP/ICH/804363/2022. Adopted December 2023. Effective June 14, 2024. Accessed April 20, 2026.

International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. 

ICH Q5A(R2) Implementation Working Group concept paper

. December 2023. Accessed April 20, 2026.

John Muganga is a senior manager of Regulatory Affairs at ICON plc.

ICH Q5A(R2) modernizes viral safety expectations for biotech products by shifting toward risk-based, technology-enabled controls aligned with contemporary modalities and manufacturing paradigms. Molecular methods, particularly next-generation sequencing, are formalized for broad adventitious agent detection, enabling potential retirement of select legacy in vivo assays when justified. The guidance expands considerations for continuous manufacturing, emphasizing demonstrable clearance over extended operations with defined segmentation and sampling logic. It also endorses structured use of platform prior knowledge to reduce product-specific studies when boundaries and evidence are robust.

CMC teams can align with Q5A(R2)’s updated guidance to shorten testing timelines and strengthen assurance of participant safety.

Quality and Regulations 2026 eBook

Using Risk-Based Quality Frameworks to Fast-Track Biopharma Development: A Q&A with Abzena’s Jeffrey C. Mocny and Cellares’ Anna McMahon

What FDA's AI Warning Letter Tells Us About GMP Accountability

Digital Standards Aim to Modernize Quality Assurance Without Compromising Confidence

Viral Safety 2.0: Plant-Ready Actions for ICH Q5A(R2) Compliance