Artis Bio blog

From Evaluation to Decision: A Practical Framework for Choosing the Right CDMO

Artis BioSolutions — Wed, 12 Aug 2026 16:38:29 GMT

From Evaluation to Decision: A Practical Framework for Choosing the Right CDMO

Over the four posts in this series, we have built a complete framework for evaluating a CDMO for advanced therapy development. We looked at the manufacturing landscape and what the FDA data tells us about CMC risk. We covered modality-specific expertise and the structural importance of integration across process development, analytics, and GMP. We went deep on process development philosophy, materials strategy, and facility readiness. And we closed with the partnership model and the questions that surface what a capability presentation rarely does.

This final post is about turning that framework into a decision. By the time you finish a CDMO evaluation, you will have absorbed a lot of information from a small number of organizations. The challenge is not gathering more data. It is organizing what you have learned in a way that makes the right partner visible.

What follows is the framework we recommend: a scorecard for synthesizing the evaluation, a sequencing model for the evaluation process itself, and a short list of decision pitfalls worth avoiding.

The Evaluation Scorecard

The seven criteria below cover the dimensions developed across this series. The weighting reflects our view of relative importance for advanced therapy programs at the IND-enabling stage. Adjust weighting for your specific program context: a viral vector program will weight different criteria than a stem cell program; a tech transfer engagement will weight different criteria than first-in-modality process development.

Criterion	Weight	What Strong Looks Like	What Should Concern You
Modality expertise	High	Specific named programs in your modality taken through IND.	General capability claims with no concrete program references.
Development integration	High	Same scientists from PD through GMP; analytics in parallel.	Sequential handoffs; documentation-as-transfer.
Process philosophy	High	CPPs defined in RUO; vessel transitions evaluated up front.	CPPs deferred to GMP; scale-up treated as logistics.
Materials strategy	Medium-High	Clinical-grade equivalents qualified in development.	Materials revisited at tech transfer, not in development.
Facility & QMS	Medium-High	Mature QMS, sponsor-audited, FDA-engaged.	QMS still being built, no sponsor audit history.
Partnership model	High	Direct scientist access; proactive communication.	Account leads as primary interface; presentation team ≠ program team.
Cultural fit	Medium	Candor about prior challenges; clear reasoning.	Defensive about programs that did not go perfectly.

Use the scorecard as a conversation tool, not a spreadsheet. The signals in the right two columns are the kind of evidence you should be able to point to from your evaluation conversations. If you cannot, you do not yet have enough data on that criterion.

"The decision becomes easier when the criteria are explicit. Most difficult CDMO decisions are difficult because the criteria were never named clearly enough to compare candidates against."

Sequencing the Evaluation

How you sequence the evaluation matters as much as which criteria you assess. A common pattern, capability deck first, then technical conversation, then pricing, then references, tends to give CDMOs the format they are most prepared for. A more discriminating sequence puts scientific conversation earlier and gives the evaluation more signal per hour invested.

Stage 1: Modality fit and scientific conversation

Start with a structured technical conversation. Bring two or three of the modality-specific questions from Part 2 of this series, and one or two of the process philosophy questions from Part 3. Ask to speak with the scientists who would be assigned to your program, not the BD lead. The depth and texture of this conversation is the single highest-leverage data point in the evaluation.

Stage 2: Integrated capability assessment

Once the scientific conversation has established that the CDMO is a credible candidate, move to capability. Walk through facility, equipment, QMS, regulatory history, and team structure. Treat this as confirming what was suggested by the scientific conversation rather than as a separate evaluation track. Capability without scientific depth is not enough; scientific depth without capability is not enough either.

Stage 3: Partnership and process

Ask the partnership questions from Part 4 directly. How does the team handle disagreement? Who is in the room during a GMP investigation? How do they describe a program that did not go perfectly? This stage is about how the CDMO will operate, not what they can do.

Stage 4: References and confirmation

Save references for last, and ask specific questions when you call them. Generic reference checks rarely produce useful information. Ask references about the specific dimensions where you still have uncertainty: how the CDMO handled a technical setback, whether the team you met during evaluation stayed on the program, how regulatory questions were navigated.

Decision Pitfalls Worth Avoiding

A few patterns recur in CDMO selection decisions that go badly. They are worth naming because they are easy to fall into:

Mistaking capability for fit. A CDMO can be highly capable and still not be the right partner for your program. Capability is a necessary condition. Modality fit, integration, and partnership quality are what determine whether the capability translates to program outcomes.

Optimizing for the lowest stage gate cost. Cost matters, but the cost of a program that runs eight months long because of a CMC question is far higher than the cost differential between two CDMOs. Evaluate cost in the context of expected timeline and regulatory risk, not in isolation.

Underweighting program team continuity. The team you meet during evaluation may not be the team that runs your program. This is one of the most consequential and most under-asked questions in CDMO selection. Surface it explicitly.

Skipping the difficult-program question. Every experienced CDMO has worked through programs that did not go as planned. The way they describe those experiences is among the most informative signals in an evaluation. Skipping the question, or accepting a polished non-answer, removes one of the few windows into how a partner behaves under pressure.

Confusing enthusiasm with depth. Enthusiastic generalities are easy. Specific, scientific, candid answers are harder to produce and more informative. Calibrate on the texture of the answers, not the energy of the conversation.

When You Have Enough to Decide

You have enough to decide when, for each of the seven scorecard criteria, you can point to a specific moment in the evaluation that informed your view. Not a general impression; a moment. A scientist's answer to a specific question. A facility tour observation. A reference call detail. A way the CDMO handled a follow-up request.

If you cannot point to those moments, the next evaluation step is not another presentation. It is a targeted conversation, with the right people, about the specific criteria where you do not yet have evidence.

The decision itself, when it comes, tends to feel less like a tradeoff and more like recognition. The right partner for your program will have shown you who they are throughout the evaluation. Trust what you have seen.

Getting the Full Framework

The Artis BioSolutions CDMO Selection Guide pulls this entire series into a single resource. It includes the full framework, all of the evaluation questions, the scorecard above, and an expanded FAQ covering the questions program leaders most frequently bring to us. It is designed to be the resource you keep open during your next round of CDMO conversations.

Reach out to [contact@artisbio.com] to request a copy. If you are actively evaluating CDMOs, we are also happy to talk through what the framework looks like applied to your specific program and modality.

About Artis BioSolutions

Artis BioSolutions partners with advanced therapy developers across process development, analytical development, GMP manufacturing, and IND-enabling CMC support. Our integrated team draws on deep drug development expertise across process development, analytical science, quality, and regulatory affairs, working as a true extension of your organization at every stage of the program.

If your program is in cell therapy, gene therapy, viral vector, or complex cell system development, we would welcome the conversation about whether the framework in this series, applied to our team, our facility, and our scientific depth, supports your program's needs.

Connect with the Artis BioSolutions team

inquiries@artisbiosolution.com | artisbiosolutions.com

Download the Full eBook

Partnership Model and the Questions That Reveal the Most: How to Tell Whether a CDMO is Actually the Right Fit

Landmark Bio Team — Mon, 20 Jul 2026 20:45:57 GMT

In the first three posts in this series, we covered the manufacturing landscape for advanced therapies, the importance of modality-specific expertise and development integration, and the process philosophy and infrastructure criteria that distinguish the strongest CDMO partners. This final post turns to the two remaining dimensions: the partnership model and the evaluation questions that surface what formal presentations rarely do.

These are the criteria that are most difficult to assess from a capability document and most important to get right. Capability infrastructure can be described on a slide. Partnership quality is revealed through conversation, behavior, and the character of the answers a CDMO gives when questions become specific.

1. Partnership Model: What Working Together Actually Looks Like

Beyond capabilities and infrastructure, the character of a CDMO partnership significantly influences program outcomes. The difference between a partner who executes what you specify and a partner who brings a scientific perspective to every program decision is not marginal. In advanced therapy development, where the science is complex and the challenges are real, that difference shows up in how quickly problems are identified, how reliably manufacturing runs go, and how confidently you can respond to regulatory inquiries.

The most productive CDMO relationships share a set of structural characteristics that are worth looking for explicitly during the evaluation:

Characteristics of a High-Quality CDMO Partnership

The CDMO brings scientific perspective to program decisions, offering clear recommendations, identifying tradeoffs, and sharing the reasoning behind their approach.
You have direct access to the scientists running your program, not just project managers or account leads.
Communication is proactive: the CDMO surfaces questions and concerns before they become problems, not after.
The engagement model is flexible enough to support your program wherever you are: first-time process development, IND preparation, or GMP scale-up.
Program team continuity: the scientists you meet during evaluation are the scientists who would work on your program.

That last point deserves emphasis. In the evaluation process, you will spend time with scientists who are enthusiastic, knowledgeable, and engaged. The question worth asking explicitly is whether those individuals would be assigned to your program or whether a different team takes over once the contract is signed. This happens more often than program leaders expect, and it is worth surfacing directly.

"Ask to speak directly with the scientists who would be assigned to your program. The depth and quality of that conversation is often the most informative part of the evaluation."

2. Evaluating Partnership Quality Before the Contract

Partnership quality is not observable from a capability presentation, but it is observable from how a CDMO engages during the evaluation. Pay attention to the following:

How do they handle hard questions? A partner who responds to difficult technical questions with specificity and candor, including acknowledgment of the limits of their experience, is demonstrating the communication style that you will depend on when challenges arise. Deflection or vague reassurance is a signal.

How do they approach disagreement? Ask them directly: How does your team handle a situation in which the scientifically preferred path differs from the sponsor's initial direction? The answer reveals their willingness to advocate for the science, their communication style under tension, and whether they operate as a genuine partner or as a service provider.

How do they describe the programs that did not go perfectly? Every experienced CDMO has worked through programs with technical challenges. The way they describe those experiences, whether they engage them with honesty, reflect on what they learned, and explain how their approach changed, tells you about their organizational culture. Reluctance to acknowledge past challenges is worth noting.

3. The Questions That Reveal What a Capability Presentation Cannot

The following questions are consistently useful across CDMO evaluations, regardless of modality or program stage. They are designed to surface scientific depth, process maturity, and organizational culture in ways that formal presentations rarely do. Use them as conversation starters; the goal is not a scripted answer, but a genuine exchange that reveals how the team thinks.

On Scientific Depth and Program Experience

"Share an example of a technically complex program your team has worked through. What made it challenging, and what did the team learn from it?"

This question works because it invites the CDMO to speak from experience. Specificity, candor, and reflection are good signs. Generic answers about embracing complexity or experienced teams are not. Listen for the scientific content of the answer, whether the challenge described is genuinely technical, whether the team's response demonstrates analytical rigor, and whether there is a clear through-line from challenge to learning to practice change.

On Process Development Philosophy

"At what stage do you typically define critical process parameters, and what does that process look like in practice?"

The right answer here is early. CPP definition during the RUO stage is a hallmark of a development-forward philosophy. If the CDMO describes the CPP definition as part of the GMP process itself or as occurring during technology transfer, that is a signal of their development approach.

On Technology Transfer and Transparency

"Walk us through your technology transfer process. What does the sponsor see at each stage, and where are the key decision points?"

This question reveals two things: the operational maturity of the CDMO's transfer process, and their transparency model with sponsors. A CDMO with a well-structured technology transfer will clearly describe it, identify decision points, and explain how sponsor visibility is maintained throughout. One that describes technology transfer as primarily a documentation exercise may be less oriented toward partnership.

On Problem-Solving and Root Cause Analysis

"How does your team approach process investigation and root cause analysis when a manufacturing run does not go as expected?"

Manufacturing challenges happen. The question is how a CDMO responds when they do. Look for a structured approach: clear escalation paths, defined investigation protocols, and a culture of rigorous root cause analysis rather than anecdotal troubleshooting. Ask for a specific example. The process by which a team works through a difficult run tells you as much about their capabilities as a perfectly executed run.

On FDA Engagement and Regulatory Depth

"What has your FDA engagement on programs in this modality looked like, and how has that feedback shaped your approach?"

CDMOs with genuine regulatory depth have a track record of FDA engagement that they can speak to specifically: pre-IND meetings, design review meetings, and responses to CRL questions. They also have a clear view of how that engagement has informed their development and manufacturing approach. This is not a common capability; a CDMO that can speak to it with specificity has earned a meaningful differentiator.

4. Choosing the Partner That Fits Your Program

The strongest CDMO partnerships share a consistent set of characteristics: genuine modality expertise, seamless integration across development and manufacturing, a process philosophy built for clinical execution from the start, and a collaborative approach that makes your team more capable throughout the engagement.

Programs that move efficiently from development to clinic do so because their manufacturing partner understands the science, anticipates the complexity, and brings the experience to execute reliably at every stage. A capability presentation can describe these qualities. The evaluation process is how you find out whether they are real.

Use the questions in this guide and the criteria across all four parts of this series to build an evaluation process that surfaces what you need to know. Ask for program-specific examples. Request time with the scientists, not just the project leads. Push on the hard questions. And pay attention not just to what a CDMO says, but to how they engage.

The CDMO right for your program will demonstrate this throughout the evaluation. The one that is not will, too.

About Landmark Bio

Landmark Bio was purpose-built to be exactly the kind of partner this series describes. From early process development through GMP manufacturing, QC, and IND-enabling CMC support, Landmark's integrated team draws on deep drug development expertise across process development, analytical science, quality, and regulatory affairs, working as a true extension of your organization at every stage of the program.

Landmark operates a 44,000 sq ft purpose-built facility in Watertown, MA, with 14,000 sq ft of ISO 7 and ISO 8 GMP cleanroom space across 9 suites, supporting cell therapy, viral vector, and complex cell system programs, including stem cells and CAR-T. The team has completed an FDA Design Review Meeting and has a track record of successful sponsor audits for GMP manufacturing and release testing.

COMING NEXT IN THIS SERIES

Coming Soon: The Full CDMO Selection Guide

The complete four-part series, expanded with a frequently asked questions section and a decision framework, is being released as a downloadable eBook. Reach out to contact@landmarkbio.com to request early access.

Process Philosophy, Materials Strategy, and Facility Readiness: The Operational Foundations of a Strong CDMO Partnership

Landmark Bio Team — Tue, 14 Jul 2026 17:02:16 GMT

In the previous post, we covered the two structural criteria that most reliably distinguish a translational development partner from a contract manufacturer: modality-specific expertise and integration across process development, analytical development, and GMP. These are necessary conditions for the kind of partnership that produces strong programs.

This post moves into the operational dimensions where development-forward thinking becomes visible at scale. The process development philosophy a CDMO brings to your program, the way it approaches materials qualification, and the maturity of its facility and quality systems are the factors that determine whether your first GMP run goes smoothly, whether your IND package holds up under FDA review, and whether the manufacturing partner you have chosen is genuinely ready for the complexity of your program.

These are not abstract criteria. They show up in concrete decisions made months before clinical manufacturing begins, and they compound through every subsequent stage.

1. Process Development Philosophy: Building for GMP from Day One

The approach a CDMO takes to process development tells you a great deal about how manufacturing will go. There is a significant difference between a CDMO that develops a process and then prepares it for GMP, and one that builds GMP readiness into every stage of development from the beginning.

The strongest development partners operate from the second model. They evaluate process changes, vessel transitions, and scale increases with the full clinical manufacturing context in mind from the outset. They define critical process parameters during the research-use-only stage, so that the process understanding on which GMP execution depends is established before scale-up decisions are made. They treat every phase of development as preparation for the phase that follows.

This philosophy shows up in specific operational practices:

The practical difference this philosophy makes becomes visible at scale. Consider what scaling an iPSC program from four vessels to eight actually involves. It is not simply an increase in capacity. It represents a meaningfully different manufacturing day, with different processing timelines, expanded downstream operations, and greater demands on team coordination. A CDMO that approaches this with thorough pre-run analysis and structured operator preparation executes reliably. One that treats scale-up as primarily a logistical exercise works through those details as they arise, during GMP execution, under time pressure, with your program on the line.

"A development-forward process philosophy means every question about scale, materials, and process control is answered before GMP execution begins, not during it."

2. Questions That Surface Process Development Philosophy

These questions are particularly useful for assessing how a CDMO approaches the development-to-GMP transition:

At what stage do you typically define critical process parameters, and what does that process look like in practice?
Walk us through your approach to a vessel transition. What does the evaluation process look like before you move forward?
How do you structure process development activities so that GMP teams have direct familiarity with the process before manufacturing begins?
Can you describe a program where a process decision made in early development had a meaningful impact on GMP execution, either positively or as a lesson learned?

A CDMO with a genuinely development-forward philosophy will engage with these questions with the specificity that comes from having consistently built processes this way.

3. Materials Strategy: Designing for Clinical Manufacturing from the Start

Materials qualification is one of the most impactful and most frequently underestimated decisions in early process development. Research-grade materials that work well in the laboratory often require replacement or requalification before clinical manufacturing, because the sourcing, testing, and documentation requirements for GMP-grade materials are fundamentally different.

When this transition is left to the technology transfer stage, it creates real problems. Swapping a critical raw material at that point can require partial or full process re-characterization, disrupt timelines, and generate regulatory complexity. Addressing it during process development, with full process context available, means the bill of materials is GMP-ready from the first clinical run.

A CDMO with strong translational expertise will approach materials strategy proactively and comprehensively:

Materials strategy is an area where engaging a CDMO early in development pays particularly high dividends. The decisions made about sourcing, qualification, and documentation in the research phase establish the supply chain and regulatory foundation for clinical manufacturing. A CDMO that treats materials strategy as a development-phase priority, rather than a transfer-phase problem, gives your program a meaningful structural advantage.

4. Facility Infrastructure and Quality Systems

A CDMO's physical infrastructure and quality management systems are the foundation on which everything else rests. Before selecting a partner, it is worth understanding both in detail and going beyond the capability deck to ask the specific questions that reveal operational maturity.

Facility Considerations

Suite classification and availability: What is the ISO classification of the GMP suites, and how many independent suites are available for your program? Programs that require long development timelines or multiple manufacturing runs benefit from dedicated suite access; shared suite environments introduce scheduling complexity that can affect timelines.

Modality support: Does the facility genuinely support your specific program type, including the specific culture formats, process equipment, and fill/finish capabilities your biology requires? Modality support at the marketing level and modality support at the operational level are not always the same thing.

Capacity and scheduling: What is the current capacity outlook, and what is the typical program scheduling lead time? A CDMO with strong demand may have significant lead times for GMP suite access, which will affect your program planning.

Quality and Regulatory Considerations

Audit track record: Has the facility been audited by sponsors for GMP manufacturing and release testing? What has the experience of those audits been? A track record of successful sponsor audits is evidence of operational maturity, not just documented processes, but processes that hold up under external scrutiny.

FDA engagement: Has the CDMO engaged directly with the FDA through a Pre-IND, Type B, or Design Review Meeting for programs in your modality? What came from those engagements, and how has that feedback shaped the CDMO's approach? Direct FDA engagement is a meaningful indicator of regulatory depth and the ability to anticipate review requirements.

QMS maturity: Is the quality management system mature and operating at scale, or still in active development? For programs targeting IND filing within 12 to 18 months, a CDMO whose QMS is still being built out represents a material risk.

Landmark Bio operates a 44,000 sq ft purpose-built facility in Watertown, MA, with 14,000 sq ft of ISO 7 and ISO 8 GMP cleanroom space across 9 suites. The facility supports cell therapy, viral vector, and complex cell system programs, including stem cells and CAR-T. Landmark has completed an FDA Design Review Meeting and has a track record of successful sponsor audits for GMP manufacturing and release testing.

Looking Ahead

Process philosophy, materials strategy, and facility readiness are the operational dimensions of a CDMO partnership. Together with modality expertise and development integration, they describe what a CDMO can do. What they do not describe is how it will feel to work together day to day, and how the partnership will hold up when challenges arise.

In the final post in this series, we turn to the dimensions that are most difficult to assess from a capability document and most important to get right: the partnership model itself, and the evaluation questions that surface what formal presentations rarely do.

COMING NEXT IN THIS SERIES

Part 4: Partnership Model and the Questions That Reveal the Most

What a high-quality CDMO partnership looks like in practice, and the evaluation questions that surface what a capability presentation cannot.

Modality Expertise and Development Integration: Why Direct Experience with Your Biology Matters Most

Landmark Bio Team — Mon, 06 Jul 2026 18:38:46 GMT

In the first post in this series, we looked at the manufacturing landscape for advanced therapies and what the FDA data tells us about why CMC strategy is so consequential to regulatory outcomes. Early manufacturing investment in a program is not a nice-to-have. It is one of the highest-leverage decisions a program leader makes.

This post moves from landscape to evaluation. We start with the first and most important criterion in any CDMO assessment, modality-specific expertise, and then look at the structural feature that most reliably distinguishes the strongest development partners from the rest of the field: integration across process development, analytical development, and GMP manufacturing.

A CDMO's overall capability profile, the facility size, ISO classifications, number of GMP suites, and client roster tell you something. But it does not tell you whether the scientists who would work on your program understand your biology. That distinction is where evaluations tend to fall apart, and where the most consequential differences between CDMOs become visible.

1. Modality-Specific Expertise: Matching Capability to Your Biology

Advanced therapy manufacturing is not a single discipline. The expertise required to develop and manufacture a CAR-T cell therapy differs substantially from what is needed for an MSC program, a viral vector, or an iPSC-derived product. Process conditions, critical quality attributes, analytical requirements, and regulatory considerations vary meaningfully across these modalities, and experience in one does not automatically translate to competence in another.

When evaluating CDMOs, direct experience with your specific modality is among the most important criteria to assess. This means experience that is scientific and operational, not just commercial. A CDMO that has listed your modality in their capabilities but lacks scientists who have navigated its technical complexity will struggle to be a genuine development partner when challenges arise. And in advanced therapy development, challenges always arise.

Stem cell programs illustrate why this matters. MSC and iPSC-derived products are among the most process-sensitive therapies in development. Identity, potency, and safety are all tightly coupled to process conditions, meaning that a CDMO's depth of biological understanding will directly influence the quality of decisions made at every stage, from vessel selection and media formulation through scale-up strategy and lot release testing.

"A CDMO with genuine depth in your modality will engage your questions with specificity and enthusiasm, drawing on real program experience rather than describing general manufacturing capabilities."

2. Productive Questions for Surfacing Modality Expertise

The following questions are designed to surface whether a CDMO has genuine depth in your modality or is extending a general manufacturing capability into a space where their scientific experience is more limited.

Program experience: What programs in this specific modality has your team taken from process development through GMP manufacturing? The answer you are looking for is specific: named program types, key process challenges encountered, how they were resolved, and what the team learned. General statements about capability without specific program experience should be weighted accordingly.

Regulatory outcomes: Have you supported IND filings in this modality? What does your CMC track record look like? Regulatory experience in your modality demonstrates scientific depth. A CDMO that has taken programs through the IND process understands what the FDA looks for and how to build a CMC package that holds up under review.

Scientific depth: What are the most technically challenging aspects of this biology, and how does your team approach them? This is the question that most reliably distinguishes genuine expertise from described capability. Scientists who know your biology will engage this question with specificity and with the kind of candor that comes from having worked through real problems.

Pay attention not just to what the CDMO says, but how they say it. Genuine expertise has a texture: specific examples, acknowledgment that the science is still evolving, and clear reasoning about trade-offs. The absence of that texture is informative.

3. Integration Across Process Development, Analytics, and GMP

One of the clearest structural differentiators between CDMO partners is whether process development, analytical development, and GMP manufacturing operate as a genuinely integrated team, or as sequential handoffs between separate groups.

In the handoff model, process development builds and documents the process, then transfers it to an analytical team that develops the assays, which then transfers the package to GMP for execution. At each transition, institutional knowledge is compressed into documentation, and the scientists who developed the process become less available. When questions arise during GMP execution, the team that runs the manufacturing run may not have full visibility into why certain decisions were made.

In an integrated model, these functions work together from the start. The analytical scientists who develop release assays are informed by process development data as it is generated. The GMP team participates in late-stage process development so that manufacturing execution is informed by direct experience, not only documentation. And when technical questions arise during clinical runs, the scientists with the deepest process knowledge are already part of the team.

"Integration does not simplify the science. It means the full complexity is visible and managed from the start, with the right people already informed."

The implications for program outcomes are significant. Integrated development produces more robust processes because the people asking the analytical questions are the same people who understand the process. It produces faster timelines because the iterative loop between process development and analytical development is compressed. And it produces stronger GMP execution because the team has continuity from development through clinical manufacturing.

4. What to Look for in an Integrated CDMO Model

When evaluating a CDMO's integration model, look for evidence rather than description. A few indicators worth probing:

Signs of Genuine Integration

The scientists who developed the process are still engaged through GMP execution, not just available for questions.
Assay development timelines are explicitly aligned with process development milestones, not treated as sequential.
GMP staff participate in late-stage development activities, so execution is informed by direct experience.
Technical questions during manufacturing runs go directly to the development scientists, not through a project management layer.
The program team you meet during evaluation is the team that would run your program.

That last point is worth emphasizing. The scientists who participate in your evaluation conversations should be representative of the team that would work on your program, not a presentation team assembled for business development purposes. The quality of that scientific conversation is among the most useful data points in the entire evaluation.

Landmark Bio is built around the integrated model. Process development, analytical development, QC, and GMP manufacturing are staffed as a unified team. The scientists who design a process are the same ones who see it through to GMP scale.

5. Assessing Integration During the Evaluation

Ask the CDMO to walk you through how a program moves from process development to GMP execution. Listen to how they describe the transitions. Do they describe them as handoffs or as a continuous process with the same core team? Do they describe cases where analytical development and process development ran in parallel, and how they coordinated? Do GMP scientists participate in that explanation, or only the project lead?

The most revealing question on integration is often a simple one: who is in the room when a technical question arises during a GMP run, and how does that work? The answer tells you a great deal about whether the CDMO's integration model is structural or aspirational.

Looking Ahead

Modality expertise and development integration are the two criteria that most reliably separate translational development partners from contract manufacturers. They are necessary conditions for the kind of partnership that produces strong programs. They are not sufficient on their own.

In the next post, we go deeper into the criteria that determine whether your first GMP run executes reliably and whether your IND package holds up under review: process development philosophy, materials strategy, and facility and quality readiness. These are the operational dimensions where a CDMO's development-forward thinking, or its absence, becomes visible at scale.

COMING NEXT IN THIS SERIES

Part 3: Process Philosophy, Materials Strategy, and Facility Readiness

How a development-forward approach builds the foundation for successful GMP execution, and what to look for in facility infrastructure and quality systems.

One Roof, Two Capabilities: Why Synthetic DNA + AAV Manufacturing Belongs Together

Landmark Bio Team — Thu, 18 Jun 2026 17:41:40 GMT

AAV is one of the most widely used delivery platforms in gene therapy, supporting approved treatments for inherited retinal diseases, spinal muscular atrophy, hemophilia, and a growing number of clinical-stage programs.

While AAV manufacturing has become increasingly standardized, one critical bottleneck remains: the transfer construct carrying the gene of interest (GOI).

The Bottleneck: One Custom Component Holding Everything Up

Unlike helper and Rep/Cap plasmids, the GOI construct must be generated for every new program, often making it the longest lead-time component in the manufacturing workflow. That process takes 3-5 months for GMP material suitable for clinical use, placing it on the critical path for most AAV programs. Synthetic DNA offers an alternative approach, reducing timelines while potentially improving manufacturing performance.

Figure 1. The standard AAV production workflow has a GOI transfer plasmid bottleneck. While Helper and Rep/Cap plasmids are shelf-stable, recurring inputs, the GOI-encoding transfer plasmid often takes 3-5 months to obtain enough material to proceed to next steps.

What the Data Shows

In a head-to-head comparison of AAV8 production using identical helper and packaging inputs, the only variable changed was the source of the GOI construct. One process used a conventional plasmid transfer vector, while the other used an equivalent synthetic DNA construct.

The synthetic DNA construct produced an approximately 49% increase in viral genome titer relative to the plasmid control. Importantly, the full-to-empty capsid ratio remained comparable between conditions, indicating that the increase in yield was not achieved at the expense of vector quality.

For AAV developers, both metrics matter. Higher particle production alone has limited value if vector quality declines. Maintaining the full-to-empty ratio while increasing output suggests that the productivity gain is associated with productive vector generation rather than simply increased particle formation.

Figure 2. AAV yield: oDNA vs. pDNA. oDNA delivered an ~49% higher viral genome titer than the matched plasmid control (statistically significant, p<0.01, n=2). Across the same comparison, the full-to-empty capsid ratio was maintained — meaning the yield increase did not come at the cost of packaging quality.

Where the lift likely comes from. The proposed mechanism is upstream of vector assembly itself. Synthetic, enzymatically produced DNA provides a highly defined starting material, free of bacterial backbone sequences, antibiotic-resistance markers, endotoxin, and other plasmid-derived impurities. Unlike conventional plasmids, the platform also enables the incorporation of engineered adapter sequences that can be tailored to specific application requirements. While the contribution of each factor remains under investigation, the result was clear: higher vector yield without compromising full-to-empty quality.

Reading the Process: Landmark Bio’s Viral Vector Platform

To understand why the starting material matters, it helps to look at the process it feeds. AAV manufacturing follows a well-established workflow consisting of upstream vector production and downstream purification, formulation, and fill/finish operations. Landmark Bio’s viral vector capability is built on exactly this architecture and supports both AAV and LVV through a shared process and analytical backbone.

Upstream. HEK293 producer cells are expanded and co-transfected with the GOI transfer construct, a Rep/Cap plasmid, and a helper plasmid to generate AAV particles.
Downstream. Following harvest, the vector is purified through chromatography, concentrated and formulated by TFF, and prepared for sterile fill and finish.

Figure 3. AAV workflow: From first run to GMP. Upstream, HEK293 suspension cultures scale from small development vessels into 50L–200L bioreactors with growth and viability curves that track tightly across vessel sizes. Downstream runs clarification, capture and polishing chromatography, TFF-based concentration and formulation, and fully integrated sterile fill/finish. The titer panels, plotted at each downstream step, separate physical particles from functional vector — because the ratio between them is the real measure of process health.

Table 1. Landmark Bio analytics panel.

Why analytics matter. A release vector is only as good as the analytics that characterize it. Two rows on the panel are especially relevant: residual plasmid and host-cell DNA. Anything carried in with the DNA input has to be measured out at the end. A cleaner, backbone-free input directly lightens the analytical and regulatory burden at release.

Why AAV + Synthetic DNA Is a Natural Fit

Not every manufacturing challenge has a clean solution. This one does. The fit between AAV production and synthetic DNA is structural, not incidental.

The process is standardized. Synthetic DNA replaces the GOI transfer construct without changing the broader AAV workflow.
The bottleneck is isolated. The GOI is often the only program-specific input.
Design iteration becomes practical. Updated constructs can be generated in 3–4 weeks rather than months.
Scale-up is no longer a capacity problem. Synthetic DNA scales without the constraints of bacterial fermentation.
It is a platform that runs many products. Shorter GOI lead times can accelerate development across multiple programs.

Synthetic DNA in Two Weeks — and Everything That Comes with It

The Artis team (at Landmark Bio in Watertown, MA and Syngoi in Bilbao, Spain) produces optimized synthetic DNA (oDNA) through a cell-free enzymatic process, delivering pure, linear, double-stranded DNA encoding only the GOI — with no bacterial backbone, no antibiotic-resistance markers, and no endotoxin.³

Figure 4. End-to-end oDNA platform. Workflow schematic: customer starting material (existing pDNA or linear synthetic DNA) → restriction/adapter ligation → closed linear xDNA intermediate (repository) → rolling-circle amplification → finishing with custom adapters → gram-scale oDNA. Annotate the entire path as enzymatic, with no bacterial fermentation step. Gram-scale DNA delivery in as little as 2 weeks.

RUO and SP GMP-grade delivery: 3–4 weeks, compared to the 3–5 months typically required for traditional plasmid manufacturing.

Beyond speed, synthetic oDNA offers several advantages:

Zero host-cell endotoxin. Cell-free production eliminates host-cell impurities.
No antibiotic-resistance markers. Contains only the GOI and engineered adapters.
No bacterial backbone. Only therapeutically relevant sequence.
High batch-to-batch consistency. A defined synthetic process produces reproducible material.
Engineered adapter sequences. Can be tailored to support specific design objectives.

From DNA to Vector — Under One Roof

Artis combines synthetic DNA production and vector manufacturing within the same organization, bringing together Syngoi's synthetic DNA capabilities and Landmark Bio's AAV and LVV manufacturing expertise.

Most programs manage DNA suppliers and CDMOs as separate relationships, creating additional handoffs, timelines, and points of failure. When DNA production and vector manufacturing are coordinated from the outset, development activities can run in parallel rather than in series. Key benefits include:

Aligned timelines. DNA production and manufacturing schedules are coordinated from day one.
Shared data and expertise. Technical questions are resolved within the same organization.
Faster iteration. Construct updates can move directly into process development.
Fewer handoffs. Reduced supply chain complexity and a single accountable partner.
One quality framework. DNA production and vector manufacturing operate within an aligned quality system.

The result is a more streamlined path from GOI sequence to GMP vector production.

Figure 5. Same vector, two timelines. Top: Integrated synthetic DNA and manufacturing workflow. Bottom: Traditional supplier-to-CDMO workflow with sequential handoffs and longer lead times.

What Changes When Both Are Made In-House

The benefit of co-locating synthetic DNA and vector manufacturing is more than a shorter calendar. It changes the shape of the workflow itself.

Steps run in coordination instead of in series. In the conventional model, the program waits for plasmid release, ships material, re-qualifies on receipt, and only then books a manufacturing slot. When both sit in one organization, GOI synthesis and the manufacturing schedule are planned together from the outset.

One quality system spans both halves. A split supply chain means two quality systems, two release processes, and a re-incoming-inspection step at the boundary. Co-location lets DNA release and the vector campaign live under a single, aligned quality framework — fewer redundant tests, cleaner chain of custody.

A cleaner input simplifies downstream analytics. Because oDNA carries no bacterial backbone, no antibiotic-resistance gene, and no endotoxin, the residual-plasmid and host-related assays on the vector side start from a better baseline. That cleaner input also feeds into the full-to-empty story — the data above suggests yield can rise without compromising packaging quality.

Iteration loops close in weeks, not quarters. When process development flags a change — a tweak to the expression cassette, a codon-optimization adjustment, a different regulatory element — the same organization can synthesize the revised construct and feed it straight back into the next development run.

Fewer handoffs means less risk. Every transfer between organizations is a place where timelines slip, material is damaged in shipping, or accountability blurs when something goes wrong. Removing the supplier-to-CDMO boundary removes a whole category of failure modes.

Looking Ahead

Cell and gene therapy has moved from emerging technology to clinical reality, with a growing number of approved therapies and thousands of programs advancing through development. As demand for viral vectors continues to increase, pressure on development and manufacturing timelines is only expected to grow.

While vector manufacturing has advanced significantly, starting-material lead times remain a persistent challenge. By combining synthetic DNA and vector manufacturing within a single organization, Artis provides a faster, more integrated path from GOI to clinical-grade vector. In AAV production, this approach resulted in a 49% yield improvement while maintaining full-to-empty quality.

As development timelines accelerate, integrated DNA-to-vector workflows have the potential to reduce complexity, shorten timelines, and support the next generation of genetic medicines.

Ready to Accelerate Your AAV Program?

Whether you need synthetic DNA starting material, AAV manufacturing, or an integrated solution across both, Artis is ready to support your program from GOI to clinical-grade vector.

→ Explore Artis on Scientist.com

References

1. FDA, Approved Cellular and Gene Therapy Products. fda.gov

2. Cell & Gene Therapy Insights, scalable suspension HEK293 process development for viral vectors. insights.bio.

3. Artis BioSolutions to acquire Syngoi Technologies (Jan 6, 2026). businesswire.com.

4. U.S. Cell and Gene Therapy CDMO Market report (Nov 2025) — 2,000+ trials; market size and CAGR. globenewswire.com.

5. Viral Vectors & Plasmid DNA Manufacturing Market, SNS Insider (Dec 2025). globenewswire.com.

6. Grand View Research, Plasmid DNA Manufacturing Market — size, CAGR, segment shares. grandviewresearch.com.

7. Mordor Intelligence, Plasmid DNA Contract Manufacturing Market — downstream purification as bottleneck. mordorintelligence.com.

From Bespoke to Off-the-Shelf: The Manufacturing Reckoning Behind CAR-T’s Hottest Race

Amalia Soenens & Lindsey Wolf — Thu, 28 May 2026 00:18:37 GMT

Last November we published POSTmark CAR-T: Delivering Therapies Without Delay, arguing that manufacturing continuity—not biology—is the rate-limiting step for most CAR-T programs. This year at ASGCT, just months later, it was abundantly clear that the conversation has shifted under everyone’s feet. In vivo CAR-T, once a distant technology, is now the most promising frontier in the quest to scale up cell and gene therapy, and the questions it raises are, once again, manufacturing questions.

Here is how fast the ground has moved, and what it means for the infrastructure and expertise that the field will need next.

From Peripheral Idea to the Center of the Field

The premise of in vivo CAR-T has always been simple: instead of extracting a patient’s T cells, engineering them in a facility, and reinfusing them weeks later, deliver the CAR construct directly into the body and let the immune system build the therapy itself. No apheresis. No lymphodepletion. No multi-week ex vivo manufacturing slot. The appeal is access, cost, and speed—reaching patients who can’t wait, or who were never candidates for autologous CAR-T at all.

What changed is that the capital has caught up with the concept. Over roughly the past year, big pharma has spent heavily to buy its way in. AstraZeneca acquired EsoBiotec for up to $1 billion in early 2025; Gilead’s Kite picked up Interius BioTherapeutics for $350 million and signed a Pregene Biopharma collaboration worth up to $1.6 billion; AbbVie bought Capstan Therapeutics for $2.1 billion; and Bristol Myers Squibb acquired Orbital Therapeutics for $1.5 billion. Eli Lilly entered twice in a single year—Orna Therapeutics for up to $2.4 billion, then Kelonia Therapeutics for $7 billion.¹² The pace has been brisk enough that analysts now talk less about whether pharma wants in and more about whether any independent targets remain to buy.³

Figure 1. In vivo CAR-T deal timeline. Pharma has spent billions in a single year, split across lentiviral and LNP/mRNA delivery; bubble size shows deal value. (Sources: Labiotech.eu, 2026; FierceBiotech, 2026)

The clinical story is moving in parallel, if more unevenly. First-in-human readouts across viral and non-viral platforms have been, in the words of one industry commentator, "a mixed bag."⁴ An early lentiviral in vivo study in advanced multiple myeloma showed encouraging responses, and a first global LNP-based study in systemic lupus erythematosus delivered proof-of-concept B-cell depletion.⁵,⁶ The signals are real, but cohorts are tiny and long-term follow-up is thin—this modality is at the very beginning of its evidence curve.

Why Now: Delivery Finally Caught Up

The shift isn’t arbitrary. It reflects a genuine inflection in delivery technology. As CPTx founder and CEO Hendrik Dietz framed it, the idea of programming T cells directly inside the patient has become realistic only recently, on the back of converging advances in viral-vector engineering and lipid-nanoparticle design.⁷ The economic logic follows from the biology: an in vivo therapy behaves more like a conventional biologic, where a single manufactured batch can, in principle, treat many patients—replacing a process in which every step is patient-specific and there is no economy of scale.⁷

It also expands what is clinically thinkable. Because duration of CAR activity becomes a tunable parameter—transient bursts for an autoimmune “reset,” durable expression for cancer—researchers can pursue repeat-dosing regimens and larger, non-life-threatening indications that were simply out of range when CAR-T meant a one-shot, permanent intervention.⁷ A recent regulatory perspective makes the same point from the manufacturing side: in vivo approaches could compress the production timeline to an estimated 1.5–3 weeks and lower projected cost per course by at least 50%, while shifting from a “scale-out” model to true “scale-up.”⁸

Figure 2. From bespoke to off-the-shelf: ex vivo vs. in vivo workflow comparison. In vivo CAR-T eliminates complex, expensive, and time-intensive steps like apheresis, patient lymphodepletion, and cell expansion. This allows a 2-4 week, patient-specific scale-out to shift to 1.5-3 week scale-up, extending a single batch to many patients. (Source: Lu et al., 2026)

The Delivery Challenge Is a Manufacturing Challenge

Here is what often gets lost in the excitement around the biology: every in vivo delivery approach rests on a distinct, demanding manufacturing capability. The therapy is no longer a cell product engineered under controlled ex vivo conditions and characterized before release—it is the vector itself, administered directly to the patient. That single fact resets the quality, analytical, and safety bar.

Lentiviral vectors remain the most clinically validated route for stable CAR integration. For in vivo use they are engineered with targeting ligands—DARPins, nanobodies, or modified envelopes—to home in on T cells in circulation. Recent work has shown that retargeted, later-generation lentiviral vectors can generate high levels of CAR-T cells and durable B-cell depletion in humanized models.⁹ But producing targeted LVV at clinical scale, with the titer, purity, and potency required for direct administration, is not a simple extension of ex vivo vector manufacturing. The specifications are tighter, the analytical demands greater, and the tolerance for impurities narrower, because the vector is the drug rather than a reagent.⁸

LNP/mRNA platforms offer a different trade space. mRNA delivery is transient by design—attractive for safety and repeat dosing, but dependent on precise particle engineering for biodistribution and T-cell targeting. The depth of the CMC challenge here was on vivid display at ASGCT 2026, where Acuitas Therapeutics reported that aldehyde impurities in ionizable lipid raw materials are a primary driver of RNA-lipid adduct formation—undesired bonds that suppress mRNA translation, frequently escape standard purity assays, and directly degrade potency.¹⁰ In other words, the quality of an incoming lipid lot can quietly determine whether the finished therapy works. That is a manufacturing and raw-material-control problem before it is ever a clinical one.

Targeting and the liver problem. Both modalities share a central engineering goal: get the construct into T cells and keep it out of the liver. The same Acuitas data showed an extended-circulation LNP with DARPin targeting achieving a 10-fold reduction in liver expression and driving CAR expression on more than 60% of circulating CD8+ T cells in non-human primates, with deep B-cell depletion at low doses.¹⁰ Sana Biotechnology, presenting at the same meeting, reported that its CD8-targeted fusosome platform achieved cell-specific delivery and complete B-cell depletion in NHPs without lymphodepletion.¹¹ Different mechanisms, same hard requirement—and each demands its own analytical methods to prove targeting specificity.

Figure 3. Where the challenges live: a modality trade-off matrix. In vivo CAR-T isn't one manufacturing problem but several — each delivery route trades off durability, redosing, targeting, and CMC burden differently. (Relative assessment; informed by Lu et al., 2026 and Acuitas, 2026)

What this means in practice: in vivo CAR-T is not one manufacturing problem but several, defined by which delivery modality a program pursues and how far along it is. And because the field hasn’t consolidated around a single approach—the deal flow above spans lentiviral, LNP/mRNA, fusosome, and circular-RNA platforms—programs increasingly need partners fluent across more than one.

The Incumbents Aren’t Standing Still

It would be a mistake to read this as a clean generational handoff. Novartis, whose T-Charge platform shortened ex vivo culture time to preserve younger, stem-like T cells, argues there is room for both approaches—and continues to evaluate in vivo as a next “stepwise” innovation rather than a replacement.¹² Ex vivo therapies remain the clinical bedrock; they have saved tens of thousands of patients and built the regulatory and operational scaffolding that in vivo development now leans on.⁷ Meanwhile, autologous innovation continues at the edges, including preconditioning-free dosing strategies that chip away at the same access barriers in vivo aims to remove.¹³

The honest read is that the field is broadening, not flipping. For a manufacturing partner, that is precisely the point: the programs most likely to succeed will move fluidly between modalities, and the infrastructure has to keep up.

Regulators Are Watching as Challenges Arise

The same properties that make in vivo CAR-T attractive raise the safety and regulatory stakes. Because CAR-T generation happens inside the body, precise control over transduction, expansion, and persistence is harder, shifting the risk profile toward off-target effects, immunogenicity, and—for viral vectors—theoretical vector-related responses.⁸ Early clinical data already hint at post-infusion immune reactions, including acute inflammation and isolated neurotoxicity.⁶,⁸

The bar rises further for non-oncology indications. When the population is autoimmune rather than end-stage cancer, the acceptable safety margin is far lower, and decades of expected survival amplify long-term risks. Regulators are responding by treating these as gene therapy products under accelerated-but-rigorous frameworks—RMAT in the U.S., PRIME in the EU—while pushing for early engagement, harmonized comparability standards, and long-term follow-up.⁸ Capital is flowing into exactly this space: CREATE Medicines recently closed a $122M Series B to advance a repeat-dose-capable mRNA-LNP CD19 program in autoimmune disease and oncology.¹⁴ The investment thesis and the regulatory caution are growing up together.

For developers, the practical implication is that the analytical and CMC package is no longer downstream paperwork—it is the product's regulatory backbone. Methods to quantify targeting efficiency, characterize conjugation and particle attributes, and detect impurities like the lipid adducts above must be built into the program from the first GMP run.

Where Integrated Manufacturing Fits

This is the backdrop against which an enabling partner earns its place. The capabilities that matter for the in vivo transition aren’t a single cell-therapy skill—they span payload, vector, and delivery, and they have to share a quality system.

Landmark Bio, an Artis BioSolutions company, was built around that kind of multi-modality coordination. Its GMP lentiviral vector platform runs at 50–200L scale on HEK293 suspension systems with third-generation plasmid systems and integrated two-stage chromatography purification, with analytical development and QC performed in-house by the same team supporting GMP—preserving method continuity from development through clinical supply. LVV and CAR-T manufacturing operate under one roof in modular cGMP suites supporting autologous and allogeneic workflows. Upstream, Artis also manufactures the synthetic DNA that underpins vector production, with GMP-ready starting material available in as little as two weeks, and brings AAV manufacturing alongside LVV for programs whose delivery strategy may evolve.

The argument from the November POSTmark piece applies with more force here: splitting vector and cell-therapy manufacturing across sites and quality systems creates scheduling, comparability, and timeline risk that compounds as a program scales. In vivo CAR-T, which demands tight coordination across payload design, vector engineering, delivery-system manufacturing, and analytical characterization, is the case where that integration stops being a convenience and becomes a strategic asset.

What’s Next

In vivo CAR-T has moved from preclinical promise toward clinical reality faster than most expected—propelled by billions in investment and genuine advances in delivery, tempered by small cohorts, real safety questions, and an evidence base still being written. The programs advancing through Phase 1 now will define the manufacturing and regulatory infrastructure the field needs next.

Whether a team is advancing an ex vivo program, evaluating a shift toward in vivo delivery, or building across multiple modalities, the manufacturing decisions made today will shape timelines, regulatory packages, and ultimately patient access for years.

References

1. Labiotech. In vivo CAR-T cell therapy gains momentum as big pharma bets billions. labiotech.eu (2026).

2. FierceBiotech. After Lilly’s $7B Kelonia deal, are there any in vivo CAR-T biotechs left to buy? fiercebiotech.com (2026).

3. FierceBiotech. Has Novartis’ T-Charge been overtaken by in vivo CAR-Ts? Execs argue there’s room for both. fiercebiotech.com (2026).

4. Lowe, D. The Latest CAR-T Work. In the Pipeline, Science. science.org (2026).

5. Xu, J. et al. In-vivo B-cell maturation antigen CAR T-cell therapy for relapsed or refractory multiple myeloma. Lancet 406, 228–231 (2025).

6. Wang, Q. et al. In vivo CD19 CAR T-cell therapy for refractory systemic lupus erythematosus. N. Engl. J. Med. 393, 1542–1544 (2025).

7. Labiotech (interview with H. Dietz, CPTx). In vivo CAR-T cell therapy momentum. labiotech.eu (2026).

8. Lu, J. et al. The in vivo revolution in CAR-T therapy medicinal products: challenges and regulatory prospects. Signal Transduct. Target. Ther. 11, 192 (2026). doi.org/10.1038/s41392-026-02633-4

9. Coradin, T. et al. Efficient in vivo generation of CAR T cells using a retargeted fourth-generation lentiviral vector. Mol. Ther. 33, 4953–4967 (2025).

10. Acuitas Therapeutics highlights in vivo CAR T cell engineering and ionizable lipid quality attributes at the 2026 ASGCT Annual Meeting. BioSpace/Business Wire. biospace.com (2026).

11. Sana Biotechnology presents preclinical data for in vivo CAR T cell therapy SG293 in NHPs. biospace.com (2026). [Presented as ASGCT 2026 Abstract No. 20.]

12. FierceBiotech. Novartis CEO ‘continuing to evaluate’ in vivo CAR-Ts, but no deals in the works. fiercebiotech.com (2026).

13. The Medicine Maker. This Week’s CGT News: Cabaletta reports preconditioning-free CAR-T data in pemphigus vulgaris. themedicinemaker.com (2026).

14. The Medicine Maker. CREATE Medicines raises $122 million to advance in vivo CAR pipeline. themedicinemaker.com (2026).

About the Contributors

Amalia Soenens, Ph.D., is Associate Director of Commercial Technical Solutions at Landmark Bio, bringing a rare combination of deep scientific expertise and commercial acumen to the cell and gene therapy space. She holds a PhD in Biotechnology from Universidad Politécnica de Madrid and has held product management and technical roles at EditCo Bio and Synthego. Her research background spans microbiology, genomics, and molecular biology across institutions including Cambridge and the John Innes Centre.

Lindsey Wolf, MS, MBA is Associate Director of Marketing at Landmark Bio, where she shapes brand and voice in the advanced therapies space through data-driven storytelling and strategic partnerships. With over a decade in the biotech industry — spanning CRISPR, genomics, and synthetic biology — she has led marketing across companies including EditCo Bio and Integrated DNA Technologies. Her foundation as a research scientist, combined with an MBA from UC San Diego's Rady School of Management, gives her a unique ability to translate complex science into compelling narratives.

Tech-Transfer and Manufacturing for Scaling Biotech

Landmark Bio Team — Mon, 27 Apr 2026 23:15:47 GMT

At Advanced Therapies Week 2026, our Chief Technology Officer, Gregg Nyberg, joined a panel of industry leaders to discuss how advanced therapy developers are navigating the path from early-stage process development to commercial manufacturing. The conversation reinforced something we see in nearly every program we support: the hardest part of scaling a cell or gene therapy isn't any single inflection point. It's the series of small, often quiet decisions made years earlier that determine whether the next stage is a smooth translation or a costly reinvention.

The Panel

Justin Skoble, Ph.D.

VP, Technical Operations

Caribou Biosciences

Lavakumar (Kumar) Karyampudi, Ph.D.

Senior Director, Cell Therapies

Moffitt Cancer Center

Nikki Bishop

VP, Marketing & Experience, Life Sciences

Emerson

Gregg Nyberg, Ph.D.

Chief Technology Officer

Landmark Bio

Yeh-Chuin Poh, Ph.D.

SVP & Head of Technical Operations

Tolerance Bio

This post distills what we believe are the most important of those decisions, drawing on the panel discussion, our own experience working across cell therapy, gene therapy, and complex biologics programs, and the patterns we see repeated in programs that land successfully in the clinic and beyond.

Tech Transfer Planning Starts on Day One

For most small biotechs, internal GMP manufacturing isn't realistic. Some form of technology transfer to a contract partner is effectively inevitable. That reality has a design implication most programs underestimate: your process and analytical development work should be built with a receiving site in mind from the earliest bench experiments.

This doesn't mean every Phase 1 process needs to be commercial-grade. It means every Phase 1 process needs to be GMP-compatible and legible to a partner who wasn't in the room when the decisions were made. The programs that struggle most at tech transfer aren't the ones with unusually complex biology. They're the ones where knowledge was captured informally, assays were optimized for a specific analyst, and critical process decisions were never written down in a form that survives a handoff.

Advanced therapies are also inherently multi-transfer products. A process developed in an academic setting or at a translational CDMO will almost certainly move again, to scale, to a new site, or to a commercial partner. Every early decision is a deposit into that future. A fellow panelist framed it as starting with the end in mind, and that framing is exactly right: the question is never whether your process will be transferred, only how many times.

What this looks like in practice

Process development documentation that reads as clearly to an external team as it does to the scientists who ran the work.
Analytical methods that aren't dependent on a single operator or a single instrument configuration.
A critical quality attribute framework that's reasonable for the program's stage but robust enough that you can trust your clinical signal when it arrives.
Explicit decisions about what's locked down for Phase 1 and what remains flexible, documented so the next team inherits the reasoning, not just the outcome.

Build vs. Buy: The Math Has Changed

Five or six years ago, in the boom years for biotech, many advanced therapy developers built their own manufacturing facilities. Capacity was constrained, capital was plentiful, and owning the clean room felt like the safer bet. The landscape today looks different. Capacity is widely available. Capital is more constrained. And the dose volumes required for early clinical work rarely justify the cost of operating a dedicated facility that sits partially empty between campaigns.

For the vast majority of early-stage programs, outsourcing to the right partner is now the right economic answer. But we'd argue it's also the right strategic answer, and that second point gets less attention than it deserves. If an independent manufacturing partner can reproduce your process successfully, the value of your program rises. "Only we can make this" isn't a strength story for investors, for potential acquirers, or for the long-term viability of a therapy. Reproducibility at arm's length is.

"If you over-invest early, you may not have a company to get to the next stage. But you also don't want to cut corners and incur technology debt that forces expensive rework later. Quality-by-design helps you focus investment on the attributes most likely to matter."

Gregg Nyberg, Chief Technology Officer, Landmark Bio

Choosing the right partner for your stage

The criteria that matter most shift as your program matures. For first-in-human work, phase-appropriate capabilities, scientific engagement, and the right level of attention often matter more than massive commercial infrastructure. As programs move toward pivotal and commercial, the filters shift: scalable GMP infrastructure, modality-specific experience, regulatory track record, and a partnership philosophy that treats tech transfer as a collaboration rather than a one-way handoff.

What we've seen work particularly well is engaging with a manufacturing partner early, even before the official tech transfer kickoff. A team supply run or an early engineering campaign tells you more about how a process will behave in GMP than any amount of documentation review can. It also surfaces the questions that are better asked now than at BLA stage.

Phase Appropriateness Is a Resource-Allocation Decision

One of the hardest judgment calls in advanced therapy development is deciding what to invest in and when. Programs that over-specify their processes at Phase 1 often arrive at the clinic years later than programs that prioritized getting meaningful human data first. Programs that under-invest in their CQA framework can't trust the signal they see when the data does arrive.

We don't believe there's a universal answer to this trade-off. What we believe is that programs need to make it deliberately, not by default. A fully qualified potency assay, for example, is not a regulatory requirement for a Phase 1 filing. It's critical for mechanism-of-action confidence and for commercial readiness, but the panel raised a fair point: programs that chase perfect assays before first-in-human sometimes fall behind peers who accepted a simpler early assay and planned for comparability work later. The right answer depends on the biology, the modality, and the program's funding runway.

One framing from the panel that resonated with us: cost, quality, and timeline are three levers you can only optimize two of at once. Want speed and quality? You'll pay for it. Want low cost and speed? Quality will suffer. Want cost and quality? Your timeline stretches. Every program is continuously tuning among those three, and the right balance shifts as the program matures.

Quality-by-design as a focus tool

Quality-by-design is sometimes treated as a commercial-stage activity, but it's most valuable earlier, when resources are tightest. Used well, it's not about maximizing quality everywhere; it's about identifying which quality investments have the highest leverage and concentrating effort there. Not every step in a process carries equal weight. Not every attribute is equally likely to be critical. The sooner a team can make that distinction, the more efficiently it allocates both time and money.

The Hardest Thing to Transfer Is Tacit Knowledge

Batch records and SOPs are necessary, but they're the easy part. The knowledge that actually determines whether a receiving site succeeds is tacit: the judgment calls, the "how we handle this edge case" intuitions, the institutional memory that accumulates over years of running similar processes. That kind of knowledge rarely lives in documents. It lives in people.

The programs that transfer well are the ones that treat tacit knowledge as something to be deliberately moved, not something that will transfer on its own. In practice, that means three things.

Document what's usually undocumented. What has worked on similar programs. What hasn't. What your site has learned that isn't in the batch record.
Invest in shadowing, not slide decks. Receiving-site staff spend time at the sending site. Sending-site staff are present on the floor for the first validation runs. The camaraderie that builds between teams is not a nice-to-have; it's what carries the partnership through the first deviation.
Build redundancy into expertise. A single long-tenured expert is a vulnerability, not an asset. Institutional knowledge has to live across multiple people to survive staff changes, and those changes happen more often than anyone plans for.

One panelist put it sharply: you can't PowerPoint your way through a tech transfer. That matches what we see. The transfers that feel easy in the planning phase but fall apart in execution are almost always the ones that leaned on presentations and documents rather than people spending real time together.

Digital Infrastructure: Start Simple, Start Early

Digital transformation in manufacturing is often framed as a commercial-stage problem. We'd argue the opposite. The bridge to commercial manufacturing doesn't start at the end of the bridge; it starts at the bench. The companies that struggle most with digital integration late aren't the ones that picked the wrong platform. They're the ones that never developed the habit of capturing decisions alongside data in the first place.

You don't need an enterprise data fabric at the research stage. What you need is a discipline around recording why decisions were made, not just what the outcome was. Why did the team change buffer composition? Why did they settle on a particular cell density? That reasoning is what makes future scale-up a translation problem rather than a re-invention problem.

Structured spreadsheets are a legitimate starting point. The sophistication grows with the program. But the habit has to start early, because by the time you need the data for a comparability package or a regulatory filing, there's no way to reconstruct what wasn't captured.

Commercial Readiness Is Really About Regulatory Strategy

A detail that often surprises developers new to advanced therapies: every mid-program change carries a comparability cost. And at commercial stage, that cost can be substantial enough to force changes in approach, or in extreme cases, to force additional clinical work.

The programs that navigate this well are the ones that treat regulatory strategy as a Phase 1 concern, not a BLA concern. A program that plans for minimal process changes between Phase 1 and pivotal can sometimes carry early clinical data all the way through filing. A program that changes its process every time something improves may find that earlier data no longer applies.

This isn't an argument for never changing a process. Processes improve, and sometimes those improvements are essential. It's an argument for making those changes deliberately, with a clear view of what they cost in comparability work and how that cost compounds across a program's life cycle.

Tech Transfer Is Owned Jointly, by Design

We're sometimes asked who owns the success of a tech transfer: the sending site or the receiving site? The honest answer, and the answer we believe in, is that neither owns it alone. That's the point.

What works is explicit joint ownership, with three things established from day one: governance, accountability across each sub-function (manufacturing, analytics, quality), and a defined path of escalation. Without clarity on any of those, the transfer drifts. Issues become blame. The relationship frays at exactly the moment both sides most need it to hold.

The partnerships that work best are the ones where both sides know what success looks like, what their specific piece of it is, and how to raise problems early enough that they can be solved rather than managed.

What the FDA's Recent CMC Flexibility Means in Practice

The FDA's recent announcement signaling greater flexibility on CMC for cell and gene therapy was welcome news to the panel, and to us. The question, as always, is how it plays out in practice. The announcement is a statement of intent, not a guidance document, and much of what's in it represents a commitment to use tools that already exist, quality-by-design approaches, prior knowledge, platform data, more consistently.

Our read: this is a genuinely constructive signal, particularly for programs approaching BLA. But programs should not plan as if the regulatory bar has shifted. Plan as if the tools for engaging flexibly with the agency have been reaffirmed, and prepare to use them well. The strongest CMC packages still come from programs that did the underlying process and analytical work carefully, not from programs that counted on flexibility to compensate for gaps.

How We Think About This

The thread that runs through all of this, from the earliest process development decisions to the partnerships that carry a program to commercial, is that scaling an advanced therapy is never a single event. It's a long sequence of decisions, most of which are made quietly, years before their consequences become visible.

At Landmark Bio, we think of our role as helping developers make those decisions deliberately, with the benefit of what we've learned across the programs we've supported. The work we care most about isn't the headline tech transfer moment. It's the hundred small decisions in the year before it that determine whether the transfer feels like a translation or a reinvention.

If your program is approaching one of those decisions, we'd welcome a conversation.

Full Session Transcript

Read the full panel transcript

Panelist Introductions

Justin Skoble, Caribou Biosciences: Hi, I'm Justin Skoble, the Vice President of Technical Operations at Caribou Biosciences. We're developing CRISPR genome-edited allogeneic CAR-T cells. I oversee our process development, analytical development, and manufacturing teams and our supply chain function at Caribou. We're really trying to expand access to CAR-T cells by making them off the shelf.

Lavakumar (Kumar) Karyampudi, Moffitt Cancer Center: Hello, everyone. I'm Kumar Karyampudi. I'm a Senior Director of Cell Therapies at Moffitt Cancer Center. I oversee a cGMP cell and vector manufacturing group at Moffitt. It's a group of 80 people that handles a wide variety of cell therapy products as well as early-stage vector manufacturing. I'm glad to be part of this panel and looking forward to an exciting discussion.

Nikki Bishop, Emerson: Good morning, everyone. Nikki Bishop. I'm the Vice President of Marketing and Customer Experience for Emerson's life sciences software portfolio. All this week when I meet somebody, they say, "Emerson, oh, that Emerson." So I'll go ahead and say, yes, that Emerson. Emerson Electric is the larger company, but we focus on manufacturing and automation software solutions for optimization in manufacturing.

Gregg Nyberg, Landmark Bio: Good morning. I'm Gregg Nyberg, Chief Technology Officer at Landmark Bio. Landmark is a U.S.-based contract development and manufacturing organization that specializes in the advanced therapy space, especially in the translational area. We were recently acquired by Artist Biosolutions, so we're part of the Artist Biosolutions group.

Yeh-Chuin Poh, Tolerance Bio: Hi, everyone. I'm Yeh-Chuin Poh. Tolerance Bio is a new startup, only been around for a year and a half to two years. We're a therapeutic company with two different platforms. The first is a regenerative medicine platform using iPSCs differentiated into thymic cells to regenerate the thymus. The other is a monoclonal antibody platform, using a monoclonal antibody to preserve thymic involution and prevent atrophy.

Part 1: Development Through Phase 1

Moderator: Let's start with tech transfer. How soon do you start planning a tech transfer? Is it something that's in mind from the beginning, or do you kick it off when the need arises?

Justin Skoble: Planning for tech transfer is something you have to do if you want to actually develop a drug. Most small biotechs and startups can't afford their own manufacturing, so you'll have to transfer your process to a CDMO. It's really important to invest early in process development and analytical development internally, so that you know how to make your product, how to analyze it, what "good" looks like, and how to maintain critical quality attributes. It doesn't have to be perfect for Phase 1, but it needs to be at least somewhat compatible with GMP principles. Further tech transfers happen later as you scale up.

Yeh-Chuin Poh: I've developed several cell therapy products over my career, and tech transfer is always something people talk about. Some companies use stage-gating internally; when you hit certain milestones, you start the transfer process. But there's a balance. You can't wait for the researchers to finish before starting tech transfer. You need to start with the end in mind and develop something that's manufacturable. That said, if you try to develop something for commercial applications at the outset, the cost will be extreme and most startups can't absorb it. You need to be phase-appropriate: build something translatable to commercial, but without spending all your resources up front before you have clinical data.

Kumar Karyampudi: Just to add, we all know for cell therapy products, there are always multiple tech transfers inherent to any product. From day one of process development, you have to keep in mind that this product will be transferred somewhere else to scale up or support late-stage manufacturing.

Moderator: How do you decide between outsourcing the work and manufacturing in-house? And if you do outsource, how do you select the right CDMO?

Yeh-Chuin Poh: This is a big question, build versus buy. Five or six years ago, when biotech was booming, you saw many companies building their own manufacturing facilities because there was a capacity constraint. Today, many of those facilities are sitting empty. There's excess capacity, and you didn't need to spend all the capital up front. A lot of CDMOs are still operating under the old mentality, slot reservation fees, limited flexibility, while the clean rooms sit partially empty. The industry needs to come to grips with the fact that we're not in the booming days anymore. For most companies, the decision is outsourcing. You lose some control, but you gain reproducibility. If an independent CDMO can repeat your process, your value proposition goes up.

Justin Skoble: I agree. I can't imagine, in this economy, early-stage companies building their own manufacturing to support early-stage assets. The demand is too low and the math doesn't work. It becomes a matter of finding the right partner for your stage. Early-phase academic CDMOs can give you phase-appropriate capabilities and the attention and expertise you need to get first-in-human clinical data.

Moderator: Nikki, how do you integrate technology and automation early on?

Nikki Bishop: The easiest answer is: just start. A lot of what the industry does, it does because that's how it's always done. The commercial manufacturing bridge doesn't start at the end of the bridge; it starts in R&D. The misnomer is that commercial-ready technology is heavy and expensive and we can't afford it early. The reality is there are small things you can do while you're still figuring out if the science works. Simple digital data capture. Spreadsheets instead of paper. Capturing the decisions, not just the numbers; why did you make that choice? When you get to commercial manufacturing, you have the science nailed, and you're just making it scalable instead of transferring the science itself. Start simple, start small. Ask people who've been through commercial manufacturing what they wish they'd done earlier.

Gregg Nyberg: Building on that, picking a good partner early can help you understand what the journey looks like. One thing we do is engage with partners very early, often on a team supply run. They transfer the process, we run it, and we learn together what's going to be challenging to scale. You can think through bite-sized chunks: how do you run the process now in a way that sets up for automation and a streamlined commercial process later?

Moderator: What about the human side, capturing institutional knowledge from a lab or an academic setting and transferring it to another site?

Kumar Karyampudi: The mindset has to be that tech transfer is not a one-off activity; it's a collaboration between sending and receiving sites. Three things matter. First, tacit knowledge. Batch records and SOPs are standard, but institutional knowledge, how the site handles the product, what's worked, what hasn't, usually lives in people's heads. Document it. Second, you can't PowerPoint your way through a tech transfer. You have to have people shadowing. Receiving-site staff should spend time at the sending site. Sending-site staff should be there for the first two validation runs. Third, build redundancy. We've been manufacturing cellular products at Moffitt for 17 years. Some of my team have been there from the beginning, but I can't depend on one or two people being the experts. You need replicas of that expertise.

Justin Skoble: I'll echo that. We've done this very successfully, bringing folks from PD or MSAT at our CDMOs out to our facility, translating batch records into their language, having our people on-plant for every run, not just the first few. That builds the partnership and the camaraderie that makes the whole thing work.

Part 2: Phase 1 to Pivotal

Moderator: How do you balance phase appropriateness in your process and analytical development with the intent to go commercial eventually?

Yeh-Chuin Poh: Process and analytical development are absolutely critical. But you need to know what phase you're in. A lot of companies follow industry norms that, when you dig in, aren't actually regulatory requirements. Take potency assays. Critical for mechanism of action, critical for commercial, but technically, not required for your first-in-human filing. I've seen programs invest so heavily in potency assay development that they're years behind programs that prioritized showing clinical efficacy first. There's a balance. You want to be able to bridge to the future, but don't spend all your eggs now on things that cripple your timeline.

Kumar Karyampudi: When we support 15 INDs and have to think about which ones advance, it comes down to the signal from the early-phase trial. But you have to be careful: you need reasonable control on your CQAs and the parameters that drive them. Without that, you can't believe the data coming out of your trial, and you can't make a good decision about advancing.

Justin Skoble: One of the things that's difficult in advanced therapies; we think we have a product with CQAs that matter, but we don't really know until we've put them in patients and seen the responses. You learn from the translational data what "good" looks like. Until then, investing too early can give you more control over the wrong process.

Moderator: How do you balance speed with being genuinely ready to advance?

Gregg Nyberg: A lot of our clients need to get to the next value inflection point; it's existential. If you over-invest early, you may not have a company to get to the next stage. But you also don't want to cut corners and incur technology debt that forces expensive rework later. Quality-by-design helps you focus investment on the attributes most likely to matter. Don't try to optimize everything; focus on what has the most leverage.

Yeh-Chuin Poh: I think of it as three buckets: cost, quality, timeline. You can only optimize two. If you want cost and time, you compromise quality. If you want cost and quality, you compromise time. If you want time and quality, you pour money into it. Every program is tuning among those three throughout its life cycle.

Nikki Bishop: And the thing about flexibility: it really centers on standardization. What can you standardize? If you leave it too late, you don't have the flexibility you need because once you're commercial, you may want to manufacture in multiple sites in multiple ways. Find three things you can standardize on early: how you capture data, tools, templates, QbD standards; not to slow yourself down, but to help you go faster later.

Part 3: Preparing for Commercial

Moderator: From a CDMO perspective, what does a "good" tech transfer look like? And is it easier to transfer something at commercial stage?

Gregg Nyberg: An ideal client knows their product and process well. Realistically, phase-appropriate, younger companies don't know either perfectly yet. What matters most is that the client understands where their gaps are and is willing to engage. On commercial-stage transfers: it depends on how they got to that stage. If it was done in isolation from GMP realities, any change at commercial triggers significant comparability work. That's why partnering with your intended commercial CDMO early has real benefits.

Nikki Bishop: Nothing about tech transfer is easy at any stage. At commercial, you've proven the science; what you're transferring is confidence. You've proven it works. Can you transfer that confidence to another site while maintaining integrity? Technology is an enabler, but it's also the human interaction. Build those human relationships across your manufacturing partners.

Yeh-Chuin Poh: And there's regulatory. Changes in early phase are easier. At commercial, the bar is significantly higher. I've seen a commercial cell therapy still using manual hemocytometer counts, three operators, three hours, because switching to automated counting at commercial stage is such a high bar. Regulatory strategy should start at the beginning, not at commercial. At Semma Therapeutics, which Vertex acquired, our pluripotent stem cell program ran one continuous trial where Phase 1 efficacy data applied all the way through pivotal, because we didn't change anything material in between. If you change things, that same data doesn't travel.

Moderator: Who owns a tech transfer?

Kumar Karyampudi: Not one party. Both. Governance, accountability, and a defined path of escalation need to be aligned between both parties from the beginning. The sending site knows the process best; they have to transfer it accurately. The receiving site has to execute precisely and meet quality standards. Within each, you have manufacturing, analytics, QA; each owns part of the responsibility. If accountability isn't clear and governance isn't in place, the transfer drifts. Neither party owns success or failure alone.

Moderator: Final question: the FDA's recent move toward CMC flexibility for cell and gene therapy. Thoughts?

Kumar Karyampudi: Welcoming news. Early-stage hasn't really changed. At BLA stage, it looks like FDA is willing to work more flexibly with sponsors. That's good news.

Gregg Nyberg: The proof will be in the pudding. It was an announcement, not a guidance, and it's not clear yet that the day-to-day reviewers have absorbed it. A lot of what's in there is really leveraging tools that already exist, but with a commitment to double down on actually using them. It comes down to execution.

Q&A: Key Questions From the Panel

When should a program actually start planning its first tech transfer?

From day one of process development. The panelists were unanimous on this. If a startup intends to commercialize, and nearly all of them do, tech transfer to a CDMO is effectively inevitable, which means process and analytical development should be designed for portability from the earliest bench work. The caveat is phase appropriateness: you're building something transferable and GMP-compatible, not something commercial-grade. Early over-investment in commercial-grade processes is one of the most common ways young biotechs run out of runway before they have clinical data.

Build in-house manufacturing or outsource to a CDMO?

For the vast majority of early-stage programs in today's market, outsource. The economic calculus that justified in-house builds five or six years ago has shifted dramatically. Capacity is widely available, capital is more constrained, and the dose volumes required for dose-escalation studies don't support the cost of operating a dedicated facility. There's also a strategic benefit to outsourcing that's often missed: if an independent CDMO can reproduce your process, the value of your program, to investors, partners, and acquirers, goes up. In-house-only is a liability, not a moat.

How do you choose the right CDMO for your stage of development?

Match the partner to the program's current stage, not where you hope it'll be in three years. Early-phase academic and translational CDMOs often give first-in-human programs the attention, expertise, and phase-appropriate flexibility they need. You may not commercialize out of that facility, and you should plan for a future transfer, but the trade-off is usually the right one. As you move toward pivotal and commercial, the criteria shift: scalable GMP infrastructure, modality-specific experience, regulatory track record, and a partnership philosophy that treats tech transfer as a collaboration rather than a handoff become the filters that matter most.

How early should you start building digital and data infrastructure?

Earlier than most teams think, but simpler than most teams fear. You don't need an enterprise data fabric at the bench stage. What you need is a habit of capturing decisions alongside data: why a condition was changed, why a parameter was set, what the team learned in the last run. That knowledge is what makes future scale-up a translation problem instead of a re-discovery problem. Structured spreadsheets are a legitimate starting point. The sophistication grows in step with the program.

How do you capture tacit knowledge so it actually transfers?

Three practices, together. First, deliberately document tacit knowledge, what has worked, what hasn't, what your site has learned across prior programs. Second, invest in shadowing rather than slide decks: receiving-site staff spend time at the sending site, and sending-site staff are present on the floor for the first validation runs. Third, build redundancy. A single long-tenured expert is a vulnerability; institutional knowledge needs to live across multiple people to survive.

Do you need a fully qualified potency assay for Phase 1?

Technically no, it's not a regulatory requirement for first-in-human. The panel's shared view was that over-investing in analytical development before clinical proof-of-concept is one of the most common ways early programs burn resources and timelines. The balance: invest enough in your critical quality attribute framework that you can trust the signal you see in the clinic, but don't try to lock down everything before you have patients in trial. Comparability work can bridge later, as long as you're planning for it.

Is it easier to tech-transfer an early-stage or a near-commercial process?

It depends less on stage than on how the process was developed. A near-commercial process built in isolation from GMP realities can be harder to transfer than a deliberately phase-appropriate early-stage one, because any change at commercial stage triggers significant comparability work and regulatory scrutiny. The takeaway: partner early with the team you intend to commercialize with, so you're not re-engineering a process under time pressure at BLA stage.

Who owns the success of a tech transfer?

Both parties, and that's the point. No single group, sending or receiving, owns the outcome. What works is explicit governance, clear accountability across each sub-function (manufacturing, analytics, QA), and a defined path of escalation set from day one. Without those, the transfer drifts and issues turn into blame. With them, both sides know what success looks like and how to raise problems before they become failures.

How do you balance speed to clinic with being genuinely ready to advance?

Use a quality-by-design lens to focus investment where it has the most leverage. You can't optimize every attribute of every step; you'll run out of runway. What you can do is identify the handful of attributes and parameters most likely to be important, invest deeply there, and accept phase-appropriate trade-offs elsewhere. The thing to avoid is accumulating "technology debt," cut corners that force expensive rework at the next stage, when you have less time and more regulatory scrutiny.

What does the FDA's recent move toward CMC flexibility actually mean for programs?

Cautiously encouraging. The panel's consensus was that the announcement signals a real willingness to engage with sponsors more flexibly on CMC, particularly at BLA stage, but the practical change will depend on how day-to-day reviewers apply it. Much of what's in the announcement is a commitment to use tools that already exist, quality-by-design approaches, prior knowledge, platform data, more consistently. The proof, as one panelist put it, will be in the pudding.

How to Choose the Right CDMO

Landmark Bio Team — Thu, 02 Apr 2026 14:15:00 GMT

Your science is ready.

The mechanism is well-characterized, the preclinical data is compelling, and the clinical development path is coming into view. The next question — and one of the most consequential you will face — is who to bring alongside you to build the manufacturing and development infrastructure that gets your therapy to patients.

Selecting a contract development and manufacturing organization (CDMO) is both a scientific and strategic decision. Get it right, and you gain a true extension of your team: scientists and engineers who are just as invested in your program's success as you are. Get it wrong, and you may not find out until a GMP run goes sideways or an FDA inquiry lands that takes eight months to resolve.

Over the next four posts, we're going to give you the full framework for making this decision well.

What this series covers:

In Part 1 (you're here), we start with the landscape, what makes advanced therapy manufacturing categorically different from traditional biologics, and what the FDA data tells us about where manufacturing investment pays off. The numbers are clarifying.

In Part 2, we move to the first and most important evaluative criterion: modality-specific expertise. Not general manufacturing capability, direct, scientific experience with your biology. We'll show you how to assess it and what genuine depth looks like in a conversation.

In Part 3, we go deeper into process development philosophy, materials strategy, and facility readiness, the criteria that determine whether your first GMP run goes smoothly and whether your IND package holds up under review.

In Part 4, we close with the partnership model and the evaluation questions that surface what a capability presentation rarely does. These are the questions we'd ask. They work.

If you're actively evaluating CDMOs right now, you may want to read the whole series before your next meeting. If you're earlier in the process, start here and follow along — by the end, you'll have a framework you can actually use.

Let's start with the landscape.

The Advanced Therapy Manufacturing Landscape

Understanding the environment your program operates in — before you choose a partner

Your science is ready. The mechanism is well-characterized, the preclinical data is compelling, and the clinical development path is coming into view. The next question — and one of the most consequential you will face — is who to bring alongside you to build the manufacturing and development infrastructure that gets your therapy to patients.

Selecting a contract development and manufacturing organization (CDMO) is both a scientific and strategic decision. The right partner accelerates your timeline, strengthens your regulatory position, and brings the manufacturing expertise that transforms research-grade processes into reliable clinical execution. A strong fit means you gain a true extension of your team: scientists and engineers who are just as invested in your program's success as you are.

Before you can evaluate a CDMO effectively, it helps to understand the environment your program is operating in. Advanced therapy development is a uniquely complex manufacturing challenge, and the regulatory landscape reflects that complexity clearly.

Why Advanced Therapies Are Different

Advanced therapies — including cell therapies, gene therapies, viral vectors, and complex cell systems such as stem cell programs — present manufacturing challenges that are fundamentally distinct from traditional biologics. The biology is more sensitive, the processes are more operator-dependent, and the path from development to GMP execution requires a level of integration that many CDMOs are still building toward.

In a traditional small molecule or recombinant protein program, manufacturing is largely a chemistry or expression problem. You optimize conditions, validate the process, and scale. In advanced therapies, you are manufacturing living cells or biology-dependent vectors — systems that respond dynamically to process conditions, where small deviations can meaningfully affect product quality, potency, and safety.

This means the CDMO you choose is not simply executing a defined protocol. They are a scientific partner who must understand your biology deeply enough to develop processes that are robust, reproducible, and ready for regulatory scrutiny. The distinction matters enormously when it comes time to evaluate your options.

"The decisions made in process development directly shape the success of GMP manufacturing. The strongest programs build that foundation early, with the right team alongside them."

What the Regulatory Data Tells Us

The FDA data on advanced therapy development makes the stakes clear. Chemistry, manufacturing, and controls (CMC) strategy is consistently among the most consequential elements in regulatory review — and the programs that invest early in manufacturing infrastructure are the ones that move most efficiently through that process.

74%

of FDA Complete Response Letters issued between 2020 and 2024 involved manufacturing or quality considerations, highlighting how central CMC strategy is to regulatory outcomes.

~40%

of IND submissions that are placed on clinical hold or require additional information cite CMC as a primary area for strengthening, underscoring the value of early and thorough process development.

8.4 months

is the average time required to resolve CMC-related inquiries from FDA — compared to 4.8 months for protocol-related questions — reflecting the technical depth these reviews require.

These figures point clearly to where manufacturing investment pays off. Programs that engage deep manufacturing expertise early, build robust CMC packages, and select development partners with strong translational experience are the ones that move efficiently and confidently through this landscape.

For program leaders, this data reinforces a counterintuitive truth: the CDMO selection decision is not just a manufacturing decision. It is a regulatory strategy decision. The partner you choose will shape your CMC package, your IND readiness, and your ability to respond to FDA inquiries with data-supported confidence.

How to Think About This Decision

CDMO selection is often framed as a capability question: Does this organization have GMP suites? Do they have experience in my modality? Do they have capacity? These are valid questions, but they are not sufficient. The capability baseline is table stakes.

The more useful frame is alignment. Does this organization's scientific depth, development philosophy, team structure, and partnership model align with the complexity of your program? The strongest CDMO partnerships are built on genuine scientific engagement — not just manufacturing execution.

In the sections that follow, we walk through the specific criteria that separate a translational development partner from a contract manufacturer, the questions that surface scientific depth and organizational maturity, and the practical framework for making a decision you will have confidence in.

Key Questions to Bring into the CDMO Landscape

As you enter the evaluation process, a few foundational questions will help orient your thinking:

What is the FDA's current view of manufacturing quality in programs like mine, and how does a strong CDMO address that specifically?
What does a translational CDMO bring to a program that a standard contract manufacturer does not?
How early in my program does CDMO selection actually matter, and what does early engagement change?
What does a development partner who is genuinely invested in program success look like in practice?

COMING NEXT IN THIS SERIES

Part 2: Modality Expertise and Development Integration — Why direct experience with your specific biology is the primary filter in any CDMO evaluation, and what integrated development looks like in practice.

📩 Connect with us to learn how our capabilities can accelerate your program.

Extracellular Vesicles: The Next Frontier in Non-Viral Therapeutic Delivery

Landmark Bio Team — Thu, 26 Feb 2026 00:13:47 GMT

Extracellular vesicles have been a subject of scientific fascination for decades. For most of that time, they were studied primarily as mediators of intercellular communication - natural couriers shuttling proteins, nucleic acids, lipids, and metabolites between cells. What has shifted dramatically in recent years is the recognition that this biology can be harnessed therapeutically: EVs can be engineered, loaded, scaled, and delivered as a drug product.

That recognition has generated significant interest. It has also exposed a significant gap. The manufacturing and analytical infrastructure needed to move EV programs from research into the clinic is still catching up to the science.

Artis BioSolutions is working to close that gap.

What Are Extracellular Vesicles?

EVs are non-replicating, lipid bilayer-delimited particles naturally released by virtually all cell types. They are intrinsic to biological function - not an artifact, not a byproduct, but active mechanism cells use to communicate across distances and biological barriers.

Their structural properties make them attractive as therapeutic vehicles. Unlike synthetic nanoparticles, EVs are inherently biocompatible. Unlike viral vectors, they are non-replicating and carry a lower immunogenicity burden. They can be loaded with diverse molecular cargo - nucleic acids, proteins, small molecules - and surface-engineered to improve targeting.

The therapeutic applications being explored span oncology, neurology, regenerative medicine, and beyond. EVs are being studied as standalone therapeutics, as delivery vehicles for RNA and gene editing payloads, and as biomarkers for disease monitoring.

Figure 1. EVs can be grouped on the basis of their constituents, ranging from native EVs to highly engineered versions. Despite being highly functional and adaptable, their drawback has been largely around consistent and scalable manufacturing. (Image credit: Herrmann et al. 2021. Nature Nanotechnology)

The Manufacturing Challenge

Interest in EVs as a therapeutic platform has outpaced the infrastructure to support clinical development. Several challenges are well recognized across the field.

Cell source variability. EV production is inseparable from the biology of the producing cell. Different cell types - primary cells, stem cells, immortalized lines like HEK293T - produce EVs with distinct size distributions, surface marker profiles, and cargo compositions. Process decisions made early in development, including whether to work in adherent or suspension culture, at shake flask or bioreactor scale, have downstream consequences for product consistency and scalability.

Purification complexity. EVs exist in a complex biological matrix alongside cell debris, protein aggregates, and non-vesicular particles. Standard downstream approaches - harvest clarification, tangential flow filtration, column chromatography - each introduce variables that can affect yield, purity, and product integrity. Establishing a purification train that is both efficient and reproducible at scale is non-trivial.

Analytical limitations. This is the most acute bottleneck in EV development today. Achieving accurate, reproducible measurement of EV size, concentration, and surface marker identity using current analytical tools is genuinely difficult. No single method gives the complete picture. NTA and DLS provide size and concentration data but differ in sensitivity and resolution. Flow cytometry, if configured correctly, can add biomarker characterization. Getting these methods to agree - consistently across lots - requires deliberate method development and cross-validation.

Artis BioSolutions Approach to EV Development

Artis BioSolutions EV capabilities sit within a broader advanced therapies platform that spans cell therapies, viral vectors, nucleic acids, and non-viral delivery. That integration matters. EV programs often require expertise across multiple disciplines simultaneously - upstream cell biology, downstream bioprocessing, and analytical chemistry - and those disciplines need to be in conversation with each other throughout development.

Upstream Production

Artis BioSolutions works across a range of EV-producing cell types, including iMSC, MSC (iPSC-derived or primary), HSC, MKSC, and HEK293T cells. Both adherent and suspension production workflows are supported, with experience adapting adherent processes into scalable suspension systems - a transition that is technically meaningful and often necessary to achieve the production volumes required for clinical supply. Production is supported from shake flask to bioreactor scale.

Downstream Production

Artis BioSolutions downstream EV workflow covers the full purification train: harvest clarification to collect EV-rich media, TFF concentration, column chromatography, sterile filtration, and formulation. The goal is a purification strategy that is not just effective at bench scale but translatable - one that holds up as production volumes increase and produces the batch-to-batch consistency regulators expect.

Analytical Development & Characterization

The core analytical challenge with EVs is that no single instrument reliably measures size, concentration, and identity simultaneously. The Artis BioSolutions analytical team developed a rapid method using conventional flow cytometry with Violet laser Side Scatter (VSSC) configuration to address this directly. Key findings from the work:

Figure 2. Consistent EV size & concentration across orthogonal methods. A) EV size measurements by NTA (122.2 +/- 2.2 nm) and Zetasizer (122.4 +/- 1.0 nm) were closely concordant, confirming measurement reliability across orthogonal instruments. B) EV concentration remained consistent across serial dilutions from 1:200 to 1:800, with a coefficient of variation of 3.77% - demonstrating excellent assay precision within the linear quantitation range.

Figure 3. Verified EV identification through dual biomarker profiling. A) Verified EV identity through robust biomarker profiling (eGFP/CD63), enabling reliable release criteria and supporting regulatory expectations. B) High-precision analytical methods (<10% variance) provide dependable data for process development, scale-up decisions, and batch-to-batch comparability.

This method is applicable to real-time monitoring across EV process development - upstream scale-up, downstream purification, and lot release - and its performance attributes are relevant to quality-controlled release of EV products.

Table 1. The full analytical panel. Artis BioSolutions maintains a comprehensive EV analytics panel covering the critical quality attributes regulators expect for a therapeutic EV product.

Integration Across Development Lifecycle

One of the consistent lessons from the broader advanced therapy field is that programs suffer most at the transition points: from process development to GMP, from one analytical method to another, from one scale to the next. EVs are no different.

Artis BioSolutions structure is designed to minimize those gaps. Process development, analytical development, quality control, and GMP manufacturing operate under one roof, with the same team maintaining continuity across stages. When an EV process moves from development to GMP-scale production, the team that developed it is the team executing it. The institutional knowledge does not have to be translated.

Looking Ahead

EVs represent one of the most scientifically compelling frontiers in advanced therapy development. The biology is real, the therapeutic rationale is strong, and clinical programs are beginning to accumulate. What has lagged is the manufacturing and analytical infrastructure to support serious clinical development at scale. This is the workArtis BioSolutions is doing.

Conclusion

Extracellular vesicles are moving from research curiosity to therapeutic reality. But realizing that potential requires solving hard problems in manufacturing and analytics - not just once, but reproducibly, at scale, under GMP conditions. Artis BioSolutions integrated EV capabilities - spanning upstream production, downstream purification, and a comprehensive analytical panel anchored by validated flow cytometry methods - are designed to support programs from early development through clinical supply.
If you are building an EV program and thinking seriously about the manufacturing path forward, let's talk.

Authors: Eli Kraus, Yun Chen, Manish Tandon, and Gregg Nyberg contributed to the research and analytical development work described in this post.

📩 Connect with us to learn how our capabilities can accelerate your program.

From Silos to Speed: Why Connected Data Is Key to Tech Transfer

Landmark Bio Team — Thu, 22 Jan 2026 02:28:39 GMT

In today’s rapidly evolving biotech landscape, the efficient flow and integration of data across various systems and functions are crucial to the success of product development and commercialization. However, many organizations face significant challenges due to siloed data, complex system interfaces, and the difficulties inherent in transferring knowledge across the extended ecosystem of internal teams, contract organizations, and external partners.

This article explores the key barriers to data integration and tech transfer within biotech organizations and proposes solutions for overcoming these challenges through unified digital platforms, standardized data models, and seamless integrations that enable continuous digital threads, accelerate decision-making, and drive business value.

Figure 1. Artis BioSolutions End-to-End Product Lifecycle Management. From early discovery to preclinical, through clinical development, regulatory review, and approval, manufacturing, and quality control, the digital thread connects the entire therapeutic translation and industrialization phases.

Challenges in Data Integration and Tech Transfer

Siloed Data and Inefficient Data Utilization

One of the most significant barriers biotech companies face is the siloing of data assets by function, preventing seamless integration across research, development, clinical, and manufacturing stages. Data from instruments, assays, and other critical sources are often poorly leveraged, limiting the ability to create a continuous digital thread that supports decision-making across the enterprise.

In addition to the siloing of data, changes to processes and systems in biotech environments are typically incremental and require extensive validation before deployment. This incremental change approach results in slow adaptations and hampers the agility needed to drive innovation.  

Interface Complexity and Integration Risk

Biotech organizations must also contend with the complexity of integrating multiple systems such as Electronic Lab Notebooks (ELN), Manufacturing Execution Systems (MES), Laboratory Information Management Systems (LIMS), and Lab Execution System (LES). These systems often don’t communicate well with one another, and traditional interfaces come with high implementation risk, often resulting in costly failures or inefficiencies.

Furthermore, integrating instrument data into data lakes or cloud environments requires substantial investment in both time and resources. Data must be carefully staged and transformed to ensure integrity, often requiring specialized tools and expertise.

Limitations of Traditional Systems

Traditional MES platforms rely on libraries of repeatable building blocks to create manufacturing recipes. However, in the fast-moving world of biotech, these recipes are rarely reused as services and products evolve constantly. This results in inefficiencies and a lack of flexibility in the production process, hindering the scaling of operations or the adaptation of new methodologies.  

Barriers to Efficient Collaboration with External Partners

Working with external partners, such as Contract Development and Manufacturing Organizations (CDMOs) and Contract Manufacturing Organizations (CMOs) is often inefficient and challenging. Sponsors often partner closely with multiple organizations, but digital maturity between these parties is often mismatched. This disparity in capabilities makes tech transfers, both inbound (from sponsors to contract organizations) and outbound (from contract organizations to sponsors), difficult, inefficient, and error-prone.

In addition, the absence of established industry standards for data representation, coupled with a lack of robust integration solutions, leads to fragmented and inefficient interactions with external partners. Current sponsor-CDMO/CMO integration solutions are often not real-time, flexible, or validated for Good Manufacturing Practice (GxP), which is critical for regulatory compliance.

Challenges in Data Integration and Tech Transfer

To address these challenges, biotech organizations must adopt a solution-oriented approach that emphasizes integration, automation, and standardization. Below are some key strategies for overcoming the barriers to effective data management and tech transfer.

Unified Digital Platform for Seamless Integration

One of the most effective solutions to the integration problem is the deployment of a unified digital platform that can integrate ELN, LIMS, LES, MES, and other critical systems. By selecting vendor solutions that offer built-in integration, organizations can streamline data flows and ensure that all systems speak the same language.

The platform should be based on a common recipe/method data model, ensuring consistency across the lifecycle of biotech products. This allows for seamless data transfer from R&D to manufacturing and quality, while maintaining full data integrity. Moreover, integrating the ELN system with SDMS (Scientific Data Management System) can automate the ingestion and parsing of instrument data, retaining data integrity, and making it available for further use.

A key component of the solution approach is linking end-to-end product data from process development (PD) to manufacturing execution. This ensures that all data related to product development, manufacturing, quality control, and other areas are connected, versioned, and auditable, allowing for transparent decision-making.

ERP integrations can help replicate product data across the entire landscape — materials, assets, inventory movements, and quality events — ensuring that all systems reflect the same "version of truth." These connections allow for seamless transitions of data from one stage of development to the next, reducing manual data entry and minimizing errors.

For external tech transfer, the key is the adoption of common data standards for recipe and method models. This enables the conversion of digital datasets into structured, ISA 88/95-compliant recipes that can be used across different systems and technology solutions.

Figure 2. Future roadmap architecture for adaptive process control in manufacturing. Closed loop MVDA decision intelligence enables models with less variability and more history.

A secure, cloud-based collaborative platform is essential for data sharing between sponsors, CDMOs, and CMOs. Such platforms should support secure document submission, revisioning, and version control, providing a repository for process knowledge, quality control, and manufacturing processes. Automated tools, such as natural language processing and machine learning, can also help extract data from unstructured documents and convert it into structured datasets, ensuring the smooth transfer of knowledge and recipes between partners.

Conclusions: Building the Core Digital Foundation

In conclusion, optimizing data integration and tech transfer in the biotech industry requires a foundational shift towards a more connected and automated data ecosystem. By building a consistent IT/OT architecture and focusing on digital platform deployment, organizations can ensure that information flows seamlessly across R&D, clinical development, manufacturing, and external partners.

Figure 3. Building the right digital foundation sets the stage for all future successes throughout the drug development process.

Key steps include:

Building Core Digital Foundations: Automate internal tech transfer by deploying a unified digital platform for tech transfer orchestration, aligned with product development phases. Replicate common ERP data across the landscape to ensure data consistency.
Scaling with Priority Use Cases: Implement IT/OT convergence to acquire, aggregate, and contextualize data across formats. Deploy AI and machine learning solutions to enhance decision-making and process optimization.
Enhancing External Collaboration: Digitize and standardize tech transfer processes between sponsors and contract organizations, enabling real-time, GxP-validated exchanges.

By building the right foundation, biotech organizations can unlock the full potential of their data, accelerating innovation, enhancing collaboration, and delivering better outcomes for patients. Realizing this goal requires an integrated digital ecosystem that democratizes data access across the enterprise, ensuring harmonized process data for every worker, interaction, and decision.

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