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Ask the Expert

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Behind the Headlines Episode 28

Emily Schoenthaler is the Content Producer for 

Matt Truppo is global head of Computational & AI Strategy, R&D, at Sanofi.

Senior Director
Education, Outreach, and Digital Experience
Open Biopharma Research and Training Institute

Brian Drapeau is founder, GxPFrame and a cell and gene therapy manufacturing and quality systems consultant.

Industry experts from Sanofi, GxP Frame, and Open Biopharma outline how AI is transforming drug discovery, manufacturing, and workforce strategy through biopharma partnerships in the latest episode of Drug Digest.

Drug Digest: How Advanced Analytics Partnerships Enhance the Biopharma Value Chain

, global head of Computational and AI Strategy for R&D at Sanofi, 

, founder and principal consultant of GxP Frame, and 

, senior director of Education, Outreach, and Digital Experience at Open Biopharma Research and Training Institute, discuss artificial intelligence (AI) integration across drug discovery, 

, supply chain operations, and workforce development in this special combined episode of 

Truppo describes AI's primary value as connecting signals across previously siloed data types, including molecular structure, biological assay results, patient demographics, and clinical site performance, to improve scientific decision quality rather than generate autonomous answers. At Sanofi, for example, disease-specific target identification engines integrate genetics, proteomics, transcriptomics, and single-cell data to help researchers determine target druggability, tissue expression, and optimal therapeutic modality, he explains. 

All three experts emphasize that human oversight must be structurally embedded in AI workflows, not applied as an afterthought. 

How should biopharma companies govern AI tools to maintain data integrity and regulatory compliance?

, Drapeau emphasizes that the most valuable AI applications are not headline-grabbing but operational, such as deviation management, batch record review, predictive maintenance, and supply and demand forecasting. In cell and gene therapy (CGT), specifically, he notes that supply chain is patient-specific, making chain of identity and chain of custody the true measures of AI's value. He also notes that no single manufacturer owns the full CGT supply chain, making cross-company partnerships essential.

Drapeau adds that effective partnerships between biopharma and AI tech and/or advanced analytics companies require a partner capable of integrating across multiple domains while maintaining strict data governance. "Completion is not competency," he stresses, cautioning against operators who can execute AI-assisted tasks but lack the independent judgment to troubleshoot or critically evaluate model outputs.

All three experts emphasize that human oversight must be structurally embedded in AI workflows, not applied as an afterthought. Jaenisch states that explainability is non-negotiable, cautioning that AI outputs used in regulated environments must be traceable and defensible to inspectors.

“There are [many] pieces that an explainable AI system is able to retrieve and provide with every output, and if you do not have that, then you are probably not going to have a good time when an audit comes,” he explains.

Truppo adds that data governance, source traceability, and clearly stated model uncertainty are prerequisites for trustworthy AI integration across biopharma R&D and manufacturing operations when dealing with a strategic partner.

Matt Truppo, Global Head of Computational & AI Strategy, R&D, Sanofi

As the global head of Computational and AI Strategy at Sanofi, Dr Truppo oversees platform capabilities across all therapeutic areas and modalities to advance the company’s drug discovery ambitions. He focuses on embedding AI and predictive technologies throughout R&D to accelerate timelines and enable the development of first-in-class therapies. Prior to Sanofi, he led Protein Therapeutics API Development and Biologics Discovery at Johnson & Johnson and spent more than a decade at Merck working across both small- and large-molecule discovery and development. He holds a PhD in Chemistry from the University of Manchester and undergraduate degrees in Chemical Engineering and Engineering Biology from Princeton University. Over a 20+ year career, Truppo has contributed to bringing 13 medicines to market and advanced many more into the clinic, a quest driven by his commitment to improve global health through innovative science.

Richard Jaenisch, Senior Director of Education, Outreach, and Digital Experience, Open Biopharma Research and Training Institute

Jaenisch has spent the past 15 years in patient advocacy since he began and fought off end stage liver disease and more than 15 years in STEM education, marketing, and consulting. Among his duties at Open Biopharma, he is leading several data projects regarding generative AI integration and exploring generative AI in biomanufacturing training. At Prompting Integration and Consulting LLC, he develops training courses and works alongside his brother, a seasoned AI/ML engineer to develop bespoke generative AI integrations for business in life sciences and healthcare and deliver educational programming on generative AI.

Brian Drapeau, Founder and Principal Consultant, GxP Frame

A private consultant to some of the largest biotech companies in the industry, Drapeau brings more than 5 years of experience in workforce development and training across the biotechnology and pharmaceutical sectors. His expertise includes new hire training, instructional program development, contract development and manufacturing organization (CDMO) training requirements, and high-volume ramp-ups. Drapeau also authors 

, a newsletter covering FDA enforcement trends, CDMO training failures, and pharmaceutical manufacturing operations.

® Group editors, who interview industry experts to discuss the emerging opportunities, obstacles, and advances in the pharmaceutical and biopharmaceutical industry for the research, development, formulation, analysis, upstream and downstream processing, manufacturing, supply chain, and packaging of drug products.

Videos

In this episode of Drug Digest, experts from Sanofi, GxP Frame, and Open Biopharma explain how AI is transforming drug discovery, manufacturing, and workforce strategy and how strategic partnerships play a role.

At BIO 2026, Thermo Fisher's Anil Kane explains how AI is reshaping CDMO workflows and what biotechs should prioritize when choosing a development partner. 

Thermo Fisher's Kane Says AI Is Reshaping How CDMOs Add Value

For Anil Kane, PhD, the question facing small biotech companies isn't just which CDMO can run the work, but which one can make their asset worth more by the time it's done.

"I think creating value and enhancing the value of their assets is something that a small biotech company should be looking at while selecting a CDMO player," says Kane, global head of technical and scientific affairs within the Pharma Services Group at Thermo Fisher Scientific. He shared that view, along with a look at how the company is weaving artificial intelligence (AI) into its CDMO workflows, in a conversation with out sister publication 

 at the 2026 BIO International Convention (BIO 2026, June 22–25 in San Diego).

Thermo Fisher has built strategic partnerships to support AI integration across its service lines, Kane says, pairing outside expertise with in-house machine learning algorithms and predictive models trained on proprietary datasets the company has accumulated over years of development work. Those AI applications now touch preclinical and clinical phases, quality management systems, supply chain operations, and contract processing, essentially the full breadth of CDMO work, from early development through commercialization.

So what should a biotech actually weigh when picking a CDMO for AI-enabled drug development? Kane points to regulatory experience, a demonstrated quality track record, and scientific depth across modalities. He's particularly bullish on the advantage of working with a single organization that can carry a program across small molecules, biologics, messenger RNA, and viral vectors through the full development and commercialization spectrum, rather than stitching together multiple specialized partners.

On where AI itself stands, Kane says it's already producing measurable speed and efficiency gains at several development stages, though he's candid that some applications still need time to mature. The near-term emphasis, he says, has been on what he calls the "low-hanging fruit": more accessible use cases first, with deeper integrations to follow. For biotechs weighing out-licensing strategies in particular, he adds, a strong CDMO partner can move the needle directly on asset value.

At BIO 2026, Thermo Fisher's Anil Kane explains how AI is reshaping CDMO workflows and what biotechs should prioritize when choosing a development partner.

Picture the inspector. They put a finger on one sentence in your Module 3 and ask the only question that matters: “Can you show me where this came from?”

If your AI wrote that section by reading fifty batch records and a human “reviewed” the output, you have a problem. Nobody can realistically trace one sentence back through fifty documents. That isn’t validation; it’s hope. And hope is how a promising AI pilot becomes a Form 483.

The instinct across our industry is to answer this by chasing accuracy. Vendors compete on “99.9%.” But accuracy is the wrong battleground. An inspector does not fail your AI because it is 

. The real bottleneck for AI in GxP is defensibility, and defensibility is a design choice, not a model choice.

Clinical AI has been wrestling with exactly this problem, under exactly this scrutiny, for longer than has pharma manufacturing. It is worth borrowing their answer.

 does something deceptively simple.1 It constrains the model to synthesize only from approved, retrieved sources; it embeds an explicit citation behind every claim; and it logs every step for retrospective audit. Most importantly, it does not try to explain the model’s internals to a regulator at all. Instead, it mirrors the workflow the clinician already trusts. It presents each recommendation like an annotated guideline with numbered references, so a physician verifies it exactly the way they would verify any evidence-based guidance. Provenance is treated as a first-class control, not a feature bolted on afterward.

That is the whole lesson in one sentence: You earn an expert’s trust by mapping the AI onto the manual process they already believe in, not by teaching them embeddings.

Clinical medicine has also been honest about the failure modes pharma is about to meet. A 2021 letter in the 

 warned that models silently degrade when the data they see in the wild drifts from the data they were trained on.2 And health systems have published real governance scaffolding for deploying these tools safely (for example, 

, 2022, on governing clinical AI across a large health system). These are not theoretical papers.3 They are field reports from people who already had to defend AI to a regulator.

The Regulatory Example: Lock the Source, Not the Model

Here is how that principle translates to a regulatory submission.

Instead of pointing an AI at a chaos of raw batch records, process maps, validation reports, and stability data, do what good quality systems already do: Have your experts write and lock a single, approved process-description report. Then constrain the AI so that when it drafts the Module 2 and Module 3 narrative, it may cite only that approved report, nothing else. The inspector’s question now has a clean answer: “It came from the approved report, this section, signed on this date.” Same AI, completely different inspection.

I recently spent some time experimenting with these ideas using AI-generated synthetic GMP-style documents created solely for learning and exploration (no proprietary data). Three observations stood out:

The “smart” guardrail made things worse.

 A guardrail that let the model override the source introduced new silent errors. The boring guardrail to only add, never override kept silent misses at zero. Sophistication is not safety.

The model’s confidence is not your control.

 The same system that was strong at catching messy, judgment-class errors confidently invented problems on a perfectly clean record. You cannot let the model’s certainty stand in for verification.

The lever was the input, not the intelligence.

 Constraining the model to one approved source, not upgrading to a bigger model, is what made the output traceable and defensible.

None of this requires a black box. It maps directly onto the language regulators are already speaking: FDA’s Computer Software Assurance guidance (2025)4 and its risk-based, intended-use lens; the FDA’s 

 framing of “is this AI trustworthy for this specific use,” human-in-the-loop control and lifecycle management;5 and 

Two failure modes are coming that almost nobody is stress-testing.

First, cross-jurisdiction drift: an AI tuned on one regulator’s submission style can quietly slip into patterns that draw deficiencies under another’s.

Second, messy inputs: clean models behave very differently when fed colored, scanned, inconsistently formatted batch records, the reality of every CDMO handoff. Clinical AI already warned us that drift is silent. Pharma should listen before, not after, the inspection.

The takeaway I’d offer any quality or regulatory leader experimenting with generative AI is don’t make the AI smarter, make it traceable. Borrow the hospital’s discipline. Lock the source. Mirror the workflow your inspector already trusts. The model that survives an inspection is not the cleverest; it’s the one whose every sentence can be walked back to an approved, signed page.


The author conducted this work independently, in a personal capacity, using synthetic and publicly available data. No employer, client, or proprietary information is referenced. This is a practitioner perspective, not regulatory advice.

1. Alu F, Oluwadare S. An Auditable and Source-verified Framework for Clinical AI Decision Support: Integrating Retrieval-augmented Generation with Data Provenance. Frontiers in Artificial Intelligence. 2026;4(9):1737532. 

2. Finlayson S, Subbaswamy A, Singh K, et al. The Clinician and Dataset Shift in Artificial Intelligence. N Engl J Med. 2021;385(3):283-286. 

3. Liao F, Adelaine S, Afshar M, Patterson B. Governance of Clinical AI Applications to Facilitate Safe and Equitable Deployment in a Large Health System: Key Elements and Early Successes. Front Digit Health. 2022;24(4):931439. 

4. FDA. Computer Software Assurance for Production and Quality System Software. 

5. European Commission. EU GMP Annex 22 (draft 2025): Artificial Intelligence. 

Articles

Regulated generative AI in GxP settings will fail inspections less from factual error than from inability to defend provenance at the sentence level. Clinical AI offers a workable template: retrieval constrained to approved sources, claim-level citations, and end-to-end audit logging that mirrors established expert verification workflows. In regulatory submissions, locking a single signed, approved source document and restricting drafting to that source makes narratives inspectable. Key risks include dataset drift, cross-jurisdiction stylistic drift, and performance collapse on messy, scanned inputs.

What pharma’s regulatory writers can borrow from clinical AI before the next inspection.

Don’t Make AI Smarter. Make It Traceable.

Life Sciences Sector Leader | Deal Advisory Strategist & Executor | Former Research Scientist & Precision Medicine Innovator | Author | KPMG

The pace of life sciences M&A has slowed in 2026, but Kristin Ciriello Pothier, life sciences sector leader, KPMG US, says lower deal volume doesn't mean lower value. It means companies are finally doing the work.

Watch the 3-part video interview with Pothier:

What's Driving Biopharma M&A Strategy in 2026

How to Maximize Value in Platform Technology Deals

Why Pharma Dealmaking Slowed in Q1 2026 and What Is Coming Next

PharmTech: How Would You Characterize the State of Life Sciences M&A In Q1 2026?

We've stopped counting deals, and we've started making them count. Volume has continued to come down, but the assets in this space are very precious. Every company we're working with is diligencing each asset to determine whether it will be a prime in their portfolio, not just another layer on top of an already complex biopharma landscape. What we're also seeing is more pricing discipline: companies are paying what the data suggests is fair, not bloating deal values. It's a very disciplined approach to life sciences in the first half of this year.

Which Therapeutic Areas Are Generating the Most Deal Activity?

Oncology continues to be very hot. We just came back from ASCO 2026 in Chicago, and the excitement around combination therapies and modality complexity was extraordinary, the largest pharma companies each brought 50 to 100 people to work through assets and IP. Beyond oncology, we're seeing tremendous activity in chronic disease, particularly around GLP-1s [glucagon-like peptide-1] and everything surrounding them. GLP-1s are no longer just about diabetes or obesity management, their effects are expanding into cardiovascular health, oncology support, and holistic patient care. That broader view of what a GLP-1 ecosystem looks like is generating significant deal interest.

How Is Manufacturing Readiness Factoring into Deal Diligence?

For cell and gene therapy, manufacturing diligence is more intensive than for almost any other asset class, and the reason is straightforward, you cannot ship cell and gene therapy from one part of the globe to the other. It must follow a corridor-based strategy, dedicated strategies for the United States, the European Union, and in the Asia-Pacific region. So when acquirers are looking at these assets, one of the first questions is whether the manufacturing plants are included in the deal. We're seeing situations where buyers want those facilities and sellers won't give them up, and when that happens, the deal valuation changes completely. The acquirer must factor in where and how they'll manufacture in each region, and what it will cost to upskill or upscale a facility in any geography they're missing.

How are Tariffs Influencing Manufacturing Strategy Decisions?

What we're not seeing is a knee-jerk reaction. Rather than immediately committing to building a plant in the Americas or fully reshoring, companies are having very careful conversations about whether regional manufacturing investment pencils out, or whether the smarter move is to absorb tariff costs and adjust their P&L accordingly. The companies we work with are running rigorous analyses rather than making reactive decisions. That's a sign of maturity in how our sector handles geopolitical and trade pressures.

What Do You Expect to Drive Deal Momentum in the Second Half Of 2026?

We see 3 levers. The first is artificial intelligence (AI) and data, not through large M&A, but through partnerships and alliances, because the best-in-class AI capability isn't being built inside life sciences companies. The second is manufacturing sites and contract manufacturing acquisitions, particularly as platform technology complexity increases in oncology and other areas. And the third is therapeutic area M&A, anchored by oncology assets and the GLP-1 ecosystem, including adjacent services, diagnostics, consumer health, and functional nutrition. As earlier deals from this year come to fruition and unlock confidence, we expect both deal volume and deal value to rise as we move into 2027.

Life sciences M&A in early 2026 is marked by lower volume but higher selectivity, with acquirers emphasizing strategic fit, rigorous diligence, and pricing discipline aligned to data. Oncology remains the most active therapeutic area, fueled by combination regimens and modality complexity, while chronic disease dealmaking is expanding around GLP-1 ecosystems beyond diabetes and obesity. Manufacturing readiness—especially for cell and gene therapies—has become a valuation determinant. Tariff uncertainty is driving analytical, not reactive, manufacturing decisions. Second-half momentum is expected from AI alliances, manufacturing acquisitions, and oncology/GLP-1 transactions.

Kristin Ciriello Pothier, KPMG US, discusses deal discipline, manufacturing diligence, and why oncology and GLP-1s are driving biopharma M&A activity in 2026.

Q&A: Why Pharma Deal Volume Is Down but Value Is Up

Global Head of Process Solutions
MilliporeSigma

MilliporeSigma's Sebastián Arana explains why modular, digital, platform-based manufacturing, not raw capacity, will define biomanufacturing's next decade. 

Biomanufacturing's Next Decade Will Be Won by Adaptability, Not Capacity

As biopharmaceutical pipelines grow more complex and clinical timelines compress, the biomanufacturers that come out ahead over the next decade may not be the ones with the most capacity. They'll be the ones built to adapt.

That's the central argument from part 1 of a two-part interview with Sebastián Arana, global head of Process Solutions at MilliporeSigma, the life science business of Merck KGaA. Arana spoke with our sister publication 

 ahead of the 2026 BIO International Convention (BIO 2026), held June 22–25 in San Diego, laying out where he sees the biggest manufacturing pressure points building as the industry moves toward increasingly targeted, increasingly varied therapies.

"I feel the winners over the next decade or so will be companies that build that adaptability in their manufacturing ecosystem, not [just] manufacturing capacity," he says. For Arana, that adaptability has a specific shape: "modular manufacturing, digital-enabled, and fundamentally platform-based."

Platform flexibility, in his view, has become the single most important operational capability a biomanufacturer can have. Shrinking clinical timelines and increasingly diverse pipelines mean a single production ecosystem now has to flex across modalities and volume requirements, not simply scale up one product at a time. That shift moves automation, digital tools, and real-time monitoring from nice-to-have into must-have territory.

Equipment upgrades alone won't get manufacturers there, though. Arana cautions that facilities designed for an earlier generation of manufacturing tend to hit a wall once new modalities are introduced: facility-flow bottlenecks, tougher segregation and contamination-control demands, and data systems that don't talk to each other. The more processes get layered onto a site, he notes, the more that data flow becomes a make-or-break factor.

People are part of the equation too. Arana treats workforce retraining as a strategic priority rather than an afterthought, since new modalities frequently demand new ways of working on the floor. He's also skeptical that any single company can master every emerging modality at once—in his view, choosing a few areas to specialize in is quickly becoming a competitive necessity rather than an option.

Susan J. Schniepp is a fellow at Regulatory Compliance Associates, Inc. and a member of Pharmaceutical Technology's Editorial Advisory Board.

Pharma Fundamentals, Susan Schniepp, distinguished fellow with Nelson Labs and a member of 

editorial advisory board, and Amnon Eylath, president and founder of Broad Spectrum GXP, discuss biologics development, the quality system, and the Parenteral Drug Association's (PDA) revision to Technical Report No. 56 (TR56),

Application of Phase-Appropriate Quality Systems and Good Manufacturing Practice to the Development of Biological Product Drug Substance

, was written in response to a practical problem: companies were applying full commercial-scale quality standards to early-phase clinical products, which wasn’t working. Certain systems simply cannot and should not, according to Eylath, be in place during early development. The report, now in its third version, provides guidance on scaling quality requirements appropriately across development phases. The report’s core principles apply broadly across both biologics and small molecules; however, the differences between its application to biologics compared with small molecules lie in complexity and in the relative level of containment and contamination control required. In addition, FDA requested that TR 56 (2012) also serve as a guide to best practices for smaller and academic biologic manufacturers who may not have the level of experience to understand what must be in place throughout the development of a biological drug substance.

Brewer RA, Eylath AA, Kirschbaum N, et al. 

Technical Report No. 56 (Revised 2026): Application of Phase-Appropriate Quality Systems and Good Manufacturing Practice to the Development of Biological Product Drug Substance

. PDA. February 2026. https://www.pda.org/bookstore/product-detail/8742-technical-report-no-56-revised-2026

Amnon Eylath, founder, Broad Spectrum GXP

Amnon Eylath is founder of Broad Spectrum GXP, which provides strategic consulting, operational services, and interim leadership, empowering organizations to close gaps, mitigate risks, and to achieve a state of inspection readiness and operational excellence. Committed to developing high-performing teams. 

Eylath is past president of the New England PDA chapter. International thought-leader on Phase-Appropriate application of cGMP to biological drug and ATMP development and manufacturing, working in collaboration with FDA, MHRA and EMA. Collaborated with FDA on sterilization and aseptic processing technology education.

This episode of Pharma Fundamentals explains the difference between a quality system for biologics compared with small-molecule drugs and the Parenteral Drug Association’s revision to Technical Report No. 56.

Pharma Fundamentals: Biologics Quality System

Dr. Thomas Ybert is chief scientific officer and co-founder of DNA Script and leads the company's research and innovation efforts. Thomas is the main inventor of DNA Script's Enzymatic DNASynthesis (EDS) technology. Holding a PhD in molecular biology and yeast genetics from Ecole Polytechnique, his background includes R&D roles at Sanofi and Total/Amyris.

Hands of a scientist hold Biotechnology vials. Advanced genetic engineering and lab research accelerate medical breakthroughs for treating rare diseases and improving global health care. | Image Source: ©NexPix -stock.adobe.com

DNA synthesis is undergoing a structural transition from a laboratory capability to a key strategic industrial element within biopharmaceutical development. As advanced therapeutic modalities, including messenger RNA (mRNA), cell and gene therapies, and synthetic biology-derived products, continue to mature, the demands placed on DNA production have shifted toward consistency, scalability, and regulatory compliance. This article examines the evolving role of DNA synthesis within therapeutic manufacturing and the emergence of biofoundries as integrated production environments. The article highlights how the future of synthetic biology will depend not only on the ability to design biological systems, but on the capacity to manufacture DNA reliably at scale within regulated frameworks.

From Enabling Technology to Industrial Requirement

Over the past 2 decades, DNA synthesis has become a foundational capability in life sciences research, enabling the design and construction of genetic sequences for applications ranging from basic biology to drug discovery.

 Yet, despite its importance, DNA synthesis has largely been treated as a supporting tool rather than a central component of industrial biopharmaceutical production.

Advanced therapies all share a common theme: using engineered biological systems to sense, and act in a more precise way than conventional drugs, finally bringing to fruition the decades-long promise of “programmable biology.” Indeed, as biology becomes increasingly programmable, this requires DNA to be considered throughout the entirety of discovery and development and embedded within the manufacturing processes of modern therapeutics.

 DNA must not only be synthesized, but produced reproducibly, at scale, and in compliance with regulatory standards. The implications of this transition are significant, reframing DNA synthesis as an industrial discipline subject to the same constraints that have historically defined other areas of biopharmaceutical manufacturing.

The evolution of DNA synthesis mirrors earlier developments in biotechnology. Only after the development of scalable, reproducible manufacturing systems did the commercialization of monoclonal antibodies become possible.

 Over time, antibody production became standardized and integrated into regulated workflows.

A similar trajectory is emerging for DNA synthesis. Innovations such as enzymatic synthesis and novel assembly methods continue to advance the field (

). Now the central challenge has shifted toward manufacturing performance wherein DNA used in therapeutic applications must meet stringent requirements for sequence fidelity, purity, and traceability.

 Variability tolerated in research settings becomes unacceptable in clinical contexts, where inconsistencies can affect product safety and efficacy. As a result, DNA synthesis is increasingly evaluated through the lens of industrial robustness rather than purely technical capability.

Figure 1. The Development of DNA Synthesis

, from the discovery of the DNA structure to Enzymatic DNA Synthesis (EDS), mapped against the applications enabled at each stage. FIgure courtesy of the author.

The Changing Demands of Therapeutic Modalities

Modalities such as mRNA vaccines, viral vectors, and gene-editing therapies rely on precisely defined genetic constructs, making DNA a critical determinant of product quality.

These applications are introducing new manufacturing requirements. DNA constructs are becoming longer and more complex, often incorporating multiple functional domains that must be assembled with high accuracy. At the same time, development timelines are compressing, particularly in areas such as personalized medicine and vaccine development, where rapid response, flexible manufacturing, and scalable production are increasingly important.

Traditional synthesis approaches can struggle to meet these demands, particularly where sequences are long, complex, repetitive, or difficult to maintain consistently in biological production systems. In plasmid-based workflows, bacterial propagation introduces inherent biological variability, which can affect predictability, increase quality control requirements, and extend production timelines.

Cell-free production systems, including emerging enzymatic DNA synthesis (EDS) approaches, offer an alternative by reducing dependence on living host cells. By operating within programmable environments, these systems can provide greater sequence fidelity, more consistent performance across challenging constructs, and faster production workflows with reduced variability. This level of control is particularly attractive for applications requiring rapid iteration, reproducibility, and scalable manufacturing within regulated environments (

Figure 2. pDNA Fermentation vs Cell-Free Enzymatic DNA Synthesis.

 ARG, antibiotic resistance gene-free; CDMO, contract development and manufacturing organization; CMC, chemistry, manufacturing, and controls; DSP, downstream processing; GMP, good manufacturing practice; IVT, in vitro transcription; pDNA, plasma DNA; QC, quality control. Figure courtesy of the author.

Consequently, there is growing interest in platforms capable of combining high fidelity, manufacturing flexibility, and scalable throughput to support the next generation of advanced therapeutics.

Biofoundries have emerged as a potential solution to the growing manufacturing demands associated with next-generation therapeutic modalities, while also representing an important evolution toward integrated, “lab-in-the-loop” development models that accelerate learning cycles from design to testing. Combining automation, robotics, software, and advanced cell-free enzymatic DNA synthesis technologies, these facilities enable high-throughput biological engineering and more agile development workflows.

For emerging applications, such as personalized medicine, mRNA vaccines, cell and gene therapies, manufacturing requirements are changing. In these environments, timing, scalability, and sequence fidelity are increasingly crucial, often requiring smaller batch sizes, faster iteration cycles, and more flexible production capabilities than traditional manufacturing models were designed to support. Biofoundries platforms may therefore offer advantages for modern therapeutic development by enabling rapid, programmable, and potentially more localized manufacturing workflows.

By integrating design, build, and test processes, biofoundries can help reduce production timelines from months to days, improve development efficiency, and provide greater control over intellectual property and supply chains. Integration with digital systems and automated quality workflows may also support compliance with good manufacturing practice (GMP) requirements and improve manufacturing reproducibility within regulated environments.

At the same time, established plasmid manufacturing approaches based on 

 fermentation remain the industry gold standard for many large-scale applications due to their maturity, scalability, and cost efficiency. Further, the establishment of in-house DNA manufacturing capabilities still requires significant investment in infrastructure, automation, and expertise.

As a result, the future manufacturing landscape is likely to be hybrid in nature, combining traditional centralized production systems with newer automated and cell-free manufacturing capabilities tailored to the requirements of advanced therapeutics.

Iteration, Control, and Development Cycles

The integration of DNA synthesis into development workflows has implications for how therapeutics are designed. Many modern platforms rely on iterative design–build–test cycles, where the speed and efficiency of iteration directly influence development outcomes.

Closer integration of DNA production can reduce delays and enable more rapid experimentation. However, in regulated environments, this flexibility must be balanced with the need for documentation, validation, and reproducibility. Maintaining this balance represents a key challenge for organizations seeking to integrate synthesis capabilities more closely into development pipelines.

While speed remains important, the value of DNA synthesis in modern biopharmaceutical manufacturing increasingly depends on quality, reproducibility, scalability, and regulatory compatibility. As advanced therapeutics such as mRNA vaccines, gene therapies, and cell therapies mature, DNA is no longer viewed as a standalone research reagent, but as a critical component of continuous manufacturing infrastructure.

This transition reflects the broader industrialization of DNA synthesis, driven by converging advances in synthesis technologies, rising therapeutic demand, and evolving regulatory expectations. Biofoundries and integrated production models may help address these needs; although, adoption will likely vary across applications and geographies. A hybrid ecosystem is expected to emerge, combining centralized manufacturing with selective in-house synthesis capabilities.

At the same time, biosecurity and governance frameworks will increasingly shape how DNA synthesis technologies are deployed globally, influencing both market structure and technological development.

Ultimately, DNA synthesis is evolving from a research-enabling technology into a foundational pillar of biopharmaceutical manufacturing. The future of synthetic biology will depend not only on advances in genetic design, but on the ability to manufacture DNA reliably, safely, and at industrial scale.

Kosuri S and Church GM. Large-scale de novo DNA synthesis: technologies and applications. 

Cameron DE, Bashor CJ, and Collins JJ. A brief history of synthetic biology. 

Walsh G. Biopharmaceutical benchmarks 2018. 

Hughes RA, and Ellington AD. Synthetic DNA synthesis and assembly. 

Cold Spring Harbor Perspectives in Biology

Pardi N, et al. mRNA vaccines—a new era in vaccinology. 

Jackson NAC, et al. The promise of mRNA vaccines. 

Chao R, et al. Engineering biological systems using automated biofoundries. 

Guidance for Industry: CGMP for Phase 1 Investigational Drugs

Carbonell P, et al. An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. 

DNA synthesis is shifting from a research utility to an industrialized manufacturing input for advanced therapeutics, driven by requirements for sequence fidelity, purity, traceability, scalability, and GMP alignment. Longer, more complex constructs and compressed timelines in mRNA, viral vectors, and gene editing are exposing limitations of plasmid/E. coli workflows, including biological variability and extended QC. Cell-free and enzymatic DNA synthesis platforms, increasingly embedded in automated biofoundries, enable faster design–build–test iteration and more reproducible production. A hybrid manufacturing ecosystem is expected, shaped by biosecurity and governance.

The future of synthetic biology will depend not only on the ability to design biological systems, but on the capacity to manufacture DNA reliably at scale within regulated frameworks.

Writing the Biological Script: Scaling DNA Manufacturing for the Next Generation of Therapeutics

Photomicrograph of a breast cancer (grade 3 invasive ductal carcinoma) with frequent mitoses (mitotic figures), including a large central atypical mitosis. | Image Credit: ©David A Litman -stock.adobe.com

On June 24, 2026, Pfizer and FDA announced the approval of a maintenance treatment for adults with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-positive (HER2+) locally advanced or metastatic breast cancer (MBC).

The combination of palbociclib (IBRANCE) with trastuzumab, with or without pertuzumab, and endocrine therapy has been approved by FDA for use following induction treatment.

Palbociclib, an oral inhibitor of CDKs 4 and 6, is currently used for the “treatment of adult patients with HR+, HER2- advanced or metastatic breast cancer in combination with an aromatase inhibitor as initial endocrine-based therapy; or with fulvestrant in patients with disease progression following endocrine therapy.”

Other indications include in combination with inavolisib and fulvestrant for the treatment of adult patients with recurring endocrine-resistant, PIK3CA-mutated, HR-positive, HER2-negative, locally advanced or metastatic breast cancer or after completing adjuvant endocrine therapy.

“Over the past decade, IBRANCE has helped transform metastatic breast cancer treatment, establishing CDK4/6 inhibition as a cornerstone of care,” said Aamir Malik, chief US commercial officer and executive vice president, Pfizer, in a press release.

 “With today’s FDA approval, IBRANCE becomes the first and only CDK4/6 inhibitor indicated for patients with HR+ metastatic breast cancer regardless of HER2 status, extending its impact to patients who continue to face challenges with treatment resistance. This milestone strengthens confidence in IBRANCE as a CDK4/6 inhibitor backbone across combination regimens, reflecting Pfizer's ongoing leadership in delivering meaningful advances for people with breast cancer.”

FDA approved the treatment based on positive results from the Alliance Foundation Trials (AFT)-sponsored phase 3 PATINA trial, which demonstrated a 24% reduction in the risk of progression or death following induction treatment with palbociclib added to trastuzumab or trastuzumab plus pertuzumab and endocrine therapies compared to anti-HER2 and endocrine therapies alone. The trial was a randomized, open-label global phase 3 study with 518 patients to evaluate the efficacy and safety of palbociclibin combination with anti-HER2 therapy and endocrine therapy compared to anti-HER2 therapy and endocrine therapy alone as a first-line maintenance therapy. Trial results were published in the 

According to FDA, “the major efficacy outcome measure was investigator-assessed progression-free survival (PFS) per RECIST version 1.1. Overall survival (OS) was an additional efficacy outcome measure. A statistically significant improvement in investigator-assessed PFS was observed for palbociclib with trastuzumab, with or without pertuzumab, and endocrine therapy compared to trastuzumab, with or without pertuzumab, and endocrine therapy alone (Hazard ratio 0.76 [95% CI: 0.59, 0.97], 1-sided p-value 0.0134). Median PFS could not be adequately described because of censoring. At the time of the PFS analysis, OS data were not mature.”

“Resistance to dual anti-HER2 and endocrine therapy remains a central clinical challenge for patients with HR+, HER2+ metastatic breast cancer —even after an excellent response to initial treatment,” said Otto Metzger, M.D., principal investigator of the trial for Alliance Foundation Trials and Medical Oncologist at the Dana-Farber Cancer Institute, in a press release.

 “Based on the results from the PATINA study, the addition of IBRANCE in the maintenance phase can meaningfully extend the time patients go without their disease progressing. This approval gives oncologists a new, evidence-based option to optimize maintenance therapy for their patients with HR+, HER2+ disease.”

Palbociclib received breakthrough therapy designation for this indication, and the review used the FDA's Assessment Aid. This may indicate a willingness by FDA to expedite CDK4/6 inhibitor reviews in new combinations, which may encourage other manufacturers to invest in similar combinatorial approaches and pursue breakthrough designation strategies.

Because this is a multi-drug regimen that includes a small molecule (palbociclib), two monoclonal antibodies (trastuzumab and pertuzumab), and a hormone therapy, multiple manufacturers across different modalities (oral oncology, biologics/biosimilars) may see an increase in demand simultaneously. There may also be a rise in the complexity of supply chain coordination across drug classes.

The fact that trastuzumab and pertuzumab are biologics with established biosimilar competition and the regimen includes these agents, biosimilar manufacturers of trastuzumab (e.g., Kanjinti, Herzuma, Ogivri) and pertuzumab may find an increase in volume demand, reinforcing the commercial viability of those biosimilar programs.

There may also be an indirect benefit to manufacturers and labs producing diagnostic reagents, IHC/ISH assay kits, and genomic testing platforms, as this drug approval reinforces the importance of HER2 and HR testing as patient selection tools.

FDA approves Pfizer’s IBRANCE regimen for HR+, HER2+ metastatic breast cancer frontline maintenance. Press release. Pfizer. June 24, 2026. 

https://www.pfizer.com/news/press-release/press-release-detail/fda-approves-pfizers-ibrance-regimen-hr-her2-metastatic

FDA approves palbociclib with trastuzumab, with or without pertuzumab, and endocrine therapy for the maintenance treatment of HR-positive, HER2-positive metastatic breast cancer. Press release. FDA. June 24, 2026. 

https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-palbociclib-trastuzumab-or-without-pertuzumab-and-endocrine-therapy-maintenance

Metzger O, Mandrekar S, Goel S, et al. Palbociclib for hormone-receptor–positive, HER2-positive advanced breast cancer. 

. 2026 394 (5) doi: 10.1056/NEJMoa2511218 

https://www.nejm.org/doi/full/10.1056/NEJMoa2511218

FDA approved palbociclib plus trastuzumab, with or without pertuzumab, and endocrine therapy as first-line maintenance after induction for adults with HR+/HER2+ locally advanced or metastatic breast cancer. Approval was supported by the phase 3 PATINA trial (n=518), showing a statistically significant investigator-assessed PFS benefit versus anti-HER2 plus endocrine therapy alone (HR 0.76; one-sided p=0.0134), with OS not yet mature and median PFS not estimable due to censoring. The decision may catalyze broader CDK4/6 combination development, affecting biologics, biosimilars, diagnostics, and supply chains.

The agency approved palbociclib with trastuzumab, with or without pertuzumab, and endocrine therapy for the maintenance treatment of HR-positive, HER2-positive metastatic breast cancer.

FDA Approves Pfizer’s Breast Cancer Treatment

Vice President of Talent Strategies at the Lehigh Valley Economic Development Corporation

Eli Lilly’s $3.5B Lehigh Valley manufacturing project is being supported by a region with above-average manufacturing intensity, proximate access to Philadelphia and New York talent, and a dense education ecosystem. Workforce readiness is anchored by data-driven talent mapping, community college modernization, and stackable credentials spanning pre-apprenticeships through state-approved apprenticeships aligned to pharma operations. The central constraint is scaling credentialed labor to fill >850 roles, prompting earlier K–12 career awareness initiatives. AI’s workforce implications are being explored via regional advisory structures, ahead of direct alignment with Lilly.

How the Lehigh Valley Economic Development Corporation is building a technical workforce pipeline to support Eli Lilly's 850-job pharmaceutical manufacturing expansion.