Historically, biopharmaceutical supply chain disruption was treated as an isolated logistical or procurement problem, a matter of managing delayed shipments, or fluctuating raw material costs. 

Today, given the global nature of logistics and the complexity of modern biologics, supply chain disruptions present systemic risks that permeate the entire product lifecycle. Operational bottlenecks can directly impact clinical development timelines, compromise commercial launch readiness, and ultimately threaten the continuous supply of life-saving therapies to patients.

As a result, biopharmaceutical companies are now gauging the supply chain resilience of the contract development and manufacturing organizations (CDMOs) when selecting their potential biopharmaceutical partners. 

Identifying this resilience requires a holistic view of supply networks, one not solely concerned with the amount of capacity available at a CDMO but also with where that capacity is located, whether it fits the product’s technical needs, and whether it can support key markets.

Mapping vulnerabilities before they impact supply

A resilient CDMO network begins with a clear understanding of where supply could become constrained. Biopharma companies should consider four areas when assessing whether a manufacturing partner has identified and prepared for these vulnerabilities:

• Geographic concentration: If critical production stages depend on a limited number of regions, disruption affecting a manufacturing hub or transport corridor can restrict downstream supply. Biopharma companies should examine where the CDMO’s facilities and suppliers are located, as well as whether its network provides credible alternatives when one region is affected.
• Material and supplier dependencies: The availability of a manufacturing suite offers little protection if a shortage of chromatography resin, cell culture media, filters, or single-use components prevents production from proceeding. Long lead times and reliance on sole-source suppliers may further limit the CDMO’s ability to respond when forecasts change or supplies are interrupted.
• Future manufacturing capacity: Clinical manufacturing generally requires flexible access to smaller batches, whereas commercial production depends on repeatable performance at greater volumes and reliable long-term scheduling. Unless the CDMO has considered this transition early, a biopharma company may reach a pivotal milestone without a clear route to the capacity required for launch.
• Technical capability: Available bioreactor volume is not necessarily usable capacity. A CDMO’s facility must also possess the containment, analytical, downstream processing capabilities, and flexibility required by the product and process. As biologics become more complex, the number of technically suitable manufacturing sites may narrow.

Assessing these areas across the CDMO’s supply network provides a more meaningful indication of resilience than available capacity alone. A partner that identifies potential constraints early can give a biopharma company more options for managing these bottlenecks and pitfalls before they affect the program.

Onshoring as regional reinforcement

Facility location can influence supply resilience, but onshoring and regionalization represent different approaches. Biopharma companies should understand what each model can offer and whether the CDMO’s wider supply network allows those benefits to be realized.

Onshoring: Involves locating or returning manufacturing activities to the biopharma company’s domestic market. Its appeal lies in shortening some supply routes, improving proximity to local teams, and reducing dependence on overseas production corridors. It may also provide access to established domestic biotechnology clusters and support national efforts to strengthen medicine security. However, onshoring does not create a self-sufficient supply chain. Domestic facilities may continue to rely on internationally sourced equipment, raw materials, and specialist services. It may also be impractical where the required expertise or infrastructure is unavailable locally, while higher operating costs can make domestic production unsuitable for some programs. Companies should therefore assess whether a CDMO’s onshore capacity is supported by a sufficiently diverse and well-managed supply base.

Regionalization: Distributes manufacturing capabilities across several major geographic markets rather than concentrating them within one country or global hub. For biopharma companies serving multiple markets, this approach can place production closer to regional demand and provide alternative supply routes if another part of the network is disrupted. It may also improve access to regional talent and make collaboration across development, manufacturing, and quality functions easier. However, operating across multiple regions introduces additional complexity. Differences between facilities, regulatory requirements, and supply bases may make production difficult to transfer, while qualifying additional sites requires time and investment. Regionalization is therefore most effective when the CDMO’s facilities operate as a connected network, with sufficient alignment in processes and quality systems to make alternative manufacturing routes credible.

Neither approach guarantees resilience through geography alone. Their value depends on whether the CDMO has built the supplier diversity and transfer readiness needed to sustain manufacturing when normal supply arrangements are disrupted.

Capacity must follow the product lifecycle

Capacity planning is particularly challenging for biopharma companies because demand for biologics is hard to predict. Early clinical programs require limited volumes, and their future needs remain uncertain. Positive clinical data, accelerated approval pathways, or stronger-than-expected market uptake can then increase demand within a compressed timeframe. Conversely, trial delays or clinical attrition may release production slots that had been reserved.

Planning for multiple outcomes

Because biologics demand can change rapidly, biopharma companies need a CDMO that plans for more than a single forecast and that can remain flexible when things change. For example, a clinical program may progress faster than expected following positive data or an accelerated approval, creating an earlier need for commercial capacity. Trial delays or attrition can have the opposite effect, reducing or postponing demand.

A resilient CDMO prepares for these possibilities through scenario-based capacity planning. Early in development, the CDMO assesses how the process may need to scale and identifies which facilities could support later clinical or commercial production. The plan can then set decision points linked to meaningful program milestones and can course-correct as needed. This determines when the biopharma company may need to reserve additional capacity, begin technology transfer, or invest in new infrastructure.

The scenarios should be proactively updated as the program develops. Biopharma companies contribute current information about clinical progress, regulatory timelines, and anticipated demand, while the CDMO translates those assumptions into capacity requirements. This structures the response to uncertainty without requiring the biopharma company to commit prematurely to capacity it may not use or need.

However, planning alone does not create resilience. A scenario has limited value if the CDMO cannot adapt its manufacturing operations when that scenario materializes.

Designing networks that can adapt

A flexible CDMO can convert capacity plans into practical action. If demand increases, the network may need to accommodate larger batches or free up production slots. If the primary site is disrupted, manufacturing may need to move elsewhere. Without suitable alternative capacity, the biopharma company remains dependent on the original arrangement even if the risk has already been anticipated.

This adaptability must be established before circumstances change. The CDMO should identify which facilities could support the product at different stages and determine what would be required to move between them. Alignment in equipment, operating practices, and quality systems can make technology transfer more predictable, although facilities do not need to be identical. What matters is that their differences are understood and that the analytical and regulatory requirements of a transfer have been assessed.

For programs with sufficient demand or supply risk, qualifying a second site may provide an alternative manufacturing route if the primary facility becomes unavailable. In other cases, identifying a technically suitable backup site and preparing a transfer strategy may offer an appropriate level of protection without immediately duplicating manufacturing and keeping the initial investment to a level that fits the client’s risk tolerance.

Biopharma companies should therefore assess whether a CDMO’s capacity is genuinely accessible as requirements change. A network may contain multiple facilities, but it is only adaptable if the product can move between appropriate scales or sites within a timeframe that protects development progress and patient supply.

Technical capability determines whether CDMO capacity is truly available

As biologics pipelines diversify, biopharma companies cannot assess CDMO capacity solely by looking at available production slots or bioreactor volume. Capacity is only usable when the facility also has the infrastructure, expertise, and analytical capabilities required to manufacture the product safely and in accordance with its control strategy.

This distinction becomes more important for complex modalities. An antibody-drug conjugate (ADC) may require antibody production alongside high-potency containment, conjugation, and specialized analytical methods. Multispecific antibodies can introduce product-specific challenges during expression and characterization. High-concentration formulations for subcutaneous delivery may also place different demands on processing and fill-finish operations than conventional intravenous products.

The more specialized the requirements become, the fewer facilities may be able to support the program. As a result, a spare slot in the CDMO’s network may offer little protection if that site lacks the necessary technical capabilities. Companies should therefore establish where the relevant expertise and infrastructure are located, whether they can support the product at later-stage volumes and how supply would be maintained if the primary facility became unavailable.

A resilient CDMO must understand where technical dependencies could restrict supply and prepare realistic ways to manage them. This may involve connecting specialist centers through established transfer processes, qualifying an additional site where the risk justifies it, or maintaining contingency plans for critical activities that cannot readily be relocated.

The future of biologics supply will depend on adaptable CDMO networks

As biopharma companies rely more on external manufacturing, their supply resilience will depend on the adaptability of their CDMO partners. CDMOs must respond to this dependence with coordination, syncing their technical capacity to critical materials across their connected networks.

This flexibility must be established before circumstances change. Capacity planning should evolve with clinical and commercial forecasts, while technology transfer and contingency strategies should prepare programs for changes in scale, location, or demand.

CDMO selection will become an increasingly important supply-chain decision. Biopharma companies must look beyond capacity available today and choose partners whose networks can adapt as their programs and the wider operating environment evolve.

Jeff Mason is Vice President and Head of U.S. Sales at Samsung Biologics, with more than 30 years of CDMO industry experience spanning large-molecule manufacturing, program and account management, and global strategic accounts across multiple modalities. He joined Samsung Biologics in December 2025.

Despite the progress made in sustainability across the medical device and drug delivery industry, one area urgently requires greater attention: raw materials. More specifically, reliance on virgin, fossil fuel-derived plastics. And there are few indicators that this reliance is reducing; one study concludes that, without significant intervention, plastic use across industry will rise by over 50% by 2040.² 

This reliance is most often discussed as a sustainability issue, but recent challenges have shown that it must also be considered as a supply chain exposure. Indeed, the complex, international supply chains that provide medical device manufacturers with the polymers they need are not immune from the disruptions we have seen over recent years, their causes ranging from the pandemic to cyberattacks, and geopolitical instability to extreme weather.³

Material dependency has become a long-term business risk that procurement and sourcing strategies often do not yet account for. Moving away from virgin materials is not simply a way of reducing emissions, but a necessity for manufacturers who wish to safeguard their ability to continue commercializing safe, effective and affordable devices. Manufacturers that continue to plan around stable access to virgin plastics may be building future risk into products that are intended to remain on the market for many years.

The plastic dependency problem

In the longer-term, manufacturers are facing a double hit of increased demand for plastics and reduced supply as oil reserves decline. For medical device manufacturers, the consequence is not simply higher material prices; it is a less predictable planning environment in which access to validated polymers may become more constrained, procurement timelines may lengthen and suppliers may need to ration stocks.

For most industries, this forecast is already prompting reflection on virgin plastic dependency and the exploration of new or alternative approaches. But medical device manufacturers generally don’t have that flexibility. The materials they use must meet stringent performance, safety, biocompatibility, and regulatory requirements. In many cases they are selected early in the development process, then validated by means of long testing and approval processes. 

This means that switching a validated polymer is not solely a sourcing decision, but also a regulatory one. A material change to an existing device can trigger new biocompatibility testing, extractables and leachables studies, sterilization validation, and potentially a new regulatory submission—a time-consuming and costly process.

This issue is compounded by the long development cycles prevalent in the industry. In a world where products can take years or even decades to reach the market, and remain there even longer, it is essential that manufacturers start acting now if they wish to reduce dependence on fragile material supply chains in the future.

Why alternative materials aren’t a true solution – yet 

Manufacturers in a number of industries are already exploring bio-based polymers, and material science in this space is advancing quickly. But for the medical device industry, any alternative materials must clear three bars simultaneously: cost, accessibility and suitability for medical applications. Today, most struggle to clear all three at once.

Among the challenges presented by biobased polymers is that they typically cost 50 to 100% more than fossil fuel-based plastics to produce.⁴ This premium stems from the high, fluctuating costs of feedstock crops, and the smaller production scales involved. Scaling bioplastic production brings its own concerns in terms of land use, waste management infrastructure, and technological maturity⁵—concerns that must be resolved to produce the required volumes without introducing new harms. There is some way to go; according to European Bioplastics, global production capacity of biobased plastics currently represents approximately 0.5% of total plastics produced annually.⁶

While some bioplastic-based medical products are already on the market, including gloves, drapes, and staplers,⁷ concerns over safety, durability, and stability remain an obstacle to their adoption in other areas such as injection devices.

None of this means alternative materials are not worth pursuing. It means they are not, on their own, a near-term resilience strategy at the scale the industry needs. The more immediate opportunity lies less in what devices are made from, and more in reducing virgin material use across the product life cycle.

Rethinking product design to preserve value

If material substitution is not currently a dependable, at-scale response for medical devices, circular design offers a stronger route to both sustainability and resilience. By engineering devices from the outset for lower material demand, longer product use and higher-value recovery, manufacturers can reduce the amount of virgin material they need while preserving more of the value already built into each product. 

Circular design is generally presented as a hierarchy of “R-strategies”: redesign, replace, reduce, reuse, repair, reprocess, repurpose, recycle.⁸ The strategies at the top of the list keep a product in use for longer, or preserve more of its components, and tend to retain more value than those that return a finished device to raw material. 

In practice, this can mean designing for multiple use, reducing part count and material use, enabling component replacement rather than discarding a whole assembly. This is already standard practice for some complex, high-value devices like imaging systems and large surgical equipment, which are designed with refurbishment and long-life maintenance in mind, rather than being treated as disposable. 

The barriers to circular design in medical devices are real and documented: safety and infection control requirements, regulatory complexity, and the technical complexity of separating multi-material assemblies for recovery. But these are reasons to design even more deliberately for circularity as early as possible in the development process, not to avoid it altogether. 

This is how manufacturers can ensure supply chain resilience into the future, extending the design logic applied to high-value equipment to higher-volume components and devices that have traditionally defaulted to single-use, and reducing the need to secure even more virgin materials each time a device reaches the end of its first use cycle.

Where recycling falls short

While all attempts to reduce virgin material dependency will improve medical device supply chain resilience, not all circular strategies preserve equal value. Recycling schemes are already operated by some manufacturers and are clearly preferable to landfill or incineration, but for complex products like autoinjectors, recycling often recovers only the base polymer in the form of low-value recycled pellets—otherwise known as downcycling. All of the value added during precision molding and assembly is lost as soon as the device is reduced down, making recycling the least economically attractive option in many cases. 

Additionally, the demanding safety and validation requirements placed on virgin polymers intended for patient contact applications also apply to even chemically equivalent recycled plastics, often requiring extensive (and costly) further testing and documentation before they can be considered as direct substitutes for the original medical-grade materials. 

Reprocessing and remanufacturing: the resilient alternative

There is therefore a clear commercial rationale for shifting to other R-strategies that preserve greater value. Reprocessing and remanufacturing both do this by taking a fundamentally different approach to recycling. Reprocessing involves the cleaning, disinfection, and sterilization of the used device, as well as restoring its technical and functional safety,⁹ while remanufacturing involves more significant changes such as refurbishing, rebuilding or altering the design or materials.¹⁰

Both preserve substantially more of the technical, functional and commercial value of complex devices than recycling, and despite historic reluctance over safety concerns, both are now increasingly accepted at a global scale as regulation and practice evolves.

Industry Perspective: “Circularity decouples growth from resource consumption. For a manufacturer, that is strategic freedom: serving more patients without exposure to fragile supply chains and volatile commodities markets. Therefore, investments into circularity are a significant economic opportunity to create resilient supply chains and ensure that patients’ needs can be served well into the future.” — Cedric Gysel, CEO, Be Circular¹¹

The U.S. reprocessing sector, strictly regulated by the Food & Drug Administration, provides the clearest evidence base available. According to the Association of Medical Device Reprocessors (AMDR), more than 39.3 million regulated, reprocessed single-use devices were sold to 11,458 hospitals and surgical centers across 18 countries in 2025 alone.¹² This not only reduced CO2-equivalent emissions by more than 125 million pounds (57 million kg), it also generated more than $449.5 million in documented hospital savings as a result of lower cost devices and waste reduction.¹³

There is a clear practical argument for treating reprocessing and remanufacturing as a resilience strategy rather than solely a potential cost or sustainability win: every device or component kept in productive use is one less unit of demand placed on virgin material supply, and one more unit of previously-invested manufacturing value retained in the system. And as virgin plastic becomes an increasingly constrained input, strategies that preserve product value for longer will reduce exposure to price volatility, support continuity of supply, and help manufacturers meet increasingly rigorous sustainability expectations.

The business case for circularity

The medical device industry’s dependency on fossil-based virgin plastics is structural, and likely to be complicated by a tightening supply-demand curve: rising demand for virgin materials, met by a supply window narrower than most planning horizons assume. Recycling, while preferable to disposal, recovers only a fraction of the value built into a finished device. Circular design, reprocessing, and remanufacturing preserve considerably more of that value, while directly reducing exposure to virgin material volatility. The manufacturers best positioned to handle the upcoming supply pressure will be those who act early to lower their virgin plastic consumption, treating circularity as a core strategic priority.

References

1. Quote shared with Owen Mumford directly by Cedric Gysel, CEO of Be Circular
2. The Pew Charitable Trust, in collaboration with the Ellen MacArthur Foundation, Imperial College London, Systemiq and the University of Oxford, Breaking the Plastic Wave 2025 https://www.pew.org/en/research-and-analysis/reports/2025/12/breaking-the-plastic-wave-2025
3. See, for instance: Forbes, Fuel Rationing, No AC, 4-Day Work Week: How Countries Are Dealing With The Iran War Oil Crisis, March 2026 https://www.forbes.com/sites/siladityaray/2026/03/25/fuel-rationing-no-ac-4-day-work-week-how-countries-are-dealing-with-the-iran-war-oil-crisis/; OECD, OECD Supply Chain Resilience Review: Navigating Risks, 2025 https://doi.org/10.1787/94e3a8ea-en; A Qazi, Systemically important supply chains in crisis: Mapping disruptions and global ripple effects, Natural Hazards Research, Volume 6, Issue 2, 2026 https://doi.org/10.1016/j.nhres.2025.09.003
4. UKHI, Bioplastics Market Report 2025-2030, November 2025 https://ukhi.com/bioplastics-market-report-2025-2030/
5. See, for instance: G Jothiprakash, S Samiappan, S Sundaram, C Hwang See, D Sun, K Subburamu, R Desikan, Toward net-zero plastics: Synthesis, applications, and global trends of bioplastics, Journal of Environmental Chemical Engineering, Volume 14, Issue 3, 2026 https://doi.org/10.1016/j.jece.2026.123097 
6. European Bioplastics, BIOPLASTICS MARKET DEVELOPMENT UPDATE 2025, https://www.european-bioplastics.org/bioplastics-market-development-update-2025/
7. Boston Consulting Group, Medtech’s Path to a Circular Future, March 2024 https://www.bcg.com/publications/2024/medtech-path-to-circular-future
8. Nathalia Silva de Souza Lima Cano, Melanie Marino, Amy L Brooks, Matthew J Eckelman, Melissa M Bilec, Advancing the circular economy of healthcare plastics: A systematic literature review, Resources, Conservation and Recycling, Volume 219, 2025 https://doi.org/10.1016/j.resconrec.2025.108317
9. European Commission, Reprocessing of devices https://health.ec.europa.eu/medical-devices-topics-interest/reprocessing-devices_en (accessed August 2026)
10. Nathalia Silva de Souza Lima Cano, Melanie Marino, Amy L Brooks, Matthew J Eckelman, Melissa M Bilec, Advancing the circular economy of healthcare plastics: A systematic literature review, Resources, Conservation and Recycling, Volume 219, 2025 https://doi.org/10.1016/j.resconrec.2025.108317
11. Quote shared with Owen Mumford directly by Cedric Gysel, CEO of Be Circular
12. Association of Medical Device Reprocessors, Reprocessing by the Numbers, https://amdr.org/newsroom/reprocessing-by-the-numbers/ (accessed August 2026)
13. Association of Medical Device Reprocessors, Reprocessing by the Numbers, https://amdr.org/newsroom/reprocessing-by-the-numbers/ (accessed August 2026)

Alex Fong, MBA, is Head of Insight at Owen Mumford Pharmaceutical Services. He has more than 10 years of experience leading market research at Owen Mumford, with an increasing focus on sustainability, alongside previous experience in analytics, strategy and consulting.

Deepening global geopolitical instability has led some experts to warn that the pharmaceutical industry could be facing one of its most severe stress tests in a decade.¹ For drug development, this raises critical questions around preparedness, with research suggesting that only 22% of global pharma companies were actively assessing geopolitical risks at the end of 2025.²

Increasing levels of disruption, whether from geopolitical instability, adverse weather events, regulatory shifts or protocol amendments, are exposing deep flaws in how clinical supply is planned and managed. Supply plans that are set to hold for the duration of a clinical trial won’t accommodate the dynamic and uncertain nature of modern drug development. The operating environment has fundamentally shifted and is now defined by constant change that can compromise sponsors’ ability to deliver safe, timely and cost-effective supply to patients. Clinical supply chains have become exceptionally good at responding to disruption after the fact, but why is it still commonplace to design studies that need recovering in the first place? 

Sponsors need to plan for change from the outset. Combining robust early planning with a flexible Just in Time (JIT) model can help them adjust supply as demand becomes clearer, while ensuring the right patients receive the right drugs at the right time and in the right condition. 

A new operating reality 

As protocols grow more complex, patient populations more disperse and timelines more ambitious, disruption has become routine. Recruitment changes. Countries are added. Protocols evolve. Manufacturing schedules shift. Cyber risk, geopolitical tension, shifting regulation and logistics constraints compound the problem, further compromising supply predictability. Yet resilience is still too often defined by what happens after these challenges emerge, not by the decisions made long before the first shipment leaves the warehouse.

A key issue is that many clinical supply models used today were designed for a bygone era, where disruption was the exception not the norm. These models assume a stable environment, committing manufacturing scale, packaging and labelling decisions upfront, against a forecast, before real demand signals exist. But that only works if the initial forecast is right and it rarely is. Up to 80% of clinical trials fail to meet original enrolment targets,³ meaning the assumptions behind those early, sometimes irreversible, decisions are often unreliable from day one.

The mismatch creates friction before a trial begins. In one recent survey, 55% of site personnel reported that time from site selection to activation was already exceeding planned timelines.⁴ When activation runs behind schedule, supply built against the original plan is left waiting: stock can expire before it’s used or need relabeling if demand shifts to a different region, adding cost and time sponsors can struggle to accommodate. The instinctive response is often to overcompensate by building more bulk supply and labelling more stock upfront to guard against the risk of running short. But this just moves the waste elsewhere. Excess inventory sits against enrolment that hasn’t materialized, tying up investment in product that may expire, or need relabeling, before it’s ever used. Changes in trade policy can add another layer of uncertainty, increasing costs, creating customs delays, and forcing adjustments to sourcing, distribution routes or inventory strategies, making it increasingly difficult to rely on fixed assumptions established at the onset of the trial.

While contingency planning still matters, defining resilience only by what happens after disruption strikes (the plans, the expedites, the buffer stock) treats the symptom, not the design flaw. Arguably, a supply chain that only becomes resilient under pressure was never resilient to begin with. What’s needed instead is an approach that empowers sponsors to adapt with speed and precision to ensure patients never miss a dose and trials remain on track. And this starts with early and robust planning at the point of study design. 

Building resilience at the point of study design

Decisions around forecasting, manufacturing strategy, packaging, labelling and distribution all shape how well a trial can absorb change later. Treated as an afterthought, they leave little room to adapt but treated as design decisions from day one, they create flexibility from the start.

Getting this right means addressing a handful of vital components early. Forecasting needs to move beyond static, single-point projections toward models able to absorb enrolment and country-mix variability, as a trial evolves. Manufacturing strategy needs to distinguish what must be fixed early, such as safety and regulatory-bound decisions, from what can be deliberately deferred until better information exists. Packaging and labelling need to be built for configurability, rather than locked to initial assumptions about country mix and site count. Likewise, distribution must be designed with appropriate contingency options, avoiding unnecessary reliance on single points of failure. And underpinning all of it, clinical operations, manufacturing and supply planning teams need to work together from shared data and shared assumptions. It’s important to note that as enrolment and country mix become clearer, the strategy can and should be revisited, sometimes shifting to a hybrid model that applies different approaches across different countries within the same study to maximize value.

The cost of getting this wrong is significant. Median waste levels for investigational medicinal product (IMP) kits sit at around 50%, largely down to poor forecasting and planning baked in at study start.⁵ Increasingly, sponsors are recognizing the value of designing flexibility in from day one, preserving the ability to respond as studies evolve rather than committing every decision upfront. This is exactly the philosophy that flexible, late-stage customization of clinical kits makes operationally possible.

An adaptive approach 

JIT approaches offer a practical mechanism for putting this design philosophy into practice. JIT doesn’t eliminate uncertainty, but it does give sponsors a way to make fewer irreversible decisions before that uncertainty has had time to resolve. Rather than committing volumes and configurations months in advance, JIT can defer selected manufacturing, packaging or labelling decisions until closer to the point of actual need. Supply is aligned to real patient or site demand rather than a forecast made at study start. This reduces reliance on long-range projections – freeing sponsors to absorb protocol amendments, recruitment changes and shifting regional requirements without triggering a full replan, while reducing the risk of overproducing, over-labelling or misallocating supply.

In practice, this takes different forms depending on where in the supply chain flexibility is needed. Just-in-Time Manufacturing keeps production runs smaller and more frequent, tied closely to confirmed demand rather than a single upfront batch sized against a forecast that may prove wrong. This is particularly beneficial for high value biologics and personalized therapies with limited shelf life or high per-unit cost, where overproducing against an inaccurate forecast is expensive. It also adds value for trials expecting mid-study protocol amendments or dose changes, where committing to fixed batch sizes early leaves little room to adjust.

Just-in-Time labelling addresses a different challenge. By postponing final labelling until a patient or site need arises, sponsors avoid locking in country-specific or language-specific labelling requirements before the final country mix and site list are confirmed. This is especially useful in multi-country trials where new sites or countries are added mid-study, and in trials facing regulatory or import changes in specific markets. This is because stock can be finalized for the market that needs it, avoiding having pre-labelled stock sitting unused (or unavailable for use) elsewhere.

In practice, many sponsors lean on JIT most heavily early in a study, when uncertainty is greatest, before transitioning towards batch manufacturing as demand becomes clearer. Continuous monitoring empowers sponsors to track how assumptions hold up against reality, and pivot their approach as external factors, from enrolment to regulation, shift throughout the trial.

The scale of the opportunity is significant. A JIT approach can deliver a 75% reduction in overall clinical trial timeframes, along with significant reductions in waste and rework.⁶ Approached this way, JIT becomes the enabling capability that allows a supply strategy designed for change to function as intended, turning flexibility from an aspiration into something operational.

The value of flexibility 

Disruption, whether geopolitical, regulatory or operational, is now the norm, not the exception. Traditional supply models built for a more predictable era are struggling to keep pace. Real resilience starts at study design, not in the contingency plan drawn up after something breaks. JIT approaches make that design philosophy workable, aligning supply with real patient demand rather than early-stage guesswork, so patients are never left waiting on a forecast made months before their need was known. This challenges a long-held assumption: that every critical supply decision must be made at the outset. Increasingly, sponsors recognize the value of designing flexibility in from day one, preserving the ability to respond as studies, and the world around them, evolve.

References

1. https://www.emjreviews.com/emj-gold/article/the-middle-east-conflict-pharmas-supply-chain-stress-test/
2. https://www.emjreviews.com/emj-gold/news/supply-chains-and-trials-hit-by-middle-east-instability/
3. https://pubmed.ncbi.nlm.nih.gov/33146627/
4. https://www.iconplc.com/news-events/press-releases/icon-survey-reveals-increasing-clinical-trial-startup-delay
5. https://www.mckinsey.com/industries/life-sciences/our-insights/clinical-supply-chains-how-to-boost-excellence-and-innovation
6. https://www.pharmtech.com/view/necessity-drives-just-in-time-approaches-to-clinical-trials-supply

Nishma Patel is a Clinical Trial Supply Chain Solutions Manager at Almac Clinical Services, joining the organization in 2021. A Doctor of Pharmacy, she specializes in managing global clinical trial supply chains, overseeing the procurement, storage, and distribution of investigational products. She works closely with cross-functional teams and partners to ensure timely, compliant delivery and successful trial execution worldwide.

Formulation of high dose large molecules like proteins and biologics for injectables has created a great opportunity for unmet medical needs.¹ Drug concentrations at and above 100 mg/mL are considered as high dose, whereas, at or above 250 mg/mL are considered as ultra-high dose with injection volumes of about 2 mL.²

The complexity with high dose formulations stems from aggregation, viscosity, particle formation, long term stability and efficacy of drug products. High dose and smaller volumes of proteins/biologics are often desired for higher clinical efficacy for treatment of life-threatening ailments like rheumatoid arthritis, psoriasis, and neurological disorders, which are preferably administered by subcutaneous (SC). Other factors such as syringeability and injectability are also governed by concentration of drugs in certain injection volumes. Over 46 high concentration antibodies or biologics marketed thus far, about 34 of them are approved by SC, and only 6 by intravenous (IV), and 1 by intramuscular (IM) routes of administration.³

The pain sensation at the injection site is very common. Usach et al. proposed that maximum volume of an SC injection is 1.5 mL with exception of no more than 4 mL if necessary as higher volume is typically associated with the injection site pain.⁴ Berteau et al. evaluated different viscosities in SC to monitor the pain at injection site and observed that highly viscous liquids with viscosity 15–20 cP caused less pain and were easily tolerated as opposed to those having viscosity 1 cP or 8–10 cP.⁵

Fransson et al. investigated the effect of pH and buffer strength and found that at neutral pH 7 and buffer strength as low as possible, the SC formulation was better tolerated upon injection.⁶ Phosphate and citrate buffers with concentration 10 mM and 7.3 mM, respectively, caused less pain at the injection site.⁴ It is, therefore, necessary to maintain pH and buffer strength, and keep low volume for SC high dose proteins or biologics.

Those SC administrations are typically used in 2 mL volume in prefilled syringe (PFS) or autoinjector (AI). Low volume, high dose SC formulation poses some challenges and to overcome those, a few approaches are used including (i) adding viscosity reducing agent to target drug concentrations as low as 100 mg and as high as 250 mg/mL (ii) use novel technologies to deliver the protein concentration as high as 250 mg/mL or higher, and (iii) use a large delivery device with the capability to deliver 2 mL to 40 mL volume.

Formulation development of high concentration biologics

Drug development requiring formulation, analysis and manufacturing can lead to multiple challenges associated with high concentrations including aggregation that negatively impacts the product’s quality, efficacy, stability and safety, and long-term immunogenicity.⁷ Physical characterization tools are necessary to understand the protein-protein interactions. Ultracentrifugation or diafiltration or tangential flow filtration can yield higher concentrations. Increasing concentrations can lead to increased protein-protein interactions, which could impact on conformational and/or colloidal stability and aggregation.

Differential scanning calorimetry (DSC) can be used to quantify free energy difference (ΔG) between natural and denatured proteins and understand the conformational stability; higher ΔG, means greater the stability of proteins or vice versa. Dynamic light and static light scattering can be used to measure particle size and colloidal stability. Higher colloidal stability means the proteins maintain stability as a monomer in native state or partially denatured states due to repulsive or electrostatic interaction.⁸

“The complexity with high dose formulations stems from aggregation, viscosity, particle formation, long term stability and efficacy of drug products.”

Analytical challenges can be overcome by utilizing rapid and simultaneous liquid chromatography and mass spectrometry methods for determination of degradants or any other impurities. An understanding of the mechanisms of protein and excipient degradation and their interactions in solutions is equally important for selecting the appropriate bio-analytical methods to determine formulation stability. Polysorbates, for example, are known to form reactive oxygen species and may react with proteins to cause significant degradation. In cases like these, LC-MS could be used to quantify the chemical degradants.

Physical degradants like conformational changes, denaturation and protein surface adsorption can also be detected by appropriate techniques including DSC and DLS. Other complementary techniques such as differential scanning fluorometry (DSF) can also be used to understand thermal stability, NMR for structural characterization, FTIR for examining the amide I and II bonds of proteins to probe the secondary structure, while circular dichroism (CD) can be used to understand the secondary and tertiary structures of proteins.⁹

Manufacturing challenges often result from lack of appropriate equipment and/or facility. Clear guidance, therefore, is required for scale-up and manufacturing high concentration biologics. For example, vessel design and size with appropriate shaft or mixer can all play an important role for high concentration formulations. Excessive mixing can increase the chances for degradation of biologics, especially at higher concentrations. Thus, the main emphasis is to reduce processing time by maximizing flux rates under controlled temperature and pressure to further curtail the risks for potential degradation in either aqueous or non-aqueous manufacturing media.⁹

Processing techniques such as spray drying, solvent extraction, dehydration, ionic liquids among others are used for manufacturing non-aqueous based suspensions. Spray drying, for example, is applied for creating small spherical fine powder with 5–30 microns in size with the intent to provide better shear thinning and injectability and to protect moisture sensitive drugs as well. The dried powder is then suspended in non-aqueous injectable vehicle preferably in high concentrations (100–250 mg/mL) with desired viscosity (<20 cP or less) and to help improve stability by reducing aggregation and minimizing risks from inherent hydrolytic degradation.

Low temperature solvent extraction and dehydration technique can also be used for thermally labile drugs. This anti-solvent technique results in an amorphous powder by precipitation wherein drug particles are embedded in polymeric matrix, allowing excellent control over particle size, morphology, and surface characteristics with abilities to maintain colloidal stability in non-aqueous suspensions. Shire et al developed a low-shear, highly concentrated biologic suspension by particle engineering of a protein for subcutaneous injection through small gauge needle by reducing the injection force.¹⁰

Manufacturing requirements for ISO cleanrooms

Table 1 lists the cleanrooms with airborne particulate classification requirements for microbial limit in sterile drug products.¹¹

Marketed high-concentration biologics

Table 2 lists of marketed high concentrate subcutaneous proteins and biologics.

Novel technologies and ionic liquids for high concentrations biologics

HILOPRO technology is used to achieve the drug concentrations as high as 250 mg/mL by reducing the viscosity of mAbs liquid < 20 cP by adding two ingredients, nicotine and tryptophan in 20 mg or less.¹² Comera Life Sciences, for example, utilized caffeine to reduce the viscosity 37 cP or less to achieve the concentration as high as 200 mg/mL of two mAbs: ipilimumab and infliximab.¹³ Avantor used bis acetyl lysine and propionyl serine to reduce viscosity and increase concentration and also to improve shelf life and stability of proteins.

Xeris Pharmaceutical utilized its Xeriject spray drying and particle engineering technology to achieve crystalline suspensions with concentrations as high as 400 mg/mL, while Lindy Biosciences used its particle engineering microglassification technology in organic solvents to achieve spherical dense, stable particles with drug concentrations as high as 300 mg/mL and 600 mg/mL. Elektrofi, on the other hand, used its Electroject technology to achieve ultra-high drug concentrations; 500 mg/mL and 700 mg/mL microparticle-based stable suspensions by reducing protein-protein interactions and maintaining its injectability.

ENHANZE, a subcutaneous proprietary technology by Halozyme, depolymerizes hyaluronan enzyme to improve mobility of protein solutions, thus allowing much greater volumes (ca.5–10 mL) of high concentration therapeutic doses of mAbs including Herceptin Hylecta (trastuzumab and hyaluronidase), Phesgo (pertuzumab, trastuzumab and hyaluronidase), among others.¹⁴

Ionic liquids (ILs) possess an ability to dissolve challenging poorly soluble molecules, therefore, have been used in formulation and drug delivery in modern times. ILs are considered environmentally green solvents since they are derived from salts and exist as liquids at temperatures below 100ºC. Created by combining cation and anion entities, ILs are stable, non-flammable and act as non-volatile solvents.

Banerjee et al. evaluated CAGE, an IL derived from choline and geranyl acid as salt, in oral insulin formulation and found that it was absorbed through intestinal membrane via modulation of intestinal mucus properties.¹⁵ In another study, Sundaram et al. used ILs derived from choline and amino acids for oral delivery of insulin and found that depending upon concentrations of amino acids, some amino acids have more profound effect on secondary structure of insulin. Other ILs comprised of choline-glycine and choline-alanine formed appreciable amounts of aggregates at high concentrations.¹⁶

Table 3 lists a range of ionic liquids derived from reacting cation and anion moieties.¹⁷

Aseptic spray drying for high concentration biologics

Aseptic spray drying was first used in 2006 for Exubera by Pfizer for an insulin inhaled powder. Other examples include Verity Pharmaceutical’s triptorelin pamoate (Trelsatar LA) as intramuscular microsphere suspension for prostate cancer, lanreotide acetate (Somatuline LA microspheres by Ispen, Raplixa, a biologic for topical formulation by ProFibrix BV and levodopa (Inbrija) by Acorda Therapeutics.¹⁸

Conclusion

Ascendia offers the expertise in high concentration and highly viscous formulations (solutions and suspensions) of small and large molecules and biologics, and with its state-of-the-art manufacturing facility equipped with fill-finish capabilities of vials, prefilled syringes and cartridges, Ascendia can help expedite the development in the clinical stages of drug products in ISO 5, 6 and 7 (Grade A, B and C) clean rooms. Ascendia’s aseptic process to fill high concentration liquids in vials, prefilled syringes and cartridges can be achieved using peristaltic and piston pumps. Ascendia’s aseptic spray drying can help expedite the formulation development of small and large molecules, proteins and biologics.

References

1. R.G. Strickley and W.J. Lambert, A review of formulations of commercially available antibodies, J. Pharm. Sci., 2021, 110, 2590-2608.

2. I. Ghosh, D. Miranda, T. A. Kulkarni, S. Deodhar, S. Tummala and D. Bindra, Development roadmap for subcutaneous delivery of high dose biologics – high concentration formulation, analytical comparability and patient preference considerations for large volume devices, J. Pharm. Sci., 2025, 114, 103914.

3. I. Ghosh, H. Gutke, M. E. Krause, R. Clemens and R. S. Kashi, A systematic review of commercial high concentration antibody drug products approved in the US: formulation composition, dosage form design and primary packaging considerations, MABS, 2023, 15, 2205540.

4. I. Usach, R. Martinez, T. Festini, and J. S. Peris, Subcutaneous injection of drugs: literature review of factors influencing pain sensation at the injection site, Adv. Ther. 2019, 36, 2986-2996.

5. C. Berteau, O. Filipe-santos, T. Wang, H. E. Rojas, E. Granger, and F. Schwarzenbach, Evaluation of the impact of viscosity, injection volume, and injection flow rate on subcutaneous injection tolerance, Med. Devices (Auckl), 2015, 8, 473-484.

6. J. Fransson and A. Espander-Jansson, Local tolerance of subcutaneous injections, J. Pharm Pharmacol., 1996, 48, 1012-1015.

7. W. Wang and S. Nema, and T. Teagarden, Protein aggregation – mechanism, detection and control, Int. J. Pharm., 2010, 390, 89-99.

8. B. Johnson and A. Rostovtsev, High concentration biologic formulations: Challenges and solutions, Drug Discovery and Development, June 29, 2017.

9. T. A. Khan, D. Bhattacharya, T. R. Christian, M. Holstein, X. Hua, R. James, B. Jiang, A. Josowitz, D. Laiacona, S. Mehta, A. More, E. Mullen, M. Myers, B. Ricart, T. Rickenbacher, Y. Su and N. K. R. Yaragudi, Adv. Drug Deliv. Rev., 2026, 235, 115885.

10. S. J. Shire, Z. Shahrokh and J. Liu, Challenges in the development of high protein concentration formulations, J. Pharm. Sci., 2004, 93, 1390-1402.

11. Who expert committee on specifications for pharmaceutical preparations: Annex 6: good manufacturing practices for sterile products. Who Tech Rep Ser. 2002, # 902:76-93.

12. Bhami’s Research: https://bhamilab.com/index.php/hilopro.

13. Y. Zeng, S. Naik, T. Tran, P. Wuthrich, N. Muni, and R. P. Mahoney, Preclinical pharmacokinetic study on caffeine as an excipient for monoclonal antibody formulations, J. Pharm. Sci., 2023, 112, 2933-2937.

14. I. Ghosh, S. Deodhar, H. Gutke, S. Sridharan, and D. Bindra, Subcutaneous drug delivery of high concentration antibody products – part 2: formulation, device options, and clinical bridging strategies for patient-centric commercial presentations, MABS, 2026, 18, 2680773.

15. A. Banerjee, K. Ibsen, T. Brown, R. Chen, C. Agatemor, and S. Mitragotri, Ionic liquids for oral insulin delivery, Proc. Natl. Acad. Sci., 2018, 115, 7296-7301.

16. V. Sundaram, R. N. Ramanan, M. Selvaraj, R. Vijyayraghavan, D. R. MacFarlane and C. W. Ooi, Enhanced structural stability of insulin aspart in cholinium aminoate ionic liquids, Int. J. Bio. Macromol. 2022, 208, 544-552.

17. M. Guncheva, Role of ionic liquids on stabilization of therapeutic proteins and model proteins, Protein J., 2022, 41, 369-380.

18. S. Ali and J. Huang, Aseptic spray drying for poorly soluble molecules, J. Nanomedicine, 2026, 9, 1076.

Jim Huang, PhD, is founder and CEO of Ascendia. He has more than 20 years of pharmaceutical experience in preclinical and clinical formulation development, manufacturing and commercialization of oral and parenteral dosage forms. His research focuses on improving solubility and bioavailability and the controlled delivery of poorly water-soluble drugs through nano-based technologies.

Shaukat Ali, PhD, is senior director of scientific affairs and technical marketing at Ascendia, with more than 32 years of pharmaceutical industry experience. He has published more than 70 scientific and technical articles, is an inventor or co-inventor on several U.S. and European patents, has served on the USP Council of Experts for more than 16 years and is an AAPS Fellow.

According to a recent report by Grand View Research, the global high potency active pharmaceutical ingredients market size was valued at $29.9 billion in 2025 and is projected to grow from $31.6 billion in 2026 to $48.3 billion by 2033 at a CAGR of 6.2%¹, primarily driven by the rising prevalence of cancer and other chronic diseases, increasing adoption of targeted therapies and oncology drugs, and the expanding pipeline of highly potent molecules such as antibody drug conjugates and cytotoxic compounds.

As drug pipelines shift toward targeted therapies, antibody-drug conjugates, protein degraders, radioligands, potent peptides and other precision medicines, more compounds are entering development with very low occupational exposure limits and increasingly complex handling requirements. This is changing what sponsors expect from development and manufacturing partners—and what CDMOs must be prepared to deliver.

This article explores how HPAPI development and containment strategies are evolving. Drawing on insights from experts across the CDMO and pharmaceutical services sector, it examines the therapeutic and modality trends driving demand, changing customer expectations, the importance of early risk assessment and containment verification, and the capabilities manufacturers will need as highly potent pipelines become more diverse, more global and more technically demanding.

Current State of the HPAPI Market

“The HPAPI landscape continues to be driven by the rapid expansion of precision medicine, particularly in oncology,” explains Hongfeng Chen, EVP of Process Chemistry, Wilmington PharmaTech.

He says, “The growing pipeline of targeted therapies, antibody-drug conjugates (ADCs), peptide therapeutics, and other highly potent compounds is increasing demand for specialized development and manufacturing partners with expertise in high-containment operations.”

John Jacobsen, Ph.D., Head of R&D Small Molecule North America, SK Pharmteco, says, “High-potency development and manufacturing are becoming increasingly important as drug pipelines shift toward more targeted therapies requiring specialized containment, complex chemistry, advanced purification, and sensitive analytical methods.”

“Oncology remains the largest opportunity, particularly for ADCs, which combine an antibody, linker, and highly potent payload with distinct development and manufacturing requirements,” Jacobsen explains.

“Protein degraders and molecular glues are creating additional demand and may require significantly larger volumes,” he adds. Furthermore, he says, “Beyond oncology, highly potent compounds are expanding into autoimmune and inflammatory diseases, rare diseases, CNS conditions, antivirals, ophthalmology, and metabolic disorders—reflecting a broader shift toward highly targeted medicines designed to achieve specific biological effects at increasingly low doses.”

According to Mario Di Giacomo, President, Farmabios, “More than 40% of new molecular entities currently in development carry a high-potency classification.”

He says, “This creates a growing need for specialist partners that can combine potent chemistry expertise with the containment infrastructure, analytical capabilities and regulatory understanding required to manufacture these compounds safely and at scale.”

Furthermore, he explains, “Building and maintaining these capabilities in-house is often neither practical nor economically rational, driving greater reliance on specialist CDMOs and putting a premium on choosing a partner with the right combination of expertise and infrastructure.”

Frank Sorce, Chief Commercial Officer, UPM Pharmaceuticals also observes a greater reliance on CDMOs. He says, “Outsourcing is increasing because building and maintaining dedicated HPAPI infrastructure requires substantial capital investment, specialized personnel and rigorous environmental, health and safety systems.”  

“For many emerging and even established pharmaceutical companies, working with an experienced CDMO is more practical than developing these capabilities internally,” Sorce remarks.

“Almost no smaller biotech has the in-house infrastructure to handle a compound active in the nanogram range, so for most sponsors, outsourcing isn’t a preference. It’s the only option,” adds Hibreniguss Terefe, Director of Product Development, Ardena.

Evolving Customer Expectations

UPM’s Frank Sorce says customer expectations regarding containment, safety, speed and overall project executions have evolved substantially. He explains, “In the past, sponsors often focused on whether a CDMO had a potent-compound suite and could meet a specified occupational exposure limit. Today, they expect the CDMO to demonstrate a comprehensive, science-based containment strategy supported by toxicological assessment, industrial-hygiene data, engineering controls, cleaning validation and documented containment performance.”

“Simply stating that a facility can handle a particular OEB is no longer sufficient,” Sorce claims. “Sponsors want evidence that the controls are appropriate for the specific molecule, process and scale.”

Masashi Takahashi, CTO, AGC Pharma Chemicals, observes, “Pipelines are becoming more fragmented, with a higher number of clinical programs requiring smaller, more specialized batches rather than traditional high-volume production.”

This is changing the type of capacity customers require. According to Takahashi, “Demand is moving toward flexible, multi-purpose facilities capable of managing a broad range of batch sizes and occupational exposure bands, while maintaining robust segregation and cross-contamination controls.”

Furthermore, he says, “Customers are seeking integrated partners that can support the complete pathway from route scouting and analytical development through GMP scale-up and commercial manufacture. The need is therefore not simply for high-containment capacity, but for scientific, engineering and manufacturing continuity throughout the project.”

“Responsiveness, transparent communication, and the ability to adapt to changing project requirements have become essential, particularly as development timelines continue to accelerate,” adds Taylor Henderson, EH&S Manager, Alcami.

He says, “Manufacturing flexibility is especially critical during preclinical and clinical development. As molecule characteristics, batch sizes, and timelines evolve, sponsors expect CDMOs to quickly adjust processes and manufacturing strategies while maintaining quality, safety, and schedule commitments.”

The importance of flexibility is cited by SK Pharmteco’s John Jacobsen, as well. He argues that customers need more than equipment and capacity. “They want experienced partners with proven containment capabilities, flexible scale, strong regulatory systems, and teams that understand how potency affects every stage of development and manufacturing.”

“Customer expectations have evolved well beyond simply finding a CDMO capable of safely handling highly potent compounds,” adds Wilmington PharmaTech’s Hongfeng Chen.

“Speed and flexibility have become critical,” he says. “Companies are looking for partners that can rapidly move projects from development to GMP manufacturing, adapt to changing timelines, and seamlessly scale processes as programs advance. Early collaboration, proactive communication, and transparent project management are now essential expectations.”

Overall, UMP’s Sorce says the expectation has shifted from “Can you contain and manufacture this compound?” to “Can you safely, flexibly and predictably manage the entire program?”

According to Sorce, “The most valued HPAPI partners are therefore those that combine demonstrated containment with formulation and analytical expertise, scalable manufacturing, transparent project management and a strong record of reliable execution from development through commercialization.”

Addressing the Challenge of Scaling Up

Alcami’s Taylor Henderson calls maintaining flexibility as a program scales “one of the biggest challenges” associated with moving and HPAPI from development through commercial-scale manufacturing.

“During early development, small batch sizes and pilot-scale equipment allow manufacturers to adapt quickly to process changes. As products move toward commercialization, however, the potency of the compound often requires specialized equipment, production lines, or even dedicated facilities to ensure appropriate containment and prevent cross-contamination,” Henderson explains.

“A procedure that performs well with a few kilograms in development may behave very differently when material quantities, processing times, equipment size and operator interactions increase,” UPM’s Frank Sorce adds. “Dust generation, powder flow, transfer losses, cleaning difficulty and potential exposure points can all become more significant at commercial scale.”

“Operations that appear straightforward in the laboratory, such as solids charging, sampling, filtration or discharge, may present significantly greater exposure risks at larger scale,” says AGC Pharma Chemical’s Masahi Takahashi. “Heat transfer, mixing, crystallization, particle formation and drying behavior can also change, affecting both product quality and containment performance.”

The solution to the challenges of scale-up? Takahashi says, “The most effective way to balance speed and safety is to develop the process and containment strategy together.”

“Containment planning should begin early and run alongside process development, analytical development, and reaction-safety evaluation,” says SK Pharmteco’s John Jacobsen. “Waiting until the GMP campaign is scheduled can reveal facility-fit problems when there is very little time to resolve them.”

Wilmington PharmaTech’s Hongfeng Chen says, “Balancing speed-to-clinic with containment starts with early planning and close collaboration between process development, engineering, manufacturing, and quality teams.”

This includes, “conducting thorough risk assessments, selecting scalable chemistries, and leveraging existing high-containment infrastructure help minimize delays while maintaining compliance.”

Mark Vlasic, Business Development Manager, Douglas CDMO, says there are three key considerations when developing an effective containment strategy for highly potent compounds: “First, understand the compound. The physical and toxicological properties should determine the level of containment required. Second, integrate containment into development rather than treating it as a separate activity later in the program. Finally, consider future commercial manufacture early so the containment strategy can support the product throughout its lifecycle.”

Updated High Containment Facilities

With evolving demand and expectations, facilities also need to be updated to support increasingly potent compounds.

“High-containment facilities look pretty different than they did even five years ago,” says Ardena’s Hibreniguss Terefe, “and that shift has been observed happening in real time, mostly because the compounds coming through the door are more potent and more complicated.”

He says, “Isolators and closed systems now come with automated sampling, robotics, and real-time particulate and vapor monitoring built in. Those used to be premium options; now they’re close to standard.”

Furthermore, he observes facilities built for classic small-molecule work at exposure limits below 10 µg/m³ being redesigned or retrofitted to handle bioconjugation too, because ADC programs need potent small-molecule containment and biologics-grade handling within a few doors of each other.

“Analytics have also kept pace,” he adds. “Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and other ultra-trace methods are now designed into facilities from the start, not bolted on as a separate lab service.”

“High-containment facilities are becoming more closed, modular, and better suited to the smaller, changing batch sizes common in HPAPI development,” adds SK Pharmteco’s John Jacobsen. “Reactor volume alone is no longer the best measure of capability. The ability to safely handle different scales, physical forms, and potency levels is increasingly important.”

“Modern facilities increasingly use isolators, contained transfer systems, split butterfly valves, sealed charging and discharging, local exhaust ventilation and equipment designed to minimize manual intervention,” UMP’s Frank Sorce says. “This approach provides greater protection for operators while also reducing cross contamination risk and improving process consistency.”

Automation and digital monitoring represent another “major area of investment,” according to Sorce. He claims, “Automated material handling, electronic batch records, continuous environmental monitoring and remote equipment operation can reduce the number of times employees must interact directly with potent materials. These technologies also provide better process data, improve deviation investigations and support more consistent execution across clinical and commercial campaigns.”

“Containment is not just about equipment,” Wilmington PharmTech’s Hongfeng Chen stresses. “It depends on people and processes.”

He emphasizes, “Well-trained personnel, standardized procedures, and a strong culture of safety are essential to maintaining consistent performance. The most effective containment strategies integrate scientific understanding, engineering controls, operational discipline, and quality systems to protect both employees and product integrity throughout the product lifecycle.”

“None of this works without the people side: training, gowning discipline, and cleaning validation protocols all have to match the specific exposure limit and the specific operations involved,” adds Ardena’s Hibreniguss Terefe. “Put a poorly trained operator in front of a beautifully engineered isolator, and the isolator won’t save you.”

Future Outlook

Over the next five years, AGC Pharma Chemicals’ Masashi Takahashi expects continued growth in both the number and diversity of highly potent development programs. He predicts, “Oncology and ADC payloads will remain major drivers, but the manufacturing landscape will become increasingly varied as new targeted small molecules and conjugate formats progress through clinical pipelines.”

“The central trend will be integration,” Takahashi forecasts. “The strongest HPAPI platforms will connect toxicology, process development, analytical science, engineering, industrial hygiene and GMP manufacturing from the earliest project stages. That integration will be essential to deliver faster development without compromising employee safety, product quality or regulatory compliance.”

“Capacity will probably stay tight for a while,” predicts Ardena’s Hibreniguss Terefe. “New high-containment suites take two-plus years to build. The bigger constraint, according to several industry observers, isn’t the buildings at all — it’s a shortage of specialized people, particularly occupational toxicologists and containment engineers.”

“Combine that with reshoring pressure in North America and the capacity race underway in Asia-Pacific, and manufacturing looks set to keep spreading out geographically rather than consolidating around any one region,” he says. “And as regulators in the U.S., Europe, and Asia keep tightening standards and edging toward convergence, sponsors will keep raising the bar on proof: not just that a containment system passed qualification once, but that it will keep working across a molecule’s entire life, from the first clinical batch to full commercial scale.”

SK Pharmteco’s John Jacobsen foresees, “Automation and remote operations will be used more widely in containment suites to reduce manual handling and improve consistency, while modular designs will help manufacturers respond to smaller batch sizes and more varied pipelines.”

UPM’s Frank Sorce believes supply chain resilience will continue to influence sourcing decisions. He says, “Sponsors will place greater value on regional capacity, alternate sources and partners that can integrate formulation, analytical development, clinical manufacturing, validation, packaging and commercial supply. The objective will be to reduce handoffs, avoid late-stage transfers and maintain better control over critical products.”

Overall, Sorce predicts the most competitive HPAPI facilities five years from now will be “closed, flexible, automated and supported by strong scientific expertise.” “The greatest opportunity will not belong simply to manufacturers that can claim a particular containment band,” Sorce says. “It will belong to organizations that can demonstrate containment performance, solve formulation and analytical challenges, accommodate changing volumes and move potent products reliably from development through commercial supply.”

Moving a therapy from clinical development into commercial supply changes the demands placed on both the product and the network supporting it. Serialization, traceability and data exchange must work across manufacturers, packagers, logistics providers and other external partners, often while specialized therapies introduce their own handling and distribution requirements. For biopharma companies, preparing early can help prevent processes that worked at clinical scale from becoming obstacles at launch.

Building serialization readiness for commercial supply

By Phase III, serialization should be part of a biopharma company’s broader commercial launch planning. Sponsors need to determine how each saleable package will be uniquely identified, where serialization data will be generated and stored, and how that information will move between organizations across the supply chain.

These decisions are central to robust supply chain management and, in many markets, regulatory compliance. In the U.S., for example, the Drug Supply Chain Security Act (DSCSA) requires covered prescription drugs to be electronically traced at the package level as they move through the supply chain.¹ Biopharma companies must therefore ensure that product identifiers and transaction data remain accurate, accessible and exchangeable throughout commercial distribution.

Maintaining this level of data control can become more complex when much of a product’s supply chain is outsourced:

• A contract manufacturing organization (CMO) may manufacture the product
• A contract packaging organization may package and serialize it
• A third-party logistics provider (3PL) may store and distribute it
• Wholesalers may manage its downstream distribution

Each handoff creates dependencies around data ownership, system connectivity and decision-making authority. Maintaining control across this network requires integrated software that provides visibility into relevant data, supported by close and responsive communication with supply partners. By adopting a coordinated approach, biopharma companies can maintain control as their supply chains grow in scale and complexity.

The complexities of emerging modalities

Emerging modalities can introduce additional complexity into an already demanding commercial supply environment. Products such as antibody conjugates, DNA- and RNA-based therapeutics and peptide therapeutics can have specialized manufacturing and handling requirements that influence how they are packaged, stored and distributed. Their supply chains may also involve multiple specialist partners, increasing the number of organizations and systems involved in managing the product and its associated data.

For biopharma companies, serialization must therefore account for the specific commercial supply model of the specific therapy. Product identifiers and serialization data need to remain aligned with the physical product as it moves between manufacturing, packaging and distribution partners. As the supply network evolves, maintaining that alignment requires clear ownership of data and visibility across partner systems.

The characteristics of certain products can therefore expose weaknesses in serialization processes that may be manageable at smaller volumes but become more difficult to control at commercial scale. Addressing those complexities requires serialization to be planned as part of the wider commercial supply strategy rather than treated as a standalone packaging activity.

A commercial serialization approach

For emerging modalities, managing serialization effectively requires early planning across the commercial supply chain. Biopharma companies need to establish the systems, processes and partner responsibilities required to maintain control of serialization data as products move toward commercial launch.

1. Map the data landscape before launch

Biopharma companies should have a full grasp of the serialization data of their emerging modality by the therapeutic’s Phase III stage. This means having a clear view of where serialization data is created, where it resides, how it moves and whether it breaks down.

Achieving this visibility requires systems capable of supporting data exchange compliant with the Electronic Product Code Information Service (EPCIS), the GS1 standard that provides a common data model for capturing and sharing supply chain visibility events. In practical terms, EPCIS data describes what happened to a product, when and where the event occurred, and the relevant business context.² Biopharma sponsors need enough visibility into this exchange, via software, to confirm that partners can generate, receive and interpret the required events consistently.

The data map should extend beyond routine product movement to reverse logistics and verification. Sponsors need defined processes for handling returns and determining their disposition, alongside clear responsibility for responding to product identifier verification requests. A Verification Router Service (VRS) can route these requests and responses between supply chain partners,³ but effective verification still relies on accurate source data and clearly assigned responsibilities.

Data ownership also needs to be established from the outset. Contract packagers and other supply partners may generate commissioning or aggregation records, but ownership and access rights depend on contractual arrangements. Contracts should clearly define these responsibilities so sponsors can access the information they need to investigate discrepancies and manage returns.

2. Remove manual workarounds while volumes are still manageable

From spreadsheet searches to email exchanges, processes developed for clinical supply can become fragile when commercial volumes begin to increase and more organizations join a supply network.

This risk is amplified when a sponsor’s traceability environment must bridge partners that use different systems and operating procedures. Data accepted by one system may be rejected by another because of inconsistent master data; an interface that passes an initial connectivity test may still encounter problems when it processes data corrections.

A biopharma’s launch testing for emerging modalities should therefore follow data through complete business processes. Packaging events should be received and validated against the corresponding logistics activity. Corrections should preserve the audit trail, and both parties should understand how technical transmission issues differ from errors in the business data itself.

The aim is to establish repeatable workflows that reduce reliance on individual knowledge. Standard validation and reconciliation processes give lean teams a more dependable way to manage growing activity. Applying the same consistency to partner onboarding makes it easier to expand the network without recreating the operating model for every new relationship.

Addressing these kinds of manual dependencies early supports scalability later. It also gives the sponsor a clearer picture of which activities require human judgment and which can be governed through established workflows.

3. Design exception handling as part of the operating model

Complex supply chains can create multiple points where serialization issues may arise. These can include aggregation discrepancies, duplicate serial numbers and commissioning failures, as well as missing events, rejected files, shipment holds and returns queries. A management team’s skill at navigating these issues is determined by how quickly they can be identified and resolved.

This requires a biopharma company to establish named owners across the partner network. Teams need to know:

• Who performs first triage
• Who can inspect source data
• Who may correct or resend a record
• Who authorizes product movement
• When quality, regulatory, security or commercial leadership must be engaged.

Escalation paths, decision rights and response expectations should also be agreed before commercial launch. Management teams need to know who conducts the initial investigation and who can access the source data. The operating model should specify who may correct or resend a record, who can authorize further product movement and when additional functions need to become involved.

For lean launch teams, testing these procedures before commercialization can reveal gaps that routine readiness checks may miss. A tabletop exercise might simulate a mismatch between a shipment’s physical hierarchy and its electronic aggregation record. Another exercise could examine a request to verify a returned package or a report of missing transaction data. By working through detection, escalation and resolution, teams can clarify responsibilities, confirm access to the necessary information and address weaknesses before commercial distribution begins.

These exercises should include external partners. GS1 US implementation guidance recognizes CMOs, contract packagers, third-party logistics and reverse-logistics providers as service agents involved in serialized data exchange.⁴ An exception can therefore move across organizational boundaries as quickly as the product and its associated data. As a result, the response model needs to work across those boundaries as reliably as routine shipments do, under realistic time pressure.

4. Treat partner connectivity as a continuing discipline

Supply chain partner connectivity needs to remain effective as commercial operations evolve. Initial integration is only one stage of an ongoing process, as partners may change their digital systems, add sites or update identifiers and operating procedures. Biopharma companies may also introduce new products, increase volumes or expand into new markets. A sustainable connectivity model should accommodate these changes without requiring a bespoke integration project each time.

Maintaining reliable connectivity starts with establishing a clear understanding of each partner’s data environment. During the initial data exchange, biopharma companies should establish how identifiers and location data are managed, which EPCIS scenarios have been tested and how rejected messages are monitored. From there, change-control procedures should account for system updates and changes to partner processes to maintain reliable data exchange after launch.

This kind of partner onboarding should be measured with a durable operational record that documents:

• Contacts and escalation paths
• Systems and versions
• Identifiers and locations
• Supported transactions
• Test evidence
• Cutover criteria
• Post-launch monitoring responsibilities.

Standardizing this package reduces friction when the network expands and gives biopharma companies a clearer view of dependencies that would otherwise remain hidden.

Building beyond the first commercial shipment

The first commercial shipment of an emerging modality marks a longer period of operational change. As the biopharma sponsor introduces additional products, packaging configurations, partners and markets, its serialization and traceability model must accommodate greater complexity without requiring workflows to be rebuilt.

Decisions made before launch therefore shape how confidently the supply chain can grow. Reliable data and durable partner connections provide a foundation for increased automation, faster decision-making and more responsive operations. Companies that establish that foundation early will be better prepared to keep products moving as their commercial footprint evolves.

References

1. U.S. Food and Drug Administration, “Enhanced Drug Distribution Security at the Package Level Under the Drug Supply Chain Security Act,” January 2024 procedural revision.
2. GS1, “EPCIS & CBV,” current standard overview.
3. GS1 US, “Drug Supply Chain Security Act (DSCSA),” product-identifier verification and VRS implementation guidance.
4. GS1 US Healthcare, “Guideline Addendum: Diagrams and XML Examples for Serialized Exceptions Processing,” DSCSA Implementation Suite.

Joe Lipari is Director of Global Traceability Solutions for Systech and is responsible for the company’s global traceability and regulatory compliance solutions. His work is closely aligned with the company’s next-generation traceability platform, helping pharmaceutical organizations improve visibility, streamline exception management and maintain compliance across increasingly complex supply chains. He brings deep expertise in packaging, serialization and traceability, shaped by years of designing and deploying solutions for global pharmaceutical manufacturers. His experience spans both the life sciences and consumer goods sectors, where he has helped organizations connect operational needs with scalable technology strategies.

In a race defined by time to market, modeling and simulation (M&S) has outgrown its former reputation as a nice-to-have. Across industries, and in conjunction with advanced manufacturing initiatives, the evidence that M&S can reduce experimental burden, improve quality, and de-risk decisions is clear and well understood. Regulators encourage its use, industry leaders talk about it openly, and a growing body of case studies documents tangible impact.

Adoption, however, remains stubbornly uneven. If the value is so well established, why hasn’t M&S become a routine part of day-to-day product and process development?

The shortfall is not one of tooling. Mature, commercially available modeling platforms exist across scales – from molecular and unit operation models to plant-wide simulations and digital twins. Nor is it a shortage of justification. Most organizations already have strategic initiatives focused on process improvement, supply chain resilience, sustainability, and risk reduction, which are precisely the areas where M&S delivers value.

The real barriers lie elsewhere, and this article examines four of the most significant:

The True Cost of In-House M&S Capability

When organizations set out to bring M&S in-house, the discussion often begins and ends with software licensing, yet licensing is only a fraction of a much larger investment.

Consider a modest team of five to six specialists in a mid-cost region: in our experience, the annual bill can easily approach $2 million, and for each full-time employee, the outlay goes well beyond salaries. A realistic in-house setup typically carries:

• Specialized talent that spans process engineering, chemistry, data science, and advanced modeling

• Onboarding, training, and knowledge transfer

• Multiple commercial-grade M&S software suites

• High-performance computing infrastructure, whether on-prem, cloud, or hybrid

• Ongoing IT support, maintenance, and upgrades

Importantly, much of this cost is fixed. M&S demand rarely holds steady, and much of the work is front-loaded, with scope and problem definition, model setup, and validation, followed by long compute runs and waiting cycles. Utilization rates of roughly 60% are typical, and that idle capacity is what inevitably puts these teams and programs at risk. Whether the project pipeline is high or low, the organization shoulders the same infrastructure and personnel costs.

Total Cost of Ownership: A More Informed Perspective

Companies often delay M&S adoption because the upfront and recurring costs feel disproportionate to near-term needs, even when the long-term value is clear. A total cost of ownership (TCO) model offers a more sophisticated way to weigh the decision. TCO generally resolves into three categories:

Software and Compute

• Licensing costs, which usually renew annually, vary widely, as do compute costs whether on-prem or in the cloud
• Scale and usage models are the primary dimensions to consider, alongside depreciation, energy usage, and data management, all of which climb as model complexity grows

Personnel

• Engineering talent is usually the single largest contributor, typically eclipsing the combined cost of hardware and software

External Expertise

• Even with strong in-house capability, organizations commonly bring in external specialists for domain-specific expertise or when extra capacity is needed

Framed this way, the build-versus-buy question changes shape. It’s not just about the upfront costs; it is a question of how much permanent capability the business truly needs.

On-Prem, Cloud, or Hybrid? Costs Still Add Up

Many organizations look to infrastructure choices in the hope of softening the investment, but each path has its own set of trade-offs.

On-prem systems offer control and can align with legacy IT policies, but they require capital expenditure, maintenance, upgrades, and internal expertise to manage performance and reliability. Cloud-based compute reduces upfront hardware costs and offers flexibility, but introduces recurring usage fees, data management considerations, and licensing constraints. For compute-heavy simulations, costs can escalate quickly. Hybrid models blend the two, and with them comes additional complexity, since integration, governance, and security still require internal ownership.

The M&S Data Readiness Gap

Over the past decade, the application of Artificial Intelligence (AI) and Machine Learning (ML) has demonstrated remarkable success in digital-native industries, where data is abundant, cheap, and generated continuously. The same playbook, however, doesn’t apply to pharmaceutical process development and manufacturing.

Pharmaceutical manufacturing processes are complex, highly regulated, and often developed under intense time pressure. Most historical datasets are sparse and fragmented, having been collected for quality assurance or regulatory compliance rather than predictive modeling applications. Data is scattered across laboratories, pilot plants, manufacturing sites, and contractors, and stored in incompatible systems with limited traceability. Even when data has been digitized, it tends to reflect a relatively narrow operating window, the “safe space” of development, rather than the broader design space needed to train robust AI or ML models.

This points to a need common to all industries looking to apply AI, ML, and data-driven models. The limiting factor is not algorithms, compute power, or even talent; it is data readiness. It’s why we see so many companies stuck in AI pilot mode.

M&S efforts stall for the same reason. Time and again, the foundational data required for purely data-driven approaches proves unavailable, untrustworthy, prohibitively expensive to generate, or fundamentally unsuitable for the decisions at hand.

Data Trustworthiness

Making data available is only half the battle; trusting it is the other. Pharmaceutical data is shaped by numerous sources of variability:

• Manual sampling

• Batch-to-batch inconsistency

• Equipment differences

• Operator effects

• Sensor drift

• Undocumented process changes

Compounding the problem, contextual information about why a run was performed, what constraints were present, and which deviations occurred is often missing or poorly recorded. Data-driven models excel at fitting patterns, including accidental or misleading ones, so without a strong foundation of trusted, contextualized data, these models are at risk of becoming high-confidence engines with low-quality insight.

The Cost of Data Generation

The challenge becomes even more pronounced when considering the cost of data generation. In contrast to digital industries, pharmaceutical data is expensive by design. Each experiment takes weeks or months of wet and dry lab work, costly raw materials, specialized equipment, GMP-grade facilities, and highly trained personnel.

Scale-up and technology transfer widen the gap even further. Much of the available data is generated at lab or pilot scale, yet the phenomena governing large-scale behavior, including mixing, heat transfer, mass transfer, and residence time distribution, are inherently scale dependent. Models trained on small-scale datasets frequently fail when extrapolated to commercial manufacturing, not because the algorithms are weak, but because the underlying physics have changed. In these scenarios, collecting more of the same data does not solve the problem; it merely reinforces the wrong assumptions and manufactures false confidence.

Improving Data Readiness

Given these inherent challenges, mechanistic and hybrid modeling approaches surface as key enablers for the pharmaceutical industry. Mechanistic models encode first principles, mass balances, energy balances, reaction kinetics, thermodynamics, and transport phenomena, providing inherent extrapolation capability and physical interpretability. They are grounded in reality rather than correlation, and critically, they do not depend on massive datasets to be useful; they hold up even when data is sparse, noisy, or imperfect.

Hybrid models, digital twins, and detailed mechanistic frameworks combine the strengths of physics-based modeling with data-driven techniques. Physics constrain the solution space, ensuring consistency with known laws and scale-dependent behavior, while data is deployed where it adds the most value: parameter estimation, model calibration, bias correction, and the capture of unknown or poorly understood effects. Rather than asking data to explain everything, hybrid models ask it to complement the understanding.

The payoff is not just a predictive model, but a decision-ready model – transparent, interpretable, scalable, and aligned with process understanding. Models of this kind support development decisions, enable more reliable scale-up, reduce experimental burden, and build confidence across technical, operational, and regulatory stakeholders.

Why Culture, not Capacity, Blocks M&S Adoption

Infrastructure cost and data readiness are formidable barriers to M&S adoption, but the most underestimated, and the most intractable, is company culture.

Most companies believe they are supportive of predictive modeling.  They endorse it in presentations and long-term vision statements, then resist it in practice.

Experimental Bias: “Show Me the Data”

Pharmaceutical development has been built on tangible experimentation for decades, and physical experiments carry an implicit legitimacy: what we see in the lab feels more real than what a model predicts. This bias runs deep, even when wet experiments are slow, expensive, or incomplete and, even though manually collected data can be inaccurate because of operator error, inappropriate test protocols, or faulty instrumentation.

In these scenarios, models arrive after the data and serve as explanatory tools rather than decision drivers. Modeling teams are brought in late, asked to fit the data, confirm what is already known, or explain why an experiment behaved the way it did, rather than to guide which experiments should be run a priori. Cast in this reactive role, the value of M&S is severely diminished.

True adoption requires a cultural shift from experiment led to decision led development, where models inform experimental design, reduce trial and error, and quantify risk before material is consumed. That shift is as much about trust and mindset as it is about math.

Risk Aversion and Regulatory Anxiety

Pharmaceutical organizations are, by necessity, risk averse. Patient safety, regulatory scrutiny, and stringent quality expectations create powerful incentives to avoid new approaches, especially anything perceived as a black box. In regulated environments, predictability and traceability count for more than novelty, and any new methodology must earn trust across technical, quality, and regulatory stakeholders.

Even though regulators have increasingly encouraged model informed development,^1,2 internal teams often hesitate to rely on M&S for primary decisions. There remains a lingering fear that if a result is not physically measured, it will not be accepted, or that simulation outputs will be difficult to defend during audits, filings, or inspections. Modeling therefore tends to be positioned as supporting evidence at best, rather than as a foundational decision-making tool.

The irony is that this cultural resistance persists even when traditional experimental approaches are statistically weak, incomplete, or poorly scalable. Limited batch counts, narrow operating ranges, and confounded experiments are routinely accepted as sufficient, simply because they are physical, while well-constructed models, especially mechanistic ones, are subjected to a much higher standard, regardless of the broader insight and predictive power they provide.

This is where Quality by Design (QbD), as described in ICH guidelines Q8 (R2) – Q11,³ fundamentally changes the conversation. Its core principles—understanding parameter relationships, establishing causality, identifying critical quality attributes (CQAs), and defining a scientifically justified design space—naturally align with mechanistic and hybrid modeling.

Mechanistic, physics-based models are uniquely suited to support QbD because they make assumptions visible, enforce mass and energy balances, and capture scale dependent phenomena that experiments alone often miss. Instead of producing opaque correlations, these models generate explainable knowledge: how inputs propagate through a process, why certain parameters matter more than others, and under what conditions failure modes emerge. Approached this way, M&S becomes a vehicle for demonstrating process understanding rather than regulatory liability.

The Silo Problem: Modeling as a Specialist Function

Organizational structure poses another cultural hurdle. In many companies, M&S lives in a small, specialized group set apart from R&D, process development, manufacturing, and tech transfer. This centralization may seem efficient, but it tends to reinforce the perception that modeling is someone else’s job. Process engineers focus on experiments, manufacturing focuses on execution, and modeling teams wait for requests. Siloed, in this way, M&S becomes transactional: a service function rather than an integrated capability. Requests arrive late, constraints are unclear, and results fail to shape real decisions because the teams responsible for execution were never part of the modeling journey. Adoption stalls not because the models fail, but because ownership is unclear.

Incentives That Work Against Modeling

Culture is shaped by incentives, and many organizations unwittingly discourage M&S adoption through the way they measure success. Experimental throughput, batch count, and on time milestone delivery are often rewarded more visibly than insight generation or risk reduction. Running “one more experiment” feels safer than trusting a model, especially when individual career incentives are tied to tangible lab outputs.

In that environment, modeling can be perceived as slowing things down, adding review steps, or introducing uncertainty, even when it ultimately saves time and cost at the program level. Without leadership explicitly rewarding model informed decisions, M&S struggles to gain traction beyond early adopters and internal champions.

Building a Decision Centered Culture

Successful M&S adoption does not begin with software or data. It begins with a cultural commitment to better decisions. In practice, that means:

• Applying modeling early, as guidance rather than validation

• Treating models as evolving knowledge assets, not final answers

• Rewarding teams for reduced uncertainty, not just experimental output

Mechanistic and hybrid models fit naturally into this cultural shift. They align with how engineers and scientists think, provide explainability, and scale beyond the data immediately available.

Why “Can the Model Run?” isn’t the Real Question

Even organizations that invest in infrastructure, assemble the right data, and voice cultural support for modeling tend to hesitate when the time comes to rely on models for real decisions. The question is rarely, “Can the model run?” It is almost always, “Can we trust the model?”

In pharmaceutical development and manufacturing, trust is not optional. Model-based decisions can affect product quality, patient safety, regulatory filings, and supply reliability. Skepticism is rational; the trouble is that the way validation is approached often turns healthy skepticism into paralysis.

“The question is rarely, ‘Can the model run?’ It is almost always, ‘Can we trust the model?'”

The Validation Expectation Gap

A common but unspoken assumption is that models must reach a level of certainty comparable to physical experiments before they are allowed to influence decisions. In practice, that translates into unrealistic expectations: exhaustive validation across every operating condition, perfect agreement with limited and noisy datasets, and guarantees of performance in scenarios where experimental data does not, and often cannot, exist. Experimental data, tellingly, is rarely held to the same standard. Small sample sizes, narrow operating ranges, and confounding variables are accepted as a practical reality, yet models are frequently expected to outperform the very inputs that feed them.

Model validation does not mean proving a model is “right.” It means demonstrating that it is fit-for-purpose. The Context of Use (COU) defines that purpose: the scope of a computational model used to address a particular question of interest, the choice of model, what the model will be used for, how its results will be applied, and how heavily the decision depends on it.^4,5 The governing question is always the same: is the model sufficiently accurate and credible for this specific COU?

A model built to rank the sensitivity of process parameters, inform scale-up risk, or guide experimental design does not require the same validation depth as one used for real-time control or product release. Yet many organizations apply a one-size-fits-all mindset, which makes early stage and strategic models nearly impossible to justify. Trustworthy models are not defined by perfection; they are defined by:

• Clear scope and intended use

• Transparent assumptions

• Quantified uncertainty

• Documented limitations

• Consistent behavior with known physics and data

Mechanistic and physics-based models support this framework naturally. Their structure is explainable, their extrapolations are grounded in first principles, and their failure modes are often easier to diagnose than those of purely empirical models.

The “Black Box” Problem

Trust erodes quickly when models are perceived as mysterious black boxes. This perception is not limited to AI or ML; even complex mechanistic models can feel opaque if not properly built, documented, and communicated. To have confidence, stakeholders need to understand:

• Why certain assumptions were made

• Which data informed calibration

• Where predictions are strong, and where they are not

• How uncertainty propagates into decisions

Without this transparency, even technically sound models struggle to gain acceptance from engineers, quality teams, and regulatory stakeholders. Validation, moreover, is not a one-time event, and organizations that treat it as such rarely build the sustained confidence that M&S demands.

Trust is Built Through Repeated Success

Trust in modeling does not come from one perfect model. It accrues through the consistent delivery of value. As models reduce experimental burden, reveal non-obvious risks, explain scale-up behavior, and prevent late-stage surprises, confidence builds organically. Leaders become more willing to rely on simulations, teams bring modeling in earlier, and validation discussions shift from “Why should we use this?” to “How can we use this more effectively?” and “Where else can we apply similar models?”

Staying Focused on the Core Mission: A Case for the M&S Contract Organization

In an industry where the cost of wrong decisions is high, trust is the currency that determines whether modeling remains theoretical or becomes transformative. The commonality among the barriers explored here is the assumption that M&S adoption must be fully resolved internally before it can deliver value. That assumption, not the technology, is what stalls programs.

The industry already has a proven template for solving exactly this kind of problem. Pharmaceutical companies long ago accepted that they do not need to own every capability required to bring a product to market. Discovery chemistry, toxicology, and clinical development are routinely entrusted to CROs, and manufacturing to CDMOs and CMOs. Treating M&S as a highly specialized service – an M&S CRO – offers a pragmatic alternative to navigating these hurdles alone. It represents a disciplined allocation of capital, providing expertise on demand without the burden of ownership.

The value of M&S is available now. Partnering with experienced modeling teams is a legitimate, practical path to capture that value without waiting for ideal conditions that seldom materialize.

References

1. U.S. Food and Drug Administration (content current as of July 7, 2026). Model-informed drug development paired meeting program. https://www.fda.gov/drugs/development-resources/model-informed-drug-development-paired-meeting-program

2. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use. (2026). ICH M15 general principles for model-informed drug development (final guideline). https://database.ich.org/sites/default/files/ICH_M15_Step4_Final_Guideline_2026_0129.pdf

3. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use. Quality Guidelines. https://www.ich.org/page/quality-guidelines

4. Viceconti, M., Pappalardo, F., Rodriguez, B., Horner, M., Bischoff, J., & Musuamba Tshinanu, F. (2021) In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods, 185, 120-127.  https://pmc.ncbi.nlm.nih.gov/articles/PMC7883933/

5. U.S. Food and Drug Administration, Center for Devices and Radiological Health. (content current as of November 16, 2023). Assessing the credibility of computational modeling and simulation in medical device submissions: Guidance for industry and Food and Drug Administration staff.  https://www.fda.gov/regulatory-information/search-fda-guidance-documents/assessing-credibility-computational-modeling-and-simulation-medical-device-submissions

Nima Yazdanpanah, PhD, is a consultant in modeling and simulation for the pharmaceutical and fine chemical industries, with expertise spanning digitalization, process design and scale-up, and advanced manufacturing. He brings over 20 years of experience across R&D, process design, regulatory, CMC, and MSAT. At Procegence, he supports customers with process and product development and optimization, scale-up, and tech transfer. His goal: to help companies apply advanced manufacturing and modeling and simulation tools to enhance product and process quality, improve margins, and get products to market faster.

Kelly B Scribner Doering, PhD, is a life sciences strategist with over 20 years of commercial and technical experience marketing to drug discovery, development, and QC. Her expertise spans go-to-market strategy, brand development, and market intelligence across bio/pharma technology and service providers. Kelly was elected to the ISPE Boston Chapter’s Board of Directors in 2025 and has been an active ISPE member and volunteer, organizing and moderating numerous virtual and in-person events. At Procegence, she advises leadership on strategic focus, brand refinement, and go-to-market alignment.

Recent advances in cheminformatics and artificial intelligence (AI) have enabled increasingly accurate predictions of molecular properties, synthetic accessibility, and feasible synthetic routes directly from molecular structure.

Whether similar approaches can be extended to predict the commercial value of organic intermediates remains an open question.

To investigate this hypothesis, we have developed and trained a multi-layer machine-learning architecture using more than 7,000 pharmaceutical intermediates represented through SMILES strings and their market price.

The objective was not just to build a pricing engine but more fundamentally to evaluate how much pricing information is encoded in molecular structure and therefore extractable through modern AI techniques.

The model recovers approximately one-third of the observed price variability from just the molecular structure, demonstrating that molecular structure contains a measurable—but incomplete—economic signal that can be extracted using the machine-learning methods applied in this study.

These results highlight the potential but also the limitations of structure-based pricing models.

Introduction

Over the last two decades, breakthroughs have been achieved in applying cheminformatics and artificial intelligence tools to molecular design and process development.

Examples include computer-assisted synthesis planning platforms like ASKCOS or Synthia, and molecular scoring systems such as Reaxys, SAS, SCScore and FSC. These tools estimate synthetic accessibility, route complexity and the synthesis feasibility of target molecules based on their structure.

While such approaches provide valuable information on how a molecule can be synthesized, these do not directly address the key question: Can the price of an organic intermediate be inferred from its molecular structure?

Prima facie, the hypothesis seems plausible because molecular structure influences synthesis complexity, stereochemical requirements, functional group density, precursor availability, and ultimately production costs.

However, contrary to melting point, toxicity, or solubility—price is not an intrinsic molecular property. Rather, it results from the interaction between chemistry, technology, supply chains, and competitive dynamics.

The objective of our work has therefore been to investigate the extent to which price information is encoded in molecular structure and whether modern AI methods can successfully extract and quantify this signal.

Chemistry, complexity and price

Tools like ASKCOS—an open-source software suite for computer-aided synthesis planning developed by MIT and E. Merck’s Chematica, and now called Synthia—allow chemists to rapidly identify feasible synthetic routes and their complexity to access target molecules whose structure has been translated into computer-readable form, such as SMILES strings or Molfiles.

Similarly, multiple systems like SAS (Synthesis Accessibility Score), SCS (Synthesis Complexity Score), or FSC (Focused Synthesizability Score) based on Machine Learning trained on millions of different reactions are routinely used in early-stage drug discovery to weed out molecules with high theoretical scores, a proxy for difficult, if not impossible, synthesis.

However, while these tools are useful in assessing how a molecule can be synthesized by providing a quantitative view of its synthesis complexity, they do not say what it will cost—synthesis complexity and price being related but not identical concepts.

Machine-learning approaches for predicting the price of chemical compounds have so far followed three main directions.

The first relies on graph neural networks trained directly on large commercial catalogs, learning the relationship between molecular structure and listed prices.¹ While this approach has the advantage of covering very large datasets—the representativeness of catalog prices is liable to be challenged. Consequently, these models learn a combination of structural and commercial effects rather than the contribution of molecular structure alone.

A second approach estimates price from predicted synthetic routes combined with the cost of starting materials.² These methods can potentially achieve higher accuracy when reliable retrosynthetic information is available – but are dependent on the quality of the route prediction itself and cannot be considered structure-only approaches

A third direction derives accessibility or cost-related scores from market data using contrastive or self-supervised learning techniques.³ These methods are mainly intended to rank compounds according to expected cost or accessibility rather than predict an absolute market price

A molecule can be synthetically challenging yet inexpensive if produced at a large scale through an optimized process. Conversely, a structurally simple molecule may have a high price due to puny volumes, scarce feedstock availability, or stringent containment requirements due to its potency.

The development of AI models trained on large product datasets and market prices to estimate the price of organic intermediates directly from molecular structure would therefore represent a compelling innovation and address substantial unmet needs.

Such models extending the structure-based analysis beyond synthetic feasibility, combining it with a price prediction, would have the potential to provide early in the product development process data-driven guidance on cost determinants, allowing screening for alternative intermediates or synthesis pathways. Expected benefits would include supporting informed decision-making across R&D and procurement, providing an integrated, predictive tool that bridges chemistry and economics.

To this end, we have combined the multi-year experience accumulated in the development and sourcing of organic intermediates for the pharmaceutical industry with expertise in AI and MLM (Machine Learning Models) for building a model to test the feasibility of predicting the price of new intermediates based solely on their molecular structure and assessing how much of the price can be traced to this single variable.

Experimental approach

Dataset

The input data comprise more than seven thousand commercially relevant pharmaceutical intermediates. For each compound, the molecular structure was represented by its SMILES string together with the latest market price; the CAS number was used for compound identification where available.

To reduce variability associated with geography, exchange rates, and transaction scale, all prices were normalized using quotations from Chinese producers for volumes of 1,000 kg and applying 2026 RMB/USD exchange rates.

The key descriptors of the molecule are provided by CAS/SMILES in a machine-readable format “understandable” by neural network models. At the same time, the price is the target variable that the model aims to learn and predict, linking it to the structural features of the molecule.

Although a dataset of this size is significant by industrial standards, it represents only a small fraction of the broader chemical universe. Consequently, some prediction errors are inevitably attributable to an incomplete coverage of chemical space. However, as discussed later, several systematic behaviors observed in the model suggest that limited dataset size alone does not fully explain the observed performance limits.

Model architecture

The architecture of the model developed is schematically illustrated in Figure 1.

Figure 1. AI-Based Molecular Price Prediction Model Architecture

It consists of a multilayered machine learning system designed to estimate the price of organic pharmaceutical intermediates in USD/kg directly from their molecular structure. It combines traditional cheminformatics, modern machine learning techniques, and large language model (LLM)–derived insights into a single predictive framework.

Features

The first step consists of converting the molecular structure into a set of numerical descriptors that machine-learning algorithms can process. These descriptors include well-established chemical characteristics such as molecular weight, polarity, ring structures, and other features reflecting the molecule composition and complexity.

In parallel, additional descriptors are generated using a Large Language Model (LLM) to capture higher-level chemical information, like perceived synthetic complexity and key functional groups.

As this process generates multiple variables, only those providing meaningful information are retained. The selected descriptors can also be combined to create new variables, helping to identify more complex relationships between molecular structure and price.

The dataset is then divided into separate subsets used for training, validation, and testing. To ensure that the results are robust and not dependent on a particular data split, the model is trained and evaluated several times using different data subsets, which also reduces overfitting risk.

Model training

The dataset was divided into independent training, validation, and test subsets.

Model development, parameter optimization, and architecture selection were performed exclusively on the training and validation data.

Final performance was evaluated on a held-out test set comprising molecules never seen during model training. Cross-validation procedures were additionally applied to reduce dependence on any specific train-test split and provide a more robust assessment of predictive performance.

Model architecture

The model combines several complementary artificial intelligence approaches, each designed to identify different relationships between molecular structure and price. Some models focus on simple and easily interpretable relationships, while others capture more complex patterns that may not be immediately apparent.

In addition, specialized models are trained to perform particularly well on specific families of molecules or regions of chemical space. This allows the system to adapt its behavior depending on the type of compound analyzed.

The predictions generated by the various models are subsequently combined into a single estimate. Additional refinements include comparing the molecule under consideration with structurally similar compounds in the training dataset—the hypothesis being that similar molecules often exhibit comparable pricing patterns.

A final AI-based review step is then applied to identify unusual cases and improve predictions for rare or particularly complex molecular structures.

Ultimately, the multilayered predictive system involving multiple models working together receives the molecular structure of a compound as input and generates the estimated market price as output.

5. Results

Table 1 summarizes dataset size and predictive performance.

5.1 General predictive performance

The model’s ability to assess molecules that it had not been exposed to has been evaluated through an independent test set comprising 869 pharmaceutical intermediates.

The overall performance achieved an R² value of 0.36 on the price logarithm, corresponding to a Root Mean Square Error (RMSE) of 0.56 and a Mean Absolute Error (MAE) of 0.44 (see Figure 2).

Figure 2. Predicted vs. Actual Pharmaceutical Intermediate Prices in the Holdout Test Set

The R² value of 0.36 indicates that approximately one-third of the observed price variability can be recovered from just the molecular structure. The remaining variability derives from non-structural price determinants and/or structural information that the model does not fully capture.

These findings suggest that based solely on the molecular structure, the model can explain approximately one-third of the observed variation in market prices – confirming that molecular structure provides a measurable economic signal and that machine-learning techniques can partly link chemistry with commercial value.

However, the practical implications of these metrics require careful interpretation. The typical prediction error for an individual molecule remains approximately a factor of 2.3, with only 43% of compounds predicted within a factor of two of their actual market prices.

Therefore, while the model successfully distinguishes broad pricing regions within the chemical space, its ability to predict the absolute prices of specific products is limited, confirming that molecular structure alone provides incomplete information on pricing.

5.2 Systematic prediction compression

Analysis of the predicted-versus-observed prices reveals a clear systematic behavior. Predictions are compressed towards the center of the distribution, with a best-fit slope of approximately 0.32 compared with the ideal value of 1.0.

In practical terms, the model tends to overestimate the cheapest molecules and underestimate the most expensive ones:

• The lowest-priced 20% of compounds being predicted to have a price of approximately 64 USD/kg versus an average actual market price of around 16 USD/kg.

• The highest-priced 20% having an average market price in the order of 2,231/USD/kg to be compared to the 339 USD/kg predicted by the model.

The observed compression suggests that molecular structure captures broad determinants of cost but not the drivers responsible for extreme prices.

5.3 Performance after removal of price extremes

To evaluate the influence of extreme values, molecules priced below USD 20/kg and above USD 20,000/kg were removed from the test set.

The resulting improvement was limited—the median prediction error decreasing from approximately 2.3-fold to 2.1-fold, while the proportion of compounds predicted within a factor of two increased from 43% to 48%.

The modest improvement obtained after removing outliers suggests that the model’s limitations are not confined to a few exceptional compounds but are instead linked to broader structural characteristics.

5.4 What the model reveals about chemical price formation

Taken together, these results support two important conclusions.

First, molecular structure undeniably contains information relevant to pricing. Functional groups, stereochemistry, molecular complexity and other structural features influence synthetic accessibility and manufacturing costs. The model successfully extracts part of this information, using it to explain approximately one-third of the observed price variation.

Second, molecular structure alone does not explain most market price differences.

Unlike physicochemical properties such as molecular weight, boiling point, density, solubility, or toxicity, price is not an intrinsic molecular property. Rather, it results from the interaction between molecular structure, manufacturing technology, production scale, supply-chain structure, and competitive dynamics. This distinction helps explain why predicting price from structure alone is inherently more difficult than predicting traditional molecular properties.

Conclusion

Our study suggests that about one-third of the variability in pharmaceutical intermediate prices can be inferred directly from the molecular structure using the machine-learning techniques applied.

Although the resulting predictions are insufficient for accurate commercial pricing, these confirm that molecular structure contains a measurable economic signal.

Substantial improvement of predictive performance can be expected using future models combining structural descriptors with synthetic processes and commercial information.

References

1. Sanchez-Garcia, R.; Havasi, D.; Takács, G.; Robinson, M. C.; Lee, A.; von Delft, F.; Deane, C. M. CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritization. Digital Discovery 2023, 2, 103–111. doi:10.1039/D2DD00071G.

2. Abderrahmane, M.; Tajmouati, H.; Barros Ribeiro da Silva, V.; Perron, Q. Predicting the price of molecules using their predicted synthetic pathways. Molecular Informatics 2025, 44 (2), 202400039. doi:10.1002/minf.202400039.

3. Hastedt, F.; Hellgardt, K.; Yaliraki, S.; et al. MolPrice: assessing synthetic accessibility of molecules based on market value. Journal of Cheminformatics 2025, 17, 150. doi:10.1186/s13321-025-01076-3.

Dr. Michele Jermini is the Managing Director of Exeris (michele.jermini@exeris.com).

Dr. Paul Hanselmann is Founder and CEO of ChemSynthDesign GmbH.

Federico Brooks is Founder and CTO of Custodian Privacy Sagl.

Dr. Enrico Polastro is a Vice-President of Arthur D.Little.

Although contract development and manufacturing organizations (CDMOs) have been expanding, demand has also grown. According to BioPlan’s 23rd Annual Survey of Biomanufacturing Capacity and Production, nearly half of biopharma companies say they have difficulties in finding a contract manufacturing organization (CMO) with the capacity they need.

In this year’s survey 49% of qualified respondents cited difficulties finding a CDMO with available capacity for mammalian production, compared to 41.3% in 2025 and 39% in 2024. Microbial fermentation was also cited by a similar percentage of respondents indicating challenges in finding a CDMO, 43.9% vs 36.9% in 2024. Gene therapy/viral and cell therapy continued to be challenged in finding CDMOs with available capacity.

This year, 55.9% indicated difficulties in finding gene therapy CDMOs and 55.6% of respondents for cell therapy (Figure 1). These advanced biotherapeutic areas continue to be experiencing significant CDMO availability constraints.

Figure 1. Availability of CMO/CDMO Capacity (2024-2026)

The challenges involved in manufacturing cell or gene therapies at clinical scale can be driven by each product’s unique vector, cell type, and potency requirements which demand highly tailored processes that deviate from standard platforms.

As a result, many CMOs either choose not to offer these services or require extended timelines to develop and validate custom workflows slowing production compared with more commoditized biologics.

Can CDMOs keep up with demand?

The regionalization of biologics manufacturing is creating a more resilient global supply chain, while also intensifying competition among CDMOs to deliver cost-effective, high-quality biologics at-scale. Smaller start-up biologics innovator companies are entering the industry and increasingly becoming reliant on CDMOs, because these start-up innovators lack facilities, expertise and infrastructure.

So, they often have no choice than to turn to CDMOs. Other companies with relevant facilities and infrastructure continue to selectively outsource their products’ bioprocessing development and manufacturing. Both approaches reduce up-front capital and financing needs, because companies can avoid $50-$100-plus million or more costs for construction of stainless steel-based facilities, with a typical fully single-use facility for commercial manufacturing costing more in the range of $20-$60 million.

Both in-house biomanufacturers and CDMOs are focusing on scaling mammalian cell culture systems. But more CMOs are projecting a 5-year expansion, which has been the trend for 3 years. In 2026, average CMO 5-year expansion for mammalian cell culture was 40.4% compared to 33.8% in 2025, suggesting that CMOs are trying to keep up with capacity demands.

Cell therapy was reported to expect average 5-year capacity expansions by 28.4% of CMOs, only slightly up from 21.8% in 2025. Gene therapy was reported to expect average capacity expansions by 25% of CMOs. This difference was also similar in 2025 with 18.2% of CMOs reporting an average 5-year growth expected expansion (Figure 2).

Figure 2. Average Planned Future Capacity Expansion: Five-Year Estimates, CMOs, by 2031

Factors leading to capacity constraints cited by CMOs included hiring constraints, analytical testing and drug product release, costs associated with downstream purification, and costs associated with upstream processing. It is increasingly evident that capacity constraints are not driven by a single dominant factor but rather by a complex and shifting combination of pressures. What emerges is not a clear, consistent culprit, but rather a dynamic interplay of industry conditions, investment cycles, and evolving operational needs.

One area where capacity is also growing to meet demand is as pharmaceutical companies offer excess capacity by adopting a CDMO business model. This approach has primarily involved capacity for mammalian cell culture.

However, there may be challenges such as difficulty to maintain CDMO services because capacity and focus can change quickly, and these companies will prioritize their own products over CDMO clients. This can lead to distrust of such services. There also might be competing products and interests, which can lead to conflicts of interest.

For example, the rise of biosimilars adds another layer of complexity, as companies must decide whether to manufacture biosimilars for others or focus on their own competing products.

Biopharma capacity perceptions

While demand for outsourced manufacturing services continues to rise, the perception of a persistent capacity shortage may not always align with market reality. Many sponsors begin their search with highly specific technical, geographic, regulatory, or timeline requirements that significantly narrow the pool of suitable CDMOs.

As a result, the challenge is often not the absence of available capacity, but the difficulty of finding capacity that meets a project’s unique needs. At the same time, sponsors’ perceptions are frequently shaped by past experiences during periods of severe industry constraints, even as many manufacturers have since expanded their facilities and capabilities.

CDMOs must have open capacity to remain competitive and attractive to sponsors. The industry is acutely aware of the persistent and evolving capacity constraints. While some of these constraints are new, others have spurred a continuous cycle of technological innovation, strategic investment, and operational transformation over the years.

Manufacturers have designed a range of solutions some strategic and forward-looking, others tactical and driven by immediate needs. Yet their sponsors continue to say they can’t find necessary manufacturing capacity.

While big pharma companies have traditionally taken to outsourcing operations to cut costs, many companies are now deciding to employ a more strategic approach. Overall, companies in the biopharmaceutical industry are evaluating their out- vs. in-sourcing options, including assessing options from a longer-term perspective.

For example, some large biologics innovators will not adopt cutting edge platforms like continuous bioprocessing/perfusion manufacturing until there is a critical mass of CMOs capable of taking on these platforms, essentially, innovators need to know if there are competent CMOs available if they decide to adopt a novel technology platform. Otherwise, they will stick with tried-and-true batch manufacturing.

“[T]he challenge is often not the absence of available capacity, but the difficulty of finding capacity that meets a project’s unique needs.” — Katrina Cordavado

Regional trends

U.S. and EU facilities are generally experiencing similar capacity constraint levels. However, higher percentages in the U.S. continue to indicate moderate and minor constraints. The fact that U.S. has significantly more moderate or significant constraints compared to European facilities might be driven by the several factors.

Especially, higher throughput expectations may translate to more strain for the U.S. facilities. Large-scale U.S. commercial facilities often operate at or near maximum throughput to meet domestic demand, support global supply chains, and rapidly scale pipelines. This “go big or go home” approach can lead to more frequent bottlenecks when something in the process like downstream purification lags.

The U.S. also tends to lead in adopting new technologies and platforms, such as continuous manufacturing, automation, and novel modalities. While this can boost long-term efficiency, it often introduces short-term disruption and variability.

Europe, by contrast, may adopt innovations slower but more steadily leading to fewer operational hiccups. Western Europe’s manufacturing is also often more distributed and modular, with redundancy built in across countries.

Even though European facilities serve wider regions, their design may intentionally mitigate constraint risks by spreading workload across more nodes. At the same time, it is possible that U.S. facilities are simply perceiving and reporting constraints differently, especially as part of internal continuous improvement or compliance initiatives. Europe by contrast might disregard minor delays and not register them as constraints.

Emerging regions, particularly India and China, are becoming major hubs with large-scale GMP facilities and double-digit growth rates, while North America and Europe continue to dominate high-value innovation. Increasingly companies are popping up to serve their domestic, regional, or other international markets that are experiencing rapid growth.

Markets for biopharmaceuticals are growing in India and China, with increased incomes, a growing middle class, and improving health care infrastructure and services. As such, their domestic CDMOs and contract research organizations (CROs) will continue to capture market share in these regions, and many will continue their push to enter established markets in the U.S. and EU.

The industry landscape is also being reshaped by huge investments by CDMOs for facility expansions aimed at meeting evolving market demands. Such expansions clearly reflect the CDMO industry’s movement toward more sophisticated and efficient production.

What’s next?

Capacity remains one of the most persistent challenges in outsourced biomanufacturing, but it is only one piece of the equation. Nearly half of biopharma companies continuing to report difficulty finding CDMOs with available capacity, and this highlights the ongoing imbalance between manufacturing demand and available infrastructure.

As investment in new facilities and manufacturing technologies continues, an important question remains: will CDMO capacity expansion be able to keep pace with the growing and increasingly diverse needs of the industry? At the same time, sponsors are looking beyond available manufacturing slots when selecting their CDMO. Flexibility, technical expertise, responsiveness, and the ability to adapt processes as programs evolve have become equally important differentiators, particularly for emerging biotechs developing complex biologics and advanced therapies.

In the current environment, strategic marketing is becoming increasingly important for CDMOs looking to differentiate themselves to potential sponsors. In an increasingly competitive outsourcing landscape, the CDMOs best positioned for long-term success will be those that combine sufficient capacity with the agility and collaborative approach needed to support rapidly changing development programs.

References

1. 23^rd Annual Report on Biomanufacturing Capacity and Production, BioPlan Associates, Inc. April 2026.

Katrina Cordovado, MBDC, is Director of Research at BioPlan Associates, where she leads research on biomanufacturing technology adoption, capacity, market demand and supply chain trends. Her work includes global biomanufacturing databases, market reports and industry studies supporting biopharma companies, CDMOs and technology suppliers.