What are the key bottlenecks preventing rapid adoption of novel technologies?

The primary bottleneck preventing the rapid adoption of novel technologies is the gap between experimental pilots and industrial scale operations. While innovation is abundant, structural hurdles often prevent tools from moving beyond the prototype stage.

Fragmentation and compliance gaps create the first major barrier. Multi-omic data is uniquely complex, and, without a standardized infrastructure, technical and regulatory hurdles rise exponentially.

There is also a profound domain gap between the computer scientists building models and the biologists using them. A highly accurate model is meaningless if it lacks interpretability or scientific relevance for the researcher.

Furthermore, human-centered design is often overlooked. If a technology forces experts to change their established habits without providing clear value, it will face cultural resistance.

Success requires moving from execution to orchestration. This means building a shared and governed foundation that aligns technical power with scientific reality. Only by prioritizing validated and audit-ready systems can the industry turn raw data into predictable clinical insights at speed.

The rapid adoption of novel bioprocessing technologies is still constrained by several persistent bottlenecks. The first is performance proof: next generation biologics often carry significant developability and manufacturability challenges, so any new technology must demonstrate clearly superior yield, control, and consistency before biopharma will consider replacing established systems. Without compelling data that meaningfully improves success rates for complex molecules, adoption stalls.

Additionally, biopharma is inherently conservative, and teams are reluctant to introduce additional technological risk into programs that already carry high intrinsic product complexity. Even when early data are promising, organizations often wait for extensive validation, peer adoption, or external endorsement before moving forward.

This leads directly to another bottleneck: the need for robust scale up evidence and regulatory clarity. Companies want to see proven performance at manufacturing scale, alongside a derisked regulatory path or explicit signals from authorities that the technology is acceptable within existing frameworks.

Infrastructure compatibility is another major constraint. Many novel platforms require changes to equipment, workflows, or facility design, creating real or perceived CAPEX barriers that slow deployment.

Ultimately, these factors reinforce a broader industry reality: pharma’s structural conservatism, which favors incremental change over rapid technological shifts, even when the potential benefits are significant.

Over the years, I’ve seen numerous innovative technologies that could have been used as a novel outcome or to help judge the quality of a clinical trial, yet these technologies, often even with scientific justification and some validation data, have been dismissed by sponsors because they have not been previously included in a past U.S. Food and Drug Administration (FDA) pivotal trial.

While precedence is important, it should not be a fear limiting the adoption of innovative and potentially revolutionary technologies that could save time, improve quality, or help us better understand the body. Incorporating these technologies could further subsequently lower measurement variability, increase the likelihood of finding a significant result, and improve the chances of a new product approval.

Therefore, we should move towards taking calculated risks. Innovative technologies can be beta tested as exploratory outcomes during early phase trials prior to the make-or-break pivotal trial. Information can be gathered during this work to justify the addition of one of these technologies to the FDA and to understand if these technologies truly make sense for a development program. If we fail to take these calculated risks, amazing technologies could die on the vine, and the community could miss out on truly transformative innovations.

Novel technologies are often difficult to adopt because standard equipment can't be modified quickly. As a fill/finish CDMO, we sometimes see this in the custom design of primary packaging components.

Standard vials, syringes, and other container formats exist for a reason: they were designed around the operating parameters of standard equipment. But a novel approach to an injectable therapy will sometimes push a developer toward a custom container. That custom design may work well in early development, where operations are largely manual and bench-scale. The problem surfaces later, when the product scales to automated equipment for commercial batches.

A container that doesn't fit standard equipment requires custom change parts, which must be designed, implemented, and validated. Lead times are rarely less than six to nine months, and the expense is considerably higher.

Novel containers can solve real problems. Developers simply need to understand the downstream cost of that novelty, in both timeline and budget, before they commit to it.

AI has the potential to shorten clinical trial timelines, drive tomorrow’s breakthroughs, and get therapies to patients faster. We’re already seeing the impact this technology can have, with recent data reporting a 90% reduction in time spent aggregating data across systems and a 25% reduction in cycle time from last patient last visit (LPLV) to database lock for organizations harnessing AI-powered clinical trial technology.

However, in order to achieve tangible ROI and business value from these technology investments, existing business processes must evolve alongside the technology. Investing in the latest tools won’t drive meaningful results unless we address the biggest barrier to adoption, which is ensuring the right operational foundation is in place to make the best use of AI’s capabilities.

Today, too many organizations continue to rely on manual workflows, such as Excel, when modern clinical data infrastructures and AI workflows exist because they’re habituated to it. The key to moving away from this is reengineering processes alongside investments, making it as easy as possible for teams to utilize these tools while maintaining the appropriate guardrails and controls. Ultimately, rapid adoption will require organizations to modernize not just the technology itself, but the processes and infrastructure needed to support it.

There are a few key bottlenecks. Arguably, the biggest challenge is whether the novel technology has actually been proven. It needs a robust data set showing that it is sustainable, reproducible, executable, scalable, and commercializable. If it hasn’t met those criteria, adoption is going to be difficult, particularly for the first application in a commercial process, where you’re moving from a stable or established technology to something novel. The value proposition therefore has to be strong enough to justify that transition.

Intellectual property can also be a challenge. As a product goes through drug development, there are plenty of encumbrances that can arise. If a technology comes with too much IP control or additional cost structures, that can limit its potential for adoption.

Training is another consideration, particularly if the technology requires specialized skills or knowledge that aren’t already in place. You also need a clear regulatory pathway and a supply chain that can support the technology. If it requires proprietary components, that adds complexity for both the vendor and the adopter.

Ultimately, the technology has to be compelling enough to overcome all of these factors.

While the theoretical promises of AI-driven predictive digital twins, continuous steady-state manufacturing, and real-time process analytical technology are set to revolutionize biopharma economics, three bottlenecks continue to stall their widespread industrial adoption. The transition from legacy batch processing to an integrated and automated ecosystem remains hampered by deep-seated operational, technical, and structural barriers.

First is regulatory ambiguity. Technology often evolves faster than regulatory interpretation, and drug developers hesitate to integrate unproven technologies if they risk delaying Investigational New Drug (IND) or Biologics License Application (BLA) approvals. Second is the capital and integration friction associated with retrofitting legacy facilities, where the cost and downtime of overhauling rigid infrastructure often outweigh immediate gains. Lastly, a significant talent gap persists, as the industry lacks a workforce skilled in both complex bioprocessing and advanced data science.

To break these bottlenecks, the biopharma ecosystem must shift from siloed in-house development to collaborative, shared-risk infrastructure models. Advanced CDMO partners are uniquely positioned to absorb the operational friction of innovation. By proactively building flexible, digitalized facilities and co-developing validation standards with global regulators, CDMOs can widen access to next-generation technologies. This collective approach would allow drug developers to scale novel modalities and processes more effectively, accelerating global access to lifesaving therapies without requiring individual drug developers to shoulder prohibitive capital risks and regulatory uncertainties alone.

By their very nature, novel technologies have much higher risks associated with them than their established counterparts, for all parties. So, how quickly they are adopted very much depends on the requirements of the technology itself and the risk tolerance of those involved.

The key risk for most novel technologies is the lack of industry alignment; the whole supply chain needs to move forward at the same time in order to reduce risk, and this is rarely the case. For example, there may be huge potential benefits gained through a new technology, but if regulatory expectations are not set, such as on how to define new parameters or how process validation is performed, then innovators could see increased timelines and delays to approval.

With CDMOs, the biggest risks are when and how to invest in new technology, if you invest too early the tech may be ‘old’ by the time real adoption begins. If you wait, you lose precious R&D time, and the opportunity to really embed and scale the technology internally.

Sterling’s strategy is to collaborate with customers and industry partners to assess potential novel technology, and then to develop industry alignment earlier. Working with our peers, original equipment manufacturers (OEMs), regulatory bodies, and academia, to name a few, provides more certainty to everyone in the supply chain and prevents bottlenecks where real opportunity for the future of the industry lies.

Adoption of novel manufacturing technologies for cell and gene therapies has slowed as biotech capital has tightened. With fewer financings and IPOs, developers are prioritizing speed to clinic over early investment in manufacturing innovation. Many companies have only 12–24 months of cash to generate the clinical data needed for follow-on funding, driving reliance on minimally viable manufacturing processes rather than more scalable or cost-efficient approaches.

Once a process is locked and a product advances through IND, meaningful changes become difficult. Comparability studies can add cost, time, complexity, and regulatory risk, particularly when adopting advanced technologies with limited precedent, such as moving from transient transfection to producer cell lines for lentiviral vector (LVV) or adeno-associated virus (AAV) manufacturing.

To prevent manufacturing innovation from stagnating, stronger alignment is needed across developers, investors, pharma partners, and CDMOs. Pharma companies, as future commercialization partners, have a strong incentive to encourage earlier investment in scalable manufacturing. Improved license structures and risk-sharing models may also support adoption of advanced technologies before clinical lock-in.

For CDMOs such as Minaris, this creates an important role in helping developers evaluate manufacturing trade-offs earlier in the program life cycle. Integrated development, manufacturing, testing, and release capabilities can provide practical insight into process scalability, comparability considerations, and long-term commercial readiness before key process decisions become difficult to change.

A major bottleneck to adopting novel therapeutic technologies is that development often focuses on proving a treatment can work before working out how it will be used in practice. Clinical benefit is essential, but adoption can stall if a therapy adds complexity for providers or does not align with the realities of patients’ lives.

In a chronic disease like HIV, that gap can be significant. Patients may be managing decades of therapy alongside comorbidities and individual treatment preferences, so even a therapy with strong clinical data can face adoption challenges if it adds meaningful burden or complexity. Those questions belong in development, not after approval.

Nobody owns the adoption decision; that's the real bottleneck. Clinical ops, IT, quality, and finance grade the same tool by their own scorecard, and when no one is empowered to call it, it just stalls in committee. Then when a tool gets approved, most teams drop it into a workflow nobody bothered to redesign. A faster document review doesn't help much if the sign-off behind it takes a week.

Trust is about specifics: what's this system allowed to do on its own, how do we know it's working, who's on the hook when it's wrong. Answer those and oversight gets proportional instead of blanket. Money matters. If a sponsor pays for a tool and a site absorbs the extra work, or a contract bills by the hour, not the outcome, don't expect anyone downstream to champion it. Pilots need a plan for scale, not a good demo, with honest numbers on integration and support costs. Most teams are stretched thin, and another login or duplicate entry adds to the burden the tool was supposed to lift.

Here's what we get backwards. New technology gets put under a microscope, while the mistakes baked into old processes get treated as how things are. Give both the same scrutiny and adoption moves.

Pharmaceutical manufacturers are increasingly moving toward digitized workflows, which require integrated, structured data, methods, and reference materials across platforms.

Challenges, such as manual transcription, vague instructions, and reliance on analog reference materials, can slow progress toward digitalization. As laboratories and quality assurance teams adopt innovative informatics systems, such as electronic lab notebooks, lab information management systems, lab execution systems, and other tools to expedite their workflows, their quality assurance tools must keep pace.

Digital reference standards, which bring the same science-based rigor and confidence as USP’s physical standards, aim to support the ongoing modernization of pharmaceutical manufacturing, often called “Pharma 4.0.” This transformation will make quality standards more accessible and integrated within digital environments, helping drug developers, manufacturers, and regulators deliver quality medicines that patients can rely on.

Scaling new technologies while ensuring quality requires consistent, trusted standards that can be applied across platforms and organizations. Digital standards can provide shared, trusted building blocks. But trust requires collaboration among method developers, manufacturers, technology providers, and regulators. Collaborative development helps ensure that emerging digital standards are practical, fit for purpose, and aligned with regulatory expectations.