Christopher Cole

supply chain management SCM | Image Credit: ©

Industry-wide anxiety regarding growth and pricing pressures has created a "self-fulfilling prophecy” in which companies are reacting with caution and reduced investment, naturally leading to the slower growth they fear, explains Mike Stenberg, vice president – Business Development at LGM Pharma. To combat these risks, the industry is moving beyond just vetting primary suppliers. Companies are now auditing their entire supply chain, including the providers of key starting materials used in APIs.

To ensure stability, many are adopting such strategies as second and third sourcing and maintaining safety stock. However, Stenberg warns that safety stock ties up essential cash, further limiting a company's ability to invest. Additionally, while there is a growing demand for geographic diversification to move supply chains away from India and China, sourcing APIs from the US or Europe remains difficult and expensive due to limited supply.

 spoke with Stenberg to get his perspective on what impacted the pharma supply chain in 2025 and where things are headed in 2026.

Which trends in 2025 impacted the pharma supply chain?

 There is a pervasive concern about the supply chain and uncertainty surrounding the supply chain. That has led to what we see, what we hear, what we feel is a pessimism around the ability for the industry to grow. It's interesting because in a way, when you're pessimistic about your growth potential and you're worried about the supply chain, then in an ever-increasing pressure on decreasing prices, your natural reaction to the industry is to slow down, be more cautious, invest less.

That becomes a self-fulfilling prophecy in that when you do those things, of course you grow slower. So, you're concerned about slow growth, and the actions you take slow your growth. We're seeing that pessimism and that concern about the supply chain cuts across the entire pharmaceutical industry, and we've noticed it in our business as well.

How is the sector reimagining supply chains to protect against global disruptions?

The industry is doing a lot of different things to try to deal with those kinds of things. The number one thing is people are really trying to secure their supply chain. So, we see this really increased emphasis on vetting the entire supply chain. It's been pretty common that you would certainly vet your primary supplier.

When we look at APIs, for example, people are always looking into the quality history and trying to detect any potentials for future problems, but that's not enough anymore. Now we are vetting, and people want you to vet, their suppliers, so whatever pressures your primary supplier is facing in their particular geography is exacerbated by who their suppliers are too. So, in the area of APIs, who's supplying the key starting materials and what are the risks associated with them?

The other thing that we see going on in trying to secure supply chains is second sourcing, and of course, now third sourcing. We see people starting to lay in safety stock, which makes a lot of sense. The safety stock means you carry more inventory, and inventory is equal to cash, so it reduces your ability to grow and invest. It has a whole range of attendant problems associated with it. People are trying to geographically diversify. We are routinely asked to find and source APIs, but not in India or not in China; that is really difficult to do. It's possible, you can get some APIs from Europe, and you can even get some from the United States, but there isn’t a plentiful supply of APIs from those sources, and they tend to be more expensive as well.

Articles

The pharmaceutical industry is experiencing anxiety over growth and pricing pressures, leading to cautious investment and slower growth. To mitigate risks, companies are auditing entire supply chains, including key starting material providers, and adopting strategies like second and third sourcing and maintaining safety stock. However, safety stock ties up cash, limiting investment potential. Geographic diversification is sought to reduce reliance on India and China, but sourcing APIs from the US or Europe is challenging and costly due to limited supply. These trends reflect a broader industry concern about supply chain stability.

To combat growth and pricing pressures, companies are auditing their supply chains and API material providers.

Pharmaceutical Supply Chain Resilience in a Shifting Market

Beena Wood, Qinecsa,  outlines how proactive PV, translational safety, AI foundations, and ecosystem collaboration will reshape drug safety in 2026.

Building the Foundations for Proactive Drug Safety in 2026

 recently spoke with Beena Wood, chief product officer, Qinecsa Solutions, to get her perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 3 of our three-part interview, Wood outlines how 2026 will mark a pivotal year for pharmacovigilance (PV), translational safety, and regulatory innovation, driven by a shift from reactive compliance to proactive, data-enabled decision making. She emphasizes that the industry’s biggest near-term priority is not deploying advanced AI use cases, but building the foundational infrastructure and governance required to make proactive PV viable at scale.

Wood argues that organizations that are willing to invest early—despite delayed returns—will be best positioned to lead. “Building the foundation… I think is going to be the most important thing that's going to happen in 2026,” she says, noting that meaningful transformation requires patience and long-term commitment.

A major theme is the evolution of translational safety, which Wood describes as connecting safety signals earlier in development rather than relying solely on post-market surveillance. She explains that development teams often stall not because of missing science, but because fragmented data systems make decisions unclear. “Teams rarely get stuck because the science is unclear; they get stuck because the decisions are unclear,” Wood says. Integrating mechanism data, toxicology, pharmacokinetics/pharmacodynamics, and real-world evidence earlier could fundamentally change how uncertainty is managed.

Wood also points to continued regulatory innovation in 2026, citing initiatives such as the 

FDA Commissioner’s National Priority Voucher

 program, expanded real-world evidence efforts, new FDA guidance, EMA reflection papers, and upcoming obligations tied to global frameworks like 

. These developments, she notes, introduce guardrails that support innovation while maintaining trust and safety.

Finally, Wood highlights the growing importance of ecosystem-based collaboration. She predicts a move away from bilateral partnerships toward orchestrated networks involving pharma companies, AI-native biotechs, technology hyperscalers, regulators, and patient organizations. This collaborative model, she suggests, will be essential for advancing AI-enabled safety, accelerating development, and navigating increasingly complex regulatory and scientific landscapes.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

So, if I take PV itself as an example, how do we get it to have that infrastructure and governance so that the proactive PV can become real? And building the foundation in 2026, I think, is going to be the most important thing that's going to happen in 2026. The organization that commits to building that proactive infrastructure, even though the use cases might take years to implement.

The second thing, again, is translational safety, which is to connect the dots earlier. So proactive shift isn't just about—and that ties to the FDA [Commissioner’s] National [Priority] Voucher program, etc.—post-market surveillance alone; it's starting much earlier, the safety in development.

What I'm seeing and what I think will continue in 2026 is a fundamental shift of the translational safety leaders, fundamentally rethinking how we make decisions under uncertainty. So, a colleague of mine framed it this way: teams rarely get stuck because the science is unclear; they get stuck because the decisions are unclear. And the pattern, for example, is like we have the mechanism data, tox findings, the PK/PD data, real-world evidence. However, these are different systems, different competence levels and functions. So, when the decision comes, then I think it's about we don't have enough data. I think to connect the data earlier and upstream, that would be the trend I would see—that translation safety going and fundamentally thinking of that.

I think the regulatory innovations and guidances will continue with important guardrails. We are already seeing the trends as I explained earlier. The [Commissioner’s] National [Priority] Voucher program is one example. The center is setting up of that real-world evidence center is another example by FDA, the FDA guidance document, for example, and then, of course, the EMA reflection paper and they're starting to work on annex 11 and 22, if I'm remembering the numbers correctly. And also the CIOMS working group and the EU AI Act will add another layer in 2026 where there are some deadlines coming up because of that. Also the precision medicines, moving from oncology to mainstream, like we've seen the GLP-1 and how that's extended into other areas like cardiovascular, NASH, etc. I think that will continue.

And lastly, to keep to number five, is the ecosystem collaborations. It's not any more going to be like a one-on-one or one-on-two kind of collaborations, bilateral, but it has to become orchestrated ecosystems for, for example, AI. Do we have tech hyperscalers? Do we have AI-native biotechs? Do we have patient organizations participating, regulatory collaborations, all with pharma and industry?

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Beena Wood, Qinecsa, explains how AI could revolutionize pharmacovigilance, if data, trust, skills, and organizational barriers are addressed first.

Why Data, Trust, and Skills Are the Foundations of AI-Driven Pharmacoviginlance

 recently spoke with Beena Wood, chief product officer, Qinecsa Solutions, to get her perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 2 of our three-part interview, Wood outlines a forward-looking vision for how AI could transform pharmacovigilance (PV), while candidly addressing the structural, regulatory, and organizational barriers that must be overcome first. She imagines a future in which AI-enabled surveillance systems can rapidly detect safety signals worldwide—across languages, geographies, and datasets—within hours rather than months. In Wood’s words, this would allow systems to “detect—not weeks, not months, but hours—the same reports across multiple languages,” cross-reference similar cases globally, and proactively alert manufacturers and regulators in near real time.

While Wood emphasizes that this vision is technically feasible, she stresses that today’s biggest obstacle is not algorithmic sophistication but weak data foundations. Fragmented, non-interoperable, and poorly validated datasets severely limit AI’s effectiveness in PV, she says. As she puts it, “No amount of AI sophistication will help” if the underlying data are not harmonized. She notes that leading organizations in 2025 are those investing in these “boring foundations” now, enabling faster and more scalable progress later.

Trust and explainability emerge as another critical theme, with Wood framing trust as a combination of transparency, auditability, accuracy, and risk-based evaluation. Black-box AI systems, she argues, will not gain traction in high-risk domains like safety monitoring, especially under such evolving regulatory frameworks as the EU AI Act. Clear guardrails, ethical frameworks, and responsible AI practices are essential for adoption.

Wood also highlights economic and organizational readiness as key barriers. PV teams must compete for investment by clearly articulating return on investment, while organizations need robust change management strategies to integrate AI effectively. She cautions that the real skills challenge is not simply technical proficiency but critical thinking—teaching professionals how to evaluate AI outputs, recognize limitations, and identify hallucinations. Ultimately, Wood sees AI not as a replacement for human expertise, but as an amplifier: tools that, when used responsibly, can make skilled professionals “super humans” rather than substitutes.

So think of a patient in rural India and, say they are on this newly approved gene therapy, and they experienced an unusual reaction. And then think about the possibility of an intelligence surveillance system that, within hours, is able to detect—not weeks, not months, but hours—the same reports across multiple languages, is able to cross reference it with about 47 similar cases globally and then it identifies that there is a genetic marker correlation using real-world data analysis and then, simultaneously, alerts the manufacturers as well as the regulators and agentic AI coordinating already the proactive measures.

That's where I want PV to get to. And it is theoretically possible with AI capabilities, with proactive surveillances, but we don't have the foundations there that we need in order to make it real. And that's where the barriers that you're talking about come into play and things that I talked about earlier. So, for example, the data foundations, the data heterogeneity problem and the fragmentation problem. There is simply… the uncomfortable truth for us is that we are building AI systems on data foundation that aren’t either standardized or they're not interoperable and they're often not validated for AI use cases. And so, when you have this data scattered with incompatible formats and legacy systems and regulatory silos, and if you want to create that AI model without a harmonization, it's going to be extremely difficult.

No amount of AI sophistication will help. And so that's, I think, an uncomfortable truth, and that made us realize this year, in 2025. The good organizations are the ones who are starting to do those boring foundations so they can scale very quickly afterwards.

I think the other barrier is still the explainability and trust of these AI systems. So, when I look at the trust, I think of it as the explainability, meaning the transparency, the auditability, as well as the accuracy, and weighed by the risk. That's how I look at trust in an AI system.

All of these play a part in either magnifying it or diminishing the use cases for AI. So, if we still talk about AI as a black box, it's not going to work. It's super important to have those guardrails. What kind of guardrails, what kind of ethical framework, responsible AI are we bringing? How can it be trusted? So I think that's another important barrier that, as vendors, as well as consumers, and put that in context of EU AI Act and in a high-risk system—I know there are still conversations about what becomes high-risk systems within our domain—I think all of that becomes really important. And then the economic reality is actually a barrier. I can say, definitely, with my domain, it's about asking for initial investment for AI promising efficiency gains. We know there'll be efficiency gains, but how do we sit in around the R&D table where PV and safety can build those ROI and use cases, compete against other use cases, in order to get those investments to be able to do it? How do we build that ROI for the company?

I have to say the organization’s readiness is another one. I think not looking at it as a small change, but actually having an entire change management for AI and even modifying the way that we look at skillset. I mean, is it a job description or are we building in a set of skills we need that are transferable across the organization? So I think those are the things that are barriers and what needs to be overcome.

What concerns me about that is I'm not sure, as an industry, if we're solving for the right problem. When we are talking about skills or barriers, and we have the contradictory report about shadow AI and 90% are using AI anyway, whether it's enterprise approved or not, the real skills challenge over there is: How are we teaching people to critically evaluate AI outputs in the context of the work? For example, in context of safety, how are we teaching people to evaluate if AI is operating within competency or is it hallucinating confidently? How do we teach that? How do we teach when AI cannot answer a question? And so that's how I would frame that question.

And also I would see it as, not thinking about technical skills in isolation. AI skills sometimes is considered as a technical skill, so not thinking about that in isolation, but thinking it as an intersection of multiple capabilities and collaboration. So, for example, data scientists and technology folks, do they understand the PV domain? Do the PV domain folks understand technology? How do we bring that together as a collaboration to bridge those gaps? Are they change management capabilities so we can actually do get to those transformative successes?

I think human intellect is inherently beautiful. And I don't think it can truly ever be replaced, but what we can do is we can enable these AI tools to maybe make us super humans, is how I would think about it.

Beena Wood, Qinecsa, saw 2025 as an AI superposition in which pharmacovigilence needed better data foundations and regulation.

Realizing the Potential of Enterprise AI within the Global Pharmacovigilance Domain

 recently spoke with Beena Wood, chief product officer, Qinecsa Solutions, to get her perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 1 of our three-part interview, Wood explores the complex landscape of pharmacovigilance (PV) in 2025, a year she characterizes as the "AI Superposition." Wood explains that the industry is currently navigating all stages of the 

 simultaneously, balancing breakthrough technological announcements against the disillusionment of failed pilots and the rise of a "shadow AI economy." Regarding this state of uncertainty, Wood states, "Unless we open the box of specific AI implementation within an organization, it's in superposition of either transformative success or an epic failure."

The discussion highlights a sharp divide between aspirational goals and implementation reality. While some organizations have achieved concrete successes, many in the PV domain are facing a significant "reality check." Wood points out that even leading pharmaceutical companies often lack the foundational data infrastructure necessary to support sophisticated AI systems. She emphasizes the high stakes involved in these advancements, noting, "very impactful use cases would be faster signal detection, earlier signal detection, improving the safety profile, and earlier patient protection."

Wood also addresses the perceived "skills gap," suggesting it may be misdiagnosed; the real issue often lies in the lack of maturity and context awareness in enterprise AI compared with consumer AI, she says. Furthermore, Wood highlights the rapid acceleration of the regulatory landscape, noting that 2025 saw pivotal new frameworks from the FDA and the EU. Ultimately, she argues that successful AI adoption requires moving beyond hype to focus on the critical prerequisites—specifically data foundations—to bridge the gap between promise and practice.


I am Beena Wood, and currently I'm chief product officer at Qinecsa Solutions, the software that brings pharmacovigilance [PV] into end-to-end, different products that ties the PV landscape together.

2025 was a year, it was all about the promise of AI and the implementation reality, and the gap in between, which that became impossible to ignore in 2025. Essentially, we experienced every phase of the Gartner Hype curve all at once, simultaneously. And what I mean by that is there were inflated expectations because of breakthrough announcements, what AI can do, and the pace at which AI was moving.

There was, of course, for a long time, there's no conversation about AI, especially from my domain, from pharmacovigilance domain. And then we were talking about cloud and SaaS, even as recently as five years ago. And then we jumped into, oh, there's AI ML use cases and NLP, and those dominated. And then the conversation very quickly shifted into now agentic AI and the possibilities, and then the inflated expectations with those breakthrough announcements of technology, and then, simultaneously living the trove of disillusionment because of failed pilots—investing money into AI, and I'll get to some of those failures and why, from my perspective, but also that disillusionment was happening. And then, simultaneously, there was a plateau of productivity of that hype curve, if we remember that, and that is from two factors: organizations getting this right, but it's also because we heard of shadow AI economy, in the MIT report, where people are plowing ahead, whether the enterprise is ready or not. They are using for their personal productivity.

So, I think that this is just an interesting confluence. Unless we open the box of specific AI implementation within an organization, it's in superposition of either transformative success or an epic failure. And so it is like, what 2025 made us, forced us, to do is open these boxes and confront what is actually working, and I still think we are in the early stages of that. I still think we are working in superposition of all of it as well as the hype curves. All these are hype curves. But what did we achieve, really in 2025, I would say, is some of AI moved to concrete use cases, from concept to concrete use cases for those who got the prerequisites right.

So, for example, clinical trial acceleration. I had been following this acquisition of Deep 6 AI, partly because one of my friends worked there, by Tempus and where they had showed the use cases of the clinical trials actually reducing by 50% the time, using real world data as well as the AI capability. So that is very interesting use cases to consider. And then, the other area of application, I think where it has been successful in this lifecycle is drug discovery and repurposing. Sanofi, for example, working on drug repurposing on their unpursued asset, or the asset that they have shelved, for rare diseases and then reducing that in silico model, the timeframe, because of AI.

And that kind of opens up… those are not just aspirational, but things that have been coming to life faster because of AI. Now, if I focus on PV itself, my area, there's a reality check. Theoretically, AI can, for example, very impactful use cases would be faster signal detection, earlier signal detection, improving the safety profile, and earlier patient protection. That exists; that enablement, the possibility, exists. But frankly, what I have experienced is most organizations, including leading pharma companies, haven't yet built the foundational layers that are necessary to make it successful.

So, for example, data foundation is an easy one. I think everybody will talk about it too, is that data infrastructure really doesn't exist yet to make these AI systems, those aspirational systems, come to life. I think that's an important thing that I experienced in 2025.

Another trend I saw was that perceived skill gap. I feel that problem of skillset is maybe partially misdiagnosed. And what I think is really look at why some of these consumer AIs are working really well. I would think it's because they are conversational, context aware, they are learning, or we can continue to help them learn and admit uncertainty if they're prompted, if they're built correctly. In enterprise AI, I think that maturity is lacking, or there's pockets of maturity but other areas that are lacking. I think that's another thing that I saw is that perception of skillset gap to reframing the problem correctly; instead of saying employees need to learn AI, is a real lesson “The teams are showing us what good AI looks like, and is that what we should expect from enterprise AI?” And I would say one more thing is regulatory. I think that's really important. I think that the evolution of regulatory guidances have accelerated. This year alone, we saw FDA, for example, coming up with their pivotal guidance on AI, their framework on risk assessments, and the CIOMS working group with their paper that just came out, which is pretty comprehensive. For example, FDA, establishing the CCRI, the Center for Real World Evidence Innovation, the EU Darwins, etc.

So, the regulators, I think, are starting to accelerate, or started to accelerate, this year, and it'll be really interesting to see how these trends take us into the next year.

Saharsh Part 3

 recently spoke with Saharsh Davuluri, Vice Chairman and Managing Director, Neuland Labs, to get his perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 2 of our three-part interview, Davuluri explores the critical necessity of modernizing global manufacturing infrastructure to support the rapidly expanding peptide and API segments. He identifies a continuing trend in which peptides are becoming a vital modality in drug development, extending far beyond popular weight-loss analogs to encompass diverse therapeutic indications, such as cardiovascular health, dermatology, and central nervous system-related conditions. Davuluri notes that as these candidates move through the pipeline, the industry must diversify its manufacturing base. "There is a need, from a development point of view, to have destinations outside China and Europe for peptide manufacturing," he explains.

A central theme of the discussion is the urgent requirement for technological evolution within API manufacturing, which Davuluri argues has not kept pace with the times. He points out that the sector has been slow to adopt modern concepts—such as flow chemistry, electronic batch records, and completely automated manufacturing execution systems—which has led to the industry being characterized as "primitive" compared with other sectors. He envisions 2026 as a pivotal year for a "catch up" in infrastructure, especially as the world maintains a strong interest in small molecules and synthetic peptides rather than moving exclusively toward biologics.

To meet this future demand, Davuluri suggests that regions like India will be essential due to their capacity for large-scale manufacturing and their status as a geopolitically neutral option. Ultimately, he stresses that drug manufacturers must prioritize digital transformation to bridge the gap with other high-tech medical fields. "I would think that drug manufacturers need to think about their infrastructure, think about how they would like to invest in automation, modernization, and digitization, so that our sector can really catch up and be on par with what you would see in modern biologics or vaccine manufacturing today in the world," Davuluri concludes.

Access 

We've talked a lot about peptides in the past. I see that as a continuing trend. There is a need, from a development point of view to have destinations outside China and Europe for peptide manufacturing. There is an increasing number of candidates from the peptide segment entering into drug development.

Yes, there've been a lot of GLP-1 analogs, but there are also a lot of amylin analogs. There are different kinds of peptides, not just for weight loss, but for different kinds of therapeutic indications: dermatology, cardiovascular, CNS, and therefore, we see that peptides will become a very important modality going forward.

And I think 2026 will be a year where we will see a stronger need for peptide scale up and commercial manufacturing, particularly from a region like India, which tends to be favorable for large-scale manufacturing like peptides and also at the same time is maybe geopolitical more neutral. I see a lot of investment potential for peptides in 2026.

I think API manufacturing, again, that's the perspective I speak from, has for a very long period, not modernized with the times… you know, concepts like flow chemistry, electronic batch records, completely automated MES systems, the adaptation of these concepts in API manufacturing has been very slow and has kind of put us together as in a category which you might even call as primitive; there hasn't been a lot of evolution in that area.

I think 2026 should be a year where a lot of catch up should happen because there is an increasing reemphasis on small molecules and synthetic peptides, and the world is not completely moving lock, stock, and barrel to biologics. And, therefore the need for having modern, synthetic, small molecule peptide manufacturing facilities is very important.

And, therefore, I would think that drug manufacturers need to think about their infrastructure, think about how they would like to invest in automation, modernization, and digitization, so that our sector can really catch up and be on par with what you would see in modern biologics or vaccine manufacturing today in the world.

Saharsh Davuluri, Neuland Labs, urges API and peptide automation by 2026 to match modern biologics standards.

The Critical Need for Digital Transformation in the Peptide and API Sectors

Mike Stenberg, LGM Pharma, explains how faster FDA approvals under CNPV shift bottlenecks to production, requiring onshoring and earlier supply chain risk.

Navigating Financial and Opportunity Risks in Accelerated Drug Launches

 recently spoke with Mike Stenberg, Vice President – Business Development, LGM Pharma, to get his perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 3 of our three-part interview, Stenberg explores the evolving manufacturing paradigm in the pharmaceutical industry, driven by accelerated FDA approval timelines. Traditionally, companies have relied on a 10-to-12-month approval window to finalize launch plans; however, with approvals now potentially occurring in one to two months under programs like the FDA 

Commissioner’s National Priority Voucher

 (CNPV) pilot, the primary bottleneck has shifted from the FDA to the production floor, Stenberg notes. He emphasizes that to take advantage of CNPV, companies must first onshore their manufacturing to the United States.

Because the "padding" between submission and approval has largely vanished under CNPV, manufacturers must now integrate commercial-scale planning from the very beginning of a drug's development, Stenberg continues. This "day one" approach requires identifying and securing supply chains upfront, a process that demands significant time, capital, and "boots on the ground." Furthermore, formulation must now be conducted with an eye toward immediate scale-up, ensuring that equipment is ready to move a product from the bench to commercial volumes without the luxury of an interim adjustment period.

Stenberg highlights that activities previously reserved for the phase following submission—such as procuring excipients and packaging—must now occur much earlier to ensure a successful launch. This shift introduces substantial financial and opportunity risks. If a drug fails to receive approval, companies are left with a "huge hole" in their manufacturing schedules and a significant loss of expected revenue, compounded by the cost of materials already brought in-house.

Ultimately, while the industry benefits from faster patient access to drugs, Stenberg notes that the transition requires a difficult adjustment. The risk has moved earlier in the development process, forcing companies to commit resources and production capacity before a final regulatory decision is reached.



Step one in shifting your playbook is you have to be in the United States to take advantage of the voucher program. So, step one: onshore your manufacturing and move to the United States. But having done that, it's a bit of a dilemma. We have in our industry, bemoaned forever the fact of how long it takes to get a drug approved, and I think 10 to 12 months is a dramatic improvement. It used to be 18 months. But having said that, we also baked it into our processes and we baked it into our timelines. And we came to depend on that time. So now what's happened is you move the bottleneck associated with FDA approval, you move that bottleneck to the production floor.

And, so, approval's not the issue anymore. It's manufacturing the drug in commercial volumes. So that means you have to do everything earlier. In fact, you have to do it right from the beginning of the program. You have to identify and secure your supply chains right up front. That's not something you can do on the fly, by the way.

You have to have already started doing that kind of stuff because it takes boots on the ground, it takes time, it takes a lot of money to do those things. You also have to start formulating with an eye towards immediate scale up. So you have to have that entire equipment trained somewhere in your facility so that you can go from the bench all the way to commercial volumes.

It really affects virtually everything we do in the manufacturing world. You're having to do so much more of that from day one, from securing your supply chain to formulating more quickly, but formulating to scale up immediately. We used to have some time to do those kinds of things. That time is gone. We'll adjust of course, and it's a good thing overall, but I think the industry's gonna miss that timeline, that padding, that they had between submission and approval.

When you look at the supply chain, alone, and when you think about bringing in the materials for a commercial scale up, especially if it's gonna be a big drug as opposed to say an orphan drug, that means, yeah, you've secured your supply chain and you understand where you're gonna get your supply, but you have to bring that supply in early, right?

You have to bring in all the materials and all of excipients and all of the packaging required for immediate scale up and commercialization. That has always been—that planning for launch—has always been typically what occurred during that interim phase, “Hey, we've submitted, things look good. We have 10 to 12 months, if not longer, to prepare for the actual manufacturing and launch across the board of this particular product.”

So yeah, you have risk associated with the materials that you have to bring in. Industries like ours have opportunity risk. If we're planning to scale up, we started to carve out space in our schedule and on our machines to manufacture that product. That goes away, you have a huge hole in your schedule, and you have a huge hole in your potential and expected revenues as well.

So yeah, the risk is substantial and it moves the risk earlier on in the process.