Christopher Cole

In an interview regarding the presentation “The State of AI in Next-Generation R&D” at CPHI Europe 2025, held Oct 28-30 in Frankfurt, Germany, Eva-Maria Hempe, head of Healthcare & Life Sciences, NVIDIA, discusses the pivotal role of artificial intelligence (AI) and accelerated computing in transforming the bio/pharmaceutical ecosystem; explains that the successful integration of AI within the bio/pharmaceutical sector requires addressing organizational, technological, and cultural challenges, according to an analysis of strategic imperatives for AI adoption; and highlights the strategic necessity of integrating AI while maintaining critical human oversight and addressing geopolitical concerns surrounding data sovereignty.

PharmTech: How are leading organizations overcoming the challenges posed by siloed and unstructured data to unlock the full potential of AI in R&D?

Eva-Maria Hempe: The management of siloed or proprietary data is a major competitive advantage in this field. Leading organizations are tackling this challenge by implementing advanced AI platforms designed to integrate, process, and validate data.

These platforms support the centralization and harmonization of disparate data sources without requiring everything to be repackaged. On top of these core platforms, specialized tools are offered, such as Parabricks for genome sequencing, MONAI for medical imaging, and BioNeMo for biological language models. Additionally, kits for agentic AI are employed. These tools accelerate data analysis, significantly reduce manual input, and enable researchers to gain rich insights from complex, unstructured datasets, which are an inherent feature and richness of this field.

What strategies are most effective in ensuring data quality, integrity, and regulatory compliance for AI-driven research?

Data quality and integrity are paramount, as the old proverb "garbage in, garbage out" holds true. Effective strategies center on enforcing compliance across the entire data lifecycle and establishing a robust data governance framework.

Organizations are rolling out concepts and frameworks like ALCOA (attributable, legible, contemporaneous, original, accurate). Best practices also include implementing the right processes for audits, training, and automated validation routines. AI platforms assist with this by allowing the tracing of data provenance and ensuring safe model development practices, which serve as the foundation for broader behavioral aspects of compliance.

Where are the greatest opportunities for AI to enable more connected and adaptive R&D workflows?

The greatest opportunity lies in what is referred to as the "lab in the loop" or bridging the wet lab and the dry lab. This initiative presents AI challenges on both sides.

In the wet lab, the focus is on automation to ensure data quality. This involves capturing all process data in a harmonized form, including the necessary high-quality metadata, and establishing repeatable workflows, such as automating the transfer of plates between different machines. This high-quality, automated wet lab data then provides the basis for better predictions regarding which experiments should be run.

By closing the loop—wet lab validation providing good quality data which feeds models to predict the next experiments—the R&D process can be significantly accelerated. This systematic, data-driven approach is essential because the chemical space is immense—larger than the number of sand grains in the universe—and researchers previously only explored areas they were comfortable with.

Another major opportunity involves foundation models, such as Evo 2. These models allow researchers to dive deeply into genetic mutations at scale, predict their functional impact, enable better candidate selection, and ultimately drive cross-disciplinary collaboration.

What key technical or organizational factors are holding back wider AI adoption among biopharma companies?

Adoption is primarily hampered by organizational issues, in addition to data silos. Key barriers include:

• GPU scarcity (a lack of sufficient compute resources)

Fragmented AI strategies contribute to GPU scarcity, as organizations lack a central overview of the infrastructure needed to run their most important AI projects. To address these barriers, professionals are advised to:

1. Establish a centralized AI strategy: AI must be positioned as a core business function that permeates the entire value chain, rather than just an add-on. Use cases are relevant across research, development, manufacturing, and commercial activities.

2. Balance projects: Organizations should balance ambitious long-term goals (like making drug discovery an intentional design rather than a serendipitous one) with projects that yield "quick wins". Quick wins, while perhaps less "sexy," provide immediate impact and help sustain momentum for more complex initiatives. Examples of quick wins include automated filing for regulatory processes, clinical writing, and improved recruitment for clinical trials.

3. Drive change management: This involves educating both staff and leadership. It is crucial to understand that AI is simultaneously incredibly powerful and incredibly non-powerful, meaning users must be able to judge the outputs effectively.

4. Fix processes before automating: If broken processes are digitized, standardized, or automated, the result is simply broken automated processes. Process improvements are a prerequisite for fully leveraging AI.

Addressing technical challenges like data interoperability, standardized ontologies, and compliance is also critical, and frameworks are emerging to help in these areas.

How do you envision the balance between AI-driven automation and human oversight evolving, especially for complex or customized experimentation in biologics development?

The future is seen as highly collaborative, where AI-driven automation handles routine tasks while human expertise remains essential. There will always be a human or the experiment in the loop, providing validation and feedback on the right direction.

• Data mining and crawling through vast amounts of data.

• Early candidate selection and literature review.

• Finding signals across more papers than a human could manage.

However, human oversight and expertise will remain crucial for:

• Coming up with experimental strategies.

• Making bold bets or applying creativity.

Crucially, humans must be aware of AI's limitations, particularly in areas with scarce data. Since the goal is often to explore new biological concepts, these areas are, by definition, where less data exists, meaning AI cannot be relied upon in the same way as in well-researched domains.

What else should bio/pharma industry professionals consider about the adoption of AI?

Professionals should consider the element of sovereignty. Due to large geopolitical developments, organizations are increasingly concerned about interdependency and data control. For example, in Europe, efforts are underway to help local pharmaceutical companies build sovereign AI models. This allows them to secure sensitive data without compromising their ability to perform large-scale biological modeling.

Europe has the opportunity to lead in trusted, ethical, and sovereign AI for healthcare and life sciences, relying on strong regulatory frameworks and growing AI infrastructure. Although the share of AI compute in Europe is currently low (around 4%), this is changing due to various public and public-private initiatives aimed at creating AI factories and AI data factories to convert unstructured information into actionable insights at scale.

What should bio/pharma industry professionals know about AI agents?

AI agents are a current buzzword defined as a reasoning model equipped with memory and tools. They are useful for tackling multi-step tasks.

For example, an AI agent given access to a literature database (like PubMed) can execute a sequence of actions:

1. Turn a given question into a literature search.

2. Analyze the results of the literature search.

4. Potentially link to a virtual screening workflow to create and explore variants of standard-of-care molecules after identifying the target protein.

5. Sum up all findings into a comprehensive report for the researcher.

In the future, the vision is that AI agents could potentially link to an automated lab, running experiments autonomously, perhaps with a human performing a final oversight step to prevent the ordering of overly expensive reagents or compounds

Articles

Artificial intelligence (AI) and accelerated computing are revolutionizing the bio/pharmaceutical industry by addressing organizational, technological, and cultural challenges. Successful AI integration requires overcoming data silos, ensuring data quality, and maintaining regulatory compliance. Opportunities for AI include enhancing R&D workflows through automation and foundation models. However, adoption is hindered by organizational silos, legacy systems, and fragmented strategies. AI-driven automation complements human oversight, especially in complex biologics development. Geopolitical concerns about data sovereignty are crucial, with Europe poised to lead in ethical AI. AI agents offer potential for multi-step task automation in research.

Eva-Maria Hempe, NVIDIA, says AI platforms must integrate R&D data, overcoming silos, with adoption demanding a centralized strategy and change management.

Best Practices for AI Adoption in Pharmaceutical Research

Maggie Saykali, Cefic, emphasizes the importance of competitiveness and innovation for European manufacturers to create added value and maintain environmental standards.

Maggie Saykali on the Critical Medicines Act and the Mandate for Local Production

In this part 2 of a 3-part interview regarding the presentation “The Critical Medicines Act: Game-Changing Opportunity for a Sustainable and Resilient Supply Chain?” at 

, held Oct 28-30 in Frankfurt, Germany, Maggie Saykali, Director – Specialty Chemicals, Cefic (European Chemical Industry Council), offers strategic insights into the necessary structural shifts for the European biopharmaceutical manufacturing sector, focusing on competitiveness, innovation, and crisis preparedness. Her analysis centers on the premise that the European industry cannot effectively compete on salaries or basic costs, necessitating a fundamental pivot toward quality and technological advancement to ensure long-term resilience and secure critical supply chains.

Saykali stresses that innovation is driven by the mandate for competitiveness, arguing that manufacturers must create added value compared to other regions that may use older techniques or processes with lower environmental requirements. By valuing criteria, such as implementing more efficient and environmentally friendly processes, achieving better control of emissions, and improving the recycling of solvents, Europe actively pushes for the technological advances that yield a competitive edge.

Furthermore, she argues that the approach to security and crisis readiness must evolve beyond simple stockpiling and instead focus on maintaining robust, adaptable production capacity. This strategic focus on maintaining industrialization is critical because if Europe loses its current level of pharmaceutical manufacturing capability, the ability to produce necessary medicines will inevitably dwindle. Saykali provides a crucial strategic overview of why production capability is paramount for crisis readiness:

“Crises, by definition, are unpredictable,” Saykali says. “It's not that you should have a stockpile of what is needed. The essential notion here is that you should be able to produce it when you need it, and actually that you should have the equipment and the infrastructure to produce what you need when you need it”.

She notes that other regions, such as the US, are adopting similar models to encourage national production, often because they have already lost significant domestic manufacturing capability, leaving them potentially more vulnerable than Europe. The proposed Critical Medicines Act addresses this dual challenge by emphasizing both preparedness (encouraging local production at the highest standards) and securing international alliances and cooperations for times of crisis, according to Saykali. Relying on only a single option for supply creates vulnerability, a risk clearly demonstrated during the early phases of the COVID-19 pandemic, when the delivery of key ingredients to Europe was disrupted. Ultimately, Saykali emphasizes that stakeholders must actively "put value on something other than just the price" to push for the innovation necessary to maintain this competitive edge, prompting the industry to question whether it can afford to rely solely on cheap medicines moving forward.

 and Part 3 of this interview and access all our 

*Editor’s Note: This transcript is a direct, unedited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

Competitiveness and innovation are two very important things for European manufacturers. This is the way that they can create an added value compared to production in other regions of the world that still use maybe older techniques or older manufacturing processes and lower environmental requirements. So by giving an advantage and valuing this, these criteria, actually, we are pushing for innovation and pushing for more efficient, more environmentally friendly processes, better control of emissions, better recycling of solvents, things like that. So innovation is pushed by the need for competitiveness. So the two go together, and we cannot compete on salaries or costs. We will compete on quality innovation, and that's it.

Well, we see that other world regions are actually adopting something very similar. I mean, look at the US. Everything that is happening to encourage national production is actually happening more or less on the same model as what is happening in the EU. I mean, it's not one acting on the same model as the other. They are advancing in parallel, because that is the solution if you want to have control on your supply chain and have the guarantee that whatever crisis happens, that you have what is needed to address the crisis. Crises, by definition, are unpredictable. So it's not that you should have a stockpile of what is needed. The essential notion here is that you should be able to produce it when you need it, and actually that you should have the equipment and the infrastructure to produce what you need when you need it. And if Europe does not maintain the level of industrialization and of manufacturing that we have for the pharmaceutical supply chain, then this ability to be able to produce what is needed when it's needed will dwindle, and therefore we have a problem. And this is more or less where the US are at now, because they've lost a lot of their manufacturing capability and are now more vulnerable even than Europe.

The Critical Medicines Act actually saw it really well. You have the chapter on preparedness, and you have the chapter on cooperations and international cooperations. And I think that if you want to be crisis ready, which is the final purpose of what we're doing, you need to act on both. You need to be able to encourage local production at the best and highest standards of quality possible to be able to make what you need when you need it, but you also need to secure alliances and cooperations for times of crisis, when you need to have other options as well. By having only one option for the moment, we are making ourselves vulnerable. And we have seen it during the early phases of COVID, where some supplies were not arriving to Europe, that there was really an issue in delivery of some of the ingredients to make the medicines that we needed. Actually, the pandemic really highlighted some of the issues that could happen if we don't prepare.

It's a very difficult question to ask, and one that I don't think I have all the information to answer it. I can only answer from my perspective, which is that for us, the only way to go forward is to really put value on something other than just the price, and to push for innovation, push for technological advances that will give us this competitive edge. If we don't do that, we lose the battle, and that is obvious. So we in some ways, have to ask ourselves, “Can we afford to go along as we have always done, and can we afford to only rely on cheap medicines?” They are cheap for a reason.

Videos

Nigel Langley of gChem discusses the company’s DMSO, proactive pharma strategies, and more in part 1 of a 2-part interview related to his presentation at CPHI Europe 2025.

Why Sound Science Is Pharma's Best Defense Against Regulatory Shifts

*Access a full transcript of the video interview below

In this part 1 of a 2-part interview regarding his presentation, “Challenges to the Sustained Availability of Excipients” at CPHI Europe 2025, held Oct 28-30 in Frankfurt, Germany, Nigel Langley, Global Technical Director of Life Sciences at gChem and immediate past chair of IPEC-Americas, discusses gChem's offerings at the conference, regulatory challenges facing excipients, and how the pharmaceutical industry can respond to them.

At CPHI, gChem is presenting its Procipient dimethyl sulfoxide (DMSO), a high-purity, pharmaceutical-grade product with applications as both an API and an excipient. This product is supported by a type two drug master file with the FDA in the US and a type one file in Canada. In addition to Procipient, gChem will promote two other DMSO grades: a synthesis grade for API and peptide synthesis, and an American Chemical Society analytical grade for laboratories and diagnostics.

Langley identifies the most pressing regulatory challenge as the "daily shift" in focus on different safety concerns for traditional excipients. He cites numerous examples, including issues with titanium dioxide (TiO2), potential nitrosamine connections, microplastics, and PFAS (per- and polyfluoroalkyl substances). More recent scrutiny in the US has targeted synthetic color additives and talc, keeping industry organizations like IPEC busy. This constant stream of emerging concerns makes it difficult for the industry to keep up.

Regarding how pharmaceutical companies can better anticipate these challenges, Langley acknowledges that it is difficult to be proactive. He stresses that the industry's response should be rooted in sound science. He criticizes how isolated or questionable scientific papers can sometimes used to drive regulatory decisions. Langley points to the industry's successful response to the proposed ban on TiO2 in Europe as a model for future action. The industry countered by presenting strong scientific evidence, highlighting the potential for massive drug shortages and the lack of viable alternatives. This fact-based, non-melodramatic approach ultimately prevailed. Langley believes this method of presenting robust scientific facts is how the pharmaceutical world should proactively tackle future regulatory challenges.

Look for Part 2 of this interview and access all our 



 Hi everyone. My name is Nigel Langley. I'm the global technical director of Life Sciences at gChem. I'm also the immediate past chair of IPEC-Americas, and the two roles actually provide the opportunity to be involved in the industry from the excipient side. So, for gChem, I help to support our customers and formulators in those companies with their challenges for formulation, supporting their work with our pharmaceutical grade dimethyl sulfoxide, which is DMSO, which is known as Procipient.

At CPHI this year, we will be presenting and promoting our Procipient dimethyl sulfoxide product, which is our high-purity, pharmaceutical-grade DMSO. And that has both an API and an excipient application. We support this with a Type II Drug Master File in the US with the FDA, and a Type I Drug Master File in Health Canada.

But it's also an excipient, so it has multiple functions. In addition to Procipient, we will be also promoting two other grades of DMSO that we have in our portfolio. One is our synthesis grade, which is targeted towards API synthesis as a solvent, and also peptide synthesis, which is a growing market and growing area.

And the third being our ACS grade, which is our analytical grade, which is really focused and targeted towards laboratories for reagent grades, for diagnostics, etc.

The most pressing challenge that we see, and particularly through IPEC-Americas and IPEC in general, is the daily shift in what to focus on next. We've had issues around safety concerns around quite a lot of the traditional excipients, like titanium dioxide, for example. We've had issues with nitrosamines and the possible connection with excipients, as well as drug products.

We've had issues in, with challenges with microplastics, with PFAS. There's a lot of impurities or that type of issue that's come through, some of which have been driven from Europe, some of which have been driven from the US. More recently in the US, there's, an opportunity, if that's the right word, of actually looking at color additives and synthetic ones that there's an initiative to scrutinize those and to ban certain colors. There's also more recently, talc is being on the microscope. And it's, it's really keeping IPEC busy, and the industry itself busy.

I think this is a difficult one because I think it's quite challenging for pharma companies to know how to react and be proactive in these types of instances. I think really, the science should prevail, the wealth of science in a particular area should be more compelling. I think what's happened more recently is isolated scientific papers are being cited that may be not representative of the bulk of the knowledge about a certain topic, and decisions are being made based on that type of science. And you can question whether that science is strong enough to actually warrant that type of discussion. This was an example that came from TiO2 [titanium dioxide].

The way that the industry responded to that, and, again, that came from Europe. The concern that TiO2 was banned in food and it looked like it was going to be banned in pharma products, in the pharmaceutical area. Well, if that happened, there would’ve been 90,000 drug products in Europe that would've had to be reformulated.

And, of course, a lot of those wouldn't have been reformulated because economically it wouldn't make any sense to do that. And there's no good alternative for TiO2. So how the industry responded, and it responded very well in my opinion, and took the science and questioned and challenged the citations that were being presented in Europe.

And although that took some time to actually re-present what's safe, and the world seemed to be divided on what was safe. The FDA actually saw no issues with the safety of TiO2. But in certain European countries, they did. The actual common sense, in my opinion, actually won the day in the end.

So, my point is I think the way that the pharma companies tackled that particular issue, because the impact of the patient could have been dramatic, you know, drug shortages if you have to reformulate, and as I mentioned, a lot of those products would probably not… the generics wouldn't have been reformulated because there's no economic gain to do it.

And they would've had to be reformulated with an inferior excipient to TiO2. It wasn't a simple case of just interchangeability with something else. The way that they reacted was a very positive way and not melodramatic, but just presenting the facts and the good science in that particular case. So I would imagine that's how, proactively, if this comes through, and I'm sure it will, another issue, how the pharma world will actually tackle that type of challenge.

Eva-Maria Hempe, NVIDIA, says bio/pharma must centralize AI strategy, tackle silos, prioritize process integrity, and balance quick wins with long-term drug design goals.

Centralizing AI Strategy, Ensuring Process Integrity, and Balancing Quick Wins with Long-Term Drug Design in Bio/pharma

In this part 2 of a 3-part interview regarding the presentation “The State of AI in Next-Generation R&D” at CPHI Europe 2025, held Oct 28-30 in Frankfurt, Germany, Eva-Maria Hempe, Head of Healthcare & Life Sciences, NVIDIA, explains that the successful integration of Artificial Intelligence (AI) within the bio/pharmaceutical sector requires addressing organizational, technological, and cultural challenges, according to an analysis of strategic imperatives for AI adoption. She notes that the adoption of AI is posited as a fundamental shift that permeates the entire value chain, encompassing applications across research and development, manufacturing, and commercial activities. Her primary recommendation for industry leaders is to solidify AI as a core central business function rather than treating it as a decentralized or non-essential add-on.

According to Hempe, the current barriers to widespread AI implementation are diverse, including pervasive organizational and data silos, outdated legacy IT systems, and fragmented AI strategies. Additionally, she says organizational compute capacity is a significant bottleneck, often manifested as GPU scarcity, which limits project scalability; this issue is frequently linked to a lack of centralized strategic oversight for infrastructure planning. To sustain AI momentum, Hempe advises organizations to strategically balance highly ambitious, long-term goals—such as shifting drug discovery toward intentional design—with short-term "quick win" projects. While transformative drug discovery requires timelines exceeding ten years, immediate impact can be achieved through applications like automated regulatory filing, clinical writing, and optimized recruitment for clinical trials, she adds.

Overcoming these obstacles mandates a significant focus on change management and culture shift, elements often grossly underestimated, according to Hempe. This involves comprehensively educating both staff and leadership on the capabilities and limitations of AI. Furthermore, process integrity is a critical prerequisite for meaningful digitization, she says, summarizing the necessity of focusing on foundational improvements as "if you digitize or standardize or automate broken processes, you get broken automated processes." Successful AI deployment, therefore, relies on improving standardized ontologies, data interoperability, and ensuring robust compliance frameworks, Hempe concludes.



 We already talked about silos, namely data silos, but I think there's also a lot of organizational silos. So, I think the big barriers to adoption are organizational silos, legacy IT systems, fragmented AI strategies, and then also change management is really massively underestimated. It's not just you're doing now AI and everybody's going to jump at it.

You have to find the right quick wins, which are helping people to buy into the opportunity. I also actually see is often GPU scarcity. So there is often just not enough compute for people to run what they would like to run. And that ties back to the centralized or the fragmented AI strategy. If you don't have a centralized AI strategy, you don't really have a good overview of what you need in terms of also infrastructure to run your most important AI projects for the organization. So, the first thing that I hope is going to be addressed, I would recommend people to address, is to really have a centralized AI strategy and have that as a core business function and not just an add-on somewhere.

This is just something that permeates across the entire value chain, and we are seeing use cases from research and development all the way to manufacturing and commercial. So this has to be a core central business function.

The second thing, which I already alluded to, is to really balance quick win projects with more ambitious initiatives. We all hope, and we are all working towards, making drug discovery less a serendipitous discovery and more an intentional design. But that's really hard, and the timelines are just 10 years plus. You still have to run through all the steps of clinical trials. Again, AI can help with clinical trials, but in a way, if you take a drug for six weeks, it's just going to take six weeks. Or if it takes two months or three months if you want to show a long-term survival, it just takes time. So this is the long-term parts of the portfolio, but there are so many also quick wins, which are returning impact immediately. They might not be as sexy as solving drug discovery, but with things like clinical writing, automated filing for regulatory, as well as better recruitment for clinical trials, you can win a lot on a very short time scale, and that's going to allow you to sustain the momentum as well for those more long-term and more ambitious initiatives.

And then I think the last thing, which I also said before, is really change management. So it's educating staff, educating leadership. One of the things about AI is that it's at the same time incredibly powerful and incredibly non-powerful. So you need to understand what it can do to basically also be able to judge what you do with the outputs.

Yeah, really driving that change management process, driving the culture shift, driving process improvements as well. We said before garbage in, garbage out. And another of these sort of sayings is that if you digitize or standardize or automate broken processes, you get broken automated processes. So doing all of those things is a real prerequisite to fully tapping into the power of AI. And then of course some of the more technical challenges like data interoperability, standardized ontologies, and then compliance. But there we are also seeing frameworks emerge and we're working with partners on a lot of these fields.

Maggie Saykali, Cefic, reviews how the EU’s proposed Critical Medicines Act aims to curb supply dependence and shortages by reforming drug procurement.

The CMA: Transforming Procurement to Ensure API and Excipient Supply Chain Resilience

In this part 1 of a 3-part interview regarding the presentation “The Critical Medicines Act: Game-Changing Opportunity for a Sustainable and Resilient Supply Chain?” at 

, held Oct 28-30 in Frankfurt, Germany, Maggie Saykali, Director – Specialty Chemicals, Cefic (European Chemical Industry Council), provides insight into the origins and development of the 

European Union’s proposed Critical Medicines Act

 (CMA). The push for the CMA originated during the 2020–2022 pandemic, during which medicine shortages underscored Europe's deep dependency on other regions—particularly Asia—for critical medicines, a situation aggravated by years of pharmaceutical offshoring. The core goal of the legislation is to identify the root causes of these shortages and drastically reduce them, explains Saykali.

The European Commission, working with major stakeholders, including API manufacturers, generics and branded medicine producers, patient associations, and member states, first developed a methodology to define what constitutes a "critical medicine." This methodology relies on such factors as a medicine’s supply chain structure, therapeutic family, and the number of suppliers. The concept of the CMA itself emerged in 2023, modeled after similar European legislation focusing on raw materials or semiconductors. This process involved the formation of a Critical Medicines Alliance, which included approximately 200 stakeholders. Published in March 2025 by the Directorate General for Health (DG SANTE), with assistance from the directorates for industry and medical emergencies, the act is now undergoing consultation with the European Parliament and Council, with a final vote anticipated by the end of this year or the first quarter of 2026, according to Saykali.

A primary focus of the CMA is reforming procurement practices. Currently, procurement is often based solely on price, resulting in a "one winner takes the whole tender" system that lacks guarantees regarding quality, security of supply, or adherence to environmental and social norms. This setup is detrimental to European manufacturers who must comply with strict EU regulatory frameworks. By reforming procurement criteria, introducing multi-winner tenders, and embedding environmental standards, the CMA seeks to ensure that compliance with stringent requirements becomes an advantage rather than a penalty for European producers. Furthermore, vulnerabilities must be addressed throughout the entire pharmaceutical supply chain. Saykali emphasized the often-overlooked components:

"We should not forget excipients because a lot of the critical medicines, generics or branded, actually depend on the excipients for their mode of action,” says Saykali. “And if we have a shortage and excipients, we… cannot make this medicine."

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My name is Maggie Saykali. I am the director of the European Fine Chemicals Group, and that is the trade association that represents the European manufacturers of active pharmaceutical ingredients, excipients. Intermediates and the key inputs, in fact. So our members are mostly CDMOs or some of them also make finished dosage forms, but mostly they are CDMOs and manufacturers of excipients or active pharmaceutical ingredients.

So the Critical Medicines Act is something that took a long time to elaborate. It originated during the pandemic, the idea of it. During the pandemic, we were witness to quite a few shortages that illustrated actually the existing situation that we all knew about, that for some critical medicines and with the offshoring that has been going on for years from Europe to Asia due to different factors that we were very dependent on other regions for the supply of some of our critical medicines. And this is more acute in generics of course, so we can go into the numbers behind that, But so, during the pandemic in 2020 until 2022, it was very obvious that the situation was exacerbated and that the solution must come. The European Commission worked with major stakeholders, meaning us as active pharmaceutical ingredient manufacturers, the generics manufacturers, and the branded medicines manufacturers mainly, but also patients associations, pharmacists, and member states to come up with a solution, first of all to identify the root causes for the shortages, and then, of course with the purpose of finding solution to alleviate them or to reduce them drastically.

So that started with a methodology to determine what is actually a critical medicine, what makes for the criticality. And the commission came up with a methodology with different factors based on the structure of the supply chain of a medicine. It's therapeutic a family factors like the number of suppliers, et cetera.

So there was a rather complex matric that would then identify what is a critical medicine and what is and differentiated from medicines that might be short but not qualify for the criticality. After that, in 2023, the idea came that we need a critical medicines act for Europe on the same model as the one for the chips or for raw materials, et cetera.

And this idea originated with a few member states that pushed it really hard together with the commission, European Commission, and there was a Critical Medicines Alliance formed. With the stakeholders, there was about 200 stakeholders, but the critical alliance board, steering board was these associations that I mentioned, plus representatives of patients, pharmacists, et cetera, and member states.

And we worked on what were the possible solutions. And then the commission went to work, put this all together. And in March, 2025, the Critical Medicines Act was published, and it was published by the director General for Health (DG SANTE). So the directorate in the European Commission that is responsible for everything that concerns health, but it was also helped by the directorate for industry and the one for medical emergencies.

So since then the Critical Medicine Act entered the European decision-making process, which involves consultations with the European Parliament and the council, meaning the member states, the European member states, and this is where the process is now. The council, the commission, and the parliament are discussing, and the final vote will happen we think, at the end of this year or first quarter of 2026. And then it'll be it'll have legal value.

First of all what we see today is a situation, for instance, in procurement that is completely based on cost, a price only meaning one winner takes the whole tender and it's only based on price. Meaning there are no guarantees on quality, on security of supply, on any other factors on the fact that it's produced, respecting environmental norms or social norms, all of that doesn't enter into account.

And is actually detrimental to European manufacturers because we have a set of standards, environmental, social that we have to comply with. That's normal. These are the European re regulatory frameworks. And some of the other suppliers from other world regions are not abiding by these, and they have different cost structures.

By addressing the issue of the criteria for procurement, this might give European manufacturers better possibilities to be competitive. Plus in putting environmental criteria in there as we asked for, that will give value. To the fact that we produce according to a very strict and very tight ESG requirements.

And this means that we will not be penalized because of it, but that will become rather an advantage for us. Plus then we are asking for not to go for the one one winner takes it all. But to have multi-winner tenders, which will put at an advantage Also people who manufacture in Europe.

Be it finished dosage, forms, active pharmaceutical ingredients excipients, et cetera. We should not forget excipients because a lot of the medicines critical or sorry, generics or branded actually depend on the excipients for their mode of action. And if we have. Shortage and excipients. We are at the same point that we cannot make this medicine.

So the role throughout the supply chain, we have to make sure that there are no causes for vulnerability and no possibilities for shortages or delivery of faulty or not up to standard quality ingredients.

Eva-Maria Hempe, NVIDIA, explains that accelerated computing, data governance, and “lab in the loop” are key to bio/pharma’s AI strategy for transforming drug discovery and R&D.

AI and Accelerated Computing Propel the Biopharma Ecosystem Forward

In this part 1 of a 3-part interview regarding the presentation “The State of AI in Next-Generation R&D” at CPHI Europe 2025, held Oct 28-30 in Frankfurt, Germany, Eva-Maria Hempe, Head of Healthcare & Life Sciences, NVIDIA, discusses the pivotal role of artificial intelligence (AI) and accelerated computing in transforming the bio/pharmaceutical ecosystem.

A core challenge and competitive strength in this sector stems from the prevalence of siloed and proprietary data. Leading organizations are tackling this by adopting advanced AI platforms designed to integrate, process, validate, and harmonize these complex, disparate data sources, Hempe explains. She adds that specialized tools operate atop these platforms, including Parabricks for genome sequencing, MONAI for medical imaging, and BioNeMo for biological language models, all aimed at accelerating analysis and extracting rich insights from often unstructured data.

According to Hempe, success in AI hinges directly on data quality and integrity, adhering to the principle that "garbage in garbage out is just a matter of fact." This necessity drives the implementation of robust data governance frameworks, such as ALCOA (attributable, legible, contemporaneous, original, accurate), alongside rigorous automated validation routines and training, she says. These AI platforms support safe model development practices by allowing the tracing of data provenance.

Hempe notes that the single greatest operational opportunity identified is what is termed the “lab in the loop”—the seamless integration of the wet lab (experimental work) and the dry lab (computational analysis). By automating wet lab processes, high-quality, repeatable data is generated, complete with necessary metadata. This reliable data then feeds computational models, which in turn predict the optimal next set of experiments to run, thereby accelerating the entire research workflow, she states. This systematic, data-driven exploration is essential given the immense scope of chemical possibilities. Speaking to this scale, Hempe notes, "This is more than sand grains in the universe." Furthermore, foundation models, such as Evo 2, are being deployed to deepen the understanding of genetic mutations at scale, predict their functional impact, enhance candidate selection, and encourage crucial cross-disciplinary collaboration.

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I'm Eva-Maria Hempe and I lead Healthcare and life Sciences for NVIDIA in Europe, Middle East, and Africa. What this means is we're working with the whole ecosystem, so both healthcare and life sciences. I sometimes joke I do everything from genomics over direct discovery, surgical robots all the way to helping physicians and nurses to do their job better. But that's a bit the span we're covering. We're working with companies of all different sizes. We're working with both established companies as well as startups and really helping them to use AI to the fullest and avoid reinventing the wheel and really tapping into the potential of accelerated computing.

It's one of the big strengths or big competitive advantages actually in this field is that there is, you can call it siloed, you can call it proprietary data, but of course it is something that has to be managed. And in general, the way we see is that leading organizations have advanced AI platform, which really allows them to integrate the data, to process the data, and to validate the data.

And then on top of such a platform, we're offering specialized tools like, for example, Parabricks for genome sequencing or MONAI for medical imaging or BioNeMo or biological language models, as well as some kits for agentic AI that then enable accelerating data analysis and really reducing the manual input and you really get this rich insights from these complex data sets, which are just, they're just a matter of fact, this ius nothing we're going to be changing in a way, that is the richness of this field, is the unstructured data, is the multitude of data. And so these platforms are then really supporting centralization without really having to repackage everything and the harmonization of those disparate data sources.

The question of data quality is actually really important, and integrity. So, the old proverb of garbage in garbage out is just a matter of fact. So, it's really about enforcing compliance at the data lifecycle and having a really robust data governance framework. So we're seeing concepts like ALCOA (attributable legible, contemporaneous, original, accurate) or other frameworks being rolled out. Then it's really about having the right processes on audits, training, and as well automated validation routines. And we can help with that by having, again, a platform that allows us to trace data provenance and to really have safe model development practices that you can rely on as as a basis for the more behavioral aspects, which I just outlined.

I think the greatest opportunity really is what we call the lab in the loop or bridging the wet lab and the dry lab, and that has kind of AI challenges on both sides. In the web lab, it's really about automation, so you want to have data, which is, as you said before, high quality. So you have to have the right metadata, and you want to have a really repeatable loop, a really repeatable workflow here. So we are seeing companies working in that space who are, automating the entire wet lab, like all the processes of transferring plates between different machines and capturing all that data in a harmonized form. And that data then provides the basis for better predictions of what experiments we should be running, and then you're basically closing the loop. So you're having the wet lab, which is the validation, which gets you the real data in a good quality thanks to automation, and that data quality then feeds models which can predict which experiments to do next. I think that's once this flywheel, you get to go, it can really accelerate things because the chemical space is just so huge. I mean, this is more than sand grains in the universe. And so far we've just been looking at where we felt comfortable in a way, looking at where we've been always looking. And with this more data-driven approach, we can really explore that space in a much more structured and systematic way.

And I think there's another opportunity. So these are foundation models like Evo 2, that's to really dive deeper into genetic mutations at scale and understand and predict the functional impact of that. And then, again, this allows better candidate selection and, again, drives cross-disciplinary collaboration.