Christopher Cole

Maggie Saykali, Cefic, explains that Western pharma vulnerability stems from market loss to Asia, caused largely by short-sighted offshoring due to environmental costs.

US and European Pharma Manufacturing Face Diverging Paths to Resilience

In this part 3 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), addresses the significant geographical shifts occurring in pharmaceutical manufacturing, drawing on data shared with the European Commission during the elaboration of the 

. The discussion focuses on the pronounced loss of market shares for both the United States and Europe in the manufacturing of small molecules, while China and India have gained corresponding ground. The market share data reveal a critical disparity: Europe currently maintains approximately 33% to 34% of the market for small molecules, whereas the US market share has fallen to around 10%, according to Saykali.

This erosion of manufacturing capability carries significant implications for supply chain resilience, she continues. The US, despite remaining powerful in the creation of innovative molecules, faces overwhelming dependence on external sources for generic drugs, with reliance exceeding 90%, states Saykali, adding that a market share as low as 10% indicates a severe loss of infrastructure, talent, and know-how, leaving the country highly vulnerable and requiring substantial financial investment to achieve a sustainable position.

Europe, however, still retains the necessary talent, know-how, and infrastructure due to its higher market share. While the situation was drastically different 40 years ago, Europe is now facing a crucial window to act, lest it reach a point where necessary recovery costs become prohibitive, Saykali says. The core difference between the current challenges facing the two regions lies in the amount of investment required to reverse the manufacturing decline.

The shift away from Western production was initially driven by economic factors, specifically the cheaper cost of manufacturing elsewhere. "But don't forget that… 30 years ago, it was also because we started in Europe and in in the US to have very strong environmental laws… It takes a lot of money to make it clean and environmentally sustainable, so one of the options was ship it off. But that was a short-sighted decision, and we are paying the price now."

*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.



This is the interesting difference between the US and Europe. So, the study that we shared with the European Commission when we were elaborating the Critical Medicines Act shows the loss of market shares for the US and for Europe, and of course, in the case of China and India, it's a gain of market share. and you could see that the US actually are now at more or less 10% market share for small molecules. Europe is at around 33-34%, and of course China and India have picked up all the slack.

So, if you go back 40 years ago, the situation was completely different. At 30-something percent market share, we still have the talent, we still have the know-how, we still have the infrastructure in Europe. 10% is too low. There you have lost the infrastructure or big parts of it. You've lost the talent, the know-how, and you're much more vulnerable or you have to put a lot more money on the table to be able to go back to a sustainable position.

Whereas for us in Europe, it's really time to act now if we don't want to go to a point… it's not the point of no return, it's the point where the return will cost you too much. So, that is where we are now and that's the essential difference between what's happening in the US and what's happening in Europe.

And the US are still very strong in making innovative molecules, but generics, totally dependent. I think the number is above 90% dependence on external sources for generics, because essentially, it's cheaper. But don't forget that a few years ago, 30 years ago, it was also because we started in Europe and in in the US to have very strong environmental laws and some of the production of some of the active ingredients and intermediates is not… I mean it takes a lot of money to make it clean and environmentally sustainable, so one of the options was ship it off. But that was a short-sighted decision, and we are paying the price now.

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We spoke with Nigel Langley, Global Technical Director of Life Sciences at gChem and immediate past chair of IPEC-Americas, regarding his presentation, “Challenges to the Sustained Availability of Excipients” at CPHI Frankfurt 2025, held Oct 28-30 in Germany. Langley addresses major industry concerns, including regulatory scrutiny over traditional excipients (such as titanium dioxide and talc), strategies for enhancing supply chain security and sustainability, and the urgent necessity of overcoming barriers to novel excipient innovation.

PharmTech: What is gChem exhibiting and promoting at CPHI 2025?

Langley: At CPHI this year, we will be promoting Procipient dimethyl sulfoxide, which is our high-purity, pharmaceutical-grade DMSO. Procipient has applications as both an API and an excipient. We support this product with a Type II Drug Master File in the US with the FDA, and a Type I Drug Master File in Health Canada. In addition to Procipient, we will also be promoting two other grades of DMSO in our portfolio:

1. Synthesis grade: Targeted toward API synthesis as a solvent, and also peptide synthesis, which is a growing area.

2. ACS grade: Our analytical grade, which is focused towards laboratories for reagent grades and diagnostics.

What is the most pressing regulatory shift currently threatening excipient availability?

The most pressing challenge, particularly seen through IPEC-Americas and IPEC in general, is the constant shift in deciding what to focus on next. There have been concerns regarding the safety of traditional excipients, such as titanium dioxide (TiO2). There have also been issues concerning nitrosamines and their possible connection to excipients and drug products, as well as challenges related to microplastics and PFAS (per- and polyfluoroalkyl substances). These issues, some driven from Europe and some from the US, involve various impurities. More recently in the US, there is an initiative to scrutinize and ban certain synthetic color additives. Talc is also currently "on the microscope". Dealing with these continuous issues keeps IPEC and the industry busy.

How can pharma companies better anticipate the next regulatory challenge?

It is challenging for pharma companies to know how to proactively react in these situations. Ideally, the science should prevail, and decisions should be based on the wealth of science in a particular area. However, recently, decisions have been made based on isolated scientific papers that may not be representative of the bulk of knowledge on a certain topic, leading to questions about the strength of the science cited.

A successful proactive strategy was demonstrated by the industry's response to the TiO2 concern that came from Europe. When TiO2 was banned in food, it looked like it would be banned in pharmaceutical products. If that ban had occurred, 90,000 drug products in Europe would have had to be reformulated. Many of these products, particularly generics, would likely not have been reformulated due to a lack of economic incentive, potentially leading to drug shortages. Furthermore, there is "no good alternative for TiO2," meaning reformulations would have required an inferior excipient.

The industry responded effectively by challenging the citations presented in Europe using sound science. Although the safety of TiO2 was debated (the FDA saw no issues, while certain European countries did), "common sense" ultimately prevailed. The appropriate proactive strategy is to present the facts and good science to tackle the challenge, rather than being melodramatic.

What proven strategies exist for strengthening excipient supply chains against global disruptions?

All pharma companies must consider the risks of single sourcing for raw materials, including excipients. To overcome this, supply opportunities should be provided, either by using a company that manufactures the same product at multiple global sites or by maintaining safety stock in different parts of the world to compensate for potential supply chain challenges.

Regarding sustainability, I see an opportunity for DMSO (gChem’s product) as a much safer, greener solvent alternative because it is a Class 3 solvent. This contrasts with Class 2 solvents currently used in drug synthesis and formulation, such as dimethylformamide (for API and peptide synthesis) and N-methylpyrrolidone. In terms of sustainability, there is no reason for formulators to choose more toxic Class 2 solvents in the future when a safe alternative exists, meaning sustainability can be rectified by alternative, safer materials that lead to much better patient outcomes.

Where are the best opportunities to integrate sustainable practices into excipient production without compromising performance?

Many companies are already working on sound ways of assessing their carbon footprint of manufacturing. Sustainability on raw materials sourcing is critical. This requires having excellent communication with suppliers and knowing where the material is being made and where the raw materials used by the excipient company are sourced.

The move toward safer solvents is also a key area of sustainability. In my opinion, the safer approach avoids Class 2 solvents, as they potentially face future bans. For instance, dichloromethane (a Class 2 solvent) has been banned, and dimethylformamide is now being banned in certain industries and by REACH in Europe. If there is a threat that these solvents will not be available in the future, they are not sustainable, especially given that many drug products are formulated with them.

How can novel excipients be introduced quickly while still ensuring safety and efficacy?

Novel excipients are needed now more than ever, but there are still very big barriers to their implementation. Without excipient innovation, the industry faces a major problem because drug modalities are increasingly complex, yet companies often rely on excipients that are 60, 70, or 80 years old. It is difficult to mold these older excipients into the desired functionality when what is truly needed is something designed fit for purpose.

Excipient innovation has not kept pace with drug innovation. There was hope three years ago when the Prime process was introduced by the FDA, as it offered a potential mechanism for new chemical entities used as novel excipients to be reviewed independently from the drug product submission. However, IPEC has not received confirmation from the FDA on whether this pilot program will proceed, and it is suspected to be either on hold or stopped.

To foster innovation, pharma companies need to have a much closer collaborative relationship with excipient suppliers. Suppliers should not be viewed merely as a vendor or a source of excipients. Collaboration requires early communication regarding formulation challenges and the specific need for an excipient to meet that challenge.

Academia has an important role in developing new chemical entities for future drug formulation, but the translation and commercialization of these ideas remain difficult. New materials must undergo the tox cycle and safety aspects before being adopted by a pharma company. Currently, pharma companies tend to be "first to be second," waiting for another company to successfully take a novel excipient through to completion (Phase III and drug approval). This is because a novel excipient is perceived as a significant risk; if a company already has a new API, adding a novel excipient is almost a "double risk". Much closer collaboration between the two parties is the way to combat this perceived risk.

Articles

Nigel Langley, Global Technical Director at gChem, highlights key challenges in excipient availability, including regulatory scrutiny and supply chain vulnerabilities. He emphasizes the importance of scientific evidence in addressing regulatory shifts, as seen in the titanium dioxide debate. Langley advocates for sustainable practices, such as using safer solvents like DMSO, and stresses the need for innovation in excipient development to keep pace with complex drug modalities. He calls for closer collaboration between pharma companies and excipient suppliers to overcome barriers to novel excipient implementation and ensure safety and efficacy.

Excipient Innovation and the Regulatory Tug-of-War: Navigating Threats to Availability and Safety

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In an interview regarding his presentation “Harnessing AI in Clinical Practice to Drug Development and R&D. From the Bedside to Benches” at 

, held Oct 28-30 in Frankfurt, Germany, Gustavo Ferrer, MD, CEO of Dr. Ferrer BioPharma and Moxie Health Group, discusses how artificial intelligence (AI) is rapidly reshaping how drug development and pharmaceutical manufacturing operate, compressing timelines, reducing costs, and enabling unprecedented real-world data integration. He explores how platforms like Moxie-Link—an AI-powered, Fast Healthcare Interoperability Resources (FHIR)-compliant clinical and research platform—are turning every clinical encounter into actionable intelligence, all while navigating the remaining barriers to wider AI adoption and maximizing innovation through cross-disciplinary collaboration.

PharmTech: How is AI supercharging drug development, discovery, and manufacturing?

Dr. Ferrer: AI is compressing timelines and reducing costs across the drug development lifecycle:

Target discovery and molecule screening: Predictive modeling identifies promising candidates thousands of times faster than traditional methods.

Protocol optimization: AI learns from studies to shape inclusion/exclusion criteria that improve enrollment speed and site performance.

Decentralized clinical trials: Remote patient monitoring, telemedicine, ePRO/eTRO, and automated documentation increase retention and diversity.

Manufacturing efficiency: AI-based anomaly detection, yield prediction, and supply chain forecasting reduce batch failures and downtime.

Device performance, usability studies, and human factors: This includes identifying patients, optimizing the protocol, and tracking patients carefully.

Platforms like Moxie-Link take this further by integrating the care environment directly into research operations, producing real-world datasets that inform formulation, device design, and post-market surveillance. This turns every clinical encounter into a regulated, research-grade signal, without burdening clinicians.

How can AI translate real-world patient data into actionable intelligence for drug development and R&D?

AI’s true power lies in pattern recognition across messy, real-world data:

Natural language processing extracts phenotypes, comorbidities, and device usability feedback from unstructured clinical notes.

Predictive analytics detect adverse trends earlier than traditional reporting.

Human-factors telemetry quantifies how patients actually interact with medical devices in the wild.

Dynamic eligibility algorithms identify trial candidates at the point of care.

In Moxie-Link, these insights automatically feed dashboards that can refine protocols, identify responder subpopulations, optimize endpoints, and flag new safety signals. This shortens the feedback loop between bedside observations and R&D decisions.

How do you measure the real-world impact of AI platforms compared with traditional R&D methods?

Enrollment velocity: AI-assisted eligibility and digital recruitment accelerate accrual in decentralized or hybrid designs.

Data completeness & fidelity: Continuous remote monitoring outperforms episodic site visits.

Diversity & representation: Digital recruitment expands access to historically underserved populations.

Operational efficiency: Integrated documentation, billing, telemedicine, and monitoring reduce administrative burden and protocol deviations.

In deployments across 55+ facilities, we’ve seen marked improvements in screening-to-randomization conversion, documentation accuracy, protocol compliance, and retention, all of which translate into accelerated therapeutic timelines.

How should cross-disciplinary collaboration be structured to maximize innovation while ensuring accountability and security?

Success requires distributed expertise with centralized governance. Clinical leadership ensures AI outputs align with safety, clinical nuance, and regulatory requirements. Data science teams validate models, monitor drift, and maintain explainability. Human-factors engineers ensure usability for medical device submissions. Security & compliance officers oversee data governance, consent, and audit trails. Regulatory affairs map evidence requirements across FDA, EMA, and MDR pathways.

What unresolved barriers slow wider AI adoption in pharmaceutical manufacturing?

Regulatory ambiguity: Evolving expectations for AI/ML lifecycle management and change control for software-as-a-medical-device.
Solution: Continuous dialogue with regulators and adoption of transparency frameworks.

Data fragmentation: Heterogeneous electronic medical records, siloed registries, incomplete device telemetry.
Solution: FHIR-compliant architectures, bidirectional integration, and standardized data models.

Trust & explainability: Operators must understand why a model recommends an action. This is why we use human intervention as quality assurance to double-check the performance of AI.
Solution: Human-in-the-loop design and bias-detection dashboards.

Workforce readiness: Manufacturing teams need digital fluency. At Moxie, we created an academy with interactive courses to train staff and improve the customer experience.
Solution: Embedded training and intuitive user experience designed 

AI is revolutionizing drug development and pharmaceutical manufacturing by compressing timelines, reducing costs, and integrating real-world data. Platforms like Moxie-Link transform clinical encounters into actionable intelligence, enhancing protocol optimization, decentralized trials, and manufacturing efficiency. AI excels in pattern recognition, extracting insights from unstructured data to refine protocols and identify trial candidates. Real-world impact is measured through enrollment velocity, data fidelity, diversity, and operational efficiency. Cross-disciplinary collaboration with centralized governance is crucial for innovation. Barriers to AI adoption include regulatory ambiguity, data fragmentation, trust, and workforce readiness.

Gustavo Ferrer, MD, explains how AI accelerates drug development and manufacturing, streamlining clinical data into actionable pharma insights.

Turning Real-World Data into Actionable Drug Development Insights With AI

AI’s Impact on the Pharma Workforce and Hiring: From Obsolescence to Opportunity

During the panel discussion " AI’s Impact on the Pharma Workforce and Hiring: From Obsolescence to Opportunity" at 

, Andrew Mears, CEO of Lead Candidate; Manuela Herrlein, managing partner at Alpine One GmbH; and Neil Kelly, CEO of Vector Talent dissected the profound impact of artificial intelligence (AI) on pharmaceutical talent, skills development, and organizational structure. The discussion was moderated by Vivian Xie, pharma editor at Informa Markets. The conversation spanned critical themes, such as the urgent requirement for upskilling, the challenges of attracting technology specialists against competition from “big tech,” managing organizational resistance to change, and mitigating biases introduced by automated AI processes in recruitment.

How are skills and organizational agility evolving?

The pharmaceutical sector, especially in manufacturing, is rapidly moving away from purely mechanical operations toward data-intensive, technologically advanced processes, according to the discussion. The panelists note that while the industry’s maturity is "increasing fast," the skills needed to support this evolution are lagging. Historically, manufacturing relied on "people on the lines trying to get the status and how things went wrong." Now, the integration of AI, robotics, and supercomputing is expected to "amplify how your workforce can manage."

In response to this accelerated change, organizations must fundamentally rethink their approach to talent management. Instead of relying on rigid "succession bonds," companies need to be highly agile, the speakers state. One key challenge is defining future needs by asking: "‘What kind of problems do I need to solve for the future? And then I need to define what is the most important skill, and then slice and dice it." This agile approach also necessitates resource fluidity, asking if a needed skill exists in another business unit and if you "can.. just borrow it."

This need for adaptation impacts the traditional concept of skill refresh cycles, which have drastically shortened, according to the panel. In the past, refreshing skills took three to five years, or even ten years; now, it is needed "every six to 12 months." This constant need for development is challenged by employee mindset, as upskilling is difficult because "people don't know what's in it for [them, asking] ‘Why should I upskill [my employees]?’" However, companies are beginning to reward and recognize proficiency through programs that offer awards or badges for certain skills.

How can pharma compete with tech talent and overcome regulatory inertia?

As the panel discussed, a major hurdle for the biopharmaceutical industry is attracting high-tech talent, who now have more choices than ever due to advancements in technology. The industry has "lost talent to other industries," and the traditional path of "you go to university, you do chemistry, you move up… doesn't exist anymore." It is difficult to attract these professionals when "everybody wants to work at Microsoft.” The concept of "taking talent from technology in particular is really difficult."

To manage technological gaps, smaller players in particular should strategically leverage partnerships, continued the discussion. Instead of trying to "be more things than what we wanted to be doing," companies should focus on what they are good at and utilize partners for "the technology pieces," the speakers agree.

Beyond talent competition, the industry faces inertia rooted in its operating environment, the panelists stated. When asked why the industry is often "too traditional," the most common response to is the regulatory framework within which the industry operates, they said. This framework is often cited despite highly regulated industries like automotive already operating plants 20 years ago that "were more advanced than plants in the pharma industry that are being built today." Achieving change requires momentum "at a regulatory framework level," alongside building a convincing "business case of why should we be doing something?"

What is AI’s role in recruitment and bias mitigation?

The discussion turned to vital role of the human element despite AI’s increasing integration into recruitment. Recruiting leaders use AI to reduce administrative tasks, thereby giving them free time to speak with the candidates. AI should be seen as an opportunity to amplify human capabilities, the panelists note.

However, the rapid adoption of AI introduces significant bias risks, with the key to mitigation lying in AI design. It was stated that if the AI is designed around a company’s "most common unconscious bias that people do as they recruit in their own design," the resulting tool will perpetuate that bias. To counteract algorithmic biases and "Chat GPT bias" from candidates who optimize their applications, companies are increasingly utilizing "assessment centers where you basically invite the candidates... and then you let them interact, give them case studies, so that you really can see, without using a tool, how good they are."

How is IP protected while preparing the next generation?

The speakers note that a central challenge for “big pharma” in leveraging collective data is the protection of intellectual property (IP), adding that companies are "very conscious about opening up," maintaining a "very close environment" around their organization. The solution to balancing technological advantage with IP protection relies on partnerships, they say. This approach allows companies to feed AI infrastructure within their established economic framework, offering a level of comfort that avoids exposing internal IP to a broader, global tool. Furthermore, new regulatory frameworks are needed to protect IP while allowing "the advantage of the technology and its capabilities."

New entrants graduating into this environment must recognize that the future career path is not vertical but horizontal, embracing "gig work," according to the panelists’ discussion. While academics often struggle with the concept of non-linear careers, the greatest predictor of success for young adults is "the adaptability to learn." Networking is essential, and graduates "need to have a very, very good network."

Looking ahead, the speakers stated that while generative AI is now "at a plateau," having yielded significant productivity gains, the "next development space" is "agentic AI." This next phase will lead to scenarios in which employees have a machine, avatar, or agent as a "colleague." Despite these accelerating changes, efforts toward standardizing the collective use of AI technology across the sector are not currently visible, meaning "everybody here is slightly different."



AI is reshaping the pharmaceutical workforce, necessitating rapid upskilling and organizational agility. The industry faces challenges in attracting tech talent due to competition from big tech companies and regulatory inertia. AI's role in recruitment is growing, but it introduces bias risks that need careful management. Protecting intellectual property while leveraging AI requires strategic partnerships. The future workforce must adapt to non-linear career paths and embrace networking. As generative AI reaches a plateau, the next phase, agentic AI, will further transform the industry, though standardization remains elusive.

A panel at CPHI Frankfurt discussed how AI is transforming pharma talent, necessitating rapid upskilling, bias mitigation, and regulatory changes.

The Bio/pharma Industry Navigates AI’s Impact on Workforce and Hiring

Nigel Langley of gChem stresses excipient innovation, safer solvents, and supply chain resilience for pharma’s future in part 2 of a 2-part interview related to his presentation at CPHI Europe 2025.

Excipient Innovation, Safer Solvents, and Supply Chain Resilience

*Access a full transcript of the video interview below.

In this part 2 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, highlights several pressing challenges and opportunities shaping the future of pharmaceutical formulation, excipient innovation, and supply chain sustainability.

Langley stresses the risks of single sourcing in the pharmaceutical supply chain, particularly for excipients and raw materials. To mitigate these vulnerabilities, he recommends strategies like multi-site global manufacturing or maintaining safety stock in strategic regions. He emphasizes that sustainability in sourcing and manufacturing must now be central to pharma operations. As an example, he points to dimethyl sulfoxide (DMSO), a Class 3 solvent, as a safer and greener alternative to the widely used but more toxic Class 2 solvents like dimethylformamide and N-methylpyrrolidone. With growing regulatory restrictions and environmental pressures, Langley argues that transitioning to safer solvents is both a sustainability imperative and a patient-safety priority.

Langley turned to excipient innovation, a space he describes as lagging behind the increasing complexity of drug modalities. Despite growing need, regulatory barriers remain a significant hurdle. FDA’s “PRIME” process, designed to review novel excipients independently of drug submissions, raised industry hopes but has stalled without clarity. Langley warns that relying on excipients that are decades old limits the ability to optimize new formulations. He advocates for stronger collaboration between pharmaceutical companies and excipient suppliers to foster innovation, moving beyond transactional relationships toward true partnerships where challenges are communicated early in development.

Langley also notes academia’s potential to drive excipient innovation by developing novel chemical entities, though he acknowledges the gap between university discoveries and commercialization. Adoption hesitancy among pharma companies—driven by perceived risk of pairing novel APIs with novel excipients—further slows progress.

Finally, he circles back to sustainability, underscoring the importance of carbon footprint assessments, responsible sourcing of raw materials, and preparation for a future in which Class 2 solvents may be banned. For Langley, proactive change, collaboration, and innovation will be essential to ensuring the resilience and advancement of pharmaceutical development in the coming decades.

Single sourcing is something that needs to be considered by all pharma companies if they're single source on it raw materials and obviously including excipients. One way to overcome single sourcing is to provide the opportunity of supply, either for a company that manufactures in more than one manufacturing site globally the same product, or if they don't, then to have safety stock in different parts of the world, so that it compensates for any challenges they may have in the supply chain. The sustainability of supply chain as well, especially as it reflects to gChem’s business with DMSO, I think I see an opportunity for DMSO as a much safer, greener solvent alternative, because it's a Class 3 solvent, compared to Class 2 solvents, which are currently used in drug formulations and drug synthesis, in particular dimethylformamide for peptide synthesis, as well as API synthesis and also N-methylpyrrolidone. These are class 2 solvents.

So for sustainability, to be honest, I see no reason why, when there's a very safe alternative solvent, why formulators in pharma companies would choose to use in the future Class 2, more toxic solvents when they don't need to So sustainability in that case, can be rectified by alternative, safer materials, which is much, much healthier and much better for the patient outcome.

Novel exceptions are really needed, in my opinion, they're needed more now than ever, but there's still some very big barriers to their implementation. We were very hopeful when the Prime process was actually introduced by the FDA, maybe three years ago now that there might be a mechanism where novel excipients, or new chemical entities as novel excipients, could be reviewed independently from the drug product submission, and four candidate products were actually started in that program. We have not heard back. IPEC has asked this question many times to the FDA. We haven't heard back from the FDA, where that prime process is actually going to be a full program pilot. We suspect that it's been either on hold or stopped, but we haven't had that confirmed. That was a good opportunity for novel excipients to have a little bit more accessibility, compared or in relation to existing excipients to be used in drug formulations from an early stage.

But it's not so easy. So, at one point you need innovation, and I personally believe that without excipient innovation, we're going to have a major problem in the future. Why? Because the drug modality is increasingly becoming more complex, and companies are using excipients, some of which are 60, 70, 80 years old. And there's a challenge to actually mold those into the desired functionality and properties without actually designing something that was fit for purpose. So, excipient innovation has not kept pace with drug innovation and how that might be possible, again, my opinion is that pharma companies need to have a much closer collaborative relationship with excipient suppliers, rather than just seeing them as a source of excipients and a vendor. They need to work very closely and really communicate early on what their challenges are in a particular formulation and what the need is for an excipient to meet that challenge. Without that, you're not going to get the innovation that we need to have.

I think for sure academia has a very important role in what's going to happen in maybe 10-20 years’ time in drug formulation. Yeah, they have the scope to actually develop new chemical entities or novel excipients. That's fine, but it's the translation of those into a drug product and commercialization that still remains. So. although there may be more candidates that potentially come out from university, there's still the tox cycle, the safety aspect of these new materials, that need to be proven. And they need to be then adopted by a pharma company. We have a saying in the industry that pharma companies like to be first to be second. They're waiting for somebody else to take a novel excipient through, in many cases, to completion, so phase III and an approval of the drug, and then the novel excipient is a little bit more, well, safer, or it's less risk. It's a big risk to take, a perceived risk for a pharma company. They only, they have their API if they have to have a novel excipient as well, that's almost a double risk that they're taking. And the way to combat that, again, I believe, is much closer collaboration between the two parties, and then it will work.

I think the first thing, and I think this is already ongoing, many companies now have a very sound way of assessing their carbon footprint of how they manufacture, as well as the pharma companies, there's obviously that sort of sense and directive, and it's part of being in the business to provide that type of information. I think sustainability on raw materials sourcing is critical. Again, that goes back to knowing your supplier very well and where the material is being made, and whether those raw materials are being sourced by the excipient company to make their excipients as well. So, it's all about communication to make it sustainable. I touched on earlier about the solvent side, or the excipient side, with respect to safety, sustainability, with respect to the DMSO, really should be continued into the safer approach, which is not the Class 2 solvents, in my opinion, in the future, as they potentially may get banned. You know, we've just had dichloromethane banned, which is a class 2 solvent. Dimethylformamide, in certain industries and REACH in Europe, is now being banned. So, it's probably a matter of time that that type of solvent will be removed. It takes time, but there's a reason to actually make those changes. Are those solvents really sustainable for the next few years if there's a threat that they will not be available in the future, and a lot of drug products are formulated with them, as I mentioned.

Eva-Maria Hempe, NVIDIA, says AI collaboration optimizes life science research via routine automation, freeing human experts for complex strategy, ethics, and exploring scarce data.

AI and Human Collaboration Essential for Bio/pharma Innovation

In this part 3 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, highlights the strategic necessity of integrating artificial intelligence (AI) while maintaining critical human oversight and addressing geopolitical concerns surrounding data sovereignty. She suggests that the future involves a truly collaborative framework in which technology optimizes routine tasks, freeing human researchers for complex decision making. Hempe specifically emphasizes Europe's burgeoning role in developing trusted, ethical, and sovereign AI tailored for the healthcare and life sciences sectors.

In this collaborative model, AI is positioned to manage high-throughput activities, such as data mining, crawling through vast datasets, early candidate selection, and performing extensive literature reviews that exceed human capability, according to Hempe. Conversely, she underscores the necessity of human expertise, particularly for interpreting complex results, devising experimental strategies, and guaranteeing ethical standards. Hempe adds that a core limitation of AI arises in exploring new areas where data is scarce; since AI relies fundamentally on data, human experts must intervene where knowledge gaps exist. In summing up this essential partnership, she stresses, "Human expertise remains really crucial for interpreting those complex results and coming up with experimental strategies, and especially also for ensuring ethical standards."

Further exploring advanced applications, Hempe notes the potential of AI agents as reasoning models equipped with memory and tools designed for multi-step tasks. She explains that an AI agent can transform a research question into a literature search, analyze findings, generate a hypothesis, and potentially initiate a virtual screening workflow to create molecule variants, ultimately summing the effort into a comprehensive report for the researcher. Geopolitically, the increasing focus on interdependency and sovereignty means helping European biopharma secure sensitive data by building sovereign AI models, according to Hempe. Although the current share of AI compute in Europe is around 4%, initiatives are underway—including public-private partnerships—to develop AI infrastructure and “AI data factories” to turn unstructured information into insights at scale, driving ethical and sovereign AI leadership

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There's always going to be a human or the experiment in the loop. In a way, that's what I really like about AI in this field. You always have still the experiment, which gives you an answer like are you going down the right track or not. So, what I see in the future is a really collaborative future. There's going to be some AI-driven automation for the more routine parts for the data mining, crawling through large amounts of data for early candidate selection, doing some of the literature review. Finding the signals across more papers than a human could do but then the human expertise remains really crucial for interpreting those complex results and coming up with experimental strategies, and especially also for ensuring ethical standards. In line with what I said before, you have to be aware of the limitations of AI and particularly in scarce data areas, AI is limited. AI is based on data, and we want to explore new things, so by definition, these are going to be areas where we're going to have less data. There you cannot rely on AI in the same way as you can in very well researched areas. So, that's then where the human expert comes in and so I see a lot of more routine steps to be automated but then for the really nuances for some of the creativity, maybe also for making a bold bet, that's where a human in the loop oversight will really be essential.

I think there is an element about sovereignty as well, and we're seeing some of that with some of the large geopolitical developments that there is increasingly a question of interdependency and sovereignty. What we do in Europe, we're really helping European pharmas to build sovereign AI models so that they can secure their sensitive data but at the same time don't have to compromise on being able to do large-scale biological modeling. and that also extends to AI infrastructure and we believe that Europe has the opportunity to really lead in trusted, ethical, and sovereign AI for healthcare and life sciences and it's going to be built on strong regulatory frameworks and also growing AI infrastructure. Right now, the share of AI compute in Europe is somewhere around 4%, but this is changing and we're seeing a lot of that. We're seeing a lot of initiatives coming in both as by driven by the public sector as well as public-private partnerships to really address this and to have AI factories, AI data factories that are turning those unstructured information into insights at scale in Europe.

One other area we are really interested in are AI agents. It's a bit of a buzzword recently, but what AI agents are is a reasoning model with some memory and some tools and you can really use this to give multi-step tasks to an AI. One of the examples is what we talked about before, literature review. What an AI agent can do if you give it the right access to PubMed or some other literature database, you can give it a question, it turns that question to literature search, it then analyzes the literature search, turns it into hypothesis, and then you could even go further and add something like a virtual screening workflow to it so once the AI has reasoned that your target protein is this and this and the standard of care is a particular molecule you can start creating variants of that molecule through a virtual screening workflow and really explore in that space further, and then the last step is really summing it all up into a report. And then this is something the researcher can go and continue working with, or with what we saw or with the lab in the loop, you could even think about it for the second step—we're not quite there yet ,but we're getting there—can you link that to an automated lab which then is running the experiments already by itself, maybe with a human in the loop who does a last oversight and kind of avoids the lab to order really expensive reagents or really expensive compounds? But that's one of the ways we see the future unfolding.