Christopher Cole

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.

*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.

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.

Videos

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

 of this interview and access all our CPHI Europe coverage!



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.

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 

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.

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.