Susan Haigney

AAPS PharmSci 360: Trends in Bioanalysis

® spoke with Long Yuan, PhD, director, Department of Drug Metabolism and Pharmacokinetics, 

, to find out how new types of instrumentation are impacting 

 and what changing regulations may be helping or hurting innovations. Liquid chromatography—mass spectrometry (LC–MS)-based assays and high-resolution mass spectrometry (HRMS) provide exceptional specificity, according to Dr. Yuan.

“Specificity, for example, for oligonucleotides, sometimes the commonly used portable mass spectrometer may not be able to differentiate the parent and metabolites,” Dr. Yuan explains. “HRMS has a unique advantage to provide additional specificity for these type of applications. Another advantage is it has the ability to obtain quantitative and qualitative information in the same run, so later, if needed, people can do data mining to dig out the needed information and avoid reassessing the samples. For chromatography technology, like microflow LC, nanoflow LC, these are very helpful tools to achieve superior sensitivity for the assay.”

Supercritical fluid chromatography (SFC), which is an instrumentation used to separate chiral compounds, is starting to be utilized for quantitative bioanalysis of chiral compounds for sample separation, according to Dr. Yuan. “There are also novel techniques, like hybridization extraction, for bioanalysis of oligonucleotides,” he continues. “This technique can achieve very clean sample extraction with high efficiency. Therefore, it can achieve highly improved sensitivity compared to the traditional sample preparation methods. It can even achieve [it] for LC–MS-based assays. It can achieve similar sensitivity as [an] enzyme-linked immunosorbent assay [ELISA] oligo binding assay. Some other techniques people are also looking into [include] microsampling, or patient-centric sampling. So, this will be very helpful to reduce the sample volumes and improve the patient convenience, and also compliance…and enable the remote home sampling for clinical studies.”

Dr. Yuan will be moderating the symposium, “Oligo Bioanalysis, Platforms and Methods” on Wednesday, Nov. 12, 2025 at 9AM in 221 AB at 

, which is being held from November 10–12 in San Antonio.

Long Yuan, PhD, Director and Head of the Bioanalytical group in the Department of Drug Metabolism & Pharmacokinetics at Biogen

Long Yuan, PhD, is currently the Director and Head of the Bioanalytical group in the Department of Drug Metabolism & Pharmacokinetics at Biogen. He leads a group of scientists to provide bioanalytical support for diverse modalities including small molecule drugs, oligonucleotides, biologics and AAV gene therapies, in drug discovery and development. Long received his B.S. in Medicinal Chemistry from Fudan University (Shanghai, China), and his Ph.D. in Medicinal Chemistry from the College of Pharmacy, University of Illinois Chicago (IL, USA). Before joining Biogen, he worked in the Department of Bioanalytical Sciences at Bristol Myers Squibb (NJ, USA). His research focuses on developing bioanalytical methodologies for various molecules, with recent emphasis on oligonucleotides, including ASO, siRNA and antibody-oligonucleotide conjugates. He has published over 50 peer-reviewed papers, including 7 book chapters.

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.

My name is Long Yuan. I'm currently the Director and Head of biomedical group in Biogen DNPK, so I'm needing a group of scientists to provide bionic support for different mortalities, including small molecule drugs, oligonucleotides, biologics and AV gene therapies.

PharmTech: What are some of the new instrumentations that are being used in bioanalysis? What makes these unique?

 There have been significant advances in new instrumentations and technologies applied in Bioanalysis, for example, for LC MS based assays, high resolution mass spectrometry, HRMS has been playing more and more important role because it has the unique advantage to provide exceptional specificity. Specificity, for example, for oligonucleotides, sometimes the commonly used portable mass spectrometer may not be able to differentiate the parent and metabolites. HRMS has a unique advantage to provide additional specificity for these type of applications. Another advantage is it has the ability to obtain quantitative, quantitative and qualitative information in the same run so later, if needed, people can do data mining to dig out the needed information and avoid reassess of the samples for chromatography technology like microflow LC, nanoflow LC, these are very helpful tools to achieve superior sensitivity for the assay. For example, for some biomarker assays, it may require ultra sensitive assay. So in this case, this type of nanoflow or microflow LC will be very helpful. Another new instrumentation for LC system is super critical fluid chromatography. SFC. Traditionally, this technique is more used for the separation of chiral compounds, but nowadays people also starting to use this SFC for the quantitative bio analysis of chiral compounds, for sample preparation. There are also novel techniques like hybridization extraction, for Bioanalysis of oligonucleotides. This technique combines can achieve very clean sample extraction with high efficiency. Therefore, it can achieve highly improved sensitivity compared to the traditional sample preparation methods. It can even achieve for LC MS based assays, it can achieve similar sensitivity as ELISA assay oligon binding assay. Some other techniques people also looking into is the micro sampling, or patient centric sampling. So this will be helpful, very helpful, to reduce the sample volumes and improve the patient convenience and also compliance. This will help to enable the remote home sampling for clinical studies.

How do changing regulations impact advancements in bioanalysis?

Changing regulations in the past few years has had a profound impact on advancement of Bioanalysis, especially for biomarker assays and the bioanalysis of new modalities like gene and cell therapies, for example, for biomarker bioanalysis. In January this year, FDA published a guidance on bioanalysis method validation for biomarkers. So in general, the standards are getting stricter for biomarker assays and the bioanalytic method validation for biomarkers, it should be addressed consistent with the principles of ich MJH guidance. But still, there's some kind of lack of clarity around the context of youth one and how we can apply the ICH guidance or biomarker essays. Another area is new modalities or new bio analytical techniques like qPCR based bilingual essays. There's still lack of clear guidance on those type of new essays or new techniques, regulatory agencies like FDA, EMA, Health Canada, those agencies are working towards harmonization of regulatory guidances. It has been a tremendous achievement with the. The publication of the ICH guidance, but still there have some regional differences, and I think still all the scientists and regulators around the world need to work more towards the harmonization of the guidance in this year's AAPS, I will moderate a session focused on bioanalysis of oligonucleotides. We will have four very interesting talks focused on different techniques has been applied for oligonucleotide Bioanalysis for both the PK assays and also the immunogenicity assay. The first three talks will be focused on the PK assays, three different speakers, they have put together a very interesting comparative study compared different platforms for bioanalysis of oligonucleotides, including LCMS based assay, ligand binding assay, and also qPCR based assays. It will be very interesting to see how these different types of binding platform, when people apply to the same oligo nucleotides, how the results will come out, how is, what's the pros and cons for these different types of methods? And the last presentation will be a speaker talk about the immunogenicity, as seen for only oligonucleotides. It will be also very interesting talk. Welcome to my session. I look forward to seeing you there. Thank you. Bye.

Videos

Long Yuan, PhD, director of the Department of Drug Metabolism and Pharmacokinetics at Biogen, provides a look into some of the trends impacting bioanalysis in biopharmaceutical development and manufacturing.

AAPS PharmSci 360 2025: Trends in Bioanalysis

Scene from the laboratory with pipette, test tube, microscope, DNA and formulas with copy space | Image Source: ©gopixa -stock.adobe.com

Bioanalysis, which is utilized in drug development to determine pharmacology, bioavailability, and bioequivalence, includes pharmacokinetic, toxicokinetic, or biomarker concentration measurements (1) in animal safety and clinical studies. Bioanalytical methods are required by regulators to be validated to ensure their data’s reliability (1). Artificial intelligence (AI), machine learning (ML), and other complex tools are helping drug developers evaluate the complex data involved in these types of studies.

® spoke with Dr. Mark Arnold, owner and principal, Bioanalytical Solution Integration to find out how bioanalysis enhances bio/pharmaceutical drug development. Arnold will be presenting the keynote session, “Reflections on Transformations in Drug Development, Bioanalysis, and Regulations”, at AAPS PharmSci 360 on Monday, Nov. 10, 2025 at 1:30 PM.

PharmTech: How can AI and ML be used to enhance bioanalysis in bio/pharmaceutical development?

Arnold (Bioanalytical Solution Integration):

 AI, large language models [LLMs], natural language processing, and ML have been discussed for a few years in bioanalysis but are now making actual inroads with deployed validated applications and others in development.

The earliest implementations are for reports and quality control (QC) of assay validations and study sample analysis. As the capabilities of the AIs have advanced, so have the applications in bioanalysis. New applications are looking at taking GLP [good laboratory practice] or GCP [good clinical practice] protocols and bioanalytical contracts to write bioanalytical plans. Other applications perform quality control checks that integrate ELN [electronic lab notebook], instrument, and LIMS [laboratory information management system] data on a daily basis to catch errors early and identify trends that lead to failure before they happen, and data analysis as part of investigations into assay failures.

Building on design of experiment concepts, AI will utilize databases of knowledge to improve method development for immunoassays and LC–MS [liquid chromatography—mass spectrometry] methods. I’ve heard that a mass spectrometer manufacturer is looking to apply AI to chromatogram integration to go past algorithm rules to more intelligent peak detection.Ultimately, biotech and pharma companies will be using AI to write the bioanalytical sections of filings. Health authorities are also looking at AI as a tool to analyze bioanalytical data in submissions and during on-site or remote inspections of bioanalytical labs. The resulting analyses will highlight both compliance with regulations and detect problems and errors that need further review.

PharmTech: How can large language models be applied to bioanalytical workflows?

 As noted above, AI, natural language processing, and ML are being implemented in bioanalysis for report writing and bioanalytical plans. Many LLM AIs are designed as predictive tools and operate as black boxes where it cannot be determined how they do what they do. A challenge in regulated bioanalysis in the use of LLM AIs is in their training and predictive nature; they need to find data (content) in different sources and formats and place it appropriately in the report.

For regulated bioanalysis, controls must be in place for the AI or the risk of hallucination (creation of data or content not provided) can occur as has been seen in some AIs. Not knowing if the report content is correct is not something bioanalytical labs can risk and would result in utilizing the same or greater QC and QA [quality assurance] resources to make sure of the report’s accuracy. Which is contrary to what AI is supposed to do: reduce human involvement and improve accuracy.

To address this, at least one AI-based vendor for bioanalytical labs has constraints in place to prevent hallucinated data creation and is including an audit trail in its report generation steps to demonstrate the data reached the right location. Pharma companies have started using LLMs for writing some portions of eCTDs [electronic common technical documents] and the bioanalytical sections are well-structured, making them good candidates for early implementation of AI writing.

Arnold (Bioanalytical Solution Integration): 

Most of the technologies in use for pharmacokinetic, immunogenicity, and pharmacodynamic measurements have been around but are being refined for improved sensitivity, accuracy, and precision. These include the various forms of immunoassays, hyphenated chromatographic assays (e.g., LC–MS/MS [liquid chromatography—tandem mass spectrometry], LC–HRMS [liquid chromatography—high-resolution mass spectrometry]), PCR [polymerase chain reaction], flow cytometry, and cell-based technologies. The improved flow cytometry instruments, with some instruments having 30 and 40 color detection ability, are being used not only for cellular characterization, but the improved sensitivities allow them to be applied to circulating exosome biomarkers that can detect and characterize many diseases. Mass cytometry is being used in research, and once suitable applications are identified, likely in measuring biomarkers, it will move into regulated bioanalysis. 

PharmTech: How do changing regulations impact advancements in bioanalysis?

ICH [International Council for Harmonisation] M10 standardized regulations for pharmacokinetic bioanalysis using immuno- and chromatographic assays. Having a standard accepted in many major markets eliminated the need for bioanalysts to have to keep up with changes in each of the individual countries and adjust their practices, especially when there were conflicting expectations. WHO [World Health Organization] recently published M10 which may expand its acceptance beyond the ICH-adherent countries. The expansion of gene and cell therapies require technologies (e.g., PCR, FLOW cytometry) not covered in ICH M10, and so far, the scientific community has been promoting science-based approaches to these technologies. These technologies have been used in clinical laboratories and adapted for use in pharmacokinetic assays.

For immunogenicity, a few countries have developed regulations that are reasonably similar so that achieving a consistent approach is not difficult. However, with the extensive experience for monoclonal antibody drugs, bioanalysts are proposing alternative approaches to simplify the traditional tiered-immunogenicity assay designs that generate as informative data with less effort and cost. Regulators have not formally responded to these proposals.

Another area without a global regulatory perspective is the analysis of biomarkers. In the United States, measuring biomarkers for a medical decision (i.e., those used by doctors and in hospitals) must follow the Clinical Laboratory Improvement Amendments, while those used to support the safety and efficacy of a new drug are to follow a 2025 FDA guidance which is focused on immuno- and chromatographic (e.g., LC–MS/MS) assays.The European Union and Japan have totally separate and different sets of regulations. These differences create not only challenges for the bioanalyst but also add complexities when trying to run clinical trials in multiple global regions.

To be successful in delivering data accepted globally, bioanalysts must be aware of a variety of regulations and how they apply to the purpose of the analysis, type of samples they are analyzing, and the technologies. When no regulations exist, scientists must continue to collaborate on science-based best practices. Since the science is always advancing faster than the regulations, regulators must learn about the science of the technologies and evaluate practices that generate quality data, and when they have enough understanding of the science generate regulations; hopefully, based on or in collaboration with the recommendations of the scientific community.

Bioanalytical Method Validation, Guidance for Industry

Mark E. Arnold, PhD, Owner and Principal of Bioanalytical Solution Integration

Mark E. Arnold, PhD is Owner and Principal of Bioanalytical Solution Integration; consulting with biotech and pharma companies to advance their portfolios to bring new therapies to patients in need. In that role, he collaboratively develops the bioanalytical scientific and regulatory strategy to meet current and future client needs. Mark was previously Executive Director of the Bioanalytical Sciences Department at Bristol-Myers Squibb Co. and Director of Science for the Scientific Affairs Department at Labcorp supporting bioanalytical and clinical laboratories. He received a B.S. in biology from Indiana University of Pennsylvania and a PhD in pharmacology from the University of Pittsburgh. For 40 years, Dr. Arnold has been involved in the application of bioanalysis to developing new therapies, including the review and interpretation of regulations and guidance as they apply to the evolving science in bioanalytical and clinical laboratories. He was co-chair of both the AAPS-FDA Crystal City V and VI Workshops and has been a member of AAPS since 1988 holding a variety of committee positions including 2023-2024 Scientific Advisory Chair, 2022 PharmSci360 Scientific Program Committee Chair, 2018 PharmSci360 Bioanalytical Track Chair and is an AAPS Fellow (2014). Dr. Arnold has over 115 peer-reviewed publications and numerous invited podium presentations.

Articles

Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into bioanalysis, enhancing drug development by improving data evaluation and method development. AI applications in bioanalysis include report writing, quality control, and bioanalytical plan creation, with constraints to prevent data inaccuracies. New instrumentation, such as advanced flow cytometry and mass cytometry, offers improved sensitivity and precision. Regulatory changes, like ICH M10, standardize bioanalytical practices globally, but challenges remain in areas like biomarker analysis. Bioanalysts must navigate diverse regulations and collaborate on science-based best practices to ensure globally accepted data.

Pharmaceutical Technology® spoke with Dr. Mark Arnold, owner and principal, Bioanalytical Solution Integration, ahead of AAPS PharmSci 360 to find out how bioanalysis enhances bio/pharmaceutical drug development.

AAPS PharmSci 360: Enhancing Bioanalysis with AI and Other Technologies

AAPS PharmSci 360 2025: The Role of LC–MS in Bioanalysis

The field of bioanalysis in the development of bio/pharmaceutical products is advancing, and a variety of techniques is available to those working in the discipline. One such tool is liquid chromatography–mass spectrometry (LC–MS). 

® spoke with Steve Lowes, senior director of Scientific Affairs at IQVIA Laboratories, ahead of AAPS PharmSci 360 to discuss how LC–MS is used in bioanalysis.

According to Lowes, while LC–MS can be more expensive and less sensitive than ligand binding assays (LBAs) for biologic analytes, the use of LC–MS is challenging the status quo. “Historically, we would also hear that the sensitivity is not what can be achieved with an immunoassay. I do think this latter challenge to LC–MS is being actively addressed, and I'm sure we'll get to talk about that in a moment,” he says. “However, the benefits of mitigating the challenges and the reliance on critical reagents associated with immunoassays leads to some interesting cost and efficiency assessments of LC–MS versus LBA.”

“I'm not saying that LC–MS is replacing LBA, but rather, with increasing frequency, we're seeing LC–MS to be the best tool for the job, including in biologic bioanalysis,” Lowes continues. “By that, I mean protein-based biologic drugs and oligonucleotide therapeutics. Integral to both of these are also the conjugated drug structures in a lot of oncology applications, where an antibody is directed to a tumor-associated antigen to deliver a cytotoxic payload. These are inherently complex molecules, and subsequently, so are the assays that we develop to address them. That is, we often are needing a free payload drug assay, a conjugated payload (i.e., the drug itself) and then the total antibody measurement as well.”

Steve will be monitoring the Rapid Fires: AI/ML in Bioanalysis session on Monday, Nov. 10, 2025 at 3PM CT at 

 being held Nov. 9–12 in San Antonio, Texas.

Click the video above to watch the interview.

Steve Lowes, Senior Director, Scientific Affairs, IQVIA Laboratories

Steve Lowes is Senior Director, Scientific Affairs at IQVIA Laboratories. Steve has 30+ years’ experience in bioanalysis with a focus on LC-MS and regulatory aspects and has been with the legacy LC-MS regulated bioanalytical lab of IQVIA Laboratories in Ithaca, NY since 1995. He is a regular presenter and author of publications on regulated bioanalysis, is co-editor of the text “Regulated Bioanalysis: Fundamentals and Practice,” and is the primary contributor to the popular 

 bioanalysis blog from IQVIA Laboratories. His most recent interests are the application of LC-MS to regulated bioanalysis of biologics and biomarkers. He received his bachelor’s degree in Analytical Chemistry at Sheffield Hallam University in the UK (1987) and a PhD in Biochemistry from the Open University, Milton Keynes, UK (1991).

Pharmaceutical Technology® spoke with Steve Lowes, senior director of Scientific Affairs at IQVIA Laboratories, ahead of AAPS PharmSci 360 to discuss how LC–MS is used in bioanalysis.

How AI, Machine Learning, and Large Language Models Enhance Bioanalysis

Artificial intelligence (AI), machine learning (ML), and large language models are tools that are seeing increasing use in a variety of pharmaceutical development and manufacturing areas. 

, to find out more about how these technologies can enhance 

“There is significantly increased interest and a great potential for AI to make a significant impact on various aspects of bioanalysis, including improving the quality, efficiency, compliance, and also reducing human errors,” Dr. Yuan says.

He believes these tools can be utilized to enhance bioanalytical method development, such as using AI to predict optimal conditions for bioanalytical assays. “This will be especially useful for assays that require significant method development, like the kind of trial-and-error type of method development, for example, for nick and binding assays, the optimization of capture and detection regions conditions,” he says.

He also points to the benefit of using AI in data processing and interpretation. “For example, AI can help to do the automated peak detection integration, calculate and generate the concentration data, and even integrate with the downstream report generation process,” he explains. AI can also be used to create study protocols and validation reports.

The specialized form of ML, large language models, can also be used to enhance the automation of biomedical workflows as well as streamlining and integrating various bioanalytical tasks, according to Dr. Yuan. “For example, we can help streamline and automate the whole biomedical process, including sample preparation, instrument calibration, routine sample analysis, data processing, documentation, and even reporting of the data,” he explains.

PharmTech: How can artificial intelligence and machine learning be used to enhance bioanalysis in bio/pharmaceutical development and manufacturing?

 AI and machine learning is still in a relatively early stage for the application in Bioanalysis. However, I do see there is significantly increased interest and a great potential for AI to make a significant impact on various aspects of biomanac sales, including improving the quality, efficiency, compliance and also reducing human errors. Here are some examples, I think they may be applied first is to enhance bioanalytical method development. For example, AI can predict optimal conditions for bioanalytical assays. This will be especially useful for assays require significant method development like that, kind of trial and error, type of method development, for example, for nick and binding assays, the optimization of capture and detection regions, conditions. The second area is the data processing and interpretation. For example, AI can help to do the automated peak detection integration, calculate and generate the concentration data and even integrate with the downstream report generation process. The other area is like to generate the study protocol, Biennale reports, validation reports to improve the compliance with regulatory.

How can large language models be applied to bioanalytical workflows?

Large language models are a specialized form of machine learning.These large language models, like ChatGPT, can significantly enhance the biomedical workflow by automating, streamlining and integrating various tasks for Bioanalysis. For example, we can help streamline and automate the whole biomedical process, including sample preparation, instrument calibration, routine sample analysis, data processing, documentation and even reporting of the data. It can also help to improve the regulatory compliance the QC, QA, large language models can help review electronic lab load books and audit trails to ensure the compliance with FDA or EMA guidelines. It may also help to connect to the new system or year end systems to pull the data, to prepare the report and draft the SOPs like those type of tasks. One unique features for large language models is it can also serve as it can be also more interactive with human. It's more like a real human. So so energies can interact with large language models to get guidance and assistance on troubleshooting a regular biological workflow, those type of tasks.

Long Yuan, PhD, director of the Department of Drug Metabolism and Pharmacokinetics at Biogen, explains how artificial intelligence, machine learning, and large language models are used in performing bioanalysis.

The COVID-19 pandemic brought light to the global importance of vaccines and ensuring they are manufactured to quality standards for every region in which they are used. Vishal Mukund Sonje, Vaccine Manufacturing Lead at the Coalition for Epidemic Preparedness Innovations (CEPI), discusses some of the challenges faced for vaccine manufacturers, especially those working hard to get these products to remote regions, including complex regulatory requirements.

PharmTech: How do complex global regulations and legal rules inhibit or help vaccine manufacturing?

 Development and manufacturing regulations for vaccine development are quite stringent. However, these requirements are essential, since it ensures product safety and efficacy. The main regulatory bodies [FDA and European Medicines Agency] enforce rigorous standards from maintaining high quality of vaccines, and this is essential, since vaccines are injected into healthy individuals.

There are also efforts to harmonize regulatory standard across countries, for example, ICH [International Council for Harmonisation] guidance, WHO [World Health Organization] pre-qualification. These guidances ensure that there is streamlined approval and global distribution possible, mainly in the LMIC [low- and middle-income] countries.

Are there specific manufacturing issues pharmaceutical companies should consider when manufacturing a vaccine that will be used globally, especially in areas where the vaccine may be most needed?

Vaccine manufacturing involves complex bioprocessing, and it needs years, if not decades, of experience to develop technical depth and know-how. It also involves significant CapEx [capital expenditure] investment. Apart from that, knowing post-pandemic COVID periods, it is essential [that] each region enables different platforms that can be used in a pandemic situation. The platforms that can be used are mainly categorized into recombinant protein, viral vector, and mRNA [messenger RNA].

Thirdly, manufacturers need to keep in mind [the] overall ecosystem and external environment that could have [a] large impact on the success of manufacturing capabilities, for example, maturity of the regulatory authority, input material, supply chain, cold chain infrastructure, finished goods supply chain. For example, shortage of critical components could be challenging to manage and can have detrimental effects on the planning of manufacturing. These factors are essential for any region or manufacturer to keep in mind before moving into vaccine manufacturing.

For these reasons, there are some advantages for manufacturers in certain regions like global north or Asian economies, like India and China, while it is challenging to set up new facilities in new LMIC regions, such as Africa or [the] Latin American region.

Are manufacturers moving to patient-centered manufacturing for vaccines and creating modular manufacturing centers closer to where the vaccines are specifically needed?

Overall, the trend in [the] pharmaceutical industry is more personalized medicine, or [closer] to the end user. However, for vaccines, we still do not have [a] personalized vaccine development base. Having said that, post-COVID pandemic, there is [an] increased sense of urgency from all regions to enable vaccines’ self-sufficiency, and many manufacture within the region, so that during [a] pandemic period, the companies can shift their manufacturing base to manufacture vaccines and counter the pandemic situation.

Keeping that in mind, as I said earlier, three platforms that are mainly looked after or developed by these companies in LMIC regions are, namely, viral vector, [recombinant] protein, and mRNA. So LMIC regions, for example, Africa, together with Africa CDC, a newly formed health agency in Africa, is looking to develop capabilities in Africa for manufacturing based on these three platforms.

The COVID-19 pandemic underscored the critical role of vaccines and the necessity for stringent manufacturing standards globally. Vaccine manufacturing faces challenges, particularly in remote regions, due to complex regulatory requirements. Harmonization efforts, like ICH guidance and WHO pre-qualification, aim to streamline approvals, especially in LMICs. Vaccine production requires significant expertise, investment, and consideration of external factors like supply chains. While personalized medicine is trending, vaccine manufacturing remains region-focused, with efforts to develop capabilities in LMICs using platforms like viral vector, recombinant protein, and mRNA.

Vishal Mukund Sonje, Vaccine Manufacturing Lead, CEPI, talks about the challenges that arise in the manufacturing of vaccines in various global regions.

Challenges for the Global Manufacturing of Vaccines

Early efforts in sustainability have moved from a "nice-to-have" benefit to a 

foundational endeavor across the pharmaceutical landscape

 (1). Understanding and implementing sustainable practices is no longer peripheral; it is a strategic focus dictated by evolving client expectations, rigorous environmental audits, and respecting planetary boundaries (1, 2). This industry shift is a response to global climate pressures—the healthcare sector contributes 4.4% of global carbon emissions—and a recognition that profitability and responsibility must converge (3,4).

The complexity of environmental impact can be seen in the pharma supply chain; up to 75% of a typical pharmaceutical company's greenhouse gas (GHG) emissions originates from scope 3 (2). According to the 

, “Scope 3 emissions are the result of activities from assets not owned or controlled by the reporting organization, but that the organization indirectly affects in its value chain. An organization’s value chain consists of both its upstream and downstream activities. Scope 3 emissions include all sources not within an organization’s scope 1 and 2 boundary. The scope 3 emissions for one organization are the scope 1 and 2 emissions of another organization. Scope 3 emissions, also referred to as value chain emissions, often represent the majority of an organization’s total GHG emissions” (5).

Because scope 3 covers the entire value chain, pharma scientists should integrate sustainability into R&D, manufacturing, and sourcing strategies (6,7).

How does green chemistry optimize API manufacturing?

significant producer of emissions from pharmaceutical companies

 (8). The largest contributors to the carbon footprint in these operations are the use of solvents, reagents, and precious metal catalysts (6). Applying the principles of green chemistry early in process development is one strategy to reduce emissions (6), because it allows for the implementation of eco-friendly processes and may foster acceptance by regulatory authorities (6).

Recycling solvents and catalysts offers measurable resource savings and carbon footprint reduction by minimizing incineration waste and the need for new raw material production (6). For instance, specialized companies perform the external transformation of used catalysts back into active species. Innovative technologies are emerging to support this, such as Evonik's process for concentrating precious metal-containing organic waste streams using membrane cross filtration (6), and the development of the "Chemistry in Water" platform, which reduces the consumption of organic solvents. Minimizing or eliminating solvent use may be the best sustainability approach (6).

How is sustainability by design used in bioprocess development?

Similar to quality by design, the concept of sustainability by design is taking root (7). This approach requires integrating sustainable practices from the beginning of the development process, because 80% of a drug’s final environmental impact is determined during the early design stages (7).

Key elements of sustainability by design are resource and energy efficiency, waste minimization, and the use of less-hazardous chemicals (7). Driving efficiency may be achieved with higher titer in smaller volumes through process intensification, which lowers the cost of goods while simultaneously reducing emissions (7). Examples of intensification include using high-density cell banking to skip lengthy seed expansions and implementing single-use bioreactors with high turndown ratios (7). Reducing utility usage by specifying a lower quality of purified water (e.g., reverse osmosis quality water) for media makeup and general early-stage buffers can create impactful improvements in resource management (7).

How do innovations in packaging and supply chain collaboration contribute?

Packaging is a significant contributor to scope 3 emissions, but optimizing packaging solutions offers a way to reduce a company’s GHG (2). Fiber-based packaging, derived from sustainably sourced wood and featuring a high recyclability rate (86.6% in the European Union in 2023), is gaining prominence as an option (2). Efforts to reduce packaging’s carbon footprint involve utilizing fossil-free energy in production and maximizing material efficiency through light weighting (2).

For single-use applications, which remain standard due to contamination risks, innovation focuses on alternatives to traditional materials (9). These include adopting PVC- and halogen-free alternatives (such as APET, PP, or PE) in blistering applications and developing recyclable materials for which the forming and lidding components are made of the same substance (9).

The pharmaceutical supply chain is wide and complex and includes a variety of suppliers, such as material producers, contract manufacturers, and packaging and distribution players. Effective sustainability requires collaboration across this entire supply chain (2,7). Because up to 92% of bioprocess emissions come from upstream activities, companies must engage suppliers early and prioritize partners committed to environmentally friendly practices, responsible sourcing, and strong environmental, social, and governance policies (4,7).

Digital technologies are essential enablers of sustainability goals (9). Smart tools like digital and laser printing technologies support traceability and increase flexibility for smaller or personalized batches (9). Energy-efficient manufacturing equipment is gaining traction, particularly technologies that capture and reuse waste heat (9).

Digital advancements, including real-time monitoring and advanced analytics, facilitate the complex assessment of environmental impacts (7,10). The integration of artificial intelligence (AI) and machine learning (ML) is proving indispensable for overcoming the challenge of insufficient or complex data needed for sustainability assessments (7). ML models can efficiently fill data gaps and provide higher accuracy predictions. Sustainability implications, therefore, may be assessed alongside technical and cost considerations (7).

The goal of sustainability is a commitment to transformative change (3). By embedding these practices into process design, sourcing strategies, and supply chain partnerships, pharmaceutical manufacturers are solidifying environmental responsibility as a strategic competitive advantage, ensuring the delivery of high-quality products in a socially responsible manner (1,7).

Cole, C. and Miller, D. Sustainability Becomes Essential: Green Manufacturing and Supply Chain Resilience in Bio/Pharma. 

https://www.pharmtech.com/view/sustainability-becomes-essential-green-manufacturing-and-supply-chain-resilience-in-bio-pharma

Haigney, S. CPHI Frankfurt 2025: Fiber-Based Packaging for Sustainability. 

Haigney, S. Working Together to Improve the Environment. 

Pharm. Technol. Bio/Pharma Outsourcing Innovation

https://www.epa.gov/climateleadership/scope-3-inventory-guidance

Challener, C. A. Improving the Sustainability of API Manufacturing with Recycling Technologies. 

Challener, C. A. Sustainability by Design in the Context of Bioprocess Development. 

Witte, C.; Perez, L.; Fernandez, M.; Weskamp, T. Decarbonizing API Manufacturing: Unpacking the Cost and Regulatory Requirements. McKinsey & Company. July 26, 2024. 

https://www.mckinsey.com/industries/life-sciences/our-insights/decarbonizing-api-manufacturing-unpacking-the-cost-and-regulatory-requirements

Lavery, P. How Smart Technology Is Helping Reach Sustainability Goals in Drug Packaging. 

Sustainability has become a strategic imperative in the pharmaceutical industry, driven by client expectations, environmental audits, and climate pressures. The sector's significant carbon footprint, particularly from scope 3 emissions, necessitates integrating sustainable practices across the value chain. Green chemistry and sustainability by design are pivotal in reducing emissions in API manufacturing and bioprocess development. Innovations in packaging and supply chain collaboration further contribute to sustainability goals. Smart technologies, including AI and ML, enhance environmental impact assessments, supporting the industry's commitment to transformative change and competitive advantage.

Pharma companies are focusing on sustainability efforts to meet client expectations, environmental audits, and climate pressures.