Digital Transformation in Pharma Manufacturing

Patent Cliff Deals, Tariff Pressures, and the In Vivo Cell Therapy Bet

Behind the Headlines, Episode 23

ICH Q12 and Post-Approval Changes

Speed, Flexibility Top Large-Molecule Manufacturing Trends

Drug Digest July 2025

Key Insights from Industry Survey Results

Bio/Pharma's Tariff Response

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The first CPHI Middle East event, to be held Dec. 10–12, 2024 at the Riyadh Front Exhibition & Convention Center Riyadh in Saudi Arabia, is expected to be host to 30,000 pharma professionals, according to the event’s planners. According to CPHI, the region is planning to expand its domestic manufacturing capabilities as new technology investments are poured into the market.

Indian contract development and manufacturing organizations are expected to invest in the region as they support emerging biotechs that have product development in India, according to CPHI, with more than 80 Indian companies exhibiting at the event. There are a total of 400 companies exhibiting, with more than 65 from Europe and 90 from China.

“The event is held under the patronage of the Saudi Ministry of Health, who will be among more than 160 experts presenting at the event,” CPHI stated in a press release (1). “A Ministerial Panel on 10 December will outline the Kingdom’s strategy to develop a biopharma ecosystem, in alignment with the Kingdom’s vision to be the leading biotech hub in MENA [Middle East and Northern Africa] by 2030 and globally by 2040. The Kingdom has ambitious plans to reduce its reliance on imported drugs. In fact, currently, Saudi Arabia imports over 81% of its generic medicines and 94% of its medical devices, however, by 2030, the Kingdom aims to produce 40% of its drug needs domestically.”

Hala Audi, CEO of UNIZIMA—which specializes in partnerships for bioproduction facilities in emerging market—is a keynote speaker at the event and will discuss building biologics capabilities in emerging markets.

“The event’s success is just another indicator of the significant investment underway in the region,” said Audi in the press release (1). “The rate of change is only accelerating, and the Kingdom is building pharma infrastructure at pace. The [g]overnment is rightly driving the momentum, by focusing on its strengths—a sizeable domestic market, growing scientific and medical talent, and financing power. I believe that with the right technology partnerships, the region is poised to build a biotech ecosystem that will compete with the best globally. Next generation technologies for vaccines and therapeutics including, for instance, RNA based treatments offer an opportunity for KSA [Kingdom of Saudi Arabia] and the region to leapfrog and lead the way in biotech.”

The conference agenda will include a variety of topics related to the future of pharmaceutical development in the Middle East and will span four show-floor theaters. Panel discussions will include talks on the expansion of Saudi Arabia’s domestic manufacturing hub.

“CPHI Middle East is set to be the portfolio’s most successful launch to date – with incredibly impressive attendee and exhibitor numbers,” April Hung, brand manager for CPHI Middle East, stated in the release. “This level of interest reflects the region’s ambitions and strong demand for partnerships. The public–private collaboration here is impressive, and by bringing CPHI to Saudi Arabia, we’re helping accelerate the development of partnerships and supply ecosystems that will fuel growth over the next five years. We’re thrilled to launch the show and play a role in building a new ‘heart of pharma’ in the Middle East.”

1.	CPHI. Saudi Arabia Poised to Expand Domestic Manufacturing Eco-system as 30,000 Attendees join Inaugural CPHI Middle East. Press Release. Dec. 9, 2024.

Articles

The first CPHI Middle East event will be held Dec. 10–12, 2024 at the Riyadh Front Exhibition & Convention Center Riyadh in Saudi Arabia and is expected to be host to 30,000 pharma professionals.

CPHI Middle East Sees Domestic Manufacturing Expansion in Saudi Arabia

Cheryl Barton is director of PharmaVision, 

Human head shape made from a large grid of pins connected with string. Communication technology and mental health concept | Image Credit: © ink drop - Stock.adobe.com

Over the past decade, researchers have developed a greater understanding of the relative contributions of the environment, genetic, and epigenetic influences on the onset and progression of Parkinson’s disease (PD). New strategies have emerged that seek to modulate inflammatory cellular mediators, mitochondrial function, and/or halt the accumulation of synuclein and neuromelanin (1–3).

In August 2024, German-based Centogene NV released new data from the landmark Rostock International Parkinson’s Disease (ROPAD) Consortium Study which aimed to characterize the genetics of PD to better understand disease progression, diagnosis, and treatment for patients (4). The study identified that approximately 15% of 12,580 patients studied harboured genetic variants that present new opportunities for genetic testing and the development of innovative therapies such as cell and gene therapies (CGTs) (5).

New formulations and novel non-biological drugs

More than 10 million people worldwide are living with PD, and it is now classed as the fastest-growing neurological condition in the world (6,7). Several companies such as NeuroDerm (a Tanabe Pharma company), Cerevel (an AbbVie company), and Swiss-based Contera Pharma A/S are working on non-biological agents with immediate or long-duration action to improve the symptoms of PD (8–10) (

). In October 2024, the US Food and Drug Administration (FDA) finally approved AbbVie’s Vyalev (foscarbidopa and foslevodopa), the first 24-hour infusion of levodopa-based therapy for motor fluctuations in adults with advanced PD (11). This single infusion helps reduce the pill burden whereby PD patients may take more than 20 tablets a day to control their symptoms. AbbVie gained approval for the drug, under the trade name Produodopa, from the European Commission (EC) in January 2024 (12). In May 2024, Portuguese BIAL Holding S/A launched a novel sublingual film formulation Kynmobi (apomorphine hydrochloride) in Germany, for the intermittent treatment of off episodes in adult patients with PD who are not sufficiently controlled by oral anti-Parkinson medication (13).

Pharma companies are actively pursuing the development of drugs that target novel pathways involved in the underlying mechanism of PD (

). These include BIAL’s BIA 28-6156, a small-molecule activator of glucocerebrosidase (GCase), a lysosomal enzyme involved in the breakdown of glycosphingolipids and UK-based Neurolixis’ NLX-112 (befiradol), a novel 5-HTA1A receptor agonist that has completed a Phase IIa proof-of-concept trial for dyskinesia in PD (14–16).

Innovative non-biological drugs under clinical development for Parkinson’s disease (PD).

Innovative biological therapies are progressing through trials

Several companies are looking to harness biologics to halt the accumulation of detrimental proteins such as alpha-synuclein (a-syn), which has been implicated in the pathology of PD (

). US-based AC Immune is evaluating ACI-7104 in Phase II, a monoclonal antibody (mAb) that has been shown to inhibit the accumulation of a-syn aggregates in preclinical animal models and the brain tissue of PD patients (17). An alternative approach is the use of adeno-associated virus (AVV) vectors to deliver genes that selectively modulate the production of proteins such as glutamic acid decarboxylase (GAD) to increase gamma-aminobutyric acid (GABA) levels, or glial cell line-derived neurotrophic factor (GDNF) to boost dopamine levels. In January 2024, Asklepios BioPharmaceuticals (AskBio, a subsidiary of Bayer AG) released positive data with its AAV2-GDNF showing that the one-time bilateral delivery of AB-1005 gene therapy was well-tolerated, and no serious adverse events (AEs) were observed (18). In October 2024, US-based MeiraGTx Holdings released positive top-line data from its clinical bridging study with MGT-GAD-025, an AAV-GAD, resulted in a significant and clinically meaningful improvement in Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) from baseline to Week 26 in patients with idiopathic PD (IPD) (19).

Cell and gene therapies under clinical development for the treatment of Parkinson’s disease (PD).

Cell-based therapy is expected to provide an alternative route to treat PD through the surgical injection of autologous induced pluripotent stem cell (iPSC)-derived dopamine neurons into the putamen, the brain region affected in PD. Denmark/China-based iRegene is evaluating chemically induced human dopaminergic neuron precursors derived from iPSCs to treat moderate to severe PD; the company entered clinical development with its gene therapy NouvNeu001 in the second half of 2023 (20). Similarly, Massachusetts-based BlueRock Therapeutics (a subsidiary of Bayer AG) has released promising Phase I follow-up data from its safety trial with its cell-based therapy bemdaneprocel (MSK-DA01) (21). Alternatively, Australia-based Living Cell Technologies (LCT) is developing an alginate-coated capsule containing clusters of neonatal porcine choroid plexus cells that contains a range of factors that support nerve cell functions and protective enzymes that are crucial for nerve growth and healthy functioning in PD patients (22).

Since 2019, the number of active clinical trials for PD has remained relatively consistent, with more than 136 trials per year. However, the proportion of disease-modifying to symptomatic therapies has been increasing during the past five years, and the diversity of drug targets across both symptomatic and disease-modifying drugs has also increased. Interestingly, the number of trials investigating drugs aiming to improve non-motor symptoms has expanded and several drugs are now being developed to target inflammation, a key driver of PD progression (23). But despite these significant advances, many drugs still fail to transition from Phase II to Phase III.

National and international organizations and foundations, such as Cure Parkinson’s, the American and European Parkinson’s Disease Association, Michael J. Fox Foundation, National Parkinson’s Foundation, and Parkinson’s UK, have an important role in raising awareness, supporting patients, and funding PD research. Pharma companies including Bayer, Biogen, Genzyme, GlaxoSmithKline, Sanofi, and Pfizer have made multi-million-dollar strategic investment investments, and several life-changing therapies, including CGTs, are steadily progressing through the clinic. If successful, these therapies could help rewire the PD brain circuits and fundamentally change the way patients are treated over the next decade (24).

1. Elsworth, J.D. Parkinson’s Disease Treatment: Past, Present, and Future. 

 May;127(5):785-791.
2. Pajares. M.; Rojo, A.I.; Manda, G.; Boscá, L.; Cuadrado, A. Inflammation in Parkinson’s Disease: Mechanisms and Therapeutic Implications. 

 Jul 14;9(7):1687.
3. Abeliovich, A.; Hefti, F.; Sevigny, J. Gene Therapy for Parkinson‘s Disease Associated with GBA1 Mutations. 

 11(s2): S183-S188.
4. Westenberger, A.; Skrahina, V.; Usnich, T.; et al. Relevance of Genetic Testing in the Gene-targeted Trial Era: The Rostock Parkinson’s Disease study. 

. 147(8) 2652-2667.
5. Centogene. Introducing the ROPAD Consortium: Unlocking Real-world Genetic Insights to Accelerate Treatment Access for Parkinson’s Disease. Webinar. October 2024.
6. Parkinson’s Foundation. Statistics. 

 (accessed 30 Oct. 2024).
7. BBC News. New Parkinson’s Drug Produodopa to be Available on NHS. 16 Feb. 2024.
8. NeuroDerm Ltd. ND0612 (accessed 30 Oct. 2024).
9. Cerevel Therapeutics. Cerevel Therapeutics Announces Positive Topline Results for Tavapadon in Phase 3 Adjunctive Trial for People Living with Parkinson’s Disease | Cerevel Therapeutics. News Release. 18 Apr. 2024.
10. Contera Pharma A/S. Contera Pharma Reports Topline Results from its Phase IIb Clinical Trial ASTORIA, evaluating JM-010 for the Treatment of Parkinson’s Disease Dyskinesia. News Release. 21 May 2024.
11. AbbVie. U.S. FDA Approves VYALEV (foscarbidopa and foslevodopa) for Adults Living with Advanced Parkinson’s Disease. News Release. 17 Oct. 2024.
12. AbbVie. AbbVie Launches PRODUODOPA (foslevodopa/foscarbidopa) for People Living with Advanced Parkinson’s Disease in the European Union. News Release. 9 Jan. 2024.
13. Bial. Bial launches Kynmobi in Germany. News Release. 3 May 2024.
14. Bial. BIA 28-6156. 

. (accessed 30 Oct. 2024).
15. Cavion. Cavion to Present Results of Phase 2 Essential Tremor Clinical Trial in Platform Presentation at the American Academy of Neurology. News Release. 24 Apr. 2019.
16. Neurolixis. Successful NLX-112 Clinical Results were Discussed in an Interview with the Parkinson’s UK Foundation. 15 Jan. 2024.
17. AC Immune. ACI-7104: Updates on Clinical Progress. 2024.
18. AskBio. AskBio Phase Ib Trial of AB-1005 Gene Therapy in Patients with Parkinson’s Disease Meets Primary Endpoint. News Release. 4 Jan. 2024.
19. MeiraGTx. MeiraGTx Announces Positive Data from Randomized, Sham-controlled Clinical Bridging Study of AAV-GAD for the Treatment of Parkinson’s Disease. News Release 15 Oct. 2024.
20. Cai M.; Wei, J.; Ren, X.; et al. NouvNeu001, A Phase 1 Stage Chemically Induced Human Dopaminergic Progenitor Cell Therapy for the Treatment of Mid- to Late-stage Parkinson’s Disease. 2024 International Conference. Abstract Number 700.
21. BlueRock Therapeutics. BlueRock’s Phase I Study with Bemdaneprocel in Patients with Parkinson’s Disease Meets the Primary Endpoint. News Release. 28 Aug. 2023.
22. LCT. NTCELL. Accessed 30 Oct. 2024.
23. Cure Parkinson’s. Parkinson’s Drug Therapies in the Clinical Trial Pipeline: 2024 Update. News Release. August 2024.
24. Kingwell, K. Gene Therapy Zeroes in on Parkinson’s Disease Brain Circuits. 

Cheryl Barton is director of Pharmavision, info@pharmavision.co.uk.


Vol. 36, No. 10
November/December 2024
Pages: 16–18

When referring to this article, please cite it as Barton, C. European Biotechs Unlocking Parkinson’s Disease. 

Centogene NV and ROPAD consortium publish data from a landmark study identifying genetic variants that may respond to innovative cell and gene therapies.

European Biotechs Unlocking Parkinson’s Disease

Cynthia A. Challener is a contributing editor to Pharmaceutical Technology.

Future and media concept | Image Credit: ©peshkova - stock.adobe.com

Use of process analytical technology (PAT) and advanced digital solutions such as artificial intelligence (AI) and machine learning (ML) is a crucial component of the move to Pharma 4.0. Online/inline/at-line PAT enables real-time monitoring of processes during development and manufacturing. That information can then be applied to support process control, leading to more consistent performance. When PAT is integrated with AI/ML algorithms, it is possible to develop predictive models that enable improved control, support more effective decision-making, and reduce risks, leading to optimized operations, enhanced customer experiences, and better financial outcomes, according to Edita Botonjic-Sehic, head of process analytics, data engineering and data science at ReciBioPharm.

Several PAT tools are particularly well-suited for integration with AI and ML algorithms. At the top of the list are soft sensors, which integrate a software layer based on a mathematical model that is employed to monitor process states, observes Stacy Shollenberger, senior manager, process analytical technology with MilliporeSigma.

Analysis of the results is complex, however, and requires a high level of expertise to identify, correlate, and quantify the signal and use it for predictive analysis, according to Kaschif Ahmed, principal data scientist with ReciBioPharm. “The best integration solution is one that has an extreme amount of flexibility for measurement and analysis and can easily be validated without the constant need for calibration,” he adds.

AI/ML models can be directly integrated inside the PAT tool, providing a data-driven means to estimate or predict parameters that are otherwise difficult to measure or cannot be measured directly, says Moritz von Stosch, chief innovation officer of DataHow.

Shollenberger points to spectroscopic sensors including Raman, nuclear magnetic resonance (NMR), and near-infrared (NIR) technologies, which offer near-instantaneous measurement of key metabolites and nutrients (e.g., lactate, glucose). However, the spectra generated by these techniques require interpretation using chemometric models leveraging AI/ML algorithms tailored for chemical analysis. Mass spectrometry, she notes, can also greatly benefit from AI/ML interpretation of the produced spectra.

High-performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography (UHPLC), meanwhile, can also be coupled to online analyzers that rely on models and used to monitor product purity in near real-time, according to Shollenberger.

Importantly, in comparison to standard linear regression approaches, ML can increase prediction accuracy when nonlinearities arise (e.g., due to superimpositions in the spectra), says von Stosch. Fusion of data from different sensors is also possible, enabling capture of the overall state of the process better and reducing the impact of measurement noise. “By fusing sensor information with predictions coming from process models, the performance of such approaches can be extended, (e.g., in the case of low signal-to-noise ratios),” he remarks.

Depending on the monitoring scope, Shollenberger notes that hybrid models combining AI/ML with mechanistic understanding can be better suited for real-time monitoring than AI/ML models alone because the former generally better capture the complexity of bioprocesses, leading to more reliable, interpretable, and generalizable simulation results. Upstream processes in particular stand to gain the most from hybrid models, she says, as they are characterized by the high degree of unpredictability inherent in living cells.

There are applications for AI/ML outside of soft sensors as well. For example, van Stosch notes that the advent of deep learning, a specific ML method, has also made real-time-based image processing possible. “Today, ML methods are used to count cells or particles, classify different types of cells or particles, inspect vials for release, and other applications,” von Stosch comments.

PAT tools integrated with AI/ML-driven models can be used in many different applications across modalities, the drug development and manufacturing cycle, and unit operations. “Such integrated tools are suitable for all types of processes, including those that produce small molecules and biotherapeutics and for both upstream and downstream unit operations at process development and manufacturing scale,” states Botonjic-Sehic.

“Real-time operation and immediate decision-making using PAT tools and digital twins will increase throughput and reduce failures during process development and manufacturing,” adds Ahmed. For these and other reasons, Botonjic-Sehic notes that the use of PAT and digital infrastructure is supported by regulators, with some guidelines available to help the industry move in this direction.

Real-time process optimization relying on process models is one specific activity that can particularly benefit from AI/ML at the upstream level, according to Shollenberger. “Integration of AI/ML/hybrid models with PAT in process development helps to accelerate process understanding, including the impact of different critical process parameters (CPPs) on product critical quality attributes (CQAs), and facilitates the extrapolation of process knowledge across different scales,” she says.

Process development activities can greatly benefit from more continuous tracking of the process state and the ability to make decisions during or after the run, rather than having to wait for the analytics for several hours or days, agrees von Stosch. “In addition to accelerating process development, PAT tools with integrated AI/ML models can render on-the-fly process optimization possible,” he states.

Such knowledge management is also crucial for a smooth process scale-up and transfer from development to pilot and manufacturing scales, all while ensuring stable and effective performance, Shollenberger contends.

As examples, Shollenberger highlights the use of multivariate statistical process control methods within a quality-by-design approach to process development to maintain CPPs and CQAs within the identified design space and new AI/ML design-of-experiment (DoE) tools such as the Bayesian DoE to reduce experimental effort and development costs by providing experiments that are the most informative for characterizing processes. Finally, she points to the use of digital twins for predicting process performance at production scale by mirroring and extrapolating insights gained from smaller-scale experiments. “This approach allows for a more accurate representation of how processes will behave in full-scale production, ultimately streamlining process development and enhancing overall efficiency,” Shollenberger comments.

In addition to supporting the scale-up from process development to production by learning to adapt scale-dependent process parameters and optimizing their configurations, AI/ML/hybrid models supporting real-time or near real-time monitoring and control can greatly improve process efficiency, reliability, yield, and product quality, says Shollenberger. PAT and digitally driven infrastructure can, agrees Botonjic-Sehic, reduce uncertainty by providing insights into process conditions through simulators enhanced with integrated AI/ML and PAT real-time data. “Real-time release testing will also be enabled by AI/ML with PAT due to the ability to achieve rapid quality verification,” she notes.

Improved responsiveness and better decision-making

The integration of AI, ML, and hybrid models with PAT tools offers numerous benefits that extend beyond simple process monitoring. Having an infrastructure that includes novel PAT tools, ML models, and automation allows researchers to identify their targets of interest faster and more accurately during process development and manufacturing, according to Ahmed. “Having predictive models available for each step in the process enhances process understanding and ultimately allows businesses to deliver drugs to market more quickly with greater quality while also reducing waste and cost,” he states.

Increased (near) real-time insight into the evolution of processes makes several actions possible, adds von Stosch. He highlights the ability to take corrective actions either manually or via closed-loop control, make more informed decisions about when to harvest or release material for further processing, introduce changes that afford greater insights into the process, and ultimately increase process capability.

Specifically, according to Shollenberger, chemometric models can interpret spectroscopic data and predict in near real-time process variables of interest. Advanced control techniques based on these predictive models can anticipate future deviations, enabling timely interventions and process adjustment. “It is the combination of real-time deviation monitoring and process optimization that enhances process understanding and control, leading to better quality, increased speed, and greater flexibility in the manufacturing process,” she concludes.

The potential presented by integrating AI/ML with PAT technologies is limited to some degree by several challenges to successful implementation of these solutions. Complexity, connectivity, and data availability and quality are the top issues.

“The complexity of model building and maintenance, particularly in the highly regulated biopharmaceutical environment, necessitates extensive qualification and validation efforts,” says Shollenberger. Identifying the right PAT tools that are suitable for the right level of analysis, developing soft sensors specifically targeting each CQA in the process, and building in the analytics and logic to enable process control must be achieved first, according to Botonjic-Sehic. Appropriate automation solutions that support collection and use of real-time information to predict process performance and identify failures as they occur must also be developed. Then once deployed, validation, compliance and data integrity components must be well understood before a drug is processed for release, she comments.

In addition, Shollenberger notes that executing models in a real-time process control environment involves managing time latencies and ensuring seamless connectivity and full compatibility between sensors, other equipment, software, and all interfaces.

Effective training of AI/ML models, meanwhile, requires substantial volumes of experimental data to ensure reliable predictions, placing a considerable burden on operators to generate these datasets. “ML data quantity and quality requirements are rarely met today as current datasets often consist of infrequent data points, such as daily measurements as PATs are mostly offline, limiting the availability of high-frequency historical data for model training,” Shollenberger observes. Hybrid models, she says, are therefore a good alternative for reducing the data quantity required for model training.

On a positive note, Botonjic-Sehic emphasizes that once digital models are developed using historical run data, the robustness and validation is integrated into the process, resulting in faster manufacturing with better quality while decreasing the cost of operation.

The implementation of PAT solutions and AI/ML-enabled PAT solutions in the pharmaceutical industry at production scale is more prevalent in the small-molecule segment. “The chemical nature and synthesis of small molecules are relatively straightforward, and well-established manufacturing technologies dominate the production environment. In the face of intense generic competition and cost pressures, however, drug makers are increasingly seeking innovative, speedy, and cost-efficient production approaches, and AI/ML-enabled PAT solutions have attracted significant interest,” Shollenberger explains.

Biotherapeutics producers are moving toward the use of integrated AI/ML/PAT, but at a slower pace due to the greater complexity of biomolecules, according to Ahmed. While there is significant potential for AI to enhance process understanding and control, enabling informed decisions in the future, today human involvement remains essential to address the current limitations of AI, Shollenberger adds. “Human expertise is still necessary to interpret analytical results accurately and to develop robust models that will leverage AI support in the future,” she says. In fact, she believes collaboration between human insight and AI capabilities will be crucial for advancing biomolecule production.

One example already under development is ReciBioPharm’s novel process for RNA-based vaccines and therapeutics, which it is developing in collaboration with the Massachusetts Institute of Technology using grant money from FDA. The continuous GMP process is extensively enabled by real-time analytical technologies leveraging AI/ML models, including new fit-for-purpose solutions. It is expected to be operational in mid-2025, according to Aaron Cowley, CSO, ReciBioPharm.

DataHow, meanwhile, released a spectral data analytics module within DataHowLab, its advanced process data analytics solution that deploys AI/ML with hybrid models (1).

Integration of AI/ML within PAT tools will, according to Ahmed, eventually make real-time data analysis more autonomous and ultimately support real-time release and thus a paradigm shift in pharma manufacturing.

Indeed, Shollenberger believes significant advances in PAT capabilities will be achieved in the coming years, with integration of AI and ML representing a crucial next step toward the envisioned “facility of the future”.

In the short term, she expects the push for cost reduction and environmental sustainability to drive the miniaturization and cross-unit factorization of automated PAT devices, making these advanced PAT solutions more accessible and cost-effective. In addition, use of calibration-free or limited-calibration PATs could help to streamline the integration of these technologies into existing processes with lower cost.

Other advances will occur in knowledge management, as it is vital for collecting and structuring knowledge coming from different devices and PATs. “Scale agnostic, cross-process, cross cell line, and cross-proteins knowledge management is essential for enabling the application of AI and ML across various processes and steps,” Shollenberger states.

Once these advances are achieved, it will be possible to deploy integrated AI and ML solutions that facilitate near real-time quality assurance, process optimization, and continuous improvement. “These advances will help ensure that final products consistently meet regulatory and quality standards, enhancing the efficiency and reliability of pharmaceutical manufacturing,” observes Shollenberger. She cautions, however, that integration of AI and ML models into GMP processes introduces regulatory challenges related to data security, algorithm transparency, and the validation of AI-driven processes against existing industry standards, all of which must be addressed to ensure their effective use while maintaining compliance with regulatory requirements.

Cynthia A. Challener, PhD, is a contributing editor to 

®
Vol. 48, No. 12
December 2024
Pages: 16–18

When referring to this article, please cite it as Challener, C.A. Integrating Advanced Technologies for Pharma Analysis. 

Automation technologies provide real potential to improve responsiveness and decision-making in drug development.

Integrating Advanced Technologies for Pharma Analysis

Feliza Mirasol is the science editor for 

Lipid nanoparticles (LNPs) have been gaining traction as a primary formulation and drug delivery tool in the manufacture of messenger RNA (mRNA)-based therapeutics, having proven their utility in the development of mRNA COVID-19 vaccines. As the development of LNP-enveloped mRNA therapeutics advances, what other nanoparticle technologies offer the opportunity for optimizing the formulation of mRNA—and potentially other nucleotide-based—therapeutics? This article explores the advancement of LNP formulations as well as additional nanoparticle technologies in this space.

Developing an mRNA therapeutic involves both the manufacture of the mRNA molecule itself and combining that molecule into some sort of delivery technology, whether that delivery technology is a LNP or other platform, says Colin McKinlay, PhD, senior director, Chemistry and Delivery Technologies, at Nutcracker Therapeutics. One of the challenges McKinlay points out is the type of approach a developer must take in manufacturing these types of drugs, which are unique in that they are personalized therapies, where every patient, each therapy, and the drug components are basically different.

“I think one of the lessons learned from the whole COVID-19 pandemic and putting together the mRNA-based vaccines for COVID-19 was that we could actually scale these [vaccines] up to worldwide scales, and that we were able to do that fairly effectively,” said McKinlay. “But starting to think about distributing and manufacturing mRNA drugs, where every patient has a different type of formulation, I think that starts to be a different type of challenge.”

Somewhat related to the logistics of distribution, McKinlay goes on to explain that many of the challenges associated with mRNA drug manufacturing are tied into having the ability to bridge smaller-scale companies—that is, their research work—into Phase I, Phase II, clinical-trial scale, and get these companies the materials they need for conducting those trials. “There are [many] commercial suppliers that are available for research-grade mRNA and for making mRNA at that small scale when [companies] are doing [their] discovery work. And obviously, if [a company] wants to invest a large amount of money into manufacturing, there are [many] opportunities available for large-scale commercialization, but getting strategies that are suited for … small clinical-trial stage where it is not too cost prohibitive upfront is something [where] there is a definite need in the market for,” McKinlay says.

With personal background experience in academia and moving up to a small startup and eventually to a mid-size startup, McKinlay relates the lessons learned in dealing with LNP manufacturing. “One of the things I’ve found throughout my career is that when you’re doing those initial discovery experiments and early stage experiments, you really want to be able to run high-throughput type experiments—screening different formulation chemistries, different combinations of components, or even different mRNA cargo types. However, at that small [of a] scale, [many] commercial options for making LNPs are not really tailored towards that small scale,” he explains.

In the course of his work, McKinlay noted that, although there are production systems available from various different suppliers, these systems generally have a rather large minimum volume or minimum scale at which they can operate. “What [many] people end up doing is to just pipette-mix or hand-mix their components, which means that the LNPs you’re making are very different than the LNPs you’re going to be making farther down in your development process during scale up,” he says.

One of the ways that a company can address this problem is by having its own engineering team in-house, McKinlay emphasizes. With this in-house capability, the company can have the team think about the challenges and ways to design LNPs on both a manufacturing level and at a system level. The results of the engineering team’s work can be used in research that is applied to much of the same core technologies. This gives the company the advantage of having information that can be translatable at every stage of development. “I think there are other companies that are starting to pick up on that importance,” McKinlay says, “but if you don’t think about that early on, you might end up optimizing for something that doesn’t end up being quite as good later on [and further] into your manufacturing process.”

Looking beyond the manufacturing challenges, innovations are in development to address these hurdles. McKinlay notes that some of those innovations are just starting to kick off. “The first wave [of innovation] has [involved the use of] a lot of the existing manufacturing technologies in the biologic space for these mRNA molecules. Now, [the industry is] actually starting to get different companies that are thinking about: how do we actually pursue this workflow for an mRNA specific platform? Not just pushing ourselves across the finish line, but really trying to adapt things to the advantages of mRNA and the unique challenges associated with mRNA?” he states.

McKinlay has also observed that some of the larger mRNA companies are starting to consider the problems in the mRNA space much more deeply with an aim to innovate in this space in a more mRNA-centric way. “For instance, one of the things I brought up earlier is the personalized medicines aspect. One of the exciting things about mRNA is some of the personalized therapies and personalized vaccines [that have come out of this space],” he remarks.

With a growing number of personalized medicines coming onto the scene, the industry is now looking at unique manufacturing challenges associated with them. “I think those are areas where we’re going to continue to see different types of innovation in the field,” McKinlay adds.

Broadly speaking, McKinlay considers the fact that not everybody is going in the same direction with mRNA therapeutic development an exciting trend. “There are a lot of different places that mRNA can be applied. In some cases, moving past the early stages of this field, one of the things that people are going to have to do is start specializing in different areas and saying, ‘Okay, this company is going to take mRNA in this direction.’ ‘This company is going to take it in a different direction.’ I think [this attitude] is one of the great things about a broad field like mRNA,” McKinlay says.

Vaccine development is an area where mRNA has truly demonstrated its effectiveness. But McKinlay points out the importance of not pigeonholing that platform technology such that mRNA is only thought of as a vaccine technology. Moving beyond infectious disease vaccines, or even COVID-19 vaccines, is going to be crucial to the growth of the mRNA field, McKinlay believes.

In terms of the LNP side of this field, McKinlay emphasizes that a number of LNP technologies exist that will focus on expression in the liver and that are directed towards the liver. “One of the [notions] that a lot of folks in the field are really focusing on is, ‘how do we actually direct these lipid nanoparticles to other organs in the body so that we can address other types of disease areas?’,” he says. Many companies are on this train of thought as well as researchers in academia, McKinlay explains. However, there has not been large-scale clinical successes in this area yet, he notes. “I think that’s definitely a place where more development is going to be taking place, and there’s going to be a lot of kind of handoffs between academia and industry in finding some strategies to address that,” he states.

Exploring other delivery vehicles and opportunities

In terms of non-viral RNA delivery, LNPS have largely dominated this field in terms of clinical approvals and later-stage development, according to McKinlay. However, he cautions against the industry getting stuck in these local maxima, “where the field does a lot of optimization, and [players] are able to climb in efficacy, but then [they] reach a point where there’s not too much [left] that [they] can really optimize anymore.”

Nutcracker’s exploration of alternative delivery vehicles has resulted in the company’s peptoid-based platform. According to McKinlay, these peptoids can be used in a traditional LNP-type formulation. “We’re able to branch away from traditional LNP type particles and have particles that are a little bit more unique to the chemistry of these peptoid molecules. We think that [this technology] is pushing us over, a little bit away from where the rest of the field is working; there may be a little bit more room to grow and a little bit more room to improve,” he states. McKinlay also believes that, with more and more people in the field exploring different respective local maxima, the industry as a whole can net benefit together by improving the overall safety and efficacy of the mRNA delivery vehicles that it has available to it.

Meanwhile, in academia there is also focus on using LNPs for other types of genetic cargo, such as other types of RNA or even DNA. “Beyond nucleic acids, there is also significant opportunities for using LNPs in, maybe, combination therapies, where you may have an mRNA molecule, but you also have a lipophilic, small molecule that you can incorporate into the LNP. By combining those two together, you can [potentially] see increased efficacy,” McKinlay says. “Regardless of what the future holds for mRNA therapeutics, a tight handshake between therapeutics discovery and manufacturing will be necessary for its success.”

®
Vol. 48, No. 12
December 2024
Pages: 20–21

When referring to this article, please cite it Mirasol, F. Exploring Opportunities for LNP Delivery in mRNA. 

LNPs have gained solid ground as a drug delivery system for mRNA due to their success in the vaccines arena.

Exploring Opportunities for LNP Delivery in mRNA

FDA announced a draft guidance on Dec. 5, 2024 that explains the procedures and criteria for the agency’s accelerated drug approval for serious conditions. The guidance, 

Expedited Program for Serious Conditions–Accelerated Approval of Drugs and Biologics, 

also explains procedures for expedited withdrawal of approval (1). Revisions made by Congress through the Consolidated Appropriations Act, 2023 (Public Law 117-328) are also discussed.

FDA’s accelerated approval pathway is one of the agency’s expedited programs for the development and review of new drugs to meet unmet medical needs for serious or life-threatening conditions in a timely manner. Sponsors whose product is granted accelerated approval conduct confirmatory trials that are completed postapproval to verify and describe the anticipated effects and benefits of the drug. If these trials confirm the clinical benefit, FDA will then count the confirmatory requirement as met. However, if the sponsor fails to conduct the trial with due diligence or the trial fails to verify effects and benefits, FDA may withdraw approval.

Per the guidance document, conditions and requirements associated with accelerated approval include:

The conducting of postapproval studies to verify and describe the anticipated clinical benefit of the drug.

FDA must specify the conditions for postapproval studies, by the date of approval. These may include a deadline to submit the final study protocol, targets for enrollment progress, and other milestones such as the target date for study completion.

The agency may require postapproval studies prior to the accelerated approval.

A succinct description of limitations of usefulness and anticipated benefits must be included in the drug label’s Indications and Usage section with a reference to the Clinical Trials section.

Copies of sponsor’s promotional materials must be submitted to FDA with specified time frames.

Progress reports on the postapproval studies must be submitted to FDA approximately every 180 days.

“Accelerated approval should not be considered if the completion of an adequate and well-controlled clinical trial to verify and describe clinical benefit will be infeasible,” the agency states in the document (1).

The guidance document specifically provides considerations for the use of surrogate and intermediate clinical endpoints, evidentiary criteria for accelerated approval; requirements for confirmatory studies; and labeling, promotional materials, postmarketing recordkeeping, and safety reporting considerations. In regard to evidentiary criteria, the document states, “Drugs granted accelerated approval must meet the same statutory standards for safety and effectiveness as those granted traditional approval. For effectiveness, the standard is substantial evidence based on adequate and well-controlled clinical investigations. For safety, the standard is having sufficient information to determine that the drug is safe for use under the conditions prescribed, recommended, or suggested in the proposed labeling. An application for accelerated approval should also include adequate evidence that a proposed surrogate endpoint or an intermediate clinical endpoint is reasonably likely to predict the intended clinical benefit of a drug.”

The document’s section on withdrawal of accelerated approval states, “The Agency generally expects that any proposal to withdraw approval of a drug that has been granted accelerated approval will be issued by the center or centers (hereafter, Center) that approved the drug. When the data or other information received by the Agency raises concerns that one or more of the criteria for withdrawing approval may have been met, responsible officials within the Center should discuss their concerns with the sponsor and seek an appropriate resolution. Such discussions may result in the sponsor’s voluntary request for withdrawal of approval under 21 

 601.5(a) or other regulatory actions, as appropriate, 552 depending on the circumstances” (1).

Expedited Program for Serious Conditions–Accelerated Approval of Drugs and Biologics, Guidance for Industry, Draft Guidance

The draft guidance provides information on the agency’s accelerated approval process and criteria for approval.

FDA Releases Guidance on Accelerated Approvals

On Dec. 5, 2024 both Eli Lilly and Company (Lilly) and Amgen announced their plans to expand their manufacturing facilities in Kenosha County, Wis., and Holly Springs, NC, respectively. Lilly said it is investing $3 billion to expand the Kenosha County manufacturing facility it acquired earlier in 2024 (1), while Amgen said it will invest $1 billion to establish a second drug substance manufacturing facility at its site in Holly Springs, which brings Amgen’s total planned investment in Holly Springs to more than $1.5 billion (2).

Lilly’s investment aims to extend the company’s global parenteral product manufacturing network to meet growing demand for its diabetes and obesity therapies as well as future pipeline medicines across therapeutic areas. Construction for the expansion is expected to begin in 2025. Lilly expects to add 750 highly skilled jobs to the current approximately 100-person workforce at the Kenosha County location. The new highly skilled jobs will include operators, technicians, engineers, and scientists and will be in addition to the more than 2000 construction jobs that will open up during the construction phase.

"Today's announcement represents our single largest US manufacturing investment outside our home state of Indiana and will add to our ability to expand capacity to make both our existing and future pipeline of medicines right here in the Midwest," said Edgardo Hernandez, executive vice-president and president of Lilly Manufacturing Operations, in a company press release (1). "We look forward to bringing high-wage, advanced manufacturing, engineering, and science jobs to people in Wisconsin, a state that is becoming a critical geography in our global manufacturing operations."

Lilly plans to use advanced automation—including guided vehicles, robotics, and production equipment—to accelerate injectable medicine production. Digital automation will be embedded throughout the site for data management to operations to accelerate processes and increase accuracy.

Lilly aims to foster partnerships with local higher education institutions and support various community initiatives in Wisconsin to advance innovation in pharmaceutical manufacturing.

Amgen grows drug substance manufacturing capacity

Amgen’s investment in its Holly Springs site will build a new manufacturing facility for drug substance production. As part of the expansion, the company will incorporate cutting-edge technologies and sustainable practices. Together with the already existing facility at the site, the company’s investments will create 370 new jobs in the region and will support a robust biomanufacturing hub (2).

"This expansion underscores our unwavering focus on bringing transformative medicines to patients around the world," said Robert A. Bradway, chairman and chief executive officer at Amgen, in a company press release (2). "North Carolina will be an important part of our global manufacturing network as we continue to meet the growing demand for our innovative therapies while generating significant local economic impact."

Amgen chose North Carolina, which has grown a reputation as a premier life sciences destination, for the state’s ecosystem of life sciences innovation and skilled workforce. “This expansion enhances the company's global biomanufacturing network, leveraging decades of operational expertise and technological advancements to ensure the reliable and efficient delivery of high-quality medicines to patients worldwide,” the company stated in its release (2).

1. Eli Lilly and Company. Lilly Announces $3 Billion Expansion of its Recently Acquired Manufacturing Facility in Wisconsin. Press Release. Dec. 5, 2024.
2. Amgen. Amgen Announces $1 Billion Manufacturing Expansion in North Carolina. Press Release. Dec. 5, 2024.

Lilly is investing $3 billion to expand its recently acquired manufacturing facility in Wisconsin, while Amgen is investing $1 billion to expand its facility in North Carolina.

Lilly and Amgen Each Announce Major Expansions of US Manufacturing Sites

Richard K. Burdick is principal of Burdick Statistical Consulting and Professor Emeritus of Statistics at Arizona State University.

3d rendering. Empty blank black metal sphere balance scale on white background. | Image Credit: ©PATARA – stock.adobe.com

To assess the impact of process changes, stability data (e.g., measurements of drug product over time) at recommended storage conditions are often required. Pre- and post-change degradation rates (slope over time) do not need to be identical, but do need to be highly similar, with scientific justification that any observed differences will not impact safety or efficacy. Because slopes at the recommended storage conditions are often quite small, the product is also exposed to non-recommended storage conditions (e.g., higher temperature) to quickly accelerate degradation. In comparability studies, it is necessary to demonstrate that the degradation rates (slopes) of the pre-change and post-change processes are comparable. Burdick and Sidor proposed a statistical test of equivalence that can be applied in these situations (1). However, it might be the case that practically sized experiments may lack sufficient power to perform the equivalence test. An alternative approach is proposed based on the quality range test recommended by FDA in their draft guidance for comparing biosimilars (2). This approach uses a visual interpretation of study results based on a heuristic rule for claiming process comparability.

A typical study design used in an accelerated experiment requires L=3 lots from both the pre-change and post-change processes. There are typically T=3 time points measured on each of the six lots with one measurement per lot at each time point. 

 displays plots of two data sets with L=3 lots, T=3 time points (

=28 days), and one measurement at each time point (a total of nine values for each process). The response variable is cation exchange (CEX) Main (%). The lines shown in the figure are the least square lines of fit for each of the six lots. The complete data set is provided in 

Of particular interest is the comparison of the line slopes for the two processes. Slopes differ within each process due to analytical error. Differences between processes indicate a difference in degradation rate between the two processes and should be investigated for impact on safety or efficacy.

A visual representation that allows one to focus exclusively on slopes is provided in 

. Here suitable constants have been added to each y-intercept so that they are all equal to the same (arbitrary) value.

 that the slopes from the two processes in Data Set 1 overlap the same region and there is no clear delineation between the two processes. In contrast, all three slopes of the post-change process are steeper than those of the pre-change process in Data Set 2, and there is no overlap. One might declare comparability in Data Set 1; whereas with Data Set 2, it appears the processes may not be comparable.

The quality range test provides a statistical heuristic based on a visual comparison of the slope values of the pre-change and post-change lots. The quality range test was introduced by FDA in their 2019 guidance document for analytical comparison of biosimilar products (2). One can perform the quality range test for the accelerated study by following these steps:

®
Vol. 48, No. 10
December 2024
Pages: 26–28

When referring this article, please cite it as Burdick, R. K. Comparability in Accelerated or Stressed Stability Studies Using a Quality Range. 

A statistical procedure based on a quality range is proposed for demonstrating comparability between pre-change and post-change process slopes in an accelerated or stressed stability study. 

Comparability in Accelerated or Stressed Stability Studies Using a Quality Range

Cambrex, a contract development and manufacturing organization, announced on Dec. 5, 2024 that it has entered into an agreement with Eli Lilly and Company (Lilly) aimed at Cambrex providing accelerated access to clinical development capabilities for Lilly's biotech collaborators.

Under the agreement, Cambrex will partner with Lilly's early external innovation and collaboration arm, Lilly Catalyze360-ExploR&D. Cambrex will provide drug substance, drug product, analytical service labs, and R&D expertise to biotech collaborators teamed with Lilly Catalyze360.

"We are excited to support Lilly's mission to enable external biotech collaborators to expedite their product development," said Brandon Fincher, president of Early Stage Development & Testing at Cambrex, in a company press release (1). "Our Longmont [Colorado] facility offers all services required to advance early stage small molecules into the clinic with speed, flexibility, and scientific excellence. We look forward to propelling Lilly Cataylze360 collaborators forward on an efficient path to success with their products."

Lilly Catalyze360 focuses on advancing science and supporting emerging medicines via investment and through its three pillars—Lilly Ventures, Lilly Gateway Labs, and Lilly ExploR&D. Under the agreement, Cambrex will partner with ExploR&D, which brings enterprise learning, scientific know-how, and best-in-class research and development capabilities to accelerate partner science, according to the company press release.

Primary services offered through the agreement will be conducted in the Longmont, Colo., facility. Additional support will be provided by Cambrex's network, as needed. Cambrex's Longmont facility specializes in the early development of investigational new drugs, from process development and drug substance manufacturing through to formulation development and drug product manufacturing. The facility houses capacity for non-good manufacturing practice (GMP) and current good manufacturing practice (CGMP) production. The site simultaneously provides full analytical development and CGMP testing support.

Cambrex also recently strengthened its process development capabilities when it formed a strategic collaboration with Atinary Technologies in September 2024 (2). Under this collaboration, Snapdragon Chemistry, a Cambrex company, will integrate Atinary’s AI-driven and automated process optimization technology with its own state-of-the-art laboratories and process development capabilities, which will allow Snapdragon to tackle complex challenges in drug development. Snapdragon specializes in API process development, with a focus on continuous flow process development, lab automation, and complex synthetic APIs.

Under the collaboration with Atinary, Snapdragon has integrated its proprietary LabOS software platform with Atinary’s SDLabs platform to build a fully automated and AI-driven, medium-throughput Solid-Phase Oligonucleotide Synthesizer (SPOS). The purpose of the SPOS is to enable autonomous multi-objective process optimizations. Atinary’s AI platform has enhanced Snapdragon’s process optimization campaign with significant improvements across tested applications. Snapdragon was able to demonstrate up to an 18% increase in product yield and a 22% reduction in cost compared to processes previously optimized by an experienced scientist (2).

“Atinary is committed to accelerating innovation and delivering cutting-edge solutions to our customers,” said Hermann Tribukait, PhD, co-founder and CEO of Atinary Technologies, in a company press release issued in September (2). “Our work with Snapdragon showcases the power of our AI and data-driven process to solve complex chemistry and molecular discovery challenges in the pharmaceutical and biotech industry, orders of magnitude faster than traditional methods.”

1. Cambrex. Cambrex Announces Strategic Agreement with Lilly to Support Biotech Collaborator Manufacturing. Press Release. Dec. 5, 2024.
2. Cambrex. Atinary Technologies Announces Strategic Collaboration with Snapdragon Chemistry, a Cambrex Company. Press Release. Sept. 26, 2024.to Support Biotech Collaborator Manufacturing. Press Release. Dec. 5, 2024.
2. Cambrex. Atinary Technologies Announces Strategic Collaboration with Snapdragon Chemistry, a Cambrex Company. Press Release. Sept. 26, 2024.

Through the agreement, Cambrex will provide accelerated access to clinical development capabilities to Lilly’s biotech collaborators.

Cambrex Enters Agreement with Lilly to Support Biotech Collaborator Manufacturing 

Felicity Thomas is the former Associate Editorial Director of Pharmaceutical Technology, Pharmaceutical Technology Europe, BioPharm International.

In this exclusive Drug Digest video interview an industry expert discusses key trends impacting solid dosage drug development and manufacturing. 

Focusing on Solid Dosage Trends and Demand

In this exclusive Drug Digest video interview, Felicity Thomas, Associate Editorial Director, 

Group, interviews Dr. Ali Rajabi-Siahboomi from Colorcon about solid dosage drug development and manufacturing. During the discussion, Dr. Ali will provide an overview of the solid dosage drug development and manufacturing sector, factors driving growth in the market, challenges facing companies and the shifts in demand, the evolution of coating design, and regulatory considerations impacting oral solid dosage development and manufacturing.

Ali Rajabi-Siahboomi is the vice president and chief innovation officer at Colorcon, based at the Global Headquarter, USA. Rajabi-Siahboomi has held various academic positions in Nottingham and Liverpool JM Universities in the UK before joining Colorcon as technical director, responsible for Europe, the Middle East, and Africa in 2000. His main research interests are solid dosage form pharmaceutics, pharmaceutical technology with emphasis on oral drug delivery systems, solubility enhancement to improve bioavailability, and consistency of drugs and bioequivalence formulations. He has published more than 300 articles, book chapters, abstracts, and patents and obtained his B.Pharm. and PhD in pharmacy from the University of Nottingham (UK).

This episode of Drug Digest is sponsored by:

Drug Digest is a tech talk video series with the 

® editors, who interview industry experts to discuss the emerging opportunities, obstacles, and advances in the pharmaceutical and biopharmaceutical industry for the research, development, formulation, analysis, upstream and downstream processing, manufacturing, supply chain, and packaging of drug products.

 Emerging Therapies and Targeted Delivery

 Advances in Small-Molecule Manufacturing

Videos

Drug Digest: Focusing on Solid Dosage Trends and Demand

Close-up of a microscope lens examining microarray slides, a crucial tool in genomic research and medical diagnostics | Image Credit: © Varunee - Stock.Adobe.com

It is well-known that developing a drug from the discovery through to commercialization is a costly endeavor, with market analysis showing that the average R&D cost to develop an asset for 2022–2023 was $2.3 billion (1). However, many drugs do not make it through to commercialization, in fact it has been reported that clinical attrition rates for drugs that have reached Phase I trials is as high as 90% of drug candidates (2).

As a result of the high costs and level of risk inherent with drug development, it is imperative that companies efficiently determine the safety and efficacy of their candidates, to minimize any potential failure. “Bioanalytical testing is essential in drug development because it ensures the safety, efficacy, and quality of pharmaceutical compounds through quantitative analysis of drugs and their metabolites in various biological matrices such as plasma, urine, blood tissue, [and so on],” asserts Sujata (Suzzy) Bhavekar, study director at SGS North America. “Early identification of side effects through rigorous testing can reduce development costs by up to 20% (3). Drugs with robust bioanalytical data are 50% more likely to gain regulatory approval (4).”

“Bioanalytical testing covers a range of techniques and technologies used to quantify drugs and their metabolites, or biomarkers, in biological media,” adds Iain Love, director, Chromatographic Bioanalysis & Discovery Sciences, Department of Chromatographic Bioanalysis, Charles River. “The concentration data generated through bioanalytical testing are important as they inform researchers on how long a drug stays in the body and the beneficial effects or otherwise while it’s in there. Knowing the concentration of drug candidates and understanding the levels at which the drug has positive or negative effects on the body is critical to drug development.”

Vital data on drug absorption, distribution, metabolism, and excretion can be gained through bioanalytical studies, which helps developers to determine dosing regimens, agrees Bhavekar. “Poor pharmacokinetics contribute to 30% of drug failures, highlighting the importance of accurate testing (5),” she says.

Additionally, bioanalytical studies help with biomarker discovery, which improves the success rate of targeted therapies by 25% and supports personalized medicines (6), Bhavekar continues. Furthermore, suitable bioanalytical testing helps ensure compliance with regulatory standards, including good laboratory practice (GLP) and good clinical practice (GCP). “Approximately 85% of drug applications meet approval criteria with solid bioanalytical data (7),” Bhavekar says.

“Regulatory considerations are extremely vital in bioanalytical studies to ensure compliance with international standards and facilitate successful drug approval,” continues Bhavekar. “Adhering to GLPs and GCPs is essential to ensure data accuracy, reliability, and reproducibility.”

“Bioanalytical testing is carried out according to strict inter(national) guidelines and quality management systems,” agrees Love. “Monitoring authorities translate globally recognized quality management systems into national regulations.”

At the national level, there are guidelines such as FDA’s guidance documents on bioanalytical method validation and immunogenicity (7,8) and the European Medicines Agency’s guidance on bioanalytical method validation (9), Bhakevar adds. “Compliance with 21 CFR [Code of Federal Regulations] Part 11 (10) is crucial for maintaining the credibility of electronic records and ensuring data integrity,” she says.

“In addition to these national regulations, bioanalytical studies are also carried out following the International Council for Harmonisation (ICH) M10 guideline,” confirms Love. In fact, EMA’s guidance on bioanalytical testing has been superseded by the ICH guidance (11), since its publication in 2022.

“The ICH M10 guideline provides a harmonized approach across various regions including the US [United States], EU [European Union], Japan, and other member countries [and] provides a robust framework for validating methods, focusing on precision, accuracy, selectivity, sensitivity, and reproducibility, thus enhancing global consistency in bioanalytical testing,” Bhakevar specifies. “These regulatory frameworks, including ICH M10 and FDA guidelines, form the backbone of robust bioanalytical testing, driving drug development toward clinical and commercial success.”

“Outsourcing partners are an increasingly important piece of the drug development puzzle,” notes Love. “Partnership with a bioanalytical provider can bring about several tangible advantages that can help get drugs to the patients who need them in the shortest time possible. This could be from simple access to appropriate analytical platforms to receiving advice on planning the development program.”

In terms of regulatory compliance, the right partnership with a contract research organization that complies with global regulatory standards can ensure that the bioanalytical data generated meets the relevant requirements, Bhakavar explains. “Comprehensive services [provided by a partner company], including method development, validation, and routine analysis, allow sponsor companies to focus on core R&D activities while relying on their outsourcing partner for specialized testing,” she says.

“Outsourcing partners are also likely to be able to apply experience gathered from across a broad portfolio of modalities and development scenarios that can inform program decision-making,” confirms Love. “More than ever, bioanalytical science is involved in shaping programs, so careful selection of an expert outsourcing partner is an activity that can bring about the significant benefits involved with an accelerated drug development program.”

There are also benefits in terms of the flexibility and scalability of operations that a partner company can provide for a sponsor’s needs, Bhavekar remarks. “Outsourcing provides flexibility in scaling bioanalytical operations according to project needs, from preclinical to late-stage clinical trials. This scalability helps manage resources efficiently and reduces the burden on in-house teams,” she says. “[Additionally], with large-scale facilities and automated processes, outsourcing partners can handle high volumes of samples, ensuring rapid data delivery and accelerating the drug development timeline.”

“[Furthermore], outsourcing partners invest in cutting-edge technologies and platforms, such as mass spectrometry and immunoassays, to deliver precise and reliable data, which may not be feasible for smaller companies to implement internally,” specifies Bhavekar. “Overall, outsourcing bioanalytical testing to a reputable partner can enhance the efficiency and quality of the drug development process, enabling sponsor companies to achieve their project goals more effectively.”

“There has never been a more vibrant time to be involved in bioanalytical testing. It’s sometimes a challenge to keep up with the innovation both from within the drug development industry and from supporting partners,” notes Love. “There have been, and continue to be, some hugely innovative therapies discovered and developed by the pharma and biotech sectors. At the same time, instrument and software manufacturers continue to push boundaries with what can be done to generate extremely powerful data in support of drug development.”

An area of great interest for Love is that of patient centric sampling (PSC), which is “the description given to a number of techniques and approaches that affords patients choice: choice on how a biosample is taken but also the choice of whether to provide biosamples in a clinical setting or remote from a clinical setting,” he says. “This enablement of decentralized trials is potentially the biggest and most impactful paradigm change in the clinical space for a long time.”

For Bhavekar, the many advancements that have been seen in the field of bioanalytical testing have helped to greatly improve the analysis of both small and large molecules in drug development. For example, she highlights the hybrid approach of combining ligand-binding assays with liquid chromatography coupled with tandem mass spectroscopy as a way of improving sensitivity and specificity of bioanalytical studies for both small and large molecules.

Platform approaches represent another advancement in the field, Bhavekar continues. “Enzyme-linked immunosorbent assay, plasmon resonance immunoassay, hybridization assay, immuno-PCR [polymerase chain reaction] assay, and the Meso Scale Discovery platform are widely utilized methodologies,” she says. “These platforms are highly specific and sufficiently sensitive, enabling the generation of controlled and customized data that meets regulatory compliance standards.”

As the field of bioanalytical testing evolves, it is important for drug developers to stay at the forefront of the advancements to ensure they remain competitive.

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