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

Cynthia A. Challener

Tablet, Capsule counting machine conveyor in production line of pharmaceutical industry. | Image Credit: ©funfunphoto -stock.adobe.com

Automation has been recognized for over a century to enable consistent, efficient manufacturing. In the past few decades, even the conservative pharmaceutical industry has begun to deploy automation solutions with the goal of increasing quality and reducing cost (1). The rate of adoption is steadily increasing as not only equipment innovations, but advances in digital technologies, have led to significant improvements in efficiency, reliability, and data analytics (2,3). While many of the earliest efforts at automation of pharmaceutical manufacturing were applied to oral solid dose (OSD) production, OSD manufacturers have been slower to pursue automation solutions than biologics producers. 

What are the drivers for adoption of automation?

There are many drivers for the more widespread automation of OSD manufacturing operations, including increased drug substance and formulation complexity and pressure to reduce time and cost. Automation offers the benefits of speed, precision, consistency, and reduced manual interventions.

While time-saving and cost pressures are key motivators in adopting automation, these factors only scratch the surface of the technology’s potential, says John Nadzam, global manager, OSD equipment and novel technology in the pharma services business of Thermo Fisher Scientific. “The ability to automate processes acts as a force multiplier for efficiency, consistency, and quality in drug manufacturing. As modern pharmaceutical operations face increasing complexity, smaller batch sizes, and demands for faster scale-up from development to commercial production, automation helps reconcile these competing needs,” he observes.

From a quality perspective, automation supports right-first-time execution and data integrity by minimizing manual data entry and transcription, Nadzam explains. Automated data capture also enables real-time process understanding and tighter control, helping reduce deviations, improve compliance, and support ongoing process verification.

Automation also helps manufacturers address expanding workforce challenges. “As the industry faces workforce shortages and skills gaps, automation amplifies limited resources, allowing scientists and operators to focus on critical process understanding rather than repetitive tasks,” Nazdam notes.

While automation in small-molecule drug product manufacturing has moved well beyond the pilot phase and is now a strategic imperative, it remains underutilized and fragmented in traditional batch manufacturing.

Many operations use automation primarily for recipe management; proportional, integral, derivative loop control; and basic process data acquisition, according to Nadzam. He observes that use cases do persist across unit operations in areas like granulation, compression, and coating, but most do not currently benefit from comprehensive data integration. “In many instances, we still see reliance on paper records, manual signatures, and standalone data systems. These factors limit the ability to derive cross-unit insights or perform predictive analytics. The contrasts between biologics production and continuous manufacturing are stark compared to small-molecule batch production, which, in comparison, has been slower to modernize,” he states.

What are the benefits beyond incremental improvement?

Automation delivers precision and consistency much faster than human-driven systems. “When it comes to throughput and uptime, real-time data acquisition through automated processes enables closed-loop adjustments, reducing out-of-spec material and increasing equipment availability,” says Doug Hausner, senior R&D staff scientist in the pharma services business of Thermo Fisher. As an example, he highlights the integration of tablet press feedback with in-line hardness and weight testers to minimize rejection and downtime.

Many unit operations involved in small-molecule drug manufacturing also require tight control of critical process parameters that impact product quality attributes, and automation provides a means for improving performance by enabling real-time monitoring and adjustment of process performance (5–7).

Electronic batch records, meanwhile, increase data security and integrity by eliminating transcription errors and streamlining review by exception. “These efforts ultimately support attributable, legible and intelligent, contemporaneous, original, accurate principles and audit readiness,” Hausner states. They also support greater process understanding. Digital connection of multiple unit operations, he observes, allows teams to correlate process data, such as granule particle size distribution, tablet hardness, and coating uniformity, to reveal root causes of variability.

Automated systems with built-in analytics can also identify early signs of drift, allowing for proactive interventions (e.g., predictive maintenance and process optimization to improve quality), Hausner notes. They can also operate continuously and with minimal downtime, enabling faster cycle times, and higher output based on high-quality operational data. In tableting and encapsulation, for example, robotics can maintain consistent production rates while reducing manual handling.

Other benefits of automation include reduced waste generation, process deviations, and batch failures, all of which contribute to higher costs (6–8). The specific ways in which processes are enhanced vary for each unique unit operation, but overall, they add up to better process control and product quality. As a result, automation is becoming a necessity for small-molecule drug manufacturers seeking to establish and maintain competitive advantage.

Combination of coordinated hardware and software solutions

Effective automation in small-molecule drug product manufacturing is not achieved with a single technology. 

Both hardware and software work in concert to achieve enhanced process performance 

, robotic solutions play an increasing role in small-molecule drug product manufacturing

 (9). Examples include robotic presses and feeders used in tableting and encapsulation processes, real-time monitoring tools such as those based on near-infrared (NIR) and Raman spectroscopy across many different unit operations, and autonomous mobile robots and robotic picking systems used in warehousing and packaging.

Software solutions today are equally important, as they enable integration of disparate automated unit operations with data flowing between them and supporting a holistic approach to small-molecule drug product manufacturing. As such, software advances are equally important to the expanding adoption of automation solutions.

Nadzam highlights the growing role of artificial intelligence (AI) and machine learning (ML) in improving the effectiveness of automation systems for small-molecule drug product manufacturing operations. “Not only can these tools forecast formulation challenges ahead of time, but they also enable innovators to tackle issues around solubility, permeability, bioavailability, optimal first-in-human dosing, packaging selection, investigational new drug data generation and scale-up planning—insights that can ultimately accelerate progress from development to commercialization,” Nadzam contends.

Although the benefits of automating small-molecule drug manufacturing are increasingly clear, there are still several challenges the industry must overcome to see widespread adoption. This complex set of challenges must be navigated with care and foresight.

The two most significant challenges, according to Hausner, continue to be the high upfront investment required and the ongoing need for robust skills development. “Configuring automation requires both capital investment and technical expertise, which is why it is often outsourced due to internal resource constraints. However, the reality is that outsourcing can also increase project timelines and costs,” he observes. The need for significant upfront capital investment can also be a barrier to adoption (10), particularly for small-volume products, such as those targeting rare diseases with limited patient populations and personalized therapies.

Moving to automated small-molecule drug product manufacturing also requires a shift in perspective and often training of operators in new skill areas beyond operation of the new systems, such as in data analysis and troubleshooting. That requires effective and appropriate change management, which can be difficult to achieve but is crucial to success, Hausner says.

“Operators may initially view automation as restrictive or complex. Demonstrating how automation relieves them of repetitive documentation, enhances process visibility, and improves quality of life on the floor is key to success,” he notes.

Another challenge highlighted by Hausner is data standardization and contextualization. “Even with open protocols like open platform communications unified architecture, integrating legacy systems across vendors and facilities remains complex. Without consistent data structures, the promise of AI/ML-driven optimization remains difficult to realize,” he explains.

Regulatory expectations for validation and assurance of data security, integrity, and traceability can also create challenges as targets continue to evolve.

A defined roadmap and comprehensive strategy support success

The key to successful automation of small-molecule drug product manufacturing is to pursue the right strategy that leverages the organization’s broad expertise and process knowledge. “Automation is not simply a technical upgrade—it’s a strategic transformation,” insists Hausner. “To realize its full potential, organizations must adopt a holistic approach that blends technology, process design, and cultural readiness,” he says.

Companies should first, adds Hausner, establish cross-functional teams involving quality, information technology, regulatory, and operations personnel to ensure alignment and data integrity. Development of digital roadmaps that provide these teams with articulate short- and long-term goals for connectivity, data standards, and system interoperability will help keep manufacturing operations on track, he says.

In-depth process understanding is also essential, as it supports the use of a quality-by-design approach to process development, which in turns leads to robust, scalable processes. In addition, because human oversight remains essential for automation to be successful, developing internal automation expertise and building or training a core team capable of vendor management, configuration, and troubleshooting are essential, Hausner observes.

Drug developers should, continues Hausner, follow an approach that begins with smaller pilot projects and systems, with the ability to scale up to future needs. As an example, he suggests starting with focused automation projects such as process analytical technology (PAT) integration on a single-unit operation to demonstrate value, and then expand incrementally. Hausner notesthat working with contract development and manufacturing organizations that have these key processes in place can help make it easier for pharmaceutical and biotech organizations to adopt automation, reduce bottlenecks across the drug development continuum, and ultimately increase efficiency in bringing critical medications to patients.

Although technology is not the only crucial element to successful automation of small-molecule drug manufacturing, it is an essential component, and effective, advanced solutions are needed to ensure the maximum benefits can be realized by implementing automation.

Innovations in both equipment and software enabling smarter systems, seamless integration, and real-time responsiveness are not only improving performance, but also making implementation more accessible across scales and product types.

Several converging trends are lowering barriers to adoption of automation solutions for small-molecule drug product manufacturing operations, says Nazdam. They include a growing availability of open vendor architects as equipment suppliers increasingly support standardized communication interfaces, enabling interoperability; advanced AI/ML tools capable of predicting formulation behaviors and, therefore, addressing key challenges throughout small-molecule drug development; cloud-based manufacturing execution systems that improve scalability and enable enterprise-wide visibility; and digital twins and simulation tools that allow for virtual commissioning, predictive tuning, and training without disrupting operations.

Ongoing innovations in technology that enable better integration of automation systems, wider applications for real-time monitoring, and greater use of AI/ML are expected to not only drive greater adoption in small-molecule drug product manufacturing but increase their performance and the benefits they provide.

A broadening array of advanced PAT tools will enable more effective real-time monitoring for better process control and improved quality while supporting higher productivity through reduction in the need for traditional batch testing. Wider implementation of PAT-driven feedback control will also, notes Hausner, enable greater integration between operations, even in traditional batch environments.

Broader adoption of AI/ML is also anticipated by Hausner as these tools become more accessible. In the short term, they will support optimization of small-molecule drug product manufacturing across all unit operations by enabling real-time adjustments and predictive maintenance. Longer-term, the integration of AI-driven decision support, digital twins, and autonomous optimization loops will redefine how pharmaceutical facilities are operated, according to Hausner. “These systems will use live data streams to continually refine process parameters and minimize human intervention while maintaining compliance through automated documentation,” he notes.

“The ultimate vision,” concludes Hausner, “is a self-regulating, fully digital plant where real-time data drives dynamic control, lifecycle management, and continuous improvement.”

Bremme, L.; Darino, L.; Parry, B.; Teo, K. X. Automation and the Future of Work in the US Biopharma Industry. McKinsey & Company Insights, 2020, 

https://www.mckinsey.com/~/media/McKinsey/Industries/Pharmaceuticals%20and%20Medical%20Products/Our%20Insights/Automation%20and%20the%20future%20of%20work%20in%20the%20US%20biopharma%20industry/Automation-and-the-future-of-work-in-the-US-biopharma-industry-vF.pdf

Meticulous Research. Pharmaceutical Automation Market Size, Share, Forecast, & Trends Analysis by Offering (Solutions and Services), Mode of Automation (Semi-automatic Systems and Fully automatic Systems), End User (Pharmaceutical Industry and Biotech Industry), & Geography - Global Forecast to 2031. August 2024. 

https://www.meticulousresearch.com/product/pharmaceutical-automation-market-5280

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https://www.pharmtech.com/view/using-pat-in-osd-manufacturing

Optimising the Oral Solid Dose Manufacturing Process: A Comprehensive Guide

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https://manufacturingchemist.com/navigating-the-barriers-to-automation-in-manufacturing

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

®
Vol. 49, No. 9
November/December 2025
Pages: 18–21

When referring to this article, please cite it as Challener, C.A. Leap Forward in Automation Anticipated for Small-Molecule Drug Product Manufacturing. 

Articles

Automation in pharmaceutical manufacturing, particularly for oral solid dose (OSD) production, is gaining traction due to its potential to enhance efficiency, consistency, and quality. Key drivers include increased drug complexity, cost pressures, and workforce challenges. Despite its benefits, automation remains underutilized due to high upfront costs and the need for skilled personnel. Effective automation requires a combination of hardware and software, with AI and machine learning playing crucial roles. Overcoming barriers involves strategic planning, cross-functional collaboration, and leveraging advanced technologies to ensure seamless integration and real-time responsiveness.

Technology advances are converging with cost and time pressures and formulation complexity to drive greater adoption of automated processes.


Leap Forward in Automation Anticipated for Small-Molecule Drug Product Manufacturing

Back view portrait of unrecognizable scientist wearing protective suit and mask working with hazardous materials in medical research laboratory

| Image Credit: ©Seventyfour - stock.adobe.com

Demand for highly potent active pharmaceutical ingredients (HPAPIs) remains high in the face of a continued focus by the pharmaceutical industry on the oncology space, targeted therapies, and precision medicine. Consequently, HPAPIs account for a growing percentage of pipeline candidates. Advances in synthetic chemistry, bioproduction, continuous manufacturing, and containment technologies are increasing the feasibility of bringing these ever more-potent therapeutics to the market.

is estimated to be expanding at a compound annual growth rate (CAGR) of approximately 9% to surpass $60 billion by 2034 (1,2). Given the specialized nature of HPAPI manufacturing, this strong demand growth is also driving healthy growth for the HPAPI contract manufacturing market. One estimate pegs the global value for this sector at $8.15 billion in 2024 and expanding at a CAGR of 10.93% to reach $23 billion by 2023 (3).

A distinguishing feature of the HPAPI market is the rapid rate of change it has been experiencing over the past decade-plus. The fast pace of evolution can be attributed to several factors, according to Ester Masllorens, senior vice president of global R&D with Olon. “The HPAPI landscape is rapidly evolving, driven by breakthroughs in oncology, rare diseases, and advanced delivery platforms such as antibody-drug conjugates (ADCs) and peptides. The growing complexity and potency of these molecules requires sophisticated containment capabilities and manufacturing strategies,” she says.

At the same time, Alessandro Agosti, director of Olon’s R&D Rodano Hub, notes that regulatory scrutiny and environmental, safety, and governance (ESG) expectations are compelling contract development and manufacturing organizations (CDMOs) to operate in a cleaner, safer, and more sustainable manner.

Technological innovations are helping to both reshape HPAPI development and scale-up and addressing evolving challenges. “For example, technologies such as flow chemistry, continuous manufacturing, and advanced membrane technologies reduce operator exposure while enhancing efficiency and consistency,” comments Agosti. He also highlights the increasing importance of automation, predictive modeling, and artificial intelligence-driven design-of-experiments (DoE), particularly for high-potency compounds requiring stringent containment.

What are some technical, operational, and regulatory challenges?

Development and production of HPAPIs have always been challenging given the need for specialized expertise to ensure their safe handling with respect to both operators and the environment. The unique combination of technical, operational, and regulatory challenges faced by HPAPIs continues to increase as the diversity and higher complexity of HPAPI candidates under development today continues to widen, according to Masllorens.

“In addition to the need for robust containment, validated cleaning procedures, and strict protection of operators, HPAPI CDMOs must also be able to implement highly specialized synthesis and purification capabilities, often under extreme containment, to generate molecules from ADCs and peptides to newer types of bioconjugates,” observes Masllorens. She adds that ease of formulation, bioavailability, and supply chain security remain persistent issues as well and are further complicated by fluctuating raw material markets and global economic pressures.

Why is a multipronged approach essential?

To overcome these many challenges, HPAPI manufacturers are pursuing strategies that involve numerous independent tactics. “To address the complexity of HPAPI development, the industry is increasingly adopting modular containment systems, green chemistry approaches, and digitalization,” states Masllorens. Regardless of the specific approach taken, she emphasizes that a strong commitment to innovation, safety, and sustainability is a fundamental requirement.

Olon has found that co-locating its high-containment R&D, quality control, and good manufacturing practice (GMP)-compliant production units has streamlined tech transfer activities and enabled rapid scale-up as client candidates progress through clinical development, according to Masllorens. In a similar vein, the company has connected its global network of 14 sites through a centralized R&D hub, which has supported harmonization of processes and facilitated best-practice sharing across all technologies.

Developing processes using DoE and quality-by-design approaches and advanced modeling tools, meanwhile, ensures safety, robustness, and reliable transfer throughout the entire lifecycle of highly potent compounds, notes Agosti.

Finally, Olon believes that its integration of biological capabilities—microbial fermentation for semisynthetic APIs and bioconjugation to ADCs—with its chemical HPAPI expertise positions the company to meet evolving scientific and sustainability demands of next-generation HPAPI development, according to Agosti. He notes this integrated approach is particularly advantageous for payloads that require both biological and chemical steps such as maytansinoids and camptothecins. In addition, Masllorens believes that the vertical integration of chemical and biological capabilities allows Olon to support clients across a wide range of production volumes and modalities, from early-phase clinical batches to late-stage commercialization.

Managing the move from small- to large-scale manufacturing

Tech transfer is one of the most critical and sensitive steps in HPAPI development, particularly when moving from small-scale R&D settings to full-scale GMP manufacturing. “Key challenges include validating cleaning procedures for ultra-potent compounds, maintaining consistent containment integrity, and ensuring personnel are fully trained to handle these materials safely,” Agosti observes. He adds that for biologically derived HPAPIs, such as bioconjugates, variability in biological systems and complex purification and downstream further complicate scale-up. Here again, integration of R&D, analytical development, and GMP production in the same location ensures that R&D teams stay involved through validation and even first commercial batches, ensuring continuity of knowledge.

What are some continued CDMO investments?

The HPAPI sector is set for sustained growth, driven by a strong pipeline in oncology, rare diseases, and immune-modulating therapies, according to Masllorens. Many of these molecules, such as ADCs and peptide therapeutics, require ultra-high containment, highly specialized synthesis or fermentation, and strict regulatory compliance, which is driving demand for services from CDMOs with advanced and demonstrated capabilities, she adds.

In addition, Agosti notes that clients are increasingly prioritizing ESG performance, supply chain resilience, and digital integration; therefore, CDMOs must be positioned to address these needs as well.

In response to these evolving expectations and the healthy growth of the HPAPI market, a high level of investment by CDMOs has been observed over the past several years, including a few notable moves in the United States in 2024 and 2025.

Olon added new occupational exposure band (OEB) 5 production lines and a state-of-the-art OEB6 facility dedicated to ultra-potent compounds such as ADC payloads and payload-linkers, according to Masllorens. “The OEB6 facility integrates analytical labs, pilot units, and GMP manufacturing and leverages technologies such as flow chemistry, photochemistry, biocatalysis, and tangential flow filtration to enable safer, greener, and more scalable operations and support of the development and production of ADCs,” she says. Plans are in place to expand the OEB6 facility to meet rising demand for ultra-potent payloads and biological intermediates by 2026 and scale Olon’s bioconjugation capabilities. With respect to implementation of newer technologies, Olon has incorporated both upstream and downstream innovations including antibiotic-free processes and codon-optimized microbial expression.

In June 2025, Piramal Pharma Solutions announced it will spend $90 million to expand ADC manufacturing capability at two facilities in the US, including HPAPI production (payload-linkers) in Michigan and ADC fill/finish capacity in Kentucky (4). 

meanwhile, launched a new CGMP analytical testing laboratory dedicated to HPAPIs at its Rancho Cordova, California site in February 2025 (5).

AGC Pharma Chemicals announced in January 2025 

the expansion of its HPAPI capabilities in Barcelona, Spain with the goal of achieving “seamless scale-up from grams to tons within a single integrated ecosystem” (6). Once completed, the company’s entire development and manufacturing chain from its new R&D facility through laboratory to commercial production will be adapted to OEB5 standards.

Sterling Pharma Solutions announced in May 2024 

that it would be investing $3 million to expand its integrated ADC development and manufacturing capabilities in Wisconsin. A dedicated GMP suite including a modular isolator with containment capabilities down to less than 1 nanogram per cubic meter will be added that will enable toxin linker manufacturing up to the kilogram scale (7).

Grand View Research. High Potency Active Pharmaceutical Ingredients Market–By Product, By Drug Type, By Manufacturer Type, By Application–Global Forecast, 2025–2034. February 2025. 

https://www.gminsights.com/industry-analysis/high-potency-active-pharmaceutical-ingredients-market

Mordor Intelligence. High Potency APIs Market Size & Share Analysis–Growth Trends & Forecasts (2025–2030). 

https://www.mordorintelligence.com/industry-reports/highpotencyapis%.

Nova One Advisor. High Potency API Contract Manufacturing Market Size to Hit USD 23.00 Bn by 2034. Nov. 7, 2024. 

https://www.biospace.com/press-releases/high-potency-api-contract-manufacturing-market-size-to-hit-usd-23-00-bn-by-2034

Piramal Pharma Solutions. Piramal Pharma Solutions Breaks Ground on $90M Expansion Plan. Press Release, June 25, 2025. 

https://www.prnewswire.com/news-releases/piramal-pharma-solutions-breaks-ground-on-90m-expansion-plan-302494239.html

SK pharmteco. SK pharmteco Launches State-of-the-Art HPAPI Laboratory. Press Release. Feb. 12, 2025. 

https://www.skpharmteco.com/news-insights/news/sk-pharmteco-launches-state-of-the-art-hpapi-laboratory/

AGC Pharma Chemicals. AGC Pharma Chemicals Announces Expansion of HPAPI Capabilities in Barcelona. Press Release, Jan. 29, 2025. 

https://www.agcpharmachemicals.com/en/news/agc-pharma-chemicals-announces-expansion-of-hpapi-capabilities-in-barcelona/

Sterling Pharma Solutions. Sterling Expands HPAPI Capabilities to Further Strengthen Integrated ADC Services. Press Release, May 30, 2024. 

https://www.sterlingpharmasolutions.com/knowledge-hub/news/sterling-pharma-solutions-expands-hpapi-capabilities-in-germantown/

®
Vol. 49, No. 9
November/December 2025
Pages: 13–14, 21

When referring to this article, please cite it as Challener, C.A. Trends and Challenges Impacting Highly Potent API Development. 

The demand for highly potent active pharmaceutical ingredients (HPAPIs) is rising due to the pharmaceutical industry's focus on oncology, targeted therapies, and precision medicine. This growth is driving the HPAPI market to expand significantly, with a projected value surpassing $60 billion by 2034. Technological advancements in synthetic chemistry, bioproduction, and containment are facilitating the development of these potent therapeutics. The HPAPI landscape is evolving rapidly, with increased regulatory scrutiny and environmental expectations. A multipronged approach, including modular containment systems and digitalization, is essential to address the complex challenges in HPAPI development.

Increasing demand, complexity, and potency are driving innovation and investment in HPAPIs.

Trends and Challenges Impacting Highly Potent API Development

A scientist in sterile coverall gown using a daily checklist check the cleanroom condition status in laboratory. Cleanroom facility. | Image Credit: ©warut – Stock.Adobe.com

As the number of biologic drugs has grown in the pharmaceutical pipeline and market, aseptic manufacturing capabilities have steadily grown in importance. Assuring sterility across many different modalities and product formats/sizes/volumes (e.g., vials, cassettes, prefilled syringes/pens, microdosing systems, intranasal delivery systems, on-body devices, bags, etc.), many of which are increasingly potent, has also become more challenging. Innovation in processing technologies has been essential to address the evolving needs of manufacturers. Key areas of development have included digitalization and automation solutions.

Repeatability and speed with improved quality and yield

Digitalization and automation provide numerous benefits to aseptic processing operations. Two of the most important, according to Alex Strauss, senior principal process equipment engineer with Curia, are greater speed combined with improved repeatability. “The key to automation is having a recipe-driven approach that is qualified. When the operator selects the recipe, the process is then repeatable. In addition, processes such as visual inspection can be completed much more rapidly than is possible by human operators and with incredible accuracy,” he explains.

New filling systems, adds Tom Clemens, senior director of engineering and facilities at Lifecore Biomedical, perform non-destructive weight checks of individual products immediately after filling, improving quality and increasing batch yields. Many also include automated camera verification systems to check vial crimping and stopper placement. “When an issue is detected, the system can automatically stop the line so the problem can be investigated immediately, avoiding costly material losses and potentially much longer production delays,” he says.

Many newer biologic products, including antibody-drug conjugates, radio-immunoconjugates, and cell and gene therapies, require high containment solutions to ensure operator safety. The diversity of products with high activity produced in multiproduct facilities has also created the need for effective solutions that reduce cross-contamination risks.

Regulations such as Annex 1 requirements in the European Union (1) have also been an important driver toward greater adoption of isolator technology to increase product safety by minimizing potential human impacts on sterile drug products, according to Clemens. Many manufacturers have therefore been shifting to the use of isolators for aseptic processing.

“Due to the nature of these closed, isolator-based filling systems, automation and robotics have been incorporated to perform necessary processing steps within the sterile environment,” Clemens observes. For instance, he notes that while older systems may have required an operator to remove a lid from a ready-to-use (RTU) tub of vials or syringes, many new isolator fillers use automated systems that heat the lid adhesive and then remove the lid from the tub with a variety of rollers. “This approach,” Clemens states, “reduces human contact and potential particle generation.”

Visual inspection operations are another area that has benefited significantly from advances in automation and digitalization technologies. These automated visual inspection systems use high-speed cameras and advanced software to automatically check for particulates and defects and can operate at fairly high speeds, according to Strauss. The Syntegon system deployed at Curia’s Albuquerque, NM facility, for instance, can inspect 400 vials per minute, matching the filling system’s filling speed.

As importantly, newer automated visual inspection systems are now available that can reliably evaluate highly viscous formulations (in excess of 100,000 centipoise) in vials and syringes, which they were previously not able to do. “Traditional automated visual inspection systems spin a vial/syringe and rely upon a camera to enable evaluation of the internal movement of the product to identify contaminants, which is not a suitable approach for viscous formulations because they do not flow in the same manner as lower viscosity solutions,” Clemens explains. Newer systems, he comments, incorporate additional cameras and advanced virtual image processing to identify particulates and defects and can even improve performance over time by incorporating machine learning algorithms.

Robotic systems do not just support the incorporation of aseptic filling processes within isolators and visual inspection activities. They have been widely implemented in sterile drug manufacturing processes to automate many different repetitive tasks, including vial filling, sealing, and labeling. By automating these activities, human interaction with sterile drug products is minimized, reducing contamination risks. Furthermore, robotic systems minimize product losses and enable the accurate filling of products that require only very small volumes.

Robotic systems can also eliminate some of the steps required when human operators are more involved in the aseptic filling process. For instance, the VarioSys Flex Line system at its Albuquerque, NM facility uses robotics to remove vials from a pre-sterilized tub, eliminating the need for a vial washer and sterilizing tunnel, according to Eric Schneider, director of global equipment engineering at Curia.

“In manually operated systems, it is not possible to aseptically remove primary containers without obstructing the first air (uninterrupted unidirectional filtered airflow used to provide a sterile environment) and/or subjecting the vials to scratches, etc., thus creating the need for additional steps,” Schneider says. With the VarioSys Flex Line, operator interventions are minimized, simplifying the process while also affording improved quality and safety outcomes.

Schneider does caution, however, that even the best robotics and automation systems still rely on human operators. “Training programs for operators are essential,” he emphasizes. To address this need, Curia operates Sterile University, a program designed to provide both training for new employees and continuing education for all manufacturing and quality staff through a simulated manufacturing environment that replicates the facilities, equipment, operations, and protocols found at the company.

Improvements in automated and digitized analytics

Automation and digitalization of analytics that support aseptic processing has several benefits as well. One of them, according to Jon Kallay, senior scientific portfolio specialist for microbial solutions with Charles River Laboratories, has been the facilitation of the adoption of rapid testing techniques that enable faster product release while still ensuring product quality.

One challenge with complex biologics highlighted by Kallay is the tendency for these products to appear turbid during routine sterility testing due to their physicochemical properties. Identification of such turbidity during manual visual inspection operations typically prompts a lengthy evaluation process to confirm the turbidity is not due to contamination.

Rapid test methods that evaluate multiple attributes, such as detection of adenosine triphosphate, respiration, and other microbial indicators, when combined with automated analysis of test results, provide accurate indicators and eliminate the subjective aspect of test results, Kallay contends. “Analysts no longer need to shake a bottle and determine if it fits their understanding of ‘turbid.’ Results are automatically analyzed and determined to be positive or negative based on the standards established during the validation of the technology,” he says. “Objective results eliminate unnecessary procedures while allowing quick release of product to patients,” Kallay concludes. In addition, more rapid access to test results allows users to respond quickly to any contamination events that could impact the facility and other product lots.

Digitizing the test process also improves data integrity. Documentation errors are not uncommon when test results are managed manually, according to Kallay. With digital test technologies, however, test materials and results are immediately documented and automatically transcribed to the laboratory information management system (LIMS) data storage system, avoiding opportunities for operator error.

The ability to collect real-time manufacturing data during aseptic processing has been, according to Clemens, a real game-changer, as it has made it possible to monitor quality in a proactive rather than a reactive manner. Lifecore Biomedical, for instance, uses a dashboard approach displayed on monitors throughout the facility to keep a watchful eye on manufacturing operations real-time and quickly identify and resolve line stoppages. “Trending and identification of variances, such as between shifts, also support identification of specific groups of operators that could benefit from additional training or opportunities for improving the mix of operator experience levels within shifts,” Clemens notes.

A specific example of an important development in real-time monitoring, according to Laura Choteau, quality control manager at Catalent Biologics, has been the introduction of real-time monitoring for visible particles. “Today, automation allows us to read culture plates from the start of incubation through to the end, with full traceability of the plate’s location and condition throughout. As a consequence, we can detect contamination risks much earlier and respond immediately. That reduces the likelihood of recurring contamination and helps us get much closer to identifying the root cause,” she comments.

Other notable examples of process analytical technology tools improving aseptic processing operations, according to Jean-François Boé, senior director of drug product development for Catalent Biologics, include onboard mass spectrometry and Raman spectroscopy systems embedded into compounding or fill/finish units. “The data generated from these instruments give us real-time visibility into critical process parameters. In the long term, this level of control could reduce the need for traditional final release testing, shifting more quality assurance upstream and closer to the point of manufacture,” he says.

Like Schneider, Boé emphasizes the role still played by human operators in many monitoring activities. For instance, air monitoring systems in Grade A areas still often require operators to insert culture plates, typically using glove ports. This need does carry a small but real risk of contamination, as well as the potential for plate insertion to be accidentally skipped, which would create a gap in monitoring and raises questions during batch review. These issues could be avoided, Boé says, with the use of robotics to install the plates, which would “add a layer of reliability to aseptic operations and increases confidence in the process.”

New data management software equally impactful

Automation in analytics has been around for some time and continues to improve with advances in technologies. What has driven a real shift, according to Choteau, has been how the generated data are managed and used. “Data management plays a central role in helping us act faster and make smarter decisions in aseptic environments. Modern LIMS platforms now allow us to automate calculations and perform trend analysis continuously, which provides more frequent and reliable insights, helps ensure accuracy, and allows quality teams to concentrate on investigations when abnormal trends appear,” she explains.

Off-the-shelf, user-friendly visualization and reporting software, meanwhile, has enabled engineering teams to use data in a way that would have required significant involvement of information technology personnel in the past, Clemens notes. For example, Lifecore Biomedical tracks all line interventions digitally. “Having operators identify reasons for interventions in a consistent manner from shift to shift enables our engineering teams to identify trends and take corrective actions before real issues arise,” he says.

Furthermore, both process efficiency and control are also increased. “Automation brings greater control and traceability. It minimizes sampling errors, confirms that all required samples have been taken, and provides a clear record for each action. This helps maintain compliance and strengthens confidence in our processes,” Choteau states.

Staff training, like with other aspects of digitalization and automation of aseptic processing, remains an essential component, however. Operator readiness to respond in the event of system failures must be maintained, and staff must be able to manage operations manually when needed, according to Choteau. Data security, she adds, is equally important. “Protecting data from cyber threats and ensuring its integrity remains a top priority as more systems become connected and automated,” Choteau observes.

Digital twins support effective process simulation

Modeling is an important aspect of digitalization that is benefiting many process development and manufacturing activities, including sterile production operations. Digital twin technology, for instance, is becoming increasingly valuable in drug substance manufacturing and beginning to show potential in aseptic fill/finish operations, according to Boé.

Digital twins in the pharmaceutical manufacturing context are virtual models of processes that are continuously updated with real-time data, allowing assessment of performance. “By creating a digital replica of equipment and processes, digital twins help us simulate scenarios, predict outcomes, and optimize parameters before moving to physical implementation,” he says. Boé finds this approach especially useful during process transfer, because it allows teams to anticipate risks, reduce variability, and avoid costly delays.

Artificial intelligence has growing value

Digital twins are not the only example of how automation and digitalization are being combined with artificial intelligence (AI), machine learning (ML), and other advanced computing capabilities to further improve aseptic processing. When leveraged together, these myriad technologies are accelerating timelines and enhancing process control, according to Boé.

As an example, Boé points to the benefits AI-driven models can provide for freeze-drying (lyophilization) processes. In particular, he notes they can be used to optimize cycle development, significantly reducing the time required to reach a robust process and positively impacting project timeliness and resource efficiency.

In fact, Boé expects AI and ML to play an even larger role and provide growing value as complexity—product, process, and equipment-related—continues to increase. “AI and ML help with not only data analysis and trending, failure prediction, and workflow optimization; they also support more informed decision making. All of these benefits ultimately lead to greater efficiency and reliability of aseptic manufacturing operations,” he concludes.

1. EC. Annex 1. Manufacture of Sterile Products. 

EudraLex–Volume 4–Good Manufacturing Practice (GMP) Guidelines


Vol. 49, No. 8
October 2025
Pages: 18–20

When referring to this article, please cite Challener, C.A. Advances in Digitalization and Automation of Aseptic Processing. 

The increasing prevalence of biologic drugs has heightened the importance of aseptic manufacturing, necessitating innovations in processing technologies. Digitalization and automation have enhanced aseptic processing by improving speed, repeatability, and quality. The adoption of isolator technology and robotics has minimized human contact, reducing contamination risks. Automated visual inspection systems and real-time monitoring have further improved quality control. Data management advancements have facilitated faster decision-making and process optimization. Digital twins and AI are increasingly valuable for simulating processes and enhancing control, ultimately improving efficiency and reliability in aseptic manufacturing.

The increasing diversity and complexity of injectable drug products is driving innovation.

Advances in Digitalization and Automation of Aseptic Processing

Advanced drug discovery tools are leading the identification of increasingly complex small molecules with physicochemical properties that lend promising biologic and pharmacologic activity while simultaneously creating tremendous challenges in developing formulations capable of delivering the drug to its intended site of action. Many different molecular properties acting alone and interdependently with each other and different formulation excipients and buffers influence the pharmacokinetics and pharmacodynamics of APIs. Using traditional empirical methods for investigating and identifying potential formulations for these complex molecules requires excessive time and resources at a time when reducing cost and accelerating development are essential to success. Formulation scientists are turning to predictive modeling approaches and platform formulation processes that leverage artificial intelligence (AI) and machine learning (ML) to more rapidly and comprehensively evaluate complicated formulation spaces including complex molecules and advanced drug delivery technologies (1–4).

Moving from academia to industrial application

The use of AI/ML for industrial pharmaceutical formulation development is at the nascent stage. “Much of the progress so far has come from academic research, which often seeds future industry innovation, as well as from emerging startups and the steady improvement of open-source models,” says Pauric Bannigan, chief science officer and cofounder of Intrepid Labs.

“Interest is growing rapidly, fueled by increasing recognition of AI/ML’s potential to navigate high-dimensional formulation design spaces, optimize excipient selection, and predict stability or release performance before extensive lab work,” Bannigan observes. Given these developments and the rapid advances in lab automation currently being made, he believes it is only a matter time before AI and ML become widely applied to formulation development.

How are growing formulation challenges overcome?

Small-molecule drug developers face several challenges, and many of them are well-suited to being addressed using AI/ML approaches, according to Bannigan.

Top of the list, says Daniel Joseph Price, head of the Excipients Business and working within Process Solutions in the Life Science business of Merck KGaA, Darmstadt, Germany, is the poor solubility of drug substances, with approximately 70–90% of drugs currently in the pipeline suffering from this problem (5). “This challenge is pronounced for newer drug candidates that often surpass Lipinski’s Rule of Five (6), which sets foundational criteria for oral bioavailability. In this context, overcoming solubility issues to ensure effective oral drug delivery and hence enhancing patient acceptance remains paramount,” Price emphasizes.

Other small-molecule formulation challenges highlighted by Bannigan include achieving precise release kinetics for controlled- and sustained-release systems, identifying robustness early for scale-up and manufacturability, and minimizing API waste during screening. “Each of these challenges is a multidimensional problem where dozens of formulation components and process parameters interact in nonlinear ways,” he says.

Formulations that address these key issues often involve multiple excipients. Different combinations of excipients must thus be investigated along with different formulation concentrations and processing parameters (e.g., mixing speed and time, temperature control, compression force, drying method and rate, etc.) to identify the optimum formulation design. Even with tight constraints, the exploration space can be enormous, according to Bannigan.

Pharmaceutical formulation, Bannigan adds, is a high-dimensional, sparse-data challenge where dozens of excipients and process parameters interact in nonlinear ways. As an example, he notes that with three excipients, five possible concentrations, and five process parameters at three settings each, the total number of possible unique formulations would be 3,645,000. And that would be a conservative case. “In reality, formulations often use more excipients in varying ratios with additional process parameters, pushing the possibilities into the tens or hundreds of millions,” he says. Exploring them all is simply not possible using conventional approaches, including predictive modeling systems leveraging conventional statistical methods.

Without AI/ML, in fact, Bannigan observes that countless breakthrough formulations, and more importantly the medicines they could enable, will remain buried in the noise, invisible to traditional trial-and-error approaches. “AI/ML can cut through this complexity by modeling these interactions, learning from each experimental result, and steering exploration toward the most informative and promising regions of the design space,” he contends.

Specifically, Bannigan notes, AI/ML can map complex, multi-objective relationships between components and performance; identify promising solutions that human intuition might overlook; and adapt in real time as new experimental data arrives through active learning. “By efficiently navigating vast combinatorial spaces, AI/ML not only accelerates the discovery of robust, scalable formulations, it reduces API waste, increases the probability of hitting target product profiles, and brings hidden, high-potential medicines into view,” he observes.

The application of AI/ML in formulation development offers a transformative approach to current formulation challenges, agrees Price. “By using AI/ML, scientists can streamline the formulation process, effectively narrowing down the design space. This allows them to concentrate on formulations and excipients that are likely to have a significant impact, rather than getting lost in exhaustive trial-and-error testing.”

“Furthermore, AI/ML enables researchers to explore a wider array of options, facilitating innovative solutions without the extensive time investment typically required for traditional formulation trials,” Price states. For instance, he anticipates companies harnessing AI/ML technologies to investigate broader ranges of chemicals and solutions by trying out things that go beyond the obvious, fostering greater innovation in drug formulation. “This strategic shift could ultimately lead to more effective therapies and improved patient outcomes,” he says.

Improving predictive formulation modeling

Formulation development has traditionally relied on mechanistic or simplified models to guide decision-making and optimize outcomes, according to Price. Examples include the use of the Arrhenius equation to predict the stability of drug substances under accelerated conditions, or Lipinski’s Rule of Five, which provides heuristic understanding for assessing the drug-likeness and oral bioavailability of small molecules. These models, which are rooted in physical chemistry and empirical rules, have delivered significant value—often requiring limited data to produce accurate and interpretable results for well-understood systems, Price notes.

The greater complexity of current drug candidates combined with the increased volume and variety of available formulation data is revealing the limitations of mechanistic models to solve some formulation challenges, Price says. Many new drug molecules fall outside the “rule-of-five” chemical space and exhibit behaviors that are not easily captured by conventional equations.

“In this evolving landscape, AI/ML models offer distinct advantages. ML algorithms can process and learn from large, multidimensional datasets, capturing subtle, nonlinear relationships between formulation variables and outcomes such as solubility, stability, and bioavailability,” Price comments. Unlike mechanistic models, he adds, ML approaches are not constrained by predefined assumptions and can uncover patterns that may be invisible to human experts, enabling formulation scientists to explore a far broader design space, accelerate optimization cycles, and address formulation challenges that would otherwise be intractable using traditional methods. Thus, while mechanistic models remain foundational tools in formulation science, Price believes AI/ML models provide powerful complementary capabilities, especially for novel drug development scenarios.

It is also advantageous, according to Bannigan, that AI/ML algorithms can be slotted directly into existing predictive modeling approaches by turning what is usually a static experimental plan into a living, adaptive process. “Instead of running a fixed set of tests designed at the start using design-of-experiment approaches, AI/ML begins with a small number of experiments, learns from the results, and instantly updates its recommendations for the next round. This cycle repeats, zeroing in on the most promising parts of the formulation space while skipping unproductive paths,” Bannigan explains. The advantages over traditional approaches include speed, efficiency, and reach. “Predictive models leveraging AI/ML enable identification of high-potential formulations faster using less API, and often find solutions that conventional methods never get close to,” he concludes.

Enhancing platform formulation strategies

AI/ML can also be used to improve platform processes for formulation development. One example, says Bannigan, is the use of these technologies as decision-making engines within self-driving labs and automated formulation platforms. “In this case, the AI/ML system analyzes incoming experimental data in real time, recommends the next most informative experiments, and identifies multiple viable formulation paths rather than prematurely converging on a single lead,” he explains. The system also standardizes workflows so that every project benefits from cumulative platform learning.

When integrated into automated laboratory platforms, AI/ML technologies can, adds Price, not only help reduce the manual effort required and variability associated with human operation, but enable the generation of large volumes of high-quality, consistent experimental data that can be leveraged to train and refine machine learning models, enabling the discovery of complex patterns and relationships that might otherwise go unnoticed.

Price also notes that the integration of ‘active learning’ strategies such as Bayesian Optimization, which works in tandem with ML models to iteratively select the most informative experiments that will most efficiently reduce model uncertainty and accelerate the search for optimal formulations, enhances this process.

“This synergy between automation, AI/ML, and active learning allows researchers to maximize the impact of each experiment, accelerate formulation optimization, and tackle challenging formulation development problems,” Price says. He adds that “these advances enable drug developers to innovate faster, reduce costs, and make more confident, evidence-based decisions throughout the formulation lifecycle.”

“The tightly integrated, data-rich, closed-loop cycle created by combining automation, AI/ML, and active learning not only accelerates the design–build–learn process and boosts experimental efficiency, it also reduces API consumption and increases the likelihood of finding scalable, high-performance formulations,” Bannigan contends. This intelligent, iterative process, he believes, will help companies identify novel formulation approaches and, ultimately, new life-altering medicines that would otherwise go undiscovered.

Increasing the likelihood of candidate success

Accelerated formulation development and identifying new formulation strategies are not the only advantages of leveraging AI/ML technologies when exploring formulation spaces. By rapidly focusing development on formulations with the highest predicted likelihood of meeting target product profiles, AI/ML also cuts down both early attrition and late-stage failures from chemistry, manufacturing, and controls-related issues, according to Bannigan.

 correlation from the start, ensures scalable formulations are identified sooner, and conserves API so teams can explore backup candidates in parallel. The result is a higher probability that promising small-molecule drugs will make it through development and into the hands of patients, essentially turning potential therapies into real, market-ready medicines faster and with less risk,” Bannigan contends.

One industrially relevant example is a predictive model developed by Merck KGaA, Darmstadt, Germany for accurately and reliably predicting cocrystal coformers for a range of APIs without the need for any prior experiments. Optimum cocrystals, according to Price, often exhibit significantly higher solubilities than the original API, but identifying them can be challenging. The AI-based model was trained using a carefully curated, large dataset of test drugs and coformers covering a large chemical space and supplemented with targeted experimental data and has been shown to dramatically improve the likelihood of finding the optimum coformer (by a factor of three compared to the trial-and-error approach) (2).

While hindrances to adoption of AI/ML in formulation development do exist, such as fragmented, inconsistent, and proprietary historical data; cultural and regulatory caution that demands interpretability; and the lack of integrated automation infrastructure to operationalize models effectively, Bannigan anticipates AI/ML becoming a standard part of formulation workflows within the next one to three years. He sees such systems becoming integrated with high-throughput platforms, driving faster design–build–learn cycles, and using active learning to adapt in real time with model design and experiment selection guided by large language models (LLMs) capable of rapidly synthesizing literature, historical data, and domain expertise.

In the longer term (3–10 years), Bannigan predicts foundation models, federated learning, and AI-driven excipient design will transform formulation development from trial-and-error to precision engineering, with LLMs evolving into specialized scientific copilots. “In this future, more therapies will advance successfully from development to the patients who need them,” he concludes.

Huanbutta, K.; et al. Artificial Intelligence-Driven Pharmaceutical Industry: A Paradigm Shift in Drug Discovery, Formulation Development, Manufacturing, Quality Control, and Post-Market Surveillance. European Journal of Pharmaceutical Sciences, 2024 203, 106938. DOI: 10.1016/j.ejps.2024.106938

Merck KgaA, Harnessing AI To Speed Up Drug Formulation.EMD Group Science Space Blog, emdgroup.com.

Bannigan, P.; et al. Machine Learning Directed Drug Formulation Development. Advanced Drug Delivery Reviews, 2021 175, 113806. DOI: 10.1016/j.addr.2021.05.016

Konagurthu, S. Revolutionizing Drug Development: AI-driven Solutions for Poor solubility and Bioavailability. Patheon Blog, Patheon.com, April 15, 2024.

Ting, J.M.; et al., Advances in Polymer Design for Enhancing Oral Drug Solubility and Delivery. Bioconjugate Chemistry, 2018 29 (4), 939-952. DOI: 10.1021/acs.bioconjchem.7b00646

Halford, B. Wrestling with Lipinski’s Rule of 5. C & E News, 2023 101 (8). https://cen.acs.org/pharmaceuticals/drug-discovery/Wrestling-Lipinski-rule-5/101/i8


Vol. 49, No. 8
October 2025
Pages: 14-17

When referring to this article, Challener, C. A. Predictive Modeling for Small-Molecule Formulation Development Using Advanced Algorithms. 

Advanced drug discovery tools are identifying complex small molecules with promising biologic and pharmacologic activity, posing challenges in formulation development. Traditional empirical methods are time-consuming and resource-intensive, prompting a shift towards AI/ML-driven predictive modeling and platform formulation processes. These technologies optimize excipient selection, predict stability, and enhance formulation efficiency. AI/ML can navigate complex formulation spaces, reduce API waste, and improve the likelihood of successful drug development. Despite challenges, AI/ML is expected to become integral to formulation workflows, transforming drug development from trial-and-error to precision engineering.

Artificial intelligence and machine learning are anticipated to boost success rates.