Behind the Headlines Episode 27

Ask the Expert

Small Molecules, APIs, and Excipients—Trends, Challenges, and Opportunities

Drug Digest September 2025

Artificial intelligence, machine learning, and novel instruments are helping drug developers.

Enhancing Bioanalysis and Drug Development with Advanced Tools and Technologies

J.P. Morgan Report Lists Biologics, Obesity, China

Top Trends Reshaping Pharma R&D

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

New chemical combination capsule drug on the colorful hand drawing chemical structure formula | Image Credit: ©HYUNGKEUN - stock.adobe.com

High-throughput screening (HTS) enables the rapid testing of numerous compounds against biological targets. By automating and miniaturizing assays, HTS significantly accelerates the identification of potential drug candidates or optimal process conditions and can be applied throughout the entire development path of a potential drug, from discovery to process development. In the discovery phase, HTS is one of several hit-identification (Hit-ID) paradigms and involves the use of sensitive and miniaturized methods for screening large libraries of small molecules in a practical and efficient manner. During process development, HTS helps researchers rapidly establish an optimal synthetic route and conditions for each process step that reliably produce sufficient material quickly.

The most widely used HTS technologies for identification of small-molecule API candidates include fluorescence-based, luminescence-based, and absorbance-based assays, according to Sachin Mahale, research leader, Discovery Sciences at Charles River. He adds that methods for detection of unlabeled biomolecules, such as those based on mass spectrometry, have also become more widely used at an HTS-scale in recent years, allowing the screening of compounds in both biochemical and cellular settings and thus expanding the breadth of targets to those that would otherwise be unscreenable. High-content imaging and automated electrophysiology are additional techniques providing multi-parametric cellular data that are becoming more widespread in high-throughput formats for discovery research, Mahale observes.

“Considering the vast number of technologies, each one of them serving a different purpose to address the scientific question, naturally different attributes, including binding affinities, reaction kinetics, enzymatic activity, functional activity, target engagement, selectivity, and specificity, are often analyzed. These attributes are critical because they provide early insights into the potential efficacy and selectivity of drug candidates, which is essential for optimizing pharmacological profiles,” says Mahale. Given the wide range of technologies available, he also emphasizes the importance of selecting a fit-for-purpose technology to address each biological question.

Numerous advantages for process development

During the early stages of candidate development, the synthetic pathway is still flexible, making HTS particularly valuable, according to Jens Schmidt, associate director of manufacturing, science, and technology at Lonza. “HTS can help process development scientists rapidly evaluate the feasibility of different synthetic routes or optimal chemical combinations, such as solvents, catalysts, and bases,” he explains. As the project advances through clinical phases, the flexibility in process steps decreases, but HTS remains useful for expanding the understanding of the process, such as its reaction kinetics.

Overall, using HTS during process development can, contends Schmidt, “help in the optimization and simplification of established workflows, integration of emerging technologies such as photochemistry and electrochemistry, and leveraging of rapidly evolving computational tools like artificial intelligence (AI) and machine learning (ML).”

One of the key advances in HTS for drug discovery and development is the adoption of a wider array of state-of-the-art analytical techniques for this application. The most notable technologies introduced recently as high-throughput platforms include affinity selection mass spectrometry (ASMS)-based screening platforms such as self-assembled monolayer desorption ionization (SAMDI), target protein degradation (TPD), and molecular glue platforms; and CRISPR (clustered regularly interspaced short palindromic repeats)-based functional screening platforms, notes Luke Alderwick, senior group leader, Discovery Sciences with Charles River.

ASMS screening is increasingly used to discover small molecules that engage a specific target, says Alderwick. “Highly advanced ASMS platforms that use rapid workflows to quickly generate high-quality data are getting traction as HTS methods. The platform is amenable to a broad spectrum of targets, including proteins, complexes, and oligonucleotides such as RNA, and can be a leading assay to initiate drug discovery programs,” he comments. Unlike traditional label-free approaches, which are often slow, restrictive, and not optimal for large screening campaigns, Alderwick adds that new solutions such as Charles River’s SAMDI ASMS platform allow efficient investigation of protein-protein interactions, TPD, molecular glues, RNA binders, etc. in a high-throughput format.

CRISPR-based functional screening, meanwhile, is employed to elucidate the biological pathways involved in disease processes, according to Alderwick. CRISPR editing of genes for knock-out and knock-in experiments have greatly advanced assay designs for cell-based phenotypic HTS approaches, he explains. In addition, by selectively tagging proteins of interest, CRISPR is advancing the understanding of target engagement and functional effects of drug treatments. “By screening small molecules alongside CRISPR-modified cells, researchers can better understand drug-target interactions at a genomic level, enabling the selection of candidates with higher precision,” says Alderwick.

Overall, Alderwick summarizes, these various innovations have improved the scalability, specificity, and throughput of HTS, enabling researchers to screen more diverse compound libraries and discover novel molecular mechanisms of action.

Automation and miniaturization technologies having an impact

Some of the biggest advances improving and expanding HTS for drug discovery and development relate to automation and miniaturization of laboratory systems. “Automation is transforming the landscape of API development and manufacturing,” Schmidt states.

Automated liquid-handling robots, says Mahale, have become faster, more accurate, and capable of working with extremely low volumes of reagents, enabling more cost-effective screening. “Advances in pipetting precision, multi-plate handling, and integration with other systems (like incubators, centrifuges, and imagers) have significantly reduced human error and improved reproducibility. Automated workflows for complex cell assays and readouts based on imaging analysis, meanwhile, allow multiple assays to be performed with minimal consumable cost and keeping sustainability in mind. Use of multimode plate readers in the automation workflow also allows for multiplex assays to maximize data collection,” he details.

By automating process development tasks, researchers can significantly accelerate the identification of optimal synthetic pathways and fine-tune processes for maximum efficiency, according to Schmidt. “This translates to faster development times, reduced errors, and more consistent API production. For drug developers, this means reduced risk and uncertainty in API development, enabling a greater focus on clinical trials and regulatory approvals,” he contends.

More specifically, automation accelerates workflows by allowing researchers to complete multiple tasks at once. “This kind of parallelization is especially helpful for processes like catalyst screening, ligand screening, or solvent selection. Automation also allows chemists to conduct experiments in high-pressure and high-temperature conditions, which are difficult to complete manually. With these features, automation makes experimentation in the lab more targeted and efficient, saving valuable time, energy, and cost,” Schmidt notes.

In addition to advances in automation, innovation in miniaturization technologies is helping to improve HTS performance. “By shrinking assay volumes, miniaturized platforms like microfluidics and nanodispensing have allowed for higher throughput while reducing reagent consumption,” Mahale remarks. Access to smaller-volume assays, he says, is especially useful in fragment-based drug discovery and the screening of complex biological samples, as it allows for cost-effective experiments with even larger libraries.

Data analysis and sharing solutions making a difference

In addition to innovation in the analytical methods and laboratory equipment used for HTS, significant advances have also occurred in the software arena, most notably the integration of AI and ML for data analysis and improvements in cloud-based platforms for collaborative research.

New AI algorithms can, observes Schmidt, analyze data from high-content screening systems, detecting complex patterns and trends that would otherwise be challenging for humans to identify. AI/ML algorithms can identify patterns and predict the activity of small-molecule candidates, even when the data are noisy or incomplete, Mahale adds. In addition to significantly speeding up the analysis of screening results and identifying promising leads faster and with higher accuracy, integration of AI into HTS processes can also improve assay optimization, he comments. Another key advantage of AI-driven HTS, according to Mahale, is its ability to adapt to new data in real time when compared to traditional HTS relying on pre-determined conditions.

“These advances in AI and ML have been pivotal in accelerating drug discovery by improving both the quality of data and the efficiency of experimental processes. In the early stages of development, meanwhile, using these advanced solutions provides a holistic view that streamlines route scouting and accelerates the transition from research and development to robust manufacturing processes,” Schmidt says. As an example, he points to Lonza’s AI Route Scouting Service, which integrates proprietary commercial data with cutting-edge synthesis planning technology. “This innovation allows for more accurate and comprehensive analysis of cellular phenotypes, providing deeper insights into the efficacy and safety of drug candidates,” he notes.

Cloud technology, meanwhile, has enhanced the ability to store, share, and analyze HTS data across multiple sites in real time, says Mahale. “The result is improved collaboration between research teams globally, reduced data redundancy, and increased application of machine learning models on large datasets,” he explains.

Combined, Mahale believes the various software innovations incorporated into HTS systems are enabling the handling of more complex datasets, improving prediction accuracy and streamlining the drug discovery pipeline.

Implementing HTS for drug discovery and development is not a simple matter. The systems are both complex and costly. Fortunately, minimizing these issues has also been a focus of technology providers and end users.

Schmidt points to significant advancements in standardization and simplification of workflows as helping to accelerate HTE experimentation timelines and reduce costs. Examples include the development of standardized protocols and data formats, which has simplified the implementation and operation of HTE assays, specifically improving their accessibility and accuracy. He also notes that software tools powered by AI have made it easier for researchers to design, execute, and analyze HTE experiments.

Another effective approach, according to Alderwick, is to leverage tailored compound libraries such as Charles River’s Lead-Like Compound Library, which include compounds with lead-like properties and diversity and excludes problem chemotypes. The company’s Fast-Track HTS offerings leverages this library to provide rapid hit discovery and lead identification while maintaining high-quality data outputs, he adds. “With a fixed-cost and streamlined program, this service is ideal for proof-of-concept studies and suits research groups or companies with limited funding because it allows for high-quality, early-stage screening without the need for extensive internal resources or infrastructure,” Alderwick says.

Alderwick also notes that modular systems in development offer the potential to customize HTS systems to the specific needs of a lab, which will further lower the initial capital investment required. “All of these advances are making HTS more cost-effective, scalable, and user-friendly, reducing barriers to entry for smaller research organizations,” he concludes.

Despite the many remarkable advances made in HTS technologies for drug discovery and process development, significant challenges impacting both the efficiency of identifying optimal small-molecule candidates and processes, and their downstream success in clinical trials, remain.

One important issue, according to Schmidt, is the need to analyze large HTE datasets that require significant computational resources. “This can be a bottleneck for many drug developers, which is why partnering with a contract development and manufacturing organization (CDMO) like Lonza can be helpful. CDMOs have the tools and technology, such as AI algorithms, to help drug developers analyze data and progress through development stages quickly,” he says.

There are also challenges to handling very small amounts of solids, Schmidt notes. “Although this may seem like a minor technical detail, understanding and accepting these limitations is crucial,” he says. One potential solution highlighted by Schmidt is the use of solid supports such as ChemBeads (MilliporeSigma), small, chemical-coated glass beads created specifically for use in HTS applications.

A primary challenge in HTS for drug discovery is identifying assays that are physiologically relevant to the disease state despite the need for simplification to ensure scalability and robustness, according to Mahale. Model systems do not always translate well. In addition, biochemical assays, while simpler, often fail to capture the nuances of cellular contexts, such as the impact of membrane permeability, metabolite production, or protein-protein interactions, while cell-based assays are closer to physiological reality but are more complex, expensive, and challenging to miniaturize.

“The complexity of human diseases, particularly multifactorial diseases like cancer or neurodegenerative disorders, makes it difficult to develop assays that can recapitulate the true pathophysiology of the disease,” Alderwick observes. “Poor assay choice,” he continues, “leads to the identification of hits that might show promise in early testing but later fail due to lack of efficacy or off-target effects in more complex disease models or clinical settings. The incorrect assay not only risks identifying false positives but also misses out on potential therapeutic candidates that could have better real-world efficacy. This limitation significantly contributes to the high attrition rates in drug development.”

Another challenge is the development of a robust screening cascade (primary, secondary, and tertiary screenings) that helps to identify, confirm, validate, and refine viable candidates and reduce the attrition rate during later stages of drug development. Further assays are then employed during lead optimization to assess toxicity, selectivity, functional activity, bioavailability, and pharmacokinetics for further hit validation. “Failures in designing an appropriate HTS cascade occur when these different stages are not sufficiently de-risked,” contends Alderwick.

While incorporating more predictive absorption, distribution, metabolism, excretion, and toxicity screens early in the process has helped de-risk candidate selection to some degree, the challenge still lies in balancing the need for throughput and speed with the inclusion of more complex, multi-parametric data, according to Alderwick. “Early prediction of 

 efficacy and safety in HTS remains a weak point, contributing to high failure rates in the transition from hit-to-lead and lead-to-clinical candidate stages,” he concludes.

Finally, Mahale notes that the selection of chemically diverse and high-quality libraries for HTS is pivotal in the success of small-molecule candidate identification, because even with the best assays, if the chemical library lacks diversity or contains unsuitable and non-drug-like compounds, the likelihood of finding optimal candidates diminishes. “Many historical compound libraries are biased toward certain chemical scaffolds that have been repeatedly screened over decades, thus reducing the chances of discovering novel small molecules with new mechanisms of action,” he observes. Charles River, Mahale adds, has tackled this issue by developing an expertly curated small-molecule compound library that covers a wide range of structural and functional diversity, thereby maximizing the probability of finding useful hits.

Several emerging strategies aim to improve the efficiency, reliability, and outcomes of HTS by leveraging advancements in technology, biology, and chemistry, according to Alderwick. Advances in organoid and 3D-cell culture technologies are helping to bridge the gap between simple 

 assays and complex human disease biology, and thus address the need for more physiologically relevant assays, he observes. “These models better mimic the physiological conditions of tissues and organs, providing a more relevant system for screening potential drug candidates,” Alderwick explains. Organoids derived from patient tissues, for example, can offer insights into how drugs might work in specific disease contexts, improving the predictive power of assays.

To shrink the gap between biochemical, cell-free assays and phenotypic, cell-based screens and thus better understand the on-target activity of identified hit molecules, including binding assays such as for target engagement and protein-protein interaction analysis in the HTS screening cascade, would greatly advance the ability to select optimal compounds for further development, believes Alderwick. He also notes that the use of advanced cellular models, such as CRISPR/Cas (

-associated protein) knockouts and induced pluripotent stem cells, for early screening is likely to improve HTS outcomes.

Continued advances in automation and the use of AI and ML will further enable HTS platforms to handle more complex assays with higher precision, reproducibility, and throughput, according to Mahale. “Fully automated HTS systems can reduce human error, improve the consistency of assay conditions, and allow for more sophisticated workflows that balance throughput with data quality, while incorporation of AI/ML algorithms can reduce the rates of false readouts, making HTS more reliable and reducing the time spent following up on false positives,” he says.

Ultimately, concludes Mahale, these emerging solutions aim to reduce the high attrition rates in drug discovery by improving the relevance and quality of the screening process, optimizing candidate selection, and expanding the chemical diversity available for exploration. “While challenges remain, these developments promise to significantly enhance the utility of HTS in the drug development pipeline,” he contends.

Looking beyond these strategies, Alderwick is excited about the trend toward integration of multi-omics approaches into HTS, which he says represent a major leap forward in the ability to identify optimal small-molecule API candidates. “Multi-omics approaches leverage data from various molecular levels, including genomics, proteomics, transcriptomics, metabolomics, and even epigenomics, providing a more comprehensive and holistic understanding of how small molecules interact with entire biological networks and pathways rather than isolated targets,” he explains. “Complex diseases such as cancer, neurodegenerative disorders, and metabolic syndromes often involve multiple, interconnected biological systems, and thus multi-omics approaches have a greater likelihood of identifying effective new drug candidates,” he states.

In today’s fast-paced drug manufacturing environment, HTS has become an indispensable tool. However, Schmidt emphasizes that advances in technology are not sufficient by themselves to enhance the performance of these platforms. “Looking ahead, we can expect continued improvements in both reliability and simplicity, but the key to success lies in having a motivated, dedicated, and technically skilled team that is ready to embrace challenges and open to adopting innovative techniques,” he insists. 

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


Vol. 48, No. 11
November 2024
Pages: 12–16

When referring to this article, please cite it as Challener, C. Accelerating Discovery and Development with Advances in High-Throughput Screening. 

Articles

Automation, miniaturization, and new software algorithms are improving throughput and accuracy.

Accelerating Discovery and Development with Advances in High-Throughput Screening

Feliza Mirasol is the science editor for 

On Oct. 30, 2024, Lonza's Synaffix announced that it has licensed its antibody-drug conjugate (ADC) technology to BigHat Biosciences (BigHat), a US-based biotech company. BigHat will combine the Synaffix technology with its machine learning (ML) antibody design platform to develop a new ADC pipeline program.

Under the agreement, BigHat will receive target-specific access to Synaffix’s late clinical-stage, site-specific ADC technology platform. This platform enables transformation of antibodies into proprietary, best-in-class ADC products. Lonza’s acquisition of Synaffix in June 2023 (1) means that BigHat also gains access to Lonza’s DNA-to-investigational new drug (IND) ADC offering. This DNA-to-IND offering enables drug developers to receive all necessary current good manufacturing practice (CGMP)/non-CGMP ADC batches leading up to the first clinical trial. The service is housed under a single quality system that encompasses all critical development and manufacturing activities, including antibody production, bioconjugation, and drug product filling.

“The ADC space continues to develop and evolve, and our collaboration with BigHat exemplifies the continued strength and appeal of our best-in-class ADC technology. We are excited to be at the forefront of next-generation ADC R&D by joining forces with BigHat and its AI [artificial intelligence]/ML-enabled antibody discovery and development platform. BigHat is the ideal partner for Synaffix to collaborate with. We look forward to supporting BigHat with every step in the ADC development and manufacturing process under the Lonza umbrella,” said Peter van de Sande, head of Synaffix, in a company press release.

Milliner, which is BigHat’s AI/ML-powered antibody design platform, works by integrating a synthetic biology-based high-speed wet lab with state-of-the-art ML technologies into a full-stack antibody discovery and engineering platform. This platform is used to engineer antibodies with more complex functions and better biophysical properties. Using this approach reduces the difficulty of designing antibodies and other therapeutic proteins when developers look to tackle conditions ranging from chronic illness to life-threatening disease. The platform also offers significant speed to reaching candidate discovery and validation.

“We are pleased to announce this collaboration with BigHat for its next-gen ADC molecule, which shows how drug developers can benefit from a fully integrated suite of services across our Biologics division. The Lonza network has continued to adapt to the evolving ADC landscape, including the recent addition of a highly potent vial fill capability at our Stein (CH) [Switzerland] facility. This dedicated ADC drug product capacity expansion completes our offer for comprehensive, end-to-end development and manufacturing services for ADCs, simplifying the path from DNA-to-IND and beyond for our customers,” Jean-Christophe Hyvert, president, Biologics, Lonza, said in the release.

Through this collaboration with Synaffix/Lonza, BigHat intends to advance its next-gen ADC program. BigHat’s ADC program is at the IND-enabling stage and is on track to be the company’s first clinical-stage program. According to the press release, BigHat’s next-gen ADC was optimized using the Milliner platform. It is designed for maximum payload delivery to tumor cells and with optimal drug-like-properties. Incorporating Synaffix's ADC technologies, such as GlycoConnect, HydraSpace, and toxSYN, BigHat expects to further improve the safety and efficacy of its next-gen ADC.

"BigHat is leveraging our AI-driven antibody design platform, Milliner, to develop advanced biologics that provide safer and more effective treatments for difficult-to-treat cancers. We are excited to combine Synaffix’s conjugation and linker-payload technologies with our custom-designed antibody to create a next-generation ADC for patients with high unmet medical need," said Mark DePristo, CEO, BigHat Biosciences, in the release.

1. Lonza. Lonza to Acquire Synaffix and Strengthen Antibody-Drug Conjugates Offering. Press Release. June 1, 2023.

Synaffix, a Lonza company, has licensed its ADC technology to BigHat Biosciences to be combined with the latter's ML design platform to develop new ADCs

Synaffix, a Lonza Company, Collaborates with BigHat Biosciences on ADCs Designed Using Machine Learning

Chris Burgess, PhD, is an analytical scientist at Burgess Analytical Consultancy Limited, ‘Rose Rae,’ The Lendings, Startforth, Barnard Castle, Co Durham, DL12 9AB, UK; Tel: +44 1833 637 446; chris@burgessconsultancy.com; www.burgessconsultancy.co

Financial data document graph chart report statistic marketing research development planning management strategy analysis accounting. Financial business technology hologram concept. 3d rendering. | Image Credit: © Chaosamran_Studio - stock.adobe.com

Nomenclature is important. Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected? The purpose of this article is to provide a structured model for these terms with their definitions and their relationships in the context of analysis and testing within a pharmaceutical quality system.

The crucial concept is that data quality, including data integrity, is only attainable via data governance, as will be illustrated in the proposed model.

In a regulatory context, the establishment of measured values and subsequent reportable results of a predefined quality is an essential activity. Reportable results are then compared with predetermined acceptance criteria and/or standards and specifications. The processes, mechanisms, and control systems necessary to establish measurement values and reportable results of a defined quality are interlinked. These interlinkages may include metrological, procedural, or organizational elements.

The overall proposed model is built up using building blocks akin to the construction of Lego brick models. This approach has been used in previous papers concerning error budgets in measurement uncertainty and Monte Carlo Simulation, and data quality within a lifecycle approach (1–2).

An analytical testing flow diagram, shown in 

, gives a high-level idea of the traceability and subsequent interactions from the marketing authorization (new drug application, NDA, or abbreviated new drug application, ANDA) to the reportable result and data quality.

 A high-level process flow indicating some of the elements of data governance, data quality, and data integrity, as well as the quality management system (QMS).

This process flow has been translated into the Lego brick model shown in 

. This model is based upon a data quality within a lifecycle approach model which has been modified and extended, as will be described in some detail (2).

 Data governance model modified and extended from the data quality within a lifecycle approach model (taken from reference 2, Figure 15).

The crucial concept is that data quality is only attainable via data governance.

The data governance and data quality Lego brick model

Data quality is not an accident but a product of design. It is a combination of data integrity and the functionality and control under the Pharmaceutical Quality System, usually termed the quality management system (QMS). These aspects are underpinned by data governance and qualified information technology (IT) infrastructure services. This is essentially a sandwich structure in which the “filling” is provided by metrological integrity, analytical procedure integrity, and quality oversight. These “fillings” are described below.

 are subject to risk assessments and risk management (3), which will not be discussed here.

The qualified IT infrastructure services with cybersecurity and access control, as seen above, also will not be covered further in this article.

It is, however, necessary to discuss the key elements of data integrity and the Pharmaceutical Quality System which, when combined, generate data quality on the foundation of data governance.

Data governance is the totality of arrangements to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data lifecycle (4).

The purpose of an analytical procedure is to provide a reportable result of the analytical characteristic or quality attribute being determined. Analysis and testing require a measurement system, and a procedure for its application to a sample.

Data integrity is underpinned by the first brick, the metrological integrity of the instrument or system’s operational performance, with demonstrable assurance that it is “fit for intended use” within a specific analytical procedure, which is “fit for intended purpose” over the data lifecycle.

Analysis and testing usually involve the use of an apparatus, analytical instrument, or system to make a measurement. Therefore, establishment of “fitness for intended use” for any apparatus, analytical instruments, or systems used in analysis and testing is necessary to ensure metrological integrity over the operational ranges required.

Therefore, it is essential to establish “fitness for intended use” before the analytical procedure is performed. The main resource for instrument and system requirements are the specific monographs and general chapters in the pharmacopeias. In particular, United States Pharmacopeia (USP) has a unique general chapter on the lifecycle processes and requirements for ensuring that any apparatus, analytical instrument, or system is “fit for intended use,” as seen in 

 Data quality outline for an analysis and testing quality control (QC) model using USP references.

analytical instrument and system qualification

calibration over the operational ranges of critical measurement functions

trend analysis to monitor an ongoing state of control.

The second component of data integrity is a validated or verified analytical procedure performed by a trained analyst.

It has been a requirement for more than 20 years that analytical methods and procedures need to be validated or verified (6–7). Recently, these requirements have been updated by the International Council for Harmonisation (ICH) and a new guideline on analytical procedure development issued (8–9). 

 General Chapter <1220> on Analytical Procedure Lifecycle should be consulted (10).

analytical target profile and development

trained analysts and second person review

Particular attention should be taken regarding analyst training and second person review within the laboratory.

Second person review is essential in ensuring data quality.

Quality control is the guardian of scientific soundness, whereas the quality assurance function is the guardian of compliance. To perform this duty of care, the quality assurance function requires a robust and comprehensive quality management system that enshrines the elements to provide and perform the necessary quality oversight over the data lifecycle.

Quality oversight involves both reviewing and auditing activities of QC but also includes the Pharmaceutical Quality System implementation itself to ensure that it is up to date (11).

Quality oversight covers key areas such as:

Much has been written on this topic, particularly regarding the three ALCOA models and the meanings of their acronyms (4). These acronyms are summarized below and illustrated in 

 Pictorial representation of the three ALCOA models.

It must be possible to identify the individual or computerized system that performed a recorded task, and when the task was performed. This also applies to any changes made to records, such as corrections, deletions, and changes, where it is important to know who made a change, when, and why.

All data, including any associated metadata, should be unambiguously readable throughout the lifecycle. Legibility also extends to any changes or modification to the original data made by an authorized individual so that the original entry is not obscured.

Data should be recorded on paper or electronically at the time the observation is made. All data entries must be dated and signed by the person entering the data.

The original record is the first capture of information, whether recorded on paper (static) or electronically (usually dynamic, depending on the complexity of the system). Data or information originally captured in a dynamic state remain in that state.

Records need to be a truthful representation of facts to be accurate. No errors in the original observation(s) and no editing are allowed without documented amendments or audit trail entries by authorized personnel. Accuracy is assured and verified by a documented review including review of audit trails.

All data from an analysis, including any data generated including original data, data before and after repeat testing, reanalysis, modification, recalculation, reintegration, and deletion. For hybrid systems, the paper output must be linked to the underlying electronic records used to produce it. A complete record of data generated electronically includes relevant metadata.

Data and information records should be created, processed, and stored in a logical manner that has a defined consistency. This includes policies or procedures that help control or standardize data (such as chronological sequencing, date formats, units of measurement, approaches to rounding, significant digits, etc.).

Data are recorded in a permanent, maintainable, authorized media form during the retention period. Records should be kept in a manner such that they continue to exist and are accessible for the entire period during which they are needed. They need to remain intact as an indelible and durable record throughout the record retention period.

Records should be available for review at any time during the required retention period, accessible in a readable format to all applicable personnel who are responsible for their review, whether for routine release decisions, investigations, trending, annual reports, audits, or inspections.

Data should be traceable though the lifecycle. Any changes to data or metadata should be explained and should be traceable without obscuring the original information. Timestamps should be traceable to a trusted time source. Metrological standards and instrument or system qualification should be traceable to international standards wherever possible.

Data quality is a combination of data integrity and overall control as part of the pharmaceutical quality system.

An example of a quality control data quality outline for analysis and testing, using examples from USP, is illustrated in 

Data quality cannot be assured without a data governance structure supported by a qualified IT infrastructure services with cybersecurity and access control.

The proposed Lego brick model provides a structural framework for assuring data quality over the lifecycle.

A short glossary of definitions of key terms is appended.

I wish to thank Bob McDowall and Oscar Quatrocchi for their review and helpful comments.

The totality of arrangements to ensure that data, irrespective of the format in which they are generated, are recorded, processed, retained, and used to ensure a complete, consistent, and accurate record throughout the data lifecycle (16).

Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable, and that these characteristics of the data are maintained throughout the data lifecycle. The data should be collected and maintained in a secure manner, so that they are attributable, legible, contemporaneously recorded, original (or a true copy), and accurate. Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices (16).

All phases in the life of the data (including raw data), from initial generation and recording through processing (including transformation or migration), use, data retention, archive and retrieval, and destruction (17).

The assurance that data produced is exactly what was intended to be produced and fit for its intended purpose (17).

Data governance, data integrity, and data quality are all widely used terms, but what do they actually mean and how are they connected?

Data Governance, Data Integrity, and Data Quality: What’s the Connection?

Jonathan Haider, j.haider@susupport.com, is product line manager at Single Use Support.

In the course of the significant transformation of the biopharmaceutical industry, modular manufacturing emerges as a game-changing approach to fluid management. This innovative methodology is reshaping how pharmaceutical companies and contract development and manufacturing organizations (CDMOs) produce and handle biopharmaceuticals, offering unprecedented flexibility, efficiency, and scalability.

Modular manufacturing in biopharmaceutical fluid management (

It allows for rapid adaptation that is due to either changing market demands or product specifications.

It enables manufacturers to optimize their production processes, reducing costs and improving product quality.

It facilitates easier compliance with regulatory requirements.

The latter is true when technologies provide better control and traceability throughout the manufacturing process. The integration of modular systems with manufacturing execution systems (MES) helps ensure seamless operations, real-time monitoring, and data-driven decision-making.

Overall, the approach to add modularity is particularly valuable when dealing with diverse product portfolios, varying batch sizes, and the potential need for quick scale-up or scale-out capabilities.

Process flexibility: a necessity in modern biomanufacturing

In today’s dynamic biopharmaceutical landscape, process flexibility is not just an advantage—it’s a necessity. CDMOs, in particular, face the challenge of producing a wide array of products with different specifications, volumes, and handling requirements.

One of the primary drivers for process flexibility is the diverse nature of biopharmaceutical products. From monoclonal antibodies (mAbs) to cell and gene therapies, each product may require unique processing conditions, volumes, and handling protocols. Such a volatile demand necessitates rapid scalability.

Will the COVID-19 pandemic remain unique? Uncertain. However, it has prompted manufacturers to be more prepared for potential further outbreaks, such as monkeypox. With pandemic preparedness comes the importance of being able to scale up production swiftly. Modular manufacturing systems provide the agility needed to respond to such sudden surges in demand. They also allow CDMOs to quickly reconfigure their production lines to accommodate these varying needs without significant downtime or retooling or alternatively piling equipment for all possible events and scales.

Why would CDMOs invest in a full set of multiple technologies when they can have one modular system for many scenarios?

Fluid management in manufacturing: the shift to automated closed systems

Fluid management is all about transferring liquids in biopharmaceutical manufacturing between different process steps. This can be the case in upstream, downstream, fill/finish, or in between. The industry is witnessing a transition from manual, open handling to automated closed systems, in large part due to Annex 1 of the European Commission’s guidelines, which favors closed systems. This regulatory requirement has been effective in shifting the interest of manufacturers and CDMOs to automated closed systems (1). The shift in attitude is primarily driven by the need for increased sterility, reduced contamination risks, and improved process control. Even for aliquoting small volumes into single-use bags, closed systems are increasingly being used (see 

 Aliquotation of biopharmaceuticals into small single-use bags.

Why would a manufacturer or CDMO want to close the fluid path system? Responses to this question include the following:

Enhanced sterility: by minimizing human intervention and exposure to the environment, closed systems significantly reduce the risk of contamination.

Improved process control: automated systems allow for precise control over filling parameters, ensuring consistency across batches.

Increased efficiency: automation reduces manual handling, leading to faster processing times and reduced labor costs.

Better traceability: closed systems integrated with MES provide comprehensive data logging, facilitating easier compliance with regulatory requirements.

Six most current pain points and challenges in fluid management

Despite the advancements in fluid management processes, several challenges persist in the biopharmaceutical industry.

 Many current systems struggle to accommodate varying production volumes efficiently. So many aspects can change, be it the batch volume or the size of the primary packaging. Does the manufacturer fill 100 L of antibody-drug conjugates into five single-use bags of 20 L? Or does the manufacturer dispense 20 L of viral vectors into 40 single-use bags of 500 mL? Do mAbs even need to be filled into 20 1-L bottles?Scaling up, down, or out often requires significant changes to equipment or processes, leading to increased costs and downtime.

 There is one closed system that CDMOs do not like—the closed ecosystem of technologies. A vendor lock-in and the resulting incompatibilities can restrict flexibility and increase costs for manufacturers. If Solution Provider A only accepts its own single-use assemblies, single-use bags, and sometimes even platform systems and not those of Vendor B or C, it limits both the drive for optimization and productivity and simply prevents CDMOs from following suit with their customers’ mandatory equipment.

 The need for different systems to handle various scales of production leads to increased capital expenditure, larger footprints, and more complex training requirements for operators, even more when platform systems are not modular. Adding a number of technologies does not necessarily equal scalability. More equipment is just a problem with regards to footprint in manufacturing suites.

 As the industry moves toward advanced therapy medicinal products (ATMPs) and personalized medicines, the need for precise handling of small volumes becomes critical. Many existing systems lack the required accuracy for these applications into single-use bags. In most cases, scales are integrated to measure larger filling quantities, while flow sensors are used to aliquot smaller volumes.

 Some biopharmaceuticals, particularly cell-based products, always require precise temperature control. Otherwise, inaccurate temperatures reduce cell viability, such as when cells are held too warm for too long. Most current systems struggle to maintain optimal temperatures in process steps before the cooling process.

 Most existing systems often leave significant residual volumes in tubing and containers, leading to product waste and reduced yield. This waste can be limited by draining single-use assemblies post filling with negative pressure.

Streamlining requirements: a (liquid) path forward

All of the mentioned pain points are issues that every manufacturer would want to be solved. But do all these pain points make sense for a specific manufacturer’s production? These points are of importance when biopharmaceutical manufacturers and CDMOs carefully consider their project criteria and priorities when selecting or designing fluid management systems.

What are the key project criteria, and in what order should these criteria be prioritized?

 Answering these questions helps a manufacturer find the most suitable filling platform and allows them to make trade-offs between some options. Examples are illustrated in 

; if it is important to fill small volumes (e.g., ATMPs) as accurately as possible, then speed of filling might not be the biggest priority. If it is the speed that counts, then the speed may reduce the accuracy and the option to homogenize drugs before the filling process to ensure aliquot-to-aliquot consistent product quality throughout.

 Examples of project priorities for aseptic filling.

Advanced filling platform systems adapting to criteria.

 Modular, adaptable platform systems are more likely to meet manufacturers’ defined criteria and priorities. Most innovative automated aseptic filling platforms offer several key features, such as:

Unified platform communication: implementing a standardized communication protocol across different scales enables seamless changes in production capacity without major system overhauls. Whatever MES is used, the platform systems require an interface to seamlessly be integrated into the current system, such as withOpen Platform Communication Unified Architecture (OPC UA).

Plug-and-play architecture: a single control unit that can accommodate different racks or modules, allowing for easy reconfiguration based on production needs. Even multiple racks can be attached to the control unit for large-scale operations.

Flexible pumping systems: incorporating pumps that can accurately handle a wide range of volumes, from microliters to liters. Large peristaltic pumps can be as accurate as +/-2 g filling accuracy, for example, but may not be fully suitable for certain drug substances. Depending on the product characteristics, different pump setups can be considered.

Advanced sealing technologies: predominantly performed manually, there is also a possibility to implement an automated sealing mechanisms to maintain system closure and sterility. Certain pinch valves can automatically seal the tubing right after the filling process, as shown in 

, which helps skip one process step of aseptically decoupling fully.

Cooling liquids for aliquotation process: connecting cooling functions directly to the filling platform helps to maintain product integrity for temperature-sensitive biopharmaceuticals. For example, cell-based products are cooled prior to the filling process during homogenization and mixing to maximize cell viability (2).

Optimized tubing design: enlarging the inner and outer diameters of tubing where appropriate to accommodate higher flow rates and reduce flow rates for low amounts of residual volumes. Integrated pinch valves need to be compatible to tubing of different sizes and characteristics. This allows a wider spectrum of scales and filling accuracy for manufacturers.

 Sealing pinch valves for aseptic decoupling.

Why modular filling platforms can no longer be overlooked by CDMOs

Modular manufacturing in biopharmaceutical fluid management marks a significant advancement in the industry, offering a multitude of benefits that are essential for modern production demands. One of the most compelling advantages is increased flexibility, which allows manufacturers and CDMOs to swiftly adapt to varying product requirements and batch sizes. This adaptability is crucial in a landscape where market needs can change rapidly.

Moreover, these modular systems contribute to improved efficiency by reducing setup times and facilitating easier changeovers between products, ultimately leading to higher overall equipment effectiveness. The cost-effectiveness of modular platforms cannot be overstated; by consolidating multiple dedicated systems into a single, versatile platform, companies can significantly lower capital expenditures and optimize their manufacturing footprint.

Quality control is enhanced through the implementation of closed, automated systems that minimize human error and contamination risks, ensuring consistent product quality. Additionally, the integrated data management and traceability features simplify regulatory compliance, making it easier for manufacturers to meet stringent industry standards.

As the biopharmaceutical landscape evolves, the adaptability of modular systems provides a vital degree of future proofing, allowing facilities to upgrade or expand as new technologies and regulations emerge. Furthermore, the ability to quickly scale up production can substantially shorten the time required to bring new products to market, giving companies a competitive edge.

In fact, those who embrace modular manufacturing will be well positioned to navigate the complexities of the biopharmaceutical industry, respond to changing demands, and thrive in an increasingly competitive environment. The future of biopharmaceutical manufacturing lies in modularity.

The Rules Governing Medicinal Products in the European Union Volume 4 EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use

. 8.127 of Annex 1 (2022).
2. Moloney, B. Homogenization of Cells & Aliquotation in Single-use Bags. 

®
Vol. 48, No. 11
November 2024
Pages: 17–19, 30

When referring to this article, please cite it as Haider, J. Automate Aseptic Filling: Closing the System at Full Modularity. 

Shifting to automated, closed modular manufacturing systems is growing increasingly crucial for biopharmaceutical production.

Automate Aseptic Filling: Closing the System at Full Modularity

Chris Stumpf is director of Drug Discovery Informatics Solutions, Revvity Signals.

Many pharmaceutical and biotechnology organizations are rich in data but need more insights. Scientific data are often siloed, lacking context, and, therefore, inaccessible. Artificial intelligence (AI) and machine learning (ML) are promising tools for enhancing R&D. In drug discovery, AI can perform a variety of tasks, from identifying potential drug targets to predicting drug efficacy and optimizing treatment strategies. However, there is often a missing link between drug discovery data and AI outcomes. The integration of AI in drug discovery hinges on a crucial need for robust data management.

Why proper data management is essential for evolving research needs

Demand for traditional drugs and new modalities has increased data digitization across the pharmaceutical industry (1). Meanwhile, the latest AI and ML methods are transforming the R&D process within drug discovery, delivering on the promise to accelerate the journey from laboratory insights to lifesaving therapies. This transformative power of AI inspires optimism for the future of drug discovery, but much of the potential is trapped in isolated and undefined data.

AI is only as powerful as the data it consumes, and it works best with data that have the proper quality, detail, and context. While there are abundant scientific data in pharmaceuticals and biotechnology, the data are often siloed, poorly modeled, and ultimately inaccessible. A meticulous approach to capturing and curating R&D data is indispensable to unlock the full potential of novel AI methodologies. Examining how data are ingested, stored, organized, and maintained is crucial. A strong AI-ready data management strategy can help drug discovery leaders leverage AI to deliver innovative drugs to patients faster.

As AI has matured, many industries have experienced the benefits of automating tedious, data-heavy tasks. Now, the drug discovery industry is facing a high demand for finding insights from high volumes of data, while cutting-edge AI technology is becoming more widely available just in time.

 Pharmaceutical R&D has always been data-intensive, involving many data types from various sources, including laboratory instruments, clinical trials, and real-world evidence.

These data are often stored in disparate systems, leading to silos that make it difficult for both humans and machines to access and utilize the data. What’s more, modern drug therapies are increasingly complex. Drug modalities such as antibodies and cell and gene therapies comprise large, intricate molecular entities, and developing these complicated solutions requires large amounts of data. This complexity is an opportunity for innovation, but it also underscores the need for AI’s interpretive capabilities in drug discovery (see 

 Newer drug modalities comprise large, intricate molecular entities, and potentially terabytes and petabytes of data, that artificial intelligence can interpret.

As science grows in complexity, researchers need new tools and different approaches to curating data to ensure quality and consistency. Simple tools for storing and organizing data were adequate for managing the work that previous generations performed, but they lacked the capabilities required for today’s complex research needs. Fortunately, AI can reduce human workload and quickly achieve targets (2). When paired with proper data management, it can rapidly find patterns and discover hidden insights that humans would otherwise miss.

 AI can consume and analyze complex datasets, and ML can identify valuable patterns within the data. Much like how the manufacturing industry has outsourced heavy lifting and repetitive tasks to robots, the pharmaceutical industry can delegate complex computing tasks to AI systems, enabling humans to find insights, make decisions, and explore new capabilities. Tools such as generative AI and ML algorithms give back quality time to scientists and fuel what scientists do best: think, experiment, and discover new therapies faster.

There are already many use cases for AI in drug discovery. In drug design and molecule generation, generative AI models can analyze existing databases of chemical compounds and learn how to generate novel molecules that are likely to have the desired efficacy and safety profile. In this way, researchers can employ AI to design new molecular structures with desired properties for treating specific diseases. Additionally, AI models can analyze biological data to understand the complex interactions within biological systems and identify points where therapeutic intervention could be beneficial.

Generative AI can also aid in discovering new biomarkers for diseases, which can lead to the development of more precise and targeted therapies. By analyzing patient data, including genetic information, AI models can predict how individual patients might respond to certain treatments, leading to more personalized and effective therapy plans.

Capturing and curating R&D data are crucial to realizing the full value of advanced analytics.

Featured Content

Behind the Headlines Episode 27

Ask the Expert

Drug Digest September 2025

Enhancing Bioanalysis and Drug Development with Advanced Tools and Technologies

Top Trends Reshaping Pharma R&D