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

Cynthia A. Challener

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The careful design and development of formulated oral drug products are key to ensuring patient acceptability and compliance for achieving the desired clinical outcomes. Many APIs are extremely bitter or have other aversive attributes that can make developing palatable drug products challenging, notes Nazim Kanji, executive director of pediatric services with Quotient Sciences. Several factors can influence palatability, including the chemical structure of the drug substance and the finished medicinal product formulation, as well as the excipients used. “Undesirable taste is a common problem seen in medicines spanning all therapeutic areas, from antibiotics and painkillers to antihistamines and decongestants,” Kanji notes.

Taste masking of oral dosage forms is, therefore, an essential component of formulation development. A one-size-fits-all approach is not available, however, according to Ashish A. Joshi, pharma technical and business manager with BASF Pharma Solutions. Taste-masking strategies, he observes, must almost always be tailored to various aspects, such as the degree of inherent bitterness of the drug, the type of dosage form, the patient age-group, and whether the final formulation is in a solid or liquid form.

As the percentage of drug substances in development suffering from bitter tastes has increased and many new formulation and delivery strategies have been introduced to increase patient convenience and compliance, taste-masking technology has therefore evolved. In addition to more traditional approaches such as the use of barrier coatings, solid dispersions, and the addition of sweeteners and flavoring agents, formulators are leveraging newer strategies such as liposomes, various forms of complexation, co-crystal and pro-drug formation, emulsification, and microsphere and encapsulation technologies.

As part of developing palatable pediatric and patient-centric drug products such as solutions, suspensions, and orally disintegrating tablets (ODT), several recognized approaches can be taken to mitigate the poor taste of these APIs. “The most common approaches include using a combination of flavors and sweeteners to mask a bitter taste or barrier coatings to prevent the release of API from the drug product into the buccal cavity prior to the dose being swallowed,” comments Kanji.

Some advances in taste-masking technology are improving on these basic approaches. Researchers are, for instance, exploring methods to enhance both the perception and physicochemical stability of long-acting flavors through encapsulation and complexation techniques, according to Joao Marcos Assis, global technical marketing manager at BASF Pharma Solutions. Joshi adds that certain zinc salts have also demonstrated the ability to mask bitter taste effectively.

Improvements in pH-dependent barrier film coatings, meanwhile, have helped to overcome the coating inefficiencies for fine (small particle size) drug substances that require extended processing times due to their large surface areas. For these APIs, additional steps or approaches such as drug granulation to increase particle size or drug layering in inert pellets, can be used, Assis observes.

Lipids and insoluble polymers such as stearic acid, ethyl cellulose, and carnauba wax, when used in combination with surfactants, have also been shown to be promising as effective taste-masking approaches for drug particles. “These hydrophobic materials have low solubility in the mouth, contributing to improved taste-masking properties,” Joshi explains. He does caution, though, that it is important to be aware that use of lipid coatings may result in changes in a drug substance’s release profile, thus potentially impacting drug bioavailability.

Reverse-enteric polymers have become increasingly attractive as taste-masking polymers due to their pH-dependent solubility; they do not dissolve in the mouth at salivary pH, but once exposed to the acidic pH of the stomach, dissolve immediately to release the drug substance.

When these types of polymers are employed, the coating composition and level must be carefully balanced to prevent drug release during the residence time in the buccal cavity (e.g., up to 30 seconds for orally disintegrating tablets) while still allowing adequate drug release in the gastrointestinal tract to ensure that pharmacokinetic (PK) performance is not compromised, according to Kanji. “Realizing this balance can be a challenging prospect and may not be accurately predicted by in vitro dissolution testing,” he observes.

One example noted by Assis is a copolymer of methyl methacrylate (MMA) and diethylaminoethyl methacrylate (DEAEMA), which exhibits a lipophilic characteristic and does not dissolve at neutral to basic pH due to the amino functional group. “This lipophilic quality is very beneficial to the process [because] it reduces the required coating level for taste-masking purposes and provides moisture protection for sensitive drugs,” he observes. With these attributes, the drug substance becomes more suitable for liquid taste-masked formulations, where well-coated particles can be suspended in a neutral pH liquid, enabling the creation of a taste-masked suspension.

In the dry-powder form, water-insoluble matrices have been used effectively in melt-granulation processes, Joshi adds. During extrusion, the polymer covers the drug substance and forms a stable barrier layer once the granules are cooled. “The key advantage of this process is that it does not use any water or solvents and thus achieves efficient taste-masking of a moisture-sensitive drug substance while minimizing its possible degradation due to exposure to an aqueous or organic coating process,” he comments.

Another benefit to melt-granulation is the reduced process time compared to film coating, according to Assis. It can also be implemented as a continuous process, he notes. Reverse-enteric polymers in the dry state may also be suitable for the development of hot-melt extrusion formulations, allowing the production of taste-masked amorphous solid dispersions with improved solubility and taste, Assis remarks.

Using a mixture of water-soluble and insoluble polymers represents another newer strategy. “Tailoring the ratio of a polyvinyl acetate (PVAc)-based water-insoluble polymer and a water-soluble pore former such as a polyvinyl alcohol-polyethylene glycol (PVA-PEG) copolymer in a barrier coating allows efficient taste-masking to be achieved without significantly altering the release profile of the formulation,” Joshi explains.

The assessment and optimization of the polymer coating can be efficiently undertaken by integrating drug product formulation development and manufacturing with clinical testing activities, says Kanji. Quotient Sciences, for instance, uses a proprietary drug-development platform that enables drug products to be manufactured, released, and dosed in healthy subjects in a matter of days so that compositions can be optimized based on emerging clinical data. In addition to PK data, each formulation’s palatability attributes (e.g., bitterness, mouthfeel, grittiness, and aftertaste) can also be assessed as part of the same study, Kanji adds.

Following development of the taste-masked dosage form, confirmation of acceptability is achieved via palatability assessment of the drug product, thereby providing assurance that poor taste will not impact patient acceptability and compliance, Kanji concludes.

Lipid excipients are also being explored as encapsulating agents. One example highlighted by Joshi involves dispersing the drug substance in molten stearic acid or glycerol monostearate followed by atomization in a cold environment using a melt-congealing microencapsulation process. “This approach is particularly useful when the API is both extremely bitter and highly sensitive to moisture because it allows for effective taste-masking without the use of a typical aqueous barrier coating,” he contends.

Masking the taste of highly bitter drug substances in liquid dosage forms (solutions or suspensions) remains a significant challenge. Despite advances, effective taste masking can still be difficult when a drug substance has multiple taste and/or sensorial attributes (e.g., bitter, metallic, burning sensation), according to Kanji. This situation is particularly true for liquid formulations, which typically rely on the use of flavors and sweeteners for taste masking, he notes. Barrier coatings may also lose their effectiveness over the shelf-life of the product due to continuous exposure of the API to water, Joshi adds.

One new solution is to use micelle-forming surfactants, liposomes, or cyclodextrins to protect the drug substance. “For instance, in an aqueous environment,” Joshi comments, “certain surfactants and poloxamers form micelles that can entrap the drug substance, forming transient inclusion complexes, thus shielding it from the tastebuds and effectively masking its taste. This approach, he observes, also has potential for use in other dosage forms, such as gummies.

The use of water-in-oil (W/O) emulsions for taste masking of soluble APIs is another promising approach for liquid formulations, according to Assis. “In this case, the drug substance is effectively encapsulated within the water phase, which is surrounded by the continuous oil phase, preventing it from coming in direct contact with the taste buds and thus masking unpleasant tastes such as bitterness,” he says.

The water-soluble API is dissolved in the aqueous phase. The continuous phase comprises an oil with an acceptable taste profile, with medium-chain triglycerides (MCTs) being of particular interest, as well as surfactants with both low and high hydrophilic–lipophilic balances (HLBs) as the emulsifiers, Assis observes. He notes that polyoxyl 40 hydrogenated castor oil with a high HLB of approximately 14 to 16 is recommended for solutions and suspensions because, unlike other emulsifiers, it has a more pleasant taste. Additionally, glycerol monostearate type II (mono- and di-glycerides) is an excellent alternative as a co-emulsifier due its low HLB value of approximately 3.8.

The water phase is dispersed within the continuous oil phase, and the resulting O/W emulsion has the drug substance “hidden” within the emulsion droplets, which act as a barrier between the API and the taste receptors, Assis says. The oil phase surrounding the water droplets, meanwhile, prevents release of the drug substance while it is in the mouth; release occurs only once emulsification and absorption take place in the gastrointestinal tract.

Solutions for high drug-loading formulations

One important trend in oral dosage drugs is development of high-drug-loading formulations. “High-dose drugs in solids can be very tricky and demand better approaches to improve their taste profiles,” Joshi says. For instance, he notes, traditional barrier coatings can be challenging as long coating processing times are often required to achieve the desired level of masking.

One alternative is to use a dual-granulation coating approach to establish a barrier coating. “In this process,” explains Joshi, “the coating polymer not only covers the API and excipients, but also acts as a binder that enables formation of larger granules. The result is effective taste masking combined with improved flowability and compressibility of the drug product.” It is necessary, though, to use appropriate cushioning agents/plasticizers to maintain the integrity of the polymer following compression into a tablet. Certain methacrylic acid polymers can be effectively used in this application.

Development of additives for oral dosage drugs that block the receptors in the mouth that perceive bitterness has been a goal for many years. Taste buds elicit taste responses using G-protein coupled receptors (GPCR) or ion-channel receptors. Modulating their responses using compounds that block these receptors would be an effective means of eliminating the perception of specific tastes, according to Joshi.

This approach remains challenging, however. Such compounds need to be safe and provide transient and reversible modulation so that a patient’s taste perception is not affected for a long time after consuming the drug, Joshi says. In addition, there are at least 25 different, highly genetically diverse bitter taste receptors, and the drug substance-taste receptor pathway is not always known, Assis observes. Despite these challenges, a few specific compounds referred to as bitter blockers have been developed that interfere with the taste transduction process, at least to some degree, by inhibiting the taste cascade (interrupting the GPCR signal cascade), says Assis.

A few other approaches are also being explored. For instance, the API, which typically exists as a salt of some kind, can be converted into a more palatable compound, such as a prodrug, freebase, or different salt, according to Assis. It is necessary when using this strategy, however, to carefully consider the formulation development time and to assess any potential changes in pharmacokinetics.

Finally, complexing agents like ion-exchange resins and cyclodextrins can be used to prevent interaction of the API with bitter receptors. When using these approaches, Assis emphasizes the importance of considering factors such as the potential for drug-resin dissociation and the amount of cyclodextrin needed, respectively, as these factors may impact dosage size and costs. Cyclodextrin, for instance, can not only be very costly, but also may need to be used in a high quantity to ensure effective complexation with the drug substance at the molecular level.

Cynthia A. Challener, PhD, is a contributing editor to Pharmaceutical Technology®.

®
Vol. 49, No. 1
January/February 2025
Pages: 17–19

When referring to this article, please cite it as Challener, C.A. Evolving Approaches to Taste Masking. 

Articles

Increasing API and formulation complexity and new delivery strategies are driving innovations in taste-masking.

Evolving Approaches to Taste Masking 

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Demand for biosimilars is predicted to be expanding at a compound annual growth rate of nearly 18% from $9.4 billion in 2023 to $66.9 billion in 2028 (1). This strong growth reflects both higher acceptance and adoption rates and increasing numbers of approvals in multiple regions. By mid-2024, 40 biosimilars had been approved by FDA in the United States, with 25 products launched to the market. While this growth is attracting new entrants, hurdles to developing cost-effective and competitive biosimilars remain. In addition to the need to clearly demonstrate biosimilarity to reference branded products, manufacturing complexity, including scaling challenges, must be overcome.

Developing a biosimilar starts with characterization of the reference product. That information is used to develop a cell line capable of producing the desired product with comparable post-translational modifications and other important product quality attributes. Process development and optimization, including upstream cell culture and downstream purification comes next. Comprehensive product characterization, formulation development, fill/finish, final product release, and packaging round out the activities.

Successful scaling of upstream, downstream, and fill/finish operations while ensuring high yields of high-quality product that is comparable to the reference molecule requires consideration of many factors. The best approach is to use a holistic strategy that takes these factors into account from the outset.

Biosimilars approved to date are largely recombinant proteins and monoclonal antibodies. The past decade has witnessed a rapid rise in approvals of more complex modalities, such as bi- and tri-specific antibodies, fusion proteins, antibody-drug conjugates, and cell and gene therapies. In a recent analysis, the Boston Consulting Group estimated that new modalities represented $168 billion in pipeline value in 2024, an increase of 14% from 2023 (2). Many products in these new drug classes will soon lose patent protection, according to Patrick Meyer, global head of business development and alliance management at Rentschler Biopharma.

“These modalities present greater challenges in development, manufacturing, and analytics compared to conventional antibodies. They often necessitate advanced cell-line development techniques, additional downstream processing steps, and sophisticated formulation and analytical methods,” Meyer says. This greater complexity means that developing scalable processes is often more difficult. Importantly, the different modalities often have specific manufacturing requirements and thus require tailored and customized development, optimization, and scaling solutions.

Meyer therefore anticipates the growing diversity of modalities will further drive the need for contract development and manufacturing organizations (CDMOs) with the willingness and flexibility to adapt to new modalities and markets.

Although it may not seem like a specific scaling challenge, Meyer argues that the need to demonstrate biosimilarity is indeed a scaling challenge because of the nature of biologics. “Ensuring biosimilarity is a multifaceted challenge because biological systems are inherently variable, and thus achieving comparability with the reference product requires addressing several important aspects throughout the development and approval process,” he observes. One crucial success factor in biosimilar process scaling, therefore, is maintaining cell growth and productivity without introducing variability when transitioning from small-scale to large-scale bioreactors.

Achieving product and process consistency from batch to batch and across scales is a notable challenge as well. Realizing this goal must start at the cell-line development stage, where all differences from the reference product must be minimized, according to Meyer. Development of highly controlled processes is then essential to ensure stringent product quality as processes move through development phases and scale-up to commercial production. “Application of advanced analytical techniques that enable comprehensive comparison of the biosimilar’s critical quality attributes (CQAs) to those of the reference product is key,” he says. Biosimilar developers therefore must have experience and expertise in the analysis of biologics and access to well-equipped, state-of-the-art analytical labs.

Successful scale-up of biosimilar manufacturing processes also requires access to a stable supply chain affording consistent supply of high-quality raw materials and up-to-date knowledge of regulatory requirements, Meyer notes. Process scaling must also be practically achievable with reasonable process economics without compromising product quality.

Fortunately, the biopharmaceutical industry is, although conservative, also highly innovative, with constant investment in new technologies geared to improving productivity and quality while reducing risk and cost. Biosimilar developers have benefited from many recent advances.

Notable examples highlighted by Meyer include single-use bioreactors, which reduce the risk of cross-contamination across all scales; process analytical technology, which allows for real-time monitoring and control of critical process for streamlined scale-up and consistent performance from lab to commercial production; and innovations in cell-line development such as clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing and high-throughput screening, which have led to more productive and robust cell lines that, in turn, support streamlined scaling.

Of course, introduction of new technologies inevitably brings new sets of challenges. For instance, new technologies cannot be integrated into complex biomanufacturing frameworks without a transition period, according to Meyer, which can include, among other things, reconfiguration of systems and processes and significant training of personnel.

The implementation of new technology also brings about new challenges regarding data management and security, Meyer comments. “As biopharmaceutical manufacturing becomes more automated and digitized, it is the responsibility of CDMOs to ensure the safe and compliant handling of client and product data,” he contends.

Overall, Meyer concludes, it is essential to balance the benefits and challenges of innovative technologies to realize the successful and sustainable scale-up of biosimilar manufacturing processes.

Adopt a holistic strategy and platform processes

Given the multiple steps involved in biosimilar process development and scale-up, planning is essential and, according to Meyer, should take into account several key factors: process consistency and robustness, analytical excellence, and regulatory compliance.

At Rentschler Biopharma, success in biosimilar development hinges on providing robust processes that deliver material of highly comparable quality to the reference product, and doing so in a manner that supports submission timelines and ensures reliability, according to Meyer. That is achieved by taking a holistic approach and considering multiple factors from the outset of a biosimilar development project, including cell-line development, analytical needs, and regulatory compliance.

Establishing state-of-the-art cell lines that ensure biosimilarity early on and provide consistent performance across scales, including high titers to lower overall cost of goods, is crucial for biosimilar market success, observes Meyer. Use of state-of-the-art analytical techniques for thorough characterization and real-time monitoring of CQAs also supports successful process development and scaling. Rentschler Biopharma leverages these approaches combined with a quality-by-design approach, which enables early identification of CQAs and process parameters, leading to greater process understanding and the ability to develop robust, scalable processes. Meanwhile, use of scalable bioreactor systems, including multiple 2000-L and 3000-L bioreactors, makes it possible for Rentschler Biopharma to optimize conditions for large-scale production.

Staying abreast of regulatory changes is also crucial to ensure ongoing compliance. Rentschler Biopharma has a dedicated regulatory affairs team with extensive experience in biosimilar development that engages with regulatory authorities early on in the development process and maintains open communication to align on expectations and requirements, according to Meyer. “By anticipating additional data requests from regulators, we prepare comprehensive data packages in advance,” he adds.

Access to modular and flexible manufacturing facilities is also essential to ensuring easy scale-up of biosimilar processes given that each molecule has unique attributes and often requires custom solutions to ensure consistent performance across scales. With its sites in Laupheim, Germany, and Milford, Massachusetts, in the United States, which leverage similar equipment and manufacturing platforms across lab to commercial scale, for instance, Meyer notes that Rentschler Biopharma can provide tailored development and manufacturing support for many different biosimilar candidates from early to late-stage through commercialization.

MarketsandMarkets. Biosimilars Market by Drug Class (Drug Class (Monoclonal Antibodies (Adalimumab, Infliximab, Rituximab, Trastuzumab), Insulin, Erythropoietin, Anticoagulants. rhGH), Indication, Region–Global Forecast to 2028. Marketandmarkets.com (accessed Dec. 23, 2024). 

https://www.marketsandmarkets.com/Market-Reports/biosimilars-40.html

Chen, L.; Bush, B.; Brochu, M.; and King, G. New Drug Modalities 2024, Boston Consulting Group. BCG.com. Sept. 24, 2024. 

https://www.bcg.com/publications/2024/new-drug-modalities-report

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

®
Vol. 49, No. 1
January/February 2025
Pages: 20–22

When referring to this article, please cite it as Challener, C.A. Overcoming Biosimilar Scaling Challenges. 

Process consistency and robustness, analytical excellence, and regulatory compliance are essential in the scale-up of biosimilars.

Overcoming Biosimilar Scaling Challenges

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Accurate and timely analytics are essential to successful development and manufacturing of biopharmaceutical products, whether they are small-molecule drugs, recombinant proteins or antibodies, or novel modalities such as cell and gene therapies. The correct choice of analytical technologies for process monitoring and optimization during process development can reduce timelines and risk while increasing the ability to develop robust, readily scalable processes. Outsourcing of analytics to contract research organizations (CROs) with state-of-the-art capabilities, meanwhile, can help drug developers realize these goals in a cost-effective manner.

Business and technical drivers for analytical outsourcing

Outsourcing of analytics and testing often occurs for a combination of business and biological reasons, according to Alex Sargent, director of process development for cell and gene therapy at Charles River Laboratories, particularly in the case of complex, novel modalities. “For instance, it may be more cost-effective and efficient to outsource routine testing rather than investing in the space, equipment, and personnel needed to run a limited number of assays, making it more practical to rely on external providers,” he explains.

In addition to providing more cost-efficient and scalable solutions, CROs can also offer faster turnaround times to adapt to the needs of client projects as they advance from early development to commercialization, notes Jincui Huang, senior vice-president and head of the analytical sciences and clinical quality control department at WuXi Biologics.

Outsourcing analytics to CROs is also driven by the need for specialized expertise and advanced capabilities, Huang observes. “Many biotech companies recognize that developing and maintaining in-house systems for comprehensive analytics for process monitoring and optimization can be resource-intensive. By partnering with CROs, biotech and biopharma gain access to advanced technologies and methodologies that may not be available internally,” she says.

As an example, Sargent highlights bioinformatics and DNA/RNA sequencing for cell and gene therapies as technologies often accessed by developers through strategic partnerships with CROs.

The increase in outsourcing of biologics production for both drug substances and drug products to contract development and manufacturing organizations (CDMOs) is also leading to more analytics outsourcing, according to Elliott Franco, vice-president of lab operations at Alcami. “Many CDMOs have become true experts in process development and gain deep understanding of each process they work on through establishment of comprehensive analytical capabilities. That expertise allows for easy correlation of data trends to process parameters and thus efficient optimization of processes to maximize reliability and robustness,” he comments.

Increasing automation with room for improvement

Driven by the demand for greater efficiency and robustness in biologics development, automation in analytics is becoming increasingly prevalent among leading CROs. Areas of application can include, according to Huang, sampling, real-time monitoring, and data analysis.

Integrated automated sampling systems, Huang says, allow for collection of samples from both continuous and traditional fed-batch processes in real time without interrupting the processes. Inline process analytical technology (PAT) tools such as Raman spectroscopy and bio-capacitance measurement, meanwhile, when applied in upstream processes, offer real-time, high-frequency, and non-invasive analysis of the cell-culture process through corresponding chemometric algorithms, she observes. In downstream processing, online liquid chromatography with robotic systems offers high-throughput and near-real-time analysis for monitoring of product quality attributes during purification steps, according to Huang.

The ability to follow process milestones (protein concentration, cell densities, critical impurities, etc.) with online sampling/testing capabilities provides the type of data needed to truly understand critical process parameters, Franco agrees.

Advanced digital tools, meanwhile, are transforming the way analytical data are analyzed and used. “Digitalized auto-integration/analysis and predictive modeling help us develop methods in a more efficient way and identify potential risks earlier while continuously improving assay accuracy by analyzing historical and real-time data,” Huang explains.

Additional improvements in automated testing are still needed, however, particularly with respect to process development and optimization for newer modalities such as cell and gene therapies, Sargent contends. “The state of automation in analytics within the cell and gene therapy sector has room to grow, especially when compared to manufacturing,” she states.

In the past few years, there has been a surge of new technologies and platforms for the automated manufacturing of cell and gene therapies that have the potential to streamline operations, expand access, and significantly reduce costs, particularly for autologous therapies, Sargent notes. He believes, however, that, although equally important, automating analytics for process monitoring and testing during development and commercial production has been more challenging. “When talking about autologous cell therapies, for instance, there are thousands of independent batches generated every year, all of which require analytics for quality control and release. To effectively reduce the costs of these life-saving therapies, prioritizing the automation of testing will be critical,” he asserts.

Many benefits of automating outsourced analytics

There are, in fact, many ways in which automation of analytics at outsourcing partners can benefit biologic drug developers. “Automated analytics capabilities provide customers with several significant advantages, particularly when offered by service providers with robust experience in PAT and other automatic analytics technologies,” Huang says.

Specific benefits highlighted by Sargent include faster, more cost-efficient, and more accurate analytics compared to manual techniques. “Removing human error and bias can improve robustness and reproducibility, and outsourcing testing to a partner with automated capabilities can really help someone scale up or scale out their clinical or commercial program,” he remarks.

“In addition to allowing drug developers to avoid the need to make significant capital investments in expensive and complex technologies, automated solutions at CROs often speed up process development and scale-up while also enhancing the robustness of large-scale production by leveraging the CRO’s technical expertise and comprehensive data analytics capabilities,” Huang agrees. This support helps clients make informed decisions, optimize their processes, and ultimately achieve higher yields and better product quality.

“Overall,” concludes Franco, “use of automated analytics typically ensures that processes are well characterized and less likely to encounter failures to meet specifications with access to better and more comprehensive process data.”

There are some challenges to establishing comprehensive automated analytics capabilities, even for CROs serving large numbers of clients. “In general,” summarizes Franco, “establishing automation is costly and requires significant expertise at the interface of manufacturing, process development, and analytical development.”

Cost is often the first challenge when implementing automation for process monitoring and optimization, as the upfront investment to implement new analytical technologies in a process workflow can be significant, Sargent observes. “When transitioning from manual methods to automated systems, it is essential to demonstrate that the new method delivers equivalent readouts and results. For therapeutics developers, particularly those in advanced clinical or regulatory phases, implementing such changes may be impractical or even infeasible, despite potential improvements in throughput or robustness. It thus falls on the CRO to identify clients well-suited to automation and new technologies, particularly early-stage developers or those looking to transition to new platforms,” he comments.

Another issue highlighted by Huang that CROs must be able to overcome is the need to integrate new automation technologies with client-specific practices, as many clients use specific and diverse systems that require harmonization with automated platforms. Use of many different platforms, meanwhile, creates a need to consolidate data from multiple sources into a cohesive format for analysis, which can be complex, she notes. It is also essential to ensure automated systems meet stringent compliance standards, which requires robust validation. In addition, training staff and adapting workflows to incorporate automation can take time.

One strategy for overcoming these challenges, according to Huang, is through investment in advanced infrastructure, development of proprietary integration frameworks, and having a commitment to operational excellence.

Several exciting advances in automation and analytical technologies are increasing CRO capabilities in the field. First, Huang points to high-throughput automated platforms leveraging robotic systems capable of executing multiple assays simultaneously, which are improving productivity and reducing turnaround times.Many of these systems are designed to be end-to-end platforms, she adds. “The use [of] multiple robotic arms or automated guided vehicles to connect different modules can provide seamless end-to-end automation, from sample management, to analysis and reporting,” she explains. Bioreactors with embedded sensors and integrated advanced analytical systems, meanwhile, enable continuous monitoring of critical process parameters. “These innovations are critical for improving efficiency, scalability, and precision in biologics development,” Huang states.

In cell and gene therapy, the rapid advance of next-generation sequencing technologies and bioinformatics is, according to Sargent, having a transformative impact. Rather than years, hundreds of genomes can be sequenced in a week. Similar advances in transcriptomics, proteomics, and other comprehensive assays are equally important, he observes.

Across all biologics, Sargent believes that the implementation of machine learning and artificial intelligence (AI) in process monitoring and analytics systems is tremendously important. “We are only at the beginning of what AI can do in analytics and process monitoring, and the potential for how it may transform this space cannot be ignored,” Sargent remarks.

While automation in analytics is providing many benefits, limitations remain for CROs looking to maximize the opportunities that can be realized for their customers. Lack of standardization between different vendor systems remains a major issue in this regard, says Huang. “Industry efforts are required to establish open-source standards from software to hardware perspectives,” she notes. That should include development of next-generation technologies for effectively managing and interpreting the overwhelming amount of data generated by automated systems.

Huang would also like to see the development of modular automation solutions, which she believes could help smaller CROs overcome the challenge of high upfront expenditures currently associated with existing automated analytics systems.

Sargent emphasizes the need for more end-to-end analytics solutions for process monitoring. As an example, he points to flow cytometry, which is an essential tool in the cell and gene therapy space. “New advances in cytometers have improved throughput, removed human subjectivity, and automated crucial analysis workflows, but sample prep and staining remain laborious, manual tasks. Having an end-to-end automated solution for sample collection, storage, preparation, testing, and analysis will be key for future development, both for CROs and their customers,” Sargent says.

Automation of potency assays, including testing and analysis, is also needed. “These assays can be a major challenge using existing technologies. Automation and innovative methods/techniques will therefore likely prove critical to enabling a deeper understanding of biology and cellular responses, which will benefit everyone in the field, from academic researchers to drug developers and their outsourcing partners,” he concludes.

®
Vol. 49, No. 1
January/February 2025
Pages: 26–28

When referring to this article, please cite it as Challener, C.A. Outsourcing Analytics for Process Monitoring and Optimization. 

Developers save money and time while accessing expertise.

Outsourcing Analytics for Process Monitoring and Optimization

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Improved responsiveness and better decision-making

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Integrating Advanced Technologies for Pharma Analysis

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. 

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

Accelerating Discovery and Development with Advances in High-Throughput Screening

Improving the sustainability of small-molecule API manufacturing is essential to ensuring the continued success of the pharmaceutical industry. For most APIs, however, evaluating the global warming potential along the whole value chain or lifecycle is difficult. The greatest contributor to the carbon footprint of API manufacturing operations is the use of solvents, reagents, and precious metal catalysts, for which significant restrictions on recycling and reuse can exist. The major argument against reuse and recycling is based on product and application safety, according to Michael Nonnenmacher, director global business development differentiating technologies with Evonik.

The ability to recycle and/or reuse these materials, however, enables measurable resource savings and carbon footprint reduction. “For multi-step syntheses, it is likely that hundreds of kilos of raw materials are used to produce a single kilogram of final product. It is still common practice to incinerate most of the waste generated from these raw materials, oftentimes resulting in tons of carbon dioxide (CO2) per kilo product. The possibility to recover some of these materials or replace them with greener alternatives would enable a tremendous reduction of the process carbon footprint and improve the overall sustainability of these operations,” Nonnenmacher states.

Solvent and catalyst recycling offers several significant advantages. In contrast to incineration or other forms of disposal, which generate substantial amounts of CO

, catalyst and solvent recycling allow repurposing in the same process or in different applications with minimal energy expenditure, according to Niklaus Künzle, senior director, global head of process technology and innovation for small molecules at Lonza. In addition to the reduced direct CO

 emissions, this approach also necessitates less production of raw materials, resulting in further substantial savings in both energy and CO

Solvent recovery is the most practiced recycling solution in established large-scale processes. “For API manufacturing, the use of recycled solvent is mostly restricted to the same process and a limited number of cycles, and a thorough risk assessment to mitigate potential impurity formation and accumulation in the process is needed,” Nonnenmacher says. “Since the discovery of nitrosamine impurities, which are potent carcinogens, stemming from solvent degradation, recovery is under a lot of scrutiny, but it should still be considered in cases where it will result in no process risk,” he adds. In cases where it is not appropriate to use recycled solvent in the API production process, Nonnenmacher observes that it is possible to find other fine-chemical applications where they are suitable.

Recycling of expensive palladium, platinum, rhodium, and other precious metal catalysts helps not only to reduce costs, but the carbon footprint associated with the mining and refining operations necessary to obtain the metals, notes Nonnenmacher. While solvent recycling is often performed in-house, the transformation of used catalysts back into active species is typically performed externally by specialized companies, according to Künzle.

In addition, although filtration of heterogeneous catalysts from process wastes is typically easy, separation of dissolved homogeneous catalysts from oftentimes dilute solutions requires more specialized technologies, comments Nonnenmacher. “Evonik has established an efficient process for the concentration of precious metal-containing organic waste streams using membrane cross filtration. This approach enables the sustainable recovery of the metal at the refinery, and thus contributes to resource saving and carbon footprint reduction,” he says.

The best strategy for realizing the greatest benefits from recycling during API manufacturing is to, according to Nonnenmacher, apply the principles of green chemistry early on during process development. “In this manner, it is possible to implement eco-friendly and more sustainable raw material and API manufacturing processes,” he notes. Key considerations highlighted by Nonnenmacher include avoiding the use of large volumes of especially hazardous organic solvents that cannot be recycled, applying process intensification approaches, and controlling the atom economy for waste reduction.

“Beyond these approaches, the introduction of recycling concepts or reuse options into processes right from the beginning can facilitate acceptance by regulatory authorities,” Nonnenmacher contends. Performance of process and product life-cycle analyses to generate important sustainability metrics such as process mass intensity and E-factor values when a candidate is in clinical development can, he says, help pharmaceutical firms and API makers visualize and understand potential adverse steps in the synthesis early enough, allowing manufacturers to implement more sustainable solutions.

Sustainability of small-molecule API manufacturing ensures continued success.