Accelerating Discovery and Development with Advances in High-Throughput Screening

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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.

Many HTS technologies for Hit-ID

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).”

Recent assay developments

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.

Addressing cost and complexity

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.

Further advances needed

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 in vivo 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.

Emerging strategies offer potential

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 in vitro 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 (CRISPR-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.

Excitement around multi-omics approaches

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.

People remain the key to success

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.

About the author

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

Article details

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

Citation

When referring to this article, please cite it as Challener, C. Accelerating Discovery and Development with Advances in High-Throughput Screening. Pharmaceutical Technology 2024 48 (11).