High-throughput screening

Key takeaways

High-throughput screening (HTS) is foundational in modern drug discovery and has transformed early development by enabling rapid evaluation of thousands of compounds or biologics.

Today’s HTS strategies focus on speed, data quality, reproducibility, predictive insights, and robust validation. Modern HTS integrates highly sensitive assays, automated workflows and advanced analytics to identify hits with greater accuracy and speed. Technologies such as surface plasmon resonance (SPR) extend the value of HTS by providing label-free, real-time, interaction analysis. Combined with emerging orthogonal tools, HTS is evolving into a more predictive and efficient decision-making framework.

• HTS enables rapid, large-scale compound and biologic screening, transforming drug discovery.
• Integration of robotics, miniaturized assays, and AI, improves speed, reproducibility, and predictive power.
• Key challenges include data management, assay validation, automation reliability, and cost.

Our Biacore™ SPR technology supports high-throughput screening, using label-free real-time analysis for small molecules, and biologics such as mAbs, PROTACs, and ADCs.

What is high-throughput screening (HTS)?

Traditionally, drug discovery has been a slow, sequential process. Today, it is transformed by the power of HTS to simultaneous test hundreds of compounds/biologics as potential drugs to target disease. This shift is powered by a convergence of robotics, miniaturized assays, highly sensitive detectors, advanced data processing, and intelligent data analytics.

HTS is no longer just about speed, it’s about quality, reproducibility, and translational insight. With the integration of AI-driven analytics, researchers can now predict drug candidate efficacy, toxicity, and off-target effects earlier in the pipeline, reducing costly late-stage failures.

HTS has long been associated with small molecule discovery. But today, it’s also revolutionizing the development of biotherapeutics—including mAbs, bispecifics, and engineered proteins. Screening platforms now support functional assays, binding kinetics, and epitope binning at scale, enabling confident selection of lead candidates for complex diseases like cancer, autoimmune disorders, and rare genetic conditions.

For example, HTS played a pivotal role in the discovery of poly-ADP ribose polymerase (PARP) inhibitors for BRCA-mutated cancers and in repurposing quinidine for rare epileptic encephalopathies linked to KCNT1 mutations—showcasing its impact in both precision oncology and neurology.

Applications of HTS in drug discovery and beyond

• Discovery of drug targets: Academic, bioinformatics, and pharmacology researchers use HTS to discover probes for chemical biology, screen active compounds, identify potential targets, expand target knowledge, and enhance therapeutic identification and insights.
• Screening large compound libraries: HTS drug discovery provides rapid and efficient evaluation of extensive collections of bioactive compounds, typically at a rate of several thousand compounds per day or week.
• Antibodies and their derivatives are increasingly important as biotherapeutics, precision diagnostics, and essential tools for biological research. Techniques to screen and characterize large numbers of antibodies and provide confident selection of the appropriate candidates.
• Assessment of drug efficacy and safety: In the pharmaceutical industry, HTS provides identification of potential drug candidates with desired biological responses, safety profiles, and low toxicity.

Key considerations for HTS

• Automation and miniaturization
  HTS relies heavily on automated systems—including robotic liquid handlers, plate readers, and integrated software—to conduct thousands of assays in parallel. Miniaturized formats (96-, 384-, or 1536-well plates) reduce reagent use and increase throughput.
• Standardization and reproducibility
  Assays must be highly standardized to ensure consistent results across large datasets. This includes uniform sample preparation, assay conditions, and data collection protocols to minimize variability and maximize reproducibility.
• Library diversity
  A core principle of HTS is screening large, diverse libraries of small molecules, peptides, or biologics. Structural diversity increases the likelihood of identifying novel hits with unique mechanisms of action.
• Data quality and statistical rigor
  HTS requires robust data analysis pipelines and statistical methods (e.g., Z’-factor, signal-to-noise ratio) to assess assay quality, identify hits, and reduce false positives/negatives.
• Hit validation and orthogonal follow-up
  Initial hits from HTS undergo secondary screening and orthogonal assays to confirm activity, assess specificity, and understand mechanisms of action. This step is critical to transition from hit to lead compound.
• Integration with informatics and AI
  Modern HTS platforms integrate with bioinformatics tools, machine learning, and cloud-based data systems to manage large datasets, predict compound behavior, and accelerate decision-making.

Target-based versus phenotypic HTS

Table 1. Comparison of HTS methods

	Target-based HTS	Phenotypic HTS
Method and application	Focuses on a specific molecular target, such as a protein, enzyme, or receptor, that is believed to play a key role in a disease.	Evaluates compounds based on their observable effects on cells, tissues, or organisms, without necessarily knowing the molecular target.
Key features	• Requires prior knowledge of the biological target. • Assays are designed to measure direct interaction (e.g., binding or inhibition). • Often used in early-stage drug discovery for rational drug design.	• Focuses on biological outcomes (e.g., cell death, morphology changes). • Can uncover novel mechanisms or targets. • Often used in complex disease models or when the target is unknown.
Advantages	• Mechanism of action is usually well understood. • Easier to optimize compounds for potency and selectivity. • High reproducibility and scalability.	• More physiologically relevant. • Can identify first-in-class drugs with new mechanisms. • Useful for diseases with multifactorial causes.
Limitations	• May miss compounds that act through unknown or indirect pathways. • Not optimal for complex diseases with poorly understood biology.	• Target deconvolution (figuring out how the compound works) can be time-consuming. • Assays are often more complex and harder to automate.

Although phenotypic screening can be especially advantageous for complex or poorly understood diseases, assays can be more variable and expensive. Target-based HTS such as SPR systems offer easier hit-to-lead optimization for drug discovery and align with regulatory expectations for mechanism of action (MOA).

Target-based screening and characterization technology: surface plasmon resonance (SPR)

Our Biacore™ SPR system offers HTS and selection of high performing:

• Compounds based on direct label-free binding data: selectivity, stoichiometry, affinity, and kinetics.
• Antibodies based on binding, selectivity, titer, affinity, kinetics, and epitope binning information. Analysis can take place directly in supernatant from cell cultures (Fig 1).

Small molecule fragment screening to lead optimization using SPR

Our Biacore™ SPR system is a powerful tool in fragment screening and lead optimization. In the pharmaceutical industry, SPR is heavily used for screening of selected HTS hits to verify elimination of false positives and promiscuous binders (Fig 1).

Fig 1. Biacore™ SPR systems are suitable for compound and fragment-based drug discovery for lead optimization and further development.

Biotherapeutics screening and characterization using SPR

Antibody development workflow (Fig 2) has evolved, and early development is no longer focused entirely on potency and functional aspects such as specificity, affinity, and kinetics for their molecular targets. While these factors are crucial, developability aspects play an increasingly important role for reducing the risk of a later failure of the development program (2).

Sample throughput is linked to the number of microwell plates that can be handled in an automated run. Higher-throughput systems are particularly well-suited for antibody screening and large-scale epitope binning experiments, while other systems have functions that make them ideal for detailed characterization studies. There is a considerable overlap in the systems and those intended for screening are also used for characterization and vice versa.

Fig 2. The antibody development workflow (A) First selection round with focus on antigen binding, and (B) Re-engineering and selection of clinical lead.

Key challenges in HTS

Despite its promise, HTS is not without hurdles. Data overload, automation reliability, and hit validation bottlenecks remain persistent challenges. In addition, regulatory alignment and target complexity demand robust assay design and traceable workflows. Yet, these challenges are fueling innovation. Platforms like our Biacore™ 8 series SPR systems now offer high-throughput and label-free analysis of binding kinetics, while cloud-based data platforms and machine-learning algorithms are streamlining hit triage and lead optimization.

Data overload and analysis bottlenecks

HTS generates massive volumes of data, especially with the rise of high-content and phenotypic screening. Managing, storing, and interpreting this data requires advanced analytics platforms, FAIR data principles, and skilled bioinformatics teams. Without these, data processing becomes a major bottleneck.

False positives and negatives

Despite automation, HTS is prone to assay artifacts and compound interference. False positives can lead to wasted resources, while false negatives may cause promising candidates to be overlooked. Rigorous assay validation and orthogonal biophysical assays are essential to mitigate these risks.

Automation reliability and integration

Robotic systems and liquid handlers are central to HTS, but they can suffer from calibration drift, software incompatibility, and mechanical failures. Seamless integration of hardware and software remains a challenge, especially in multi-vendor environments.

Cost and resource constraints

HTS platforms require significant investment in instrumentation, consumables, and skilled personnel. For smaller biotech firms and academic labs, the cost of maintaining high-throughput capabilities can be prohibitive.

Complex biological targets

Membrane proteins, protein-protein interactions, and intrinsically disordered proteins are difficult to screen using traditional HTS formats. These targets often require custom assay development and specialized detection technologies.

Emerging trends in HTS

AI and machine-learning integration

AI is transforming HTS by enabling predictive modeling, virtual screening, and intelligent hit triage. AI-driven platforms can now forecast compound efficacy, toxicity, and developability, significantly reducing the need for brute-force screening (3).

Miniaturization and ultra-high-throughput

Assay miniaturization (e.g., 1536-well plates, microfluidics) is reducing reagent costs and increasing throughput. Ultrahigh-throughput screening (uHTS) is now capable of testing over 100 000 compounds per day, accelerating early stage discovery.

Real-time kinetic profiling technologies provide detailed interaction parameters (k_a, k_d, K_D), facilitating rapid triage and prioritization of hits. These insights are critical for progressing candidates through early development stages.

Integrated automation and sensitive detection

Advances in integrated automation platforms include temperature-controlled sample handling systems such as hotel modules in Biacore™ SPR instruments that ensure provide consistent assay conditions and reduce manual intervention.

Specialized assay formats and high-sensitivity detection methods enable the study of challenging targets. Biacore™ SPR systems allow for the analysis of complex molecules and can analyze low-affinity interactions and complex binding mechanisms, including small chemical fragments.

Cloud-based data infrastructure

Modern HTS platforms are adopting cloud-native solutions for data storage, sharing, and analysis. Integration with electronic lab notebooks (ELNs), laboratory information management systems (LIMS), and FAIR-compliant pipelines is becoming standard.

Biacore™ systems offer standardized protocols, audit trails, and GLP-compliant data formats to ensure that screening data is reproducible across development phases and that data is suitable for regulatory documentation.

Collaborative ecosystems

Pharma companies, CROs, academic labs, and tech providers are forming strategic collaborations to share resources, expertise, and infrastructure. This collaborative model is helping democratize access to HTS technologies and can lead to improved treatment and new medicines.

Conclusion: The future of HTS is smart, scalable, and strategic

Looking ahead, HTS will become even more intelligent and integrated. Advances in synthetic biology, microfluidics, and multi-omics will expand the scope of what can be screened. Regulatory frameworks are also evolving, with accelerated approval pathways and global work-sharing initiatives like Project Orbis and the Access Consortium supporting faster translation of early discoveries into clinical impact.

The process of developing new drugs is complex, challenging, and often have lengthy timelines. To expedite patient access to treatments, especially for serious and life-threatening diseases with unmet medical needs, regulatory authorities have implemented pathways to accelerate drug development and approval. Some key pathways include orphan drug designation, accelerated approval, priority review, conditional approvals for drugs with promising early-stage data and post-marketing commitments.

In this new era, HTS is not just a tool, it’s a strategic enabler of innovation, collaboration, and patient-centric drug development.

 

REFERENCES

1. Elder D. High-Throughput Screening: Principles, Applications and Advancements. Drug Discovery from Technology Networks. Published September 25, 2025. https://www.technologynetworks.com/drug-discovery/articles/high-throughput-screening-principles-applications-and-advancements-405121
2. Jarasch, A, Koll, H, Regula, J, T, Bader, M, Papdimitriou, A, Kettenberger, H. Developability Assessment during the Selection of Novel Therapeutic Antibodies. J Pharm Sci 104(6), 1885–1898 (2015). doi: 10.1002/jps.24430. https://pubmed.ncbi.nlm.nih.gov/25821140
3. Pelago Bioscience AB. The Top 5 Drug Discovery Trends Defining 2025: What They Mean for the Future of Innovation. https://www.pelagobio.com/blog/drug-discovery-trends-2025/

 

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