High-Throughput Screening (HTS) & Analysis

We support high-throughput studies ranging from 50 to more than 200 samples per study, enabling comprehensive analysis across large datasets within defined timelines.

Most studies are completed within 2–3 weeks from sample receipt, depending on scope and complexity. Our ability to measure across >100 samples within this timeframe, without sacrificing structural depth, is what distinguishes our high-throughput workflows from traditional approaches.

Yes. We have extensive experience working with complex biologics, including fusion proteins, ADCs and non-globular proteins. We are often engaged where standard approaches have failed to deliver meaningful data.

We offer both, but our high-throughput workflows are designed to provide multi-attribute structural insight in a single integrated study, reducing the need for multiple vendors.

We provide interpreted datasets, reviewed by experienced scientists, with clear insights to support development decisions.

Yes. Our analytical approaches are aligned with current regulatory guidance and designed to support development programs through to later-stage requirements.

Yes. Where standard HTS assays are not appropriate — or where sensitive assays are needed for non-standard molecules or complex biologics — our scientists can develop and optimize assays tailored to your molecule and study requirements.

Assay development is integrated into our study setup process, so it does not create a separate workstream or extend timelines unnecessarily. If bespoke HTS assay development is needed alongside your high-throughput study, this is scoped as part of the initial discussion with our scientists.

Speak to our team early in your planning to ensure the right analytical approach is in place before sample submission.

From target identification through to lead optimization, structural insights generated through HTS can play a critical role at key decision points in the drug discovery process — particularly where early candidate selection, molecule ranking, or lead optimization depends on more than basic physicochemical data.

By characterizing multiple structural attributes simultaneously, including glycosylation profiles, aggregation behavior, charge heterogeneity, and higher-order structure, teams gain a more complete picture of each candidate early in the process. This reduces the risk of progressing molecules that carry structural liabilities which only become apparent later, at far greater cost.

For drug discovery programmes working across large candidate sets, our multi-attribute workflows make it practical to apply this depth of insight at scale, without sacrificing turnaround times.

Accuracy in high-throughput screening depends on more than instrumentation — it requires workflows designed to minimize human error, reduce false positives, and support robust hit confirmation at scale.

Our automated, standardized workflows reduce the points of manual intervention where human error is most likely to occur, ensuring consistency across every sample in a large dataset. This is particularly important when results are being used to rank or compare candidates, where a single inconsistency can skew decision-making.

Alongside our automated workflows, computational approaches are applied during data analysis to further validate findings across large sample sets, reducing the risk of false positives before hit confirmation.

False positives are a known risk in any high-throughput setting. Our multi-attribute approach mitigates this by cross-referencing findings across multiple orthogonal assays simultaneously, so an anomalous result in one assay is evaluated in the context of the full structural picture, rather than acted upon in isolation.

Where a candidate shows potential, hit confirmation is built into our workflow as a structured step, providing the additional analytical depth needed to validate initial findings with confidence before progressing to the next stage of development.

The result is a dataset you can trust for the development decisions that depend on it.

Modern advances in analytical instrumentation, workflow automation, and data interpretation have fundamentally changed what high-throughput screening can deliver. Where speed once meant sacrificing structural depth, it is now possible to characterize a large library of attributes simultaneously across large sample sets — within timelines that align with active development programmes.