Analytical measurement in drug development spans multiple attributes, from concentration and binding to quality, impurities and function. Aligning these measurements with workflow needs is critical for enabling timely and confident decision-making

Ruizhi Wang and Paolo Romele at Abselion

As drug discovery and development workflows evolve, the role of analytical measurement is also changing. Increasing modality complexity, from monoclonal antibodies and recombinant proteins to emerging cell and gene therapies, has led to more iterative and less linear development processes, where decisions are made across multiple parallel conditions and timepoints rather than at fixed stages.1

Analytical measurement in this context spans several dimensions. These include quantifying protein concentration and yield, assessing binding behaviour, evaluating product quality and impurity profiles, confirming molecular identity, and measuring functional activity. Each provides different analytical insight and is typically addressed using different analytical approaches.

Development decisions, therefore, rarely depend on a single measurement. Instead, they rely on a combination of analytical outputs that together provide a more complete understanding of the molecule or process under investigation. This aligns with broader industry thinking around fit-for-purpose analytics, where measurement is selected based on its ability to inform a specific decision rather than its standalone performance.2

Different analytical approaches are used to address different questions across development workflows. Quantitative measurements such as protein concentration or viral titre are commonly used to assess yield and productivity. These are often performed using plate-based immunoassays such as enzyme-linked immunosorbent assay, or chromatographic techniques such as high-performance liquid chromatography, depending on the stage of development. Binding and interaction measurements provide insight into affinity, kinetics and target engagement. Techniques such as bio-layer interferometry (BLI) and surface plasmon resonance (SPR) are widely used, particularly in discovery and characterisation workflows.3

Product quality and impurity profiling represent another important dimension. Analytical approaches such as size-exclusion chromatography, capillary electrophoresis and liquid chromatography-mass spectrometry are used to assess aggregation, fragmentation and process-related impurities. These include host cell proteins (HCP) and residual Protein A, which are particularly relevant in downstream process development and purification workflows.4

Identity and structural characterisation are typically addressed using mass spectrometry-based workflows, including peptide mapping and analysis of post-translational modifications. Finally, functional or potency measurements, often based on cell assays, provide information on biological activity and complement analytical readouts.

In many biologics workflows, these measurements are not performed independently, but are used together to guide progression decisions across development stages. For example, early cell line development may initially prioritise productivity and expression, while later process development places greater emphasis on product quality, impurity clearance and consistency across batches. Similarly, in antibody discovery workflows, binding affinity alone is rarely sufficient for candidate selection without supporting information on manufacturability, stability and functional performance. As a result, analytical strategies are increasingly expected to provide a broader and more connected view of both molecule quality and process behaviour. This is contributing to growing interest in analytical workflows that can support faster iteration while maintaining consistency across multiple measurement types.

Taken together, these approaches form a multidimensional analytical landscape. Each method provides a specific perspective, but also introduces its own constraints in terms of throughput, sensitivity, reproducibility and workflow integration. In practice, development workflows increasingly rely on combining these complementary measurements to build a more holistic understanding of candidate quality, manufacturability and performance.

Table 1: Common analytical challenges across measurement types

Given this diversity, there is no single analytical method that is optimal across all stages. Instead, measurement strategies are selected based on the attribute being assessed and the decision being supported. In practice, this often means balancing analytical depth, operational complexity, throughput and the speed at which data is needed to support development decisions. This principle is reflected in regulatory and analytical frameworks, including ICH Q2, where method suitability is defined in relation to intended use.5

In early-stage workflows, the emphasis is often on throughput and speed. Large numbers of samples may be screened in parallel using relatively simple measurements to rank candidates or conditions. throughput with analytical depth. High-throughput workflows require rapid and scalable methods, while more detailed measurements, such as impurity profiling or structural characterisation, typically involve longer processing times and more operationally demanding workflows.

Method-specific limitations also contribute. Quantitative assays may require dilution to operate within a defined dynamic range, while interaction-based techniques such as BLI or SPR are sensitive to binding conditions. Chromatographic and mass spectrometry-based methods provide detailed information on product quality and impurities, but often require additional sample preparation and data analysis.

Sample context adds further complexity. Measurements are frequently performed on samples ranging from crude lysates to partially purified intermediates. These differences in composition can affect assay performance, introducing variability through matrix effects or sample preparation steps.6 In practice, and based on experience working with development teams, these challenges often arise not from the analytical method itself, but from how methods are applied across different workflow contexts and constraints.

As development progresses, the analytical focus shifts towards deeper characterisation. Measurements of binding behaviour, product quality and impurity profiles, including HCP and residual Protein A, become increasingly important, particularly in process development and comparability studies. These later-stage measurements often rely on more specialised or orthogonal techniques, including chromatographic and mass spectrometry-based approaches. As a result, data generated at different stages may not be directly comparable, particularly when different attributes or analytical formats are involved.

Implementing a fit-for-purpose approach introduces several practical challenges (Table 1). One key issue is balancing

A further challenge is data comparability across measurement types. Different methods produce fundamentally different outputs. Quantitative measurements are often expressed in absolute terms, while binding assays provide kinetic or affinity parameters. Quality and impurity measurements may be reported as distributions or relative abundances.

In practice, development decisions often require integrating these different data sets. For example, a candidate may demonstrate favourable expression levels but exhibit less optimal binding characteristics or impurity profiles. Variability can also arise from differences in assay format, sample preparation, or implementation. Even well-established methods can produce different results when applied across workflows or teams.7

Table 2: Iterative measurement and decision-making across development workflows

As a result, comparability is not solely a technical issue but also an interpretative one. Aligning data across measurement types is essential for consistent decision-making.

Drug development workflows are inherently iterative. Measurement and decision-making are closely linked, with each cycle informing the next.

Where data is generated efficiently, workflows can progress quickly. However, when measurements require multiple steps, confirmation across methods, or additional analysis, decision cycles can be extended (Table 2).

This is particularly relevant in multi-attribute workflows. The overall timeline may depend on the slowest measurement step, particularly when combining concentration, binding and impurity data. Discrepancies between measurements may require additional experiments, further slowing progress. In many cases, delays are driven by the difficulty of integrating and interpreting outputs generated from different analytical approaches, rather than a lack of data. separate platforms and teams. Coordinating these activities can introduce inefficiencies, particularly in high-throughput environments.

There is, therefore, growing interest in approaches that improve alignment between measurement and workflow requirements. This includes not only reducing manual steps and improving reproducibility, but also increasing the level of automation in sample handling, assay execution and data generation.8 More automated workflows can help reduce variability between users and support more consistent data across large sample sets.9

In parallel, there is increasing pressure to shorten development timelines, while expanding the amount of analytical information generated at each stage. This is driving broader interest in analytical workflows that are not only high-performing, but also easier to implement, scale and integrate into day-to-day laboratory operations. In many organisations, the practical challenge is no longer access to analytical capability alone, but how to deploy that capability efficiently across multiple projects, teams and workflow stages.

At the same time, infrastructure considerations are becoming more prominent. Traditional analytical techniques often rely on complex instrumentation or centralised facilities, which can limit accessibility and slow decision-making.

As workflows scale, integrating analytical measurement into routine operations becomes increasingly important. Different measurement types are often performed across

In this context, alternative detection strategies, including electrochemical approaches such as redox electrochemical detection that can convert biochemical interactions into precise, measurable electrical signals, are being explored to support simplified workflows and more distributed measurement. These approaches don’t require optical components or complex fluidics, and can help to reduce reliance on complex instrumentation while enabling faster and more accessible data generation within development workflows.10

More broadly, the direction of travel across the industry points towards analytical ecosystems that are increasingly integrated, automated and workflow-aware, where measurement is designed not only around technical performance, but around how effectively data can move through development processes and support decision-making in real time.

Analytical measurement in drug development is inherently multidimensional, spanning concentration, binding, quality, impurities, identity and function.

The challenge is not simply generating data, but integrating multiple analytical outputs in a way that supports timely and consistent decision-making. Aligning measurement strategies with workflow requirements, including throughput, comparability, scalability and increasing levels of automation, is therefore increasingly important.

As development workflows continue to become more iterative, data-rich and operationally complex, the role of analytical measurement is also evolving. Measurement is increasingly expected to support not only analytical understanding, but also workflow efficiency, faster iteration and more integrated decision-making across development stages. In this context, fit-for-purpose measurement represents a practical framework for supporting effective drug development.

Ultimately, the future value of analytical measurement will depend not only on what can be measured, but on how effectively analytical insights can be translated into timely and confident development decisions.

References:

1. Visit: nature.com/articles/s41587-022-01582-x

2. Visit: pubmed.ncbi.nlm.nih.gov/16397743/

3. Visit: pubmed.ncbi.nlm.nih.gov/19758119/

4. Visit: pubmed.ncbi.nlm.nih.gov/22327428/

5. Visit: database.ich.org/sites/default/files/ICH_Q2%28R2%29_Guideline_2023_1130.pdf.

6. Visit: pubmed.ncbi.nlm.nih.gov/25819785/

7. Visit: nature.com/articles/533452a

8. Visit: tandfonline.com/doi/full/10.1080/19420862.2021.1 978131

9. Visit: analyticalsciencejournals.onlinelibrary.wiley.com/ doi/10.1002/bit.24978

10. Visit: researchgate.net/publication/339037563_ Electrochemical_biosensors_perspective_on_functional_ nanomaterials_for_on-site_analysis

Dr Ruizhi Wang is chief executive officer and co-founder of Abselion, a biotechnology company developing analytical technologies for biologics research and development. His work focuses on advancing measurement platforms that support more efficient decision-making across protein discovery, process development and bioprocessing workflows. Ruizhi has experience spanning biotechnology innovation, platform development and commercialisation, working closely with both academic and industry partners to translate emerging analytical technologies into practical tools for life sciences applications. Ruizhi holds a PhD from the University of Cambridge, UK, and MSc/ BSc degrees from ETH Zurich, Switzerland.

Dr Paolo Romele is senior vice president of Technology at Abselion, where he leads technology development across biosensing platforms, assay development, analytical consumables, and customer-focused application workflows for biologics research and bioprocessing. His work spans platform development, measurement technologies and the translation of analytical methods into robust and practical laboratory workflows. Prior to joining Abselion, Paolo conducted advanced research at STMicroelectronics and the Max Planck Institute for Polymer Research, building expertise in electrochemical sensing, materials science and applied engineering. He holds a PhD from the University of Brescia, Italy.