Introduction

Immunoassays have become an important workhorse in biopharmaceutical research and development and basic research. Applications are broad, from determining the pharmacokinetics of biologics, to measuring biomarkers, and measuring the levels of host cell protein impurities in release testing. Immunoassays are also playing a key role in emerging therapeutics, such as recombinant proteins and nanobodies, and in cell and gene therapy, for example to determine the immunogenicity of viral vectors.

The drive to increase productivity in biopharma R&D, from lead discovery and preclinical/clinical trials through bioprocess development to manufacturing, has accelerated the search for efficient bioanalytical methods that deliver reliable results to instill confidence in data driven decision making and meet regulatory needs. Immunoassays should deliver accurate and precise results quickly, using a minimum of sample and reagents, and in a cost-effective manner. Reductions in manual operations and hands on time also mean more time for data analysis and less risk of operator fatigue.

  • We will look at how all this can be achieved using immunoassays, including:
  • Understanding the fundamentals of how to get good immunoassay data,
  • Overcoming challenges in immunoassay development, and
  • Determining how to cost-effectively get as much data as possible from as little sample as possible with the least effort.

Maximizing immunoassay speed and quality in drug development

Meeting the demanding timelines of biopharma, for example for cell and gene therapy, includes the development and validation of fit-for-purpose immunoassays that can quickly and efficiently deliver high quality data time and time again, even in the presence of varying amounts of interfering matrix substances. Achieving this involves a combination of high quality reagents, optimized protocols, and automation.

High quality data starts with high quality reagents

High quality reagents are key, including high performance antibodies that capture and detect the analyte with high specificity, selectivity and affinity to provide the most accurate, precise and sensitive detection. These reagents can be incorporated into a quality-controlled ready-to-use kit, and this is also the most direct route to shortening assay development time. An alternative is a detailed assay protocol provided by the immunoassay vendor combined with an open and reliable immunoassay platform that supports efficient validation for the development of fit-for-purpose assays.

Automation boosts assay performance and efficiency

Assay performance can be greatly improved by avoiding errors in pipetting, handling samples and reagents, and simplification of the workflow. Replacing manual operations through automation will enable assay performance to routinely meet the targets for accuracy and precision specified in regulatory guidelines. Automation is also key to improving robustness, reducing hands on time and operator fatigue, increasing throughput, and smoothening the transfer of assays between pharma and CRO. And with the right combination of reagents, workflows and automation, assay performance can reach the levels required for singlicate analysis that is becoming increasingly accepted, providing it is backed by appropriate validation.

Overcoming common immunoassay challenges

Generating accurate and precise data on analytes in the complex matrices frequently encountered in biological R&D means overcoming many challenges. These can include interference that reduces robustness, selectivity and specificity, and the poor performance of cross-reacting antibody reagents.

Sources of interference vary depending on the nature of the assay, and interference should be determined early in method development by assessing a number of factors:

  • Parallelism/linearity
  • Recovery of spiked analyte
  • Effects of blocking agents
  • Changes in therapy and sampling techniques
  • Trends and inconsistencies in study results

Assay interference can be avoided by careful choice of reagents, including dilution, or depletion steps in the protocol, or blocking with specific reagents. Drug-target studies can be complicated by drug-target complex dissociation, which can be reduced by optimizing reagent concentrations and incubation/assay times. The development of anti-drug antibody (ADA) assays should include evaluation of drug tolerance early in assay development and validation.

Minimize contact times to reduce matrix interference

Interference is often countered by dilution, but this lowers assay sensitivity. Alternatively, contact times can be reduced to favor specific high-affinity antibody-antigen interactions and minimize low affinity interference. This is possible in a flow-through device that shortens contact times between reagents, the sample and its matrix.

Avoid cross reactivity of antibody reagents

Cross-reactivity (antibody binding to different antigens in the matrix) is widespread and selecting antibodies based on high specificity/low cross-reactivity is critical in assay validation. For example, a monoclonal antibody should be chosen as the primary antibody (e.g. for capture) to establish high assay specificity, and a polyclonal antibody can be used as the detection reagent.

Miniaturize assays to save sample and reagent

Getting more data from precious samples and reagents is an increasing challenge. This can include microsampling in animal studies to meet the requirements of the 3R’s (Replace, Reduce, Refine), or the analysis of precious biotherapeutics used in cell and gene therapy. Reagent availability can also be an issue, for example when analyzing lead compounds in early drug development. Miniaturizing immunoassays to the nanoliter scale enables more data to be generated from smaller amounts of sample and reagents and even makes it possible to perform ‘one mouse, one PK’ studies that reduce animal use and biological variation in preclinical studies and maximize data quality.

Avoid sample dilution and repeats by staying in range

Extensive sample dilution to stay in range can limit sensitivity and result in errors that require laborious assay repeats. Analytical efficiency can be greatly improved with methods that have high precision and accuracy, and a broad analytical range.

Implement automation to save time

Productivity relies on the rapid development and validation of immunoassays. Automation is a great support for this, with many benefits:

  • Minimal hands-on time
  • Reduced risk for errors that require assay repeats
  • Improved precision in liquid handling
  • Support for Design of Experiments (DOE) to accelerate method development

Maximizing productivity – weighing up the advantages and disadvantages of multiplex assays

With the need to increase analytical throughput in biotherapeutic R&D and cell and gene therapy, multiplexing assays can appear to be an obvious approach. The possible advantages of multiplexing include:

  • More data points/unit sample
  • Reduced cost/data point
  • Less sample handling means less error
  • Fewer wells and/or plates to handle
  • Increased throughput

It is certainly true that a well-designed multiplex immunoassay can deliver, but method development can be a complex task and in the end it can be more productive to run time- and volume-efficient singleplex assays in parallel. Let’s look at the downsides of developing multiplex immunoassays.

Cross-reactivity

As was mentioned above, the performance of an immunoassay is only as good as the specificity of the component antibody reagents and identifying antibodies with high specificity is a challenge for a single analyte, let alone several that need to work together in a multiplex assay. In fact, antibody cross-reactivity (the opposite of specificity) is widespread and is one of the biggest obstacles to developing high performance multiplexed immunoassays.

Dynamic range

Multiplex assays can be challenged by the need to measure different analytes over widely different ranges. Having to dilute the sample two or three times to get everything in range defeats the purpose of having a multiplex assay.

Assay development time

Developing a multiplex assay is a time-consuming process involving optimization of many parameters that must match in the same reaction. It can be more efficient to develop and run singleplex assays in parallel.

Commitment to vendor-supplied assays

The shear complexity of multiplex assays means that researchers regularly turn to external providers providing fixed assay panels rather than developing assays in house to meet specific needs.

Gyroplexing on an open platform – a unique alternative

One alternative to meeting the needs addressed by multiplexing is to run time- and volume-efficient singleplex assays in parallel using the Gyrolab automated miniaturized immunoassay platform:

  1. More data points/unit sample

    Miniaturization means more data points per microliter of sample. Running singleplex assays for different analytes in parallel on the same sample means that you can get all the data you need at once.

  2. Reduced cost/data point

    Miniaturization and automation reduce costly reagent consumption, costly hands-on time, and delivers broad analytical ranges that reduce repeats. Assay development can also be speeded up.

  3. Less sample handling means less error

    Automation, in combination with a system that defines volumes through precision microfluidics greatly reduces error.

  4. Fewer wells and/or plates to handle

    Automation solves this problem as well.

  5. Increased throughput

    Automation again. And the possibility to do overnight runs can boost throughput to approximately 1700 data points/24 h.

All this is possible using the Gyrolab platform, which runs multiple singleplex assays in parallel with no cross talk to deliver accurate high-precision data for multiple analytes with minimum sample and reagent consumption. We call it Gyroplexing.  This approach also offers several more advantages:

  • Gyroplex assays perform as well as regular Gyrolab assays.
  • The wide dynamic range of Gyrolab assays enables the same sample dilution to be used for several analytes.
  • Gyroplex assays can be readily tailored to match specific analytical needs.

And the Gyrolab platform is open to development of in-house assays, which means your analytical possibilities are only limited by your imagination and access to suitable reagents.

Conclusions

Success in using immunoassays in biopharma R&D builds on high quality reagents used to develop robust assays quickly, efficiently and cost-effectively on an automated platform. Gyrolab system offers a number of advantages when it comes to increasing productivity in biopharmaceutical R&D:

  • Miniaturization reduces sample and reagent consumption to enable more data to be reliably generated from less material. This is especially important when addressing the needs of the 3Rs of preclinical work.
  • Automation and miniaturization brings high precision and robustness to give you confidence in your results.
  • Immunoassays run in Gyrolab CDs involve flow-through that shortens contact times to greatly reduce matrix interference.
  • Immunoassays run on Gyrolab system generally have a broader analytical range than corresponding ELISAs, which reduces the need to re-run assays.
  • Analysis of multiple analytes in parallel without cross talk.
  • Flexible open platform – Gyrolab system supports more than just sandwich assays. This open platform means your analytical possibilities are only limited by your imagination and access to suitable reagents.
  • Automation of robust assays smoothens transfer between pharma and CRO.

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