Accelerating assay development optimisation for drug discovery

Dr. Zack Gurard-Levin, Chief Scientific Officer, SAMDI Tech offers five challenges and solutions for optimal assay development in drug discovery

Developing a suitable assay is critical to identifying and characterising novel drug leads. While designing an assay may appear straightforward, developing the assay in an optimised format for a particular target and project can become an expensive and time-consuming process.

Regardless of the target, assay developers often share common goals while also facing similar challenges (Table 1). Achieving the goals while mitigating assay challenges is key to a successful assay development program and drug discovery project. This article highlights five common challenges for developing functional enzyme activity assays and binding assays and offers solutions for rapid and optimal assay development to accelerate drug discovery.

 

GoalsChallenges
Measure biochemical/binding activityMatch the assay with the target
Robust and reproducible assayVariability in reagent behaviour
Achieve balanced conditions optimised for the targetFind conditions tolerated by assay methodology
Generate high quality dataRule out false positive and negative results
Rapidly identify drug leadsPartner with the right service provider

Table 1

  1. Matching the assay methodology for the target.

The drug discovery assay market features an abundance of assay kits designed to measure diverse activities and binding interactions. Many kits rely on common laboratory equipment such as optical plate readers, making them a convenient option to initiate assay development. Selecting the most appropriate kit or choosing to develop a novel assay for your target of interest, requires careful consideration of the project goals and an understanding of (1) what will be measured, (2) the required level of sensitivity and signal to background, and (3) the necessary throughput. Understanding the opportunities afforded by certain assay methodologies, along with the limitations and potential pitfalls, will offer a faster path toward an optimised assay and the hit identification phase.

To illustrate the decision-making process of matching the assay methodology and the target, imagine being tasked with developing a protease assay. Proteases have become particularly popular recently due to the role of distinct protease enzymes in SARS-CoV-2 replication. Commercial protease assay methodologies are widely available, the majority of which utilise a fluorescence resonance energy transfer (FRET) approach.In this assay, the substrate features a fluorescent emitter and a fluorescent quencher that when the substrate is intact, the quencher blocks the fluorescent signal. Upon protease activity, the substrate is cleaved, and the distance between the emitter and quencher increases, allowing a fluorescent signal detected by the plate reader.

Protease FRET assays are available as off-the-shelf assay kits, providing a convenient starting point. However, is this commercial FRET assay the best methodology for this particular protease? Considering the three questions above, it is important to first understand whether the protease of interest will recognise the commercial substrate. If yes, is it the most appropriate substrate to use? How will the particular FRET labels (emitter and quencher molecules) impact data quality? These questions are critical given that some fluorescent molecules have been shown to impact enzyme activity, specificity, and even the behaviour of small molecule modulators (Ref 1).

Next, if the goal is to develop a balanced assay (see Ref 2-3 for more information on establishing balanced conditions), what is the KMof the substrate, and is the assay sufficiently sensitive and robust at that concentration? This is important for FRET-based approaches that are prone to an inner filter effect (Ref 4) where high substrate concentrations are prone to intermolecular quenching, which can introduce artifacts and impact data quality. Lastly, if the assay will be used for screening, what throughput is required? FRET assays are high-throughput and, therefore, useful for screening. Prioritising which of the aforementioned factors are most important for your target will guide assay choice.

  1. Labeled reagents are susceptible to variability, false positives.

Optimising a robust assay is a milestone in a drug discovery program. It is equally important to achieve reproducible results over the life of the project. A challenge arises when assays rely on reagents such as antibodies, beads, or fluorescent or radioactive reporters, where activity, purity and quality can vary by batch. One solution is to secure sufficient quantities of a single lot of all reagents ahead of time, provided the shelf life is stable throughout the lifetime of the project. However, the amount of material needed is often unknown and/or cost prohibitive. In cases where multiple lots are unavoidable, it is important to establish a series of quality control measures by assessing enzyme activity, substrate KM, antibody specificity, and linear range of detection agents, among others. Taking time to validate reagents and adopting quality control measures internally, rather than relying on certificates from manufacturers, will eliminate delays or stalling the project.

The major pitfall of labeled assays, particularly those that rely on optical readouts, is the high rate of false positive and negative results due to interference with small molecules present in libraries. Many small molecules have properties that can interfere with optical signals – quenching properties or autofluorescence – that will not necessarily show up during assay development, but will become an issue during the compound characterisation phase. To eliminate optical interference effects, assay developers may select label-free assay methodologies.

  1. Label-free assay methodologies may impose reaction restrictions.

In the last decade, the drug discovery community has seen a shift toward label-free methodologies including mass spectrometry (MS). By reporting on the mass of the analyte of interest, MS assays offer an information-rich and quantitative readout well-suited for measuring biochemical activities and binding interactions. MS assays offer an opportunity to multiplex provided the analytes differ in mass, and offer a powerful tool to discover unanticipated reactions. However, like label-based approaches, sample preparation protocols and limitations challenge the development of optimised MS assays for drug discovery.

Conventional MS instruments are sensitive to salts, detergents, carrier proteins, and organic additives, all of which are often critical components of an optimised biochemical or binding reaction. These components induce ion suppression that negatively impacts data quality. MS approaches utilise a variety of approaches to tackle this challenge. Traditional electrospray ionisation (ESI) MS includes a chromatography step prior to MS analysis, often through solid-phase extraction. The sample cleanup step removes the salts and detergents from the reaction and enriches the analyte of interest. As a consequence, the throughput suffers, with a 384-well plate requiring over an hour for analysis. When throughput is most critical, such as when performing large screening campaigns, higher-throughput MS instruments such as matrix-assisted laser desorption ionisation (MALDI) and acoustic transfer systems coupled to MS instruments are attractive alternatives. One reason for the higher throughput is that MALDI and acoustic MS do not include the sample preparation step. However, they remain sensitive to ion suppression from common buffer components. Therefore, assay developers may spend up to one year developing buffers that are suitable for a particular MS instrument, sacrificing optimisation based on the target needs and focusing more on the ability to use the high-throughput MS instrument. Balancing speed and data quality must be strongly considered. Alternatively, innovations with self-assembled monolayer chemistry combined with MALDI TOF offer an ultra-high-throughput sample preparation process that enables the development of assays without any buffer restrictions (Ref 5). The high-throughput sample preparation and readout can complete million-compound screens in one week (Ref 6-7), accelerating the process toward an optimised assay for hit identification and validation.

  1. Quality assays from start to finish.

Given the caveats with many of the common assay methodologies discussed above, including the variability of reagents, optical interference, and assay restrictions, it is important to develop orthogonal assays to ensure only the most promising molecules move toward developmental efforts. Many label-based assays offer standard-counter screen kits, especially for enzyme-coupled approaches, where multiple enzymes are sensitive to inhibition. Orthogonal assays are often integrated in a program during a phase where only a limited number of compounds will be analysed, therefore throughput is not a driving factor for assay selection. However, orthogonal assays should complement the primary assays to put together a comprehensive data package for making better go/no-go decisions in drug discovery and development.

In addition to orthogonal biochemical assays, integrating biophysical approaches can build additional confidence in hits. Similar to biochemical assays, the biophysical toolbox includes label-based and label-free approaches spanning low- to high-throughput, with advantages and caveats. Selecting the right assay requires consideration of the desired affinities, the requirement to synthesise a labelled reporter, reagent consumption, and costs. For example, applications of MS to analyse binding interactions have grown in the last few years. Affinity selection MS (ASMS) approaches have become powerful tools for protein and oligonucleotide targets. Traditional ASMS approaches require high concentrations of the target and pool hundreds of compounds together to balance the low throughput due to multiple-column chromatography steps. The target consumption requirement may limit wide-scale use for challenging to express targets, and the presence of hundreds of compounds allows opportunities for compound misbehaviour to impact data quality. Alternatively, innovations with surface chemistry have enabled ASMS assays that utilise approximately 10-fold less of the target and offer a high-throughput readout amenable to screening smaller pools to minimise compound misbehaviour (Ref 8). One aspect to consider is whether the target tolerates a handle for specific immobilisation. Several immobilisation chemistries are available and protocols for introducing bioorthogonal handles are well-established. Regardless of the initial approach, utilising multiple assay methodologies to generate complementary datasets will build confidence in driving critical decisions in drug discovery.

  1. Accelerating assay development with external collaborations.

With more assay methodologies becoming available or in development, the wealth of information needed to stay current with cutting-edge technologies can be overwhelming. Rather than potentially miss opportunities that can benefit any drug discovery program, biotech and pharma companies often partner with service providers or contract research organisations (CROs) that specialise in assay development and screening. CROs offer an established infrastructure and extensive expertise around developing assays. They also have access to a suite of technologies to not only develop initial assays but also validate compounds across multiple platforms. Partnering with CROs can see challenges around ensuring the data quality matches industry standards, waiting for data delivery, and gaining access to not only cutting-edge assay technologies but also small molecule library collections for hit identification.

When considering CROs for assay development, it is important to inquire about how assays are optimised, whether assays are ‘off-the-shelf’ with fixed conditions or tailored to a specific target, whether assays are run under kinetically balanced conditions, and if they have realistic data delivery times. Further, some CROs offer unique technologies that may enable measuring activities from targets previously considered intractable using conventional assay methodologies.

Conclusion

There are many choices when considering an assay methodology for any given target. Rather than focusing on selecting the ‘right assay methodology’, it is more beneficial to focus on making an informed decision. When it comes to developing optimised assays, knowledge of how the assay works, the advantages and potential pitfalls, and how to interpret the data to draw conclusions will enable faster, smarter decisions to accelerate drug discovery.

Volume 22, Issue 1 – Winter 2020/21

About the author

Dr. Zack Gurard-Levin is Chief Scientific Officer at SAMDI Tech. He brings more than 10 years of multi-disciplinary research experience with expertise in chemistry, biochemistry, cellular biology, and translational research. He has been a pioneer user of SAMDI technology and co-developed SAMDI as a high-throughput, label-free solution for drug discovery research. Prior to SAMDI Tech, he was a research scientist at the Institut Curie in Paris, France, leading epigenetics drug discovery and diagnostics projects in oncology.

References:

  1. Kaeberlein M, McDonagh T, Heltweg B et al., Substrate-specific activation of sirtuins by resveratrol. J. Biol. Chem. 2005, 280, 17038-45.
  2. Copeland RA. Mechanistic considerations in high-throughput screening. Anal. Biochem. 2003, 320, 1-12.
  3. Copeland RA. Evaluation of enzyme inhibitors in drug discovery: a guide for medicinal chemists and pharmacologists. 2nd: Wiley, Hoboken, NH 2013.
  4. Pamier MO, Van Doren SR. Rapid determination of enzyme kinetics from fluorescence: overcoming the inner filter effect. Anal. Biochem 2007, 371, 1, 43-51.
  5. Mrksich M. Mass spectrometry of self assembled monolayers: a new tool for molecular surface science. ACS Nano 2008, 2, 7-18.
  6. Gurard-Levin ZA, Scholle MD, Eisenberg AH et al., High-throughput screening of small molecule libraries using SAMDI mass spectrometry. ACS Comb Sci 2011, 13, 347-350.
  7. Patel K, Sherrill J, Mrksich M et al., Discovery of SIRT3 inhibitors using SAMDI mass spectrometry. J Biomol Screen 2015, 20, 842-848.
  8. VanderPorten EC, Scholle MD, Sherrill J et al., Identification of small molecule non-covalent binders utilizing SAMDI technology. SLAS Discov 2017, 22, 1211-1217.

 

 

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