Some of the most exciting new therapies are antibody based, including monoclonal antibodies, antibody-drug conjugates, and even some cancer immunotherapies. Each approach delivers or harnesses therapeutic-grade antibodies to target disease with high precision. With hundreds of FDA-approved antibody drugs now on the market, antibody discovery workflows are more essential than ever to the drug development process.
Unfortunately, these workflows are notoriously inefficient. Conventional processes involve repeated testing of the same candidates across multiple platforms, extending timelines and increasing costs. At the same time, they only capture a fraction of the immune system’s full diversity.
What the community needs is a faster, more efficient approach that can probe a broader immune repertoire while also gleaning deep functional insights at the single-cell level. Today, functional analysis is often delayed until late in the antibody discovery process, a costly choice if candidates underperform in these critical tests. Assessing function earlier in the process could alleviate these issues, but most platforms lack the required throughput and economic parameters to make this a practical option.
Conventional screening tools
First introduced in the 1970s, hybridoma technology reigned for decades as the gold standard in antibody discovery. By fusing antibody-producing B cells with myeloma cells, it became possible to create hybridomas with immortal properties: producing monoclonal antibodies over and over. Despite its broad popularity, the hybridoma approach suffers from an inefficient fusion process that results in the loss of many valuable plasma cells. This greatly limits the diversity of antibodies that can be recovered, making much of the immune repertoire inaccessible and potentially excluding superior therapy candidates. It also requires the use of many more animals in the workflow, which is not only costly but also increasingly problematic as government and other guidelines aim to reduce the use of animals in research.
An alternative to hybridomas is B cell screening, which analyzes antigen-specific B cells directly. Techniques like fluorescence-activated cell sorting allow scientists to identify heavy and light chain genes found in single cells, offering access to more of the immune repertoire. B cell screening enables faster therapeutic development. Notable examples include the rapid discovery of a monoclonal antibody cocktail for the Ebola virus and a monoclonal antibody for SARS-CoV-2, highlighting the value of B cell screening for emerging infectious diseases in particular. Compared to hybridoma technology, B cell screening is higher throughput and provides results at single-cell resolution.
However, both methods generally rely on a “binding-first” mentality, delaying functional characterization until later stages. But binding is not always a reliable proxy for function, as numerous cases have shown. One example is enlimomab, a monoclonal antibody against ICAM-1 developed using a hybridoma process. Although it bound its target, enlimomab failed in later clinical stages when it led to worsening outcomes for stroke patients. Had functional analysis occurred earlier, its lack of functional efficacy may have been identified much sooner. For hybridomas and B cell screening, incorporating functional characterization earlier in the workflow could help scientists focus on more promising candidates and reduce costly failures in the discovery and development process.
A function-focused approach
Logically, it seems like a no-brainer that functional analysis would be ideally performed early in the discovery process; after all, antibody-based therapeutics have to bind to a target and have the desired functional effect in vivo. But establishing function is difficult. It generally requires many different assays to evaluate neutralization, cytotoxicity, and more. Each assay is performed individually, meaning either some cells have to be moved from one platform to another or that new cell populations are needed for each test. Either way, the conventional process is cumbersome, time-consuming, and expensive.
As single-cell technologies improve, integrating streamlined functional analysis earlier in the discovery workflow should become increasingly feasible. This would improve lead selection by allowing scientists to advance only the candidates that have been functionally validated. It would also reduce costs by minimizing the reliance on multiple secondary assays and shorten development timelines by eliminating non-functional candidates earlier. Overall, a function-focused approach should translate to higher success rates, with fewer late-stage failures and a greater proportion of candidates advancing through the development gauntlet.
Fortunately, technology developers around the world are designing or preparing to release new platforms to meet these needs. As these tools come to market, it will be important for scientists to evaluate their usefulness in existing discovery and development workflows. Here are some key elements to look for:
- Flexibility: The microfluidic tools already used for functional analysis often share an Achilles’ heel: Devices are custom-designed for specific assays and cannot easily be repurposed. For optimal utility, new platforms should be far more flexible, allowing users to change assays and experimental steps as often as they’d like.
- Ability to perform sequential functional assays: Functional testing is rarely successful with a single test. Scientists want to be able to query a population of cells to identify a subset with a certain surface marker and then test only those cells for their response to a specific reaction. Instead of having to move cells from one platform to another to run those queries, a high-value platform would allow scientists to perform assays sequentially in situ without having to move the cells or start over with new populations between experiments.
- Necessary scale: While the functional assay may not require millions of cells, it could be useful to test, say, tens of thousands of cells. For that reason, the ideal analysis platform will need to be able to operate at the scale demanded by the drug discovery and development team.
- Direct functional readout: Unlike many single-cell technologies that infer function through predictions based on genetic code or gene expression levels, the most reliable functional analysis must come from a direct readout. Questions such as “does this compound kill this cell population?” or “what’s the biological effect of this surface marker?” can only be answered definitively through a functional assay.
With the right mix of features, the incoming generation of analysis platforms could finally allow scientists to focus on function earlier in the discovery process, improving candidate success rates while reducing costs for antibody-based therapies.
This article was contributed by Jonathan Didier, senior field applications scientist at Lightcast, where he focuses on single-cell functional analysis.
