Monoclonal antibodies offer a major advantage in drug discovery, and it’s no longer difficult to quickly make a lot of new antibodies. But this leads to their biggest drawback. Screening and then characterizing all those antibodies to find promising drug candidates is so laborious and expensive that many potentially useful candidates languish unstudied indefinitely. But advanced screening technology can help.
By screening and characterizing antibodies simultaneously, a technology called high-throughput surface plasmon resonance (HT-SPR) can help researchers identify the best drug candidates from a vast library of antibodies in just hours or days, simultaneously harvesting rich data about how they work. This can ensure that good candidates are not overlooked, and can help to identify promising drug candidates faster and at lower cost than before.
Monoclonal antibodies are one of the hottest classes of drugs in the pharmaceutical market. More than 60 have been approved for use, and upward of 500 more are in clinical development. An antibody drug can be a blockbuster. AbbVie’s Humira (adalimumab), which treats autoimmune conditions like Crohn’s disease, is the world’s best-selling drug, with sales of almost $20 billion in 2019.
Despite their potential, antibodies that target a specific epitope — a region of an antigen — cannot be designed from scratch. Instead, scientists must mine them from large and diverse pools of antibodies derived from one of several sources: animals immunized with a protein of interest, libraries of recombinant antibody genes selected by phage display, or samples of antibody-producing B cells from people who have been immunized or have recovered from a disease.
“You can generate a large number of antibodies that you don’t know much about,” says Daniel Bedinger, lead applications scientist at Carterra, a company that specializes in antibody screening technology.
The delays mount, however, when trying to determine which of the antibodies might make viable drugs. The enzyme-linked immunosorbent assay (ELISA), for example, has been a staple of antibody research since the 1970s. Researchers can examine a lot of antibodies using ELISA, but the technique tells them only whether an antibody binds to a target, forcing them to use additional techniques to glean other key details that indicate how it could act in living tissues.
To uncover those details, scientists use methods such as surface plasmon resonance (SPR) to deduce which epitope each antibody binds, how strongly it binds, and its mechanism of action. This technique has been used to characterize antibody interactions since the 1980s, and it detects interactions between two molecules, such as an antibody and its target. When a molecule in solution binds to a molecule tethered to the surface of a thin metal film, the mass increases. This changes the refractive index of the solution near the film’s surface, making it possible to optically detect when binding has occurred.
For the past several years, the most common SPR system has been GE’s Biacore product line. Its most recent version has eight channels that allow researchers to study eight antibodies at a time. But even that did not speed antibody characterization enough to unblock the bottleneck. The new HT-SPR systems, however, do. For example, Carterra’s LSA platform, which was released in 2018, uses sensor chips to study 384 antibodies simultaneously. It generates 100 times the data in 10% of the time, using just 1% of the sample required by Biacore or other platforms, according to the company.
These advances allow scientists to characterize antibodies in more detail. Carterra’s LSA platform, which uses the company’s proprietary Epitope software, can sort thousands of antibodies that bind to the same or similar epitopes and group them into families, or bins, of antibodies that share a target. It can also study how rapidly antibodies bind and let go of their target molecules, which reveals both their affinity for a target and their binding kinetics. The LSA also yields information about an antibody’s mechanism of action. Together this information produces a detailed picture of how different antibodies function, which makes it easier to select the most promising candidates from a large library.
“The LSA platform allows you to do all the characterization at the first step,” says Bedinger. “It gives a really rich picture of what you have.”
Built for Speed
Several research teams are taking advantage of the LSA’s greater resolution to quickly identify promising antibodies that can block the spike protein of SARS-CoV-2, the virus behind the COVID-19 pandemic, from binding its cellular receptor and prying open the door into human cells. The platform’s speed has enabled researchers to identify, rank, and characterize potentially useful antibodies within just a few weeks of starting an antibody discovery project, Bedinger says.
Others are using the LSA to better understand libraries of antibodies. Aaron Sato, Chief Scientific Officer of Biopharma at Twist Bioscience, turned to the LSA platform to screen and characterize the libraries that his company builds to license to pharmaceutical and biotechnology companies. The system helped determine which targets the antibodies in those libraries cover and what kind of binding activity they have. “The LSA gives us a tremendous amount of data to make the right decisions about what leads to move ahead,” Sato says.
The LSA can also be used further upstream to help validate new methods of antibody production, as Ligand Pharmaceuticals is doing. The company creates transgenic animals that, once immunized, produce a wide range of potentially useful antibodies. Ligand uses Carterra’s LSA to validate new strains of transgenic animals, such as transgenic chickens, to confirm that the antibody panels that the animals produce represent a good diversity of epitopes and binding kinetics.
“We are selling diverse sets of antibodies,” says Bill Harriman, Vice President, Antibody Discovery, at Ligand. “The LSA lets us demonstrate that we are generating that diversity.”
The demand for new antibody drugs is expected to continue to grow, including therapies for diseases such as COVID-19. Researchers who can quickly and accurately find the best candidates for the job will have a head start on finding the next blockbuster.