Today, therapeutic antibodies are reaching the clinic in unprecedented numbers. However the path from laboratory to the clinic is far from smooth. Antibodies face a range of obstacles during development, from insufficient efficacy, to manufacturing difficulties to immunogenicity – any of which can spell the costly end of an antibody development program.

Since 2012, Fusion Antibodies has delivered over 160 successful antibody humanization projects to customers worldwide. For many of these projects, we and our customers observed that our humanized antibodies retained and even improved antigen affinity, compared to the parent antibody.

Antibody candidates originate from many sources, using ever-improving discovery technologies. However, achieving a balanced antibody profile can be challenging. Our customers need to identify and eliminate those initially promising antibodies that bind tightly, but later turn out to have problems with manufacturing, stability or immunogenicity. They need to be sure they have the right antibody before establishing a stable cell line, and be confident that their lead candidate can get through CMC testing and make it to clinic.

RAMP™ – a 2 step affinity maturation platform

To help customers face these challenges, we have poured our expertise into creating RAMP™ – our rational affinity maturation platform designed to accelerate and optimize selection of your lead antibody candidate.

RAMP™ combines innovative library design with stringent in silico screening of variants. This sieves out the strongest candidates into a micro-library that can be expressed in mammalian cells for further characterization.

Rational library design

Taking the parent antibody, we create a massive library of around 10^20 variants. Our proprietary rational design approach takes a leaf from nature, inspired by how B cells use somatic hypermutation to generate antibody diversity.

RAMP™ introduces mutations in both the CDR and framework regions to create diversity, allowing only amino acids that can naturally occur at each position in the human antibody sequence. This “natural” approach reduces the likelihood of hydrophobic patches of amino acids and the downstream risks of aggregation and immunogenicity.

At the same time, strict sequence checks are applied to screen out primary sequence liabilities such as deamidation sites, cleavage sites and free cysteines. These checks and balances help create a library of variants that are “pre-screened” for manufacturing and clinical use.

In silico refinement

The library is then refined using in silico software that rapidly models variant-antigen binding and predicts affinity and stability. Over the 3-week in silico phase, the initial library is funneled down into a micro-library of the 100 strongest variants. At this stage we either express the micro-library as full length IgGs in CHO mammalian cells or hand the micro-library back to the client for further in-house testing.

RAMP™ up your chances of getting to the clinic

RAMP™ for affinity maturation is a fast, reliable method for improving affinity and selecting your lead antibody candidate. In a promising performance test, RAMP™ improved the affinity of the best-selling breast cancer drug trastuzumab in silico and we’re currently validating the best variants experimentally.

RAMP™ can also be applied to “rescue” molecules, with promising functional activity but poor developability profiles, where finessing of the sequence is required. Our novel library design approach can also open up new sequence space to potentially build on your patent family and increase the value of your program.

Want to select the best possible antibody for the clinic?

Design, development and clinical testing of therapeutic antibodies is a race against the clock, and against competitors. Companies are increasingly turning to in silico approaches to turbo boost the process. At Fusion Antibodies, we were early adopters of in silico techniques. We’ve seen first hand how embracing this technology accelerates antibody design and development, which reduces costs.

AI and machine learning

Despite the buzz, the holy grail of true artificial intelligence (AI) – machines that can “think” and reason like humans do – remains elusive. In the meantime, deep machine learning is the established star of the in silico show. Machines are “trained” by feeding them large sets of experimental data and teaching them (via algorithms) how to perform a task. With each repetition of the task, the machine adds the experience to their knowledge bank, improves performance and comes up with results that humans didn’t necessarily expect. So how do in silico techniques, including machine learning, fit in to antibody development?

In silico antibody development

Antibody engineers have a plethora of options in their in silico toolkit for optimising antibodies. These bioinformatics tools include homology modelling to predict antibody structure, molecular docking to identify antibody-antigen interactions and algorithms to calculate energy changes in mutated versions of the antibody. Each of these processes can benefit from machine learning to speed up predictions and improve decision making. Stitching these processes together into an automating streamlined workflow saves even more valuable time.

Focus on libraries and screening

In silico techniques in antibody development have been described as third generation, following second generation in vitro and first generation in vivo methods1. Affinity maturation is a good example of how throughput has soared with in silico methodology. In silico libraries of around 10^25 variants have smashed through the experimental library size ceilings of mammalian display (10^10 variants) and phage display (10^12 variants).

Similarly, the time needed to screen through these libraries for the best sequence has decreased drastically from 3 months with mammalian cells or phage display, down to just 3-4 weeks in silico.

An added bonus is that in silico libraries and screening avoid the potential expression and biophysical issues related with phage display systems, while leaving the door open to promising variants that may not have expressed well in phage.

Challenges remain

The in silico approaches currently used in antibody design remain overwhelmingly knowledge-based. However, machine learning is only as good as the data you train it with. A current challenge is the scarcity of robust experimental open-access datasets, and a lack of widely accepted standards for validating data quality.

Another challenge is the changing skillsets needed by today’s biologists. Data scientists, programmers and technologists are now staple members of biology teams, and everyone needs to learn to speak a common language. Training university students in such interdisciplinary working will ensure the teams of tomorrow are well placed to harness the power of machine learning and in silico techniques.

Future applications

An exciting application of in silico technologies would be to open avenues of investigation previously hampered by experimental roadblocks. For example, G-protein coupled receptors (GPCRs) are a rational antibody target for many disease processes, but are notoriously difficult proteins to isolate out of the cell membrane where they are firmly embedded. In silico modelling could sidestep the difficulty posed by purification and antibody development. This means that proteins that were previously very difficult to raise antibodies against can now be targeted.

RAMP™Rational Affinity Maturation Platform

At Fusion Antibodies, we are firm believers in integrating bioinformatics and in silico techniques to accelerate our workflows and therefore your journey to the clinic. That’s why we developed RAMP™ – our rational affinity maturation platform. It takes RAMP™ just 3 weeks to create a massive library of around 10^25 variants of the parent antibody and to select out the best candidates using rapid in silico screening. The result is a micro-library of the 100 best candidates, selected for binding potential and stability.

Harness the power of our in silico RAMP™ technology to optimise your lead antibody faster.

1              https://www.ncbi.nlm.nih.gov/pubmed/30298157

Fusion Antibodies are working with a number of clients on pilot projects for their latest technology – RAMP™ – Rational Affinity Maturation Platform.

RAMP™ was launched at the end of 2018 and clients have been coming on board to benefit from the innovative in silico approach to Affinity Maturation, particularly for time sensitive projects.

The range of clients includes one of the world’s top ten pharmaceutical companies, multiple biotech companies in Asia and Europe, and a UK based academic research institution.

Dr. Paul Kerr, CEO, Fusion Antibodies, said, “Having presented the technology platform at Antibody Engineering and Therapeutics, San Diego at the end of 2018, we are delighted that a number of projects have now commenced with us. We welcome the opportunity to help these clients address difficult roadblocks in their antibody development projects.”

Chief Technology Officer, Dr. Richard Buick commented, “We have already tested the power of our platform on one of the world’s bestselling drugs, Trastuzumab, and significantly improved its affinity. We are excited for the opportunity to demonstrate the power that lies in our approach.”

RAMP™, a Rational Affinity Maturation Platform, is designed to improve the ability of an antibody to bind without damaging the overall profile, has delivered results beyond affinity maturation. Factors that can be improved using the platform include stability, aggregation, yield, specificity and cross-reactivity, which are key considerations for clients who want to get their antibody to the clinic and market approval.