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In silico – the future of antibody development

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