Rapid and accurate in silico solubility screening of a monoclonal antibody library

Pietro Sormanni,Leanne Amery,Sofia Ekizoglou,Bojana Popovic,Michele Vendruscolo

doi:10.1038/s41598-017-07800-w

Pietro Sormanni, Leanne Amery + Show 3 more

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Antibodies represent essential tools in research and diagnostics and are rapidly growing in importance as therapeutics. Commonly used methods to obtain novel antibodies typically yield several candidates capable of engaging a given target. The development steps that follow, however, are usually performed with only one or few candidates since they can be resource demanding, thereby increasing the risk of failure of the overall antibody discovery program. In particular, insufficient solubility, which may lead to aggregation under typical storage conditions, often hinders the ability of a candidate antibody to be developed and manufactured. Here we show that the selection of soluble lead antibodies from an initial library screening can be greatly facilitated by a fast computational prediction of solubility that requires only the amino acid sequence as input. We quantitatively validate this approach on a panel of nine distinct monoclonal antibodies targeting nerve growth factor (NGF), for which we compare the predicted and measured solubilities finding a very close match, and we further benchmark our predictions with published experimental data on aggregation hotspots and solubility of mutational variants of one of these antibodies.

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Owing to their high affinity and specificity, as well as their inherently low toxicity, biologics, and in particular monoclonal antibodies, represent the fastest growing class of therapeutic molecules in the biopharmaceutical market[1,2,3]
That bind nerve growth factor (NGF), which were generated from phage display followed by targeted mutagenesis of a parent antibody[21, 22]
We have used the CamSol method to quantitatively predict the solubility of a panel of monoclonal antibodies against NGF using only their amino acid sequences as input (Figure 4)

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Introduction

Owing to their high affinity and specificity, as well as their inherently low toxicity, biologics, and in particular monoclonal antibodies, represent the fastest growing class of therapeutic molecules in the biopharmaceutical market[1,2,3]. If an antibody is to serve a therapeutic purpose, it usually needs to be formulated at the high concentrations necessary for subcutaneous delivery (>50 mg/mL), and it must remain active over the shelf-life of the product (>1 year)[7] Because of these requirements, a bottleneck for the successful development of antibody therapeutics is often insufficient solubility, which can lead to aggregation at the conditions of storage[8,9,10]. To be effective this assessment should be rapid, inexpensive (in particular in terms of material needed), and readily applicable to most, if not all, of the screened antibodies, so that those antibodies embodying the best balance between strength of target binding and solubility can be selected from the very beginning (Figure 1) Given these requirements and the large number of antibody variants that typically result from the screening (up to thousands), it would be convenient to do this early assessment computationally. Computational approaches are already increasingly employed at this stage, for instance to identify potential sequence liabilities, which could impact on deamidation or other possible sources of instability (e.g. oxidation, fragmentation, etc.)[13,14,15,16,17,18]

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