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disordered aggregates. In addition, self-association fosters high viscosities at high antibody concentrations.13,14

Risk Mitigation Strategies

A fundamental diff erence between small molecule drug discovery and biologics projects is that aggregation-related problems outlined above only become evident when the fi rst eff orts are made to produce the new biologic in larger quantities and prepare solutions with high protein concentrations. At this stage drug discovery projects are already quite advanced and changes in the sequence would denote a substantial setback and delay. It is therefore critical to address developability-related issues early on through selection of sequences with a minimum of potential liabilities, through removal of potential risk factors during sequence optimization and to have research and development in close alignment at all stages of this process. Optimization strategies for biologics utilize a mixture of complementary experimental methods and computational analyses to detect such potential risk factors.

The property which is ultimately to be optimized is long-term stability, also commonly referred to as shelf-life. However, it is usually not practicable to run long-term stability experiments for multiple candidates as these experiments consume substantial amounts of material and last several months. Therefore, in-silico technologies have become a valuable resource to assess antibody developability.

Computational approaches benefi t from the tremendous progress that has been made over the last years in sequencing technology, hardware and software development, and the wealth of structural information from more than 1,000 antibody Fab structures deposited in the protein data bank.15,16

The availability of a large number of antibody structures allows researchers to build very accurate 3-dimensional models of lead candidates, allowing for precise prediction of the position of every amino acid.17-19

This is of particular importance for sites of potential

chemical degradation like deamidation and oxidation sites since solvent accessibility and local protein fl exibility, two important factors, can only be reliably estimated if a 3-dimensional model of a protein is available.

A second important source of information are antibody sequences of which thousands are available in public databases.20

Since antibody

variable domains are highly diverse in their sequences, huge sequence databases are an invaluable source of information which allow for rigorous statistical analysis of antibody sequences and to suggest somatic mutations which might be potentially detrimental for the conformational stability and thus could potentially compromise long- term stability.21

Detecting sequence patterns which are prone to form ordered cross- beta sheet aggregates and rationally engineer these sequences has also been shown to improve antibody developability. Here modern biotechnology benefi ts from the decades of basic research which have been invested in understanding protein aggregation on the atomistic level and algorithms which have been developed to predict aggregation tendencies of proteins and peptides.9, 12

Hence, a variety of computational methods are available to identify potential risk factors for successful antibody development and to complement experimental approaches.22

The attractiveness of

computational methods is that they pinpoint potential liabilities to distinct sequence patterns and therefore can directly guide protein engineering towards improved developability properties. It has been shown in a variety of studies that single or very few changes in protein sequences can have dramatic eff ects on expression, aggregation and thermal stability, thereby leaving biological function untouched.23


is important to point out, however, that computational approaches/ methods are still in the early stages of application to biologics, and more work is necessary to establish the generalizability and ultimate robustness of these approaches.

Figure 2. Interdependence of antibody degradation pathways:

Chemical modifi cations can compromise conformational stability which results in a higher fraction of partially unfolded states. Partially unfolded antibodies are prone to form disordered aggregates but also fi brilic structures if sequence stretches with high propensities for beta-sheet aggregation are present. Ultimately, aggregates grow irreversibly into high-molecular weight particles which eventually precipitate.

As soon as initial quantities of a biologic become available, infor- mation from experimental methods will further inform about the developability profi le of a molecule. Generally, the challenge in defi ning predictive methods lays in identifying experimental conditions that are “representative” for a multitude of parameters that will be optimized during development. Commonly, a small set of e.g. solution conditions is chosen to explore the intrinsic antibody properties. The previously gained information from computational approaches is orthogonal and independent of experimental conditions. Thus, the combination of computational and experimental methods proves most powerful at the interface between research and development. Synergistically, all information is used to select the best candidate. In addition, fl agging risk factors informs the developer about challenges that may occur during development.

As described above, aggregation propensity is linked to protein folding stability (or conformational stability) which is commonly studied in unfolding experiments. Such experiments are well established

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