Antibody Developability Prediction

Developability refers to the possibility for the successful development of a lead candidate into a stable, safe, manufacturable, and efficacious drug, which is enable to be used as an additional selection criterion. By using a class of small-scale, fast, and predictive tests addressing biochemical and biophysical features, as well as in silico analysis, enable to evaluate a clinical candidate molecule with promising properties at an early stage of drug development. With years of exploration in antibody engineering, Creative Biolabs has built a full-scale in silico technology platform. Based on our advanced platform, we offer high quality antibody developability prediction services for customers all over the world, including Antibody Aggregation Prediction and Antibody Immunogenicity prediction.

A variety of in silico analysis approaches are available in Creative Biolabs, our scientists are confident in proving the first class antibody developability prediction services to contribute to your antibody development process.

N-Glycosylation Sites and Cysteines in Fv Domains Analysis

Typically, Fv glycosylation can influence target binding, and it may also play a crucial role in biodistribution or pharmacokinetics. An additional N-glycosylation site in the Fv domain means another source of product heterogeneity, and the production of such therapeutic antibodies with a consistent human-like glycoform profile remains a big challenge. Fortunately, potential N-glycosylation sites in the Fv domains enable to be determined in the amino acid sequence via their consensus motif NXS or NXT (X may be any amino acid except proline).

Lysine Glycation Sites Analysis

Generally, glycation is distributed over many lysines in antibodies, however, a number of researchers describe glycation hotspots, which considers that certain structural features govern the rate of glycation. Apart from solvent accessibility, a contribution of a neighboring acidic residue acting catalytically in the conversion of the initial glucose adduct, a Schiff base, into a more stable Amadori product was proposed taking advantaged of structural and mutational data. Therefore, via a structural model, we can search for exposed lysine residues in the vicinity of suitable acidic residues, so as to forecast glycation hotspots.

Fig 1. The left part shows the impaired stability of an example IgG1 mAb containing a free Cys in CDR H2, and the right part shows calculated isoelectric points of monoclonal antibodies. (Jarasch, A., 2015)

Isoelectric Point and Charge Distribution Analysis

An isoelectric point high enough (e.g., >7) is regarded as a prerequisite for binding and nonbinding to cation and anion exchange chromatography media, providing two common purification methods. Aggregation and solubility property of a protein is usually favorable at pH values far away from the protein’s isoelectric point, albeit specific interactions of the protein with anions or cations present in the solution enables to modulate solubility to a great extent. Hence, a sequence-based prediction of the protein’s net charge as a function of pH, and its isoelectric point, helps to estimate whether or not the values are in the desired range. Besides, a structure-based method to measure net charge and isoelectric point using 3D homology can offer a more accurate result.

Prediction of Asparagine and Aspartate Degradation Hotspots in Fv Domains Analysis

Usually, during therapeutic proteins production, storage, or in vivo after administration, deamidation of asparagine residues, isomerization of aspartate residues, and formation of the general cyclic succinimide intermediate are frequent degradation reactions. Our scientists had established a hotspot prediction method which displays greatly increased rates of correct predictions, depending on a large set of antibodies with experimentally determined data. This method, implemented in an automatic homology modeling and hotspot prediction workflow, provides a facile way to assess many candidates.

Immunogenicity Analysis

Although a variety of factors are related to immunogenicity, including sequence, structure, modifications, route of administration, patient population, others are still only poorly understood. Thus, many attempts have been done to predict the potential immunogenicity of drug candidates during the lead selection process by in silico, in vitro, or in vivo models. Essentially, in silico models address potential T-cell epitopes, which are essential (but not sufficient) for T-cell-mediated immunogenicity. These algorithms enable to offer value as a ranking tool for otherwise equivalent drug candidates.

With our comprehensive antibody developability prediction services, designing and engineering novel antibodies with desired therapeutic properties is available. We customize the service according to the specific requirements from the customers. We also provide other structure-based antibody reformatting services. Please contact us for more information and a detailed quote.

Reference

1. Jarasch, A., (2015). “Developability assessment during the selection of novel therapeutic antibodies.” Journal of pharmaceutical sciences, 104(6), 1885-1898.

All services provided on this site are intended to support preclinical research only. Do not use our services or final products on humans.