Before the road towards clinical trials, the developability properties of therapeutic antibodies need to be evaluated. Antibody molecules are assessed for developability characteristics including aggregation, immunogenicity, pharmacokinetic clearance, viscosity, and half-life span.1 This is important to ensure a stable and efficacious drug before clinical trials.
These properties can be understood through in silico methods, such as homology modelling, docking or interface prediction. During the Lead Identification and Optimization phases these tools can help to researchers to generate three-dimensional models of the antibodies and predict or identify the key residues involved in antigen binding. Machine learning tools can also predict these properties by assessing hydrophobicity, electrostatic charge, and topology pattern interaction of the amino acid sequence.2,3
Computational Tools
Spatial-Charge-Map (SCM) is a tool developed by Agrawal et al. (2016) that can screen monoclonal antibodies for their viscosities. SCM works by quantifying the electronegative potential patch on the Fv domain of antibodies. High viscosities, commonly found with highly concentrated antibody solutions, poses several problems to develop, manufacture, and administer the potential drug. Since the antibody sequence is a component for viscosity, this SCM tool is extremely helpful for the selection of low-viscosity antibodies.4
The therapeutic antibody profiler (TAP) is an application which compares your antibody variable domain sequence against five factors: total complementarity-determining region (CDR) length, surface hydrophobicity, asymmetry in net heavy- and light-chain surface charges, and positive charge and negative charge in the CDRs. Raybould et al. (2019) published this tool to reveal antibodies that have rare characteristics in clinical-stage mAb therapeutics.5,6
Machine Learning Tools
Jain et al. (2017) used a random forest machine learning approach to estimate the hydrophobic chromatography (HIC) retention time directly from an antibody sequence. Quantifying hydrophobicity of a monoclonal antibody is important to assess downstream risks. The researchers successfully developed predictive models to predict the surface exposure of amino-acid side-chains in the antibody’s variable region.7
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Raybould, M.I.J., Deane, C.M. (2022). The Therapeutic Antibody Profiler for Computational Developability Assessment. In: Houen, G. (eds) Therapeutic Antibodies. Methods in Molecular Biology, vol 2313. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1450-1_5
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023). Computational and artificial intelligence-based methods for antibody development. Trends in Pharmacological Sciences, 44(3), 175–189. https://doi.org/10.1016/J.TIPS.2022.12.005
Mieczkowski, C., Zhang, X., Lee, D., Nguyen, K., Lv, W., Wang, Y., Zhang, Y., Way, J., & Gries, M. (2023). Blueprint for antibody biologics developability. MAbs, 15(1). https://doi.org/10.1080/19420862.2023.2185924
Agrawal, N. J., Helk, B., Kumar, S., Mody, N., Sathish, H. A., Samra, H. S., Buck, P. M., Li, L., & Trout, B. L. (2016). Computational tool for the early screening of monoclonal antibodies for their viscosities. MAbs, 8(1), 43-48. https://doi.org/10.1080/19420862.2015.1099773
J. Raybould, M. I., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A., Shi, J., & Deane, C. M. (2019). Five computational developability guidelines for therapeutic antibody profiling. Proceedings of the National Academy of Sciences of the United States of America, 116(10), 4025-4030. https://doi.org/10.1073/pnas.1810576116
James Dunbar and others, SAbPred: a structure-based antibody prediction server, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W474–W478, https://doi.org/10.1093/nar/gkw361
Jain, T., Boland, T., Lilov, A., Burnina, I., Brown, M., Xu, Y., & Vásquez, M. (2017). Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning. Bioinformatics, 33(23), 3758-3766. https://doi.org/10.1093/bioinformatics/btx519
Antibody specificity refers to an antibody's ability to selectively bind to a unique epitope on a target antigen while avoiding interactions with unrelated antigens. This property arises from the highly specialized antigen-binding site located in the variable region of the antibody, which determines its unique binding characteristics.
Antibody affinity refers to the strength of the binding interaction between a single antigen epitope and the paratope (binding site) of an antibody. This interaction is a fundamental measure of how well an antibody recognizes its specific antigen target.
Recombinant antibodies are produced using genetic engineering techniques, unlike traditional antibody production, where the immune system generates antibodies without direct control over their sequence. By introducing genes encoding antibody fragments into host cells, such as bacteria or mammalian cells, recombinant antibodies can be expressed, purified, and deployed for applications including research, diagnostics, and therapeutics.
Recombinant antibody expression is a biotechnological process that involves engineering and producing antibodies outside their natural context using recombinant DNA technology.
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