Optimization of therapeutic antibodies for reduced self-association … – Nature.com

Jain, T. et al. Biophysical properties of the clinical-stage antibody landscape. Proc. Natl Acad. Sci. USA 114, 944949 (2017).

Article CAS PubMed PubMed Central Google Scholar

Makowski, E. K., Wu, L., Gupta, P. & Tessier, P. M. Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods. mAbs 13, 1895540 (2021).

Labrijn, A. F., Janmaat, M. L., Reichert, J. M. & Parren, P. Bispecific antibodies: a mechanistic review of the pipeline. Nat. Rev. Drug Discov. 18, 585608 (2019).

Article CAS PubMed Google Scholar

Shim, H. Bispecific antibodies and antibody-drug conjugates for cancer therapy: technological considerations. Biomolecules 10, 360 (2020).

Article CAS PubMed PubMed Central Google Scholar

Drago, J. Z., Modi, S. & Chandarlapaty, S. Unlocking the potential of antibodydrug conjugates for cancer therapy. Nat. Rev. Clin. Oncol. 18, 327344 (2021).

Article PubMed PubMed Central Google Scholar

Dean, A. Q., Luo, S., Twomey, J. D. & Zhang, B. Targeting cancer with antibody-drug conjugates: promises and challenges. mAbs 13, 1951427 (2021).

Carter, P. J. Potent antibody therapeutics by design. Nat. Rev. Immunol. 6, 343357 (2006).

Article CAS PubMed Google Scholar

Carter, P. J. & Rajpal, A. Designing antibodies as therapeutics. Cell 185, 27892805 (2022).

Article CAS PubMed Google Scholar

Leavy, O. Therapeutic antibodies: past, present and future. Nat. Rev. Immunol. 10, 297 (2010).

Article CAS PubMed Google Scholar

Chames, P., Van Regenmortel, M., Weiss, E. & Baty, D. Therapeutic antibodies: successes, limitations and hopes for the future. Br. J. Pharmacol. 157, 220233 (2009).

Article CAS PubMed PubMed Central Google Scholar

Makowski, E. K. et al. Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space. Nat. Commun. 13, 3788 (2022).

Article CAS PubMed PubMed Central Google Scholar

Gupta, P. et al. Antibodies with weakly basic isoelectric points minimize trade-offs between formulation and physiological colloidal properties. Mol. Pharm. 19, 775787 (2022).

Article CAS PubMed PubMed Central Google Scholar

Rabia, L. A., Desai, A. A., Jhajj, H. S. & Tessier, P. M. Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility. Biochem. Eng. J. 137, 365374 (2018).

Article CAS PubMed PubMed Central Google Scholar

Kingsbury, J. S. et al. A single molecular descriptor to predict solution behavior of therapeutic antibodies. Sci. Adv. 6, eabb0372 (2020).

Article CAS PubMed PubMed Central Google Scholar

Starr, C. G. et al. Ultradilute measurements of self-association for the identification of antibodies with favorable high-concentration solution properties. Mol. Pharm. 18, 27442753 (2021).

Article CAS PubMed Google Scholar

Makowski, E. K., Wu, L., Desai, A. A. & Tessier, P. M. Highly sensitive detection of antibody nonspecific interactions using flow cytometry. mAbs 13, 1951426 (2021).

Xu, Y. et al. Addressing polyspecificity of antibodies selected from an in vitro yeast presentation system: a FACS-based, high-throughput selection and analytical tool. Protein Eng. Des. Sel. 26, 663670 (2013).

Article CAS PubMed Google Scholar

Ahmed, L. et al. Intrinsic physicochemical profile of marketed antibody-based biotherapeutics. Proc. Natl Acad. Sci. USA 118, e2020577118 (2021).

Yadav, S., Laue, T. M., Kalonia, D. S., Singh, S. N. & Shire, S. J. The influence of charge distribution on self-association and viscosity behavior of monoclonal antibody solutions. Mol. Pharm. 9, 791802 (2012).

Article CAS PubMed Google Scholar

Yadav, S. et al. Establishing a link between amino acid sequences and self-associating and viscoelastic behavior of two closely related monoclonal antibodies. Pharm. Res. 28, 17501764 (2011).

Article CAS PubMed Google Scholar

Xolair. Prescribing Information (Genentech Inc., 2021).

Dyson, M. R. et al. Beyond affinity: selection of antibody variants with optimal biophysical properties and reduced immunogenicity from mammalian display libraries. mAbs 12, 1829335 (2020).

Wang, N. et al. Opalescence of an IgG1 monoclonal antibody formulation is mediated by ionic strength and excipients. Biopharm Int. 22, 3647 (2009).

Salinas, B. A. et al. Understanding and modulating opalescence and viscosity in a monoclonal antibody formulation. J. Pharm. Sci. 99, 8293 (2010).

Article CAS PubMed PubMed Central Google Scholar

Goldberg, D. S., Bishop, S. M., Shah, A. U. & Sathish, H. A. Formulation development of therapeutic monoclonal antibodies using high-throughput fluorescence and static light scattering techniques: role of conformational and colloidal stability. J. Pharm. Sci. 100, 13061315 (2011).

Article CAS PubMed Google Scholar

Shi, G. H. et al. Subcutaneous injection site pain of formulation matrices. Pharm. Res. 38, 779793 (2021).

Article CAS PubMed Google Scholar

Chabra, S. et al. Ixekizumab citrate-free formulation: results from two clinical trials. Adv. Ther. 39, 28622872 (2022).

Article CAS PubMed PubMed Central Google Scholar

Grinshpun, B. et al. Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. mAbs 13, 1932230 (2021).

Htzel, I. et al. A strategy for risk mitigation of antibodies with fast clearance. mAbs 4, 753760 (2012).

Neergaard, M. S., Nielsen, A. D., Parshad, H. & Van De Weert, M. Stability of monoclonal antibodies at high-concentration: head-to-head comparison of the IgG1 and IgG4 subclass. J. Pharm. Sci. 103, 115127 (2014).

Article CAS PubMed Google Scholar

Lai, P. K. et al. Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: experimental assessment and computational predictions of domain interactions. mAbs 13, 1991256 (2021).

Sickmier, E. A. et al. The panitumumab EGFR complex reveals a binding mechanism that overcomes cetuximab induced resistance. PLoS ONE 11, e0163366 (2016).

Article PubMed PubMed Central Google Scholar

Bohrmann, B. et al. Gantenerumab: a novel human anti-A antibody demonstrates sustained cerebral amyloid- binding and elicits cell-mediated removal of human amyloid-. J. Alzheimers Dis. 28, 4969 (2012).

Article CAS PubMed Google Scholar

Weihofen, A. et al. Development of an aggregate-selective, human-derived -synuclein antibody BIIB054 that ameliorates disease phenotypes in Parkinsons disease models. Neurobiol. Dis. 124, 276288 (2019).

Article CAS PubMed Google Scholar

De Groot, A. S. & Martin, W. Reducing risk, improving outcomes: bioengineering less immunogenic protein therapeutics. Clin. Immunol. 131, 189201 (2009).

Article PubMed Google Scholar

De Groot, A. S. & Scott, D. W. Immunogenicity of protein therapeutics. Trends Immunol. 28, 482490 (2007).

Article PubMed Google Scholar

Apgar, J. R. et al. Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design. PLoS ONE 15, e0232713 (2020).

Article CAS PubMed PubMed Central Google Scholar

Tomar, D. S. et al. In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions. mAbs 9, 476489 (2017).

Boughter, C. T. et al. Biochemical patterns of antibody polyreactivity revealed through a bioinformatics-based analysis of CDR loops. eLife 9, e61393 (2020).

Lai, P. K., Gallegos, A., Mody, N., Sathish, H. A. & Trout, B. L. Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics. mAbs 14, 2026208 (2022).

Han, X., Shih, J., Lin, Y., Chai, Q. & Cramer, S. M. Development of QSAR models for in silico screening of antibody solubility. mAbs 14, 2062807 (2022).

Mason, D. M. et al. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nat. Biomed. Eng. 5, 600612 (2021).

Article CAS PubMed Google Scholar

Saka, K. et al. Antibody design using LSTM based deep generative model from phage display library for affinity maturation. Sci. Rep. 11, 5852 (2021).

Article CAS PubMed PubMed Central Google Scholar

Hie, B. L. et al. Efficient evolution of human antibodies from general protein language models and sequence information alone. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01763-2 (2023).

Shin, J. E. et al. Protein design and variant prediction using autoregressive generative models. Nat. Commun. 12, 2403 (2021).

Article CAS PubMed PubMed Central Google Scholar

Neal, B. L., Asthagiri, D. & Lenhoff, A. M. Molecular origins of osmotic second virial coefficients of proteins. Biophys. J. 75, 24692477 (1998).

Article CAS PubMed PubMed Central Google Scholar

Lomakin, A., Asherie, N. & Benedek, G. B. Aeolotopic interactions of globular proteins. Proc. Natl Acad. Sci. USA 96, 94659468 (1999).

Article CAS PubMed PubMed Central Google Scholar

Elcock, A. H., Sept, D. & McCammon, J. A. Computer simulation of proteinprotein interactions. J. Phys. Chem. B 105, 15041518 (2001).

Article CAS Google Scholar

Sharma, V. K. et al. In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability. Proc. Natl Acad. Sci. USA 111, 1860118606 (2014).

Article CAS PubMed PubMed Central Google Scholar

Raybould, M. I. J. et al. Five computational developability guidelines for therapeutic antibody profiling. Proc. Natl Acad. Sci. USA 116, 40254030 (2019).

Article CAS PubMed PubMed Central Google Scholar

Negron, C., Fang, J., McPherson, M. J., Stine, W. B. Jr. & McCluskey, A. J. Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment. mAbs 14, 2080628 (2022).

Connolly, B. D. et al. Weak interactions govern the viscosity of concentrated antibody solutions: high-throughput analysis using the diffusion interaction parameter. Biophys. J. 103, 6978 (2012).

Article CAS PubMed PubMed Central Google Scholar

Kelly, R. L. et al. Chaperone proteins as single component reagents to assess antibody nonspecificity. mAbs 9, 10361040 (2017).

Datta-Mannan, A. et al. The interplay of non-specific binding, target-mediated clearance and FcRn interactions on the pharmacokinetics of humanized antibodies. mAbs 7, 10841093 (2015).

Chung, S. et al. An in vitro FcRn- dependent transcytosis assay as a screening tool for predictive assessment of nonspecific clearance of antibody therapeutics in humans. mAbs 11, 942955 (2019).

Liu, Y. et al. High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy. mAbs 6, 483492 (2014).

Sule, S. V., Dickinson, C. D., Lu, J., Chow, C. K. & Tessier, P. M. Rapid analysis of antibody self-association in complex mixtures using immunogold conjugates. Mol. Pharm. 10, 13221331 (2013).

Article CAS PubMed Google Scholar

Sun, T. et al. High throughput detection of antibody self-interaction by bio-layer interferometry. mAbs 5, 838841 (2013).

Mouquet, H. et al. Polyreactivity increases the apparent affinity of anti-HIV antibodies by heteroligation. Nature 467, 591595 (2010).

Article CAS PubMed PubMed Central Google Scholar

Wardemann, H. et al. Predominant autoantibody production by early human B cell precursors. Science 301, 13741377 (2003).

Article CAS PubMed Google Scholar

Jacobs, S. A., Wu, S. J., Feng, Y., Bethea, D. & ONeil, K. T. Cross-interaction chromatography: a rapid method to identify highly soluble monoclonal antibody candidates. Pharm. Res. 27, 6571 (2010).

See the original post here:
Optimization of therapeutic antibodies for reduced self-association ... - Nature.com

Related Posts

Comments are closed.