HER2-ECD (human epidermal growth factor receptor 2 - extracellular domain) is a prominent therapeutic target validated for treating HER2-positive breast and gastric cancer, but HER2-specific therapeutic options for treating advanced gastric cancer remain limited. We have developed antibody-drug conjugates (ADCs), comprising IgG1 linked via valine-citrulline to monomethyl auristatin E, with potential to treat HER2-positive gastric cancer in humans. The antibodies optimally selected from the ADC discovery platform, which was developed to discover antibody candidates suitable for immunoconjugates from synthetic antibody libraries designed using antibody-antigen interaction principles, were demonstrated to be superior immunoconjugate targeting modules in terms of efficacy and off-target toxicity. In comparison with the two control humanized antibodies (trastuzumab and H32) derived from murine antibody repertoires, the antibodies derived from the synthetic antibody libraries had enhanced receptor-mediated internalization rate, which could result in ADCs with optimal efficacies. Along with the ADCs, two other forms of immunoconjugates (scFv-PE38KDEL and IgG1-AL1-PE38KDEL) were used to test the antibodies for delivering cytotoxic payloads to xenograft tumor models in vivo and to cultured cells in vitro. The in vivo experiments with the three forms of immunoconjugates revealed minimal off-target toxicities of the selected antibodies from the synthetic antibody libraries; the off-target toxicities of the control antibodies could have resulted from the antibodies' propensity to target the liver in the animal models. Our ADC discovery platform and the knowledge gained from our in vivo tests on xenograft models with the three forms of immunoconjugates could be useful to anyone developing optimal ADC cancer therapeutics.

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; VH: variable heavy; VL: variable light.