Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

Abstract

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

Keywords: affinity; aggregation; computational modeling; design; developability; high throughput; humanization; immunogenicity; mAb; monoclonal antibody; pharmacokinetics; polyspecificity; prediction; solubility; specificity; therapeutic; viscosity.

Figure 1.

The development process for antibody therapeutics is being improved by early assessment of antibody developability using emerging high-throughput experimental and computational methods. The development pipeline for antibody therapeutics is limited by the difficulty in assessing key biophysical properties at the antibody discovery and protein engineering stages to identify drug-like antibodies with increased likelihood of success in the clinic. These experimental limitations stem from extremely limited quantities of high-quality (purified) protein available for analysis, especially at the stage of antibody discovery. This has motivated recent advances in high-throughput experimental methods that are compatible with extremely dilute antibody solutions, as well as computational methods for evaluating antibody properties and designing antibody variants with improved properties. These advances stand to substantially improve the development of efficacious therapeutic antibodies. Each developability property is highlighted at one or more stages at which it is typically evaluated

Figure 2.

Computational design methods for minimizing stability trade-offs during antibody humanization. (a) A novel computational design method (CoDAH) for humanizing antibodies was used to predict humanizing mutations (green) for a murine antibody that otherwise would not be mutated using traditional CDR grafting humanizing designs (purple), as well as additional mutations common to both methods (blue). (b) Both traditional CDR grafting and CoDAH humanization results in antibodies with improved humanness, but the CoDAH designs also maintain, and even improve, rotomeric stability. (c) CoDAH designs also generally exhibit similar or improved thermal stability. (d) Both humanization methods result in antibody variants with similar affinity as the parental antibody, although several CDR grafting designs display reduced affinity. The figure is adapted with permission from a previous publication.

Figure 3.

Evaluation of mutations that reduce viscosity at high antibody concentrations. (a) The impact of Fv mutations that modify antibody charge on viscosity was systematically evaluated to test the role of charge-related properties in mediating viscous antibody behavior. Systematic alteration of Fv charge in an anti-PDGF antibody rarely resulted in reduced viscosity, even when predicted to reduce viscosity by multiple conventional scoring methods. (b) Antibody variants with modest reductions in viscosity achieved in Round 1 (green) were further optimized in Round 2 through mutagenesis of negatively charged patches in the variable regions, which produced two antibody mutants that could be concentrated above 150 mg/mL while maintaining low viscosity (<20 cP). (c) Fv charge partially but incompletely describes the viscosity behavior, demonstrating the complicated relationship between antibody viscosity and charge properties. The figure is adapted with permission from a previous publication.

Figure 4.

A computational method (Solubis) for predicting and remediating antibody aggregation hotspots. (a) Solubis identified two aggregation hotspots in an anti-VEGF antibody (PDB: 2FJF), namely one in light chain CDR2 (L2; blue) and one in heavy chain CDR3 (H3; green). (b) The two regions (circled in red) were predicted to be aggregation prone (high TANGO Score) and unstable (less negative free energy of folding). (c) Mutations in both CDRs L2 and H3 that were predicted to reduce aggregation (reduced TANGO Score) and improve folding stability (larger negative change in the free energy of folding) were selected to be evaluated. (d) Experimental evaluation of the antibody mutants revealed increased resistance to aggregation, as judged by increased aggregation temperatures that approached the antibody melting temperatures, particularly for variants with mutations in both CDRs. The figure is adapted with permission from a previous publication.

Figure 5.

Cell culture-based evaluation of FcRn-mediated antibody transcytosis correlates well with antibody clearance in vivo. (a) An in vitro transcytosis assay involves growing a monolayer of Madin–Darby canine kidney (MDCK) cells that overexpress human FcRn and measuring the amount of IgG in the outer chamber after extended incubation with excess IgG loaded in the inner chamber. (b) Measured rates of in vitro antibody transcytosis correlate with antibody clearance measured in humans (Pearson’s r = 0.90 and Spearman’s ρ = 0.77). (c) For a subset of antibodies that have been further experimentally characterized, isoelectric point (pI) displays some correlation with in vivo antibody clearance rates, as antibodies with high pIs have increased risk for fast antibody clearance (Pearson’s r = 0.57 and Spearman’s ρ = 0.57). The figure is adapted with permission from a previous publication.

Figure 6.

Repeated administration of therapeutic antibodies can lead to the development of anti-drug antibodies and immunogenicity. Antibody therapeutics are first internalized by APCs and processed into peptides, some of which are presented by the major histocompatibility complex II (MHCII) as foreign antigens to naïve T cells (top). Activated T cells then proliferate, release chemokines, and activate naïve B-cells (middle). Activated B-cells mature into plasma cells and produce ADAs in large quantities, leading to immunogenicity (bottom). While there are currently no reported assays for directly predicting clinical immunogenicity, various steps in the ADA development process can be modeled and predicted