Structure-based engineering of pH-dependent antibody binding for selective targeting of solid-tumor microenvironment

Abstract

Recent development of monoclonal antibodies as mainstream anticancer agents demands further optimization of their safety for use in humans. Potent targeting and/or effector activities on normal tissues is an obvious toxicity concern. Optimization of specific tumor targeting could be achieved by taking advantage of the extracellular acidity of solid tumors relative to normal tissues. Here, we applied a structure-based computational approach to engineer anti-human epidermal growth factor receptor 2 (Her2) antibodies with selective binding in the acidic tumor microenvironment. We used an affinity maturation platform in which dual-pH histidine-scanning mutagenesis was implemented for pH selectivity optimization. Testing of a small set of designs for binding to the recombinant Her2 ectodomain led to the identification of antigen-binding fragment (Fab) variants with the desired pH-dependent binding behavior. Binding selectivity toward acidic pH was improved by as much as 25-fold relative to the parental bH1-Fab. In vitro experiments on cells expressing intact Her2 confirmed that designed variants formatted as IgG1/k full-size antibodies have high affinity and inhibit the growth of tumor spheroids at a level comparable to that of the benchmark anti-Her2 antibody trastuzumab (Herceptin®) at acidic pH, whereas these effects were significantly reduced at physiological pH. In contrast, both Herceptin and the parental bH1 antibody exhibited strong cell binding and growth inhibition irrespective of pH. This work demonstrates the feasibility of computational optimization of antibodies for selective targeting of the acidic environment such as that found in many solid tumors.

Keywords: Virtual histidine scanning; acidic pH selectivity; cell binding; spheroid growth; tumor targeting.

Figure 1.

Definition of relative binding free energy function for optimization of pH dependence. The main property to be optimized is ∆∆∆G_binding, the binding free energy gap between the Acidic and Physiological pH environments of a Mutant relative to the Parent, which has to be as negative as possible. This is shown in the upper diagram as the difference given by (1) – (2). Computationally, we simulate (3) – (4) instead, which from the thermodynamic cycle yields the same quantity. The bottom diagram provides an illustrative example for a possible distribution of free energies for the four states shown in the thermodynamic cycle, and how ∆∆∆G_binding can be calculated based on their free energies.

Figure 2.

Structural location of selected histidine mutations. Shown is the crystal structure of the parental bH1-Her2 complex (PDB code 3BE1)⁴⁰ as prepared for molecular simulations. Only the antigen-binding Fv domains of the antibody are shown, and colored cyan and magenta for the heavy and light chains, respectively. Selected positions for mutation to histidine are shown as Cα-sphere models and are labeled. Domain IV (residues C489-N607) of the Her2 antigen including the epitope is rendered as a black ribbon inside a translucent gray molecular surface.

Figure 3.

Iso-affinity plots from SPR data. Acidic pH data are plotted with red symbols, physiological pH data with black symbols. Arrows indicate moving the data point on the iso-affinity plot from physiological to acidic environments for various variants (labeled, mean data from Table 2) .

Figure 4.

Representative SPR sensorgrams for select Fab variants. Interaction of the parent bH1-Fab, the lead single mutant bH1-P5 (H-R58H) and the double mutant bH1-P5P8 (H-R58H,L-S30bH) with immobilized Her2 ectodomain. The black lines represent raw data and the red lines are global fits to a 1:1 bimolecular interaction model. See the Materials and Methods section for experimental details.

Figure 5.

pH dependence of Fab variants binding to Her2-expressing SKOV3 cells. Selected anti-Her2 Fab variants were analyzed for cell-based binding by flow cytometry under acidic and physiological pH conditions (using Method A, Materials and Methods section). Error bars represent standard deviations between technical replicates.

Figure 6.

pH dependence of FSA variants binding to cells expressing Her2. (A) High-density Her2 cells (SKOV3) were tested in environments with varying pH between 5.2 and 7.3 and cell binding was analyzed by flow cytometry (using Method A, Materials and Methods section). Error bars represent standard deviations between technical replicates. (B) Apparent dissociation constants (K_D) of tested FSA variants from binding experiments to high-density Her2 cells (SKOV3) at various pHs.

Figure 7.

pH dependence of FSA variants binding to cells expressing Her2 at low-density. Low-density Her2 expressing JIMT-1 cells were tested under acidic and physiological pH conditions and cell binding was analyzed by flow cytometry (using Method A, Materials and Methods section). The left panel representing binding to the normal cell model (low-density Her2 and physiological pH) is to be compared with binding of the same variants to the tumor cell model consisting of high-density Her2 (SKOV3 cells) within a pH range of 6.0–6.8 (Figure 6A). Error bars represent standard deviations between technical replicates.

Figure 8.

Tumor spheroid growth inhibition. Human breast primary carcinoma BT474 spheroids were cultured in physiological pH and in acidic pH of 6.4 in the presence of antibodies Herceptin, bH1-FSA, bH1-P5P8-FSA or Ctrl-hIgG (human IgG1 isotype antibody, negative control), and spheroid size monitored over time (see Materials and Methods section). (A) Dose-response effect of antibody variants in different pH conditions at the 192 h time point. Spheroid size is normalized to time zero and Ctrl-hIgG. (B) Spheroid growth over time for different antibody variants in different pH conditions. Spheroid size is normalized to time zero and Ctrl-hIgG. The p-values for each treatment relative to the control were calculated using Student’s t-test (*, p ≤ 0.05; **, p ≤ 0.01). (C) Representative images of the spheroids at 100 μg/mL antibody concentration in different pH conditions at the end of experiment. Error bars in (A) and (B) represent standard deviations between biological replicates.

Figure 9.

Structural details of pH-sensitive histidine mutations. Antibody chains of the parental bH1 antibody are colored as in Figure 2, and the antigen is rendered as gray tube. At each mutated position, the parental side chain and its histidine side chain substitutions in the acidic conditions (dark-green C atoms) and physiological conditions (bright-green C atoms) are overlaid and rendered as sticks model. The main interacting side chains of the antigen are shown in sticks models and labeled. Rosetta models for histidine mutants shown.