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. 2016;8(3):585-92.
doi: 10.1080/19420862.2016.1141160. Epub 2016 Feb 24.

The Application of Mathematical Modelling to the Design of Bispecific Monoclonal Antibodies

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Free PMC article

The Application of Mathematical Modelling to the Design of Bispecific Monoclonal Antibodies

Tamara J van Steeg et al. MAbs. .
Free PMC article

Abstract

Targeting multiple receptors with bispecific antibodies is a novel approach that may prevent the development of resistance to cancer treatments. Despite the initial promise, full clinical benefit of this technology has yet to be realized. We hypothesized that in order to optimally exploit bispecific antibody technology, thorough fundamental knowledge of their pharmacological properties compared to that of single agent combinations was needed. Therefore, we developed a mathematical model for the binding of bispecific antibodies to their targets that accounts for the spatial distribution of the binding receptors and the kinetics of binding, and is scalable for increasing valency. The model provided an adequate description of internal and literature-reported in vitro data on bispecific binding. Simulations of in vitro binding with the model indicated that bispecific antibodies are not always superior in their binding potency to combination of antibodies, and the affinity of bispecific arms must be optimized for maximum binding potency. Our results suggest that this tool can be used for the design and development of the next generation of anti-cancer bispecific compounds.

Keywords: Affinity; Monovalent; avidity; binding; bispecific; limitation; mathematical; model; spatial.

Figures

Figure 1.
Figure 1.
Model verification against binding data of a monovalent BSAb and its reference monoclonal antibodies (anti-EGFR or anti-IGF1R) for different cell types (BxPC-3, H358, NIH3T3) In this simulation, the fraction of the maximal binding signal (left y-axis) was predicted as a function of antibody concentration in nM (x-axis) and plotted against real-life binding data (MFIR, right y-axis). The purple symbols and lines represent the binding data and model prediction for the monovalent BSAb, respectively. The red symbols and lines represent the binding data and model prediction for the anti-EGFR mAb, respectively. The green symbols and lines represent the binding data and model prediction for the anti-IGF1R mAb, respectively. A. Binding to BxPC-3 cells; B. Binding to H358 cells; C. Binding to NIH3T3 (decoy) cells.
Figure 2.
Figure 2.
Model verification against binding data from Harms et al. Simulation of total bound (black solid line), bivalent bound (gray solid line) and monovalent bound (black dashed line) plotted as the fraction of the maximum signal (y-axis) against antibody concentration (pM) for a parent antibody using the parameters as reported by Harms et al. A. Simulation for U87MG cells with a receptor density of 5.8 ×104 receptors/cell; B. Simulation for H1975 cells with a receptor density of 3.6 × 105 receptors/cell; C. Simulation for A431 cells with a receptor density of 2 × 106 receptors/cell. The parameters kon and KD were obtained from the original publication. The reaction volume (Vr) was set to the relevant range reported in the publication (3.4 × 10−5 L). The effective concentration (Ceff) was adjusted to 0.01 to reflect the observed binding curve.
Figure 3.
Figure 3.
Combination versus Bispecific treatment: Influence of receptor density on target and decoy binding. A. Simulation of total concentration bound at tumor cells in a decoy cell scenario with decoy-target cell ratio of 1; B. Simulation of IGF1R concentration bound at decoy cells (decoy-target cell ratio of 1). Simulations were performed with a fold10- lower receptor density for IGF1R compared to EGFR on tumor cells, which resembles BxPC-3 cells. The receptor density on the decoy cells was assumed to be 2-fold higher than the EGFR receptor density on tumor cells which resembles the difference between tumor cells (BxPC-3) and decoy cells (NIH3T3).
Figure 4.
Figure 4.
, Bispecific vs. Combination treatment: Influence of decoy-tumor cell ratio (ccr). Simulation of total receptor occupancy (y-axis) against antibody concentration (pM). Similar receptor densities for IGF1R and EGFR on tumor cells (EGFR = 1 × 106 receptors/cell, IGF1R = 0.9 × 106 receptors/cell) were assumed, which resembles H-358 cells. The receptor density on the decoy cells was assumed to be 1.5-fold higher than the EGFR receptor density on tumor cells.
Figure 5.
Figure 5.
Influence of IGF1R affinity on total binding on tumor cells. A surface plot in which receptor occupancy (%, z-axis) was simulated as function of IGF1R affinity (pM, x-axis) and antibody concentration (pM, y-axis). Simulation of total receptor occupancy on the tumor cell. Kd ranges from high (0.1pM) to low (1×106 pM) affinity for IGF1R. The antibody concentration ranges from 100 to 10000 pM. Simulations were performed for a high receptor density (˜1×106 receptors/cell) on tumor and decoy cells with target-decoy cell ratio of 3. The affinity for EGFR was kept constant to 1 nM (medium affinity).
Figure 6.
Figure 6.
EGFR therapy, total concentration bound for high-high and low-low affinity compounds. Receptor binding as a function of receptor density on the cell surface: the influence of bivalent binding with different affinities. The green, red, blue and black solid lines represent binding of the antibody at an affinity of 750, 75, 7.5 and 0.75 nM, respectively. The black vertical lines indicate the receptor density for decoy cells (low receptor density = 104 receptors/cell) and target cells (high receptor density = 106 receptors/cell).

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References

    1. Groenendijk FH, Bernards R. Drug resistance to targeted therapies: Déjà vu all over again. Mol Oncol 2014; 12:1-17; PMID:24910388; http://dx.doi.org/:10.1016/j.molonc.2014.05.004.24060863
    1. Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG.. Cancer drug resistance: an evolving paradigm. Nat Rev Cancer 2013; 13:714-26; PMID:24060863; http://dx.doi.org/10.1038/nrc3599 - DOI - PubMed
    1. Mazor Y, Oganesyan V, Yang C, Hansen A, Wang J, Liu H, Sachsenmeier K, Carlson M, Gadre D V, Borrok MJ, et al. Improving target cell specificity using a novel monovalent bispecific IgG design. MAbs 2015; 7:37-41 PMID:25621507; http://dx.doi.org/10.1080/19420862.2015.1007816.22464987 - PMC - PubMed
    1. Croasdale R, Wartha K, Schanzer JM, Kuenkele K-P, Ries C, Mayer K, Gassner C, Wagner M, Dimoudis N, Herter S, et al. Development of tetravalent IgG1 dual targeting IGF-1R-EGFR antibodies with potent tumor inhibition. Arch Biochem Biophys 2012; 526:206-18; PMID:22464987; http://dx.doi.org/10.1016/j.abb.2012.03.016 - DOI - PubMed
    1. Dong J, Sereno A, Aivazian D, Langley E, Miller BR, Snyder WB, Chan E, Cantele M, Morena R, Joseph IBJK, et al. A stable IgG-like bispecific antibody targeting the epidermal growth factor receptor and the type I insulin-like growth factor receptor demonstrates superior anti-tumor activity. MAbs 2011; 3:273-88; PMID:21393993; http://dx.doi.org/10.4161/mabs.3.3.15188 - DOI - PMC - PubMed

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