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A biophysical model of cell adhesion mediated by immunoadhesin drugs and antibodies.

Gutenkunst RN, Coombs D, Starr T, Dustin ML, Goldstein B - PLoS ONE (2011)

Bottom Line: Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells.We also quantitatively describe the parameter space in which binding occurs.Our model elaborates substantially on previous work, and our results offer guidance for the refinement of therapeutic immunoadhesins.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona, United States of America.

ABSTRACT
A promising direction in drug development is to exploit the ability of natural killer cells to kill antibody-labeled target cells. Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells. Thus it is important to understand adhesion of cells by antibodies and similar molecules. We present an equilibrium model of such adhesion, incorporating heterogeneity in target cell epitope density, nonspecific adhesion forces, and epitope immobility. We compare with experiments on the adhesion of Jurkat T cells to bilayers containing the relevant natural killer cell receptor, with adhesion mediated by the drug alefacept. We show that a model in which all target cell epitopes are mobile and available is inconsistent with the data, suggesting that more complex mechanisms are at work. We hypothesize that the immobile epitope fraction may change with cell adhesion, and we find that such a model is more consistent with the data, although discrepancies remain. We also quantitatively describe the parameter space in which binding occurs. Our model elaborates substantially on previous work, and our results offer guidance for the refinement of therapeutic immunoadhesins. Furthermore, our comparison with data from Jurkat T cells also points toward mechanisms relating epitope immobility to cell adhesion.

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Related in: MedlinePlus

Adhesion model predictions.A: Curves shown enclose the region of greater than 50% cell adhesion for the best-fit all-mobile model (blue), the best-fit model with  (red), the  model with  of nonspecific IgG (black), and the  model with ligand-epitope binding constants  and  each divided by 10 and no nonspecific IgG (green). The thin solid lines show our approximations for the bounding ligand concentrations  and . B: Experimental data on the inhibition of adhesion by nonspecific IgG (open circles) compared with predictions from our model with the immobile epitope fraction a function of the contact area. The solid line is from the best-fit model, and the dashed lines denote 95% confidence intervals from our bootstrap parameter uncertainties. Inset plots the same data and prediction on a logarithmic scale.
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pone-0019701-g004: Adhesion model predictions.A: Curves shown enclose the region of greater than 50% cell adhesion for the best-fit all-mobile model (blue), the best-fit model with (red), the model with of nonspecific IgG (black), and the model with ligand-epitope binding constants and each divided by 10 and no nonspecific IgG (green). The thin solid lines show our approximations for the bounding ligand concentrations and . B: Experimental data on the inhibition of adhesion by nonspecific IgG (open circles) compared with predictions from our model with the immobile epitope fraction a function of the contact area. The solid line is from the best-fit model, and the dashed lines denote 95% confidence intervals from our bootstrap parameter uncertainties. Inset plots the same data and prediction on a logarithmic scale.

Mentions: For drug design, an important consideration is what combinations of ligand concentration and target cell epitope count will yield adhesion. The curves in Fig. 4A separate the region where more than 50% of cells are adhered (inside each curve) from the region where less than 50% are adhered for three different scenarios. The outermost blue curve is the predicted separation curve for the parameters obtained from the fit with all receptors mobile (that shown by the blue lines in Fig. 3B). From this curve we can see that the minimal ligand concentration for adhesion is inversely proportional to the square of the epitope density: the bottom portion of the curve has a slope of approximately negative two.


A biophysical model of cell adhesion mediated by immunoadhesin drugs and antibodies.

Gutenkunst RN, Coombs D, Starr T, Dustin ML, Goldstein B - PLoS ONE (2011)

Adhesion model predictions.A: Curves shown enclose the region of greater than 50% cell adhesion for the best-fit all-mobile model (blue), the best-fit model with  (red), the  model with  of nonspecific IgG (black), and the  model with ligand-epitope binding constants  and  each divided by 10 and no nonspecific IgG (green). The thin solid lines show our approximations for the bounding ligand concentrations  and . B: Experimental data on the inhibition of adhesion by nonspecific IgG (open circles) compared with predictions from our model with the immobile epitope fraction a function of the contact area. The solid line is from the best-fit model, and the dashed lines denote 95% confidence intervals from our bootstrap parameter uncertainties. Inset plots the same data and prediction on a logarithmic scale.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3100730&req=5

pone-0019701-g004: Adhesion model predictions.A: Curves shown enclose the region of greater than 50% cell adhesion for the best-fit all-mobile model (blue), the best-fit model with (red), the model with of nonspecific IgG (black), and the model with ligand-epitope binding constants and each divided by 10 and no nonspecific IgG (green). The thin solid lines show our approximations for the bounding ligand concentrations and . B: Experimental data on the inhibition of adhesion by nonspecific IgG (open circles) compared with predictions from our model with the immobile epitope fraction a function of the contact area. The solid line is from the best-fit model, and the dashed lines denote 95% confidence intervals from our bootstrap parameter uncertainties. Inset plots the same data and prediction on a logarithmic scale.
Mentions: For drug design, an important consideration is what combinations of ligand concentration and target cell epitope count will yield adhesion. The curves in Fig. 4A separate the region where more than 50% of cells are adhered (inside each curve) from the region where less than 50% are adhered for three different scenarios. The outermost blue curve is the predicted separation curve for the parameters obtained from the fit with all receptors mobile (that shown by the blue lines in Fig. 3B). From this curve we can see that the minimal ligand concentration for adhesion is inversely proportional to the square of the epitope density: the bottom portion of the curve has a slope of approximately negative two.

Bottom Line: Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells.We also quantitatively describe the parameter space in which binding occurs.Our model elaborates substantially on previous work, and our results offer guidance for the refinement of therapeutic immunoadhesins.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona, United States of America.

ABSTRACT
A promising direction in drug development is to exploit the ability of natural killer cells to kill antibody-labeled target cells. Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells. Thus it is important to understand adhesion of cells by antibodies and similar molecules. We present an equilibrium model of such adhesion, incorporating heterogeneity in target cell epitope density, nonspecific adhesion forces, and epitope immobility. We compare with experiments on the adhesion of Jurkat T cells to bilayers containing the relevant natural killer cell receptor, with adhesion mediated by the drug alefacept. We show that a model in which all target cell epitopes are mobile and available is inconsistent with the data, suggesting that more complex mechanisms are at work. We hypothesize that the immobile epitope fraction may change with cell adhesion, and we find that such a model is more consistent with the data, although discrepancies remain. We also quantitatively describe the parameter space in which binding occurs. Our model elaborates substantially on previous work, and our results offer guidance for the refinement of therapeutic immunoadhesins. Furthermore, our comparison with data from Jurkat T cells also points toward mechanisms relating epitope immobility to cell adhesion.

Show MeSH
Related in: MedlinePlus