Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors
Fig 2
Optimization of binding association and dissociation constants to experimental data.
The cost function is calculated as the sum of the squared differences between the normalized model output and the normalized experimental data at each antibody concentration used. “All Norm” includes all of the optimized parameter sets from each of the different normalization options described in the Methods, and “BS1 Norm” highlights the parameter sets where the model output was normalized against the bound concentration of BS1 at the initial concentrations used to normalize the experimental data, which was the best-performing normalization. Figures separated by normalization scheme and figures with a narrow range of parameter values are available in the Supporting Information (S2–S4 Figs). A, Relationship between initial guesses and optimized values for each binding reaction rate constant. kon,8R* is not pictured because its initial and ‘optimized’ values were determined from the other parameters using the thermodynamic cycle relationship. B, Distribution of optimized parameter values across all optimizations performed. Marked points indicate the values of the lowest cost parameter set (values are listed in Table 2). C, Relationship between optimized parameter values and the cost of the optimized parameter sets compared to experimental data, separated by parameter. Optimized points with the same value are grouped into a single point, with the point size indicating how many optimized parameter values are in the group.