Discussion
The Bliss independence–based model has fewer restrictions
than Loewe additivity–based models. First, Loewe
additivity models rely on accurately estimated doseresponse
curves to support the calculation of the effective
dose for a given response. When a 4PL model is used for
estimating a dose response, it is mandatory that the
response has to fall between the estimated Emin and Emax
parameters—a result that is often not possible. When the
data point is not between Emin and Emax, it has to be manually
removed from the analysis, which is undesirable for
statistical analysis. Second, the Loewe additivity model
becomes unusable when a dose-response curve is not
available or difficult to model. Third, the Loewe additivity
model is far more computationally challenging than the
Bliss independence model. When both methods have solutions,
their results are very similar (comparisons not
shown). The Bliss independence model presented in this
article requires only a linear model technique, which is
readily available in most software.
Screening for effective combination drugs among hundreds
of possible candidates is a challenging task. Those
analytical methods without proper statistical modeling can
easily lead to false decisions. The methodology introduced
in this article rigorously considers the variances in both
monotherapy and combination drug experiments and provides
a practical way to integrate those variances into one
comprehensive model. The confidence intervals constructed
using this method have successfully helped us to
identify synergistic regions of the test antibodies. Finally,
it should be emphasized that most other methods provide
only a simple score to quantify synergism. In contrast, the
method reported here provides both a synergy score as
well as a statistical confidence interval to qualify that synergy
score