Statistically testable parameter drug interaction modeling

The purpose of this research is to (1) produce a model allowing statistical tests of interaction behavior, (2) assess the model's difference from classical interactions, (3) compare to previous models, (4) find an experimental design to determine the drug ratio of most interaction, and (5) apply the model to a dataset that samples a scope of data where 50% of one drug is not achieved. The proposed model or STP model gives fits similar in its ability to describe data observed to a polynomial model described by Minto. The model introduces statistically testable summary parameters (STP) for interactions, best ratio for maximum interaction, change in Hill slope, and change in maximum effect. The differences in classification between STP interactions and traditional interactions are small. The STP model can approximate Finney, Single Hill Constant and Minto models. The drug ratio giving the most interaction can be estimated by administering set ratios of drugs to at least 10 patients. Without sampling data including the 50% effect of one drug, interaction statements are difficult to make with the STP model alone. Still, the STP model adequately predicts the interaction surfaces.