A Novel Approach for Modeling Biphasic Dose–Response Curves

Abstract

Estimates of EC 50 1 from dose–response data play an important role in comparing drug potencies. When the sampling data of dose–response studies fail to follow a sigmoidal shaped curve, and the data display a biphasic property at higher dose levels where the response profile concaves and takes an inverted U-shape, this is known as the hook or prozone effect. To address this concern, some research investigators may pursue data removal. Others may choose to ignore the data shape and fit a model blindly. Unfortunately for both practices, the estimates of the fitting parameters, such as the EC 50, will be of poor quality and result in misleading inference. The authors propose the use of an empirical and novel extension of a sigmoid model to properly and effectively capture the information from all of the dose–response data, including that of the inverted U-shaped tail. Methods for using 3- and 4-parameter logistic models with examples, are discussed.