Response surface models in the field of anesthesia: A crash course

Abstract

Drug interaction is fundamental in performing anesthesia. A response surface model (RSM) is a very useful tool for investigating drug interactions. The methodology appeared many decades ago, but did not receive attention in the field of anesthesia until the 1990s. Drug investigations typically start with pharmacokinetics, but it is the effects on the body clinical anesthesiologists really care about. Typically, drug interactions are divided into additive, synergistic, or infra-additive. Traditional isobolographic analysis or concentration-effect curve shifts are limited to a single endpoint. Response surface holds the complete package of isobolograms and concentration effect curves in one equation for a given endpoint, e.g., loss of response to laryngoscopy. As a pharmacodynamic tool, RSM helps anesthesiologists guide their drug therapy by navigating the surface. We reviewed the most commonly used models: (1) the Greco model; (2) Reduced Greco model; (3) Minto model; and (4) the Hierarchy models. Each one has its unique concept and strengths. These models served as groundwork for researchers to modify the formula to fit their drug of interest. RSM usually work with two drugs, but three-drug models can be constructed at the expense of greatly increasing the complexity. A wide range of clinical applications are made possible with the help of pharmacokinetic simulation. Pharmacokinetic-pharmcodynamic modeling using the RSMs gives anesthesiologists the versatility to work with precision and safe drug interactions. Currently, RSMs have been used for predicting patient responses, estimating wake up time, pinpointing the optimal drug concentration, guide therapy with respect to patient's well-being, and aid in procedures that require rapid patient arousal such as awake craniotomy or Stagnara wake-up test. There is no other model that is universally better than the others. Researches are encouraged to find the best fitting model for different occasions with an objective measure.