Fuzzy modeling, a novel approach to studying pharmacodynamics

Abstract

Fuzzy logic can handle questions to which the answers may be "yes" at one time and "no" at the other, or may be partially true and untrue. Pharmacodynamic data deal with questions such as "Does a patient respond to a particular drug dose or not," or "Does a drug cause the same effects at the same time in the same subject or not." Such questions are typically of a fuzzy nature and might, therefore, benefit from an analysis based on fuzzy logic.The objective was to assess whether fuzzy logic can improve the precision of predictive models for pharmacodynamic data.The methods and results were as follows: (1). The quantal pharmacodynamic effects of different induction dosages of thiopental on numbers of responding subjects were used as the first example. Regression analysis of the fuzzy-modeled outcome data on the input data provided a much better fit than did the unmodeled output values with r-square values of 0.852 (F-value = 40.34) and 0.555 (F-value = 8.74), respectively. (2). The time-response effect propranolol on peripheral arterial flow was used as a second example. Regression analysis of the fuzzy-modeled outcome data on the input data provided a better fit than did the unmodeled output values with r-square values of 0.990 (F-value = 416) and 0.977 (F-value = 168), respectively.Fuzzy modeling may better than conventional statistical method fit and predict pharmacodynamic data, such as, for example, quantal dose response and time response data. This may be relevant to future pharmacodynamic research.