Prediction of Cyclosporine Blood Levels in Heart Transplantation Patients Using a Pharmacokinetic Model Identified by Evolutionary Algorithms

Abstract

Artificial intelligence (AI)-based computation methods have been recently shown to be applicable in several clinical diagnostic fields. The purpose of this study was to introduce a novel AI method called evolutionary algorithms (EAs) to clinical predictions. The technique was used to create a pharmacokinetic model for the prediction of whole blood levels of cyclosporine (CyA). One hundred one adult cardiac transplant recipients were randomly selected and included in this study. All patients had been receiving oral cyclosporine twice daily, and the trough levels in whole blood were measured by monoclonal-specific radioimmunoassay. An evolutionary algorithm (EA)-based software tool was trained with pre- and post-operative variables from 64 patients. The results of this process were then tested on data sets from 37 patients. The mean value of the predicted CyA level throughout the measurement period for the test data was 175 +/- 27 ng/ml, which compared well with the mean observed CyA level of 180 +/- 31 ng/ml. The system bias expressed as the mean percent error (MPE) for the training and test data sets were 7.1 +/- 5.4% (0.1% to 26.7%) and 8.0 +/- 6.7% (0.8% to 28.8%), respectively. The prediction accuracy ranged from 80% to 90%. The correlation coefficient between predicted and observed CyA concentration for the training data were 0.93 (p < 0.001) and for the test data were 0.85 (p < 0.001), respectively. The results of this study suggest that the use of evolutionary algorithms to identify pharmacokinetic models yields accurate prediction of cyclosporine whole blood levels in heart transplant recipients. This and other similar technologies should be considered as future clinical tools to reduce costs in our health systems.