Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles

Abstract

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development.

Figure 1. The IFS curve of first three responses prediction.

The order-1 response is the most possible response according to the prediction. The highest prediction accuracy of first order response was 63.9% with 141 features. The highest cumulated prediction accuracies of first two responses and first three responses were also achieved with these 141 features. The red color points represent the highest accuracy points of each kind of accuracy.