ANTICANCER DRUG CHARACTERIZATION USING A HUMAN CELL-LINE PANEL REPRESENTING DEFINED TYPES OF DRUG-RESISTANCE

Differential drug response in a human cell line panel representing def ined types of cytotoxic drug resistance was measured using the non-clo nogenic fluorometric microculture cytotoxicity assay (FMCA). In total 37 drugs were analysed; eight topoisomerase II inhibitors, eight anti- metabolites, eight alkylating agents, eight tubulin-active agents and five compounds with other or unknown mechanisms of action, including o ne topoisomerase I inhibitor. Correlation analysis of log IC50 values obtained from the panel showed a high degree of similarity among the d rugs with a similar mechanism of action. The mean percentage of mechan istically similar drugs included among the ten highest correlations, w hen each drug was compared with the remaining data set, was 100%, 92%, 88% and 52% for the topoisomerase II inhibitors, alkylators, tubulin- active agents and anti-metabolites respectively. Classification of dru gs into the four categories representing different mechanisms of actio n using a probabilistic neural network (PNN) analysis resulted in 29 ( 91%) correct predictions. The results indicate the feasibility of usin g a limited number of cell lines for prediction of mechanism of action of anti-cancer drugs. The present approach may be well suited for ini tial classification and evaluation of novel anticancer drugs and as a potential tool to guide lead compound optimisation.