A New Approach to Drug Molecular Design based on Neural Network Analysis and Molecular Orbital Calculation : Molecular Modeling to Circumvent Cancer Drug Resistance Associated with ABCG2

Abstract

Irinotecan (CPT-11) is a widely-used potent antitumor drug that inhibits mammalian DNA topoisomerase I (Topo I). However, overexpression of ABCG2 can confer cancer cells resistance to SN-38, that is, the active form of CPT-11. In the present study, to develop a platform for the molecular modeling to circumvent cancer drug resistance associated with ABCG2, we have characterized a total of fourteen new SN-38 analogues by some typical properties, which were evaluated by molecular orbital (MO) calculations and neural network (NN) QSAR technique. The NN was applied to estimate hydrophobic properties (LogP) of the analogues. Thereafter, the electrostatic potential (ESP) and the salvation free energy (dG) were evaluated by MO calculation. These indexes were found to be well correlated with the drug resistance ratio experimentally observed in ABCG2-overexpressing cells. It is suggested that hydrophilic analogues carrying OH- or NH2-groups are good substrates for ABCG2 and therefore exported from cancer cells. In contrast, SN-38 analogues with Cl atom have similar LogP values and high affinities toward the putative active site of ABCG2, however they were not substrates of ABCG2. From these results, it is strongly suggested that hydrogen bonds are critically involved in the transport mechanism of ABCG2.