A classification model to predict synergism/antagonism of cytotoxic mixtures using protein-drug docking scores.

ABSTRACT:

Background

Safer and more effective mixtures of anticancer drugs are needed, and modeling can assist in this endeavor. This paper describes classification models that were constructed to predict which fixed-ratio mixtures created from a pool of 10 drugs would show a high degree of in-vitro synergism against H460 human lung cancer cells. One of the tested drugs was doxorubicin and the others were natural compounds including quercetin, curcumin, and EGCG. Explanatory variables were based on virtual docking profiles. Docking profiles for the 10 drugs were obtained for 1087 proteins using commercial docking software. The cytotoxicity of all 10 drugs and of 45 of the 1,013 possible mixtures was tested in the laboratory and synergism indices were generated using the MixLow method. Model

SUBMITTER: Boik JC 

PROVIDER: S-EPMC2526994 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature