Format

Send to

Choose Destination
Talanta. 2013 Oct 15;115:84-93. doi: 10.1016/j.talanta.2013.04.031. Epub 2013 Apr 18.

Development and validation of a general approach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks.

Author information

1
Chemical and Geological Sciences Department, University of Cagliari, 09042 Monserrato (CA), Italy. Electronic address: tpivetta@unica.it.

Abstract

The combination of two or more drugs using multidrug mixtures is a trend in the treatment of cancer. The goal is to search for a synergistic effect and thereby reduce the required dose and inhibit the development of resistance. An advanced model-free approach for data exploration and analysis, based on artificial neural networks (ANN) and experimental design is proposed to predict and quantify the synergism of drugs. The proposed method non-linearly correlates the concentrations of drugs with the cytotoxicity of the mixture, providing the possibility of choosing the optimal drug combination that gives the maximum synergism. The use of ANN allows for the prediction of the cytotoxicity of each combination of drugs in the chosen concentration interval. The method was validated by preparing and experimentally testing the combinations with the predicted highest synergistic effect. In all cases, the data predicted by the network were experimentally confirmed. The method was applied to several binary mixtures of cisplatin and [Cu(1,10-orthophenanthroline)2(H2O)](ClO4)2, Cu(1,10-orthophenanthroline)(H2O)2(ClO4)2 or [Cu(1,10-orthophenanthroline)2(imidazolidine-2-thione)](ClO4)2. The cytotoxicity of the two drugs, alone and in combination, was determined against human acute T-lymphoblastic leukemia cells (CCRF-CEM). For all systems, a synergistic effect was found for selected combinations.

KEYWORDS:

Artificial neural networks; Cancer; Cisplatin; Copper complexes; Experimental design; Synergism

PMID:
24054565
DOI:
10.1016/j.talanta.2013.04.031
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center