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

PIVETTA, Tiziana, Francesco ISAIA, Federica TRUDU, Alessandra PANI, Matteo MANCA, Daniela PERRA, Filippo AMATO and Josef HAVEL. Development and validation of a general appoach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks. Talanta. Elsevier, 2013, vol. 115, No 2013, p. 84-93. ISSN 0039-9140. Available from: https://dx.doi.org/10.1016/j.talanta.2013.04.031.

Basic information

• Original name: Development and validation of a general appoach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks
• Authors: PIVETTA, Tiziana (380 Italy, guarantor), Francesco ISAIA (380 Italy), Federica TRUDU (380 Italy), Alessandra PANI (380 Italy), Matteo MANCA (380 Italy), Daniela PERRA (380 Italy), Filippo AMATO (380 Italy, belonging to the institution) and Josef HAVEL (203 Czech Republic, belonging to the institution).
• Edition: Talanta, Elsevier, 2013, 0039-9140.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10406 Analytical chemistry
• Country of publisher: Netherlands
• Confidentiality degree: is not subject to a state or trade secret
• Impact factor: Impact factor: 3.511
• RIV identification code: RIV/00216224:14310/13:00068012
• Organization unit: Faculty of Science
• Doi: http://dx.doi.org/10.1016/j.talanta.2013.04.031
• UT WoS: 000328095600012
• Keywords in English: synergism of drugs; artificial neural network; experimental design; cancer
• Tags: AKR, rivok, ZR
• Tags: International impact, Reviewed

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.

Links

• MSM0021622411, plan (intention): Name: Studium a aplikace plazmochemických reakcí v neizotermickém nízkoteplotním plazmatu a jeho interakcí s povrchem pevných látek

Investor: Ministry of Education, Youth and Sports of the CR, Study and application of plasma chemical reactions in non-isothermic low temperature plasma and its interaction with solid surface