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

J 2013

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 et. al.

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

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10406 Analytical chemistry

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

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

Abstract

V originále

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

Citovat

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.

@article{1093943,
   author = {Pivetta, Tiziana and Isaia, Francesco and Trudu, Federica and Pani, Alessandra and Manca, Matteo and Perra, Daniela and Amato, Filippo and Havel, Josef},
   article_number = {2013},
   doi = {http://dx.doi.org/10.1016/j.talanta.2013.04.031},
   keywords = {synergism of drugs; artificial neural network; experimental design; cancer},
   language = {eng},
   issn = {0039-9140},
   journal = {Talanta},
   title = {Development and validation of a general appoach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks},
   volume = {115},
   year = {2013}
}

TY  - JOUR
ID  - 1093943
AU  - Pivetta, Tiziana - Isaia, Francesco - Trudu, Federica - Pani, Alessandra - Manca, Matteo - Perra, Daniela - Amato, Filippo - Havel, Josef
PY  - 2013
TI  - Development and validation of a general appoach to predict and quantify the synergism of anti-cancer drugs using experimental design and artificial neural networks
JF  - Talanta
VL  - 115
IS  - 2013
SP  - 84-93
EP  - 84-93
PB  - Elsevier
SN  - 00399140
KW  - synergism of drugs
KW  - artificial neural network
KW  - experimental design
KW  - cancer
N2  - 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.
ER  -

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. \textit{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.

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