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.

Základní údaje

Originální název

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

Autoři

PIVETTA, Tiziana (380 Itálie, garant), Francesco ISAIA (380 Itálie), Federica TRUDU (380 Itálie), Alessandra PANI (380 Itálie), Matteo MANCA (380 Itálie), Daniela PERRA (380 Itálie), Filippo AMATO (380 Itálie, domácí) a Josef HAVEL (203 Česká republika, domácí)

Vydání

Talanta, Elsevier, 2013, 0039-9140

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10406 Analytical chemistry

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 3.511

Kód RIV

RIV/00216224:14310/13:00068012

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1016/j.talanta.2013.04.031

UT WoS

000328095600012

Klíčová slova anglicky

synergism of drugs; artificial neural network; experimental design; cancer

Štítky

AKR, rivok, ZR

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 4. 2014 10:51, Ing. Zdeňka Rašková

Anotace

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.

Návaznosti

MSM0021622411, záměr

Název: Studium a aplikace plazmochemických reakcí v neizotermickém nízkoteplotním plazmatu a jeho interakcí s povrchem pevných látek

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Studium a aplikace plazmochemických reakcí v neizotermickém nízkoteplotním plazmatu a jeho interakcí s povrchem pevných látek

Citovat

PIVETTA, Tiziana, Francesco ISAIA, Federica TRUDU, Alessandra PANI, Matteo MANCA, Daniela PERRA, Filippo AMATO a 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, roč. 115, č. 2013, s. 84-93. ISSN 0039-9140. Dostupné z: 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 a 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, roč.~115, č.~2013, s.~84-93. ISSN~0039-9140. Dostupné z: https://dx.doi.org/10.1016/j.talanta.2013.04.031.

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