Multivariate analysis of chromatographic retention data and lipophilicity of phenylacetamide derivatives

Vastag, Gyöngyi; Apostolov, Suzana; Perišić-Janjić, Nada U.; Matijević, Borko

doi:10.1016/j.aca.2013.01.002

Cited by 33 publications

(14 citation statements)

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“…In contrast to the classical linear regression, multivariate methods can give a more comprehensive insight into the dependencies that exist among the analyzed data. [39][40][41][42][43] In order to establish a model that defines a qualitative and/or quantitative relationship between the chemical structure and the studied properties of molecules, as well as the relationship between the studied parameters of potential biological activity, all the obtained results, R M 0 , m, determined in both modifiers and both stationary phases, including the standard parameter of lipophilicity, pharmacokinetic predictors, and toxicity parameters, were analyzed using cluster analysis (CA) and principal component analysis (PCA). The CA and PCA were performed on the matrix in which R M 0 , m, logP, pharmacokinetic predictors, and toxicological parameters of the tested compounds were variables (columns), and the diphenylacetamides rows.…”

Section: Application Of Multivariate Analysis For the Study Of The mentioning

confidence: 99%

Chemometric study of chromatographic and computational bioactivity parameters of diphenylacetamides

Vastag

Apostolov

Mijin

et al. 2018

Journal of Chemometrics

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Selected chemometric methods were used to form models that define the qualitative and quantitative dependence between the chemical structure and the parameters of potential biological activity (lipophilicity, retention, pharmacokinetic, and toxic properties) of the selected diphenylacetamide derivatives. The chromatographic retention parameters of the selected diphenylacetamides were determined by applying reversed‐phase thin‐layer chromatography (RPTLC) on C18 and cyano modified carriers in mixtures of water‐methanol and water‐acetone. It was found that the polarity of the substituent R has dominant influence and its electronic effects to a lesser extent, on the studied parameters of the biological activity of diphenylacetamides. Also, results suggest a better similarity of the retention constants, RM0, with parameters of lipophilicity and pharmacokinetic predictors. Contrary to that, chromatographic parameter, m, exhibits a greater resemblance with toxicity parameters.

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Section: Application Of Multivariate Analysis For the Study Of The mentioning

confidence: 99%

Chemometric study of chromatographic and computational bioactivity parameters of diphenylacetamides

Vastag

Apostolov

Mijin

et al. 2018

Journal of Chemometrics

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“…Glavna komponenta, PC, je u stvari linearna kombinacija originalnih promenljivih. U praktičnom radu obično je dovoljno zadržati samo nekoliko glavnih komponenti, čiji zbir obuhvata veliki procenat ukupne promenljive [14][15][16].Faktor analiza koristi se za opis međusobne zavisnosti velikog broja promenjivih korišćenjem manjeg broja osnovnih, ali neopažljivih slučajnih promenljivih povezanih kao faktori. Ova metoda takođe se koristi za redukciju velikog broja podataka, ali i za otkrivanje strukture povezanosti među promenjivim (varijable), odnosno za njihovu klasifikaciju [15].…”

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Multivariate statistical analysis of parameters of surface water quality in Vojvodina

Matijević¹,

Vastag²,

Bečelić‐Tomin³

et al. 2014

Zas Mat

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1. UVOD Kvalitet površinskih voda zavisi od više faktora, a najviše od atmomsferskih padavina, erozije tla u slivu, naseljenosti i razvoja industrije u slivnom području. Promena temperature u toku godišnjih doba, kao i mešanje različitih vrsta voda takođe su bitni faktori koji utiču na kvalitet površinskih voda [1].Da bi se postigao zadovoljavajući kvalitet površinskih voda neophodan je monitoring koji je izrazito bitan segment u kvalitetnom i pravilnom upravljanju vodama. Monitoring program površinskih voda uključuje analizu vode, sedimenta i biote [2]. Krajnja informacija koja se dobija monitoringom površinskih voda ključna je za donošenje odluka o pravilnom i blagovremenom upravljanju vodama i zahteva odgovarajući način obrade podataka dobijenih merenjima u toku samog monitoringa. Rezultati, koji su dobijeni sprovođenjem različitih monitoring programa su suviše obimni i za njihovu pravilnu interpretaciju se koriste različite statističke metode. Najčešće primenjene metode su multivarijacione statističke metode, od kojih su najznačajnije: klaster analiza, faktor analiza i analiza glavnih komponenti (PCA). Pomoću ovih statističkih metoda moguće je značajna redukcija velikog broja rezultata monitoringa i markiranje mernih stanica sličnog kvaliteta, kao i izdvajanje onih mernih stanica koje značajno odstupaju u kvalitetu vode od ostalih mernih stanica.Adresa autora: Prirodno-matematički fakultet, Departman za hemiju, biohemiju i zaštitu životne sredine, Univerzitet u Novom Sadu, Trg Dositeja Obradovića 3, Novi Sad, Srbija Primljeno za publikovanje: 14. 09. 2013. Prihvaćeno za publikovanje: 22. 11. 2013.Multivarijacione statističke metode omogućavaju identifikaciju mogućih faktora/izvora koji su odgovorni za varijabilnost kvaliteta vode. Takođe, omogućavaju identifikaciju rasporeda izvora zagađenja i na taj način predstavljaju koristan alat za razvoj odgovarajuće strategije kako bi se ostvarilo efiksano upravljanje vodnim resursima [3][4][5][6][7][8][9][10][11][12][13].Klaster analiza se koristi za redukciju obimnih podataka, kombinuju se objekti u grupe relativno homogenih sastava. Ona pomaže u grupisanju parametara (slučajeva) u klastere na osnovu slič-nosti ili razlike između njih. Izazov mnogih istraži-vanja u kojima se radi sa velikim brojem podataka upravo je identifikovanje i grupisanje elemenata u manje grupe na osnovu neke povezanosti [14,15].Analiza glavnih komponenti (Principal Components Analysis, PCA) ima sposobnost da prepoznaje i eliminiše suvišne podatke iz eksperimentalnih rezultata. Primenom analize glavnih komponenti redukuje se veliki broj raspoloživih podataka, a kao rezultat se dobija različiti broj novih promenljivih tzv. glavne komponente (principal components, PC). Glavna komponenta, PC, je u stvari linearna kombinacija originalnih promenljivih. U praktičnom radu obično je dovoljno zadržati samo nekoliko glavnih komponenti, čiji zbir obuhvata veliki procenat ukupne promenljive [14][15][16].Faktor analiza koristi se za opis međusobne zavisnosti velikog broja promenjivih korišćenjem manjeg br...

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“…Principal component analysis (PCA) and hierarchical clustering (HC) were employed widely in liquid and/or gas chromatography to identify patterns and relationships between the columns since these methods can handle samples of large sizes and complexities. Therefore, these tools have been used for identification, classification, and qualitative control [32, 33], evaluation of the stationary phases [18, 25, 27, 28], and elucidation of retention mechanisms [27, 28, 34]. Moreover, hyphenated chromatographic instruments such as HPLC-DAD or LC-MS can generate tremendous amounts of data in a short time.…”

Section: Introductionmentioning

confidence: 99%

Supercritical Fluid Chromatography of Drugs: Parallel Factor Analysis for Column Testing in a Wide Range of Operational Conditions

Bakain

Al‐Degs

Andri

et al. 2017

Journal of Analytical Methods in Chemistry

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Retention mechanisms involved in supercritical fluid chromatography (SFC) are influenced by interdependent parameters (temperature, pressure, chemistry of the mobile phase, and nature of the stationary phase), a complexity which makes the selection of a proper stationary phase for a given separation a challenging step. For the first time in SFC studies, Parallel Factor Analysis (PARAFAC) was employed to evaluate the chromatographic behavior of eight different stationary phases in a wide range of chromatographic conditions (temperature, pressure, and gradient elution composition). Design of Experiment was used to optimize experiments involving 14 pharmaceutical compounds present in biological and/or environmental samples and with dissimilar physicochemical properties. The results showed the superiority of PARAFAC for the analysis of the three-way (column × drug × condition) data array over unfolding the multiway array to matrices and performing several classical principal component analyses. Thanks to the PARAFAC components, similarity in columns' function, chromatographic trend of drugs, and correlation between separation conditions could be simply depicted: columns were grouped according to their H-bonding forces, while gradient composition was dominating for condition classification. Also, the number of drugs could be efficiently reduced for columns classification as some of them exhibited a similar behavior, as shown by hierarchical clustering based on PARAFAC components.

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Multivariate analysis of chromatographic retention data and lipophilicity of phenylacetamide derivatives

Cited by 33 publications

References 40 publications

Chemometric study of chromatographic and computational bioactivity parameters of diphenylacetamides

Chemometric study of chromatographic and computational bioactivity parameters of diphenylacetamides

Multivariate statistical analysis of parameters of surface water quality in Vojvodina

Supercritical Fluid Chromatography of Drugs: Parallel Factor Analysis for Column Testing in a Wide Range of Operational Conditions

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