Modelling of retention of pesticides in reversed-phase high-performance liquid chromatography: quantitative structure-retention relationships based on solute quantum-chemical descriptors and experimental (solvatochromic and spin-probe) mobile phase descriptors.
A quantitative structure-retention relationship (QSRR) analysis based on multilinear regression (MLR) and artificial neural networks (ANNs) is carried out to model the combined effect of solute structure and eluent composition on the retention behaviour of pesticides in isocratic reversed-phase high-performance liquid chromatography (RP-HPLC). The octanol-water partition coefficient and four quantum chemical descriptors (the total dipole moment, the mean polarizability, the anisotropy of the polarizability and a descriptor of hydrogen-bonding based on the atomic charges on acidic and basic chemical functionalities) are considered as solute descriptors. In order to identify suitable mobile phase descriptors, encoding composition-dependent properties of both methanol- and acetonitrile-containing mobile phases, the Kamlet-Taft solvatochromic parameters (polarity-dipolarity, hydrogen-bond acidity and hydrogen-bond basicity, pi*, alpha and beta, respectively) and the 14N hyperfine-splitting constant (aN) of a spin-probe dissolved in the eluent are examined. A satisfactory description of mobile phase properties influencing the solute retention is provided by aN and beta or alternatively pi* and beta. The two seven-parameter models resulting from combination of aN and beta, or pi* and beta, with the solute descriptors were tested on a set of 26 pesticides representative of 10 different chemical classes in a wide range of mobile phase composition (30-60% (v/v) water-methanol and 30-70% (v/v) water-acetonitrile). Within the explored experimental range, the acidity of the eluent, as quantified by alpha, is almost constant, and this parameter is in fact irrelevant. The results reveal that aN and pi*, that can be considered as interchangeable mobile phase descriptors, are the most influent variables in the respective models. The predictive ability of the proposed models, as tested on an external data set, is quite good (Q2 close to 0.94) when a MLR approach is used, but the modelling capability can be further improved using an artificial neural network.
D'Archivio
Angelo Antonio
AA
Dipartimento di Chimica, Ingegneria Chimica e Materiali, Università degli Studi di L'Aquila, Via Vetoio, 67010 Coppito, L'Aquila, Italy.
Ruggieri
Fabrizio
F
Mazzeo
Pietro
P
Tettamanti
Enzo
E
D002626:Chemistry, Pharmaceutical / Q000379:methods*; D002851:Chromatography, High Pressure Liquid / Q000379:methods; D006860:Hydrogen Bonding; D006863:Hydrogen-Ion Concentration; D008956:Models, Chemical; D010575:Pesticides / Q000032:analysis*; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012996:Solutions
D010575:Pesticides; D012996:Solutions