Use of ANN modelling in structure-retention relationships of diuretics in RP-HPLC

Structure-retention relationship study, conducted by RP-HPLC, was used to i nvestigate physical chemical parameters related to the RP retention times o f amiloride, hydrochlorothiazide and methyldopa in order to predict the sep aration of amiloride and methylclothiazide from Lometazid(R) tablets. Reten tion data were obtained with an ODS column using a mobile phase methanol-wa ter (pH adjusted with phosphoric acid). Physical chemical properties were c alculated directly from the molecular structure. Artificial neural networks (ANNs) were used to correlate chromatograms retention times with mobile ph ase composition and pH, and with physical chemical properties of amiloride, hydrochlorothiazide and methyldopa and to predict separation of amiloride and methylclothiazide from Lometazid(R) tablets. Sensitivity analysis was p erformed to interpret the meaning of the descriptors included in the models . Results confirmed the dominant role of the polar modifier in such chromat ographic systems. Within a series of solutes chromatographed under identica l conditions, the retention parameters could be approximated by a non-linea r combination of log P, log D, pK(a), surface tension, parachor, molar volu me and to minor extend by polarisability, rexractivity index and density. T his study has demonstrated that the use ANNs techniques can result in much more efficient use of experimental information. As HPLC is the most popular analytical technique, improvements in HPLC methods development can yield s ignificant gains in the overall analytical effort. The ANNs extension prese nted could be the method of choice in some advanced research settings and s erves as an indication of the broad potential of neural networks in chromat ography analysis. (C) 1999 Elsevier Science B.V. All rights reserved.