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

Abstract

Structure-retention relationship study, conducted by RP-HPLC, was used to investigate physical chemical parameters related to the RP retention times of amiloride, hydrochlorothiazide and methyldopa in order to predict the separation of amiloride and methylclothiazide from Lometazid(R) tablets. Retention data were obtained with an ODS column using a mobile phase methanol-water (pH adjusted with phosphoric acid). Physical chemical properties were calculated directly from the molecular structure. Artificial neural networks (ANNs) were used to correlate chromatograms retention times with mobile phase 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 performed to interpret the meaning of the descriptors included in the models. Results confirmed the dominant role of the polar modifier in such chromatographic systems. Within a series of solutes chromatographed under identical conditions, the retention parameters could be approximated by a non-linear combination of log P, log D, pK(a), surface tension, parachor, molar volume and to minor extend by polarisability, rexractivity index and density. This 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 significant gains in the overall analytical effort. The ANNs extension presented could be the method of choice in some advanced research settings and serves as an indication of the broad potential of neural networks in chromatography analysis.