Dual retention behavior is observed for triphenylmethane (TPM) derivatives in RPTLC on silica gel plates when the composition of acetone-water mobile phases is varied. The physicochemical and molecular properties of the TPM derivatives causing this unusual retention behavior have been investigated by traditional quantitative structure-retention relationship (QSRR) modeling and by 3D molecular modeling. The QSRR studies were performed by using PLS regression analysis based upon use of selected sets of Dragon molecular descriptors and pharmacokinetically relevant VolSurf descriptors to identify the physicochemical properties that govern chromatographic behavior. Comparative molecular similarity indices analysis (CoMSIA) was used to create the 3D isoenergy contours of favored and unfavored contributions of the molecular fields around the molecules in the RPTLC systems used. The dual retention behavior of the TPM derivatives can be attributed to the propensity of these molecules to become oriented with different parts of their surface toward the silica gel layer when mobile phases with high or low water content are used. This hypothesis was supported by the 3D modeling results, which revealed that the isoenergy contours of the electrostatic, hydrophobic, and hydrogen bond acceptor fields of CoMSIA around the molecules were very different in systems with high or low water content. Furthermore, the VolSurf descriptors, which represent pharmacokinetic properties, correlated better with RM values measured using mobile phases with high water content than with those measured using mobile phases with low water content, and thus may be related to chromatographic behavior. Statistically significant PLS models with high predictive power ( q2 > 0.8) have been developed for prediction of RM values for the four mobile phases used. Lipophilicity was found to be the most important molecular property governing the retention of TPM derivatives.
- Comparative molecular similarity indices analysis (CoMSIA)
- Dual retention mechanism
- Partial least-squares (PLS) regression
- Quantitative structure-property relationship (QSPR)
ASJC Scopus subject areas
- Analytical Chemistry
- Clinical Biochemistry