Retention time and peak width in the combined pH/organic modifier gradient high performance liquid chromatography

Confirming therapeutic target of protopine using immobilized β2 -adrenoceptor coupled with site-directed molecular docking and the target-drug interaction by frontal analysis and injection amount-dependent method

Drug-protein interaction analysis is pregnant in designing new leads during drug discovery. We prepared the stationary phase containing immobilized β₂ -adrenoceptor (β₂ -AR) by linkage of the receptor on macroporous silica gel surface through N,N'-carbonyldiimidazole method. The stationary phase was applied in identifying antiasthmatic target of protopine guided by the prediction of site-directed molecular docking. Subsequent application of immobilized β₂ -AR in exploring the binding of protopine to the receptor was realized by frontal analysis and injection amount-dependent method. The association constants of protopine to β₂ -AR by the 2 methods were (1.00 ± 0.06) × 10⁵ M^-1 and (1.52 ± 0.14) × 10⁴ M^-1 . The numbers of binding sites were (1.23 ± 0.07) × 10^-7 M and (9.09 ± 0.06) × 10^-7 M, respectively. These results indicated that β₂ -AR is the specific target for therapeutic action of protopine in vivo. The target-drug binding occurred on Ser¹⁶⁹ in crystal structure of the receptor. Compared with frontal analysis, injection amount-dependent method is advantageous to drug saving, improvement of sampling efficiency, and performing speed. It has grave potential in high-throughput drug-receptor interaction analysis.

A simple approach for retention prediction in the pH-gradient reversed-phase liquid chromatography

A simple approach for retention modeling of solutes under pH-gradient conditions at various organic contents in the mobile phase is proposed. This approach is based on a retention model arising from the evaluation of the retention data of a set of 17 OPA derivatives of amino acids obtained in two series of 22 pH-gradient runs performed between a given initial and final pH value (between 2.8 and 10.7 or 3.2 and 9.0) with different gradient duration and with different organic modifier content in the eluent. The derived model is a fifth-parameter equation easily manageable through a linear least-squares fitting. It requires only 6 initial pH-gradient experiments, allows a very satisfactory prediction for various pH-changes of the same kind with those used in the fitting procedure and seems to be very promising in separation optimization under pH-gradient conditions. The pH-gradient method appears to be especially suitable and effective for separation of amino acid derivatives whereas the application of pH-gradients from 3.2 to 9.0 proved to be beneficial.