Drug-protein interaction studied by technique of microdialysis combined with high performance liquid chromatography

Drug-protein binding is an important process in determining the activity and fate of a pharmaceutical agent once it has entered the body. This review examines the method of microdialysis combined with high-performance liquid chromatography (HPLC) that has been developed;by ours to study such interactions, in which the microdialysis was applied to sample the free drug in the mixed solution of drug with protein, and HPLC to quantify the concentration of free drug in the microdialysate. This technique has successfully been used for determining various types of binding interactions between the low affinity drugs, high affinity drugs and enantiomers to HSA. For the case of competitive binding of two drugs to a protein in solution, a displacement equation has been derived and examined with four nonsteroidal anti-inflammatory drugs and HSA as model drugs and protein, respectively. Microdialysis with HPLC was adopted to determine simultaneously the free solute and displacing agent in drug-protein solutions. The method is able to locate the binding site and determine affinity constants even up to 10(7) L/mol accurately.

Study of interaction between drug enantiomers and serum albumin by capillary electrophoresis

The interaction between drugs and human serum albumin (HSA) was investigated by capillary electrophoresis (CE). It involves stereoselectivity, drug displacement and synergism effects. Under protein-drug binding equilibrium, the unbound concentrations of drug enantiomers were measured by frontal analysis (FA). The stereoselectivity of verapamil (VER) binding to HSA was proved by the different free fractions of two enantiomers. In physiological pH (7.4, ionic strength 0.17 phosphate buffer) when 300 mu M (+/-) VER were equilibrated with 500 mu M HSA, the concentration of unbound S-VER was about 1.7 times its antipode. The binding constants of two enantiomers, KR-VER and KS-VER, were 2670 and 850 M-1, respectively. However, no obvious stereoselective binding of propranolol (PRO) to HSA was observed. Trimethyl-beta-cyclodextrin (45 mM) was used as a chiral selector in pH 2.5 phosphate buffer. Several drug systems were studied by the method. When ibuprofen (IBU) was added into VER-HSA solution. R-VER was partially displaced while S-VER was not displaced at all. A binding synergism effect between bupivacaine (BUP) and verapamil was observed and further study suggested that verapamil and bupivacaine occupy different binding site of HSA (site II and site III, respectively).

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.