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Digoxin transport

Drescher, S., Glaeser, H., Murdter, T., Hitzl, M., Eichelbaum, M. and Fromm, M.F. (2003) P-glycoprotein-mediated intestinal and biliary digoxin transport in humans. Clinical Pharmacology and Therapeutics, 73, 223—231. [Pg.363]

The model produced a reasonable rank ordering of the 27 inhibitors of digoxin transport... [Pg.374]

Xu J, Go ML, Lim LY. Modulation of digoxin transport across Caco-2 cell monolayers by citrus fruit juices lime, lemon, grapefruit, and pummelo. Pharm Res 2003 20(2) 169-176. [Pg.182]

Mikkaichi T, Suzuki T, Onogawa T, et al. Isolation and characterization of a digoxin transporter and its rat homologue expressed in the kidney. Proc Natl Acad Sci U S A 2004 101 3569-3574. [Pg.186]

B. P-gp inhibition (log IC50) of digoxin transport in Caco-2 cells for 35 molecules [56]... [Pg.413]

Ekins et al. built QSAR models using Catalyst software to rank and predict inhibitors for P-gp substrate transport. In their first attempt, four different pharmacophores were derived from the analysis of inhibitors of digoxin transport, vinblastine binding, or intracellular accumulation of vinblastine and calcein [61]. These data were then combined with experiments using verapamil as inhibitor and led to the construction of a unique pharmacophore consisting of one hydrogen-bond acceptor, one aromatic ring, and two hydrophobic centers [62]. [Pg.510]

This picture has been supported by various pharmacophore modeling studies, particularly the most comprehensive studies by the group of Ekins [24,25]. They used several different training sets, such as inhibitors of digoxin transport, inhibitors of... [Pg.203]

Drescher S, Glaeser H, Miirdter T, Hitzl M, Eichelbaum M, Fromm MF. P-glycoprotein-me-diated intestinal and biliary digoxin transport in humans. CUn Pharmacol Ther (2003) 73, 223-31. [Pg.937]

Using the digoxin transport model to predict 51 of the molecules from the vinblastine binding, vinblastine accumulation, and verapamil accumulation data sets results in a correlation of observed versus predicted values (r = 0.63) and a Spearmans rho ranking correlation coefficient of 0.75 p = < 0.0001, Figure 6). This is quite acceptable as a computational model for drug discovery where large databases need to be filtered in a cost-effective manner. [Pg.359]

Figure 6 Evaluation of the digoxin transport P-glycoprotein pharmacophore tested on 51 molecules. Observed versus predicted ICjo r — 0.63, Spearmans rho = 0.75, p < 0.0001, Elipse = 95% confidence interval. Figure 6 Evaluation of the digoxin transport P-glycoprotein pharmacophore tested on 51 molecules. Observed versus predicted ICjo r — 0.63, Spearmans rho = 0.75, p < 0.0001, Elipse = 95% confidence interval.

See other pages where Digoxin transport is mentioned: [Pg.503]    [Pg.162]    [Pg.357]    [Pg.374]    [Pg.374]    [Pg.374]    [Pg.379]    [Pg.380]    [Pg.305]    [Pg.120]    [Pg.136]    [Pg.557]    [Pg.301]    [Pg.406]    [Pg.110]    [Pg.108]    [Pg.218]    [Pg.273]    [Pg.115]    [Pg.602]    [Pg.925]    [Pg.357]    [Pg.357]    [Pg.358]   
See also in sourсe #XX -- [ Pg.305 ]

See also in sourсe #XX -- [ Pg.357 ]




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