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. 2010 Nov 2;53(3):811-8.
doi: 10.1016/j.jpba.2010.04.035. Epub 2010 May 6.

The effects of glycation on the binding of human serum albumin to warfarin and L-tryptophan

Affiliations

The effects of glycation on the binding of human serum albumin to warfarin and L-tryptophan

K S Joseph et al. J Pharm Biomed Anal. .

Abstract

Diabetes leads to elevated levels of glucose in blood which, in turn, can lead to the non-enzymatic glycation of serum proteins such as human serum albumin (HSA). It has been suggested that this increase in glycation can alter the ability of HSA to bind to drugs and other small solutes. This study used high-performance affinity chromatography (HPAC) to see if there is any significant change related to glycation in the binding of HSA to warfarin and l-tryptophan, which are often used as probe compounds for Sudlow sites I and II of HSA in drug binding studies with this protein. It was found through frontal analysis studies that both of these compounds gave a good fit to a single-site binding model with glycated HSA under the conditions used in this study. There was no significant change in the association equilibrium constants or specific activities for warfarin with HSA at pH 7.4 and 37 degrees C under glycation conditions that were representative of those expected in pre-diabetes or diabetes, but a 4.7- to 5.8-fold increase in binding affinity for l-tryptophan with glycated HSA was observed. These results indicate that warfarin and l-tryptophan can be successively used as site-selective probes for glycated HSA; however, changes in the affinity of l-tryptophan may need to be considered in such an application. These results should be valuable in future competition studies using these compounds as probes to examine the interactions of other drugs and solutes with Sudlow sites I and II and to determine how changes in HSA glycation can affect the serum protein binding of various pharmaceutical agents during diabetes.

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Figures

Figure 1
Figure 1
Structures of warfarin and L-tryptophan.
Figure 2
Figure 2
(a) Competition studies for the injection of R-warfarin onto glycated HSA 1 column with acetohexamide solutions in the mobile phase. Acetohexamide concentrations from left to right: 20, 15, 10, 5, and 1 μM. The R-warfarin samples had a concentration of 5 μM and the injection volume was 20 μL. (b) Plots of 1/k vs. [Acetohexamide] while injecting R-warfarin on to a normal HSA column (●) and glycated HSA column 1 (■). The best-fit lines were as follows: normal HSA, y = 779 (± 51) x + 0.0184 (± 0.0006) (r = 0.991); glycated HSA, y = 1075 (± 72) x + 0.0183 (± 0.0008) (r = 0.991).
Figure 3
Figure 3
Breakthrough curves for warfarin at applied concentrations 10, 5, 2.5, 1.5, and 1 μM (from left to right) on glycated HSA column 1. Conditions for these studies are given in the text.
Figure 4
Figure 4
Double reciprocal plots prepared according to Eqn. (3) for warfarin binding to columns prepared with samples of gHSA1 (▲), gHSA2 (■) or gHSA3 column (●). The solid line shows the best-fit line for each data set. The error bars represent a range of ± 1 S.D. The best-fit lines were as follows: gHSA1, y = 424 (± 15) x + [9.62 (± 0.87)] × 107 (r = 0.998); gHSA2, y = 270 (± 10) x + [6.10 (± 0.56)] × 107 (r = 0.998); gHSA3, y = 287 (± 13) x + [7.80 (± 0.77)] × 107 (r = 0.997).
Figure 5
Figure 5
Plots of mLapp versus [Warfarin] prepared according to Eqn. (2) for the gHSA1 column (▲), gHSA2 column (■) and gHSA3 column (●). That data used in these plots were the same as used in Figure 4. The best-fit lines were as follows: gHSA1, y = [{1.81 (± 0.15)} × 105 × {1.19 (± 0.05) × 10−8 x]/[1 + {1.81 (± 0.15)} × 105 x] (r = 0.999); gHSA2, y = [{1.89 (± 0.09)} × 105 × {1.83 (± 0.05) × 10−8 x]/[1 + {1.89 (± 0.09)} × 105 x] (r = 0.999); gHSA3, y = [{2.01 (± 0.20)} × 105 × {1.52 (± 0.05) × 10−8 x]/[1 + {2.01 (± 0.20)} × 105 x] (r = 0.998).
Figure 6
Figure 6
Double reciprocal plots of L-tryptophan binding to affinity columns containing glycated HSA. These results are for the gHSA1 column (▲), gHSA2 column (■), and gHSA3 column (●). The solid line shows the best-fit line for each data set. The error bars represent a range of ± 1 S.D. The best-fit lines were as follows: gHSA1, y = [3.89 (± 0.06)] × 103 x + [2.02 (± 0.36)] × 108 (r = 0.999); gHSA2, y = [2.34 (± 0.07)] × 103 x + [1.49 (± 0.39)] × 108 (r = 0.999); gHSA3, y = [3.04 (± 0.04)] × 103 x + [1.72 (± 0.16)] × 108 (r = 0.999).

References

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