Corticosteroid-binding Globulin Binding Affinity Research Articles

Pyrexia and acidosis act independently of neutrophil elastase reactive center loop cleavage to effect cortisol release from corticosteroid-binding globulin.

Corticosteroid-binding globulin (CBG) transports cortisol and other steroids. High-affinity CBG (haCBG) undergoes proteolysis of the reactive center loop (RCL) by neutrophil elastase (NE) altering conformation to low-affinity CBG (laCBG). Elevated temperature reduces CBG:cortisol binding affinity. Surface plasmon resonance was used to determine binding profiles of 19 steroids to haCBG and laCBG at 25, 37, and 39°C mimicking pyrexia and pH 7.4 and 7.0 mimicking acidosis, pathophysiological conditions relevant to sepsis. An expected 4-8-fold reduction in affinity for cortisol, cortisone, corticosterone, 11-deoxycortisol, progesterone, 17-hydroxyprogesterone, and prednisolone occurred with NE-mediated haCBG-to-laCBG conversion. CBG:cortisol binding affinity was further reduced 3.5-fold at 39°C relative to 37°C, binding affinity was also reduced by acidosis for both haCBG and laCBG. Using a conformational antibody generated against the RCL, we confirmed RCL antibody binding was eliminated by NE cleavage, but preserved in pyrexia and acidosis. Molecular modeling studies performed at 40°C confirmed a critical role for Trp371, positioned within the steroid-binding pocket, in ligand binding. These studies demonstrated CBG binding affinity to range of steroids is ligand specific and is reduced with NE-mediated haCBG-to-laCBG transition. Reduced CBG:cortisol binding occurs with increased temperature and in acidosis. Increased flexibility of the Trp371 side chain is proposed in the thermo-coupling mechanism of cortisol release. The synergy of NE cleavage, pyrexia, and acidosis on CBG:cortisol binding may serve to enhance cortisol delivery to the interstitial space in inflammation.

Measurement of free glucocorticoids: quantifying corticosteroid-binding globulin binding affinity and its variation within and among mammalian species.

Plasma glucocorticoids (GCs) are commonly used as measures of stress in wildlife. A great deal of evidence indicates that only free GC (GC not bound by the specific binding protein, corticosteroid-binding globulin, CBG) leaves the circulation and exerts biological effects on GC-sensitive tissues. Free hormone concentrations are difficult to measure directly, so researchers estimate free GC using two measures: the binding affinity and the binding capacity in plasma. We provide an inexpensive saturation binding method for calculating the binding affinity (equilibrium dissociation constant, K d) of CBG that can be run without specialized laboratory equipment. Given that other plasma proteins, such as albumin, also bind GCs, the method compensates for this non-specific binding. Separation of bound GC from free GC was achieved with dextran-coated charcoal. The method provides repeatable estimates (12% coefficient of variation in the red squirrel, Tamiasciurus hudsonicus), and there is little evidence of inter-individual variation in K d (range 2.0-7.3 nM for 16 Richardson's ground squirrels, Urocitellus richardsonii). The K d values of 28 mammalian species we assessed were mostly clustered around a median of 4 nM, but five species had values between 13 and 61 nM. This pattern may be distinct from birds, for which published values are more tightly distributed (1.5-5.1 nM). The charcoal separation method provides a reliable and robust method for measuring the K d in a wide range of species. It uses basic laboratory equipment to provide rapid results at very low cost. Given the importance of CBG in regulating the biological activity of GCs, this method is a useful tool for physiological ecologists.

How Changes in Affinity of Corticosteroid-binding Globulin Modulate Free Cortisol Concentration

Context:Recent studies of corticosteroid-binding globulin (CBG) indicate that it does not merely transport cortisol passively but also actively regulates its release in the circulation. We show how CBG binding affinity can vary to give changes in free cortisol concentration in a physiologically relevant range.Objective:The objective was to determine how the binding affinity of plasma CBG is affected by glycosylation, changes in body temperature, and the conformational change induced by proteases at sites of inflammation.Design:Binding assays were performed over a range of temperatures with plasma and recombinant CBG to determine the contribution of glycosylation. The role of conformational change was assessed by measuring binding affinities of plasma CBG before and after reactive loop cleavage by neutrophil elastase.Main Outcome Measures:Determination of binding constants allows calculation of clinically relevant changes in CBG saturation and free cortisol concentrations.Results:On reactive loop cleavage at inflammation sites, CBG can continue to act as a buffered source of cortisol, although with a much reduced affinity, to give a potential quadrupling of free cortisol. Predicted increases in systemic free cortisol resulting from elevated body temperatures, previously reported based on affinity measurements using nonglycosylated recombinant CBG, were shown here to be considerably increased using glycosylated plasma CBG, with a doubling for every 2°C rise in body temperature.Conclusions:The ability of CBG to modulate free cortisol levels in blood must be considered in the understanding and management of disease processes, as illustrated here with predictable changes in inflammation and fever.

Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification

Non-stochastic and stochastic 2D bilinear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design we have modeled the angiotensin-converting enzyme inhibitory activity of perindoprilate's σ-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models had shown an accuracy of 95.65% for the training set and 100% for the external prediction set. Next the prediction of the σ-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by multiple linear regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic ( R 2 = 0.953 and s = 0.238) and stochastic ( R 2 = 0.961 and s = 0.219) 3D-chiral bilinear indices were used. These models showed adequate predictive power (assessed by the leave-one-out cross-validation experiment) yielding values of q 2 = 0.935 ( s cv = 0.259) and q 2 = 0.946 ( s cv = 0.235), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The obtained results are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.

Atom-based 3D-chiral quadratic indices. Part 2: Prediction of the corticosteroid-binding globulinbinding affinity of the 31 benchmark steroids data set

A quantitative structure–activity relationship (QSAR) study to predict the relative affinities of the steroid ‘benchmark’ data set to the corticosteroid-binding globulin (CBG) is described. It is shown that the 3D-chiral quadratic indices closely correlate with the measured CBG affinity values for the 31 steroids. The calculated descriptors were correlated with biological data through multiple linear regressions. Two statistically significant models were obtained when non-stochastic ( R = 0.924 and s = 0.46) as well as stochastic ( R = 0.929 and s = 0.46) 3D-chiral quadratic indices were used. A leave-one-out (LOO) approach to model validation is used here; the best results obtained in the cross-validation procedure with non-stochastic ( q 2 = 0.781) and stochastic ( q 2 = 0.735) 3D-chiral quadratic indices are better or similar to most of the 3D-QSAR approaches reported so far. These results support the idea that the 3D-chiral quadratic indices may be helpful in prediction of the corticosteroid-binding affinity for new compounds.

3D-chiral Atom, Atom-type, and Total Non-stochastic and Stochastic Molecular Linear Indices and their Applications to Central Chirality Codification

Non-stochastic and stochastic 2D linear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These descriptors circumvent the inability of conventional 2D non-stochastic [Y. Marrero-Ponce. J. Chem. Inf. Comp., Sci. l 44 (2004) 2010] and stochastic [Y. Marrero-Ponce, et al. Bioorg. Med. Chem., 13 (2005) 1293] linear indices to distinguish sigma-stereoisomers. In order to test the potential of this novel approach in drug design we have modelled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models showed an accuracy of 100% and 96.65% for the training set; and 88.88% and 100% in the external test set, respectively. Canonical regression analysis corroborated the statistical quality of these models (R(can) of 0.78 and of 0.77) and was also used to compute biology activity canonical scores for each compound. After that, the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by linear multiple regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R2 = 0.982 and s = 0.157) and stochastic (R2 = 0.941 and s = 0.267) 3D-chiral linear indices were used. The predictive power was assessed by the leave-one-out cross-validation experiment, yielding values of q2 = 0.982 (s(cv) = 0.186) and q2 = 0.90 (s(cv) = 0.319), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The best results obtained in the cross-validation procedure with non-stochastic (q2 = 0.904) and stochastic (q2 = 0.88) 3D-chiral linear indices are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide an interesting alternative to other more common 3D-QSAR descriptors.

Combined MEDV-GA-MLR method for QSAR of three panels of steroids, dipeptides, and COX-2 inhibitors.

The MEDV-13, molecular electronegativity distance vector based on 13 atomic types, has at best 91 descriptors. It is impossible to indirectly use multiple linear regression (MLR) to derive a quantitative structure-activity relationship (QSAR) model. Although principal component regression (PCR) or partial least-squares regression (PLSR) can be employed to develop a latent QSAR model, it is still difficult how to determine the principal components (PCs) and depict the physical meaning of the PCs. So, a genetic algorithm (GA) is first employed to select an optimal subset of the descriptors from original MEDV-13 descriptor set. Then MLR is utilized to build a QSAR model between the optimal subset and the biological activities of three sets of compounds. For 31 benchmark steroids, a 5-descriptor QSAR model (M1) between the corticosteroid-binding globulin (CBG) binding affinity of the steroids and 5-descriptor subset is developed. The root-mean-square error of estimations (RMSEE) and the correlation coefficient of estimations (r) between the CBG binding affinity (BA) observed and the BA estimated by M1 are 0.422 and 0.9182, respectively. The root-mean-square error of predictions (RMSEP) and the correlation coefficient of predictions (q) between the BA observed and the BA predicted by leave-one-out cross validations are 0.504 and 0.8818, respectively. For 58 dipeptides inhibiting angiotensin-converting enzyme (ACE), a 5-variable QSAR model (M2) between the pIC(50) of peptides and 5-descriptor subset is derived. The M2 has a high quality with RMSEE = 0.339 and r = 0.9398 and RMSEP = 0.370 and q = 0.9280. For 16 indomethacin amides and esters (ImAE) inhibiting cyclooxygenase-2 (COX-2), a 6-variable QSAR model (M3) with RMSEE = 0.079 and r = 0.9839 and RMSEP = 0.151 and q = 0.9413 is built.

Self-organizing molecular field analysis: a tool for structure-activity studies.

Self-organizing molecular field analysis (SOMFA) is a novel technique for three-dimensional quantitative structure-activity relations (3D-QSAR). It is simple and intuitive in concept and avoids the complex statistical tools and variable selection procedures favored by other methods. Our calculations show the method to be as predictive as the best 3D-QSAR methods available. Importantly, steric and electrostatic maps can be produced to aid the molecular design process by highlighting important molecular features. The simplicity of the technique leaves scope for further development, particularly with regard to handling molecular alignment and conformation selection. Here, the method has been used to predict the corticosteroid-binding globulin binding affinity of the "benchmark" steroids, expanded from the usual 31 compounds to 43 compounds. Test predictions have also been performed on a set of sulfonamide endothelin inhibitors.