Allosterically coupled multi-site binding of T to human serum albumin

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Allosterically coupled multi-site binding of testosterone to human serum albumin

Abhilash Jayaraj, Heidi A. Schwanz, Daniel J. Spencer, Shalender Bhasin, James A. Hamilton, B. Jayaram, Anna L. Goldman, Meenakshi Krishna, Maya Krishnan, Aashay Shah, Zhendong Jin, Eileen Krenzel, Sashi N. Nair, Sid Ramesh, Wen Guo, Gerhard Wagner, Haribabu Arthanari, Liming Peng, Brian Lawney, Ravi Jasuja





ABSTRACT

Human serum albumin (HSA) acts as a carrier for testosterone, other sex hormones, fatty acids, and drugs. However, the dynamics of testosterone's binding to HSA and the structure of its binding sites remain incompletely understood. Here, we characterized the dynamics of testosterone's binding to HSA and the stoichiometry and structural location of the binding sites using two-dimensional nuclear magnetic resonance (2D NMR), fluorescence spectroscopy, bis-ANS partitioning, and equilibrium dialysis, complemented by molecular modeling.

2D NMR studies showed that testosterone competitively displaced 18-[ 13C]- oleic acid from at least three known fatty acid-binding sites on HSA that also bind many drugs. Binding isotherms of testosterone's binding to HSA generated using fluorescence spectroscopy and equilibrium dialysis were nonlinear and the apparent Kd varied with different concentrations of testosterone and HSA. The binding isotherms neither conformed to a linear binding model with 1:1 stoichiometry nor to two independent binding sites; the binding isotherms were most consistent with two or more allosterically coupled binding sites. Molecular dynamics studies revealed that testosterone's binding to fatty acid-binding site 3 on HSA was associated with conformational changes at site 6, indicating that residues in these two distinct binding sites are allosterically coupled.




Conclusion

There are multiple, allosterically coupled binding sites for testosterone on HSA. Testosterone shares these binding sites on HSA with free fatty acids, which could displace testosterone from HSA under various physiological states or disease conditions, affecting its bioavailability.






INTRODUCTION

Human serum albumin (HSA) is the most abundant protein in human plasma, with concentrations ranging from 30-50 g·L-1 (approximately 450-750 µM). HSA acts as a carrier for endogenous lipophilic compounds such as steroid hormones, fatty acids, some nutrients, and many drugs (1, 2). The steroid hormones, testosterone (T), dihydrotestosterone (DHT), and estradiol (E2), bind reversibly with high affinity to sex hormone-binding globulin (SHBG) and with lower affinity to HSA. Because of relatively high HSA concentrations, a substantial (33 to 54%) fraction of testosterone in the plasma is carried by HSA. Total testosterone levels represent the sum of the concentrations of protein-bound and unbound (free) testosterone in circulation. Free testosterone refers to the fraction of circulating testosterone that is not bound to any circulating protein while the bioavailable fraction refers to the circulating testosterone that is not bound to SHBG, reflecting the view that HSA-bound testosterone can dissociate from HSA at the capillary level, especially in tissues with long transit time (due to low affinity), and may be available for biological action. Laurent et al have shown that binding of testosterone to SHBG affects its bioavailability and that the biological activity of circulating testosterone correlates with the free testosterone levels (3-5). The binding to SHBG and HSA regulates the distribution of circulating testosterone into its bound and free fractions. HSA, even with its relatively lower affinity than SHBG, binds a higher fraction of circulating testosterone in men and women than SHBG due to its high binding capacity and high concentration. However, the dynamics of testosterone's binding to HSA remain incompletely understood and were the subject of this investigation.

Structurally, HSA is comprised of three homologous domains (6); domains II and III both contain a binding pocket formed mostly of hydrophobic and positively charged residues in which a variety of compounds bind (7–15). Although it has been shown that HSA possesses multiple binding sites for several biomolecules, including fatty acids and some drugs, it is generally believed that testosterone binds to HSA at a single site on domain IIA (10–12) with a low-to-moderate affinity (Ka ≈ 2.0 − 4.1 × 104 M -1 at 37°C) (13–18) and 1:1 stoichiometry. Because HSA transports a number of hormones, nutrients, and exogenous drugs, these endogenous ligands and drugs could potentially compete with testosterone for the same binding pocket(s) on HSA, thereby affecting its binding and bioavailability.

Since the publication of the crystal structure of HSA by He and Carter in 1992 (19), structural aspects of the binding sites of HSA in its physiologically relevant solution state have been characterized using nuclear magnetic resonance (NMR). Our group pioneered the novel strategy of using 13C-enrichment of a specific carbon in a fatty acid as a non-perturbing probe to enable visualization of small ligands complexed with a large protein, for which a solution structure has not been obtained (20). Since then, the use of two-dimensional (2D) NMR of 13C-enriched fatty acids has facilitated the identification of common binding sites for many biomolecules and drugs on HSA. The application of 2D NMR to characterize 18-[ 13C]-oleic acid (OA) binding to HSA led to the identification of nine individual OA binding sites within the three domains of HSA; seven of these nine binding sites have since been mapped onto the crystal structure (19). Some of these fatty acid-binding sites also participate in binding other biomolecules and drugs (6), each of which can competitively alter the binding of the other ligands (22, 23). OA bound to three low-affinity sites can be displaced by drugs from Sudlow’s drug sites I and II, as well as from the fatty acid-binding site 6 (23). Along similar lines, free fatty acids have been shown to modulate the binding of steroid hormones, including testosterone, to HSA, (24-26) leading us to consider the hypothesis that testosterone binds to one or more of the fatty acid-binding sites on HSA. Accordingly, we utilized the well-characterized HSA: OA 2D NMR system, described above, to investigate the competitive displacement of 18- [ 13C]-oleic acid by testosterone and determine the stoichiometry and the structural/spatial location of the testosterone binding pocket(s) on HSA.

Because chemical shift perturbations detected by NMR can be caused by either a direct binding event in the immediate binding pocket or by allosteric coupling distant from the binding site, we performed molecular dynamics studies to characterize the binding affinities of each site for testosterone and OA and explain the sequence of displacement and binding observed experimentally. Structural perturbations in HSA binding regions distant from the immediate binding site caused by the binding of testosterone to a specific binding site were evaluated by the following root mean square fluctuations (RMSF), residue cross-correlations, and principal component analysis (PCA). Additionally, the findings of the 2D NMR experiments were confirmed by studying the binding dynamics of testosterone using two independent methods: steady-state fluorescence spectroscopy and equilibrium dialysis. Collectively, the data reported in this manuscript on the chemical shift perturbation observed in the 2D NMR studies; the results of the fluorescence spectroscopy and equilibrium dialysis experiments; and the molecular modeling provide important insights into the locations of the multiple binding sites of testosterone on HSA, the dynamics of binding pocket residue coupling in the HSA: testosterone complex, and the molecular understanding of the binding stoichiometry.







DISCUSSION

Here, we employed multiple biophysical techniques – 2D NMR, fluorescence spectroscopy, bis-ANS partitioning, and equilibrium dialysis experiments – along with complementary molecular modeling studies to characterize the stoichiometry and the structure of the testosterone binding pockets on HSA. Our 2D NMR data offer direct experimental evidence that testosterone binds to at least three FA binding sites on HSA and the steady-state fluorescence quenching of tryptophan residues and repartitioning of bis-ANS probe show that the testosterone binding to HSA is a multiphasic process. The detailed molecular modeling studies suggest that these binding events are associated with significant conformational rearrangement in the binding pockets – distant from the site of testosterone's binding – indicating that they are allosterically coupled. These data do not support the prevailing model of 1:1 stoichiometry of testosterone's binding to a single binding site on HSA. These findings are novel and significant in several aspects. First, they show that there are multiple binding sites for testosterone on HSA (the prevalent view is that testosterone binds to HSA at a single binding site with a single Kd). Second, we show for the first time that these binding sites are allosterically-coupled. Finally, our data show for the first time that testosterone shares these binding sites on HSA with free fatty acids and some commonly used drugs and provide a mechanistic explanation for how commonly used drugs and free fatty acids – especially in the postprandial state and in disease conditions characterized by elevated free FA concentrations – can displace testosterone from its binding sites on HSA and potentially affect its bioavailability.

The 2D NMR data suggest the presence of at least three testosterone binding sites on HSA and the fluorescence spectroscopy data showed that these binding sites have distinct binding affinities. Equilibrium dialysis experiments revealed that the apparent Kd changes depending on the ratio of the concentrations of testosterone and HSA, providing further evidence of two or more binding sites and the possibility of allosteric coupling between the sites. The molecular modeling results confirmed the presence of allosterically coupled testosterone binding sites and provided novel insights into the energetics of the binding and the relative affinities of the binding sites for testosterone. The cross-correlation matrices provided confirmation of the interaction between the sites participating in testosterone binding.


The two Kd values resulting from the fit of the steady-state data (Figure 3B) are 17.8 nM and 12.3 µM, which differ by three orders of magnitude. The range of circulating testosterone concentration in men is 10-35 nM, that of HSA 450-750 µM, and that of SHBG 13.5-87.3 nM. With such high serum concentration of HSA, comparatively lower concentrations of testosterone and SHBG, and a high-affinity binding pocket on HSA in the low nanomolar range, virtually all of the testosterone in the blood would expect to be bound to HSA, with a negligible amount bound to SHBG or unbound to any protein. However, clinical data (13, 18, 48) show that 33- 45% of testosterone in the blood is bound to SHBG, while 50-67% is bound to HSA and 1-4% is unbound. The only explanation for this, while staying consistent with the derived dissociation constant values from our data, is that the low-affinity site with a Kd of 12.3 µM is the only available site on HSA in the unbound state. The binding of testosterone to the low-affinity binding site causes a long-range conformational change that renders the second, high-affinity binding site available for binding testosterone. We have previously observed similar allosteric coupling between the monomers of dimeric SHBG upon testosterone binding (49). The overly simplified linear models with 1:1 stoichiometry have overlooked the complexities in the dynamics of the binding of testosterone to HSA and SHBG.






CONCLUSION

Using 2D NMR, equilibrium dialysis, fluorescence spectroscopy, and molecular modeling, we provide evidence of at least three allosterically coupled binding sites for testosterone on HSA.
We show that testosterone binds to the same binding sites that are known to bind with fatty acids (FAs) and other endogenous ligands, and many commonly used drugs. These data also provide a mechanistic explanation for how commonly used drugs and free FAs – especially in the postprandial state and in disease conditions characterized by elevated free FA concentrations – could displace testosterone from its binding sites on HSA and potentially affect its bioavailability.
 
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The two Kd values resulting from the fit of the steady-state data (Figure 3B) are 17.8 nM and 12.3 µM, which differ by three orders of magnitude. The range of circulating testosterone concentration in men is 10-35 nM, that of HSA 450-750 µM, and that of SHBG 13.5-87.3 nM. With such high serum concentration of HSA, comparatively lower concentrations of testosterone and SHBG, and a high-affinity binding pocket on HSA in the low nanomolar range, virtually all of the testosterone in the blood would expect to be bound to HSA, with a negligible amount bound to SHBG or unbound to any protein. However, clinical data (13, 18, 48) show that 33- 45% of testosterone in the blood is bound to SHBG, while 50-67% is bound to HSA and 1-4% is unbound. The only explanation for this, while staying consistent with the derived dissociation constant values from our data, is that the low-affinity site with a Kd of 12.3 µM is the only available site on HSA in the unbound state. The binding of testosterone to the low-affinity binding site causes a long-range conformational change that renders the second, high-affinity binding site available for binding testosterone. We have previously observed similar allosteric coupling between the monomers of dimeric SHBG upon testosterone binding (49). The overly simplified linear models with 1:1 stoichiometry have overlooked the complexities in the dynamics of the binding of testosterone to HSA and SHBG.
 

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Table 1: Binding affinity of OA and testosterone with HSA for different simulations. Data were calculated using MM-BAPPL.
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Figure 1: Synthesis schema and NMR analysis of labeled testosterone upon binding to HSA. (A) Chemical synthesis schema for [13C]-labeled testosterone. A sharp doublet is observed in the testosterone solution. The resonances shift downfield in the presence of HSA and change multiplicity to a multiplet splitting pattern, denoting multiple interaction environments for HSA-bound testosterone. (B) 13C NMR spectral overlay of 1 mM [13C]-T and 1 mM [13C]-T in the presence of 0.5 mM HSA
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Figure 2: (Figure 2A) HSA crystal structure showing the fatty acid-binding sites which have been resolved in the crystal structure. Figure 2B. Panel A: 9 resonances observed from 13C-OA binding to HSA (2 mM OA, 0.5 mM HSA) bound sites. Panels B, C, D show a decrease in NMR signal from binding pockets corresponding to peaks A, E, and F as the OA is displaced by increasing testosterone concentrations. Red resonances in panels B, C, and D are from sites that are occupied by OA in the presence of testosterone. Panel A shows the spectra obtained from 4:1 ratio of OA: HSA in absence of testosterone; Panel B: in presence of 0.5 molar equivalents of testosterone, Panel C: upon addition of 1 molar equivalent of testosterone; and Panel D: in presence of 2 molar equivalents of testosterone. Panel E shows the four spectra analyzed using CCPNMR software to determine the peak intensity at corresponding concentrations of testosterone.
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Figure 3: Fluorescence spectroscopy analysis provides direct evidence for multiple binding sites and allostery. testosterone was titrated into a solution of 1 µM HSA in a single quartz cuvette and the system was excited at 295 nm to target tryptophan residues. As shown in Figure 3A, titration of testosterone into HSA: OA mixture, led to quenching of fluorescence emission from tryptophan residues. Testosterone was sequentially increased from 0 to 60 µM and at each concentration, the total delta intensity (defined in the text) was calculated and plotted as a function of [T] (Figure 3B). The data were then fit with three distinct binding models: (i) the extent linear model assuming 1:1 binding stoichiometry for HSA: T complex formation (Figure 3B, red curve). The unconstrained best fit to this model found a Kd of 528 nM, but it is clear that it does not adequately describe the data. (ii) Two, non-interacting, independent binding sites with distinct Kds. Figure 3B, the blue curve shows the best fit to the two-site specific binding model while constraining the Bmax value to one-half of the delta intensities (in the absence of relative quantum yield for the intermediate binding states, the two Bmax values were constrained to 1.8 × 106, the midpoint between the minimum and maximum Σ∆I values. The model with two independent sites also poorly fit (two discrete Kd values - 81.9 nM and 18.1 µM) the data. (iii) Two interacting binding sites (Figure 3B, green curve). The data fits resulted in the determination of two interacting sites, exhibiting a high-affinity Kd of 17.8 nM and a low-affinity Kd of 12.3 µM. Figure 3C shows changes in emission from bis-ANS repartitioning upon titration of testosterone from 0 to 1 mM into µM HSA incubated with 10 µM bis-ANS. Figure 3C, the red curve shows the fits to two-site specific binding model fit using the Kd constraints obtained from the fit of the steady-state tryptophan emission data from the green curve in Figure 3B. Collectively, these independent measures of testosterone-induced perturbations in fluorescence emission from intrinsic (tryptophan) and extrinsic (bis-ANS) probes provide evidence of multiple, interacting testosterone binding sites on HSA.
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Figure 4: The apparent dissociation constant (Kd) at 37°C changes as a function of testosterone concentration. The Kd was obtained by using the law of mass action and the measured testosterone concentrations from equilibrium dialysis and mass spectrometry for four different concentrations of HSA: 450 µM (Red circles), 600 µM (orange squares), 750 µM (green triangles), and 875 µM (blue diamonds). The apparent Kd depends on the ratio of HSA to testosterone, which is suggestive of allostery and interaction between the binding sites. If there were one binding site for testosterone on HSA, then the Kd should remain unchanged at a given temperature, but these data show that apparent Kd values for testosterone binding to HSA changes with their relative concentrations
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Figure 5: Binding of testosterone to site 3 allosterically alters the orientation of residues in site 6, disrupting the hydrogen bonding in site 6. 5A shows the presence of hydrogen bonds with the S480 residue (Sites 3 and 6 in the crystal structure of HSA (PDB ID: 1GNI)). 5B shows the rearrangement of residue orientation in site 6 due to the binding of testosterone at site 3 and loss of hydrogen bond with S480 and moves away from F204. OA of HSA:7OA (Panel A) at site 3 is depicted in green and OA at site 6 is depicted in cyan; testosterone of HSA:6OA: T (Panel B) at site 3 is depicted in pink and OA at site 6 is shown in orange
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Figure 6: Root Mean Square Fluctuation (RMSF) analysis and dynamic cross-correlation matrices (DCCM) of HSA complexes. 6A: Unliganded HSA is shown in blue, the HSA:7OA complex is in orange, and the HSA:6OA: T (testosterone at site 3) complex is depicted in grey. 6B: Percent increase in RMSF of HSA:6OA: T compared to HSA:7OA upon replacement of OA at site 3 with testosterone. This shows that site 6, comprising regions 1, 2, and 3, is affected by this replacement. Dynamic cross-correlation matrices for liganded structures: (6C) HSA:7OA complex (6D) HSA:6OA: T (testosterone at fatty acid site 3) complex show that the binding of testosterone to site 3 leads to the increased correlated motion of HSA residues at site 6, demoted by the change in color to red in the square box marked in blue.
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Figure 7: Spatial rearrangements in HSA structure upon testosterone binding and principal component analysis (PCA) for inter-domain dynamics. (7A) Specific movement of R348 hydrogen-bonded with OA (green) to HSA. A superposition of crystal structure (PDB ID: 1GNI, yellow) and MD snapshot of HSA:6OA: T (red). testosterone (pink) is unable to form a hydrogen bond due to its compact, smaller structure. This enables domain IIB to move toward domain III, leading to a decrease in affinity for OA at site 6. (7B) PCA and PCA vector field representation of HSA only (with fatty acid site 3 occupied by testosterone and remaining sites with OA). This depicts the primary atomic motions in HSA upon testosterone binding to site 3.
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Figure 8: Root means square fluctuation (RMSF) analysis of HSA elicits the long-range rearrangements in the HSA backbone by testosterone binding (8A) unliganded HSA showed in blue, the HSA:7OA complex is in orange, and the HSA:5OA:2T (testosterone at site 3 and 6) complex is plotted in grey. The figure depicts an increase in RMSF at sites 1 and 5 upon binding of sites 3 and 6 with testosterone. (8B) testosterone-induced long-range temporal rearrangement examined by the distance between distant Cα atoms of D183 and K519. HSA only is in blue, HSA:7OA is in orange, and HSA:5OT:2T is in grey. The yellow line represents the average distance in all PDB structures having HSA with fatty acid bound.
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Much more going on here than SHBG:T binding when it comes to cFT!

Patiently waiting on the completion of Phase II for the TruT (cFTZ) Algorithm




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NIH/NIA
Phase II: Research and Commercialization of TruT Algorithm
Role: Principal Investigator

Sep 15, 2017 - May 31, 2021


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A Reappraisal of Testosterone's Binding in Circulation: Physiological and Clinical Implications (2017)
Anna L Goldman, Shalender Bhasin, Frederick C W Wu, Meenakshi Krishna, Alvin M Matsumoto, Ravi Jasuja




Abstract

In the circulation, testosterone and other sex hormones are bound to binding proteins, which play an important role in regulating their transport, distribution, metabolism, and biological activity. According to the free hormone hypothesis, which has been debated extensively, only the unbound or free fraction is biologically active in target tissues. Consequently, accurate determination of the partitioning of testosterone between bound and free fractions is central to our understanding of how it's delivery to the target tissues and biological activity are regulated and consequently to the diagnosis and treatment of androgen disorders in men and women. Here, we present a historical perspective on the evolution of our understanding of the binding of testosterone to circulating binding proteins. On the basis of an appraisal of the literature as well as experimental data, we show that the assumptions of stoichiometry, binding dynamics, and the affinity of the prevailing models of testosterone binding to sex hormone-binding globulin and human serum albumin are not supported by published experimental data and are most likely inaccurate. This review offers some guiding principles for the application of free testosterone measurements in the diagnosis and treatment of patients with androgen disorders. The growing number of testosterone prescriptions and widely recognized problems with the direct measurement as well as the computation of free testosterone concentrations render this critical review timely and clinically relevant.




Essential Points

Most circulating testosterone is bound to its cognate binding proteins—sex hormone−binding globulin (SHBG), human serum albumin (HSA), cortisol-binding globulin, and orosomucoid; these binding proteins play an important role in regulating the transport, tissue delivery, bioactivity, and metabolism of testosterone

The physiochemical characteristics and dynamics of the binding of testosterone to its binding proteins are poorly understood; oversimplified assumptions of stoichiometry, binding dynamics, and binding affinity have contributed to the development of inaccurate linear binding models of testosterone to SHBG and
HSA

The ensemble allosteric model of the binding of testosterone to SHBG developed from recent studies using modern biophysical techniques suggests that testosterone binding to SHBG is a complex, multistep process that involves interbinding site allostery

The dynamics of the binding of testosterone to HSA, orosomucoid, and corticosteroid-binding globulin also require careful reexamination because the roles of these binding proteins in regulating circulating testosterone concentrations remain incompletely understood

If the free hormone hypothesis is correct (i.e., only free testosterone is biologically active), accurate determination and harmonized reference ranges for free testosterone are necessary to diagnose androgen disorders in men and women

Methods for the measurement of free testosterone levels are fraught with potential problems, including poor precision, inaccuracy, and low specificity, and reliable assays are not readily available to practicing clinicians; therefore,
algorithms based on valid binding models that can be used to estimate circulating free testosterone levels are needed to facilitate sound clinical decision making




Binding proteins in the peripheral circulation are important in regulating the transport, bioavailability, and metabolism of their cognate ligands, such as steroid hormones, fatty acids, vitamins, and drugs. The major sex steroid hormones—testosterone, 5α-dihydrotestosterone, and 17β-estradiol—bind predominantly to sex hormone−binding globulin (SHBG) and to
human serum albumin (HSA) and to a lesser extent to corticosteroid-binding globulin (CBG) and orosomucoid. SHBG, which is secreted by the liver, binds to testosterone with high affinity and is an important determinant of the distribution of circulating testosterone into its bound and free fractions (1). HSA is one of the most abundant and versatile proteins in circulation; although it binds testosterone with lower affinity than SHBG does, its high binding capacity and high concentration allow it to buffer fluctuations in testosterone levels (1). The characteristics of testosterone binding to CBG and orosomucoid and the biological roles of these binding proteins in regulating testosterone bioavailability remain incompletely understood.




Total testosterone refers to the sum of the concentrations of protein-bound and unbound testosterone in circulation. The fraction of circulating testosterone that is unbound to any plasma protein is referred to as the free testosterone fraction. The term bioavailable testosterone refers to the fraction of circulating testosterone that is not bound to SHBG and largely represents the sum of free testosterone plus HSA-bound testosterone (Fig. 1) (2); the term reflects the view that HSA-bound testosterone, which is bound with low affinity, can dissociate from HSA in the tissue capillaries and effectively be available for biological activity. The free testosterone fraction can be measured directly by the equilibrium dialysis or ultrafiltration method or calculated from total testosterone, SHBG, and HSA concentrations using published mass action binding algorithms (3–6). The bioavailable fraction can be measured using the ammonium sulfate precipitation method or the concanavalin A method, or it can be calculated from total testosterone, SHBG, and HSA concentrations (7). Although the pioneers who originated the concept of bioavailable testosterone envisioned it as the sum of HSA-bound and unbound fractions of circulating testosterone (2), the methods used to measure bioavailable testosterone concentrations, namely, the ammonium sulfate precipitation and concanavalin A methods, quantitate it as the non−SHBG-bound fraction of circulating testosterone, which approximates but is not equivalent to its original conceptualization as the sum of HSA-bound plus unbound testosterone levels (8).

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Partitioning of testosterone in the systemic circulation. Circulating testosterone is bound tightly to SHBG (green = high-affinity binding) and weakly to albumin, orosomucoid (ORM), and CBG (blue = low-affinity binding) (11). Only 1% to 4% of circulating testosterone is unbound or free. The combination of free and albumin-bound testosterone is also referred to as the “bioavailable testosterone” fraction.




The validity of calculated bioavailable and free testosterone is predicated on the accuracy of binding protein and testosterone concentrations and on the veracity of the assumptions of the association stoichiometry, binding affinities, and binding dynamics underlying the molecular binding model. The foundational assumptions about the relationship between testosterone and its binding proteins and estimates of the biophysical parameters of testosterone binding to its cognate binding proteins, upon which many extant algorithms for computing free testosterone are based, have undergone recent reappraisal and are discussed later. Data from the experimental studies performed in the 1960s and 1970s have been extrapolated without acknowledgment of the lack of experimental support for the underlying assumptions about linearity (3, 9–11) or of the methodological limitations described by the original authors. Collectively, these have led to an oversimplification of binding models based on somewhat erroneous assumptions of stoichiometry, binding affinity, and binding dynamics.

The rapid growth of testosterone prescriptions during the last decade (12) has refocused attention on the critical need for accurate determination of free testosterone in the diagnostic evaluation of men with a suspected androgen deficiency and for rational dosing and monitoring of testosterone replacement therapy. Accordingly, an expository review of the published data and prevailing models of testosterone binding is timely. Here, we present a historical perspective of the evolution of our understanding of the binding and bioavailability of testosterone. This review attempts to provide a comprehensive and critical appraisal of the prevailing models of testosterone binding to SHBG and HSA, the associated biophysical parameters, and their underlying assumptions and limitations. We discuss how recent advances in the computational and biophysical techniques have begun to unravel the multistep dynamics of testosterone binding to its cognate binding proteins, including the allosteric interactions between the testosterone binding sites on the SHBG dimer. This review also provides a contemporary perspective on the validity of the free hormone hypothesis and the clinical implications of these findings in the diagnosis, treatment, and monitoring of men with hypogonadism.




Biology of Binding Proteins and Their Role in the Transport, Distribution, Metabolism, and Bioavailability of Testosterone

At least four structurally distinct binding proteins are known to bind testosterone in human circulation: SHBG, HSA, CBG, and orosomucoid. Among these, SHBG has received the most attention because of its high binding affinity for testosterone. These binding proteins influence the tissue bioavailability and metabolic clearance rate of testosterone by regulating the amount of free testosterone available for biological action in the tissue. The roles of HSA, CBG, and orosomucoid in regulating testosterone’s bioavailability are less well understood, and we do not know how disease states or conditions that may differentially alter the circulating concentrations of HSA, CBG, and orosomucoid impact the binding of testosterone to SHBG. Current computations of free and bioavailable testosterone account only for the potential impact of alterations in HSA and SHBG, ignoring CBG and orosomucoid and other potentially interacting proteins and steroid hormones.



HSA

HSA is the most abundant protein in the human circulation, accounting for 60% of the total serum protein content and having a concentration of 30 to 50 g/L (450 to 750 µM) (35, 36). From 33% to 54% of testosterone binds with low affinity to HSA, with an association constant of 2.0 to 4.1 × 104 L/mol at 37°C (4, 5, 37–39). Albumin Catania (580 Lys-Leu-Pro-COOH) (40) and albumin Roma (321 Glu-Lys) (41) are known variants that impact the affinity of HSA for testosterone; albumin Roma has a decreased affinity for testosterone, and it is unknown if albumin Catania has an increased or decreased affinity.

The high capacity of HSA for binding steroids is particularly highlighted during pregnancy when the circulating sex steroid concentrations increase very substantially; however, even during pregnancy, more than 99% of available binding sites on HSA remain unoccupied (11). It has long been hypothesized that HSA-bound testosterone may dissociate in the capillary bed of organs with long transit times, such as the liver and the brain, and may become biologically active (bioavailable) in these organs in addition to the unbound testosterone (2, 42).

The HSA protein is encoded by a gene on chromosome 4 (43), which contains 15 exons placed symmetrically in three domains that likely arose by triplication of a single ancestral gene. The HSA gene is translated into a 609−amino acid product from which a signal peptide and a propeptide are cleaved, yielding a 585−amino acid mature protein that is secreted into the circulation. HSA in circulation can undergo nonenzymatic glycation by the formation of a Schiff base between ε-amino groups of lysine and arginine residues and glucose (44). HSA is generally measured with dye-binding assays such as bromocresol green or bromocresol purple or with immunoassays (45). The bromocresol green methods may overestimate HSA because of interference by acute-phase reactant proteins (46–48), whereas the bromocresol purple method reportedly has high concordance with immunoassays (49, 50).

Major gaps remain in our understanding of the dynamics of free testosterone regulation by HSA. Pardridge (42) hypothesized that within the tissue capillaries, conformational changes in the HSA molecule caused by interactions between HSA and the endothelial wall could lead to an opening of the binding site coil and enhanced dissociation of testosterone from HSA. Indeed, the dissociation of testosterone from bovine serum albumin in the brain capillary is ∼50 times faster than dissociation from albumin in vitro (42). This increase in transportability of HSA-bound testosterone may result from interactions of HSA with specific receptors in the microcirculation; however, in vivo studies of HSA transport into the brain (51) or liver (52) microcirculation showed that the volume of distribution of HSA was no greater than in the vascular space. Others have postulated that the enhanced dissociation of testosterone from HSA in the capillaries results from the secretion of binding inhibitors from the endothelium (18). Current models of the binding of testosterone to HSA are discussed further in a subsequent section.



Appraisal of the Prevailing Models of Testosterone Binding to Plasma Proteins

Most of the experimental data characterizing the association of testosterone with HSA and SHBG, which led to the conception of linear binding models of testosterone’s association with SHBG and HSA, including those by Vermeulen et al. (3), Södergard et al. (4), and Mazur (5), were generated in the 1950s through the 1980s. The resolution of the crystal structure of the liganded SHBG domains in the early 2000s was a major advance in our understanding of testosterone binding to SHBG. However, as we discuss subsequently, a paucity of experimental data supports the widely used assumptions of stoichiometry and the affinity of testosterone’s binding to SHBG (Table 1).




Table 1.
Factors Contributing to Erroneous Assumptions of Binding Affinity and Stoichiometry in Linear Models of Testosterone Binding to Its Cognate Binding Proteins

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Contributing Factor

1:1 Binding stoichiometry assumed without supporting experimental data

Use of Scatchard plots to force a straight line through nonlinear experimental binding data

Failure to account for alteration of binding equilibria during the separation of free and bound testosterone

Variations in the estimates of binding affinity because of differences in the temperature at which binding isotherms and dialysis experiments were performed

Variations in the estimates of binding affinity due to differences in dialysis conditions, including differences in the assay buffer composition and relative volumes of serum and assay buffers

Limited ability to detect additional binding sites on SHBG and HSA because of the narrow range of testosterone concentrations used in the binding experiments





Critical evaluation of the current model of testosterone binding to HSA

HSA consists of three domains [Fig. 2(a)] (60); both domains II and III have a binding pocket formed mostly of hydrophobic and positively charged residues in which a variety of compounds bind (61). It is widely believed that testosterone binds to HSA at a single site on domain IIA (62–64) with a low-to-moderate affinity (i.e., an association constant of 2.0 to 4.1 × 104 L/mol at 37°C) and a fast dissociation half-time (∼1 second) (4, 5, 38, 39, 65). As a result, all the equations for calculating free testosterone have used 1:1 stoichiometry of testosterone binding to HSA [Fig. 2(b)] (3–5, 34).

In the years since these studies were published, limited experimental data in the literature have supported the commonly assumed 1:1 stoichiometry for the binding of testosterone to HSA (Table 1) (66). For instance, experimental data published as early as 1954 by Eik-Nes et al. (67) suggested multiple, noninteracting testosterone binding sites on HSA. In 1978, Moll et al. (68) performed a detailed evaluation of the association of testosterone with HSA, and these authors also suspected multiple, identical, noninteracting testosterone binding sites on HSA. In 1982, Södergard et al. (4) conducted thermodynamic studies of the association of dihydrotestosterone with HSA and reported that the data pointed toward multiple binding sites on HSA. Ryan (69) suggested the possibility of multiple binding sites for testosterone on HSA and a nonlinear binding relationship. The calculations of binding parameters based on the assumption of 1:1 stoichiometry may also be invalid (Table 1) (70). Thus, although these trailblazers were suspicious of 1:1 stoichiometry, the methods and computational tools available to them were inherently limited in providing definitive evidence of stoichiometry, multiple binding sites with different binding affinities, or allostery in the binding of testosterone to HSA. Regardless, this same set of papers has been cited repeatedly over the years as the basis of the 1:1 stoichiometry for the binding of testosterone to HSA, although, in fact, these pioneering studies did not provide experimental data to support this assumption (Table 1).

As recently as the 1990s, Fischer et al. (71) concluded on the basis of studies that used equilibrium dialysis and circular dichroism that the second domain of the HSA molecule contained the primary binding site(s) for testosterone and acknowledged that “the data indicated the existence of cooperativity between secondary fatty acid-binding sites and the primary testosterone binding site.” Others also showed that for many ligands, the multiple binding sites on the HSA domains are allosterically coupled (72). It is conceivable that testosterone, like other ligands, may also have multiple binding sites with distinct affinities on HSA. Oversimplification of binding models and potentially erroneous assumptions can have major implications not only on estimates of testosterone’s bioavailability but also on putative competitive interactions with fatty acids and other hormones and drugs.

The dynamics of testosterone binding to HSA requires careful reexamination using modern experimental tools. Previous methods of comparing the solubility of testosterone in an aqueous buffer solution with its solubility in similarly buffered bovine serum albumin and using Scatchard analysis for equilibrium dialysis of testosterone with HSA were incapable of confirming multiple binding sites or identifying allosteric interactions between binding sites. For instance, novel conformational probes that exhibit perturbations in their ground or excited-state optical properties in response to changes in their electronic environment can facilitate the characterization of the binding of hormones and drugs to HSA and evaluation of the competitive displacement by ligands. In addition, magnetic resonance spectroscopy using 13C-enriched probes can help map the spatial pockets of testosterone binding to HSA.





Methods for Determination of Free Testosterone

Considering the high affinity of SHBG for testosterone binding, the SHBG-bound fraction is generally considered unavailable for biological action, and only the free and bioavailable testosterone fractions have been viewed as biologically active. The need for accurate assessment of free testosterone levels in the diagnosis and treatment of hypogonadism has stimulated the development of a variety of methods (Table 3), which are discussed in detail in the following sections.




Table 3.
The Relative Merits and Demerits of Various Methods of Measuring Free and Bioavailable Testosterone Levels

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Bioavailable Testosterone

Method

Merits

Problems

Ammonium sulfate precipitation of SHBG-bound testosterone

• Correlates well with free testosterone obtained by equilibrium dialysis

• Technically difficult
• Not easily automated
• Few clinical laboratories measure it routinely
• Conceptually measures non−SHBG-bound testosterone, which approximates but does not equal HSA-bound plus unbound testosterone

Concanavalin A method

• More selective and less variable than ammonium sulfate precipitation to precipitate SHBG

• Technically difficult
• Not easily automated
• Not used currently by clinical laboratories
• Measures non−SHBG-bound testosterone, which approximates but does not equal HSA-bound plus unbound testosterone

Calculated bioavailable testosterone

• Based on law-of-mass-action theory or empirical equations
• Simple to obtain

• Correlation between different algorithms is poor unless revalidated in a local laboratory
• Dependent on correct estimation of the association constants for the binding of testosterone to SHBG (KT) and HSA (KHSA)
• Results affected by the quality of total testosterone and SHBG and HSA measurements


Free Testosterone

Equilibrium dialysis

• The reference method against which other methods are compared

Technically difficult; operations in which the dialysis is performed vary across laboratories, contributing to high interlaboratory variability
• Not easily automated
• Few hospitals clinical laboratories perform this assay
• Expensive
• Relies on accuracy and precision of total testosterone

Ultracentrifugation

• Comparable to equilibrium dialysis

• Technically difficult
• Not easily automated
• Few clinical laboratories measure it routinely
• Expensive
• Relies on accuracy and precision of total testosterone

Free androgen index

• Represents the ratio of total testosterone/SHBG
• Has been shown to correlate with free testosterone measurements
• Simple to obtain

• Overly simplistic and inaccurate measure of free testosterone concentrations
• Poor indicator of gonadal status
• Dependent on accurate measurements of total testosterone and SHBG
• Most experts do not favor its use

Analogue immunoassays

• Commercially available kits
• High throughput and precision
• Has been shown to correlate with free testosterone measurements

• Provides inaccurate estimates of free testosterone
• Experts recommend against the use of direct analog assays for the measurement of free testosterone.

Salivary testosterone

• Simple to obtain

• May not be an accurate marker of circulating free testosterone concentrations
• Affected by sample desiccation, contamination by food and blood

Calculated free testosterone

• Easy to use algorithms based on various models of testosterone binding to SHBG or empirical equations
• Simple to obtain

Dependent upon correct estimates of the association constants and stoichiometry for binding of testosterone to SHBG and HSA
• Accuracy and precision affected by the accuracy and precision of the total testosterone and SHBG assays





Equilibrium dialysis and its various embodiments

Equilibrium dialysis is widely considered the reference method against which other methods are compared. It is technically demanding, and its performance is affected by assay conditions, which can result in high assay variability (192). Typically, the equilibrium dialysis procedure involves the dialysis of serum or plasma samples across a semipermeable cellulose membrane with a low-molecular-weight cutoff; protein-bound testosterone is retained, whereas free testosterone equilibrates across the dialysis membrane and can be measured in the dialysate either directly using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay or immunoassay or indirectly using a tracer. Indirect methods require adding a trace amount of radioactively labeled testosterone to the sample, and after equilibrium has been achieved, the proportion of tracer in the dialysate provides a measure of the percentage of free testosterone. Because free testosterone concentration can then be calculated by multiplying the percentage of the free fraction with the total testosterone concentration obtained from the same sample in a separate assay, accurate determination of total testosterone levels is necessary for accurate determination of free testosterone levels by this method.

Although a diligently conducted equilibrium dialysis assay accurately measures free testosterone levels, the method is fraught with operator-dependent errors. The protocol itself is labor-intensive, requiring repeated purification of the radioactive tracer, and is not readily amenable to high throughput. Even some large commercial diagnostic laboratories have stopped offering this assay. Although equilibrium dialysis is widely considered to be the gold standard for measuring free testosterone, this method is subject to various sources of error that may contribute to inaccuracy and imprecision. For instance, the dilution of serum or plasma may disturb the equilibrium between SHBG and its ligands (193). Results may also be altered when solutes become attached to the dialysis apparatus or membrane or when there is an unequal distribution of free ligands between the two compartments as a result of (1) inadequate time to reach equilibrium; (2) release of materials from the plate or membrane that interferes with the determination of concentration; and (3) the Donnan effect at low ionic strengths, which alters the distribution of charged particles near a semipermeable membrane so that they may not distribute evenly across the two sides of the membrane (194, 195). The ionic strength and pH of the dialysis buffer and the temperature at which dialysis is performed affect the equilibrium and the estimates of binding parameters. The batch-to-batch variability in adsorption characteristics of dialysis plates from different manufacturers may be an additional source of interassay variation. The Centers for Disease Control and Prevention’s (CDC’s) hormone standardization program is invested in improving clinical assays and minimizing factors that affect measurement variability (196).


Effects of temperature variations

Steroid binding is affected by the temperature and may be 2.5 times higher at 4°C than at 37°C (9, 197, 198). The seminal testosterone-binding experiments were performed at varying temperatures—some studies were performed with ice-cold ammonium sulfate (4°C) (2) or at 25°C (9), which may affect binding equilibrium (Table 1). For example, in a separate study characterizing temperature effects on cortisol protein binding by the equilibrium dialysis method, raising the temperature from 37°C to 41°C led to an increase of ∼80% in serum-free cortisol level (199).

Effects of assay buffer composition and buffer volumes

The composition and ionic strength of the dialysis buffer affect the results of equilibrium dialysis experiments. Experiments should ideally be performed using a dialysis buffer with an ionic composition that resembles that of human plasma, but this has not been the case in all studies. The assumption that the concentration of free ligands is equal on both sides of the membrane at equilibrium is not always valid (Table 1). Most proteins have a charge and accumulate a set of neutralizing counterions. The Donnan effect, discussed previously, is a consequence of maintaining the overall electrical neutrality of the solution and may give spurious evidence of an association between a ligand and a protein of opposite charge when charged counterions are present in the buffer. Differences in the ratios of volumes of dialysis buffer to sample may also affect estimates of free testosterone; when the binding is nonlinear, the decrease in total analyte concentration can alter the free fraction (Table 1).

Alteration of equilibria during physical separation of free and bound testosterone fractions

Traditional assays for determining stoichiometry and association constants usually involve separation of bound and free forms of testosterone using equilibrium dialysis, ultracentrifugation, ammonium sulfate precipitation, or other chromatographic separation methods with a subsequent Scatchard plot of the ratio of bound testosterone to unbound testosterone [(bound/free testosterone); ordinate] plotted against the bound testosterone concentration [(bound); abscissa] (200). The Scatchard analysis is a method of “linearizing” data from a saturation binding experiment to determine binding constants and estimates of the stoichiometry of the noninteracting sites. However, under several experimental conditions, the underlying assumptions in the Scatchard analysis are not met, and the use of the Scatchard analysis may yield inaccurate parameter estimation (Table 1).

Achieving standardization of dialysis conditions across laboratories has been difficult, resulting in substantial interlaboratory variations in reported results. Authors who measure free testosterone by equilibrium dialysis should provide details about their methodology to ensure reproducibility and interlaboratory comparability.




Computational Algorithms for Estimating Free and Bioavailable Testosterone Concentrations: Pitfalls and the Compelling Need for Accuracy in Calculated Free Testosterone


Most hospital and commercial laboratories do not offer an equilibrium dialysis assay for free testosterone, most likely because of operational complexities in performing the assay and difficulties in automating the procedure; only a few academic and commercial laboratories offer this assay. Furthermore, efforts to standardize experimental conditions for the performance of equilibrium dialysis across the few commercial and academic laboratories that offer it have proven challenging. Fortunately, LC-MS/MS methods for precise total testosterone measurements and high-sensitivity SHBG enzyme-linked immunosorbent assay are widely available. Accordingly, an accurate algorithm, validated against the equilibrium dialysis measurement, can provide calculated free testosterone values with significantly higher precision and lower cost than can be achieved with equilibrium dialysis in many hospital laboratories.

Recognizing the practical difficulties that practicing clinicians face in obtaining precise and accurate measurements of free testosterone concentrations by the equilibrium dialysis method, an expert panel of the Endocrine Society concluded that “calculated free testosterone, using high-quality testosterone and SHBG assays with well-defined reference intervals, is the most useful clinical marker…” (205). Accordingly, several groups have developed frameworks for computing free testosterone from SHBG, total testosterone, and HSA concentrations that can be broadly classified into three categories: (1) algorithms based on linear models of testosterone binding to SHBG, (2) algorithms derived from empiric bootstrapping of data fits to mathematical forms, and (3) algorithms based on nonlinear models incorporating allostery in the SHBG dimer.




Calculated free testosterone based on linear models

The algorithms published by Vermeulen et al. (3), Södergard et al. (4), and Mazer (5) are all based on the linear model of testosterone binding to SHBG [Fig. 3(a)]. They all used Scatchard analysis to linearize data from a saturation-binding experiment to determine binding constants and estimates of the stoichiometry of noninteracting sites. However, under several experimental conditions, the underlying assumptions in the Scatchard analysis are not met, and the Scatchard analysis may yield inaccurate parameter estimation (Table 1). For instance, the assumptions of linear regression, that the scatter of points about a line follows a normal (or Gaussian) distribution and the standard deviation is the same at every concentration of the analyte, are violated in a Scatchard plot, which alters the relationship between bound and free fractions. The use of the calculated values of the bound/free steroids further violates the assumption of linear regression that all uncertainty is in the Y variable, whereas the X variable is known with complete certainty. Because of the nonlinear nature of binding and allosteric interactions between binding sites, the linear transformation of the binding data to force a straight line through nonlinear data renders these historical estimates of binding affinity and capacity prone to error. Nonlinear computational tools may be more suitable for binding events, which involve allosteric interactions and are nonlinear; however, these methods have not been used in the literature for the analyses of data related to testosterone binding to SHBG or HSA.

These algorithms use different association constants of testosterone binding to HSA and SHBG and therefore yield slightly different estimates of free testosterone. For example, the Vermeulen et al. (3) equation used association constants of 3.6 × 104 and 1 × 109 L/mol for HSA and SHBG, respectively, whereas the Södergard et al. (4) algorithm used 4.06 × 104 and 5.97 × 108 L/ mol, respectively. All three equations, especially the Vermeulen et al. (3) equation, have been widely used in the literature and in commercial laboratories. The calculated free testosterone values derived using these equations correlated with free testosterone concentrations measured by equilibrium dialysis in some studies (3, 4, 214) but displayed substantial systematic differences from values derived using the equilibrium dialysis method and from each othe
r (6, 34, 215218).

Hackbarth et al. (218) evaluated five separate equations in two patient groups with different sex distributions. They defined percentage differences above 20% to be unacceptable; depending on the equation, 32% to 72% of males and 29% to 57% of females displayed an unacceptable agreement between levels of calculated free testosterone and measured free testosterone by equilibrium dialysis. In addition, 14.9% of males and 11.1% of females showed poor fit by all five equations.




Calculated free testosterone from empiric and bootstrap fitting approaches

Ly and Handelsman (6) analyzed a data set comprising >4000 blood samples in which free and total testosterone and SHBG concentrations were measured; dividing the data set into samples with serum total testosterone above and below 5 nM, they used a bootstrap regression modeling approach free of assumptions about theoretical binding equilibria to develop an empirical equation for free testosterone in terms of total testosterone and SHBG. Later, Sartorius et al. (217) created a variety of formulas for evaluation by bootstrap resampling to identify the best-fit model according to entropy reduction and improve upon the previous empirical calculated free testosterone equation. This algorithm, like the others, is highly dependent on the accuracy and precision of the total testosterone and SHBG assays, which affects the accuracy and precision of calculated free testosterone (219). Furthermore, regression equations derived empirically in one patient population may not necessarily apply to another population, especially to a population with substantially different SHBG concentrations. In a different patient population, there is no reason to believe that best-fit parameters will be the same as in the test population. In addition, these methods do not have a testable binding model that can be subjected to experimental validation or improved upon to incorporate other variables or new knowledge of the dynamics of testosterone binding to its cognate binding proteins or for personalization to specific conditions or disease states.




Calculated free testosterone using an algorithm that incorporates experimentally observed nonlinear binding dynamics and allosteric interaction between binding sites


We recently investigated the source of systematic discrepancies between free testosterone values computed using the simple linear model, which formed the basis of the Vermeulen et al. (3), Södergard et al. (4), and Mazer (5) equations, and free testosterone measured using equilibrium dialysis. These discrepancies between free testosterone calculated using these linear binding models and free testosterone measured using equilibrium dialysis are most likely the result of the erroneous assumptions of the dynamics of testosterone binding to SHBG. Recent studies of testosterone binding to SHBG using modern biophysical techniques suggest that SHBG circulates as a homodimer and that there is complex allosteric interaction between the two binding sites on the SHBG dimer, such that the binding affinities of the two sites are not identical (34). The computational algorithm based on this novel multistep ensemble allosteric model (EAM) (34) of testosterone binding to SHBG provided estimates of free testosterone levels that closely matched free testosterone levels measured using the equilibrium dialysis method in samples derived from men and women in two randomized clinical trials (220, 221). The calculated free testosterone level obtained using the prevailing linear model was systematically lower than those measured by equilibrium dialysis. In Table 4, we show that calculated free testosterone values across age deciles in the Framingham Heart Study computed by the linear model are lower than those computed by the allosteric model. The EAM model is based on experimentally derived binding affinity and dynamics, which can be verified experimentally and improved upon with additional information about other variables that determine free testosterone concentrations.






Lack of Standardization of Free Testosterone Measurement Methods and Unavailability of Harmonized Reference Ranges for Free Testosterone

Le et al. (222) surveyed 120 academic and community laboratories in the United States to characterize the distribution of assays and the associated reference values for free testosterone. In all, 84% of the surveyed laboratories sent their samples for free testosterone measurement to larger centralized reference laboratories (222). These large commercial laboratories offered a variety of methods, including ultracentrifugation, radioimmunoassay, and calculation-based algorithms, as well as equilibrium dialysis (222). Many clinical laboratories used calculated free testosterone based on published linear equations (3). The laboratories reported wide variations in the reference ranges. Only 30 of the laboratories surveyed would confirm that validation studies had been performed, and the authors advised that reference ranges provided by manufacturers and laboratories should be interpreted with caution.

In a survey of 12 academic laboratories, 12 community medical laboratories, and one national laboratory, Lazarou et al. (223) found 17 and 13 different sets of reference values for total and free testosterone, respectively, which were established largely without clinical considerations.
Recently, Bhasin et al. (224) reported reference ranges for calculated free testosterone concentrations in a large, rigorously collected sample of community-dwelling men. In healthy young men of the Framingham Heart Study who were 19 to 40 years of age, the lower limit of the normal range, defined as the 2.5th percentile of calculated free testosterone, was 70 pg/mL (242.7 pmol/L) (198).







Clinical Implications and Recommendations

Male hypogonadism is a clinical condition characterized by the presence of typical signs and symptoms in the setting of consistently low serum testosterone concentrations. The Endocrine Society guidelines currently suggest measuring free testosterone levels in men in whom total testosterone concentrations are near the lower limit of the normal range and in men with conditions that affect SHBG concentrations and render total testosterone a less reliable index of gonadal function (206). If the free hormone hypothesis is correct, free testosterone should serve as the benchmark for biochemical confirmation of hypogonadism. Accurate determination of free testosterone values is therefore central to an accurate diagnosis of hypogonadism.

The direct analog assays for free testosterone determination are inaccurate and should not be used. However, a confluence of factors related to the regulatory process, economic considerations, and difficulties in performing equilibrium dialysis methods in many hospital laboratories has led to their surprising endurance despite their known inaccuracy. Historically, laboratory-certifying bodies, such as the Clinical Laboratory Improvement Amendments, have certified laboratories and assays mostly on the basis of process measures; unlike the CDC and its Hormone Assay Standardization program for testosterone, these bodies have generally not required accuracy-based benchmarks. Similarly, the requirement in the assay approval process for demonstration of comparability to a previously approved assay enables new tracer analog assays to be approved because they can demonstrate comparability to previously approved analog methods.

Equilibrium dialysis is the reference method for free testosterone determination, but this assay is not always available to clinicians in all hospital laboratories; in addition, there are substantial interlaboratory variations because of the lack of standardization of assay conditions, making it difficult for practicing endocrinologists to interpret free testosterone levels. Mechanisms to harmonize the equilibrium dialysis procedure across laboratories are needed. Until equilibrium dialysis methods can be standardized across laboratories, a computational framework that accurately captures the dynamics of testosterone to SHBG and HSA interactions in calculating free testosterone values is an unmet need for precise clinical diagnosis. The EAM appears to be an accurate and testable model for calculating free testosterone levels, but this model needs further validation in large populations.
 

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readalot

Member
The EAM appears to be an accurate and testable model for calculating free testosterone levels, but this model needs further validation in large populations.

Excellent post @madman. Only issue I observe with all of this is the sentence quoted above. Majority of the data currently available would indicate either lots of labs are measuring free T incorrectly or the EAM is going to have to be adjusted significantly to fit the data. Perhaps the EAM will need more inputs (fitting inputs) to allow it to be thermodynamically self consistent while also fitting the lion share of actual dialysis free T data. Thank you.
 

madman

Super Moderator
Excellent post @madman. Only issue I observe with all of this is the sentence quoted above. Majority of the data currently available would indicate either lots of labs are measuring free T incorrectly or the EAM is going to have to be adjusted significantly to fit the data. Perhaps the EAM will need more inputs (fitting inputs) to allow it to be thermodynamically self consistent while also fitting the lion share of actual dialysis free T data. Thank you.

The EAM appears to be an accurate and testable model for calculating free testosterone levels, but this model needs further validation in large populations.

This will be a part of the ongoing phase II.
 

madman

Super Moderator
EAM (cFTZ) SHBG: T binding

The relation between Percent FT with Total Testosterone and SHBG. Intra-dimer complex allostery suggests that SHBG can regulate FT fraction over a wide range of total testosterone concentrations without getting saturated.
Indeed, it was found that percent FT calculated using the new model changed very modestly over a wide range of total testosterone concentrations. In contrast, Vermeulen's equation suggests a negative relation between percent FT and total testosterone. Furthermore, as SHBG concentrations increase, the percent FT calculated using our new model shows only a modest decline in contrast to the marked decline in percent FT calculated using Vermeulen's equation.




The new dynamic model leads to the reconsideration of several dogmas related to testosterone's binding to SHBG and has important physiologic and clinical implications. First, the fraction of circulating testosterone which is free is substantially greater (2.9±0.4%) than has been generally assumed (% cFTV 1.5±0.4%). Second, percent FT is not significantly related to total testosterone over a wide range of total testosterone concentrations. However, the percent FT declines as SHBG concentrations increase, although it does not decline as precipitously as predicted by Vermeulen's model. Due to the allostery between the two binding sites, SHBG is able to regulate FT levels in a much larger dynamic range.






*Intra-dimer complex allostery suggests that SHBG can regulate FT fraction over a wide range of total testosterone concentrations without getting saturated.

*Indeed, it was found that percent FT calculated using the new model changed very modestly over a wide range of total testosterone concentrations.

*Due to the allostery between the two binding sites, SHBG is able to regulate FT levels in a much larger dynamic range.
 

madman

Super Moderator


*The binding of T to SHBG is complex, which results in many different methods that directly measure or calculate free T. Some of these methods do not measure the free fraction of T and some formulae may provide less accurate results [40]

*Recent evidence suggests that the law of mass action formula which is based on the assumption that two T molecules bind to two binding sites on the SHBG with similar binding affinity may be incorrect. And further argues that the binding of T to SHBG may be a multistep, dynamic process with complex allosteric characteristics [65]. Based on this new model, investigators used a new formula to calculate free T in younger men in the Framingham Heart Study and showed that the newly calculated values were similar to those measured by equilibrium dialysis. They further verified that the calculated free T values had clinical diagnostic validity using data from the European Male Aging Study

*Currently, the CDC is developing a harmonized method for free T based on calculated free T using revised formulae. This may bring the measurement of free T to a referable standard in clinical laboratories and common reference intervals that all clinicians can use


*Perhaps the newer formula for calculated free T validated in multiple laboratories [65], will become generally available, correlate with free T by equilibrium dialysis and demonstrate improved correlation with clinical symptoms and therapeutic responsiveness. If all these prove to be true, then this formula to calculate free T may be a justified replacement for free T measurement by the equilibrium dialysis methodology




Phase II: Research and Commercialization of TruT Algorithm for Free Testosterone

Jasuja, Ravi

https://grantome.com/grant/NIH/R44-AG045011-02


The measurement of testosterone (T) levels is central to the diagnosis of androgen disorders, such as hypogonadism in men and polycystic ovary syndrome (PCOS) in women. Circulating T is bound with high affinity to sex hormone-binding globulin (SHBG) and with substantially lower affinity to albumin; only the free fraction is biologically active. Conditions that affect SHBG concentrations, such as aging and obesity, alter total but not free T concentrations; in these conditions, the determination of free T is necessary to obtain an accurate assessment of androgen status. The tracer analog method, the most widely used method for free T, has been shown to be inaccurate. The equilibrium dialysis method, considered the reference method, is technically difficult to implement and standardize, and is not available in most hospital laboratories, leading the Endocrine Society's Expert Panel to conclude that ?? the calculation of free testosterone is the most useful estimate of free testosterone in plasma?? Therefore, there is an unmet need for algorithms that provide accurate estimates of free T that match those derived from equilibrium dialysis. We have designed a novel and accurate TruTTM algorithm for the determination of free T, based on the characterization of testosterone's binding to SHBG using modern biophysical techniques. We have discovered that testosterone's binding to SHBG is a dynamic multistep process that includes allosteric interaction between the two binding sites on an SHBG dimer. Our computational framework incorporates the correct binding parameters derived experimentally in these studies, the non-linear dynamics in T: SHBG association, and allostery

In phase I studies, we demonstrated that the TruTTM algorithm provides accurate free T values that match those obtained using the equilibrium dialysis in healthy and hypogonadal men
. We have also shown that the binding parameters that have formed the basis of previous equations (e.g., Vermeulen) are incorrect, and that free T values derived using these equations deviate substantially from free T measured by equilibrium dialysis. The phase I studies have led to the adoption of the TruTTM algorithm at several institutions.

The phase II program will continue the development of the TruTTM algorithm by validating it in common conditions characterized by altered SHBG concentrations, such as obesity and aging (AIM 1), in healthy women across the menstrual cycle, and in women with PCOS (Aim 2).
We will generate population-based reference ranges for free T (Aim 3). Phase II also includes plans for the commercialization of the TruTTM algorithm using a HIPAA-compliant infrastructure for its clinical adoption

The phase II program will provide validation of the TruTTM algorithm in the two most common clinical indications for free T measurement? men suspected of hypogonadism and altered SHBG levels, and women with hyperandrogenic disorders. It will also enable the development of a HIPAA-compliant platform that can be embedded into the electronic medical record for wider clinical adoption and for improving clinical care
 
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