|Home | About | Journals | Submit | Contact Us | Français|
We characterized the isoform specific glucuronidation of six isoflavones genistein, daidzein, glycitein, formononetin, biochanin A and prunetin using 12 expressed human UGTs and human intestinal and liver microsomes. The results indicated that these isoflavones are metabolized most rapidly at three different concentrations by one of these four UGT isoforms: UGT1A1, UGT1A8, UGT1A9 and UGT1A10. Furthermore, glycitein was usually metabolized the fastest whereas prunetin the slowest. Using the rates of metabolism by 12 UGT isoforms as a means to establish the metabolic “fingerprint”, we found that each isoflavone had distinctive concentration-dependent patterns. Determination of kinetic parameters of glucuronidation using genistein and prunetin indicated that the distinct concentration-dependent metabolic patterns were the result of differences in Km and Vmax values. We then measured how well metabolic “fingerprinting” predicted metabolism of these isoflavones by human intestinal and liver microsomes. We found that the prediction was rather successful for five isoflavones in the liver microsomes, but not successful in the intestinal microsomes. We propose that a newly discovered UGT3A1 isoform capable of metabolizing phenols and estrogens might be responsible for the metabolism of isoflavones such as formononetin in humans. In conclusion, the first systematic study of metabolic “fingerprinting” of six common isoflavones showed that each isoflavone has UGT isoform-specific metabolic patterns that are concentration-dependent and predictive of metabolism of the isoflavones in liver microsomes.
Isoflavones such as, genistein, daidzein, glycitein, biochanin A, prunetin, and formononetin, belong to a class of phytochemicals called phytoestrogens. Natural sources of isoflavones include soy and soy foods, alfalfa sprouts, and red clover1,2. Soybeans have abundant amounts of genistein, daidzein, glycitein, and their glycosides3. On the other hand, red clover has large amounts of formononetin and biochanin A in addition to genistein, and daidzein4. Prunetin is mainly found in Kudzu roots, a Chinese herb found to be active against alcohol abuse5. Isoflavones have been shown to possess significant biological activities ranging from anticancer to cardiovascular protective effects3,6,7. Their in vitro and in vivo mechanisms of actions range from antioxidants to antiproliferation6.
Despite of these claimed health benefits and demonstrated preclinical activities, there are significant challenges associated with development of isoflavones into chemo-preventive and chemo-therapeutic agents. The major challenge currently is their low bioavailabilities (<5%), as the result of extensive first-pass metabolism by phase II enzymes including UGTs and SULTs8,9. A large body of work has been done in multiple research laboratories including our own to understand the metabolic pathways of isoflavones and how they relate to poor bioavailabilities.
Our previous studies have shown that glucuronidation of isoflavones in intestine and subsequent excretion of respective glucuronides from intestine was affected by the structure of isoflavones and possibly the structure of isoflavone glucuronides, respectively2,10. Additional studies of isoflavones in Caco-2 cells showed that glucuronidation of isoflavones and excretion of their glucuronides were strongly influenced by their structure11. Our recent study indicated that prunetin might be metabolized into multiple glucuronides and that formation of a particular metabolite (e.g., prunetin-4′-O-glucuronide) was sometimes determined only by the presence of a specific UGT isoform (i.e., UGT1A10)12. Therefore, systematic metabolic profiling studies utilizing expressed human UGT isoforms is of great importance in determining major isoform(s) responsible for isoflavone metabolism, determining major metabolite(s) formed by particular isoform, and predicting the major organ (site) of metabolism. The information so derived may also be used to gauge the safety and efficacy concerns which may arise from potential drug-drug interactions and genetic polymorphism. Metabolic profiling (or reaction phenotyping) may also be used to determine the likelihood of success of approaches developed to improve human isoflavone bioavailability.
The present study represented a detailed and systematic study of UGT isoform-specific metabolism of six isoflavones commonly used in studies of isoflavones in the literature: genistein, daidzein, glycitein, biochanin A, formononetin, and prunetin. As a first study of this kind, the overall goal of this research was to demonstrate the potential utilities of UGT metabolic characterization in predicting safety and efficacy of a chemical (in this case an isoflavone). Toward this goal, the first two objectives of this study were to determine if these structurally diverse isoflavones were mainly metabolized by the same set of UGT isoform(s) and to determine how changes in isoflavone structures and differences in substrate concentrations affect the UGT isoform-specific metabolism of isoflavones. Another objective was to determine if isoform specific metabolic pattern, determined from metabolic profiling, might be used to predict glucuronidation rates of isoflavones in microsomes derived from human intestine and liver, two major organs responsible for first-pass metabolism as well as recycling via enteric and enterohepatic schemes13,14.
Daidzein and glycitein were purchased from LC Laboratories (Woburn, MA). Biochanin A, formononetin, prunetin, and genistein were purchased from Indofine Chemicals (Somerville, NJ). Expressed human UGT isoforms (Supersomes), pooled female human liver and intestinal microsomes were purchased from BD Biosciences (Woburn, MA). Uridine diphosphoglucuronic acid (UDPGA), alamethicin, D-saccharic-1,4-lactone monohydrate, magnesium chloride, and Hanks' balanced salt solution (powder form) were purchased from Sigma-Aldrich (St Louis, MO). All other materials (typically analytical grade or better) were used as received.
The incubation procedures for measuring UGTs' activities were essentially the same as published before12,15. Briefly, incubation procedures using microsomes or supersomes were as follows: (1) microsomes/supersomes (final concentration ≈ in range of 0.0053~0.053 mg of protein per mL as optimum for the reaction), magnesium chloride (0.88 mM), saccharolactone (4.4 mM), alamethicin (0.022 mg/mL), different concentrations of substrates in a 50 mM potassium phosphate buffer (pH 7.4), and UDPGA (3.5 mM, add last) were mixed; 16 the mixture (final volume ) 200 μL) was incubated at 37°C for a predetermined period of time (60 min); and (3) the reaction was stopped by the addition of 50μL of 94% acetonitrile/6% glacial acetic acid containing 100 μM testosterone as the internal standard. For UGT profiling, three concentrations, 2.5, 10 and 35 μM (40μM for prunetin) were used. For kinetic profiling of genistein and prunetin with UGT 1A1, 1A8, 1A9 and 1A10, seven substrate concentrations in the range of 1-50μM were used.
We analyzed six isoflavones and their respective glucuronides by using the following common method: system, Waters Acquity UPLC with photodiode array detector and Empower software; column, BEH C18, 1.7 μm, 2.1 × 50 mm; mobile phase B, 100% acetonitrile, mobile phase A, 100% aqueous buffer (2.5mM NH4Ac, pH 3.0); flow rate 0.5 mL/min; gradient, 0 to 0.3min, 10% B, 0.3 to 2.9 min, 0–50%B, 2.90 to 3.2 min, 50–90%B, 3.2 to 4.0 min, 90% B, wavelength, 254 nm for isoflavones and their respective glucuronides and testosterone; and injection volume, 10μL. Linearity was established in the range of 0.78-100 μM (total 8 concentrations) for all compounds. The LLOQ for all compounds was 0.78μM. Analytical methods for each compound were validated for inter-day and intra-day variation using 6 samples at three concentrations (50, 12.5 and 1.56 μM). Precision and accuracy for all compounds were in acceptable range of 85% to 115%.
For quantification of metabolites such as genistein glucuronides, a previously published method was used17. This method is essentially a method that compared the peak area change in aglycone after glucuronide is hydrolyzed by b-glucuronidases that result in peak area change in glucuronides. In this method, the change in concentrations as the result of hydrolysis can be expressed as:
Where ΔPGG is the change in peak areas of isoflavone glucuronide (or IG), ΔPG is the change in the peak area of its corresponding isoflavone aglycone (or I) obtained from the extracted samples before and after hydrolysis, and aI and aIG are the slopes of the corresponding calibration curve that goes through the origin.
The term aIG was expressed in the term of aI, since commercial standards of isoflavone glucuronides were not available. The latter is accomplished by
where K is the conversion factor of molar extinction coefficients of glucuronides to their corresponding aglycones. We performed the same experiments at three different concentrations to calculate an average K value. This conversion factor K was used to calculate the concentration of glucuronides use the standard curve of aglycones. The following is a list of the conversion factor used: biochanin A, 1.26; daidzein, 1.02; formononetin, 1.2; genistein, 1.06; glycitein, 1.25; and prunetin, 0.98. Plugging various K values into the following equation using peak areas of metabolite, the metabolite concentration (CIG) can be calculated by:
where PIG is the metabolite peak area in the chromatogram.
Six isoflavones and their respective glucuronides were separated by the same UPLC system but using slightly different chromatographic conditions because of mass spectrometer requirements. Here, mobile phase A was ammonium acetate buffer (pH 7.5) and mobile phase B was 100% acetonitrile with the gradient as follows: 0 to 2.0 min, 10–35% B, 2.0 to 3.0 min, 35–70% B, 3.2 to 3.5 min, 70–10% B, 3.5 to 3.7 min, 10% B. The flow rate was 0.5 mL/min. The effluent was introduced into an API 3200 Qtrap triple-quadrupole mass spectrometer (Applied Biosystem/MDS SCIEX, Foster City, CA) equipped with a TurboIonSpray™ source. The mass spectrometer was operated in negative ion mode to perform the analysis of six isoflavones and their glucuronides. The main working parameters for the mass spectrometers were set as follows: ion-spray voltage, -4.5 kV; ion source temperature, 400°C; nebulizer gas (gas1), nitrogen, 40 psi; turbo gas (gas2), nitrogen, 40 psi; curtain gas, nitrogen, 20 psi and minor adjustments were then made for each isoflavones. Isoflavone mono-glucuronides were identified by MS and MS2 full scan modes (Fig.2)
Rates of metabolism in expressed human UGT isoforms and human liver and intestine microsomes were expressed as amounts of metabolites formed per min per mg protein or nmol/min/mg. Kinetic parameters were then obtained based on the profile of Eadie-Hofstee plots2. If Eadie-Hofstee plot was linear, formation rates (V) of isoflavone glucuronides at various respective substrate concentrations (C) were fit to the standard Michaelis-Menten equation:
where Km is the Michaelis-Menten constant and Vmax is the maximum rate of formation of glucuronides.
When Eadie-Hofstee plots showed characteristic profiles of atypical kinetics (autoactivation and biphasic kinetics)18,19, the data from these atypical profiles were fit to equations 16 or (3), using the ADAPT II program20. To determine the best-fit model, the model candidates were discriminated using the minimum Akaike's information criterion (AIC)21 value, and the rule of parsimony was applied. Therefore, using this minimum AIC estimation (MAICE), a negative AIC value (i.e. -54.2) would be considered a better representation of the data versus a set of data having a positive AIC value (i.e. 0.83)21.
When the kinetics of enzyme reactions showed autoactivation, formation rates (V) of isoflavone glucuronides at various substrate concentrations (C) were fit to the following equation:
|Vmax-0||- intrinsic enzyme activity|
|Vmax-d||- maximum induction of enzyme activity|
|R||- rate of enzyme activity induction|
|C||- concentration of substrate|
|Km||- concentration of substrate to achieve 50% of (Vmax-0 + Vmax-d)|
When the enzymatic reactions showed substrate inhibition kinetics (in which the substrate compound inhibits the glucuronidation velocity especially at higher concentrations), formation rates (V) of isoflavone glucuronides at various substrate concentrations (C) were fit to the following equation:
|Vmax1||- maximum enzyme activity of UGT isoform|
|C||- concentration of substrate|
|Km1||- concentration of substrate to achieve 50% of (Vmax) for one UGT isoform|
|Ksi||- substrate inhibition constant|
One-way ANOVA with or without Tukey-Kramer multiple comparison (posthoc) tests were used to evaluate statistical differences. Differences were considered significant when p values were less than 0.05.
We conducted simple LC-MS/MS studies of the metabolites to show that all glucuronides formed using the expressed human UGTs were mono-glucuronides (Fig.2). We did not find any di-glucuronide of isoflavones in this study. However, we occasionally identified a second minor mono-glucuronide when high concentration of genistein and glycitein were used but the peak area of the minor metabolite was usually less than 5% of the respective major mono-glucuronide peak. We did not quantify the second minor monoglucuronide in this paper.
To determine the main UGT isoform(s) responsible for metabolizing the selected isoflavones, we incubated the isoflavones with various UGT isoforms for up to 1 hour, based on the thermostability studies of specific UGT isoforms previously done in our lab12. We then examined the isoform specific metabolic patterns of all isoflavones, and found that there was no single UGT isoform that could metabolize all six isoflavones at the tested concentration at the most rapid rate (Fig.3--5).5). In fact, the top isoform kept changing according to the compounds and concentrations, and the only commonality we could identify was that the top four isoforms were always the same: UGT1A1, 1A8, 1A9 and 1A10. Additionally, 1A7 was found to be an important isoform for glycitein and prunetin. In contrast, 1A4 and 2B4 did not make a detectable contribution to the formation of any glucuronides, whereas 1A3, 1A6, 1A7, 2B7, 2B15 and 2B17 showed small but variable extent of glucuronidation of the selected isoflavones (Fig.3--5).5). Moreover, “fingerprints” of M1 and M2 for prunetin was significantly different especially since UGT1A9 was not able to make M2 (Fig.5). Taken together, at low concentration of 2.5 μM, the two most important isoform were UGT1A1 and 1A9, whereas at high concentration of 35 mM, the two most important isoform were UGT1A8 and UGT1A10 for all isoflavones except formononetin and daidzein (Fig 2).
We used 12 commercially available recombinant UGT isoforms to determine how each isoflavone was metabolized by different UGT isoforms. Since, no two isoflavones shared the same metabolic pattern (shown in Fig.3--5),5), we assigned the pattern of metabolism of an isoflavone by the 12 UGTs as a metabolic “fingerprint” for that isoflavone. We believed that it was valuable to assign a metabolic pattern to each compound because it might be useful to predict how a compound might metabolize in a particular organ and what the possible underlying causes of drug-drug interactions might be, if any (more discussion later).
Concentration changes had significant impact on the isoform specific metabolic patterns of genistein, glycitein, biochanin A and prunetin (formation rates of prunetin-5-O-glucuronide or M1), because there was a significant change in the rank order of the most important isoforms as a function of concentration (Fig.3--5).5). For example, UGT1A9 (the largest contributor) and 1A1 were the top two isoforms responsible for genistein metabolism at 2.5 μM, but UGT1A8 became the largest contributor at 10 μM concentration, followed by UGT 1A9 and UGT1A1. Concentration changes had smaller but still significant impact on the isoform specific metabolic patterns of formononetin and daidzein because there were less significant changes in the rank order as concentration changed. In the case of formononetin, UGT1A1 was always the most important but other isoforms (UGT 1A8, 1A9 and 1A10) also became relevant at higher concentrations.
Analysis of the results of glucuronidation of isoflavones by UGT1A1, 1A8, 1A9 and 1A10 showed that there were three different kinds of concentration-dependent glucuronidation profiles (Fig.6). The first profile showed that the substrate concentration had an inverse relation with the glucuronidation rate, such that the highest glucuronidation rate was achieved at the lowest substrate concentration while the lowest glucuronidation rate was achieved at the highest substrate concentration. For example, for UGT1A1, the rank order for the rates of glucuronidation (measured in nmol per min per mg of protein) for genistein was 2.5μM (0.57±0.02)>12.5μM (0.35±0.01)>35μM (0.27±0.03) and that for biochanin A was 2.5μM (0.93 ±0.07)>12.5μM (0.37±0.01)>35μM (0.27±0.03) (Fig.6A). Based on known kinetic profile, this probably represented substrate inhibition kinetics (see later).
The second kind of profile showed an increase in the rates of glucuronidation with an increase in substrate concentrations. For example, genistein was glucuronidated by UGT1A8 at the rates of 0.25±0.08, 1.7±0.20 and 1.98±0.09 (nmol per min per mg of protein) at 2.5, 12.5 and 35 μM genistein concentrations, respectively (Fig.6B). Based on known kinetic profile, this probably represented simple Michaelis-Menten kinetics (see later).
The third kind of profile showed that the compound was glucuronidated the fastest at the middle substrate concentration, i.e.12.5 μM. For example, biochanin A was glucuronidated the fastest at 12.5 μM followed by 2.5 and 35μM, both by UGT1A9 and UGT1A10 (p<0.05, one way ANOVA) (Fig.6C and D).
Expressed UGT 1A1, 1A8, 1A9 and 1A10 were shown to be the four most important isoforms for isoflavones glucuronidation at all three selected concentrations. Here we determined how changes in chemical structures affected the glucuronidation rates (Fig.7).
For UGT1A1, at 2.5μM substrate concentration, glucuronidation of glycitein was the fastest (5.15 ± 0.35 nmol per min per mg of protein), followed by biochanin A (0.93 ±0.07), formononetin (0.61 ±0.02), genistein (0.57±0.02), daidzein (0.50 ±0.07), and prunetin (0.02±0.002). At 12.5 and 40 μM substrate concentrations, the trend was approximately the same, but there was a bigger difference between the slowest and the fastest glucuronidation rates (Fig. 7A).
For UGT1A8, at 2.5μM substrate concentration, glycitein was glucuronidated at the fastest rate (0.68±0.03 nmol per min per mg of protein), followed by genistein (0.25±0.08) and prunetin (0.08±0.003), while biochanin A, formononetin, and daidzein showed no detectable metabolism. At 12.5 and 40 μM substrate concentrations, the isoflavone-specific metabolic profile did not change very much but differences between the fastest and the slowest glucuronidation rates increased compared to that at the 2.5 μM substrate concentration (Fig. 7B).
For UGT1A9, at 2.5μM substrate concentration, the rank order of the UGT1A9-mediated glucuronidation rates (nmol per min per mg of protein) was glycitein (1.15±0.04) > daidzein (0.73±0.12) > genistein (0.73±0.04) > prunetin (0.21±0.01) > biochanin A (0.15±0.02) > formononetin (0.0±0.00). At 12.5μM substrate concentration, the rank order changed to daidzein (3.03±0.16) > glycitein (1.45±0.28) > genistein (1.16±0.03) > biochanin A (0.59±0.02) > prunetin (0.41±0.01) > formononetin (0.08±0.00). At 35μM (40μM for prunetin) substrate concentration, metabolism rates of glycitein (3.85±0.43) became the fastest again but the rank order of metabolism of other isoflavones did not change from those at the 12.5 μM substrate concentration (Fig. 7C).
For UGT1A10, at 2.5μM substrate concentration, glycitein was again glucuronidated at the fastest rate (0.52±0.07 nmol per min per mg of protein, p<0.05), followed by genistein and biochanin A, whereas formononetin, prunetin and daidzein showed no detectable metabolism. At 12.5 and 35μM substrate concentration, glycitein was again glucuronidated at the fastest rate, and the rest of the isoflavone-specific profile showed minimal differences (Fig. 7D).
We hypothesized that the reason why each isoflavone showed different concentration-dependent metabolic “finger-print” was because their kinetic parameters (i.e., Km and Vmax values) were isoform-dependent. To confirm this hypothesis, the kinetics of genistein and prunetin glucuronidation was determined using concentration versus rates of metabolism and Eadie-Hofstee plots (Fig 8 and and99).
For genistein, UGT 1A9- and 1A10-mediated glucuronidation followed classic Michaelis-Menten kinetics, whereas UGT1A8-mediated glucuronidation followed autoactivation kinetics (Fig. 8A, Fig. 9A-C). UGT1A1-mediated glucuronidation of genistein was decreased as concentration increased, and its Eadie-Hofstee plot did not match with any known kinetic profile (Fig. 8A, ,9D).9D). For prunetin, UGT 1A7-, 1A9- and 1A10-mediated glucuronidation also followed classic Michaelis-Menten kinetics, whereas UGT1A8-mediated glucuronidation followed autoactivation kinetics (Fig. 8B, Fig. 9E-H).
The kinetic parameters were determined for both Michaelis-Menten and autoactivation kinetic profiles using Adapt II kinetic modeling software. For genistein, Vmax and Clint values (=Vmax/Km) were the highest for UGT1A8, followed by 1A9, followed by 1A10, while Km values had the exact opposite rank order (Table 1). For prunetin, Clint values showed following rank order, UGT 1A8>1A9>1A1> 1A10, however Vmax and Km values did not follow the same order in any direction (Table 2).
The rates of glucuronidation of the selected isoflavones by human liver microsomes were determined at three concentrations of 2.5μM, 12.5μM and 35μM (Fig.10A). Previously published research in our lab had shown that prunetin was glucuronidated mainly to metabolite 1 (M1 or prunetin-5-O-glucuronic acid) in liver, and to metabolite 2 (M2 or prunetin-4′-O-glucuronic acid) in intestine12. The results of this study showed that the rate of glucuronidation of glycitein was always the highest of all isoflavones at all of the three substrate concentrations, while prunetin (M1) was glucuronidated the lowest by human liver microsomes.
At 2.5μM substrate concentration, the rank order of the glucuronidation rates (nmol per min per mg of protein) was glycitein (2.66±0.06) > formononetin (1.71±0.02) > biochanin A (1.20±0.02) > genistein (0.87±0.03) > daidzein (0.75±0.01) > prunetin (0.17±0.01). At 12.5μM and 35μM (40μM for prunetin) substrate concentrations, the rank order of the glucuronidation rates (nmol per min per mg of protein) was essentially maintained although daidzein's rank order was elevated (or more aglycone was metabolized) when compared those at 2.5 μM substrate concentration.
The results clearly indicated that at different concentrations, there were significant differences in the glucuronidation of the six isoflavones by human liver microsomes (p<0.05, one way ANOVA) (Fig. 10A). In addition, the effects of concentration also showed different trends as we observed in UGT isoforms; namely, glucuronidation rates increased with concentration except in biochanin A, whereas glucuronidation rates decreased with increase in concentration.
When compared to glucuronidation by human liver microsomes, human intestinal microsomes usually metabolized the isoflavones much faster with typical rates at least 2 times faster (Fig.10B). The maximum difference was found in prunetin metabolism, because liver microsomes nearly did not form prunetin-4′-glucuronide but intestinal microsomes made a lot of this metabolite. The apparent rate of prunetin glucuronidation in intestinal microsomes was about 20 folds higher than liver (Fig.10B).
When metabolism rates of six isoflavones in intestine were compared, we found that at 2.5 μM, formononetin and biochanin A were metabolized the fastest (≈7 nmol per min per mg of protein), followed by glycitein and genistein, prunetin, and daidzein. At 12.5 μM substrate concentration, the rank order changed quite significantly, and glycitein and formononetin showed the highest metabolism rates (≈9 nmol per min per mg of protein). At 35 μM (40μM for prunetin) substrate concentration, the rank order was essentially the same as observed at 12.5μM, except that formononetin was metabolized faster than glycitein (Fig.10B).
The results also clearly indicated differences in the effects of substrate concentrations on the glucuronidation of the six isoflavones by human intestinal microsomes (p<0.05, one way ANOVA) (Fig. 10B). In addition, the effects of concentration also showed different trends as compared to trends observed with different UGT isoforms; namely, glucuronidation rates of two isoflavones (daidzein and formononetin) increased with concentration, two isoflavones (biochanin A and prunetin) did not change with concentration, and the remaining two (genistein and glycitein) first went up and then came down as the concentration increased (Fig.10B).
Our metabolic profiling studies have clearly shown that each isoflavone had a distinctive UGT isoform-specific metabolic pattern that could be used to define or characterize a metabolic “fingerprint.”Because this “fingerprint” was always dependent on isoflavone structure and frequently on isoflavone concentrations (Fig.3--5),5), it allowed us to gain a better understanding of isoflavone metabolic processes. It also had a variety of general utilities as described in great details below. Our studies represent major advances in this area, since previous studies of this type have used fewer compounds12,15, less UGT isoforms or both22. Most studies also used just one or two isoflavone concentrations22.
The first utility was to predict the potentials of drug-drug interactions and the impacts of genetic polymorphisms, both of which influence safety and/or efficacy of drugs23. Metabolic profiling of phase I metabolic enzymes especially CYP isoforms had been done for an extended period of time, and proven to be very useful for the purpose of safe and effective use of drugs24,25. In fact, phase I metabolic profiling is required by FDA as a part of new drug application dossier23. A major task here was to define the main UGT isoforms capable of metabolizing isoflavones based on the “fingerprint.” The results showed that four UGT1A isoforms mainly shared the responsibilities of metabolizing the selected isoflavones. In other words, isoflavones were just a class of shared substrates for 4 distinctive UGT isoforms. This was quite unusual if we wanted to compare them to the CYP-catalyzed metabolism, where only one or two isoforms were likely to be responsible for the metabolism of a single substrate23. Therefore, isoflavones, which were subjected to the rapid metabolism by four UGT isoforms with significant overlapping specificities, should have been quite resistant to metabolic interactions. Indeed, we have observed in rodents that bioavailability of intact isoflavones were very difficult to change using glucuronidation inhibitors such as probenecid (data not shown). Similarly, low potentials for changes in isoflavone bioavailabilities were expected as results of genetic polymorphism. Hence, if isoflavones were to be developed into drugs, the potential for isoflavone-drug interaction and impact of genetic polymorphism would be small, a desirable trait for the safe use of drugs.
The second utility of metabolic “fingerprint” was to predict the main organ responsible for the metabolism of isoflavones.This was again done quite frequently for CYP. For example,if a substrate (e.g., debrisoquine26) was mainly metabolized by expressed human CYP2D6, which is an isoform mainly expressed in human liver26, we could predict that the compound was mainly metabolized in liver. In the present study, the four major isoforms responsible were UGT1A1, 1A8, 1A9 and 1A10.Qualitative and quantitative expression of these UGT isoforms in two main metabolic organ, liver and intestine were quite different27. UGT1A1 was expressed in all the organs although its expression in the liver appeared to be more pronounced than that in the intestine26. UGT1A9 was only expressed in liver whereas UGT1A8 and UGT1A10 were mostly expressed in the small intestine. Therefore, based on the UGT metabolic fingerprinting of the isoflavones, we predicted that at high isoflavone concentrations (>10 μM), the main organ of metabolism for genistein, glycitein, biochanin A and prunetin to be the intestine (microsomes) and the main organ of metabolism for formononetin and daidzein (more so for daidzein) to be the liver (microsomes).The prediction for the first four isoflavones turned out to be right but for the second two isoflavones, it appeared to be incorrect.Prediction for formononetin also fell short in the extent of metabolism aspect (discussed later). We believed this was due to the absence of a metabolic “fingerprint” or a certain major UGT isoform(s), which was especially capable of rapidly metabolizing isoflavones lacking a 5-OH or 6-OCH3 group (i.e., daidzein, glycitein and formononetin). We speculated that the recently characterized UGT3A1 might be involved since this isoform metabolizes phenols such as nitrophenol and estradiol (estrogens)28. Additional studies are necessary to identify which UGT isoforms are capable of rapidly metabolizing flavonoids without 5-OH or 6-OCH3 group.
The third utility of the metabolic “fingerprint” was to predict the extent of metabolism and metabolic rank-order in target tissues (e.g., intestine and liver). Based on Fig.11A, this would have translated into rapid metabolism of glycitein, daidzein, and genistein, as well as slow metabolism of biochanin A, formononetin and prunetin in tissues. In liver microsomes, the prediction was essentially accurate for glycitein, genistein and prunetin and reasonable for biochanin A and daidzein. A correlation between the rates of glucuronidation of six isoflavones by human liver microsomes and the combined rates of glucuronidation by UGT1A1 and UGT1A9 together was established (Fig.11A). UGT1A1 and UGT1A9 were chosen together for establishing correlation, as they were one of the most abundantly expressed isoforms in human liver, which contribute significantly to isoflavones metabolism. The results showed a moderate correlation coefficient of 0.54 when all the six isoflavones data were plotted, whereas the correlation coefficient was significantly improved to 0.8, when data was plotted after excluding formononetin rates of glucuronidation (Fig 11A). The fact that we missed on predicting metabolism of formononetin suggested that one (or more) major isoflavone metabolizing UGT isoform(s) was missing from the present “fingerprint” studies, as stated previously. When the same approach was used to correlate intestinal microsomal metabolism of isoflavones with expressed UGT data, the outcome was unexpected and showed no apparent correlation (Fig.11B). It was uncertain as to why there was no apparent correlation but we speculated that one of the reasons was that one or more major intestinal UGT isoforms capable of metabolizing isoflavones was missing.
Another use of the metabolic “fingerprint” was to determine structure-activity (metabolism) relationship (SAR/SMR). Since all isoflavones shared the same structural backbone, the SAR/SMR studies mainly examined how changes in the position of methoxy group and hydroxyl group (the site of glucuronidation) affected its glucuronidation by the most important expressed human UGT isoforms: UGT1A1, 1A8, 1A9 and 1A10. Our analysis indicated that prunetin was really different from others because it was the only compound among the six that did not have a 7-OH group. This structural characteristic might have drastically decreased its glucuronidation by UGT1A1 (Fig 6A), but more compounds possessing this structural characteristic may be needed to make an affirmative statement. This lack of metabolism of prunetin was not surprising since it was also shown to be poor substrate for glucuronidation in Caco-2 cells11, which expressed multiple UGT1As with UGT1A1 as one of its major UGT isoforms15. The “fingerprint” studies of additional UGT isoforms also indicated that UGT2B7 only glucuronidated isoflavones with 5-hydroxyl group. Therefore, three isoflavones that had 5-hydroxyl group (genistein, biochanin A and prunetin) were its substrates, and isoflavones without the 5-hydroxyl group (daidzein and formononetin) were not.
Having stated the need for and utilities of “fingerprinting,” a relevant question was then what constitutes a reasonable “fingerprint.” When studying effects of substrate concentration on metabolic profiling, we found that changes in substrate concentrations could change “fingerprint” and therefore it became necessary to conduct metabolic profiling at multiple concentrations. We have chosen three different concentrations and found that this appeared to be satisfactory. These three concentrations should cover good range with concentration higher and lower than expected Km value. The third concentration should be in between. In this case, we used a low concentration of 2.5 μM since a concentration lower than 2.5 μM will hinder quantitation of metabolites via UV. We did not propose to use a high concentration greater than 35 μM because of solubility limits. The medium concentration we used here is 12.5 μM, since this was close to the average of the low and the high. Based on the success we had, we believed that “fingerprint” studies at three concentrations were essential if we wanted to develop a useful “fingerprinting” profile. Another necessary piece of “fingerprinting” was more obvious, which was to have all the fingers. The fact that we missed badly on formononetin metabolism prediction suggested that we did not have all the “fingers” necessary for its finger printing. We will conduct additional studies to define what we have missed.
In conclusion, we have shown for the first time that the glucuronidation of six common isoflavones were all UGT isoform-, substrate structure- and concentration-dependent, which can be effectively captured by metabolic “fingerprint” using expressed human UGT isoforms. We demonstrated for the first time the multifaceted utilities of UGT metabolic “fingerprint” in defining drug interaction potentials, genetic polymorphism consequence, major organs for metabolism, and SMR. We believe that the approach developed here may be of general utilities in defining the metabolic “fingerprint” of other UGT substrates, which in turn could improve the safety as well as efficacy of drugs that are inactivated (occasionally activated) or eliminated by the glucuronidation pathway.
Footnote. Work is supported by NIH GM-070737 to MH. LT is supported in part by a SMU's training grant. ZL is supported by a 115 Key Project of the Ministry of Science and Technology of PR China (No.2006BAI11B08-4).