Abstract
A typical prescription of traditional Chinese medicine (TCM) contains up to a few hundred prototype components. Studying their absorption, metabolism, distribution, and elimination (ADME) presents great challenges. The objective of this study was to develop a practical approach for investigating ADME of individual prototypes in TCM. An active fraction of Xiao-Xu-Ming decoction (AF-XXMD) as a model TCM prescription was orally administered to rats. AF-XXMD–related components in plasma, urine, bile, and feces were detected using high-resolution mass spectrometry and background subtraction, an untargeted data-mining tool. Components were then structurally characterized on the basis of MSn spectral data. Connection of detected AF-XXMD metabolites to their precursor species, either prototypes or upstream metabolites, were determined on the basis of mass spectral similarity and the matching of biotransformation reactions. As a result, 247 AF-XXMD–related components were detected and structurally characterized in rats, 134 of which were metabolites. Among 198 AF-XXMD prototypes dosed, 65 were fully or partially absorbed and 13 prototypes and 34 metabolites were found in the circulation. Glucuronidation, isomerization, and deglycosylation followed by biliary and urinary excretions and direct elimination of prototypes via kidney and liver were the major clearance pathways of AF-XXMD prototypes. As an example, the ADME profile of H56, the single major AF-XXMD component in rat plasma, was elucidated on the basis of profiles of H56-related components in plasma and excreta. The results demonstrate that the new analytical approach is a useful tool for rapid and comprehensive detection and characterization of TCM components in biologic matrix in a TCM ADME study.
Introduction
The bioactive substances of traditional Chinese medicine (TCM) and understanding their action mechanisms are of great interest to drug discovery scientists and clinicians. The effectiveness of TCM is generally recognized to be associated with chemical constituents of TCM in the circulation (Wang et al., 2011; Zhang et al., 2011, 2013). Concentrations and duration of individual TCM components in the circulation depend on their absorption, distribution, metabolism, and excretion (ADME), processes that are often mediated by metabolizing enzymes and transporters. Inhibition or induction of the involved enzymes or transporters by a coadministered TCM component or a pharmaceutical drug could significantly change exposure levels of bioactive components, leading to drug-drug interactions among herbal components and between an herbal component and a pharmaceutical drug (Posadzki et al., 2013; Cheng et al., 2014; Ma et al., 2014; Jia et al., 2015). In addition, the study of ADME of herbal medicines is very important for the elucidation of mechanisms of TCM-induced toxicity. Herbal medicine–mediated organ toxicity is often related to high exposure and accumulation of certain toxic components in the organs (Qiu et al., 2000; Zhu, 2002; Hu et al., 2004; Yue et al., 2009; Yuan et al., 2011; Ding and Chen, 2012; Xiong et al., 2014).
ADME studies of a drug in human and animal are often carried out using a radiolabeled drug. However, it is not practical to use multiple radiolabeled TCM prototype compounds in a TCM ADME study. Therefore, the success of ADME study of a TCM prescription relies on liquid chromatography (LC)/mass spectrometry (MS) technology (Song et al., 2014). In the past ten years, many TCM research groups have made significant efforts in the development and application of a variety of LC/MS approaches for detection and characterization of TCM components in the circulation and excreta (Yang et al., 2012; Wu et al., 2012; Yan et al., 2013; Geng et al., 2014; Zuo et al., 2015). The first analytical challenge faced in studying the ADME of herbal medicines using high-resolution mass spectrometry (HRMS) is to sensitively and comprehensively detect TCM components in plasma, urine, feces, and/or bile samples. The task is often accomplished by processing accurate MS and MS/MS datasets using targeted data-mining tools (Wu et al., 2012; Yang et al., 2012), including mass defect filter (MDF), extracted ion chromatography (EIC), product ion filter (PIF), neutral loss filter (NLF), and isotope pattern filter. These HRMS-based data-mining technologies were developed originally for the detection and identification of drug metabolites in complex biologic systems (Bateman et al., 2007; Ma and Zhu 2009; Zhu et al., 2011; Ma and Chowdhury, 2012; Geng et al., 2014; Du et al., 2015). In addition, mass spectral trees similarity filter (MTSF) technology (Jin et al., 2013) is employed for the detection and identification of TCM metabolites on the basis of the similarity of their product ion spectra to those of their precursor species. These targeted data-mining approaches are capable of searching for metabolites of individual TCM prototypes on the basis of the metabolite’s predicted mass defect (MDF), fragmentation pattern (NLF and PIF), or molecular weight (EIC). However, since an herbal medicine can contain up to a few hundred parent components, searching for their metabolite components on the basis of their masses, mass defects, or product ion spectra predicted from individual TCM prototypes is truly time-consuming and labor-intensive. More importantly, many major TCM metabolite components in plasma or excreta are formed via multiple steps of biotransformation so that their detection by targeted data-mining tools may fail. The second analytical challenge faced in a TCM ADME study is determination of metabolic pathways of individual TCM parent components, especially when dealing with a few hundred in vivo TCM components.
The main objective of the current study was to develop a practical and integrated approach for in vivo ADME study of TCM medicine. To evaluate the utility and effectiveness of this approach, metabolism and disposition of an active fraction of Xiao-Xu-Ming decoction (AF-XXMD) as a model TCM prescription were determined in rats. The prescription of Xiao-Xu-Ming decoction is used for the treatment of theoplegia and the sequelae of theoplegia. The formula of XXMD consists of 12 crude herbal medicines (Supplemental Table 1). AF-XXMD was a kind gift from Professor Hailin Qin and shows similar pharmacological effects as XXMD. About 68 prototype components were previously identified in AF-XXMD (Wang et al., 2014). However, to date there has been no report on the detection and characterization of XXMD or AF-XXMD metabolites in animals and humans.
Materials and Methods
Materials.
Methanol and acetonitrile of MS grade and formic acid of analytical grade were purchased from Mallinckrodt Baker Co. (Phillipsburg, NJ). Purified water used in the study was provided by Wahaha Co., Ltd. (Hangzhou, China). Wistar rats (200 ± 20 g) were purchased from Beijing Vital River Experimental Animal Co. Ltd. (Beijing, China).
Animal Experiment.
Rats were kept in an environmentally controlled breeding room for 3 days before the experiment and then fasted (water only) for 12 hours in metabolic cages prior to the dosing of AF-XXMD (0.5 g/kg). The rats [24 intact rats and three bile duct–cannulated rats (BDC)] were divided to nine groups (three rats per group). Bile samples were collected from the group of BDC rats 2 hours prior to dosing (control bile samples) and 0–4, 4–12, and 12–24 hours postdosing (test bile samples). Another group of rats were kept in metabolic cages, and control samples of urine and feces were collected 0–4 hours prior to the administration. Urine and feces samples for testing were collected 0−12 hours and 12−24 hours postadministration. Blood samples were collected at 0 (control samples), 0.5, 1.25, 3, 8, 12, and 18 hours from the abdominal artery of the remaining seven groups of the rats (one time point per group of rats). Plasma samples prepared from collected blood samples were pooled at equal volumes in individual time points across rats and then placed in 15-ml plastic centrifuge tubes. The centrifuge tubes were then vigorously vortexed for 30 seconds prior to storage at −80°C until use. The urine, bile, and feces samples were pooled in equal volumes or weights for each time period across rats, then they were kept in a refrigerator at –80°C. The experiment was approved by the Animal Care and Welfare Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College (Beijing, China).
Sample Preparation.
Pooled bile (1.0 ml), urine (2.0 ml), or plasma samples (2.0 ml) were mixed with 5 volumes of methanol in test tubes and then vigorously vortexed for 30 seconds. After centrifugation at 1721g for 10 minutes, supernatants were evaporated to dryness under a stream of nitrogen at 40°C. The residues were dissolved in 0.1 ml methanol. Each dissolved sample was then centrifuged at 15,493g for 10 minutes, and 5 μl of supernatant was injected into the LC-HRMS system. Pooled feces samples were powdered, weighed, and added to an equal volume of saline. A 10× volume of methanol was then mixed with the fecal solutions. After vortexing, ultrasonic extraction for 30 minutes, and centrifugation at 1721g for 10 minutes, supernatants were then collected and filtered through a 0.45-μm nylon filter film, and 5-μl aliquots were injected into the LC-HRMS system.
Chromatography/Mass Spectrometry Analysis.
Analyses of AF-XXMD components in the dosing solution and rat plasma, urine, bile, and feces samples were carried out using an LC-HRMS system. A Surveyor LC plus system (Thermo Fisher Scientific, San Jose, CA) equipped with a Surveyor MS pump plus, a Surveyor autosampler, a Thermo BDS HYPERSIL C18 column (150 × 2.1 mm, 3 μm), and an Agilent SB-C8 guard column (12.5 × 2.1 mm, 5 μm) was employed for separation. The mobile phase consisted of water containing 0.1% formic acid (A) and acetonitrile (B) delivered at a flow rate of 0.2 ml/min using a gradient program as follows: 0–5 minutes, A: 95–95%, B: 5–5%; 5–25 minutes, A: 95–70%, B: 5–30%; 25–35 minutes, A: 70–60%, B: 30–40%; 35–45 minutes, A: 60–20%, B: 40–80%; 45–50 minutes, A: 20–20%, B: 80–80%; 50–51 minutes, A: 20–95%, B: 80–5%; 51–60 minutes, A: 95–95%, B: 5–5%. The column temperature was maintained at 30°C and the sample injection volume was 5 μl.
An LTQ FT mass spectrometer (Thermo Fisher Scientific) was coupled to the LC system via an electrospray ionization interface. Ultrahigh-purity helium (He) was used as collision gas and high-purity nitrogen (N2), as nebulizing gas. The operating parameters in the positive ion mode were as follows: ion spray voltage at 4.0 kV, capillary temperature at 250°C, capillary voltage at 40 V, sheath gas flow rate of 40 (arbitrary units), auxiliary gas flow rate of 10 (arbitrary units), sweep gas flow rate of 3 (arbitrary units), and tube lens at 90 V. Compounds were detected by full-scan mass analysis from m/z 100 to 1200 at a resolving power of 50,000 with data-dependent MS/MS analysis triggered by the two most abundant ions from full-scan mass analysis, followed by MSn analysis on the most abundant product ions. Collision-induced dissociation was conducted with an isolation width of 2 Da. The collision energy was set to 35%. Dynamic exclusion was conducted by utilizing a repeat count of one prior to exclusion. Each mass-to-charge (m/z) value resided on the dynamic exclusion list for 30 seconds after performing a data-dependent MSn experiment to generate MS2 and MS3 spectra. Data acquisition was performed with Xcalibur version 2.0 SR 2 software (Thermo Fisher Scientific).
Background Subtraction Data Processing.
Background subtraction was performed using an in-house developed precise-and-thorough background-subtraction (PATBS) algorithm, which was previously described and applied to the detection of drug metabolites (Zhang and Yang, 2008; Zhang et al., 2008). In the PATBA processing, a specified time window was set at ±0.5 minutes around a chromatographic time point of a full MS dataset of a dosed sample. Mass tolerance window around the same ions present in the full MS dataset of the dosing sample was set to ±10 ppm and a specified scaling factor that was multiplied with the highest intensity of the identified ion in the spectra of the control sample was set to 2.
Data Processing by MTSF.
MTSF was applied to generate information on the spectral similarity between two TCM components. LC-HRMS data acquired with Xcalibur version 2.0 SR2 software were processed using the Mass Frontier 7.0 software (ThermoFisher Scientific) to construct mass spectral trees. To convert HRMS and multiple-stage mass spectrometric data (including MS2 and MS3 data) of all detected compounds to mass spectral trees data, a total extraction component detection algorithm was used to detect components with the setting as follows: Tree-branching began at the second MS stage with a tree match factor of 90%. The library of mass spectral trees was built by importing mass spectral trees data for template compounds consisting of the parent components in the dosing solution. The MTSF technique with the search type setting of “similarity” was applied to screen the potential TCM-related compounds on the basis of similarity by comparing the mass spectral trees of detected compounds with those of template compounds in the library. The similarity score thus obtained, among which the highest was 1000, was connected with the deviation of the parent ion and daughter ion mass, as well as with the matching of daughter ion categories. A higher score indicated a stronger structural correlation between two compounds tested.
Biotransformation Matching.
Once a spectral similarity score between two components was determined to be higher than 200, the shift of molecular weight from one to another component was searched automatically against mass shifts from the parent drug to a metabolite via common biotransformation reactions listed in an Excel spreadsheet. If the shift matched a biotransformation reaction (mass error less than 10 ppm), the metabolic relationship between the two TCM components was established. Furthermore, the established metabolite formation pathways were confirmed on the basis of the interpretation of their accurate mass MSn spectral data.
Results
Integrated Analytical Strategy for Study of ADME of Multiple Components of TCM in Vivo.
A new integrated approach for determining exposure, metabolism, and disposition of multiple TCM prototype components in animal species and human was developed and evaluated (Fig. 1). In this study, dosed plasma, urine, feces, and bile samples (dosed samples) were collected from rats after the administration of a TCM prescription that contained more than two hundred prototype components. Control samples were collected from the same rats prior to administration. As an alternative, control samples could also have been collected from a control group in which dosing formulation without TCM had been administered. The dosed and control samples as well as the dosing solution were subjected to analysis by LC/HRMS. The first step of the analytical approach was to acquire MS and MS/MS spectral data sets for TCM chromatographic components using a data-dependent MS/MS acquisition method on an Fourier transform ion cyclotron resonance instrument. The second step was to discover both TCM-prototype and -metabolite components by processing collected full-scan MS datasets using a background-subtraction tool (PATBS). If a TCM component was uncovered by the PATBS, but its MS/MS spectrum was not acquired by the data-dependent method, an additional injection to record its MS/MS or MSn spectral data would also need to be performed. The third step was to connect TCM metabolite components to their parents or metabolic precursors (upstream metabolites) on the basis of the similarity of their MS/MS spectra and matching of biotransformation reactions. The MS/MS spectral similarity among AF-XXMD components was determined using MTSF. The biotransformation reaction matching was carried out using an Excel spreadsheet in which common metabolic reactions were listed. The fourth step was to determine metabolite structures on the basis of their MS/MS spectral data and their formation pathways. The spectral data of the parent components were used to facilitate metabolite structural elucidation. Finally, an ADME profile of a key individual TCM prototype was determined on the basis of its biotransformation network established in plasma, urine, bile and feces.
An integral analytical strategy for detection, structural characterization, and metabolic pathway identification of TCM components in animals and human using HRMS and data-processing tools.
Profiling and Characterization of AF-XXMD Prototype Components in the Dosing Solution.
Figure 2A displays a total ion chromatogram (TIC) of a high-resolution, accurate MS dataset of the AF-XXMD dosing solution, in which a total of 198 AF-XXMD prototypes were detected. Molecular formulas and MS2 and MS3 spectral data of these AF-XXMD prototypes are summarized in Supplemental Table 1. Among the 198 prototypes, 68 components were structurally characterized (Supplemental Fig. 1) on the basis of their MSn spectra and comparisons with those of 14 reference standards. The same prototypes were previously identified in an AF-XXMD dosing solution (Wang et al., 2014). Additionally, 14 new, minor AF-XXMD prototypes were structurally characterized on a basis of their msn spectra (Supplemental Fig. 1). Structures of the remaining 116 prototype components in the AF-XXMD dosing solution were not characterized, although their MS/MS spectra were recorded by HRMS. Large scale isolation of these unknowns followed by NMR analysis would be an ideal way to determine their structures.
Untargeted analysis of multicomponents of AF-XXMD in a bile sample (postdose 0–4 hours). (A) Zoomed area (24–30 minutes) of TIC of the AF- XXMD dosing solution. (B) Zoomed area (24–30 minutes) of TIC of a pooled rat bile sample. (C) Zoomed area (24–30 minutes) of TIC of the same bile sample after PATBS process against an LC/MS dataset from a pooled control rat bile. (D) Zoomed area (24–30 minutes) of TIC of the same bile sample after sequential PATBS processes against LC/MS datasets from the pooled control rate bile sample and the dosing solution. Red ∇: AF-XXMD–parent components. Blue ∇: AF-XXMD–related components displayed in the TIC of the rat bile sample without data process Green ∇: metabolites of the AF-XXMD prototype components in the rat bile sample revealed and confirmed via PATBS and MTSF processes, respectively. Yellow ∇: unknown components displayed in the ion chromatogram of the rat bile sample after sequential PATBS processes. Labeled peaks: H stands for AF-XXMD prototype components presented in the dosing solution, M stands for metabolites of the AF-XXMD prototype components, and U stands for unknown components.
Detection and Characterization of AF-XXMD–Related Components in Rat Bile, Urine, and Feces.
There were only three AF-XXMD components displayed in the TIC of full MS dataset of a pooled bile sample (0–4 hours) at retention times between 24–30 minutes (Fig. 2B and Supplemental Fig. 2A). A majority of the AF-XXMD components were buried under high levels of background noise or coeluted with intense endogenous components. After background subtraction against a full MS dataset collected from a pooled, predose rat bile sample (control sample), most endogenous chromatographic ion components were removed (Fig. 2C). As a result, 116 AF-XXMD–related components were found in bile samples (0–4 hours, 4–12 hours, and 12–24 hours; Fig. 2C, Supplemental Fig. 2B and Supplemental Table 2). To differentiate metabolite components from AF-XXMD prototype components, the processed dataset was further subtracted by the full MS dataset of the dosing solution (Fig. 2A). The resultant ion chromatogram of the dosed bile sample only exhibited AF-XXMD metabolite components without the AF-XXMD prototype components (Fig. 2D). Thus, about 27 AF-XXMD prototypes and 89 metabolites were confirmed in the bile samples (0–24 hours) (Table 1). Similarly, urine and feces samples were processed using the PATBS process (Supplemental Figs. 3 and 4). As a result, about 101 AF-XXMD–related components, including 70 metabolites, were found in urine samples (24 hours) and about 121 AF-XXMD–related components including 22 metabolites were detected in the fecal samples (0–24 hours) (Table 1).
The total number of individual AF-XXMD components and unknowns detected in rat samples by HRMS and background subtraction
Detection and Characterization of AF-XXMD–Related Components in Rat Plasma.
A total ion chromatogram of a full MS analysis of a pooled, dosed plasma sample (1.25 hours postdose) showed only one AF-XXMD component, H56, with multiple intense endogenous components and high background noises were also present (Fig. 3A). After PATBS processing, many minor AF-XXMD components were revealed in the processed TIC (Fig. 3B). About 21 components were displayed in zoomed area (15–30 minutes) of the ion chromatogram (Fig. 3C) that were absent or present in significantly lower abundance in the control plasma. On the basis of structures and formation pathways of the metabolites determined, 17 components were confirmed as AF-XXMD–related components in this plasma sample (1.25 hours postdose), 11 of which were AF-XXMD metabolites (Supplemental Table 3). LC/UV profiles of plasma samples showed that H56 was a single AF-XXMD component detected by UV (data not shown), suggesting that H56 was the single major AF-XXMD component in rat plasma. Furthermore, the structure of H56 was determined to be cimicifugin (data not shown). Similarly, other plasma samples (0.5, 3, 8, 12, and 18 hours postdose) were processed using the same method. As a result, a total of 13 AF-XXMD prototypes and 34 metabolites were found in the dosed plasma samples (Tables 1 and Fig. 3C and Supplemental Table 2).
Untargeted analysis of multi-components of AF-XXMD in a pooled rat plasma (postdose 75 minutes). (A) TIC of the rat plasma sample without the data processing. (B) TIC of the same sample from background subtraction using PATBS. (C) Zoomed area (15–30 minutes) of the TIC displayed in Figure 3B. Blue ∇: AF-XXMD–related components detected in the plasma sample without data processing. Green ∇: AF-XXMD–related components in the plasma sample revealed by background subtraction. Yellow ∇: unknown components displayed in the TIC of the rat bile sample after sequential PATBS processes. Red: endogenous components. H stands for AF-XXMD prototype components presented in the dosing solution. M stands for metabolites of the AF-XXMD prototype components. U stands for unknown components.
Characterization of Structures and Formation Pathways of AF-XXMD Metabolite Components.
As stated above, a total of 339 components, which were either absent or present at significantly lower levels in control samples, were revealed in dosed plasma, bile, urine, and feces samples (Table 1 and Supplemental Table 2). Among them, 247 were identified AF-XXMD components, including 113 prototypes and 134 metabolites. The rest (92) of the detected components were either unidentified AF-XXMD metabolites or elevated endogamous components. None of the identified AF-XXMD metabolites had been previously reported (Wang et al., 2009). To build the connections between a metabolite and its precursor species, which could be either a prototype or an upstream metabolite, MTSF was employed to process MS2 and MS3 spectral data to determine structural similarity among all of detected AF-XXMD components. First, the 198 parent compounds in the dosing solution were used as template compounds to set up a mass spectral trees library. Then, the mass spectral trees were established for the 226 components (134 identified AF-XXMD metabolites and 92 unidentified components) discovered by PATBS. Finally, a similarity score between two components was calculated. A metabolite that had a similarity score greater than 200 with respect to an AF-XXMD prototype component or another metabolite was considered to be related to each other structurally. Furthermore, a mass difference between the two components was calculated and then matched with mass shifts of common metabolites from its parent drug using biotransformation matching to confirm their metabolic relationship. In the end, the formation pathway and structure of the metabolite were determined on the basis of the structural similarity, matching of the biotransformation reaction, and interpretation of their MS2 and MS3 spectra. For example, M29 was detected in plasma, bile, and urine by PATBS (Fig. 3C, Supplemental Figs. 2B and 3B, and Supplemental Table 2), and its MS2 and MS3 were retrieved from MSn dataset (Fig. 4A). Searches for similar structures of M29 on the basis of its spectral tree against the MSn spectral library of AF-XXMD components led to the identification of H35, H25, and H56, each of which had a Similarity Score (against M29) greater than 650 (Fig. 4B). Thus, M29 was determined to have a structure similar to those of H35, H25, and H56 (Fig. 4C). The follow-up biotransformation matching found that the molecular ion of M29 was at m/z 483.1493 (Supplemental Table 2), 176 greater than that of H56 and a match for a glucuronidation reaction. Therefore, M29 was identified as a glucuronide conjugate of H56 (Figs. 4D). Likewise, H33 and H35 were identified as linked to H56. In addition, M17, M19, M27, M29, and M42 were determined to be associated with H56 (Supplemental Tables 2 and 3). On the basis of results from processing by MTSF and biotransformation matching, the biotransformation network of H56 was determined and showed the connections of H56 with various AF-XXMD components via metabolism, (Fig. 5).
Identification of AF-XXMD components that have similar structures to M29 using MTSF. (A) Mass spectral tree of M29; (B) Detection of H35, H25, and H56 using M29 as a template and MTSF; (C) Structures of H35, H25, and H56; (D) Structure of M29 that was determined by comparing spectral trees of H56 and M29.
Proposed biotransformation network of H56 in rats. The term of “-glc” stands for loss of glucose. The term of “-xyl” stands for loss of xylose.
Metabolism and Disposition of H56 in Rat.
The ADME profile of H56 in rat was determined (Fig. 6) on the basis of the proposed biotransformation network of H56 (Fig. 5) and the AF-XXMD component profiles determined in plasma, urine, bile, and feces (Figs. 2 and 3, Supplemental Figs. 2B, 3B, 4B, and Supplemental Tables 2 and 3). H56 was a prototype in the dosing solution (Supplemental Table 1) and observed in feces, bile, plasma, and urine (Supplemental Table 2), suggesting that a part of H56 was absorbed via the gastrointestinal (GI) track and unabsorbed H56 was eliminated directly into feces (Fig. 6). Additionally, H33 (a prototype) can be converted to H35 (a prototype) via the loss of the xylose moiety, and then H35 can be converted to H56 via deglycosylation in the GI track (Fig. 5). Absorbed H56 went to the liver and underwent extensive hepatic metabolism. A majority of metabolites (M17, M19, M29, M42) and precursor species (H33 and H35) of H56 were excreted into bile (Supplemental Fig. 2 and Supplemental Table 2), and H56 and some of its metabolites, M19 and M29, entered the circulation and then were excreted into urine via kidney. M17 and M27, two metabolites of H56, were not seen in the circulation (Figs. 3 and 6) but were observed in urine (Supplemental Fig. 3 and Supplemental Table 2), suggesting these metabolites were quickly eliminated via kidney after forming in the liver and quickly passing through the circulation (Fig. 6).
Proposed ADME profile of H56 in rats.
Discussion
In this study, we took advantages of PATBS, a unique background subtraction algorithm, for sensitively and comprehensively detecting TCM components in a complex biologic matrix (Fig. 1). PATBS is originally developed for untargeted detection of drug metabolites, such as in vitro drug metabolites (Zhang and Yang, 2008), in vivo drug metabolites (Zhang et al., 2008; Zhu et al., 2009), and modified peptides hydrolyzed from protein-drug adducts (Zhang et al., 2015). In addition, the combination of PATBS and targeted data mining (EIC, MDF, isotope pattern filter) significantly reduces false positives and improves detection sensitivity (Zhang et al., 2008; Xing et al., 2015). In the current study, TCM component profiles in bile (Fig. 2), plasma (Fig. 3), urine (Supplemental Fig. 3), and feces (Supplemental Fig. 4) samples were quickly generated by PATBS, revealing any components that were present solely in a dosed sample or had concentrations significantly higher in a dosed sample than those in a control sample. A majority of these detected components, shown in Figs. 2C, 2D, 3B, 3C, Supplemental Figs. 2B, 3B, and 4B, were AF-XXMD–related components. Additionally, endogenous metabolites significantly elevated owing to the exposure to AF-XXMD components were detected by the process (Table 1). The detection of elevated endogenous biomarkers by PATBS was reported previously (Zhang et al., 2010).
To overcome the limitations of targeted data-mining techniques, a few untargeted analysis methods, including metabolomics (Xie et al., 2012; Yan et al., 2013) and in-house-developed background subtraction techniques (Gong et al., 2012; Yan et al., 2013), have been applied to the detection of TCM components in vivo. In this study, PATBS has demonstrated several distinct advantages over targeted and other untargeted LC/MS approaches in detecting TCM components in complex biologic samples. First, PATBS was capable of rapid and unbiased detection of TCM components regardless of their molecular weights, mass defects, and fragmentation patterns. Additionally, detected TCM metabolite (Fig. 2D) can be immediately differentiated from TCM prototypes via the subtraction of prototypes in the dosing solution (Fig. 2A) from the detected total TCM-related components (Fig. 2C). Second, PATBS had superior sensitivity and coverage in finding TCM components in complex biologic samples. The background subtraction processing not only completely removed intensive endogenous chromatographic components to reveal overlapped TCM components in rat plasma (Fig. 3C), bile (Fig. 2C), urine (Supplemental Fig. 3B), and feces (Supplemental Fig. 4B) but also significantly reduced background levels so that minor TCM components were revealed. Third, PATBS had good selectivity in detecting TCM components in complex biologic samples. All of the significant components displayed in PATBS-processed chromatograms of the dosed-bile (Fig. 2C) and plasma (Fig. 3C) samples were those that either did not exist (TCM components or other xenobiotics) or had much higher abundance (elevated endogenous metabolites) in these dosed samples compared with those in the corresponding control samples.
Among 339 components detected in the plasma, urine, bile, and feces by background subtraction (Table 1 and Supplemental Table 2), 247 components were identified to be AF-XXMD–related components, including 134 newly characterized metabolites of individual AF-XXMD prototypes. It was demonstrated previously that MTSF can quantitatively evaluate structural similarity among multiple unknown components by comparing their fragmentation patterns and product ions (Sheldon et al., 2009; van der Hooft et al., 2011; Ridder et al., 2012; Rojas-Cherto et al., 2012). Furthermore, a biotransformation-matching method was employed to define the relationship between two TCM components after high structural similarity was determined by MTSF. The utility of the MTSF and biotransformation-matching processes in analyzing metabolic pathways of individual prototypes was demonstrated in the determination of the biotransformation network of H56 in rats (Fig. 5).
Determination of absorption of individual TCM components after oral administration of a TCM prescription to animals and humans is one of the key tasks of a TCM ADME study. A majority of TCM component profiling studies reported in the literature focused on the detection and identification of TCM components in plasma using HRMS-based targeted data-mining tools (Xue et al., 2011, 2014; Sun et al., 2013; Tao et al., 2015). However, such an experimental approach cannot fully evaluate which TCM prototypes were absorbed, since absorbed TCM prototypes may not be present in the circulation owing to fast metabolism, high affinity to certain tissues, or direct elimination via bile. TCM component profiles in plasma, urine, bile, and feces provided comprehensive information on the absorption of individual prototypes in a TCM prescription. For example, AF-XXMD component profiles of all dosed samples found 68 AF-XXMD prototypes and one metabolite of another prototype in feces but not in plasma, urine, or bile, suggesting that the 69 AF-XXMD prototypes were not absorbed via the GI track in rat (Supplemental Table 2). About 26 AF-XXMD prototypes were completely absorbed, since they were absent in feces. Additionally, 39 prototypes were partially absorbed because they were present in feces (Supplemental Table 2). The number of absorbed AF-XXMD prototype components determined on the basis of plasma profiles was only 20% of the total of absorbed AF-XXMD prototypes determined on the basis of information collected from profiling plasma, urine, bile, and feces (Supplemental Table 2), suggesting that TCM component profiles in plasma did not provide accurate information on the absorption. The ADME data clearly demonstrate that a few of the absorbed AF-XXMD prototypes (such as H32) were directly eliminated via bile before entering the circulation and a few of the absorbed AF-XXMD prototypes (such as H101) were extensively metabolized in the GI tract followed by direct biliary excretion. Several absorbed AF-XXMD components (such as H71) or their metabolites (such as M57) were present in urine but not found in plasma because these components were quickly eliminated via kidney after entering the circulation. These TCM components can express biologic effects in kidney, even though they were not detected in plasma because of low abundances or absence.
To further demonstrate the utility and effectiveness of the approach (Fig. 1) for studying ADME of individual TCM prototypes in vivo, we constructed the formation, metabolism, and elimination pathways of H56 (Fig. 6). H56, identified as cimicifugin, was the single major AF-XXMD component in rat plasma. Its role in expressing the pharmacological effects of AF-XXMD is currently under investigation. In addition to direct absorption via the GI track, H56 in plasma could be formed from H35 via deglycosylation (Fig. 5). H33 could be also metabolically converted to H56 via the formation of H35. Both H35 and H56 were major prototype components in the AF-XXMD dosing solution (Wang, et al., 2014). Good oral absorption of H56, rapid conversion from H33 and H35 to H56, and slow metabolism and urinary excretion of H56 in rats could be the reasons why the H56 level in rat plasma was very high compared with other AF-XXMD prototype components or metabolites (Fig. 3C). Results from the ADME profiling experiment also suggest that H56 underwent three major metabolic reactions: glucuronidation to M19, sulfation to M17 and M29, and hydroxylation to M27. These metabolites were mainly eliminated via biliary and urinary excretions. M17 and M27 were also found in the feces, suggesting that the two metabolites may pass through intestinal membrane into feces or were formed in the GI track (Fig. 6).
In summary, a new and integrated approach (Fig. 1) for study of exposure, metabolism, and disposition of multiple herbal prototypes in vivo was developed and applied to ADME study of AF-XXMD in rats after an oral administration. The approach used HRMS to acquire accurate full MS and MSn data in plasma and excreta. Confirmation of TCM prototypes and detection of unknown TCM metabolites were accomplished using PATBS, a unique background subtraction algorithm. As a result, over 247 AP-XXMD–related components were detected and structurally characterized in rat plasma, urine, bile, and feces (Table 1 and Supplemental Table 2). It was evident that this untargeted data-mining technique has significant advantages over targeted data-mining technologies with respect to sensitivity, selectivity, analytical speed, and comprehensiveness in finding TCM components in complex biologic matrixes. Among 198 AF-XXMD prototype components dosed in rats (Supplemental Table 1), 26 prototypes were completely absorbed and 39 prototypes were partially absorbed (Supplemental Table 2). In the rat plasma, 13 AF-XXMD prototypes and 34 metabolites were detected and structurally characterized (Fig. 3 and Supplemental Table 3), among which H56 was determined to be the single dominant component in the circulation. Glucuronidation, isomerization, and deglycosylation followed by biliary and urinary excretions played major roles in the clearance of a majority of AF-XXMD prototype components in rats. About 31 and 27 prototype components were found in urine and bile, respectively (Supplemental Table 2), suggesting that direct elimination of absorbed prototypes via kidney and liver were significant clearance pathways for some of these prototypes. Furthermore, an approach that combined MTSF and biotransformation matching was applied to connect metabolites to their metabolic precursors, either prototypes or upstream metabolites, which enabled the rapid establishment of metabolic pathways of individual TCM prototypes. As an example, ADME profile of H56 was determined (Figs. 5 and 6). These results demonstrate that the integrated approach is a useful tool for qualitative study of ADME of multiple components in a TCM prescription in animals and humans.
Acknowledgments
We thank Xin Wang for the development, and implementation of the precise-and-thorough background-subtraction (PATBS) data processing software.
Authorship Contributions
Participated in research design: Wu, Zhu, J. Zhang.
Conducted experiments: Wu, H. Zhang, Wang.
Contributed reagents: Qin.
Performed data analysis: Wu, H. Zhang.
Wrote or contributed to the writing of the manuscript: Wu, Zhu, J. Zhang.
Footnotes
- Received November 2, 2015.
- Accepted March 23, 2016.
This work was supported by the Beijing Natural Science Foundation [Grant 7133252] and the National Natural Science Foundation of China [Grant 81302740].
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This article has supplemental material available at dmd.aspetjournals.org.
Abbreviations
- ADME
- absorption, distribution, metabolism, and elimination
- AF-XXMD
- active fraction of Xiao-Xu-Ming decoction
- EIC
- extracted ion chromatography
- GI
- gastrointestinal
- HRMS
- high-resolution mass spectrometry
- LC
- liquid chromatography
- MDF
- mass defect filter
- MS
- mass spectrometry
- MTSF
- mass spectral trees similarity filter
- NLF
- neutral loss filter
- PATBS
- precise-and-thorough background-subtraction
- TCM
- traditional Chinese medicine
- TIC
- total ion chromatogram
- Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics
References
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ADME of TCM Components by HRMS and Data Processing
