An Integrated Approach for Studying Exposure, Metabolism and Disposition of Multiple Component Herbal Medicines Using High Resolution

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An integrated approach for studying exposure, metabolism and disposition of multiple component herbal medicines using high resolution

mass spectrometry and multiple data processing tools

Caisheng Wu, Haiying Zhang, Caihong Wang, Hailin Qin, Mingshe Zhu and Jinlan Zhang Downloaded from

State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences& Peking Union Medical College, Beijing, China (C.W., C.W., H.Q., J.Z.), and Department of Biotransformation, Bristol-Myers Squibb

Company, Princeton, USA (H.Z., M.Z.) dmd.aspetjournals.org

at ASPET Journals on September 27, 2021

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Running Title

Study ADME of multiple TCM components using HRMS technology

Corresponding author:

Mingshe Zhu

Department of Biotransformation, Bristol-Myers Squibb Company, Princeton, NJ 08540, USA;

Tel (609) 252-3324; E-mail: [email protected]

Jinlan Zhang

State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Downloaded from Materia Medica, Chinese Academy of Medical Sciences& Peking Union Medical College, 2

Nanwei Road，Beijing 100050, China; Tel & Fax: 086 10 83154880; E-mail: [email protected]

dmd.aspetjournals.org

# Text pages : 38

# Tables: 1 at ASPET Journals on September 27, 2021

# Figures: 6

#References: 45

# Words in Abstract: 250

# Words in Introduction: 748

#Words in Discussion: 1502

ABBREVIATIONS:

Absorption, metabolism, distribution and elimination, ADME; active fraction of Xiao-Xu-Ming

Decoction, AF-XXMD; extracted ion chromatography, EIC; high resolution mass spectrometry,

HRMS; isotope pattern filer, IPF; mass defect filter, MDF; mass spectral trees similarity filter,

MTSF: neutral loss filter, NLF; precise and thorough background subtraction, PATBS; traditional

Chinese medicine, TCM; Yin-Chen-Hao-Tang ,YCHT.

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Abstract

A typical prescription of traditional Chinese medicine (TCM) contains up to a few hundreds of prototype components. Studying their absorption, metabolism, distribution and elimination (ADME) represents 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 Downloaded from model TCM prescription was orally administered to rats. AF-XXMD-related components in plasma, urine, bile and feces were detected using high resolution mass dmd.aspetjournals.org spectrometry and background subtraction, an untargeted data-mining tool, and structurally characterized based on MSn spectral data. Connection of detected

AF-XXMD metabolites to their precursor species, either prototypes or upstream at ASPET Journals on September 27, 2021 metabolites, were determined based on 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 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 based on 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

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DMD # 68189 of TCM components in biological matrix and study of ADME of a TCM prescription in vivo.

Downloaded from dmd.aspetjournals.org at ASPET Journals on September 27, 2021

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Introduction

Understanding of bioactive substances of Traditional Chinese medicine (TCM) and their action mechanisms is 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;

Zhang et al., 2013). Concentrations and duration of individual TCM components in Downloaded from the circulation depend on its absorption, distribution, metabolism and excretion

(ADME) processes that are often mediated by metabolizing enzymes and transporters. dmd.aspetjournals.org Inhibition or induction of the involved enzymes or transporters by a co-administered

TCM component or a pharmaceutical drug could significantly change exposure levels of bioactive plasma components, leading to drug-drug interactions (DDI) among at ASPET Journals on September 27, 2021 herbal components and between an herbal component and a pharmaceutical drug

(Cheng et al., 2014; Jia et al., 2015; Ma et al., 2014; Posadzki et al., 2013). 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 (Xiong et al., 2014, Qiu et al., 2000; Hu et al., 2004, Yue et al., 2009; Su et al.,

2004, Yuan et al., 2011; Ding et al., 2012, Zhu, 2002).

An ADME study of a drug in human and animal is often carried out using radioactivity profiling after dosing 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 LC/MS

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DMD # 68189 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 excrete (Yang et al., 2012; Wu et al., 2012; Geng et al., 2014; Zuo et al., 2015; Yan et al., 2013). The first analytical challenge faced in studying the ADME of herbal medicines using HRMS is to sensitively and comprehensively detect TCM Downloaded from components in plasma, urine, feces and bile samples. The task is often accomplished by processing accurate MS and MS/MS datasets using targeted data-mining tools dmd.aspetjournals.org (Yang et al., 2011; Wu et al., 2012), including mass defect filter (MDF), extracted ion chromatography (EIC), product ion filter (PIF), neutral loss filter (NLF) and isotope pattern filter (IPF). These high resolution mass spectrometry (HRMS)-based at ASPET Journals on September 27, 2021 data-mining technologies are originally developed for the detection and identification of drug metabolites in complex biological systems (Bateman et al., 2007; Geng et al.,

2014; Du et al., 2015; Ma et al., 2012; Ma and Zhu 2009; Zhu et al., 2011). In addition, mass spectral trees similarity filter (MTSF) technology (Jin et al., 2013) is employed for the detection and identification of TCM metabolite based on similarity of product ion spectra of metabolites to those of their precursor species. These targeted data-mining approaches are capable of searching for metabolites based on their predicted mass defects (MDF), fragmentation patterns (NLF and PIF) or molecular weights (EIC) from individual TCM prototypes. However, since an herbal medicine can contain up to a few hundred parent components, searching for their metabolite components based on their masses, mass defects or product ion spectra

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DMD # 68189 predicted from individual TCM prototypes is truly time-consuming and labor-intensive. More importantly, many major TCM metabolite components in plasma or excretes 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 the determination of metabolic pathways of individual TCM parent components, especially when dealing with a few hundreds of Downloaded from in vivo TCM components.

The main objective of the current study was to develop a practical and integrated dmd.aspetjournals.org approach for study of in vivo ADME of TCM medicine. To evaluate the utility and effectiveness of this approach, metabolism and disposition of a model TCM prescription, an active fraction of Xiao-Xu-Ming Decoction (AF-XXMD), in rats at ASPET Journals on September 27, 2021 were determined. The prescription of Xiao-Xu-Ming decoction is used for the treatment of theoplegia and the sequela of theoplegia. The formula of XXMD consists of 14 crude herbal medicines (Supplemental Table 1). AF-XXMD is previously characterized and isolated from XXMD (Wang et al. 2006). and shows similar pharmacological effects as XXMD. About 68 prototype components are previously identified in AF-XXMD (Wang et al, 2014). However, there is no report on metabolism and disposition of XXMD or AF-XXMD in animals and humans in the literature.

Materials and Methods

Materials. Methanol and acetonitrile of MS grade and formic acid of analytical grade were purchased from Mallinckrodt Baker Co. (Phillipsburg, NJ, USA). Purified 8

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DMD # 68189 water used in the study was provided by Wahaha Co., Ltd (Hangzhou China).

AF-XXMD was prepared from XXMD following a previous procedure (Wang et al.,

2006). Wistar rats (200±20g) were purchased from Beijing Vital River Experimental

Animal Co. Ltd (Beijing, China).

Animal Experiment. Rats were kept in an environmentally controlled breeding room for three days before the experiment and then fasted (water only) for 12 h in Downloaded from metabolic cages prior to the dosing of AF-XXMD (0.5 g/kg). The rats (24 intact rats and three bile duct-cannulated rats) were divided to nine groups (three rats per group). dmd.aspetjournals.org Bile samples were collected from the group of BDC rats 2 h prior to dosing (control bile samples) and 0–4 and 4–12 h post-dosing (test bile samples). Another group of rats were kept in metabolic cages, and control samples of urine and feces were at ASPET Journals on September 27, 2021 collected 0-4 h prior to the administration. Testing urine and feces samples were collected 0−12 h and 12 h−24 h post administration. Blood samples were collected at

0 (control samples), 0.5, 1.25, 3, 8, 12 and 18 h from the abdominal artery of the remaining seven groups of the rats (one time point per a 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 sec prior to storage at

−80˚C until use. The urine, bile and feces samples were pooled in equal volumes or weights in individual time periods across rats, then 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

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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 five volumes of methanol in test tubes and then vigorously vortexed for 30 sec. After centrifugation at 1721 × g for 10 min, 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 15493 × g for 10 Downloaded from min, and 5 μL of supernatant was injected into the LC-HRMS system. Pooled feces samples were powdered, weighed, and adding an equal volume of saline to the dried dmd.aspetjournals.org feces. Then 10 times of methanol were mixed with the fecal solutions. After vortexing, ultrasonic extraction for 30 min and centrifuging at 1721 × g for 10 min, supernatants were then collected and filtered through a 0.45 μm nylon filter film, and 5 μL aliquots at ASPET Journals on September 27, 2021 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 a LC/HRMS system. A Surveyor LC plus system (Thermo Fisher

Scientific, San Jose, CA, USA) 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 min, A: 95-95%, B: 5-5%；5-25 min，A: 95-70%, B: 5-30%; 25-35 min, A: 70-60%, B:

30-40%; 35-45 min, A: 60-20%, B: 40-80%; 45-50 min, A: 20-20%, B: 80-80%;

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50-51 min, A: 20-95%, B: 80-5%; 51-60 min, A: 95%-95%, B: 5%-5%. The column temperature was maintained at 30˚C and the sample injection volume was 5μL.

An LTQ FT mass spectrometer (Thermo Fisher Scientific) was coupled to the LC system via an electrospray ionization (ESI) 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 Downloaded from 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 dmd.aspetjournals.org rate of 3 (arbitrary units), and tube lens at 90 V. Compounds were detected by full-scan mass analysis from m/z 100 to 1,200 at a resolving power of 50,000 with data-dependent MS/MS analysis triggered by the two most abundant ions from at ASPET Journals on September 27, 2021 full-scan mass analysis, followed by MS/MS/MS analysis on the most abundant product ions. Collision-induced dissociation (CID) 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 s after performing a

n 2 data-dependent MS experiment to generate MS 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 et al., 2008a; Zhang et al., 2008b). In the

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PATBA processing, a specified time window was set at ± 0.5 min 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 the information of Downloaded from spectral similarity between two TCM components. LC-HRMS data acquired with

Xcalibur version 2.0 SR2 software were processed using the Mass Frontier TM 7.0 dmd.aspetjournals.org software (ThermoFisher Scientific, San Jose, CA, USA) 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 at ASPET Journals on September 27, 2021 component detection (TECD) algorithm was used to detect components with the setting as follows: tree-branching began at the second MS stage with a trees match factor of 90%. The library of mass spectral trees was built by importing mass spectral trees data of 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 based on the similarity by comparing the mass spectral trees of detected compounds with those of template compounds in the library. The obtained similarity score, among which the highest was 1000, was connected with the deviation of the parent ion and daughter ion mass, as well as matching of daughter ion categories. The higher score indicates the stronger structural correlation between the two compounds tested.

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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 matches a biotransformation reaction (mass error less than 10 ppm), the metabolic relationship between the two TCM components was established. Downloaded from Furthermore, the established metabolite formation pathways were confirmed based on the interpretation of their accurate mass MSn spectral data. dmd.aspetjournals.org

RESULTS

Integrated Analytical Strategy for Study of ADME of Multiple Components at ASPET Journals on September 27, 2021 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 (Figure 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 hundreds of prototype components.

Control samples were collected from the same rats prior to the administration. As an alternative, control samples can also be collected from a control group in which dosing formulation without TCM is 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 a

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FTICR instrument. The 2nd 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 is uncovered by the PATBS, but its

MS/MS spectrum is acquired by the data-dependent method, an additional injection to record its MS/MS or MSn spectral data will be performed. The 3rd step was to connect TCM metabolite components to their parents or metabolic precursors Downloaded from (upstream metabolites) based on the similarity of their MS/MS spectra and the matching of biotransformation reactions. The MS/MS spectral similarity among dmd.aspetjournals.org AF-XXMD components was determined using MTSF. The biotransformation reaction matching was carried out using an Excel spreadsheet where common metabolic reactions were listed. The 4th step was to determine metabolite structures based on at ASPET Journals on September 27, 2021 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 based on its biotransformation network established in plasma, urine, bile and faces.

Profiling and Characterization of AF-XXMD Prototype Components in the

Dosing Solution. Figure 2A displays a total ion chromatogram (TIC) of 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 Figure 1) based on their MSn spectra and comparisons with those of 14

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DMD # 68189 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 using HRMS and comparison of their MSn spectra with those of the 14 reference standards (Supplemental Figure 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 Downloaded from isolation of these unknowns followed by NMR analysis would be an ideal way to determine their structures. dmd.aspetjournals.org 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 (0-4h) at retention times between at ASPET Journals on September 27, 2021 24-30 min (Figure 2B and Supplemental Figure 2A). A majority of the AF-XXMD components were buried under high levels of background noises or co-eluted with intense endogenous components. After background subtraction against a full MS dataset collected from a pooled, pre-dose rat bile sample (control sample), most endogenous chromatographic ion components were removed. As a result, 116

AF-XXMD-related components were found in bile samples (0-4h, 4-12 h and 12-24 h). (Figure 2C, Supplemental Figure 2B and Supplemental Table 2). In order to differentiate metabolite components from AF-XXMD prototype components, the processed dataset was further subtracted by the full MS dataset of the dosing solution

(Figure 2A). The resultant ion chromatogram of the dosed bile sample (0-4 h) only exhibited AF-XXMD metabolite components without the AF-XXMD prototype

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DMD # 68189 components (Figure 2D). Thus, about 27 AF-XXMD prototypes and 89 metabolites were confirmed in the bile samples (0-4, 4-12, 12-24 h) (Table 1). Similarly, urine and feces samples were processed using the PATBS process (Supplemental Figure 3 and

Supplemental Figure 4). As a result, about 101 AF-XXMD-related components including 70 metabolites were found in urine samples (0-12 h and 12-24 h) and bout

121 AF-XXMD-related components including 22 metabolites were detected in the Downloaded from fecal samples (0-12, 12-24 h) (Table 1).

Detection and Characterization of AF-XXMD-related Components in Rat dmd.aspetjournals.org Plasma. A TIC of full MS analysis of a pooled, dosed plasma sample (1.25 h post-dose) showed only one AF-XXMD component, H56, while multiple intense endogenous components and high background noises were present (Figure 3A). After at ASPET Journals on September 27, 2021 PATBS processing, many minor AF-XXMD components were revealed in the processed TIC (Figure 3B). About 21 components were displayed in zoomed area

(15-30 min) of the ion chromatogram (Figure 3C), which were absent or present at significantly lower abundance in the control plasma. Based on structures and formation pathways of the metabolites determined, 17 components were confirmed as

AF-XXMD-related components in this plasma sample (1.25 h post-dose), 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 using the synthetic standard. In summary, a total of 13 AF-XXMD prototypes and 34

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DMD # 68189 metabolites were found in the dosed plasma samples (0.5, 1.25, 3, 8, 12, 18 h post-dose) (Tables 1 and Figures 3B, 3C).

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). Downloaded from Among them, the AF-XXMD prototype components accounted for 113 and metabolites accounted for 134. None of those AF-XXMD metabolites were previously dmd.aspetjournals.org reported (Wang et al., 2009). To build the connections between a metabolite to its precursor species that can be either a prototype or an upstream metabolite, MTSF was employed to process MS2 and MS3 spectral data to determine structural similarity at ASPET Journals on September 27, 2021 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 of the 226 potential AF-XXMD metabolite components (339 detected components - 113 prototypes found in vivo) discovered by

PATBS were established. 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

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DMD # 68189 metabolite were determined based on 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 (Figure 3C,

Supplemental Figures 2B, 3B and Supplemental Table 2) and its MS2 and MS3 were retrieved from MSn dataset (Figure 4A). Search for similar structures of M29 based on its spectral tree against the MSn spectral library of AF-XXMD components led to the Downloaded from identification of H35, H25 and H56, each of which had Similarity Score (against M29) greater than 650 (Figure 4B). Thus, M29 was determined to have a similar structure to dmd.aspetjournals.org those of H35, H25 and H56 (Figure 4C). The follow-up Biotransformation Matching found out that the molecular ion of M29 (m/z 483.1493 in Supplemental Table 2) was

176 greater than that of H56, which matched a glucuronidation reaction. Therefore, at ASPET Journals on September 27, 2021 M29 was identified as a glucuronide conjugate of H56 (Figures 4D). Similarly, H33 and H35 were identified to be linked to H56. In addition, M17, M19, M27, M29 and

M42 were determined to be associated with H56 (Supplemental Tables 2 and 3).

Based on results from processing by MTSF and Biotransformation Matching, the biotransformation network of H56, which showed connections of H56 with various

AF-XXMD components via metabolism, was determined (Figure 5).

Metabolism and Disposition of H56 in Rat. The ADME profile of H56 in rat was determined (Figure 6) based on the proposed biotransformation network of H56

(Figure 5) and the AF-XXMD component profiles determined in plasma, urine, bile and feces (Figures 2, 3, Supplemental figures 2B, 3B, 4B and Supplemental Tables 2 and 3). H56 was a prototype in the dosing solution (Supplemental Table 1) and

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DMD # 68189 observed in feces, bile, plasma and urine (Supplemental Tables 2 and 3), suggesting that a part of H56 was absorbed via GI track and unabsorbed H56 was eliminated directly into feces (Figure 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 GI track (Figure 5). Absorbed H56 went to liver and underwent extensive hepatic metabolism. A majority of metabolites (M17, M19, M29, M42) and Downloaded from precursor species (H33 and H35) of H56 were excreted into bile (Supplemental Figure

2 and Supplemental Table 2), while H56 and some of its metabolites, M19 and M29, dmd.aspetjournals.org entered the circulation and then were excreted into urine via kidney. M17 and M27, two metabolites of H56, were not seen in the circulation (Figures 3, 6), but were observed in urine (Supplemental Figure 3 and Supplemental Table 2), suggesting at ASPET Journals on September 27, 2021 these metabolites were quickly eliminated via kidney after forming in liver and quickly passing through the circulation (Figure 6).

DISCUSSONS

In this study, we took advantages of PATBS, a unique background subtraction algorithm, for sensitively and comprehensively detecting TCM components in complex biological matrix (Figure 1). PATBS is originally developed for untargeted analysis of in vitro drug metabolites (Zhang et al., 2008a), in vivo drug metabolites

(Zhang et al., 2008b; Zhu et al., 2009), and modified peptides hydrolyzed from protein-drug adducts (Zhang et al., 2015). In addition, combinations of (PATBS and targeted data mining (EIC, MDF, IPF) significantly reduces false positives and

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DMD # 68189 improves detection sensitivity (Zhang et al., 2008b; Xing et al. 2015). In the current study, TCM component profiles in bile (Figure 2), plasma (Figure 3), urine

(Supplemental Figure 3) and feces (Supplemental Figure 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 Figures 2C, 2D, Downloaded from 3B, 3C, Supplemental Figures 2B, 3B and 4B were AF-XXMD-related components.

Additionally, endogenous metabolites significantly elevated due to the exposure to dmd.aspetjournals.org AF-XXMD components were detected by the process (Table 1). The detection of elevated endogenous biomarkers by PATBS was reported previously (Zhang et al.,

2011). at ASPET Journals on September 27, 2021 To overcome the limitations of targeted data-mining techniques, a few untargeted analysis methods, including metabolomics (Yan et al., 2013; Xie et al., 2012) and in-house developed background subtraction techniques (Yan et al., 2013; Gong et al.,

2012), 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 biological 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 (Figure 2D) can be immediately differentiated from TCM prototypes via the subtraction of the detected total

TCM-related components (Figure 2C) by prototypes in the dosing solution (Figure

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2A). Second, PATBS had superior sensitivity and coverage in finding TCM components in complex biological samples. The background subtraction processing not only completely removed intensive endogenous chromatographic components to reveal overlapped TCM components in rat plasma (Figure 3C), bile (Supplemental

Figure 2B), urine (Supplemental Figure 3B) and feces (Supplemental Figure 4B), but also significantly reduced background levels so that minor TCM components were Downloaded from revealed. Third, PATBS had good selectivity in detecting TCM components in complex biological samples. All of significant components displayed in dmd.aspetjournals.org PATBS-processed chromatograms of the dosed bile (Figures 2C and 2D) and plasma

(Figure 3C) samples were those that either did not exist (TCM components or other xenobiotics) or had much higher abundance (elevated endogenous metabolites) in at ASPET Journals on September 27, 2021 these dosed samples as compared to 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 found to be associated with AF-XXMD, 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; Rojas-Cherto et al., 2012; van der Hooft et al., 2011; Ridder et al., 2012).

Furthermore, a Biotransformation Matching processing 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 Marching

DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version.

DMD # 68189 processes in analyzing metabolic pathways of individual prototypes was demonstrated in the determination of the biotransformation network of H56 in rats (Figure 5).

Determination of absorption of individual TCM components after oral administration of a TCM prescription to animals and humans is one of 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 Downloaded from using HRMS-based targeted data-mining tools (Xu et al., 2014; Xue et al., 2011; Tao et al., 2015; Sun et al., 2013). However, such experimental approach cannot fully dmd.aspetjournals.org evaluate what TCM prototypes were absorbed since absorbed TCM prototypes may not be present in the circulation due to fast metabolism, high affinity to certain tissues or direct elimination via bile. TCM component profiles in plasma, urine, bile and at ASPET Journals on September 27, 2021 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 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 that they were present in feces

(Supplemental Table 2). The number of absorbed AF-XXMD prototype components determined based the plasma profiles was only 20% of the total of absorbed

AF-XXMD prototypes determined based on the information collected from profiling plasma, urine, bile and feces (Supplemental Table 2), suggesting TCM component

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DMD # 68189 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 GI 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 Downloaded from plasma because these components were quickly eliminated via kidney after entering the circulation. These TCM components can express biological effects in kidney even dmd.aspetjournals.org thought they were not detected in plasma due low abundances or absence.

To further demonstrate the utility and effectiveness of the approach (Figure 1) in the studying ADME of individual TCM prototypes in vivo, we constructed the at ASPET Journals on September 27, 2021 formation, metabolism and elimination pathways of H56 (Figure 6). H56 identified as cimicifuge was the single major AF-XXMD component in rat plasma. Its role in the expressing pharmacological effects of AF-XXMD is currently under investigation. In addition to direct absorption via GI track, H56 in plasma could be formed from H35 via deglycosylation (Figure 5). H33 could be also metabolically converted to H56 via the formation of H35. Both H33 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 as compared to other AF-XXMD prototype components or metabolites (Figure 3C).

Results from the ADME profiling experiment also suggest that H56 underwent three

DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version.

DMD # 68189 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

GI track (Figure 6).

In summary, a new and integrated approach (Figure 1) for study of exposure, Downloaded from 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 dmd.aspetjournals.org approach used HRMS to acquire accurate full MS and MSn data in plasma and excretes. Confirmation of TCM prototypes and detection of unknown TCM metabolites were accomplished using PATBS, a unique background subtraction at ASPET Journals on September 27, 2021 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 biological 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 (Figure 3 and Supplemental Table 3), among which H56 was determined to be the single dominant component in the circulation. Glucuronidation,

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DMD # 68189 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 eliminations of absorbed prototypes via kidney and liver were significant clearances pathways for some of these prototypes.

Furthermore, an approach that combined MTSF and Biotransformation Matching was Downloaded from applied to connect metabolites to their metabolic precursors, either prototypes or upstream metabolites, which enabled the rapid establishment of metabolic pathways dmd.aspetjournals.org of individual TCM prototypes. As an example, ADME profile of H56 was determined

(Figures 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 at ASPET Journals on September 27, 2021 animals and humans.

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Acknowledgments

We thank Xin Wang for the development and implementation of the precision and through background subtraction (PATBS) data processing software.

Downloaded from dmd.aspetjournals.org at ASPET Journals on September 27, 2021

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Authorship Contributions

Participated in research design: Wu, Zhu, and J.L. Zhang.

Conducted experiments: Wu, H.Y Zhang, and Wang.

Contributed reagents: Qin

Performed data analysis: Wu, H.Y. Zhang.

Wrote or contributed to the writing of the manuscript: Wu, Zhu, .J.L. Zhang, Downloaded from dmd.aspetjournals.org at ASPET Journals on September 27, 2021

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Footnotes

This work was supported by the Beijing Natural Science Foundation [Grant 7133252] and the National Natural Science Foundation of China [Grant 81302740].

Downloaded from

dmd.aspetjournals.org at ASPET Journals on September 27, 2021

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Figure legends

Figure 1. An integral analytical strategy for detection, structural characterization and metabolic pathway identification of TCM components in animals and humans using

HRMS and data processing tools.

Figure 2. Untargeted analysis of multi-components of AF-XXMD in a bile sample Downloaded from (postdose 0-4 hr). (A) Zoomed area (24-30 min) of TIC of the AF- XXMD dosing solution. (B) Zoomed area (24-30 min) of TIC of a pooled rat bile sample. (C) dmd.aspetjournals.org Zoomed area (24-30 min) 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 min) of TIC of the same bile sample after sequential PATBS processes against LC/MS datasets at ASPET Journals on September 27, 2021 from the pooled control rate bile sample and the dosing solution. Red ∇:

AF-XXMD-parent components. Bule ∇ 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, U stands for unknown components.

Figure 3. Untargeted analysis of multi-components of AF-XXMD in a pooled rat

DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version.

DMD # 68189 plasma (post-dose 75 min). (A) TIC of the rat plasma sample without the data processing. (B) TIC of the same sample from background subtraction using PATBS.

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 Downloaded from the rat bile sample after sequential PATBS processes. H stands for AF-XXMD prototype components presented in the dosing solution. M stands for metabolites of dmd.aspetjournals.org the AF-XXMD prototype components. U stands for unknown components.

Figure 4. Identification of AF-XXMD components that have similar structures to at ASPET Journals on September 27, 2021 M29 using MTSF. (A) Mass spectral tree of M29; (B) Detection of H35, H25, H56 using M29 as a temple and MTSF; (C) Structures of H35, H25, H56; (D)

Structure of M29 that was determined by comparing spectral trees of H56 and M29.

Figure 5. Proposed biotransformation network of H56 in rats. The term of “-glc” stands for glucose. The term of “-xyl” stands for xylose.

Figure 6. Proposed ADME profile of H56 in rats.

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Table 1. The total number of individual AF-XXMD components and unknowns detected in rat samples by HRMS and background subtraction.

Samplea AF-XXDM AF-XXMD Unknownsd Prototypesb Metabolitesc Plasma (0.5,1.25,3.8,12,18 h)e 13 34 15 Urine (0-12, 12-24 h) 31 70 30 Bile (0-4, 4-12, 12-24 h) 27 89 39 Feces (0-4, 4-12, 12-24 h) 99 22 26 All of samples above 113 134 92

Rats were dosed with AF-XXMD (0.5 g/kg) and plasma samples (6 time points), Downloaded from urine, bile and feces were collected. bProposed sructures and mass spectral data of AF-XXMD prototypes are shown in Supplemental Table 1 and Supplemental Figure 1. cProposed structures and mass spectral data of AF-XXMD metabolites are shown in Supplemental Table 2. dmd.aspetjournals.org dUnknown components included (1) AF-XXMD-related components that were either unknown AF-XXMD prototypes or unknown metabolites of AF-XXMD prototypes and (2) endogenous components whose levels were significantly elevated in the dosed samples. e

Summary of AF-XXMD prototypes and metabolites found in plasma is shown in at ASPET Journals on September 27, 2021 Supplemental Table 3.

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Fig. 1. dmd.aspetjournals.org MS dataset of MS dataset of MS dataset of dosing a dosed a control solution sample sample

- PATBS processing at ASPET Journals on September 27, 2021 Total TCM components Total TCM components in in dosing solution the dosed sample

- PATBS processing

TCM prototype in TCM metabolites in the dosed sample the dosed sample

MS and MS/MS MS and MS/MS datasets datasets of the TCM of TCM metabolites from parents all of samples

- MTSF processing - Biotransformation matching

Structures and formation pathways of individual TCM metabolites DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from

H105 H106-108 100 H124-126H128-131 H138-140 H109-112 (A)

H100-101 H113 dmd.aspetjournals.org Fig. 2. H123 H132-133 H97 H114-116H119-120 H127 H141 H96 H143 H142 144

0 at ASPET Journals on September 27, 2021 100 (B) M101 M88-89 M103

0 M101 U65 100 (C) M88-89 M109 Relative Abundance Relative M103 M80 H138 H124 M84 M105 U71 M81 M92 U56 U57 U61 M83 U54 H129 M110 M82 M86 H141 U68

M101 M109 (D) 100 M88-89 M103 M80 U71 M84 M105U61 M81 M92 U56 U57 M82 M83 U54 M110 M86 U68

0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 Time (min) DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from

Fig. 3. dmd.aspetjournals.org

3 (A)

1 at ASPET Journals on September 27, 2021

0 3 (B) 2

Relative Abundance Relative 0 5 10 15 20 25 30 35 40 45 50 H56 10 H138 (C) 8 U33 M89M90 H129 6 M15 M101 U21 M29 M104 M81 4 H21 U19 M46 M93 M110 H19 M26 H124 U66 2 0 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Time (min) DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from

H 483.1493 MS O O 100 ID Match Mol Mass Formula Title

dmd.aspetjournals.org + OH Score [MH ] 263.1391 O O OH O O H35 938.2 469.1704 C22H29O11 Cimifugoside OH 0 OH H25 842.5 631.223 C28H39O16 Cimifugin- 200 400 600 800 1000 H35 OH diglu

H O O H56 672.8 307.1176 C16H19O6 Cimigugin at ASPET Journals on September 27, 2021 307 MS2 100 OH O O (B) OH O O OH H OH O O OO OH 0 150 250 350 450 H25 OH O OH O O OH OH OH O O OH 235 H OH 100 289 MS3 O O M29 OH 259 221 OH O O 0 100 200 300 400 500 600 OH (D) m/z H56 (A) (C)

Fig. 4. DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from

Fig. 5 dmd.aspetjournals.org O O OH O O O HO

HO O O HO O HO O HO O O O O M42 OH M17 +SO OH at ASPET Journals on September 27, 2021 O OH 3 [Related: H35; Score: 794] H33 HO [Related: H35; Score: 944] OH O OH -xyl OH +O -CH2-2H +SO3

O O O O O O -glc +O - CH2 -2H HO HO O O HO O O HO O HO O O O OH H56 OH OH M27 HO +gluA OH [Related: H56; Score: 213] O O O O H35

HO O O O O OH O HO O OH O C O OH OH O HO C HO OH OH O M19 and M29 [Related: H35; Score: 953 and 938] DMD Fast Forward. Published on March 24, 2016 as DOI: 10.1124/dmd.115.068189 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from

Fig.6. dmd.aspetjournals.org

Dosing H33,H35, GI track H33 H35 H56 M27 at ASPET Journals on September 27, 2021 solution H56,M17, M27 M17 Feces

H33 H56 M27 Liver M42 H35 M17 M19,M29

Plasma H35, H56, H33,H35,H56,M17, M19, M29 Circulation M19,M27,M29,M42

Bile

H35,H56, M17, M27 Tissues M19,M29 H35,H56,M17,M19, M27,M29 Kidney Urine