Rapid discovery and identification of multiple absorbed chemical constituents and metabolites in rat cerebrospinal fluid after oral administration of Jitai tablets by a LC-MS based metabolomics approach

Abstract

An integrative strategy using LC-Q/TOF-MS and LC-QqQ-MS/MS coupled with multi-variate statistical analysis of principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) was developed to screen and identify the constituents transported into the rat cerebrospinal fluid (CSF) after oral administration of Jitai tablets (JTTs). CSF samples were pretreated with cold methanol prior to liquid chromatography, and the separation was carried out on a HSS T3 column with a linear gradient elution. Mass spectra were acquired in positive ion mode for analyte identification and targeted MS/MS mode for quantification. In the S-plot of OPLS-DA, twenty-six interested ions were extracted, among which, sixteen absorbed prototype components of JTT and seven metabolites were identified in vivo. Cysteine conjugation, demethylation and glucuronidation were the major metabolic reaction types of the identified constituents. An LC-MS/MS method with targeted multiple-reaction monitoring of scopolamine, tetrahydrocoptisine, tetrahydroberineper, protopine, tetrahydropulmatine and corydaline in rat CSF was developed to validate the identification results. The concentration of the six compounds in rat CSF was in the range of 0.014–1.678 ng mL⁻¹. In conclusion, a LC-MS based metabolomics approach can provide a rapid and sensitive method for characterizing bioactive components of JTT at micro concentration on pathological biopsy, which benefits further pharmacology and mechanism research of JTT.

---

1. Introduction

Opioid addiction brings about significant medical and social issues worldwide. For the current first-line medication (methadone, buprenorphine, naloxone, etc.), high risk of abuse and relapse has always been a consideration. Traditional Chinese medicine (TCM), in the meantime, attracts attention for its complementary and alternative therapeutic efficacy to western drugs, but with low toxicity in treating opiate addiction.^1–3

Jitai tablet (JTT), a well-accepted traditional Chinese medicine prescription (TCMP) approved for the treatment of opiate addiction by the State Food and Drug Administration of China, is prepared from 15 herbs, including Rhizoma Corydalis, Radix Salviae Miltiorrhiae, Radix Angelicae sinensis, Ligusticum Chuanxiong, Semen Persicae, Flos Carthami, Radix Aconite, Radix Ginseng, Cortex Cinnamomi, Rhizoma Zingiberis, Semen Myristicae, Flos Daturae, Radix Aucklandiae, Lignum Aquilariae Resinatrm and Margarita. Previous clinical trials demonstrated that this formula exhibited notable curative effect on inhibiting withdrawal symptoms and rehabilitating the abnormal physiology induced by chronic drug use with fewer harmful side-effect.^4–7 The complexity and diversity of multi-component mixtures enhanced the challenge to elucidate pharmacological mechanism of JTT. It is generally considered that the therapeutic effect of TCMP are mainly from the synergistic effect of their chemical components, whereas not all of which are certainly worthwhile for the pharmacological activity or toxicity. Therefore, it is fundamental to characterize and analyze the chemical components of JTT in vivo to elucidate the safety, efficacy and stability of this TCMP in clinical usage.

Previously, our group screened and analyzed the potential bioactive components and metabolites of JTT in orally dosed rat plasma, resulting in twenty-six chemical components and five metabolites from JTT characterized.⁸ As a complex TCMP used for treating neurological disorder, JTT's main pathological effect targets the central nervous system (CNS). Therefore, the absorbed and metabolized components in pathological tissue should be subjected for further in-depth investigation. Cerebrospinal fluid (CSF) is the ideal choice for the chemical components screening because it is most proximal to the site of neuropathology in the brain.⁹ However, CSF is a challenging matrix to work with due to the following issues: (1) only limited constituents can pass through the blood–brain barrier into CSF; (2) micro or trace concentration of endogenous and exogenous metabolites with limited volume; (3) prone to contamination with blood in sample collection; (4) limited techniques on either qualitative or quantitative analysis of CSF in vivo.

To address the limitations mentioned above, high-throughput metabolomics, incorporating advanced analytical instruments, with aims at the comprehensive characterization of metabolites in complex samples, is of great importance.¹⁰ Being the most commonly used state-of-the-art analytical approaches in metabolomics applications, liquid chromatography tandem mass spectrometry (LC-MS) technologies are popularly applied for qualitative and quantitative measurement of metabolites in complex biological systems.^11–14 With the sizable amount of raw data processed by LC-MS, a robust discrimination method should be developed to facilitate the identification results, because the analytical method searching manually and intuitionally may leave important components undiscovered, especially for some metabolites in micro amount of TCMP. Multi-variate statistic analysis, such as principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), have been typically applied to study metabolites and differences between groups.^15–17 The multi-variate statistic analysis can extract the constituents with no dependence of previous knowledge of the compound structure. Therefore, in combination with bioinformatics tools, successful studies demonstrated that metabolomics incorporating state-of-the-art analytical approaches enables systematic molecular characterization of complex samples and providing an integrated biochemical view of TCM.^18–20

In the present study, an integrative strategy using liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (LC-Q/TOF-MS) and liquid chromatography tandem mass spectrometry (LC-QqQ-MS/MS) followed by multi-variate statistic analysis was applied to explore and identify the constituents or metabolites transported into the rat CSF after oral administration of JTT.

2. Experimental

2.1. Chemicals and reagents

Reference standards such as scopolamine, protopine, tetrahydropulmatine were purchased from the National Institute for Food and Drug Control (Beijing, China). Tetrahydroberineper, tetrahydrocoptisine and corydaline were obtained from Shanghai Sunny Biotech Co., Ltd (Shanghai, China). JTT samples (batch number: 120102) were kindly supplied by National Engineering Research Center for TCM Shanghai TCM technology Co., Ltd. (Shanghai, China).

Acetonitrile and methanol of mass-spectrometry grade were obtained from Merck (Darmstadt, Germany). Formic acid was purchased from Sigma-Aldrich (St. Louis, MO, USA). All aqueous solutions were prepared with ultra pure water produced from a Milli-Q50 SP Reagent Water System (Bedford, MA, USA). Other reagents were of analytical grade or higher if not otherwise stated.

2.2. Preparation of analytical sample of JTT extract and standard solutions

JTT was ground into fine powder and sieved through a no. 40 mesh sieve to get a homogeneous size. 80 mg was accurately weighed into a 10 mL volumetric flask. Methanol–water (50 : 50, v/v) was added and ultrasonic extracted for 30 min, and then cooled at room temperature. The same solvent was added to compensate for the lost volume to get a uniform suspension, then centrifuged at 13 500 × g for 10 min at 4 °C (Universal 320R, Hettich, Germany) and the supernatant was filtered through a syringe filter (0.22 μm), and an aliquot (5 μL) of supernatant was subjected to LC analysis.

The stock solutions of scopolamine, protopine, tetrahydropulmatine, tetrahydroberineper, tetrahydrocoptisine and corydaline were individually prepared in methanol to a final concentration of 200 μg mL⁻¹. All the stock solutions were stored in the refrigerator at 4 °C and were stable for 3 months. Different volumes of each stock solution were transferred into volumetric flasks and then diluted to volume to make working standard solution with methanol. Calibration work solutions were prepared by diluting stock solutions with methanol, a series of final concentration were obtained. Calibration work solutions were prepared before analysis.

2.3. Animals and drug administration

Twelve male Sprague-Dawley (SD) rats (body weight: 180–220 g) were obtained from Shanghai SLAC Lab. Animal Co., Ltd. (Shanghai, China). The animals were maintained in propylene cages (target conditions: temperature 22 to 26 °C, relative humidity 40 to 70% and 12 h dark–light cycle) with free access to standard laboratory chow and water for 5 days acclimation. The animals were randomly divided into two groups: dosed group and control group with six rats in each. Animal experiments were carried out according to the Guidelines for the Care and Use of Laboratory Animals, and were approved by the Animal Ethics Committee of the Second Military Medical University. JTT extracts were dissolved with 0.5% carboxymethyl cellulose sodium salt (CMC-Na) aqueous solution as stock solution (0.75 g mL⁻¹). The prepared suspension was intragastrically administered to dosed group (15 mL kg⁻¹ body weight) once a day for three consecutive days. The equivalent volume of 0.5% CMC-Na aqueous solution was orally administered to control group. One hour after last oral administration of JTT and vehicle, the animals were anaesthetized by intraperitoneal injection of 1% pentobarbital sodium (0.15 mL/100 g body weight). CSF samples were taken from the cisterna magna by percutaneous puncture while animals were under pentobarbital anesthesia. The CSF samples were then centrifuged at 13 500 × g at 4 °C for 10 min to remove cells and the supernatant was stored at −80 °C for further analysis.

2.4. CSF sample preparation

CSF samples were thawed at room temperature and sample preparation started within 30 min of thawing the samples. Four-fold volume of cold methanol was added to 100 μL CSF sample, vortexed for 2 min, and then centrifuged at 13 500 × g at 4 °C for 10 min. The supernatant from centrifugation was collected and then dried using a vacuum evaporator (Labconco, Hettich AG, Bäch, Switzerland). The residue was dissolved in 50 μL water–acetonitrile (50 : 50, v/v) and centrifuged at 13 500 × g, 4 °C for 5 min again to obtain a clear extract.

2.5. LC-Q/TOF-MS analysis

Liquid chromatography was performed on an Agilent 1200 Series liquid chromatography (Agilent Technologies, Santa Clara, CA, USA), equipped with a quaternary pump with on-line degasser, auto-sampler and column oven. Chromatographic separation was performed on an ACQUITY HSS T3 (100 mm × 2.1 mm, 1.8 μm, Waters, MA, Ireland). Analytical column was maintained at 40 °C and eluted with a mobile phase consisting of (A) acetonitrile containing 0.1% formic acid and (B) water containing 0.1% formic acid using the following gradient program: 10% → 30% A at 0–8.0 min; 30% A → 95% A at 8–18 min; 95% A at 18–20 min at a flow rate of 0.4 mL min⁻¹. The equilibration time was 5 min. The injection volume of reference compounds and samples was 10 μL.

Mass spectrometry was performed on an Agilent 6538 series accurate quadrupole-time-of-flight mass spectrometer (Agilent Technologies, Santa Clara, CA, USA), equipped with electrospray ionization (ESI) source. The mass spectrometer was operated in positive ion mode. The drying gas temperature was maintained at 350 °C at a flow rate of 11 L min⁻¹, and the nebulizing gas (N₂) pressure was set at 45 psi. The scan parameters were set as follows: capillary, 3500 V; skimmer voltage, 60 V; octapole RF peak, 750 V; fragmentor, 135 V. Data were collected in centroid mode over the m/z 100–1500 range at a scan rate of 0.2 s per spectrum. The mass axis was calibrated for both positive and negative every day before analysis over the m/z 100–1500 range using TOF tuning mix bottle B obtained from Agilent Technologies. To ensure accuracy and reproducibility, a sprayer with a reference solution was used as continuous calibration in positive ion mode using the reference masses: 121.0509 and 922.0098 m/z. The MS/MS analysis was conducted in targeted MS/MS mode at four different collision energies 10, 20, 30, 40 V in positive mode.

2.6. Pattern recognition analysis

The raw data of all determined samples was further processed by molecular feature extraction (MFE) in Agilent MassHunter Workstation Software (Agilent Technologies, Santa Clara, CA, USA, version B.03.01) to search all the ions that represent real molecules. The parameters for data processing method were set as follows: retention time range 0.1–20.0 min, mass range 100–1500 Da, with a signal threshold of 100 counts (height). This processing step created MHD files containing compound IDs (based on neutral mass and retention times). Mass Profiler software (version B.02.00, Agilent Technologies) was then employed for peak alignment, normalization and further filtering. The alignment parameters were set as follows: retention time tolerance 0.1 min, mass tolerance 0.01 Da. This step created an Excel file containing a list of the intensities of the detected peaks using retention time (t_R) and mass data (m/z) pairs as the identifier of each peak. The resulting three-dimensional data comprising of peak number (t_R–m/z pair), sample name and ion intensity were exported to the software SIMCA-P (Ver 11, Umetrics, Umea, Sweden) for PCA and OPLS-DA.

2.7. Validation and quantitative determination of the identified compounds by LC-QqQ-MS/MS

To further confirm identified compounds were specific to the dosed group, the peaks were quantitatively analyzed by LC-QqQ-MS/MS with an Agilent 6410 triple quadrupole mass spectrometer. CSF extract (10 μL) was injected and analyzed with the same analytical column and mobile phases as LC-Q/TOF-MS analysis. The temperature was maintained at 35 °C and eluted with a mobile phase consisting of acetonitrile (A) and water containing 0.1% formic acid (B) using the following gradient program: 20% A → 45% A at 0–1.0 min; 45% A at 1.0–2.5 min; 45% A → 20% A at 2.5–4.0 min; 20% A at 4.0–7.0 min at a flow rate of 0.3 mL min⁻¹. The total run time was 7 min and the equilibrated time was 2 min. Peaks were automatically integrated using instrument software.

3. Results and discussion

3.1. Optimization of LC-MS condition

For LC condition optimization, a sub-micron particle size ACQUITY HSS T3 column was selected for the separation of untargeted constituents, which can not only maintain the retention of low polarity compounds but also enhance the retention of water-soluble organic molecules. In order to obtain high efficient chromatographic separation and enhanced ionization efficiency, different mobile phases (methanol–water and acetonitrile–water) were compared, acetonitrile–water was chosen for its efficient chromatography, appropriate ionization and shorter run time for the analytes. Meanwhile, the addition of 0.1% (v/v) formic acid could significantly improve the peak shape and restrain the peak tailing, as well as enhance the ion response of analytes in positive ion mode. Gradient elution was employed to obtain an effective separation and avoid interferences from endogenous matrix in CSF. A higher column temperature (40 °C) was adopted to reduce the high pressure caused by sub-micron particle size column.

For MS condition optimization, positive ion mode was considered suitable for detecting most constituents of JTT. For instance, alkaloids, phthalides, etc. showed better response under this condition. Generally, the higher fragmentor voltage was applied, the more information of fragment ions could be acquired, meanwhile the lower of sensitivity was obtained under such circumstance, because the intensity of the [M + H]⁺ ions was reduced by forming fragment ions. As a trade-off between sensitivity and fragmentation, the voltage at the exit of the capillary (fragmentor voltage) was found to be optimum at 135 V.

3.2. Optimization of sample preparation

Effective sample preparation could not only increase the coverage of target trace compounds but also decrease ion suppression or enhancement caused by co-eluting endogenous materials. Solid-phase extraction (SPE) with Waters Oasis HLB cartridges (Milford, MA, USA) was initially tested. However, it showed limited trapping efficiency to compounds with required range of polarity and acid-base property. Considering the physicochemical property of target components, the protein precipitation with methanol was eventually chosen to ensure the simultaneous extraction of the compounds and less interference from the co-eluted endogenous matrix. Meanwhile, cold methanol was selected to prevent degradation of chemical components in CSF.

3.3. PCA and OPLS-DA analysis

PCA and supervised OPLS-DA were performed to phenotypically discriminate the dosed group and the control. After Pareto scaling with mean-centering, the data was displayed as score plot (Fig. 1A). In the PCA score plots, each spot represented a CSF sample. The scores plot showed that the two groups were clearly clustered into two classes, indicating that the administration of JTT induced metabolic changes in the CSF. In order to explore the constituents for the discrimination between dosed group and control group, a more advanced OPLS-DA was conducted to generate S-plot (Fig. 1B). In the S-plot, each spot represented a unique compound ion detected by LC-Q/TOF-MS, the x axis represented variable contribution, where the farther the distance of the ion points from zero, the more the ion contributed to the difference between two groups; the y axis represented variable confidence, where the farther the distance the ion points from zero, the higher the confidence level of the ion to the difference between two groups. Consequently, the ion pair points at the two ends represented characteristic markers with the most confidence to each group. From S-plot, the ion even with subtle difference between dosed group and control group can be easily obtained. From the trend plots, the ions presented in dosed group and absent in control group can be directly extracted in the red frame at the top of the S-plot (Fig. 1C shows the t_R–m/z 5.22–319.1180 ion as an example). Based on OPLS-DA of the datasets of the dosed group and control group as illustrated above, twenty-six interested ions were extracted, and their information is shown in Table 1.

Fig. 1 Multivariate statistical analysis of LC-MS constituents in CSF dosed with JTT in positive mode. (A) PCA score plot of LC-MS spectra of dosed group and control group, (B) S-plot of OPLS-DA model for dosed group and control group. (C) The trend plot of 5.22–319.1180.

Table 1 MS and MS/MS data of the identified components in rat CSF after oral administration of JTT in positive ion mode

Peak no.	Name	Source herb	tR (min)	Molecular formula	Measured [M + H]^+	Theoretical exact mass (Da)	Mass accuracy (ppm)	Fragment ions (m/z)	Fragment ions formula
1	Atropine	Flos Daturae	2.80	C17H23NO3	290.1798	289.1678	−1.22	124.1119	[M + H–C9H10O3]^+
2	Scopolamine	Flos Daturae	2.85	C17H21NO4	304.1564	303.1471	0.84	156.1024	[M + H–C9H8O2]^+
								138.0914	[M + H–C9H8O2–H2O]^+
								121.0650	[M + H–C9H8O2–2H2O]^+
3	Fuziline	Radix Aconite	3.41	C24H39NO7	454.2801	453.2727	−0.33	436.2659	[M + H–H2O]^+
								422.2556	[M + H–CH3OH]^+
								418.2497	[M + H–2H2O]^+
4	Neoline	Radix Aconite	3.75	C24H39NO6	438.2857	437.2777	−1.51	420.2737	[M + H–H2O]^+
								388.2523	[M + H–H2O–CH3OH]^+
								370.2350	[M + H–2H2O–CH3OH]^+
5	Talatizamine	Radix Aconite	4.58	C24H39NO5	422.2908	421.2828	−1.70	390.2738	[M + H–CH3OH]^+
								372.2535	[M + H–CH3OH–H2O]^+
6	Agarotetrol	Radix Aucklandiae	5.22	C17H18O6	319.1180	318.1103	−0.79	301.1121	[M + H–H2O]^+
								283.1006	[M + H–2H2O]^+
								255.1058	[M + H–2H2O–CO]^+
7	Chasmanine	Radix Aconite	5.48	C25H41NO6	452.3031	451.2934	−2.52	434.2568	[M + H–H2O]^+
								420.2717	[M + H–CH3OH]^+
								370.2356	[M + H–H2O–2CH3OH]^+
8	Corypalmine	Rhizoma Corydalis	5.51	C20H23NO4	342.1761	341.1627	−1.25	178.0896	[M + H–C10H11O2]^+
								163.0655	[M + H–C10H12NO2]^+
9	14-Acetyltalatizamine	Radix Aconite	6.20	C26H41NO6	464.3021	463.2934	−3.04	432.2778	[M + H–CH3OH]^+
								400.2533	[M + H–2CH3OH]^+
10	Protopine	Rhizoma Corydalis	6.77	C20H19NO5	354.1339	353.1263	0.95	206.0788	[M + H–C9H8O2]^+
								189.0780	[M + H–C9H8O2–H2O]^+
								149.0596	[M + H–C11H11NO3]^+
11	Allocryptopine	Rhizoma Corydalis	7.41	C21H23NO5	370.1657	369.1576	−0.01	352.1542	[M + H–H2O]^+
								336.1235	[M + H–H2O–CH4]^+
								306.0882	[M + H–H2O–CH4–2CH3]^+
								290.0932	[M + H–H2O–2CH4–CO]^+
								206.0816	[M + H–C10H12NO2]^+
								188.0714	[M + H–C10H12NO2–H2O]^+
12	Tetrahydropalmatine	Rhizoma Corydalis	7.45	C21H25NO4	356.1872	355.1784	−0.09	340.1523	[M + H–H2O]^+
								192.1011	[M + H–C10H12O2]^+
								165.0906	[M + H–C11H13NO2]^+
13	Tetrahydrocoptisine	Rhizoma Corydalis	7.57	C19H17NO4	324.1228	323.1158	−0.08	176.0711	[M + H–C9H8O2]^+
								149.0602	[M + H–C10H9NO2]^+
14	Senkyunolide F	Ligusticum Chuanxiong	7.77	C12H14O3	207.1011	206.0943	−2.18	189.0905	[M + H–H2O]^+
								161.0953	[M + H–H2O–CO]^+
15	Tetrahydroberineper	Rhizoma Corydalis	8.14	C20H22NO4^+	340.1541	340.1549	−0.08	176.0704	[M + H–C10H12O2]^+
								149.0592	[M + H–C11H13NO2]^+
16	Corydaline	Rhizoma Corydalis	8.41	C22H27NO4	370.2024	369.1940	−0.08	192.1019	[M + H–C11H14O2]^+
								165.0918	[M + H–C12H15NO2]^+
M1	Demethylated metabolite of talatizamine	Radix Aconite	2.72	C23H37NO5	408.2758	407.2672	0.06	390.2632	[M + H–H2O]^+
								372.2529	[M + H–2H2O]^+
								358.2357	[M + H–H2O–CH3OH]^+
M2	Glucuronide conjugate of demethyltetrahydropalmatine	Rhizoma Corydalis	3.62	C26H31NO10	518.2068	517.1948	0.18	342.1732	[M + H–(GluA–H2O)]^+
								178.0864	[M + H–(GluA–H2O)–C10H12O2]^+
M3	Demethylated metabolite of 14-acetyltalatizamine	Radix Aconite	4.03	C25H39NO6	450.2856	449.2777	4.23	418.2632	[M + H–CH3OH]^+
								386.2874	[M + H–2CH3OH]^+
M4	Glucuronide conjugate of tetrahydropalmatine	Rhizoma Corydalis	5.80	C27H33NO10	532.2198	531.2104	−3.73	356.1883	[M + H–(GluA–H2O)]^+
								192.1011	[M + H–(GluA–H2O)–C10H8O3]^+
M5	Cysteine conjugate of senkyunolide I	Ligusticum Chuanxiong	6.93	C15H21NO5S	328.1239	327.114	0.13	282.1180	[M + H–HCOOH]^+
								207.1070	[M + H–C3H7NO2S]^+
M6	Acetylcysteine conjugate of senkyunolide I	Ligusticum Chuanxiong	7.23	C22H31N3O9S	514.1859	513.1781	−0.19	439.1546	[M + H–C2H5NO2]^+
								385.1445	[M + H–C5H7NO3]^+
								282.1168	[M + H–C5H7NO3–C3H5NO3]^+
								207.1027	[M + H–C5H7NO3–C3H5NO3–C3H7NO2S]^+
M7	Unknown	Unknown	7.89	C14H25NO11	384.1498	383.1428	0.61	208.1185	[M–(GluA–H2O)]^+
M8	Unknown	Unknown	8.13	C19H15NO6	354.0982	353.0899	−3.09	336.0887	[M + H–H2O]^+
								320.0589	[M + H–H2O–CH4]^+
M9	Unknown	Unknown	10.30	C20H19NO6	370.1301	369.1212	−4.23	352.1321	[M + H–H2O]^+
								336.1223	[M + H–H2O–CH4]^+
M10	Glucuronide conjugate of protopine	Rhizoma Corydalis	10.47	C26H27NO11	530.1668	529.1584	−2.21	354.1339	[M + H–(GluA–H2O)]^+

---

3.4. Analysis of prototype constituents of JTT in rat CSF

In this study, the LC-Q/TOF-MS technique was developed to identify the multiple absorbed components in CSF after oral administration of JTT. The chemical structure of reference standards is shown in Fig. 2. Total ion chromatograms (TIC) of scopolamine, tetrahydrocoptisine, tetrahydroberineper, protopine, tetrahydropulmatine and corydaline in neat solution, JTT extracts, control CSF, and dosed CSF are shown in Fig. 3. The retention time (t_R), MS data, and fragments of absorbed constituents are listed in Table 1. The constituents in rat CSF after oral administration of JTT were well separated and identified by their retention time and mass spectra. In order to obtain MS fragmentation patterns of constituents in CSF, MS/MS spectra of six reference standards were detected by LC-Q/TOF-MS under the above optimum MS condition. The information of their retention time and mass spectra for the comparison with those of peaks detected in the dosed CSF was obtained from the chromatogram. Their fragmentation patterns were summarized. In the full scan mass spectra, [M + H]⁺ and [M + H–H₂O]⁺ were observed in positive ion mode. The information of [M + H]⁺ was utilized to determine molecular weight (MW). The identification of these compounds was carried out referring to their MS and MS/MS spectra. As a result, twenty-six compounds, including two scopola alkaloids (peak 1 and 2) from Flos Daturae, five aconitine-type alkaloids (peak 3, 4, 5, 7 and 9) from Radix Aconite, seven isoquinoline alkoloids (peak 8, 10, 11, 12, 13, 15 and 16) from Radix Aucklandiae, one 2-(2-phenylethyl) chromon (peak 6) from Radix Aucklandiae, one phthalide (peak 14) from Radix Angelicae sinensis and Ligusticum chuanxiong were eventually identified by comparing the extracted ion chromatograms (EICs) of target peaks in TIC of dosed rat CSF and JTT. Among of which, peak 2, 10, 12, 13, 15 and 16 were unambiguously identified as scopolamine, protopine, tetrahydropulmatine, tetrahydrocoptisine, tetrahydroberineper and corydaline, respectively. Owing to the unavailability of reference standards, peak 1, 3, 4, 5, 6, 7, 8, 9, 11, and 14 could only be tentatively identified as atropine,²¹ fuziline,²² neoline,²² talatizamine,²³ chasmanine,²³ 14-acetyltalatizamine,²³ agarotetrol,²⁴ corypalmine,²⁵ allocryptopine²⁶ and senkyunolide F²⁷ by comparing their retention behaviors and MS spectra with literature data. Here, allocryptopine at the retention time of 7.41 min is chosen as an example for the illustration of the identification approach of isoquinoline alkoloids originated from Rhizoma Corydalis. Protonated molecular ion m/z 370.1657 [M + H]⁺ was observed in the MS spectra. In the MS/MS spectra, m/z 352.1542 [M + H–H₂O]⁺ was formed by the neutral loss of H₂O, while m/z 206.0816 [M + H–C₁₀H₁₂O₂]⁺ was produced by undergoing similar Retro-Diels–Alder (RDA) fragmentation reaction. Furthermore, the neutral loss of H₂O (m/z 206.0816 → 188.0714) was also observed, and the ion at m/z 188.0714 can be assigned to an isoquinoline fragment formed by the cleavage of the central ring system, which was also significant in the MS/MS spectra. The MS/MS fragmentation of allocryptopine was in good agreement with the literature (Fig. 4).²⁶

Fig. 2 Chemical structures of identified compounds in rat CSF after oral administration of JTT.

Fig. 3 Total ion chromatography of (A) controlled rat CSF, (B) dosed rat CSF and (C) JTT extract in positive ion mode.

Fig. 4 Chemical structure, mass fragment information and possible mechanistic pathway of fragmentations of allocryptopine in rats CSF.

3.5. Characterization of metabolites of JTT in rat CSF

TIC of tentatively identified metabolites in rat CSF after oral administration of JTT is shown in Fig. 3. The possible metabolic process of major constituents is shown in Table 1. Reduction, hydration, demethylation, as well as glucuronide conjugation and sulfate conjugation, were the common metabolic processes affecting most constituents of JTT in vivo.

M1 exhibited a protonated molecule [M + H]⁺ ion at m/z 408.2758 (C₂₃H₃₇NO₅), 14 Da (–CH₂) less than that of the protonated molecular ion of talatizamine (m/z 422.2908, C₂₄H₃₉NO₅). When the ion was cracked in high collision energy, it yielded fragment ions at m/z 390.2632 [M + H–H₂O]⁺, 372.2529 [M + H–2H₂O]⁺, and 358.2357 [M + H–H₂O–CH₃OH]⁺, which were 14 Da (–CH₂) less than that of the corresponding fragment ions of talatizamine. Based on the MS pattern and literature data,²³ M1 was proposed to be demethylated metabolite of talatizamine. In the same way, M3 was tentatively identified to be demethylated metabolite of 14-acetyltalatizamine probably due to its unique neutral losses of CH₃OH.

Glucuronidation is a major phase II reaction type in biotransformation, metabolism and disposition of drug. M4 at the retention time of 5.80 min was chosen as an example to explain the procedure of identification. Predominant [M + H]⁺ ion at m/z 531.2104 was observed in MS spectra, which showed 176 Da (C₆H₈O₆) higher than that of tetrahydropalmatine. In its MS/MS spectra, fragment ions m/z 356.1883 [M + H–(GluA–H₂O)]⁺ and m/z 192.1011 [M + H–(GluA–H₂O)–C₁₀H₈O₃]⁺ were found, indicating the neutral loss of H₂O and a typical result of RDA cleavage from quinoline alkaloids. Therefore, M4 was proposed to be glucuronide conjugate of tetrahydropalmatine. In the same way, M2 was identified as glucuronide conjugate of demethyl tetrahydropalmatine in our published report.²⁸

M5 at the retention time of 6.93 min was suspected to be a metabolite of senkyunolide I. Predominant [M + H]⁺ ion at m/z 328.1239 was observed in the MS spectra. The characteristic ion indicated MW of 327, 103 Da higher than the MW of senkyunolide I (224), suggesting the presence of one cysteine residue in M5. In the MS/MS spectra, fragment ions at m/z 282.1180, 207.107 were formed by loss of 46 Da (CH₂O₂) and 121 Da (cysteine) from the [M + H]⁺ ion. By comparison with literature data, M5 was designated as cysteine conjugate of senkyunolide I.²⁹ In the same way, M6 was tentatively identified as acetylcysteine conjugate of senkyunolide I (Fig. 5).²⁹

Fig. 5 Chemical structure, mass fragment information and possible mechanistic pathway of fragmentations of cysteine conjugate of senkyunolide I in rats CSF.

3.6. Validation and quantitative determination of the identified compounds by LC-QqQ-MS/MS

To further confirm the identification results and the concentration of the identified component in CSF, a targeted multiple reaction monitoring (MRM) of scopolamine, tetrahydrocoptisine, tetrahydroberineper, protopine, tetrahydropulmatine and corydaline in rat CSF by LC-QqQ-MS/MS was developed. The MRM transitions were chosen as m/z 304.2 → 156.0 for scopolamine, m/z 324.1 → 176.0 for tetrahydrocoptisine, m/z 340.2 → 176.0 for tetrahydrocoptisine, m/z 354.1 → 188.0 for protopine, m/z 356.2 → 192.0 for tetrahydropulmatine and m/z 370.2 → 192.0 for corydaline in positive mode, while the retention time was 1.6 min, 4.7 min, 4.8 min, 4.5 min, 4.6 min and 4.9 min for each number, respectively (Fig. 6).

Fig. 6 Representative MRM chromatograms of the six analytes: (A) control CSF sample, (B) dosed CSF sample.

Method validations including the calibration curves, lower limit of quantification (LLOQ) were carried out. Calibration work solutions were prepared by serially diluted stock solutions to the desired concentrations ranged from 0.01 ng mL⁻¹ to 10 ng mL⁻¹ for all the six analytes. Calibration curves were constructed from the peak area of each compound containing six non-zero concentrations using least square weighted (1/x²) linear regression. The LLOQ was calculated as the lowest concentration of each sample that could be determined with an acceptable accuracy and precision. In the present study, the concentrations of six analytes were successfully determined by the established method. The mean concentration was 1.678 ng mL⁻¹ for scopolamine, 0.024 ng mL⁻¹ for tetrahydrocoptisine, 0.014 ng mL⁻¹ for tetrahydroberineper, 0.162 ng mL⁻¹ for protopine 0.587 ng mL⁻¹ for tetrahydropulmatine and 0.051 ng mL⁻¹ for corydaline, respectively (the results are shown in Table 2).

Table 2 The regression data, LLOQ and quantification results of six analytes in rat CSF

Analyte	Regression equation	r	Linear range (ng mL^−1)	LLOQ (ng mL^−1)	CSF conc. (ng mL^−1, n = 6)
Scopolamine	y = 0.3574x + 2.5023	0.9987	0.01–10	0.01	1.678
Tetrahydrocoptisine	y = 0.3746x + 2.9195	0.9981	0.01–10	0.01	0.024
Tetrahydroberineper	y = 0.4676x + 4.7325	0.9983	0.01–10	0.01	0.014
Protopine	y = 0.5286x + 4.8869	0.9983	0.01–10	0.01	0.162
Tetrahydropulmatine	y = 2.2834x + 1.2376	0.9992	0.01–10	0.01	0.587
Corydaline	y = 1.3191x + 6.5471	0.9993	0.01–10	0.01	0.051

---

4. Conclusion

In the present study, a rapid, sensitive and reliable method using LC-Q/TOF-MS and LC-QqQ-MS/MS coupled with multi-variate statistic analysis was applied to explore and identify the constituents or metabolites transported into the rat cerebrospinal fluid (CSF) after oral administration of JTT. In the S-plot of OPLS-DA, twenty-six interested ions were identified, among which, sixteen absorbed prototype components of JTT and seven metabolites were characterized in vivo. Major metabolic reactions of identified compounds were cysteine conjugation, demethylation and glucuronidation. Moreover, the simultaneous determination of scopolamine, tetrahydrocoptisine, tetrahydroberineper, protopine, tetrahydropulmatine and corydaline in rat CSF was carried out by established LC-QqQ-MS/MS method. This is the first report on systematic analysis of JTT on its pharmacological target using LC-MS based metabolomics approach. These works could provide more in-depth insights into the active constituents working in vivo and would be fundamental for further revealing the pharmacology and mechanism of JTT.

Acknowledgements

The work was supported by program NCET Foundation, NSFC (81230090, 81402819, 81402844), partially supported by Global Research Network for Medicinal Plants (GRNMP), King Saud University, Shanghai Leading Academic Discipline Project (B906), Key laboratory of drug research for special environments, PLA, Shanghai Engineering Research Center for the Preparation of Bioactive Natural Products (10DZ2251300), the Scientific Foundation of Shanghai China (12401900801, 13401900101, 13ZR1408500), National Major Project of China (2011ZX09307-002-03) and the National Key Technology R&D Program of China (2012BAI29B06).

References

1. P. Lobmaier, M. Gossop, H. Waal and J. Bramness, Eur. J. Clin. Pharmacol., 2010, 66, 537–545.
2. C. T. Keith, A. A. Borisy and B. R. Stockwell, Nat. Rev. Drug Discovery, 2005, 4, 71–78.
3. L. Wang, G. B. Zhou, P. Liu, J. H. Song, Y. Liang, X. J. Yan, F. Xu, B. S. Wang, J. H. Mao, Z. X. Shen, S. J. Chen and Z. Chen, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 4826–4831.
4. S. A. Tu, J. L. Gao, J. Liu, J. M. Zhang, Y. Y. Huang, S. S. Xu, M. Han and J. H. Liang, Phytother. Res., 2015, 29, 687–694.
5. S. Q. Hao, M. Zhao, R. W. Zhang, J. C. Zhang, J. Zhang and X. S. Feng, Addict. Behav.: Mol. Mankind, 2013, 38, 2596–2600.
6. Y. Liu, M. Han, X. D. Liu, Y. P. Deng, Y. Li, J. Yuan, R. B. Lv, Y. K. Wang, G. M. Zhang and J. L. Gao, Psychopharmacology, 2013, 230, 235–244.
7. H. Y. Fan, L. Sun, X. X. Li, S. B. Zhou, J. C. Liang, B. Y. Yan, Y. Li and Y. P. Deng, Jpn. J. Compl. Alternative Med., 2014, 20, 527–534.
8. S. P. Wang, L. Liu, L. L. Wang, P. Jiang, J. Q. Zhang, W. D. Zhang and R. H. Liu, Rapid Commun. Mass Spectrom., 2010, 24, 1641–1652.
9. W. R. Wikoff, G. Pendyala, G. Siuzdak and H. S. Fox, J. Clin. Invest., 2008, 118, 2661–2669.
10. C. Hu and G. Xu, TrAC, Trends Anal. Chem., 2014, 61, 207–214.
11. K. Li, Q. Fu, H. Xin, Y. Ke, Y. Jin and X. Liang, Analyst, 2014, 139, 3577–3587.
12. X. Ma, A. K. M. Leung, C. L. Chan, T. Su, W. Li, S. Li, D. W. F. Fong and Z. L. Yu, Analyst, 2014, 139, 505–516.
13. H. Sun, B. Ni, A. Zhang, M. Wang, H. Dong and X. Wang, Analyst, 2012, 137, 170–185.
14. S. L. Li, H. Shen, L. Y. Zhu, J. Xu, X. B. Jia, H. M. Zhang, G. Lin, H. Cai, B. C. Cai, S. L. Chen and H. X. Xu, J. Chromatogr. A, 2012, 1231, 31–45.
15. W. Y. Lu, B. D. Bennett and J. D. Rabinowitz, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci., 2008, 871, 236–242.
16. G. X. Xie, Y. Ni, M. M. Su, Y. Y. Zhang, A. H. Zhao, X. F. Gao, Z. Liu, P. G. Xiao and W. Jia, Metabolomics, 2008, 4, 248–260.
17. A. Zhang, H. Sun, G. Yan, P. Wang, Y. Han and X. Wang, Cancer Lett., 2014, 345, 17–20.
18. L. Yan, W. Nie, T. Parker, Z. Upton and H. Lu, Future Med. Chem., 2013, 5, 1953–1965.
19. P. Wang, H. Sun, H. T. Lv, W. Sun, Y. Yuan, Y. Han, D. Wang, A. Zhang and X. Wang, J. Pharm. Biomed. Anal., 2010, 53, 631–645.
20. Z. H. Su, S. Q. Li, G. A. Zou, C. Y. Yu, Y. G. Sun, H. W. Zhang, Y. Gu and Z. M. Zou, J. Pharm. Biomed. Anal., 2011, 55, 533–539.
21. H. Chen, Y. Chen, H. Wang and P. Du, Talanta, 2005, 67, 984–991.
22. H. Sun, B. Ni, A. Zhang, M. Wang, H. Dong and X. Wang, Analyst, 2012, 137, 170–185.
23. G. Tan, Z. Lou, J. Jing, W. Li, Z. Zhu, L. Zhao, G. Zhang and Y. Chai, Biomed. Chromatogr., 2011, 12, 1343–1351.
24. C. Lancaster and E. Espinoza, Rapid Commun. Mass Spectrom., 2012, 26, 2649–2656.
25. L. Li, M. Ye, K. Bi and D. Guo, Biomed. Chromatogr., 2006, 20, 95–100.
26. E. Jeong, S. Y. Lee, S. M. Yu, N. H. Park, H. Lee, Y. Yim, G. Hwang, C. Cheong, J. H. Jung and J. Hong, Rapid Commun. Mass Spectrom., 2012, 26, 1661–1674.
27. C. He, S. Wang, Y. Feng, S. Liang, X. Lin, D. Xu and K. Ruan, J. Ethnopharmacol., 2012, 142, 706–713.
28. S. P. Wang, L. Liu, L. L. Wang, P. Jiang, J. Q. Zhang, W. D. Zhang and R. H. Liu, Rapid Commun. Mass Spectrom., 2010, 24, 1641–1652.
29. A. Zuo, L. Wang, H. Xiao, L. Li, Y. Liu and J. Yi, J. Pharm. Biomed. Anal., 2011, 56, 1046–1056.

---

Footnote

† These authors contributed equally to this work and should be considered as the first authors.

---