Abstract
Nowadays, breast disorders seriously affect women’s health in an
increasing number. In China, Xiaojin Pills are commonly used in the
treatment of breast diseases. Doctors have concluded that the combined
use of Xiaojin Pills with conventional therapy can significantly
improve the efficacy with fewer side effects. However, the prescription
of Xiaojin Pills is complicated and their quality control methods
cannot completely ensure the quality of Xiaojin Pills. On the basis of
its mechanism, our study combined chemical evaluation and biological
evaluation to identify the anti-inflammatory markers of Xiaojin Pills.
In this manuscript, 13 compounds in Xiaojin Pills were quantified. At
the same time, the cyclooxygenase-2 inhibition rates of different
Xiaojin Pills were measured and the possible markers were screened by
spectrum-effect relationship. Further, anti-inflammatory activities of
markers were verified and protein interaction network was analyzed,
identifying the components of Protocatechuate, Beta-Boswellic acid and
Levistilide A as the anti-inflammatory quality markers of Xiaojin
Pills. We hope our studies can provide a scientific theoretical basis
for accurately quality control of Xiaojin Pills and reasonable
suggestions for pharmaceutical companies and new ideas for the quality
control of other medicines.
Introduction
With the acceleration of life pace and dramatic changes in society and
natural environment, breast disease has become one of the most
important factors threatening women’s health^[42]1,[43]2. Among them,
hyperplasia of mammary gland (HMG) is the most common problem troubled
middle-aged women for its high morbidity, and recurrence
rate^[44]3,[45]4. What’s more, the prevalence of breast cancer in
cyclomastopathy patients is as high as 2–4 times in healthy
individuals^[46]5–[47]8. Although the etiology of HMG is still not
fully clear and appropriate therapies are limited^[48]9, more and more
evidences^[49]10–[50]13 indicate that the high expression of
cyclooxygenase-2 (COX-2) is closely related to the inflammatory
response and cell carcinogenesis.
At present, although conventional hormone therapy has a certain effect
on the treatment of HMG, it is easy to relapse, and lead to endocrine
disorders. In order to improve the effectiveness and alleviate the side
effects, Chinese clinicians often combined Western medicine and
Traditional Chinese Medicine (TCM) to treat HMG. The first choice of
Chinese patent medicine is Xiaojin Pills, a famous prescription with a
history of 200 years^[51]14. Xiaojin Pills have definite effects of
dispersing swelling, detumescence, promoting blood circulation, and
relieving pain. The whole regulation, multi-target and multi-channel
action model makes it have a better therapeutic advantage^[52]2,[53]15.
Meta-analysis of 858 cases showed that on the basis of conventional
western medicine treatment, combined with Xiaojin Pills treated for
HMG, the total effective rate and cure rate was significantly higher
than that of the control group, and no obvious adverse reaction was
found^[54]16. In addition, it was also found that Xiaojin Pills showed
a good effect on prostatitis^[55]17, arthralgia, thyroid nodule,
mammary cancer, thyroid cancer and other cancers^[56]18–[57]20 and the
alleviating effect on prostatitis was achieved by inhibiting the
expression of COX-2^[58]17,[59]21. These results provided an important
basis for screening the quality markers (Q-markers) of Xiaojin Pills
from the anti-inflammatory effect and mechanism.
Xiaojin Pills consist of 10 kinds of Chinese herbal medicine (CHM),
including Moschus, Momordicae Semen, Aconiti Kusnezoffii Radix Cocta,
Liquidambaris Resina, Olibanum, Myrrha, Faeces Trogopterori, Angelicae
Sinensis Radix, Lumbricus rubellus and Fragrant Ink. The sources of
these drugs are complex, including plant medicine, resin medicine,
animal medicine and mineral medicine. Due to the reliable efficacy, it
was first recorded in the 1977 edition of Chinese Pharmacopoeia (CP).
However, the latest edition of CP only defined the lower limit of
muscone to evaluate the effectiveness, and identified three diester
diterpenoid alkaloids by TLC method to control toxicity. Muscone is
recognized as a component of effective anti-inflammatory activity and
the single indicator is obviously unable to evaluate its quality and
clinical effects, thus, it is necessary to study and screen the
anti-inflammatory components other than muscone.
In order to accurately control the quality of Chinese medicine and
Chinese patent medicine, the concept of Q-markers was proposed by Prof.
Liu Chang-xiao (China) in 2016^[60]22. Q-markers could meet the
following basic conditions. They are the chemical substances inherent
in the products or formed during processing, and closely related to the
functional properties of traditional Chinese medicine, and have
definite chemical structures. Meanwhile, they should be the substances
can be qualitatively identified and quantitatively measured. Due to the
inherent difficulty in characterizing all chemical constituents in
botanical drugs, Botanical Drug Development Guidance (BDDG) for
Industry by United States FDA’s in 2016 clearly suggested the relative
content or activity can be characterized by bioassay. Based on this
concept and anti-inflammatory mechanism of inhibiting the high
expression of COX-2, we put forward a new idea to evaluate the
anti-inflammatory activity of Xiaojin pill by COX-2 inhibition test and
screen the anti-inflammatory substances.
In the present study, a rapid, sensitive, and accurate HPLC-MS/MS
analytical method was developed to detected 13 kinds of candidate
anti-inflammatory and analgesic components in Xiaojin Pills, including
Aconitine (AC) Mesaconitine (MA), Hypaconitine (HA), Benzoylaconitine
(BAC), Benzoylmesaconitine (BMA), Benzoylhypacoitine (BHA), Levistilide
A (Lev), Inosine (Ino), Ligustilide (Lig), Acetyl-11-keto-b-boswellic
acid (Ace), Beta-Boswellic acid (Bet), Ferulic Acid (Fer) and
Protocatechuate (Pro). Validation parameters of the HPLC-MS/MS method
were studied systematically^[61]23, and 10 batches of Xiaojin Pills
were evaluated. Then, COX-2 inhibitor screening tests were carried out,
and the correlation analysis was further performed to find possible
anti-inflammatory ingredients. Finally, the screened markers were
verified by COX-2 inhibition tests and protein interaction network
analysis. Figure [62]1 showed the screening process of Xiaojin Pills’
quality markers. We hope that our study is benefit for the precise
quality control and clinical application of Xiaojin Pills. The approach
also provides examples for exploring the bioactive components of
Chinese patent medicine and other ethnic drugs.
Figure 1.
[63]Figure 1
[64]Open in a new tab
The screening process of Xiaojin Pills’ quality markers based on multi
component content determination, COX-2 anti-inflammatory experiment and
network interaction analysis.
Results
Clustering analysis (CA) results
The HPLC-MS/MS method was validated in terms of linearity, limit of
detection (LOD) and limit of quantification (LOQ), precision,
stability, repeatability and recovery based on ICH guidelines^[65]24.
Their results were described in Supplementary Tables [66]S1 and [67]S2.
The contents of 13 compounds in 10 samples of Xiaojin Pills were
presented in Table [68]S3. Contents of diester diterpenoid alkaloids in
all samples were lower than 0.0031%, which showed that Xiaojin Pills
were fairly safe. The other ten components differed greatly in
different samples. To show the difference intuitively, the
concentrations of thirteen components in 10 batches were used to create
a 13 × 10 matrix where all the numerical values were displayed in terms
of thermography (Fig. [69]2). Using an appropriate distance level,
samples were obviously classified into three clusters. S1, S2 and S6
were categorized into cluster I. S5, S3, S4, S7 and S8 were grouped
into cluster II. S9 and S10 were put into cluster III. It was clear
that the contents of Fer, Pro, MA, Lig, Bet, HA, Lev, BMA and BHA in
cluster III were obviously higher than others.
Figure 2.
[70]Figure 2
[71]Open in a new tab
CA result of different batches of Xiaojin Pills based on chemical
analysis.
Results of feasibility
Results of inhibition curve (S4) were shown in Fig. [72]3A, and the
IC[50] was about 0.5076 mg/ml. At the concentration of 0.5 mg/ml, the
actual inhibition rate of S1 and S6 was 34.04% and 37.64%,
respectively. The inhibition rate curves of S1 and S6 were shown in
Figs [73]3B [74]and C. It was illustrated that the theoretical
inhibition rate of S1 and S6 was 36.04%, and 39.86%, respectively. The
error rate of S1 and S6 calculated was 5.55% and 5.57%, which indicated
a direct determination method was of good feasibility.
Figure 3.
[75]Figure 3
[76]Open in a new tab
Concentration-COX-2 inhibition rate- curves of different Xiaojin Pills’
samples and three compounds. S4 (A), S1 (B), S6 (C), Pro (D), Bet (E)
and Lev (F).
Results of COX-2 inhibition rate of samples
At the concentration of 0.5 mg/ml, the COX-2 inhibition rate of S1, S2,
S3, S4, S5, S6, S7, S8, S9 and S10 were 34.25%, 28.12%, 21.66%, 47.04%,
44.08%, 37.64%, 43.47%, 52.07%, 51.72% and 35.91%, respectively.
Results of regression analysis
Multiple linear regression analysis generalizes directly to multiple
predictor variables and the multiple linear regression equation relates
the continuous response variables (Y) to predictor variables (x). The
regression equation was listed as:
[MATH: Y=−0.237852+0.013772x1+0.000203x2+0.014667x3 :MATH]
1
Among them, Y represented COX-2 inhibition rate, and x[1], x[2], x[3]
represented Pro, Bet and Lev, respectively.
The results showed that the F value was 37.974 and the corresponding P
value was 0.000 (ie., less than 0.05), demonstrating that the multiple
linear regression analysis model was satisfactory. It was found that a
strong linear relationship between COX-2 inhibition rate and the
contents of Pro, Bet and Lev.
The equation above described the degree which components in Xiaojin
Pills contributed to the anti-inflammatory activity. Pro had the
greatest important influence on COX-2 inhibition ratio, with
correlation coefficient was 0.611. Bet and Lev were also positively
correlated to inhibition ratio, and the correlation coefficients were
0.525 and 0.487, respectively. Therefore, Pro, Bet and Lev could be
considered as the candidate anti-inflammatory markers.
Results of anti-inflammatory activity of three components
Curve fitting method was used to evaluate the anti-inflammatory
activity of three components. The results were shown in Fig. [77]3D–F.
It was clear that the best inhibition rate of Pro was as high as
91.57%, while that of Bet and Lev was about 78.96% and 42.92%. It could
be concluded that the anti-inflammatory inhibition activity of Pro was
higher than Bet, and Lev, which was consistent with the correlation
coefficient above.
Results of protein interaction network
Compound target information
73 targets points were obtained by removing the repeated targets,
including 45 of Pro, 33 of Bet, and 6 of Lev. From the number of
targets, Pro contributed the most to the network. Among the targets we
obtained, PTGS1 (prostaglandin 1/COX-1) and PTGS 2 (prostaglandin
2/COX-2) were the most important node in the process of prostaglandin
metabolism. Therefore, Xiaojin Pills might be involved in the
prostaglandin metabolic process to play a role in anti-inflammatory
effect.
Construction of protein interaction and module analysis
Protein interaction information of targets from each compound was
introduced into Cytoscape 2.8.3 and the protein interaction network
diagram of Xiaojin Pills was obtained, including 186 nodes and 834
edges. Detailed information on these hubs is provided in Fig. [78]4A.
Module analysis of protein interaction network had been conducted. As
showed in Fig. [79]4B, 13 modules were identified. Using BinGo, the
functions of the protein contained in the modules were annotated. Each
module’s biological process was shown in Table [80]1. The module was
graded according to the MCODE algorithm. The correlation of proteins in
the modules became stronger as the score increased. Modules of 4th and
6th were mainly related to the prostaglandin metabolism process,
including PTGS1 (prostaglandin peroxidase 1/COX-1), PTGS2
(prostaglandin peroxidase 2/COX-2). Prostaglandin (PG) was the
metabolites produced by PTGS catalytic arachidonic acid. The PG
synthesis regulated by PTGS was considered to be a marker of
proinflammatory response and played an important role in the initiation
of inflammation. The 10th module also played a role in inflammation,
and the proteins it contained was related to the leukocyte chemotaxis
mediated by chemotactic factors. This module included RB1CC1, PRKCZ,
CHUK and IKBKB. Chemokines could cause an inflammatory reaction by
inducing leukocytes to express integrins, releasing cytokines, inducing
endothelial cells to express adhesion molecules, and collecting more
inflammatory cells through the wall of vessels.
Figure 4.
[81]Figure 4
[82]Open in a new tab
(A) The protein interaction network of Pro, Bet and Lev; (B) the
modules of the protein interaction network of Pro, Bet and Lev.
Table 1.
The main biological process of the modules.
Cluster Score P-value KEGG pathways
1 14 2.6 × 10^−6 Neuroactive liqand-receptor interaction
2 12 1.3 × 10^−11 Fanconi anemia pathway
3 11 9.8 × 10^−7 Prostanoid metabolic process
4 5.385 1.2 × 10^−14 Pathways in cancer
5 5 6.6 × 10^−12 Steroid hormone biosynthesis
6 4 1.0 × 10^−5 Arachidonic acid metabolism
7 4 6.4 × 10^−5 Inflammatory mediator regulation of TRP channels
8 4 2.9 × 10^−6 Platelet activation, signaling and aggregation
9 3.556 2.9 × 10^−6 Central carbon metabolism in cancer
10 3.333 7.2 × 10^−4 Chemokine signaling pathway
11 3 3.2 × 10^−5 Cell cycle
[83]Open in a new tab
During three anti-inflammatory markers screened, Pro and Bet both acted
on COX-1 and COX-2, which were directly involved in the arachidonic
acid metabolism pathway. However, it was not found a target directly
related to this pathway associated with Lev. Through the analysis of
protein interaction network, it was predicted that the inhibition
effect of Pro and Bet on COX-2 was better than that of Lev.
Discussions and Conclusions
During the study, different chromatography conditions were examined and
compared, including various columns, mobile phases, and gradient
elution conditions to achieve good resolution, high detection
sensitivity, symmetric peak shapes, and short run time. Three kinds of
reversed-phase columns, Kromasil C18 column (4.6 mm × 200 mm, 5 μm),
Phenomenonex Gemini C18 column (4.6 mm × 150 mm, 5 μm) and Agilent
Technologies Zorbax Eclipse XDB-C18 (4.6 mm × 150 mm, 5 μm), were
investigated. The Agilent Technologies Zorbax Eclipse XDB-C18 column
had good peak separation and sharp peaks. Poor resolution among the
peaks was found when methanol was used as the organic solvent of the
mobile phase. However, when methanol was replaced by acetonitrile, the
resolution was greatly improved. Different concentrations of formic
acid (0.05%, 0.1%, and 0.2%) or acetic acid (0.05%, 0.1%, and 0.2%)
were added to the aqueous phase, respectively. 0.1% formic acid was
selected with the best response and peak shape. The overall
chromatographic run time was 30 min. The column temperature was set at
30 °C and the flow rate was 0.3 mL·min^−1 to ensure good separation.
Mass spectral conditions were optimized in MRM scan type using the
reference compounds. LC-MS/MS data of samples were collected by using
an Agilent Triple Quadrupole mass spectrometer with an electrospray
interface (ESI) operated both in positive and negative modes. The mode
of the ESI was depended on the properties of compounds in gaining or
losing electrons^[84]25. In order to get good response, AC, MA, HA,
BAC, BMA, BHA, Lev, Ino and Lig were detected by the ESI^+ mode, while
Ace, Bet, Fer and Pro were detected by the ESI^− mode. In MRM mode, all
compounds could be detected in different channels without interference.
In addition, the temperature of 35 °C showed the highest extraction
efficiency. For 0.2 g powder, 5 ml methanol was shown to be the optimal
volume. The extraction time was studied in the range of 20 to 50 min.
Finally, 30 min showed the highest extraction efficiency. As a result,
the optimum conditions were: temperature of 35 °C, 5 ml methanol to
0.2 g powder and the extraction time of 30 min.
COX, also known as prostaglandin-endoperoxide synthase (PTGS), is an
enzyme responsible for the formation of important biological mediators,
including prostaglandins, prostacyclin and thromboxane. COX is the
central enzyme in the biosynthetic pathway to prostanoids from
arachidonic acid. COX-2 is one of the known isoenzymes, which is not
expressed on normal conditions in most cells, but elevated levels are
found during inflammation. Pharmacological inhibition of COX by
non-steroidal anti-inflammatory drugs (NSAID) can provide relief from
symptoms of inflammation and pain^[85]10,[86]11. The experiment
indicated that Xiaojin Pills played good anti-inflammatory and
analgesic effects of reducing the expression of COX-2^[87]21.
The quality of Chinese patent medicine is mainly evaluated by the
contents of active ingredients. However, it is a huge challenge to
select the quality markers accurately for the complex effective
material basis of patent medicine. In this study, a method of screening
anti-inflammation markers based on spectrum-effect relationship has
been practiced and the results provide a scientific basis for the
formulation of quality control indicators. We believe our methods could
provide a significant way to combine chemical methods and bioactivity
evaluation to screen the biological activity markers in Chinese patent
medicines or other valuable materials. It will be of interest for
pharmaceutical companies and pharmaceutical workers.
Materials and Methods
Materials
Ten batches of Xiaojin Pills samples were collected from four
pharmaceutical factories and all the information was listed in
Table [88]2.
Table 2.
Sample information.
Sample no. Pharmaceutical factory Batch no. Production date
S1 Jiuzhaigou, Sichuan 151201 December 1, 2015
S2 Jiuzhaigou, Sichuan 150905 September 24, 2015
S3 Jiuzhitang, Schuan 160601 June 2, 2016
S4 Jiuzhitang, Schuan 151102 November 16, 2015
S5 Jiuzhitang, Schuan 150601.1 June 8, 2015
S6 Kaijing, Sichuan 20151003 October 28, 2015
S7 Kaijing, Sichuan 20160501 May 1, 2016
S8 Kaijing, Sichuan 20160502 May 2, 2016
S9 Yongkang, Sichuan 151009NO.015 November 4, 2015
S10 Yongkang, Sichuan 160806NO.073 November 3, 2015
[89]Open in a new tab
HPLC-grade acetonitrile and methanol were purchased from Fisher
Chemical (Pittsburg, PA, USA). HPLC-grade formic acid, ammonium
acetate, and analytical-grade dimethyl sulfoxide (DMSO) were purchased
from Chengdu KeLong Chemical Factory (Chengdu, China). The ultrapure
water was obtained by Millipore Milli-Q water purification system
(Millipore, Billerica, MA, USA). All solutions were filtered through
0.22 μm membranes (Jinteng, Tianjin, China) and degassed by ultrasonic
bath before use. Screening kits for COX-2 inhibitors were purchased
from Beyotiome Biotechnology (Shanghai, China). The fluorescence values
were measured by Fluorescence microplate reader (GEMINIXS, USA) and the
SOFTmaxPRO software was the production of Molecular Devices Company in
USA.
Standards of MA(MUST-16032504), AC (MUST-16062206), HA (MUST-16032106),
BMA (CHB151203), BAC (CHB160912), BHA (CHB160326) and Bet (CHB 170801)
were purchased from Chroma-Biotechnology Co., Ltd (Chengdu, China). Ace
(PRF8051801), Pro (15121808), Fer (PRF7101144) and Lig (PRF 15092501)
were purchased from Chengdu Biopurify Phytochemicals Ltd (Chengdu,
China). Lev (PSO756-0010) was obtained from Chengdu PUSH Bio-Technology
Co., Ltd (Chengdu, China). Ino (151014) was a gift from Chengdu
PureChem-Standard Co., Ltd (Chengdu, China). The purity of all
standards is more than 98%.
HPLC-MS/MS analysis
MS Conditions
Samples were analyzed by an Agilent1260 high performance liquid
chromatograph and Agilent6460C triple-quadrupole tandem mass
spectrometry (Agilent Technologies, Santa Clara, CA, USA) using an
Agilent Technologies Zorbax Eclipse XDB-C18 column (4.6 mm × 150 mm, 5
μm). The column temperature was 30 °C and 0.1 μL of the sample solution
was injected into the system. The mobile phase was composed of (A) 0.1%
aqueous formic acid in water (ESI^+ mode) or 10 mmol/L ammonium acid
(ESI^− mode) and (B) acetonitrile using a gradient program of 100–80% B
for 0–1 min, 80–50% B for 1–2 min, 50–30% B for 2–3 min, 30–80% B for
3–5 min, 80–100% B for 5–30 min, with a mobile flow rate of 0.3 mL/min.
Mass spectrometric scan were obtained by ESI in ESI^+ mode and ESI^−
mode with a scanning interval 100–1000 m/z. The main parameters for MS
were set as follows: gas temperature, 300 °C; gas flow, 11 L/min;
nebulizer, 35 psig; capillary voltage, 4000 V; atomizer pressure 15 psi
(1 psi = 6.895 Kpa). MS parameters and MRM transitions of each analyte
are shown in Table [90]3. Based on this condition, the MRM chromatogram
and chemical structure of each compound are shown in Fig. [91]5.
Tablet 3.
Summary of molecular weight, multiple-reaction monitoring transitions,
DP and CE of the 13 components determined by HPLC-MS/MS.
Compound Molecular weight Monitoring ion Precursor ion (m/z) Product
ion (m/z) DP (V) CE (eV)
AC 645.7 ESI^+ 646.3 105.1 180 50
MA 631.7 ESI^+ 632.2 105.1 180 46
HA 615.7 ESI^+ 616.3 105.0 180 46
BAC 603.7 ESI^+ 604.3 105.0 180 45
BMA 589.7 ESI^+ 590.3 105.0 205 46
BHA 573.3 ESI^+ 574.3 105.0 200 46
Lev 380.2 ESI^+ 381.0 191.0 90 10
Ino 268.2 ESI^+ 268.9 136.9 80 5
Lig 190.2 ESI^+ 191.0 90.9 100 15
Ace 512.4 ESI^− 511.3 59.1 110 12
Bet 456.4 ESI^− 455.3 377.4 110 48
Fer 194.1 ESI^− 193.0 133.8 100 12
Pro 154.0 ESI^− 153.0 109.0 90 12
[92]Open in a new tab
Figure 5.
[93]Figure 5
[94]Open in a new tab
Typical MRM and chemical structures of the 13 compounds in Xiaojin
Pills.
Preparation of standard solutions
The mixed standard containing 1.27 μg/mL AC, 1.02 μg/mL MA, 0.97 μg/mL
HA, 1.12 μg/mL BAC, 1.03 μg/mL BMA, 0.90 μg/mL BHA, 0.96 μg/mL Lev,
1.03 μg/mL Ino, 0.998 μg/mL Lig, 1.013 μg/mL Fer, 0.98 μg/mL Pro,
1.09 μg/mL Ace and 0.96 μg/mL Bet was prepared stock into a volumetric
flask and dissolved with 10 mL methanol. These solutions were stored in
dark glass bottles at 4 °C and stable for at least 1 week. Working
standard solutions were freshly prepared by diluting suitable amounts
of the above solutions with methanol before injection.
Preparation of sample solutions
Break different batch of Xiaojin Pills to powder through a 50-mesh
sieve. A total of 0.2 g of powder was accurately weighed and extracted
with 5 mL of methanol solution by ultrasonic extraction for 30 min.
Extracted solution was cooled, contributed to weight loss during the
extraction procedure, and filtered through a 0.22 μm micropore film to
yield the subsequent filtrate solution.
Determination of the sample
10 batches of Xiaojin Pills were determined by the method above.
CA
CA is a multivariate analysis method that is used to sort samples into
groups^[95]26. In the present study, the CA of samples was performed
using Metabo Analyst 3.0. Ward’s method as the amalgamation rule and
squared Euclidean distance as metric were used to establish clusters.
To visualize the similarity and dissimilarity of the samples, all the
data were expressed as a thermograph.
COX-2 inhibition rate measurement
Sample preparation
An aliquot of 0.2 g Xiaojin Pills powder (through a 50-mesh sieve) was
accurately weighed and transferred into a 15-ml-flask. After being
added with 10 mL of DMSO, the flask was weighed and extracted by
ultrasonic extraction for 30 min. Extracted solution was cooled and the
lost weight was supplied. Then the mixture was centrifuged at 4000 rpm
for 10 min, the supernatant was transferred into another 15-ml-flask
and the high concentration sample solutions for each batch were
obtained.
Determination of suitable sample concentration
It is difficult to complete inhibition curve for each sample and
calculate the IC[50] value for the large amounts. Therefore, one sample
(S4) was selected to calculate the IC[50] value randomly, and it was
used as a reference concentration. Firstly, S4 was prepared into
solutions with a concentration of 20 mg/ml, 15 mg/ml, 0.5 mg/ml,
0.3 mg/ml, 0.1 mg/ml, 0.05 mg/ml, 0.01 mg/ml, and 0.005 mg/ml,
respectively. Then, the inhibition rate of COX-2 in different
concentration samples was measured and the inhibition curve was
plotted. IC[50] of S4 was calculated to be 0.5076 mg/ml. Based on it,
each sample was diluted to a concentration of 0.5 mg/ml to determine
the COX-2 inhibition rate.
Experimental procedure
According to the instructions of COX-2 inhibitor screening kit, the
relative fluorescence unit (RFU) was measured after a period of
incubation. Excitation wavelength was 560 nm, and emission wavelength
was 590 nm. Inhibition rate of each sample was calculated as follows:
[MATH:
Inhibit<
mi>ionrate(%)=RFU2−<
mi>RFUS
RFU2−RF<
mi>U1×
100% :MATH]
2
Among them, RFU[1], RFU[2], and RFU[S] represented the fluorescence
value of the blank control group, 100% enzyme activity control group
and the sample group, respectively.
Feasibility verification
S1 and S6 were randomly selected to compare the results difference
between direct determination method and curve fitting method. The
former was determined at the fixed concentration of 0.5 mg/ml. It was
considered as an actual value. The later was calculated by the best
fitted curve equation after the study of relationship between
concentrations and inhibition rates. This result was accepted as a
theoretical value. Error rate of direct determination method was
calculated by formula 2.
[MATH: Errorrate(%)=IRa−I<
msub>Rt
IRt×100%
:MATH]
3
Among them, IR[a], IR[t] represented the fluorescence value of the
actual inhibition rate and the theoretical inhibition rate,
respectively.
Multiple linear regression analysis
Multiple linear regression analysis was used to model the best
combination of two or more independent variables (x[i]) to predict or
estimate the dependent variable (Y) by fitting a linear
equation^[96]27. It showed the contribution of each independent
variable to the dependent variable in the following form:
[MATH: Y=b0+∑i=1nbixi(n=1,2,3,4…) :MATH]
4
where Y was the estimated value and represents the dependent variable
and x[i] were the uncorrelated variables; b[0] represented the
estimated constant, and b[i] was regression coefficients. In this
study, multiple linear regression analysis was introduced to combine
data from the chemical contents determination and anti-inflammatory
activity of Xiaojin Pills. SPSS statistical software (SPSS for Windows
13.0, SPSS Inc., USA) was used to establish the chemical-activity
relationships and further explore the anti-inflammatory markers.
Verification of anti-inflammation markers
The anti-inflammation activities of the markers we screened were
compared by the curve fitting method. After the study of relationship
between concentrations and inhibition rates, several best fitted curves
of the markers were obtained respectively, and the anti-inflammation
effects of several markers could be verified by comparing their best
inhibition rates.
Protein interaction network
Collection of the potential targets
Target data of the anti-inflammatory markers were excavated and the
target information of candidate components were derived from STITCH
([97]http://stitch.em-bl.de/), BATMAN-TCM
([98]http://bionet.ncpsb.org/batman-tcm), TCMID
([99]https://omictools.com/traditio), TCMSP
([100]http://ibts.hkbu.edu.hk/LSP/tc) and ChEMBL
([101]https://www.ebi.ac.uk/chembl/). STITCH database could generate a
score for each component target relationship, which ones with high
confidence data (score >0.7) was approved to ensure the reliability of
data^[102]28.
Construction and analysis of protein interaction network
The protein interaction information of the target was derived from the
String database ([103]http://www.string-db.org/). The protein
interaction data we obtained were introduced into Cytoscape 2.8.3 and
each of the target protein interaction networks was calculated by the
Union method^[104]13,[105]29. After removing the isolated points,
repeated and self-loop edges, we got the interaction network of
anti-inflammatory markers.
Pathway enrichment analysis
Because of the great number of compounds of an herb and the multiple
targets of each compound, the total number of the targets of all the
key herbs is substantial. The great amount of targets makes it
difficult to understand the biological meaning, so a pathway enrichment
analysis was performed using the Database Visualization and Integrated
Discovery software (DAVID, [106]http://david.abcc.ncifcrf.gov/home.jsp,
version 6.7.) and based on the pathway data obtained from the Kyoto
Encyclopedia of Genes and Genomes database (KEGG,
[107]http://www.genome.jp/kegg/, updated on April 18, 2016)^[108]30.
Electronic supplementary material
[109]Supplementary information^ (83.5KB, doc)
Acknowledgements