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Article(id=1201096922959409788, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1201096916940579367, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2023-1187, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1697731200000, receivedDateStr=2023-10-20, revisedDate=1703001600000, revisedDateStr=2023-12-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1764293421734, onlineDateStr=2025-11-28, pubDate=1712851200000, pubDateStr=2024-04-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764293421734, onlineIssueDateStr=2025-11-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764293421734, creator=13701087609, updateTime=1764293421734, updator=13701087609, issue=Issue{id=1201096916940579367, tenantId=1146029695717560320, journalId=1189982191388893191, year='2024', volume='59', issue='4', pageStart='789', pageEnd='1100', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764293420298, creator=13701087609, updateTime=1764293534792, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1201097397242912862, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1201096916940579367, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1201097397242912863, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1201096916940579367, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1040, endPage=1047, ext={EN=ArticleExt(id=1201096923487892114, articleId=1201096922959409788, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Analysis of inorganic elements in different batches of earthworm polypeptides by ICP-MS combined with chemometrics technology, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

To establish a method for determining 26 inorganic elements in earthworm polypeptide and determine the elemental content in different batches of earthworm polypeptide, microwave digestion method was used to pre-treat the samples, and ICP-MS method was used to determine the content of 26 elements in different batches of earthworm polypeptide. The linear relationships of 26 elements were good in the range of 0-1 000 μg·L-1, with R2 greater than 0.999, precision RSD 0.21%-2.71%, repeatability RSD 0.19%-4.69%, stability RSD 0.11%-4.24%, and recovery rates of 82.41%-116.16%. The data was plotted using Origin 2022 software to characterize the distribution of elements content. SPSS 27.0 was used for principal component analysis, and SIMCA 14.1 software was used for OPLS-DA analysis. The results showed that among the 26 elements, the higher content of earthworm polypeptide was K, Na, and Ca, followed by Zn, Fe, Al, B and other trace elements, In, Sc, Co, Pb, Bi and other elements had little or no detectable content, and there were differences in the content of polypeptide in different batches. This study provides a theoretical basis for the production quality control, quality evaluation and drug efficacy application of earthworm polypeptide through the determination and analysis of the elements and content of earthworm polypeptide.

, correspAuthors=Yong-gang LIU, Tao MA, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2024 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Hong-liu YANG, Wei-ting ZHONG, Yu-shi GUO, Shu-qi LI, Jin-chai QI, Yong-gang LIU, Tao MA), CN=ArticleExt(id=1201096924641325805, articleId=1201096922959409788, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=ICP-MS法结合化学计量学分析不同批次地龙多肽中无机元素的含量, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

为建立测定地龙多肽中26种无机元素的方法, 测定不同批次地龙多肽中的元素含量, 本研究采用微波消解法对样品进行前处理, 以ICP-MS法对不同批次地龙多肽中26种元素进行含量测定。26种元素在0~1 000 μg·L-1内线性关系良好, R2大于0.999, 精密度RSD为0.21%~2.71%, 重复性RSD为0.19%~4.69%, 稳定性RSD为0.11%~4.24%, 加样回收率为82.41%~116.16%。数据采用Origin 2022软件绘制元素含量分布特征图, 利用SPSS 27.0进行主成分分析, 采用SIMCA 14.1软件进行OPLS-DA分析。结果表明, 26种元素中, 地龙多肽含量较高的常量元素是K、Na和Ca, 其次主要含有Zn、Fe、Al、B等微量元素, In、Sc、Co、Pb和Bi等元素含量极少甚至未能检出, 且不同批次地龙多肽元素含量存在差异。本研究通过对地龙多肽元素种类和含量进行测定分析, 为地龙多肽的生产质量控制及质量评价、药效应用提供一定的理论基础和依据。

, correspAuthors=刘永刚, 马涛, authorNote=null, correspAuthorsNote=
*刘永刚, E-mail: ;
马涛, E-mail:
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#共同第一作者.

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Chin J Ethnomed Ethnopharm (中国民族民间医药), 2019, 28: 67-73., articleTitle=null, refAbstract=null)], funds=[Fund(id=1201096933935903086, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, awardId=2018YFC1706505, language=CN, fundingSource=国家重点研发计划(2018YFC1706505), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1201096924985258763, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, xref=null, ext=[AuthorCompanyExt(id=1201096924993647374, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, companyId=1201096924985258763, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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ElementLinear equationR2Detection limit/μg·L-1Quantification limit/μg·L-1
BY = 205.769 5x + 571.570 71.000 00.218 10.727 0
NaY = 20 636.768 8x + 412 987.158 41.000 00.195 00.650 0
MgY = 1 891.490 8x + 9 702.217 21.000 00.136 60.455 3
AlY = 439.215 7x + 1 711.007 91.000 00.256 00.853 3
KY = 1 566.145 9x + 2 356.477 11.000 00.149 00.496 7
CaY = 151.837 5x + 2 604.939 61.000 00.110 30.367 7
ScY = 3 207.444 8x + 54 727.035 50.999 20.002 70.009 0
VY = 16 845.352 6x + 12 330.536 71.000 00.003 40.011 3
CrY = 28 532.697 6x + 71 492.580 21.000 00.017 80.059 3
MnY = 9 183.859 4x + 149 058.681 20.999 40.162 80.542 7
FeY = 931.471 3x + 2 345.333 10.999 60.172 00.573 3
CoY = 56 666.774 0x + 73 388.274 90.999 90.043 40.144 7
NiY = 16 614.544 5x + 20 118.446 51.000 00.086 40.288 0
CuY = 46 343.764 6x + 31 158.402 50.999 80.009 50.031 7
ZnY = 5 373.501 2x + 92 958.053 01.000 00.090 30.301 0
GaY = 4 464.326 9x + 436.896 91.000 00.010 50.035 0
AsY = 2 303.449 0x + 7 192.516 20.999 80.018 80.062 7
SeY = 28.867 5x + 1 306.069 41.000 00.021 60.072 0
ZrY = 34 025.723 5x + 4 056.447 51.000 00.002 70.009 0
MoY = 23 509.309 1x + 3 917.508 91.000 00.001 30.004 3
CdY = 11 460.457 8x + 780.469 51.000 00.001 00.003 3
InY = 44 574.342 2x + 209.335 31.000 00.000 30.001 0
TeY = 301.609 0x + 48.444 61.000 00.003 60.012 0
BaY = 5 875.419 7x + 20 510.415 90.999 90.026 80.089 3
PbY = 206 723.543 2x + 1 215 523.538 20.999 90.005 50.018 3
BiY = 268 061.272 8x + 474.676 11.000 00.000 30.001 0
), ArticleFig(id=1201096932681806099, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=CN, label=Table 1, caption=

Linear equation, detection limit, and quantification limit (linear range, 0-1 000 μg·L-1)

, figureFileSmall=null, figureFileBig=null, tableContent=
ElementLinear equationR2Detection limit/μg·L-1Quantification limit/μg·L-1
BY = 205.769 5x + 571.570 71.000 00.218 10.727 0
NaY = 20 636.768 8x + 412 987.158 41.000 00.195 00.650 0
MgY = 1 891.490 8x + 9 702.217 21.000 00.136 60.455 3
AlY = 439.215 7x + 1 711.007 91.000 00.256 00.853 3
KY = 1 566.145 9x + 2 356.477 11.000 00.149 00.496 7
CaY = 151.837 5x + 2 604.939 61.000 00.110 30.367 7
ScY = 3 207.444 8x + 54 727.035 50.999 20.002 70.009 0
VY = 16 845.352 6x + 12 330.536 71.000 00.003 40.011 3
CrY = 28 532.697 6x + 71 492.580 21.000 00.017 80.059 3
MnY = 9 183.859 4x + 149 058.681 20.999 40.162 80.542 7
FeY = 931.471 3x + 2 345.333 10.999 60.172 00.573 3
CoY = 56 666.774 0x + 73 388.274 90.999 90.043 40.144 7
NiY = 16 614.544 5x + 20 118.446 51.000 00.086 40.288 0
CuY = 46 343.764 6x + 31 158.402 50.999 80.009 50.031 7
ZnY = 5 373.501 2x + 92 958.053 01.000 00.090 30.301 0
GaY = 4 464.326 9x + 436.896 91.000 00.010 50.035 0
AsY = 2 303.449 0x + 7 192.516 20.999 80.018 80.062 7
SeY = 28.867 5x + 1 306.069 41.000 00.021 60.072 0
ZrY = 34 025.723 5x + 4 056.447 51.000 00.002 70.009 0
MoY = 23 509.309 1x + 3 917.508 91.000 00.001 30.004 3
CdY = 11 460.457 8x + 780.469 51.000 00.001 00.003 3
InY = 44 574.342 2x + 209.335 31.000 00.000 30.001 0
TeY = 301.609 0x + 48.444 61.000 00.003 60.012 0
BaY = 5 875.419 7x + 20 510.415 90.999 90.026 80.089 3
PbY = 206 723.543 2x + 1 215 523.538 20.999 90.005 50.018 3
BiY = 268 061.272 8x + 474.676 11.000 00.000 30.001 0
), ArticleFig(id=1201096932799246622, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
ElementRepeatability/%Precision/%Stability/%Sample recovery
Rate of recovery/%RSD/%
B2.161.420.72115.641.87
Na0.702.400.26116.162.14
Mg0.601.230.19103.530.54
Al1.111.160.51103.332.06
K0.361.340.21111.060.41
Ca0.392.710.82107.990.19
Sc2.721.670.56102.651.46
V1.051.320.7887.752.99
Cr0.540.640.1290.531.07
Mn0.191.191.3993.220.82
Fe0.501.614.24108.850.41
Co1.601.220.81113.221.02
Ni0.221.110.1883.040.19
Cu0.530.840.11115.740.76
Zn0.790.950.2596.660.74
Ga1.841.290.23106.881.03
As3.650.970.82114.870.32
Se3.600.800.9891.621.83
Zr0.240.672.33100.031.08
Mo3.080.320.8782.410.97
Cd2.780.470.46114.040.66
In4.091.170.7293.130.81
Te2.200.210.5086.270.77
Ba1.240.620.2082.962.11
Pb1.100.891.9991.210.66
Bi4.690.591.37101.952.03
), ArticleFig(id=1201096932962824496, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=CN, label=Table 2, caption=

Precision, repeatability, stability, and sample recovery of each element

, figureFileSmall=null, figureFileBig=null, tableContent=
ElementRepeatability/%Precision/%Stability/%Sample recovery
Rate of recovery/%RSD/%
B2.161.420.72115.641.87
Na0.702.400.26116.162.14
Mg0.601.230.19103.530.54
Al1.111.160.51103.332.06
K0.361.340.21111.060.41
Ca0.392.710.82107.990.19
Sc2.721.670.56102.651.46
V1.051.320.7887.752.99
Cr0.540.640.1290.531.07
Mn0.191.191.3993.220.82
Fe0.501.614.24108.850.41
Co1.601.220.81113.221.02
Ni0.221.110.1883.040.19
Cu0.530.840.11115.740.76
Zn0.790.950.2596.660.74
Ga1.841.290.23106.881.03
As3.650.970.82114.870.32
Se3.600.800.9891.621.83
Zr0.240.672.33100.031.08
Mo3.080.320.8782.410.97
Cd2.780.470.46114.040.66
In4.091.170.7293.130.81
Te2.200.210.5086.270.77
Ba1.240.620.2082.962.11
Pb1.100.891.9991.210.66
Bi4.690.591.37101.952.03
), ArticleFig(id=1201096933109625148, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
ElementY1Y2Y3Y4Y5Y6Mean valueRSD/%
B4.485.243.396.245.032.864.5425.06
Na756.27791.92851.121 055.44988.13968.89901.9712.09
Mg184.82192.82208.50257.03297.03289.26238.2418.94
Al18.3019.8318.0223.9214.4812.1417.7821.15
K13 284.0914 149.111 592.5319 322.2516 506.8016 381.0613 539.3141.94
Ca538.61568.93506.36637.26424.95374.73508.4717.22
Sc--0.001 90.002 70.001 4-0.001 0106.93
V0.060.060.090.170.120.080.0926.08
Cr2.472.562.573.132.722.612.688.03
Mn1.061.111.351.711.561.551.3917.31
Fe51.9953.0756.1468.8446.8245.0553.6514.45
Co--------
Ni2.842.863.063.713.473.553.2510.55
Cu0.070.070.420.500.300.550.3260.53
Zn25.1325.8021.8627.0011.0013.0220.6430.64
Ga0.011 70.012 30.008 30.010 30.006 60.006 50.009 324.83
As0.390.400.541.160.700.580.6239.58
Se0.120.090.130.130.120.110.1211.05
Zr0.230.230.260.320.160.060.2137.84
Mo0.220.220.140.160.110.100.1630.31
Cd0.050.050.040.050.020.020.0432.08
In0.000 80.000 90.001 20.001 60.000 90.001 00.001 125.19
Te0.006 70.003 20.004 50.005 40.002 70.002 50.004 236.55
Ba1.541.611.081.470.810.681.2030.36
Pb--------
Bi--------
), ArticleFig(id=1201096933222871369, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=CN, label=Table 3, caption=

Content of 26 inorganic elements in different batches of earthworm polypeptides (mg·kg-1). Y1-Y6 represent six batches of earthworm polypeptide samples respectively. -: Not detected

, figureFileSmall=null, figureFileBig=null, tableContent=
ElementY1Y2Y3Y4Y5Y6Mean valueRSD/%
B4.485.243.396.245.032.864.5425.06
Na756.27791.92851.121 055.44988.13968.89901.9712.09
Mg184.82192.82208.50257.03297.03289.26238.2418.94
Al18.3019.8318.0223.9214.4812.1417.7821.15
K13 284.0914 149.111 592.5319 322.2516 506.8016 381.0613 539.3141.94
Ca538.61568.93506.36637.26424.95374.73508.4717.22
Sc--0.001 90.002 70.001 4-0.001 0106.93
V0.060.060.090.170.120.080.0926.08
Cr2.472.562.573.132.722.612.688.03
Mn1.061.111.351.711.561.551.3917.31
Fe51.9953.0756.1468.8446.8245.0553.6514.45
Co--------
Ni2.842.863.063.713.473.553.2510.55
Cu0.070.070.420.500.300.550.3260.53
Zn25.1325.8021.8627.0011.0013.0220.6430.64
Ga0.011 70.012 30.008 30.010 30.006 60.006 50.009 324.83
As0.390.400.541.160.700.580.6239.58
Se0.120.090.130.130.120.110.1211.05
Zr0.230.230.260.320.160.060.2137.84
Mo0.220.220.140.160.110.100.1630.31
Cd0.050.050.040.050.020.020.0432.08
In0.000 80.000 90.001 20.001 60.000 90.001 00.001 125.19
Te0.006 70.003 20.004 50.005 40.002 70.002 50.004 236.55
Ba1.541.611.081.470.810.681.2030.36
Pb--------
Bi--------
), ArticleFig(id=1201096933361283409, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Principal componentEigenvalueVariance contribution rate/%Cumulative variance contribution rate/%
110.86151.72051.720
27.62136.28988.009
31.4546.92394.932
), ArticleFig(id=1201096933520666967, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=CN, label=Table 4, caption=

Principal component eigenvalues, contribution rates, and cumulative variance contribution rates

, figureFileSmall=null, figureFileBig=null, tableContent=
Principal componentEigenvalueVariance contribution rate/%Cumulative variance contribution rate/%
110.86151.72051.720
27.62136.28988.009
31.4546.92394.932
), ArticleFig(id=1201096933633913184, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
ElementRotated factor loading matrix
123
B0.7630.361-0.388
Na-0.1490.985-0.014
Mg-0.5800.783-0.141
Al0.9820.1430.049
K0.1090.9820.083
Ca0.9970.0250.012
V-0.2000.8810.095
Cr0.4260.901-0.020
Mn-0.1940.9730.106
Fe0.8570.4060.255
Ni-0.2100.9540.022
Cu-0.3290.7620.364
Zn0.927-0.2690.103
Ga0.834-0.495-0.207
As0.3060.9420.112
Se0.1020.4440.850
Zr0.9150.0540.249
Mo0.727-0.659-0.151
Cd0.952-0.2960.054
Te0.687-0.2470.573
Ba0.916-0.367-0.142
), ArticleFig(id=1201096933784908134, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1201096922959409788, language=CN, label=Table 5, caption=

Component matrix

, figureFileSmall=null, figureFileBig=null, tableContent=
ElementRotated factor loading matrix
123
B0.7630.361-0.388
Na-0.1490.985-0.014
Mg-0.5800.783-0.141
Al0.9820.1430.049
K0.1090.9820.083
Ca0.9970.0250.012
V-0.2000.8810.095
Cr0.4260.901-0.020
Mn-0.1940.9730.106
Fe0.8570.4060.255
Ni-0.2100.9540.022
Cu-0.3290.7620.364
Zn0.927-0.2690.103
Ga0.834-0.495-0.207
As0.3060.9420.112
Se0.1020.4440.850
Zr0.9150.0540.249
Mo0.727-0.659-0.151
Cd0.952-0.2960.054
Te0.687-0.2470.573
Ba0.916-0.367-0.142
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ICP-MS法结合化学计量学分析不同批次地龙多肽中无机元素的含量
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杨洪柳 1, # , 仲伟婷 2, # , 郭雨师 3 , 李姝琪 1 , 亓金钗 1 , 刘永刚 1, * , 马涛 1, *
药学学报 | 研究论文 2024,59(4): 1040-1047
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药学学报 | 研究论文 2024, 59(4): 1040-1047
ICP-MS法结合化学计量学分析不同批次地龙多肽中无机元素的含量
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杨洪柳1, #, 仲伟婷2, #, 郭雨师3, 李姝琪1, 亓金钗1, 刘永刚1, * , 马涛1, *
作者信息
  • 1.北京中医药大学中药学院, 北京 102401
  • 2.北京泰德制药股份有限公司, 北京 100176
  • 3.包头市昆都仑区医院, 内蒙古 包头 014010

通讯作者:

*刘永刚, E-mail: ;
马涛, E-mail:
Analysis of inorganic elements in different batches of earthworm polypeptides by ICP-MS combined with chemometrics technology
Hong-liu YANG1, Wei-ting ZHONG2, Yu-shi GUO3, Shu-qi LI1, Jin-chai QI1, Yong-gang LIU1, * , Tao MA1, *
Affiliations
  • 1. School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102401, China
  • 2. Beijing Tide Pharmaceutical Co., Ltd., Beijing 100176, China
  • 3. Baotou Kundulun District Hospital, Baotou 014010, China
出版时间: 2024-04-12 doi: 10.16438/j.0513-4870.2023-1187
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为建立测定地龙多肽中26种无机元素的方法, 测定不同批次地龙多肽中的元素含量, 本研究采用微波消解法对样品进行前处理, 以ICP-MS法对不同批次地龙多肽中26种元素进行含量测定。26种元素在0~1 000 μg·L-1内线性关系良好, R2大于0.999, 精密度RSD为0.21%~2.71%, 重复性RSD为0.19%~4.69%, 稳定性RSD为0.11%~4.24%, 加样回收率为82.41%~116.16%。数据采用Origin 2022软件绘制元素含量分布特征图, 利用SPSS 27.0进行主成分分析, 采用SIMCA 14.1软件进行OPLS-DA分析。结果表明, 26种元素中, 地龙多肽含量较高的常量元素是K、Na和Ca, 其次主要含有Zn、Fe、Al、B等微量元素, In、Sc、Co、Pb和Bi等元素含量极少甚至未能检出, 且不同批次地龙多肽元素含量存在差异。本研究通过对地龙多肽元素种类和含量进行测定分析, 为地龙多肽的生产质量控制及质量评价、药效应用提供一定的理论基础和依据。

电感耦合等离子体-质谱法  /  微波消解  /  地龙  /  地龙多肽  /  无机元素

To establish a method for determining 26 inorganic elements in earthworm polypeptide and determine the elemental content in different batches of earthworm polypeptide, microwave digestion method was used to pre-treat the samples, and ICP-MS method was used to determine the content of 26 elements in different batches of earthworm polypeptide. The linear relationships of 26 elements were good in the range of 0-1 000 μg·L-1, with R2 greater than 0.999, precision RSD 0.21%-2.71%, repeatability RSD 0.19%-4.69%, stability RSD 0.11%-4.24%, and recovery rates of 82.41%-116.16%. The data was plotted using Origin 2022 software to characterize the distribution of elements content. SPSS 27.0 was used for principal component analysis, and SIMCA 14.1 software was used for OPLS-DA analysis. The results showed that among the 26 elements, the higher content of earthworm polypeptide was K, Na, and Ca, followed by Zn, Fe, Al, B and other trace elements, In, Sc, Co, Pb, Bi and other elements had little or no detectable content, and there were differences in the content of polypeptide in different batches. This study provides a theoretical basis for the production quality control, quality evaluation and drug efficacy application of earthworm polypeptide through the determination and analysis of the elements and content of earthworm polypeptide.

ICP-MS  /  microwave digestion  /  earthworm  /  earthworm polypeptide  /  inorganic element
杨洪柳, 仲伟婷, 郭雨师, 李姝琪, 亓金钗, 刘永刚, 马涛. ICP-MS法结合化学计量学分析不同批次地龙多肽中无机元素的含量. 药学学报, 2024 , 59 (4) : 1040 -1047 . DOI: 10.16438/j.0513-4870.2023-1187
Hong-liu YANG, Wei-ting ZHONG, Yu-shi GUO, Shu-qi LI, Jin-chai QI, Yong-gang LIU, Tao MA. Analysis of inorganic elements in different batches of earthworm polypeptides by ICP-MS combined with chemometrics technology[J]. Acta Pharmaceutica Sinica, 2024 , 59 (4) : 1040 -1047 . DOI: 10.16438/j.0513-4870.2023-1187
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地龙作为中医临床常用动物类中药, 药用历史悠久, 中国药典(2020版) 收载的地龙来源为钜蚓科动物参环毛蚓Pheretima aspergillum (E. Perrier)、通俗环毛蚓Pheretima vulgaris Chen、威廉环毛蚓Pheretima guillelmi (Michaelsen) 或栉盲环毛蚓Pheretima pectinifera Michaelsen的干燥体, 主要品种为“广地龙”和“沪地龙”[1-3]。地龙性味寒咸, 能够清热定惊、通经活络、平喘利尿, 主治高热神昏、关节痹痛、肢体麻木、高血压等症[4]。现代研究表明地龙有降压、平喘、抗凝血、抗血栓、抗炎、抗菌、抗肿瘤、免疫调节等多种药理作用[5, 6]。
地龙中主要含有蛋白质、肽类、酶类、氨基酸、二肽类、核苷类、脂类及无机元素。其中蛋白多肽类成分含量较高, 是地龙的主要成分, 也是其主要药效成分之一[7]。对于地龙蛋白的提取, Wu等[8]比较了直接浸提、匀浆浸提、超声提取三种不同提取方法对于地龙可溶性蛋白的影响, 其中匀浆提取效率高且简单易行。Peng等[9]比较了水提醇沉法和盐析法对可溶性蛋白的提取率及活性影响, 实验表明水提醇沉法对可溶性蛋白的提取率及活性优于盐析法。Dong等[10]采用SDS-PAGE法对新鲜广地龙进行总蛋白分离, 利用纳升高效液相色谱-四极杆-线性离子阱-静电场轨道阱高分辨质谱技术检索鉴定出386个蛋白质。目前, 已从地龙中提取得到抗菌肽、纤维蛋白溶酶、催产素相关肽等具有生物活性的肽类[11, 12]。现代药理研究表明, 地龙蛋白多肽类成分具有抗凝血、抗血栓、抗肿瘤、抗纤维化、降血脂、抗氧化、增强免疫等药理作用[13]。例如, Li等[14]从蚯蚓的腔液中纯化出两种新型镇痛肽和抗炎肽VQ-5和AQ-5。Wang等[15]通过体外酶解方法对地龙药材进行体外酶解, 采用电渗析法、超滤法、DA201-C树脂法等方法对地龙酶解液进行分离, 得到抗血栓肽。He等[16]采用体外消化模型研究了蚯蚓蛋白在胃肠道消化降解后产生的抗氧化肽的氨基酸序列, 表明蚯蚓蛋白胃肠消化产物具有良好的抗氧化活性。Chen等[17]通过观察地龙多肽对高血脂大鼠的防治作用, 初步探讨了地龙多肽的降脂作用机制。Tian等[18]研究表明从广地龙中纯化的蛋白组分具有促进创伤修复的作用。地龙中的蛋白多肽类成分含量高且具有多种药理活性, 已开发出多肽地龙蛋白、地龙蛋白溶血栓胶囊、蚓激酶肠溶胶囊等蛋白多肽类产品[19]。Qi等[20]通过对小鼠的凝血及出血时间, 血栓造模实验以及对大鼠凝血三项的检测实验表明, 地龙胶囊具有抑制血栓生成的作用。Liu等[21]研究表明蚓激酶肠溶胶囊可以提高急性脑梗死患者的治疗效果, 可以有效改善凝血指标和血液流变学指标, 提升患者神经功能和生活活动能力。目前, 地龙蛋白多肽的研究逐渐增多, 地龙蛋白多肽类成分的研究也逐渐深入和广泛。
无机元素与人体健康密切相关, 尤其具有重要生理功能的微量元素在机体内发挥着重要作用[22]。动植物等中药材中, 含有多种无机元素, 无机元素与中药功效之间关系紧密, 无机元素与中药性味、归经、中药的质量研究、炮制等方面也有一定的关系[23, 24]。目前, 微量元素分析技术有原子吸收光谱法、原子荧光光谱法、电感耦合等离子体原子发射光谱法和电感耦合等离子体质谱法等, 其中电感耦合等离子体质谱法(inductively coupled plasma mass spectrometry, ICP-MS) 具有多元素同时测定、干扰较少、分析简便快速、精密度较高、检测限低等多种优势。中药含有的无机元素种类丰富且含量差异大, 该技术可以更好地应用于中药无机元素的分析[25-27]。地龙多肽具有多种药理活性, 其药理活性可能与所含无机元素种类和含量相关, 因此本研究采用电感耦合等离子体质谱法检测地龙多肽中的无机元素, 探究地龙多肽中无机元素的种类、含量与临床疗效和质量研究等关系, 为地龙多肽的质量控制和应用提供一定的理论基础和依据。
材料与方法
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仪器  十万分之一电子天平(BT125D型, 德国Satorious公司); MARS Xpress微波消解仪(美国CEM公司); 电热赶酸仪(EHD-24型, 北京东航科仪仪器有限公司); 电感耦合等离子体质谱仪(美国Thermo Fisher Scientific公司)。
试剂  65.0%~68.0% HNO3、30%过氧化氢(优级纯GR, 北京化工厂有限公司); 纯净水(屈臣氏饮用水, 广州屈臣氏食品饮料有限公司); 高纯氩气(北京氦普北分气体工业有限公司); 高纯氦气(北京北氧利来科技发展有限公司); Re、Rh单元素标准溶液(1 000 μg·mL-1, 国家有色金属及电子材料分析测试中心); B、Na、Mg、Al、K、Ca、Sc、V、Cr、Mn、Fe、Co、Ni、Cu、Zn、Ga、As、Se、Zr、Mo、Cd、In、Te、Ba、Pb、Bi混合标准溶液(100 μg·mL-1, 上海麦克林生化科技有限公司); 地龙多肽(北京艾克赛德生物工程有限公司, 批号: 20230324, 20230325, 20230326, 20230327, 20230328, 20230329)。
ICP-MS测定条件  氩气压力: 0.60~0.65 MPa, 氦气压力: 0.1~0.2 MPa, 冷却气流速: 14.000 0 L·min-1, 辅助气流速: 0.800 0 L·min-1, 雾化气流速: 0.981 6 L·min-1, 射频功率: 1 550 W, 雾化室温度: 2.5 ℃, 采样深度: 5 mm, 扫描次数30次, 数据采集模式: KED, 重复3次。
微波消解程序  微波消解程序总共分为三步: 第一步为20 min内从室温升至100 ℃, 维持5 min, 输出功率为1 600 W; 第二步为10 min内从100 ℃升至150 ℃, 维持5 min, 输出功率为1 600 W; 第三步为10 min内从150 ℃升至200 ℃, 维持30 min, 输出功率为1 600 W。
内标溶液的制备  精密移取Rh、Re单元素标准溶液适量, 用2%硝酸溶液稀释成浓度为10 μg·L-1, 作为内标溶液。
混合标准溶液的制备  精密移取含有26种元素的多元素混合标准溶液适量, 用2%硝酸溶液进行逐级稀释, 配制成各元素含量浓度依次为0、1、10、100、1 000 μg·L-1的系列标准溶液。
样品制备  精密称量约0.1 g地龙多肽样品, 置于聚四氟乙烯消解罐中消解, 移取5 mL浓硝酸和1 mL 30%过氧化氢至消解罐中, 旋紧罐盖, 室温下放置20 min进行预消解后, 将消解罐放入微波消解仪, 按照微波消解程序进行消解, 消解程序结束后运行冷却程序15 min。待温度降至室温后取出, 将消解罐置于电热赶酸仪中, 设置110 ℃, 赶酸约3 h, 结束后冷却至室温, 将消解罐内剩余少量液体过0.45 μm滤膜转移至25 mL量瓶中, 用纯净水洗涤消解罐数次, 同时将洗涤液合并于量瓶中, 并用纯净水定容至刻度, 即得供试品溶液。
空白溶液的制备  配制空白溶液即除不加地龙多肽样品之外, 其余所有配制步骤与供试品溶液的配制方法相同。标准溶液配制所用的器具均用2%硝酸溶液浸泡24 h以上, 用水洗净晾干备用。
结果
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1 线性方程、检出限和定量限
在ICP-MS测定条件下, 将配制的标准溶液依次进行测定, 以标准溶液的浓度作为横坐标, 分析信号与内标信号的比值作为纵坐标, 绘制标准曲线, 将配制的空白溶液连续测定11次, 并对测定结果进行分析处理, 得到该方法的检出限和定量限, 结果见表 1。
2 精密度试验
在ICP-MS工作条件下, 将配制的1 000 μg·L-1标准溶液测定6次, 根据测定值计算26种无机元素的精密度RSD为0.21%~2.71%, 结果见表 2。
3 重复性试验
准确称量6份约0.1 g的地龙多肽样品, 按照前述样品制备方法配制供试品溶液, 在ICP-MS工作条件下, 对配制的6份供试品溶液进行测定, 根据测定值计算26种无机元素的重复性RSD为0.19%~4.69%, 结果见表 2。
4 稳定性试验
准确称量1份约0.1 g的地龙多肽样品, 按照前述样品制备方法配制供试品溶液, 在ICP-MS工作条件下, 分别于0、2、4、8、12、24 h时取样进行测定, 根据测定值计算26种无机元素的稳定性RSD为0.11%~4.24%。结果见表 2。
5 加样回收率试验
准确称量6份约0.05 g的地龙多肽样品, 加入与上述地龙多肽样品中各无机元素含量相当的标准溶液, 按照前述样品制备方法配制供试品溶液, 在ICP-MS工作条件下, 对配制的6份供试品溶液进行测定, 根据测定值计算26种无机元素的回收率, 加样回收率为82.41%~116.16%。结果见表 2。
6 不同批次地龙多肽中无机元素含量
取6批不同批次的地龙多肽样品, 分别编号为Y1~Y6, 每批样品各3份, 每份约0.1 g, 按照前述样品制备方法配制供试品溶液, 在ICP-MS工作条件下, 依次对配制的供试品溶液进行测定, 根据测定值计算6批地龙多肽样品中26种无机元素的含量, 结果见表 3。由表可知, 在26种元素中, 地龙多肽主要含有K、Ca、Na、Mg等常量元素; 微量元素含量较多的为Fe、Zn、Al和B元素, 平均含量分别为53.65、20.64、17.78和4.54 mg·kg-1; In、Sc、Co、Pb和Bi等元素含量极低, 甚至在部分批次未能检出。
7 元素含量分布
为了更直观地分析各元素的含量, 将26种元素数据导入Origin 2022软件绘制元素含量分布特征图, 整体分析地龙多肽中26种无机元素含量, 结果见图 1。不同批次地龙多肽间部分元素含量差异明显, 其中Cu、As、Zr、B、Mg和Ca元素在不同批次中含量有较大差异。因此, 不同批次地龙多肽中的元素含量存在差异, 能够为地龙多肽的质量控制及质量评价提供一定的理论依据。
8 主成分分析
主成分分析(principal component analysis, PCA) 是采用降维的方法, 将多个变量转换为能够反映原始变量的大部分信息的少数变量[28]。在检测的26种不同元素中, 除去含量极少以及未能检出的In、Sc、Co、Pb和Bi元素, 剩余21种元素数据通过SPSS 27.0软件进行主成分分析, 得到主成分特征值、方差贡献率和累积方差贡献率及旋转后的载荷矩阵图, 结果如表 4、5所示。将数据导入SIMCA 14.1软件, 得到PCA得分图, 结果见图 2。其中有三个主成分的特征值大于1, 主成分1的方差贡献率为51.720%, 主成分2的方差贡献率为36.289%, 主成分3的方差贡献率为6.923%。前3个主成分累计方差贡献率达94.937%, 能够反映不同批次地龙多肽中不同元素的大部分信息。从表 5可以看出, Ca、Al和Cd元素在第一主成分上有较高的载荷, Na、K和Mn元素在第二主成分上有较高的载荷, Se、Cu和Te元素有较高的载荷。说明第一主成分主要反映Ca、Al和Cd元素的信息, 第二主成分主要反映Na、K和Mn元素的信息, 第三主成分主要反映Se、Cu和Te元素的信息。
9 正交偏最小二乘法判别(OPLS-DA) 分析
在不同批次地龙多肽检测的26种元素中, 除去含量极少以及未能检出的In、Sc、Co、Pb和Bi元素, 将剩余21种元素数据导入SIMCA 14.1软件, 进行正交偏最小二乘判别分析(OPLS-DA)。建立的模型R2X (cum) 为0.909, 稳定性R2Y (cum) 为0.675, 预测率Q2 (cum) 为0.924, 均大于0.5, 说明所建模型稳定、可靠。地龙多肽OPLS-DA模型得分图及VIP图如图 3所示, 以VIP > 1为筛选标准, 得出B、Cu、Fe、Cr四种微量元素为不同批次地龙多肽的质量差异性成分。
讨论
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本文通过电感耦合等离子体质谱法对不同批次的地龙多肽样品中26种元素进行了检测, 结果显示地龙多肽中含量较丰富的常量元素是K、Na和Ca; 其次, 微量元素主要含有Zn、Fe、Al、B等, Ga、Te和In元素含量极少, Co、Pb和Bi元素并未检出, Sc元素在部分批次中检出。研究表明, K元素具有促进组织细胞再生修复、促进血液凝固等作用; Ca可以降低神经兴奋性, 还具有止血消肿的作用; Zn具有促进人体生长发育, 促进创口愈合, 提高机体免疫力, 杀菌等作用; Mg具有调节神经肌肉的兴奋性, 扩张血管等作用; Fe是人体内含量最高的必需微量元素, 参与人体氧的运输和储存, 参与体内过氧化物酶、过氧化氢酶等多种酶的合成, 同时有助于伤口愈合[29, 30]。这可能与地龙中的蛋白质和肽类成分所具有的促进生长发育、抗凝血、促进伤口愈合、增强免疫、抗肿瘤、抗肝纤维化及抗菌等作用有关[31]。
中药资源丰富, 产地众多, 微量元素在地壳表面分布不均, 不同地区的微量元素分布存在差异, 动植物所处的环境对其所含微量元素也有一定影响, 因此中药中微量元素的含量可作为药材质量控制的依据之一[32]。地龙独特的生活习性, 使其容易受到土壤环境的影响。地龙种类繁多, 产地不同, 品种不同都会对地龙的质量和药效产生影响。地龙的品种主要为产于广西、广东、福建等地的广地龙以及产于浙江、湖北、上海等地的沪地龙两种[33]。Wu等[34]采用电感耦合等离子体发射光谱和电感耦合等离子体质谱对不同产地的广地龙中的25种元素含量进行了测定, 并运用主成分分析和判别分析进行分析, 结果表明, 该方法可以溯源广地龙的生产产地。地龙中含有多种无机元素, 例如钙、钾、镁、铁、锌、锶、硒、铬、钴、锰、铜、铅、镉、镍等[35, 36]。地龙多肽中所含元素更多源于地龙药材, 因此, 不同品种、不同产地所产的地龙多肽中所含的无机元素也会存在一定的差异。
地龙蛋白多肽类的提取主要包括酶解法、匀浆浸提法、超声提取以及水提醇沉等方法, 所使用的溶剂主要有机溶剂, 酸碱溶液和水等, 不同的提取方法和提取溶剂对地龙多肽类成分的提取和保留有一定的影响, 对其所含的无机元素含量也有一定的影响[37]。化学计量学运用数学、统计学、计算机科学等相关学科的理论与方法, 来优化化学量测过程, 从而在所获数据中能够最大程度地获取有用的信息[38]。本研究测定了不同批次地龙多肽中的无机元素, 并通过元素含量分布分析、主成分分析以及正交偏最小二乘法判别分析对测定结果进行分析。根据元素含量分布特征图来看, 不同批次的地龙多肽之间元素含量存在较大差异, 主要差异元素为Cu、As、Zr、B、Mg和Ca元素。元素含量分布特征图可方便直观地看出元素含量差异等方面信息, 可以作为样品质量评价的依据之一。通过降维的方法进行主成分分析, 特征值大于1的3个主成分累计方差贡献率达94.937%, 可反映地龙多肽不同批次中不同元素的大部分信息。经正交偏最小二乘法判别分析, 不同批次地龙多肽的质量差异性成分为B、Cu、Fe和Cr四种微量元素。
中药中所含的微量元素对中药药效的发挥有一定的影响, 对中药中的微量元素进行分析可为中药的药效、药性、质量评价及用药安全提供更多的参考[39]。本研究基于ICP-MS建立了对地龙多肽中26种无机元素的测定方法, 完成了对地龙多肽元素的种类和含量测定及分析, 为其质量控制及进一步应用提供一定的理论基础。
作者贡献: 杨洪柳负责设计实验、样品检测、数据处理及文章撰写; 仲伟婷负责数据处理及文章撰写; 郭雨师和李姝琪负责文献查阅及整理; 亓金钗负责文章修改; 刘永刚和马涛负责实验设计及文章审阅。
利益冲突: 所有作者均声明无任何利益冲突。
基金
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  • 国家重点研发计划(2018YFC1706505)
文献
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参考文献 引证文献
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2024年第59卷第4期
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doi: 10.16438/j.0513-4870.2023-1187
  • 接收时间：2023-10-20
  • 首发时间：2025-11-28
  • 出版时间：2024-04-12
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  • 收稿日期：2023-10-20
  • 修回日期：2023-12-20
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国家重点研发计划(2018YFC1706505)
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    1.北京中医药大学中药学院, 北京 102401
    2.北京泰德制药股份有限公司, 北京 100176
    3.包头市昆都仑区医院, 内蒙古 包头 014010

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*刘永刚, E-mail: ;
马涛, E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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