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Article(id=1236689048513860009, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236689043514257626, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1657468800000, acceptedDateStr=2022-07-11, onlineDate=1772779245737, onlineDateStr=2026-03-06, pubDate=1680105600000, pubDateStr=2023-03-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772779245737, onlineIssueDateStr=2026-03-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772779245737, creator=13701087609, updateTime=1772779245737, updator=13701087609, issue=Issue{id=1236689043514257626, tenantId=1146029695717560320, journalId=1235980733773295621, year='2023', volume='32', issue='6', pageStart='553', pageEnd='656', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772779244545, creator=13701087609, updateTime=1772779562627, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236690377705575144, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236689043514257626, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236690377705575145, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236689043514257626, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=576, endPage=582, ext={EN=ArticleExt(id=1236689048769712572, articleId=1236689048513860009, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=Study on the formulation robustness of biopharmaceutical drug products based on multivariate data analysis, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective:

To explore the feasibility of multivariate data analysis (MVDA) in formulation robustness study of biopharmaceutical drug products by taking an antibody-drug conjugate (ADC) HS630 as example.

Methods:

A two-level fractional factorial design with five factors was used to investigate the effects of protein content, contents of three excipients and pH on critical quality attributes. After preparation, the samples of each group were exposed to high temperature for 28 d and light for 10 d, respectively. The contents of polymers and free DM1 were detected and analyzed.

Results:

Partial least squares (PLS) method was used for model fitting and data analysis, and the model seemed good with an acceptable predictive capability. All the excipients and pH had no significant effect on the contents of polymers and free DM1. Although it was not significant, a higher free DM1 content was noticed with higher protein content and higher pH conditions. Light and high temperature had significant effects on the attributes of drug product with a similar trend but small ranges. The formulation in the studied range was regarded as robust.

Conclusion:

Taking MVDA method into formulation robustness study of drug products can analyze the multi-dimensional data obtained under different treatment conditions. The critical components of the formulation can be identified, and the influence of treatment conditions on the critical quality attributes can be investigated.

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目的：

以抗体偶联小分子药物（ADC）HS630为例，探讨多变量数据分析（MVDA）在生物药制剂处方稳健性研究中的应用。

方法：

采用5因子2水平的部分析因设计，考察蛋白质含量、3种辅料含量及pH值对关键质量属性的影响规律。各组制剂样品制备后分别置于高温条件下28 d和光照条件下10 d，进行多聚体含量和游离DM1含量检测和结果分析。

结果：

采用偏最小二乘法（PLS）进行模型拟合和数据分析，模型拟合良好，预测性能尚可。处方中各辅料和pH的变异对多聚体含量和游离DM1含量均无显著影响。蛋白质含量和pH的升高会在一定限度上增加冻干制剂中游离DM1的含量，但影响并不显著。光照和高温对各处方均有显著影响，但影响趋势一致且整体变化幅度不大。本研究范围内制剂处方稳健。

结论：

采用MVDA方法进行生物药制剂稳健性研究，可以对不同处理条件获得的多维数据进行分析，识别关键制剂组分，并考察处理条件对关键质量属性的影响。

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瞿海斌，男，教授，博士生导师，主要从事药品质量控制及过程分析技术研究。联系电话：(0571)88208428，E-mail: 。
王海彬，女，博士，正高级工程师，主要从事生物制药研究。联系电话：(0571)63281566，E-mail: 。
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聂磊，男，博士，正高级工程师，主要从事生物制药研究。联系电话：(0571)63281566，E-mail: 。

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聂磊，男，博士，正高级工程师，主要从事生物制药研究。联系电话：(0571)63281566，E-mail: 。

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聂磊，男，博士，正高级工程师，主要从事生物制药研究。联系电话：(0571)63281566，E-mail: 。

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基于多变量数据分析的生物药制剂处方稳健性研究
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聂磊 1, 2 , 方伟杰 3 , 郭婷婷 1, 2 , 钱慈 2 , 章熠 2 , 瞿海斌 3 , 王海彬 1, 2
中国新药杂志 | 生物医药前沿 2023,32(6): 576-582
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中国新药杂志 | 生物医药前沿 2023, 32(6): 576-582
基于多变量数据分析的生物药制剂处方稳健性研究
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聂磊1, 2 , 方伟杰3, 郭婷婷1, 2, 钱慈2, 章熠2, 瞿海斌3 , 王海彬1, 2
作者信息
  • 1浙江博锐生物制药有限公司，台州 318000
  • 2海正生物制药有限公司，杭州 311404
  • 3浙江大学药学院，杭州 310058

    • 聂磊，男，博士，正高级工程师，主要从事生物制药研究。联系电话：(0571)63281566，E-mail: 。

通讯作者:

瞿海斌，男，教授，博士生导师，主要从事药品质量控制及过程分析技术研究。联系电话：(0571)88208428，E-mail: 。
王海彬，女，博士，正高级工程师，主要从事生物制药研究。联系电话：(0571)63281566，E-mail: 。
Study on the formulation robustness of biopharmaceutical drug products based on multivariate data analysis
Lei NIE1, 2 , Wei-jie FANG3, Ting-ting GUO1, 2, Ci QIAN2, Yi ZHANG2, Hai-bin QU3 , Hai-bin WANG1, 2
Affiliations
  • 1BioRay Pharmaceutical Co., Ltd., Taizhou 318000, China
  • 2Hisun Biopharmaceutical Co., Ltd., Hangzhou 311404, China
  • 3Zhejiang University, Hangzhou 310058, China
出版时间: 2023-03-30
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摘要
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目的：

以抗体偶联小分子药物（ADC）HS630为例，探讨多变量数据分析（MVDA）在生物药制剂处方稳健性研究中的应用。

方法：

采用5因子2水平的部分析因设计，考察蛋白质含量、3种辅料含量及pH值对关键质量属性的影响规律。各组制剂样品制备后分别置于高温条件下28 d和光照条件下10 d，进行多聚体含量和游离DM1含量检测和结果分析。

结果：

采用偏最小二乘法（PLS）进行模型拟合和数据分析，模型拟合良好，预测性能尚可。处方中各辅料和pH的变异对多聚体含量和游离DM1含量均无显著影响。蛋白质含量和pH的升高会在一定限度上增加冻干制剂中游离DM1的含量，但影响并不显著。光照和高温对各处方均有显著影响，但影响趋势一致且整体变化幅度不大。本研究范围内制剂处方稳健。

结论：

采用MVDA方法进行生物药制剂稳健性研究，可以对不同处理条件获得的多维数据进行分析，识别关键制剂组分，并考察处理条件对关键质量属性的影响。

多变量数据分析  /  生物药制剂  /  处方稳健性
Objective:

To explore the feasibility of multivariate data analysis (MVDA) in formulation robustness study of biopharmaceutical drug products by taking an antibody-drug conjugate (ADC) HS630 as example.

Methods:

A two-level fractional factorial design with five factors was used to investigate the effects of protein content, contents of three excipients and pH on critical quality attributes. After preparation, the samples of each group were exposed to high temperature for 28 d and light for 10 d, respectively. The contents of polymers and free DM1 were detected and analyzed.

Results:

Partial least squares (PLS) method was used for model fitting and data analysis, and the model seemed good with an acceptable predictive capability. All the excipients and pH had no significant effect on the contents of polymers and free DM1. Although it was not significant, a higher free DM1 content was noticed with higher protein content and higher pH conditions. Light and high temperature had significant effects on the attributes of drug product with a similar trend but small ranges. The formulation in the studied range was regarded as robust.

Conclusion:

Taking MVDA method into formulation robustness study of drug products can analyze the multi-dimensional data obtained under different treatment conditions. The critical components of the formulation can be identified, and the influence of treatment conditions on the critical quality attributes can be investigated.

multivariate data analysis  /  biopharmaceutical drug product  /  formulation robustness
聂磊, 方伟杰, 郭婷婷, 钱慈, 章熠, 瞿海斌, 王海彬. 基于多变量数据分析的生物药制剂处方稳健性研究. 中国新药杂志, 2023 , 32 (6) : 576 -582 .
Lei NIE, Wei-jie FANG, Ting-ting GUO, Ci QIAN, Yi ZHANG, Hai-bin QU, Hai-bin WANG. Study on the formulation robustness of biopharmaceutical drug products based on multivariate data analysis[J]. Chinese Journal of New Drugs, 2023 , 32 (6) : 576 -582 .
基金
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  • 国家重点研发计划资助项目(2021YEE0201800)
  • 浙江省重点研发计划(2021C03089)
2023年第32卷第6期
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  • 首发时间：2026-03-06
  • 出版时间：2023-03-30
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  • 录用日期：2022-07-11
基金
国家重点研发计划资助项目(2021YEE0201800)
浙江省重点研发计划(2021C03089)
作者信息
    1浙江博锐生物制药有限公司，台州 318000
    2海正生物制药有限公司，杭州 311404
    3浙江大学药学院，杭州 310058

通讯作者:

瞿海斌，男，教授，博士生导师，主要从事药品质量控制及过程分析技术研究。联系电话：(0571)88208428，E-mail: 。
王海彬，女，博士，正高级工程师，主要从事生物制药研究。联系电话：(0571)63281566，E-mail: 。
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https://castjournals.cast.org.cn/joweb/zgxyzz/CN/1236689048513860009
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2种不同金属材料的力学参数
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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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