通用性近红外模型的验证和维护
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摘要
近年来,中国药品生物制品检定所研制并在全国推广了药品检测车,其中配备了以近红外光谱仪为主的多种快速检验设备,用于对市场上的药品进行现场筛查。在近红外药品快速筛查系统中,以通用性定性鉴别模型与通用性定量分析模型联用来筛查假药和劣药,通过通用性定性鉴别模型来快速鉴别、识别可疑药品,并使用通用性定量分析模型来对药物制剂中的API(主要活性成分)或者水分含量进行快速的定量分析,以确定含量是否合格。
近红外模型是药品检测车中近红外药品快速识别系统的核心,是药品检测车能否有效的发挥其实际作用的关键所在。本论文首先针对现有的通用性定性鉴别模型进行了大规模的验证,综合评价了模型的质量。结果表明,现有的近红外通用性定性鉴别模型总体上具有良好的预测结果,从而证实了之前关于通用性定性鉴别模型的建模思路是正确的、可行的。同时,针对前人的工作中建立的一些具体的通用性定量分析模型,这些模型的成功是特例还是具有普遍意义?对于这个问题,本论文选用9种不同结构的β-内酰胺类粉针剂的产品,以其中的水分为待测对象,模拟建立不同工艺、来源但具有相同INN名称药品制剂的通用性定量分析模型的过程,来进一步验证建立通用性模型的可行性,为其提供理论基础。
实践证明,尽管各个厂家采取的工艺、辅料、包装材料等不尽相同,针对药物制剂建立的通用性定性鉴别模型和通用性定量分析模型是行之有效的。然而,随着生产某品种药物的新企业不断出现、制剂的生产工艺也可能会改进,在建模过程中采集的有限的样品将无法代表日益更新的无限的产品,此时的主要问题在于原模型建模样品的代表性不足导致了在对未知样品进行预测时可能会出现错误的判别结果或严重的预测偏差。本论文对于通用性定性鉴别模型提出了对其进行维护和更新的原则依据和具体方法,以基于制剂光谱内部差异性的光谱距离正态性分布为主要依据,来判断原模型中某种药品制剂的光谱分组是否合理,并对市场上新产品的光谱如何加入到原模型中进行维护提出了在三种不同情况下的应对策略。针对通用性定量分析模型的局限性问题,可通过两种方法来扩展通用性模型的适用范围,一是经典的模型更新方法,将新产品的信息加入到原模型中去,扩充模型所包含的变异范围,从而扩展模型的适用性;二是使用PDS算法对临时出现的新样品进行模型传递,也可解决这个问题。
For the purpose of quick inspection of medicines in countryside and fighting against counterfeit drugs,the National Institute for the Control of Pharmaceutical and Biological Products(NICPBP) have designed mobile vehicles(mobile labs) which was equipped with many instruments including near-infrared(NIR) spectrometers and put them into use in China in the past few years.In NIR fast drug prescreening system, universal identification and quantification models are combined to screen counterfeit medicines,while the former models identify and discriminate suspicious medicines rapidly and the latter models quantify the contents of API(Active Principal Ingredient) or moisture in the formulations to determine whether the contents meet the requirements.
NIR models are the essential part of NIR fast drug prescreening system and vital for mobile labs to exert their practical actions.In this dissertation,the current universal identification models were validated comprehensively and the performances of them were evaluated on the whole.The results indicate that they have favorable identification performances,and thus confirm the development of universal identification models is appropriate and feasible.In the meantime,it is also successful to construct universal quantification models for concrete formulations in previous work,however,are they special examples or of universal sense? In this paper,nine types of different beta-lactam antibiotics powder injections were chosen to validate the feasibility of universal quantification models specifying moisture content as the interested component, simulating the process of building universal calibration models for the samples with same INN(International Nonproprietary Name) from diverse formulations and sources.
It has been proved that the universal identification and quantification models are effective though different manufactures employed different process,excipients and packages.However,when new enterprises emerge or new formulations are employed in pharmaceutical industry,original universal identification and quantification models might become invalid for the new products and may yield wrong discriminate results or unacceptable prediction errors due to the occurrence of new products.In this dissertation, the principle and methods to maintain and update universal identification models have been discussed and investigated.The normality plot of spectral distances indicating the internal differences of the spectra of the same medicine preparations is the basic principle to judge whether the spectra grouping is rational,and the strategies to update original universal identification models using the spectra of new products were proposed under three different circumstances.As to the limit of universal quantification models,two solutions were proposed to solve the problem of model invalidation.One is the classical model updating method by which information of new products are added to original models so as to include more variations and expand the applicability.Another solution is calibration transfer from known samples to new products by PDS algorithm to extend the performance of universal models.
近红外模型是药品检测车中近红外药品快速识别系统的核心,是药品检测车能否有效的发挥其实际作用的关键所在。本论文首先针对现有的通用性定性鉴别模型进行了大规模的验证,综合评价了模型的质量。结果表明,现有的近红外通用性定性鉴别模型总体上具有良好的预测结果,从而证实了之前关于通用性定性鉴别模型的建模思路是正确的、可行的。同时,针对前人的工作中建立的一些具体的通用性定量分析模型,这些模型的成功是特例还是具有普遍意义?对于这个问题,本论文选用9种不同结构的β-内酰胺类粉针剂的产品,以其中的水分为待测对象,模拟建立不同工艺、来源但具有相同INN名称药品制剂的通用性定量分析模型的过程,来进一步验证建立通用性模型的可行性,为其提供理论基础。
实践证明,尽管各个厂家采取的工艺、辅料、包装材料等不尽相同,针对药物制剂建立的通用性定性鉴别模型和通用性定量分析模型是行之有效的。然而,随着生产某品种药物的新企业不断出现、制剂的生产工艺也可能会改进,在建模过程中采集的有限的样品将无法代表日益更新的无限的产品,此时的主要问题在于原模型建模样品的代表性不足导致了在对未知样品进行预测时可能会出现错误的判别结果或严重的预测偏差。本论文对于通用性定性鉴别模型提出了对其进行维护和更新的原则依据和具体方法,以基于制剂光谱内部差异性的光谱距离正态性分布为主要依据,来判断原模型中某种药品制剂的光谱分组是否合理,并对市场上新产品的光谱如何加入到原模型中进行维护提出了在三种不同情况下的应对策略。针对通用性定量分析模型的局限性问题,可通过两种方法来扩展通用性模型的适用范围,一是经典的模型更新方法,将新产品的信息加入到原模型中去,扩充模型所包含的变异范围,从而扩展模型的适用性;二是使用PDS算法对临时出现的新样品进行模型传递,也可解决这个问题。
For the purpose of quick inspection of medicines in countryside and fighting against counterfeit drugs,the National Institute for the Control of Pharmaceutical and Biological Products(NICPBP) have designed mobile vehicles(mobile labs) which was equipped with many instruments including near-infrared(NIR) spectrometers and put them into use in China in the past few years.In NIR fast drug prescreening system, universal identification and quantification models are combined to screen counterfeit medicines,while the former models identify and discriminate suspicious medicines rapidly and the latter models quantify the contents of API(Active Principal Ingredient) or moisture in the formulations to determine whether the contents meet the requirements.
NIR models are the essential part of NIR fast drug prescreening system and vital for mobile labs to exert their practical actions.In this dissertation,the current universal identification models were validated comprehensively and the performances of them were evaluated on the whole.The results indicate that they have favorable identification performances,and thus confirm the development of universal identification models is appropriate and feasible.In the meantime,it is also successful to construct universal quantification models for concrete formulations in previous work,however,are they special examples or of universal sense? In this paper,nine types of different beta-lactam antibiotics powder injections were chosen to validate the feasibility of universal quantification models specifying moisture content as the interested component, simulating the process of building universal calibration models for the samples with same INN(International Nonproprietary Name) from diverse formulations and sources.
It has been proved that the universal identification and quantification models are effective though different manufactures employed different process,excipients and packages.However,when new enterprises emerge or new formulations are employed in pharmaceutical industry,original universal identification and quantification models might become invalid for the new products and may yield wrong discriminate results or unacceptable prediction errors due to the occurrence of new products.In this dissertation, the principle and methods to maintain and update universal identification models have been discussed and investigated.The normality plot of spectral distances indicating the internal differences of the spectra of the same medicine preparations is the basic principle to judge whether the spectra grouping is rational,and the strategies to update original universal identification models using the spectra of new products were proposed under three different circumstances.As to the limit of universal quantification models,two solutions were proposed to solve the problem of model invalidation.One is the classical model updating method by which information of new products are added to original models so as to include more variations and expand the applicability.Another solution is calibration transfer from known samples to new products by PDS algorithm to extend the performance of universal models.
引文
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