摘要
糖胺聚糖(glucosaminoglycan, GAG)是一类生物大分子多糖,其生物活性与本身的物化特性和生化特性有关,多数无毒。随着生物可降解材料、结构分子生物学和天然药物食品的兴起、发展,糖胺聚糖在药理学、分子生物学及临床应用等方面取得了很大的进展。近些年开发的透明质酸(HA)、肝素(Hep)和硫酸软骨素(CS)这三种药物均属于糖胺聚糖类药物,临床应用都比较理想。肝素作为抗凝血和抗血栓药物,已有90多年历史的临床应用,在血渗析和体外血循环中能防止血液凝固,防止血栓的生成。透明质酸可增加关节间的润滑性,在剧烈运动时起减震作用,使关节免受伤害。硫酸软骨素具有特殊免疫抑制的药理作用,可有效地防止动脉粥状硬化,且在临床上对于治疗关节炎、风湿痛、神经痛及腰痛都有较好的效果。
透明质酸、肝素、硫酸软骨素这三种物质的化学结构非常相似,表观上又都是白色的粉末,从外观性状上很难区分开来。由于市场价格和制剂问题等诸多原因,这三种物质在应用过程中屡有掺假或混用的现象发生。为了严格把握药物中有效成分的组成及含量,保证患者用药的安全性和有效性,对药物中的杂质、相似物质等进行准确确认和测定显得尤为重要。然而常规的定性方法存在诸多不利因素,如：抽样有损检测,消耗试剂,样品预处理过程繁琐,并造成一定的污染等等。因此,在生产及原料采购过程中寻找简便、快速有效的鉴别、分析测定方法显得尤为重要。
近红外分析技术是一种绿色分析技术,其优点是分析速度快,可对多种成分同时分析,无污染,样品不需要预处理,不使用有毒、有害试剂,不对样品造成损害,而且可以进行在线分析和远距离测定,操作简单,分析成本低,现已被广泛应用于石油化工业、农业、食品业、制药业、临床医学等领域。
本实验利用近红外光谱仪对透明质酸、肝素和硫酸软骨素分别采集图谱后,建立相应的数学模型,先通过无监督识别中系统聚类分析法(HCA)、K-均值聚类法分别对上述三种样品进行成分上的分类,效果很明显,图形显示三种样品可以很清晰的分开。接着利用主成分分析(PCA)结合有监督识别中的SIMCA识别模式鉴别上述这三种样品,通过考察不同光谱预处理方法(5点平滑、一阶微分5点平滑、SNV)、样品集不同的划分方式以及建模波段的选取对建模的影响,结果证明采用5点平滑预处理方法、总取样量为25、全光谱区段内建立的数学模型的预测效果最好,可将三者快速鉴别分离。本实验的结论可以为近红外光谱鉴别其他性状外观相似的药物奠定基础。
Glycosaminoglycan is a kind of Biological Macromolecule. It's bioactivity is tightly related to the physicochemical property, and the majority of it is non-toxic. With the development of biodegradable polymer, natural medicine and structural molecular biology, the glycosaminoglycan has been made great progress in the area of Pharmacology, Molecular biology and clinical application. In the last few years, Hyaluronic acid(HA), heparin(Hep), chondroitin sulfate(CS) are glycosaminoglycans, and they are ideal drugs in clinical application. As the anticoagulant and antithrombotic drugs heparin has been used in blood dialysis and in the blood circulation in vitro to prevent blood coagulation and thrombus formation over 90 years of clinical application. Hyaluronic acid increases the lubrication between the joints, damps the effect of strenuous exercise, and avoids the joints harm. Chondroitin sulfate has a special role in the pharmacological immunosuppression, it can prevent atherosclerosis, and has good results in clinical treatment for arthritis, rheumatic pain, neuralgia and low back pain.
The chemical structure of HA, Hep and CS are similar, and they are all white powder, so it is difficult to identify them. Because of the price and preparation in medicine, it is narmal to falsify the glycosaminoglycans. In order to completely eradicate the fake and inferior drugs to get better economic efficiency, it's quite important to identify impurity and different glycosaminoglycans in raw materal and preparation. However, the routine analysis method is destructive, and it needs pretreatment and organic reagent and it nees long time most of all. Therefore, it's quite necessary to use an undestructive, rapid and non-pollution method.
Near infrared spectroscopy techonology (NIRS) is a green method without specail pretreatment, pollution and destructive. It can be used in inline and remote analysis, and it has been widely used in chemical, agriculture, food and pharmaceutical.
In the experiment we collected the spectrums of HA, Hep, and CS with near infrared spectroscopy instrument, then we used nonsupervised and supervised classification methods to identify the three differernt types of glycosaminoglycans. We used hierarchical clustering analysis (HCA) and K-means method to separate the three types, and we got good results. In the supervised classification we used soft independent modeling of class analogy (SIMCA) to identify three different types of glycosaminoglycans with different pretreatment including smoothing with 5 points, SNV, and first derivative with smoothing of 5 points. Meanwhile we investigated the effect of different wave range and different sample number to the mathematical model. It is proved that the mathematical model with the preteatment of smoothing with 5 points,25 samples and whole wave range was good enough to separate the three glycosaminoglycans rapidly and accurately. The conclution of the thesis lay foundations to identify drugs with similar aspect.
引文
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