Modified Nanoprecipitation Method for Preparation of Cytarabine-Loaded

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高效液相色谱手性固定相法直接拆分4种吲哚类衍生物

高效液相色谱手性固定相法直接拆分4种吲哚类衍生物李秀娟1,2,3　赵亮2　明永飞2,3　彭先芝1　周永章31　陈立仁21(中国科学院广州地球化学研究所,广州510640)2(中国科学院兰州化学物理研究所,兰州730000) 3(中国科学院研究生院,北京100039)摘　要　将纤维素2三(3,52二甲基苯基氨基甲酸酯)(C DMPC )涂敷于自制的球形氨丙基硅胶上,制备成了纤维素2三(3,52二甲基苯基氨基甲酸酯)手性固定相(C DMPC 2CSP )。

利用正相高效液相色谱,在该固定相上对新合成的4种吲哚类衍生物对映体进行了手性拆分。

通过选择不同结构和浓度的醇类改性剂,优化了色谱分离条件,同时探讨了醇的结构和浓度对于对映体拆分和保留的影响。

结果表明,适合Ⅱ～Ⅳ号样品拆分的醇类改性剂分别为正丁醇、乙醇和乙醇,而适合Ⅰ号样品的醇类改性剂为乙醇和正丙醇组成的混合体系。

在优化的各流动相体系下,4种吲哚类衍生物的对映体都得到了很好的分离。

在此基础上计算了它们的对映体过量值(e .e .值)。

实验结果令人满意,表明高效液相色谱手性固定相法是拆分这类化合物的一种理想方法。

关键词　高效液相色谱,手性拆分,吲哚类衍生物,纤维素2三(3,52二甲基苯基氨基甲酸酯)手性固定相　2006208224收稿;2006210231接受本文系国家自然科学基金(No .20375045)、中国科学院“西部之光”(2003)和广东省自然科学基金(No .021428)资助项目3E 2mail:eeszhou@zsu .edu .cn1　引　言高效液相色谱手性固定相法拆分手性化合物,在近20年来得到了迅速的发展。

各种类型的手性固定相相继被合成,用于分离众多的手性化合物[1]。

其中多糖类手性固定相是应用最为广泛、手性拆分能力最强的一类[2]。

在所有的纤维素衍生物中,纤维素2三(3,52二甲基苯基氨基甲酸酯)(CDMPC )对众多类型的手性化合物都表现出了较高的手性识别性能[3,4]。

PDMS固相微萃取膜的研制及对水样中多环芳烃的分析应用

PDMS固相微萃取膜的研制及对水样中多环芳烃的分析应用黄健祥杨运云李攻科*（中山大学化学与化学工程学院广州510275）摘要本文考察了聚二甲基硅氧烷(PDMS)固相微萃取膜的制备条件，采用扫描电镜、热重分析、红外光谱等手段表征膜的性质。

测试证实膜的表面均匀一致，涂层材料热稳定性好、耐溶剂性能好。

采用自制PDMS固相微萃取膜，建立了SPMEM—GC/MS测定水样中PAHs 的分析方法。

方法的线性范围在0.10～1000.00 μg/L之间，检出限在0.02～0.51 μg/L之间，相对标准偏差(RSD)在5.2 %～15.2 %之间，分析实际江水样品，回收率在75.8 %～101.5 %之间，RSD在5.3 %～23.8 %之间。

关键词固相微萃取膜（SPMEM）；多环芳烃（PAHs）1 前言固相微萃取(Solid Phase Microextraction, SPME)[1~7]是二十世纪九十年代初提出并发展起来的快速、灵敏、方便、无溶剂、易于实现自动化并适用于气体、液体和固体样品分析的新颖的样品前处理技术。

但也存在装置价格昂贵，现涂层种类有限，选择性差，对无机离子的萃取分离技术不成熟，对复杂基体样品的萃取选择性和重现性不理想等不足。

固相微萃取膜(Solid-Phase Microextraction Membrane，SPMEM)是将固相微萃取涂层材料均匀的涂布于膜状基材上，将针状的固相微萃取装置发展制作成膜状的固相微萃取膜。

固相微萃取膜继承了固相微萃取的萃取机理，保留了固相微萃取的优点，具有与固相微萃取纤维相似的萃取性能。

固相微萃取膜集提取与浓缩为一体，采用溶剂洗脱的方法解吸，可以通过改变萃取膜的大小和厚度来提高萃取分析的灵敏度，方便地实现与气相色谱、液相色谱以及其它各种分析仪器的联用，是固相微萃取技术发展的一个重要延伸。

多环芳烃（PAHs）是一大类广泛存在于环境中的有机污染物，也是最早被发现和研究的化学致癌物，它们是指两个以上苯环连在一起的化合物，具有相当强的致癌性。

《2024年两种制备方法对水飞蓟宾纳米混悬剂体内外行为的影响》范文

《两种制备方法对水飞蓟宾纳米混悬剂体内外行为的影响》篇一一、引言水飞蓟宾是一种具有重要药用价值的化合物，广泛应用于临床治疗多种疾病。

然而，由于水飞蓟宾的溶解性差，其生物利用度常常受到限制。

为了解决这一问题，研究者们尝试了多种方法制备水飞蓟宾纳米混悬剂，以提高其溶解度和生物利用度。

本文将探讨两种制备方法对水飞蓟宾纳米混悬剂体内外行为的影响。

二、制备方法一第一种制备方法主要采用乳化溶剂挥发法（Emulsification-Evaporization Method），简称E-E法。

这种方法主要包括将水飞蓟宾溶解在有机溶剂中，再通过高速搅拌或乳化使药物与辅料混合形成均匀的油滴状。

然后通过降低温度或使溶剂挥发的方式使油滴固化，最终形成纳米混悬剂。

三、制备方法二第二种制备方法为纳米沉淀法（Nanoprecipitation Method）。

该方法首先将水飞蓟宾与稳定剂混合，然后将其溶于有机溶剂中。

接着在搅拌条件下，将该混合物与另一溶液迅速混合，产生高浓度过饱和的体系。

过饱和体系中由于大量的沉淀过程导致形成稳定而小尺寸的颗粒。

再经一定的处理方法获得最终的产品纳米混悬剂。

四、两种制备方法对体内外行为的影响1. 体外释放研究对于两种制备方法所得的纳米混悬剂进行体外释放研究，可以观察到通过纳米沉淀法制备的混悬剂药物释放速率相对较快，因为其能够产生较高的过饱和度并迅速产生大量的药物沉淀颗粒。

而E-E法由于在固化过程中可能存在药物与辅料之间的相互作用，导致药物释放速率相对较慢。

2. 体内吸收研究在体内吸收方面，由于纳米混悬剂具有较小的颗粒尺寸和较大的表面积，能够显著提高药物的溶解度和生物利用度。

通过比较两种制备方法得到的纳米混悬剂在体内的吸收情况，可以观察到采用纳米沉淀法制备的混悬剂能够更好地促进药物在体内的吸收，提高生物利用度。

而E-E法虽然过程相对复杂，但其所得到的纳米混悬剂也能在体内产生一定的药效。

3. 体内外相关性研究在体内外相关性研究中，通过对两种制备方法得到的纳米混悬剂进行体内外释放及吸收的研究，发现其具有较好的相关性。

甘草次酸修饰多西紫杉醇磁性纳米粒的制备与表征

甘草次酸修饰多西紫杉醇磁性纳米粒的制备与表征作者：王莎莎陈家琦王华华黄胜楠贾永艳祝侠丽来源：《中国药房》2020年第19期摘要目的：制备甘草次酸修饰多西紫杉醇磁性纳米粒（GA-DTX-NGO/IONP-NPs），并对其理化性质进行评价。

方法：以磁性纳米氧化石墨烯（NGO/IONP）作为抗肿瘤药物载体，多西紫杉醇（DTX）为模型药物，甘草次酸（GA）為靶头分子。

采用水热法合成NGO/IONP、酰胺化反应合成GA修饰的壳聚糖（GA-CS）后，采用傅里叶红外光谱法、差示扫描量热法及振动样品磁测量法等对两者进行表征。

采用离子凝胶化法制备GA-DTX-NGO/IONP-NPs;采用透射电镜、纳米粒度分析仪等对其微观形态、粒径及Zeta电位进行观察和测定;采用超滤离心法测定其包封率和载药量;通过观察有无外加磁场时的状态考察其磁性;结合808 nm激光对其进行光热转换试验。

结果：成功合成了NGO/IONP和GA-CS，且NGO/IONP呈现超顺磁性。

GA-DTX-NGO/IONP-NPs在透射电镜下呈圆球状，粒径为（262.8±4.23） nm，Zeta电位为（13.6±1.51） mV，包封率为（94.29±0.50）%，载药量为（17.12±0.12）%。

GA-DTX-NGO/IONP-NPs的外观呈黑色，分散均匀;其在外加磁场下磁性纳米粒可定向移动，显示出良好的磁定向性。

在808 nm激光照射下，GA-DTX-NGO/IONP-NPs 具有良好的光热转换效应，且呈浓度和时间依赖趋势。

结论：本研究成功制备了一种磁性纳米载药系统GA-DTX-NGO/IONP-NPs，可为肿瘤的磁热-化疗联合治疗提供一定的理论依据。

关键词磁性纳米氧化石墨烯;甘草次酸;多西紫杉醇;磁性纳米粒ABSTRACT OBJECTIVE： To prepare Glycyrrhetinic acid-modified docetaxel magnetic nanoparticles （GA-DTX-NGO/IONP- NPs）， and to evaluate its physicochemical properties. METHODS： Magnetic nano graphene oxide （NGO/IONP） was chosen as the anti-tumor drug carrier， docetaxel （DTX） as the model drug and glycyrrhetinic acid （GA） as the target molecule. Firstly， NGO/IONP was synthesized by hydrothermal method and GA-CS was synthesized by amidation reaction. Fourier IR spectrometer， DSC and vibration sample magnetic measuring instrument were used to characterize NGO/IONP and GA-CS. GA-DTX-NGO/IONP-NPs were prepared by the ion gelation method. TEM and particle size analyzer were used to observe and determine the morphology， particle size and Zeta potential of GA-DTX-NGO/IONP-NPs; the ultrafiltration-centrifugation method was used to determine encapsulation efficiency and drug loading amount; the magnetic properties were investigated by investigating the state with or without external magnetic field; the photothermal conversion test was carried out with laser irradiation of 808 nm. RESULTS： NGO/IONP and GA-CS were successfully synthesized， and NGO/IONP exhibited superparamagnetism characteristics. GA-DTX-NGO/IONP-NPs were spherical under TEM， the particle size was （262.8±4.23） nm and the Zeta potential was （13.6±1.51） mV. The encapsulation rate and drug loading amount were （94.29±0.50）% and （17.12±0.12）%，respectively. GA-DTX-NGO/IONP-NPs were black in appearance and evenly dispersed. Under the external magnetic field， the magnetic nanoparticles could move directionally， showing good magnetic properties. GA-DTX-NGO/IONP-NPs showed a good concentration- and time-dependent photothermal conversion effect under 808 nm laser irradiation. CONCLUSIONS： GA-DTX-NGO/IONP-NPs are successfully prepared. This study could provide some theoretical basis for the combined treatment of magnetic heating-chemotherapy for liver tumors.KEYWORDS Magnetic nano graphene oxide; Glycyrrhetinic acid; Docetaxel; Magnetic nanoparticles肝癌是危害人类健康的重大疾病之一 [1-2]。

任现职以来发表的主要论着、承担的科研项目等

重庆医科大学申报教授专业技术职务任职资格评审综合情况(公示)表填表时间：2016 年6月12日填报单位: 重庆医科大学基础医学院学科: 生物化学与分子生物学
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USP 1223 VALIDATION OF ALTERNATIVE MICROBIOLOGICAL METHODS

1223VALIDATION OF ALTERNATIVE MICROBIOLOGICAL METHODSINTRODUCTIONThe purpose of this chapter is to provide guidance for validating methods for use as alternatives to the official compendial microbiological methods. For microbial recovery and identification, microbiological testing laboratories sometimes use alternative test methods to those described in the general chapters for a variety of reasons, such as economics, throughput, and convenience. Validation of these methods is required. Some guidance on validation of the use of alternate methods is provided in the Tests and Assays section in the General Notices and Requirements. This section also notes that in the event of a dispute, only the result obtained by the compendial test is conclusive.Validation studies of alternate microbiological methods should take a large degree of variability into account. When conducting microbiological testing by conventional plate count, for example, one frequently encounters a range of results that is broader (%RSD 15 to 35) than ranges in commonly used chemical assays (%RSD 1 to 3). Many conventional microbiological methods are subject to sampling error, dilution error, plating error, incubation error, and operator error.Validation of Compendial Procedures 1225defines characteristics such as accuracy, precision, specificity, detection limit, quantification limit, linearity, range, ruggedness, and robustness in their application to analytical methods. These definitions are less appropriate for alternate microbiological m ethod validation as ―at least equivalent to the compendial method‖ given the comparative nature of the question (see the Tests and Assays—Procedures section in General Notices and Requirements). The critical question is whether or not the alternate method will yield results equivalent to, or better than, the results generated by the conventional method.Other industry organizations have provided guidance for the validation of alternate microbiological methods.* The suitability of a new or modified method should be demonstrated in a comparison study between the USP compendial method and the alternate method. The characteristics defined in this chapter may be used to establish this comparison.TYPES OF MICROBIOLOGICAL TESTSIt is critical to the validation effort to identify the portion of the test addressed by an alternate technology. For example, there is a variety of technologies available to detect the presence of viable cells. These techniques may have application in a variety of tests (e.g., bioburden, sterility test) but may not, in fact, replace the critical aspects of the test entirely. For example, a sterility test by membrane filtration may be performed according to the compendial procedure up to the point of combining the processed filter with therecovery media, and after that the presence of viable cells might then be demonstrated by use of some of the available technologies. Validation of this application would, therefore, require validation of the recovery system employed rather than the entire test.There are three major types of determinations specific to microbiological tests. These include tests to determine whether microorganisms are present in a sample, tests to quantify the number of microorganisms (or to enumerate a specific subpopulation of the sample), and tests designed to identify microorganisms. This chapter does not address microbial identification.Qualitative Tests for the Presence or Absence of MicroorganismsThis type of test is characterized by the use of turbidity in a liquid growth medium as evidence of the presence of viable microorganisms in the test sample. The most common example of this test is the sterility test. Other examples of this type of testing are those tests designed to evaluate the presence or absence of a particular type of microorganism in a sample (e.g., coliforms in potable water and E. coli in oral dosage forms).Quantitative Tests for MicroorganismsThe plate count method is the most common example of this class of tests used to estimate the number of viable microorganisms present in a sample. The membrane filtration and Most Probable Number (MPN) multiple-tube methods are other examples of these tests. The latter was developed as a means to estimate the number of viable microorganisms present in a sample not amenable to direct plating or membrane filtration.General ConcernsValidation of a microbiological method is the process by which it is experimentally established that the performance characteristics of the method meet the requirements for the intended application, in comparison to the traditional method. For example, it may not be necessary to fully validate the equivalence of a new quantitative method for use in the antimicrobial efficacy test by comparative studies, as the critical comparison is between the new method of enumeration and the plate count method (the current method for enumeration). As quantitative tests, by their nature, yield numerical data, they allow for the use of parametric statistical techniques. In contrast, qualitative microbial assays, e.g., the sterility test in the example above, may require analysis by nonparametric statistical methods. The validation of analytical methods for chemical assays followswell-established parameters as described in Validation of Compendial Procedures 1225. Validation of microbiological methods shares some of the same concerns, although consideration must be given to the unique nature of microbiological assays (see Table 1).Table 1. Validation Parameters by Type of Microbiological TestVALIDATION OF QUALITATIVE TESTS FOR DEMONSTRATION OF VIABLEMICROORGANISMS IN A SAMPLESpecificityThe specificity of an alternate qualitative microbiological method is its ability to detect a range of microorganisms that may be present in the test article. This concern is adequately addressed by growth promotion of the media for qualitative methods that rely upon growth to demonstrate presence or absence of microorganisms. However, for those methods that do not require growth as an indicator of microbial presence, the specificity of the assay for microbes assures that extraneous matter in the test system does not interfere with the test.Limit of DetectionThe limit of detection is the lowest number of microorganisms in a sample that can be detected under the stated experimental conditions. A microbiological limit test determines the presence or absence of microorganisms, e.g., absence of Salmonella spp. in 10 g. Due to the nature of microbiology, the limit of detection refers to the number of organisms present in the original sample before any dilution or incubation steps; it does not refer to the number of organisms present at the point of assay.One method to demonstrate the limit of detection for a quantitative assay would be to evaluate the two methods (alternative and compendial) by inoculation with a low number of challenge microorganisms (not more than 5 cfu per unit) followed by a measurement of recovery. The level of inoculation should be adjusted until at least 50% of the samples show growth in the compendial test. It is necessary to repeat this determination several times, as the limit of detection of an assay is determined from a number of replicates (notless than 5). The ability of the two methods to detect the presence of low numbers of microorganisms can be demonstrated using the Chi square test. A second method to demonstrate equivalence between the two quantitative methods could be through the use of the Most Probable Number technique. In this method, a 5-tube design in a ten-fold dilution series could be used for both methods. These would then be challenged with equivalent inoculums (for example, a 10–1, 10–2, and 10–3 dilution from a stock suspension of approximately 50 cfu per mL to yield target inocula of 5, 0.5, and 0.05 cfu per tube) and the MPN of the original stock determined by each method. If the 95% confidence intervals overlapped, then the methods would be considered equivalent.RuggednessThe ruggedness of a qualitative microbiological method is the degree of precision of test results obtained by analysis of the same samples under a variety of normal test conditions, such as different analysts, instruments, reagent lots, and laboratories. Ruggedness can be defined as the intrinsic resistance to the influences exerted by operational and environmental variables on the results of the microbiological method. Ruggedness is a validation parameter best suited to determination by the supplier of the test method who has easy access to multiple instruments and batches of components.RobustnessThe robustness of a qualitative microbiological method is a measure of its capacity to remain unaffected by small but deliberate variations in method parameters, and provides an indication of its reliability during normal usage. Robustness is a validation parameter best suited to determination by the supplier of the test method. As there are no agreed upon standards for current methods, acceptance criteria are problematic and must be tailored to the specific technique. It is essential, however, that an estimate of the ruggedness of the alternate procedure be developed. The measure of robustness is not necessarily a comparison between the alternate method and the traditional, but rather a necessary component of validation of the alternate method so that the user knows the operating parameters of the method.VALIDATION OF QUANTITATIVE ESTIMATION OF VIABLE MICROORGANISMS IN ASAMPLEAs colony-forming units follow a Poisson distribution, the use of statistical tools appropriate to the Poisson rather than those used to analyze normal distributions is encouraged. If the user is more comfortable using tools geared towards normally distributed data, the use of a data transformation is frequently useful. Two techniques are available and convenient for microbiological data. Raw counts can be transformed to normally distributed data either by taking the log10 unit value for that count, or by takingthe square root of count +1. The latter transformation is especially helpful if the data contain zero counts.AccuracyThe accuracy of this type of microbiological method is the closeness of the test results obtained by the alternate test method to the value obtained by the traditional method. It should be demonstrated across the operational range of the test. Accuracy is usually expressed as the percentage of recovery of microorganisms by the assay method. Accuracy in a quantitative microbiological test may be shown by preparing a suspension of microorganisms at the upper end of the range of the test, that has been serially diluted down to the lower end of the range of the test. The operational range of the alternate method should overlap that of the traditional method. For example, if the alternate method is meant to replace the traditional plate count method for viable counts, then a reasonable range might be from 100 to 106 cfu per mL. At least 5 suspensions across the range of the test should be analyzed for each challenge organism. The alternate method should provide an estimate of viable microorganisms not less than 70% of the estimate provided by the traditional method, or the new method should be shown to recover at least as many organisms as the traditional method by appropriate statistical analysis, an example being an ANOVA analysis of the log10 unit transforms of the data points. Note that the possibility exists that an alternate method may recover an apparent higher number of microorganisms if it is not dependent on the growth of the microorganisms to form colonies or develop turbidity. This is determined in the Specificity evaluation.PrecisionThe precision of a quantitative microbiological method is the degree of agreement among individual test results when the procedure is applied repeatedly to multiple samplings of suspensions of laboratory microorganisms across the range of the test. The precision of a microbiological method is usually expressed as the standard deviation or relative standard deviation (coefficient of variation). However, other appropriate measures may be applied. One method to demonstrate precision uses a suspension of microorganisms at the upper end of the range of the test that has been serially diluted down to the lower end of the range of the test. At least 5 suspensions across the range of the test should be analyzed. For each suspension at least 10 replicates should be assayed in order to be able to calculate statistically significant estimates of the standard deviation or relative standard deviation (coefficient of variation). Generally, a RSD in the 15% to 35% range would be acceptable. Irrespective of the specific results, the alternate method should have a coefficient of variation that is not larger than that of the traditional method. For example, a plate count method might have the RSD ranges as shown in the following table.Table 2. Expected RSD as a Function of cfu per PlateThe specificity of a quantitative microbiological method is its ability to detect a panel of microorganisms suitable to demonstrate that the method is fit for its intended purpose. This is demonstrated using the organisms appropriate for the purpose of the alternate method. It is important to challenge the alternate technology in a manner that would encourage false positive results (specific to that alternate technology) to demonstrate the suitability of the alternate method in comparison to the traditional method. This is especially important with those alternate methods that do not require growth for microbial enumeration (for example, any that do not require enrichment or can enumerate microorganisms into the range of 1–50 cells).Limit of QuantificationThe limit of quantification is the lowest number of microorganisms that can be accurately counted. As it is not possible to obtain a reliable sample containing a known number of microorganisms, it is essential that the limit of quantification of an assay is determined from a number of replicates (n > 5) at each of at least 5 different points across the operational range of the assay. The limit of quantification should not be a number greater than that of the traditional method. Note that this may have an inherent limit due to the nature of bacterial enumeration and the Poisson distribution of bacterial counts (see Validation of Microbial Recovery from Pharmacopeial Articles 1227). Therefore, the alternate method need only demonstrate that it is at least as sensitive as the traditional method to similar lower limits.LinearityThe linearity of a quantitative microbiological test is its ability to produce results that are proportional to the concentration of microorganisms present in the sample within a given range. The linearity should be determined over the range of the test. A method to determine this would be to select at least 5 concentrations of each standard challenge microorganism and conduct at least 5 replicate readings of each concentration. An appropriate measure would be to calculate the square of the correlation coefficient, r2, from a linear regression analysis of the data generated above. While the correlation coefficient does not provide an estimate of linearity, it is a convenient and commonly applied measure to approximate the relationship. The alternate method should not have an r2 value less than 0.95.Limit of DetectionSee Limit of Detection under Validation of Qualitative Tests for Demonstration of Viable Microorganisms in a Sample.RangeThe operational range of a quantitative microbiological method is the interval between the upper and lower levels of microorganisms that have been demonstrated to be determined with precision, accuracy, and linearity.RuggednessSee Ruggedness under Validation of Qualitative Tests for Demonstration of Viable Microorganisms in a Sample.RobustnessSee Robustness under Validation of Qualitative Tests for Demonstration of Viable Microorganisms in a Sample.* PDA Technical Report No. 33. The Evaluation, Validation and Implementation of New Microbiological Testing Methods. PDA Journal of Pharmaceutical Science & Technology.54 Supplement TR#33 (3) 2000 and Official Methods Programs of AOAC International.。

PLGA纳米粒作为药物载体的靶向作用研究进展

动物医学进展，２０２０，４１（１２）：９６１０１ＰｒｏｇｒｅｓｓｉｎＶｅｔｅｒｉｎａｒｙＭｅｄｉｃｉｎｅＰＬＧＡ纳米粒作为药物载体的靶向作用研究进展　收稿日期：２０２００６０６　基金项目：国家自然科学基金项目（３１８７２５１１）　作者简介：胡　馨（１９９７－），女，重庆人，硕士研究生，主要从事兽药学研究。

通讯作者胡　馨，支　慧，杨　艳，杨　杰，柴东坤，林　浪，刘云杰，宋振辉，封海波（西南大学动物科学学院，重庆４０２４６０）摘　要：纳米科技在现代医学及药学的应用方面广泛发展，纳米药物载体在实现靶向性给药、缓释药物、降低药物的毒副作用等方面有重大优势。

聚乳酸羟基乙酸聚合物（ＰＬＧＡ）是一种高分子有机化合物，具有生物相容性及生物可降解性，当前聚乳酸羟基乙酸聚合物纳米粒（ＰＬＧＡＮＰｓ）被广泛地作为药物载体进行靶向治疗。

论文归纳总结了近年来国内外的相关文献报道，概述了ＰＬＧＡＮＰｓ的特点、制备方法与表征以及靶向作用的研究进展，着重讨论了ＰＬＧＡＮＰｓ作为药物载体在肿瘤组织、心脑血管、骨组织、免疫和基因类疾病中靶向作用的研究进展，并对未来发展前景进行了展望，为相关的科研提供参考。

关键词：聚乳酸羟基乙酸聚合物；纳米粒；药物载体；靶向作用中图分类号：Ｓ８５４．５３；Ｓ８５９．７９７文献标识码：Ａ文章编号：１００７５０３８（２０２０）１２００９６０６靶向制剂是指通过局部给药的方式将药物输送至特定的组织、器官、细胞内，以提高药物的疗效和生物利用度，并减少毒副作用带来的危害。

聚乳酸羟基乙酸聚合物［ｐｏｌｙ（ｌａｃｔｉｃｃｏｇｌｙｃｏｌｉｃａｃｉｄ），ＰＬＧＡ］是由乳酸和羟基乙酸的单体聚合而成的可降解的高分子有机化合物。

纳米粒（ｎａｎｏｐａｒｔｉｃｌｅｓ，ＮＰｓ）是大小介于１ｎｍ～１０００ｎｍ之间的一种固态胶体颗粒，可作为药物靶向传递的载体。

ＰＬＧＡ是乳酸（ｌａｃｔｉｃａｃｉｄ，ＬＡ）与羟基乙酸（ｇｌｙｃｏｌｉｃａｃｉｄ，ＧＡ）共聚合而成，当ＰＬＧＡ进入体内，通过酯键水解生成相应的单体酸、乳酸和羟基乙酸，然后经过三羧酸循环后转变成二氧化碳和水，因此该聚合物对人体无刺激性，无毒且拥有良好的生物相容性和降解性［１］；ＰＬＧＡＮＰｓ极易于被吞噬细胞摄取，因此通过在纳米颗粒偶联吸附相应的配体可定位到需要的组织和器官。

一种快速可控制备纳米粒子的新方法——瞬时纳米沉淀法研究进展

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石河子大学学报：自然科学版
第３４卷
装形成表面为亲水层、内部包载功能性物质粒子的水分散性ＮＰｓ，即可有效解决疏水性功能性物质在水溶液中稳定分散和运送的问题［７－－８１。同时，由表面活性剂分子组装形成的纳米载体的引入还可以带来更好的稳定性、生物相容性、以及更长的循环时间等【９】。然而，制备这类ＮＰｓ的传统方法均基于体系的热力学平衡，是一个缓慢的、自发的热力学过程［１０］，如透析法、乳化法等，其制备时间较长，甚至可以达到数天；另外，ＮＰｓ的尺寸不易调控，并且其尺寸分布也较宽；纳米粒子的载药量较低。为了解决这些问题，普林斯顿大学的Ｐｒｕｄ’ｈｅｍ— ｍｅ教授采用了一种名为瞬时纳米沉淀法（ＦｌａｓｈＮａｎｏｐｒｅｃｉｐｉｔａｔｉｏｎ，简称ＦＮＰ）的制备纳米粒子的新方法［１１－１２】。ＦＮＰ法不同于通常的热力学控制的方法，是一种通过动力学控制的强化自组装制备纳米粒子的方法。通过外加的作用力进行的动力学控制，ＮＰｓ的制备时间急剧下降至数秒，其中最重要的ＮＰｓ的形成则是在毫秒级的时间尺度内完成的吲；通过控制ＦＮＰ法制备ＮＰｓ过程的工艺参数，可以轻易地调控ＮＰｓ的粒径，并且其尺寸分布也可以控制得非常窄；ＦＮＰ法制备的ＮＰｓ中功能性疏水有机分子的装载率很高［１４】。这些优势使得ＦＮＰ法十分适合于控制制备功能性的ＮＰｓ。１

N-取代苝酰亚胺小分子自组装形貌及生物学应用研究进展

ｏｆ３ｗａｓｉｎｊｅｃｔｅｄｉｎｔｏｔｈｅｍｉｘｅｄｓｏｌｕｔｉｏｎｏｆｗａｔｅｒ／ｍｅｔｈａｎｏｌ（ｖ／ｖ ∶ ３５／６５）（ｄ）ｔｈｅｍａｇｎｉｆｉｅｄｉｍａｇｅｏｆＤＨ⁃ＰＤＩ
这样的聚集体的形貌与我们研究有机⁃多金属氧簇化合物的自组装形态有相似之处［８－１０］：连有疏水基团的衍生物在溶液中自组装，疏水基团较短时分子之间较强 π－π 作用力，使得化合物在有机溶剂中自组装成具有一定长度的一维形貌；如果疏水基团相对较大，
Ｆｉｇ．２Ｓｃｈｅｍｅｏｆｃｏｍｐｏｕｎｄｓ１，２，３ａｎｄｓｃａｎｎｉｎｇｅｌｅｃｔｒｏｎｍｉｃｒｏｇｒａｐｈｓ（ａ）ＤＰ⁃ＰＤＩｓｅｌｆ⁃ａｓｓｅｍｂｌｙ：ｔｈｅｃｈｌｏｒｏｆｏｒｍｓｏｌｕｔｉｏｎｏｆ１ｗａｓｉｎｊｅｃｔｅｄｉｎｔｏａｎｅｘｃｅｓｓｏｆｅｔｈａｎｏｌｓｏｌｖｅｎｔ；（ｂ）ＤＴ⁃ＰＤＩｓｅｌｆ⁃ａｓｓｅｍｂｌｙ：ｔｈｅｃｈｌｏｒｏｆｏｒｍｓｏｌｕｔｉｏｎｏｆ２ｗａｓｉｎｊｅｃｔｅｄｉｎｔｏｔｈｅｅｘｃｅｓｓｅｔｈａｎｏｌｓｏｌｖｅｎｔ；（ｃ）ＤＨ⁃ＰＤＩｓｅｌｆ⁃ａｓｓｅｍｂｌｙ：ｔｈｅｃｈｌｏｒｏｆｏｒｍｓｏｌｕｔｉｏｎ
图４化合物５（１０ μｍｏｌ／Ｌ）在不同溶剂中的扫描电镜图片Ｆｉｇ．４ＳＥＭｉｍａｇｅｓｏｆｃｏｍｐｏｕｎｄ５（１０ μｍｏｌ／Ｌ）ｉｎｄｉｆｆｅｒｅｎｔｓｏｌｖｅｎｔｓ
在苝环的海湾处进行取代基修饰的结构，使得分子自组装过程中依赖苝酐核 π－π 堆积变得困难。文献报道的碳酸烯丙酯官能化的苝二酰亚胺衍生物６［１３］，可以很容易地在ＣＨ３ＣＮ中 π－π 相互作用，自组装成数十毫米长的纳米棒；在３０％Ｈ２Ｏ、ＤＭＳＯ混

碱基切除修复抑制剂甲氧胺联合β-榄香烯治疗恶性脑胶质瘤的实验研究

序言β-榄香烯属国家二类非细胞毒性抗肿瘤新药，临床研究证实其对包括脑胶质瘤在内的多种肿瘤疗效确切，且无其他传统化疗药常有的骨髓抑制、肝肾功能损害等毒副作用。

但目前临床应用的榄香烯乳注射液因其存在静脉炎发生率很高、剂型性质不稳定等缺点，其进一步的应用受到了较大的限制。

碱基切除修复抑制剂甲氧胺（Methoxyamine），可通过裂解核酸内切酶破坏DNA碱基切除修复过程，从而抑制肿瘤细胞对损伤作用的修复反应。

据此，可认为抑制DNA 碱基切除修复可能是增强肿瘤细胞化疗敏感性的潜在靶点，目前多项实验报道也已证实了甲氧胺可增强烷化剂和放疗的抗肿瘤效果。

近年来，通过纳米技术构建的纳米脂质体在提高药物溶解度、增加药物稳定性、降低药物副作用、缓控释给药等方面较普通的脂质体有了显著的提高。

研究表明，纳米脂质体对正常细胞和组织无损伤作用，并可长时间吸附于靶细胞周围，因此使药物能充分向靶组织渗透，也可以通过静电吸附效应与细胞膜接触而融合而进入细胞内。

因此将药物包封于纳米脂质体被认为可以改变被包封药物的体内分布，提高药物治疗指数，降低药物毒性。

基于增强β-榄香烯的疗效，减少毒副作用的目的，本课题研究内容分两部分：（一）联合碱基切除修复抑制剂甲氧胺，探讨是否在体内外抗瘤活性上具有协同作用，以期减少榄香烯用量，降低毒副反应，为其在临床的应用提供实验和理论依据。

（二）、利用纳米脂质体技术构建新型的β-榄香烯-纳米脂质体药物传递系统，初步探讨其体外抗瘤活性。

II碱基切除修复抑制剂甲氧胺联合β-榄香烯治疗恶性脑胶质瘤的实验研究中文摘要胶质瘤是成人神经系统最常见的原发性肿瘤，手术全切除率很低，复发率高，当前多种治疗效果仍不理想。

榄香烯属国家二类非细胞毒性抗肿瘤新药，临床研究发现其对多种肿瘤疗效确切，而且还具有提高和改善机体免疫功能，与放化疗协同作用等独特效果。

但是肿瘤细胞具有强大的DNA损伤修复机制，会对化疗药物产生抗性。

因此抑制这种内在的DNA修复过程，如碱基切除修复抑制剂甲氧胺的联合应用有利于提高化疗药物的抗瘤效果。

NatMethods西湖大学党波波博士等开发出蛋白质定点化学剪切方法以代替传统生物酶切

NatMethods西湖大学党波波博士等开发出蛋白质定点化学剪切方法以代替传统生物酶切责编丨迦溆蛋白质生物表达过程中，不同标签肽（如His-Tag、FLAG-Tag）、蛋白（如GST、MBP）往往需要被融合到目标蛋白的C端或N端以提高目标蛋白表达产率、可溶性，实现蛋白纯化。

然而被融合的标签肽、蛋白最终往往需要从目标蛋白上切掉【1】。

目前唯一的生物兼容性蛋白剪切方法是生物酶切，包括常用的TEV酶、thrombin酶等。

然而，生物酶切经常面临许多问题：如生物酶的加入增加了后续再除去的纯化步骤；高价蛋白酶增加了大量制备目标蛋白的成本；另外很多情况下，特别对于膜蛋白，生物酶切效率往往很低【2】。

蛋白质化学剪切方法因为操作方便，成本低等优势有望解决生物酶切涉及的各种问题。

然而传统蛋白质化学剪切方法因为选择性差，反应条件苛刻，并没有被广泛应用【3-7】。

2019年3月25日，西湖大学生命科学学院党波波研究员在Nature Methods上发表了题为SNAC-tag for Sequence-specific Chemical Protein Cleavage的研究论文，报道了一种可以用二价镍离子在生物兼容条件下实现蛋白质定点化学剪切的方法。

该研究将底物噬菌体展示技术引入到蛋白质化学剪切方法开发当中，采用不同金属离子对底物噬菌体文库进行高通量筛选，最终鉴定出可以被二价镍离子高效切断的多肽序列特点。

研究者进而高通量合成了上百个多肽序列鉴定每个位点氨基酸性质对于剪切效率的影响，最终确定-GSHHW-剪切效果最佳，在含-GSHHW-序列的多肽剪切测试中，在0.1 M CHES, pH 8.2, 1 mM NiCl2, 22 °C, 16 h反应条件下剪切效率>95%。

研究者将-GSHHW-序列命名为SNAC-tag （Sequence-specific Nickel Assisted Cleavage tag）。

纳米酶 ros清除氧化铈异质结构

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聚吡咯_碳纳米管分子印迹修饰电极对槲皮素的选择性测定

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CH I 660C 电化学工作站 (上海辰华仪器有限公司 ); 实验采用三电极系统: 参比电极为饱和甘汞电极 ( SCE ) , 辅助电极为铂丝 , 工作电极为 PPy /CNT /GCEM IP。吡咯 ( Py , 上海科丰化学试剂有限公司 ) ; 槲皮素 ( 上海试剂二厂 ) ; K3 [ Fe( CN ) 6 ]、 K4 [ F e( CN ) 6 ] ( 天津市广成化学试剂有限公司 ) ; 桑色素 ( M orin , E - M erck); 碳纳米管 ( CNT, 清华大学化工系提供 ), 上述试剂均为分析纯。所有溶液均用二次蒸馏水配制。 - 4 电聚合溶液由 Py ( 01 1 m o l/L ) + CNT ( 01 5 g /L ) + Qu ( 41 0 @ 10 mo l /L ) + H 2 SO4 ( 0101 mo l /L ) 组成。

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2. PTBP1 promotes migration and invasion of lung cancer cells
3. PTBP1 enhances exon11a skipping of Mena premRNA in lung cancer cells
Results
1. PTBP1 is highly expressed in lung adenocarcinoma (LUAD) tissues and 95-D cells and upregulation of PTBP1 is associated with EMT progress
2. PTBP1 promotes migration and invasion of lung cancer cells
Master Research
PTBP1 enhances exon11a skipping in Mena premRNA to promote migration and invasion in lung
carcinoma cells
Background Objectives Technology Methods Results Conclusions
5. PTBP1-mediated migration and invasion of 95-D cells are partially dependent on MenaINV
Results
2.1. Overexpressed PTBP1 promotes levels of EMT-related proteins in lung cancer cells
Technology Methods
Results
1. PTBP1 is highly expressed in lung adenocarcinoma (LUAD) tissues and 95-D cells and upregulation of PTBP1 is associated with EMT progress

聚集诱导发光分子在光动力治疗中的研究进展

聚集诱导发光分子在光动力治疗中的研究进展许敏;龙资;易小庆;娄筱叮;夏帆【摘要】光动力治疗(PDT)已成为治疗癌症的重要方法之一.传统光敏剂由于存在选择性差、易光漂白等问题,极大地限制了其在临床上的应用.而具有聚集诱导发光特性的荧光分子(AIEgens),在光照条件下能产生活性氧,并能够将肿瘤细胞杀死,具有治疗癌症的功效.此外,AIEgens还具有易制备、荧光特性优异、生物相容性好以及被动靶向效应(EPR效应)等优点,已被广泛应用于光动力治疗领域,并取得了巨大的发展,具有潜在的医学应用价值.该文主要概括并讨论了近5年来AIEgens在光动力治疗中的研究进展,并进行了展望.【期刊名称】《分析测试学报》【年(卷),期】2018(037)010【总页数】11页(P1147-1157)【关键词】光动力治疗;光敏剂;聚集诱导发光【作者】许敏;龙资;易小庆;娄筱叮;夏帆【作者单位】中国地质大学(武汉)纳米矿物材料及应用教育部工程研究中心,材料与化学学院,湖北武汉430074;中国地质大学(武汉)纳米矿物材料及应用教育部工程研究中心,材料与化学学院,湖北武汉430074;中国地质大学(武汉)纳米矿物材料及应用教育部工程研究中心,材料与化学学院,湖北武汉430074;中国地质大学(武汉)纳米矿物材料及应用教育部工程研究中心,材料与化学学院,湖北武汉430074;中国地质大学(武汉)纳米矿物材料及应用教育部工程研究中心,材料与化学学院,湖北武汉430074;华中科技大学化学与化工学院,湖北武汉430074【正文语种】中文【中图分类】O657.3;G353.11光动力疗法[1]是目前获得临床认可的一种较新颖的微创治疗方法，其作用机理通常是利用光敏剂所制备的纳米颗粒能选择性地聚集在肿瘤部位，并通过特定波长的光照射使肿瘤部位的光敏剂活化，将能量传递给周围的氧使其生成具有细胞毒性的活性氧(主要为单线态氧，ROS)，从而破坏肿瘤组织，达到治疗效果。

不同修饰磁纳米颗粒提取DNA对HRM基因分型能力的影响探讨

论著•临床研究不同修饰磁纳米颗粒提取DNA 对HRM 基因分型能力的影响探讨** 基金项目：国家自然科学基金项目（81560343）；甘肃省兰州市城关区科技计划项目（20161-3）。

作者简介：张敏，女，检验技师，主要从事分子生物学方向的研究。

# 通信作者，E-mail ：youchg@lzu. edu. cn （本文引用格式：张敏，张俊平，杨美娟，等.不同修饰磁纳米颗粒提取DNA 对HRM 基因分型能力的影响探讨:J + .国际检验医学杂志#2019,40（11） !2931297.张敏】，张俊平2,杨美娟】，李凌霄2,郜莉娜】，沈明辉】，尤崇革(1.兰州大学第二医院检验医学中心，甘肃兰州730030#.中国科学院兰州化学物理研究所，甘肃兰州730030)摘要：目的评价不同修饰基团的磁性纳米颗粒提取的基因组DNA 对高分辨熔解曲线(HRM)进行 SNP 基因分型能力的影响&方法以商品化硅基质吸附柱提法基因组DNA 提取试剂盒为对照，评价二氧化硅 (SiOJ 、壳聚糖(CTA )修饰磁珠及四氧化三铁(Fe 3O 4)裸核磁珠DNA 的提取得率及纯度；针对rs12641369G>A 、rs3114018C>A 、rs2302515C>G 3个SNP 位点，分别取上述方法提取的DNA 作为模板进行PCR-HRM 检测；以Sanger 测序作为"金标准"，评价检测的灵敏度和特异度；通过重复性实验评价其重复性和再现性&结果 4种方法DNA 提取质量的比较结果显示,SiO 2@Fe 3O 4法提取得率与离心柱法相当，且差异无统计学意义(P >0.05)；CTA @FeO 』法及Fe 3O 4法DNA 提取得率均比柱提法低，差异有统计学意义(P V0.01)。

灵敏度和特异度实验结果显示，除2例样本的熔解曲线均发生飘移接受复查外，其余标本3个位点均可直接基因分型；4种核酸提取方法的最终分型结果灵敏度和特异度均达到10 0 % &批内和批间重复试验均显示重复性和再现性良好：柱提法3个位点野生型与纯合突变型样本熔解峰Tm 值的变异系数在0. 6%〜0. 8% ； SiO 2 @Fe 3 O 4法3个位点野生型与纯合突变型样本熔解峰Tm 值的变异系数在0. 5%〜0. 8% ；CTA @Fe 3O 4法3个位点野生型与纯合突变型样本熔解峰Tm 值的变异系数在0. 5%〜0. 8% ；Fe 3 O 4法3个位点野生型与纯合突变型样本熔解峰Tm 值的变异系数在1. 2%〜1.5% &结论本研究自建的3种DNA 磁珠法提取体系对PCR-HRM 检测结果的影响很小，可对rs12641369G>A 、rs3114018C>A 、rs2302515C>G 3个SNP 位点进行常规化基因分型，且具备良好的检测性能，具有一定的临床应用价值&关键词：磁固相萃取技术；高分辨熔解曲线；基因分型；检验性能；单核苷酸多态性DOI !0. 3969". issn. 16734130. 2019. 11. 003 中图法分类号:R394. 8文章编号= 1673-4130(2019)111293-05 文献标识码:AEffects of different modified magnetic nanoparticle ofthe extracted DNA on HRM genotyping capability *ZHANG Min 1，ZHANG J unping 2，YANG Meijuan 1 , LI Lingxiao 2，GAO Lina 1，SH.EN Minghui 1，YOU Chongge 1(1. Laboratory Medicine Center ，Lanzhou University Second Hospital ，Lanzhou ，Gansu 730030，China #. Lanzhou Institute of Chemical Physics ，Chinese AcademyofSciences Lanzhou Gansu 730030 China )Abstra&t ：Obje&tive To evaluate the e f ect of genomic DNA extracted from magneticnanoparticlespre-paredbydi f erentmodifyinggroupsontheSNPgenotypingcapabilityofhighresolution melting (HRM )tech- nique . Methods The yield and purity of DNA extracted from silica(SiO2) , chitosan(CTA)modified magneticbeads and ferric oxide bare nuclear magnetic beads were evaluated by the centrifugal column DNA extraction kit of Tiangen company . For the three SNP sites of rs12641369 G 〉A,rs3114018 C 〉A,rs2302515 C 〉G,the DNA extracted by the above methods was taken as the template for PCR-HRM detection Sangersequencing wasusedasthegoldstandardtoevaluatesensitivityandspecificity Repeatability and reproducibility were e valuated by repeated experiments. Results The results showed that the yield of S1O 2 @Fe 3O 4 was comparableto that of the centrifugal column method, and the difference was not statistically significant (P %0. 05). The rates of nucleic acid extraction by CTA @Fe 3 O 4 and Fe 3 O 4 were lower than those by centrifugal column meth-od (P V0.01).Theresul>sof3locishowed>ha>a l >hesamplescouldbedirec>lygeno>ypedexcep>for2sam- pleswhosemelingcurveswerea l drif>ingforreview.Thesensi>iviyandspecifici>yof>hefournucleicacidextraction methods were100%.Repeated tests at three sites showed good repeatability and reproducibility. The coefficient of variation of Tm(melting temperature)between wild and homozygous mutant samples of three loci were between0.6%and0.8%；The coefficient of variation of Tm(melting temperature)between wild-type and homozygous mutant samples by SiO z@Fe3O4of three SNPs were between0.5%and0.8%.The coefficient of variation of Tm between wild type and homozygous mutant samples by CTA@Fe3O4method was between0.5%and0.8%‘respectively.The variation coefficients of Tm values of fusion peaks of wild and homozygous mutant samples by Fe3O4method were between 1.2%and 1.5%,respectively.Conclusion The three self-established DNA magnetic bead extraction systems in this study have little impact on the PCR-HRM detection results,and can be used to classify the three SNPs,including rs12641369G>A,rs3114018C>A,and rs2302515C>G,which have good detection performance and certain clinical application value.Key words:magnetic solid p hase extraction technology；high resolution melting curve；genotyping；testing performance；single nucleotide polymorphism高分辨熔解曲线（HRM）作为一种新兴的分子诊断技术，其不受突变位点和突变类型的限制，特异度和灵敏度高，操作简便，目前已被广泛地用于生物学及医学各个领域研究［15］（由于HRM检测的高灵敏性，其对核酸质量的要求也相对较高。

廖明阳简历

廖明阳简历廖明阳，男，1950年12月生，湖南衡山人，医学硕士。

军事医学科学院毒物药物研究所研究员，博士生导师。

军事医学科学院药物安全评价研究中心首席专家，国家药品监督管理局新药审评专家和国家药品监督管理局GLP认证专家，享受国务院特殊津贴待遇。

1975年8月毕业于天津医学院（现天津医科大学）医学系，1987年7月于军事医学科学院卫生毒理学专业硕士研究生毕业。

1996年赴美国印地安那大学药理毒理系和美国礼来制药公司药物安全评价中心（GLP）高级访问学者。

主要学术兼职：中国毒理学会副理事长；中国药理学会药物毒理专业委员主任委员；中国环境诱变剂学会致突变专业委员会主任委员；全国实验动物标准化技术委员会委员；全国危险化学品管理标准化技术委员会化学品毒性检测技术委员会委员；毒理学杂志》副主编；中国新药杂志等国内多家学术刊物编委。

学科专业：药物毒理学；研究方向：新药临床前安全性评价；新药早期毒性优化筛选；药物毒理机制与再评价。

承担了多项国家863 、973和军队基金课题。

主编或参编专著8部，公开发表学术论文近150篇。

共获得军队科技进步二等奖四项。

近期发表的文章：Wang Q, Jiang Y, Wu C, Zhao J, Yu S, Yuan B, Yan X, Liao M. Study of a novel indolin-2-ketone compound Z24 induced hepatotoxicity by NMR-spectroscopy-based metabonomics of rat urine, blood plasma, and liver extracts. Toxicology and Applied Pharmacology 2006 (215) 71–82 Shichang,wu chunqi liao mingyang.NMR-spectroscopy-based metabonomic approach to the analysis of Bay41-4109, a novel anti-HBV compound, induced hepatotoxicity in rats，Toxicology Letters， 2007,173:161-167Ronghui Lei，Chunqi Wu，Baohua Yang，Huazhi Ma, Chang Shi, Quanjun Wang, Qiuxiu Wang,Ye Yuan,Mingyang Liao. Integrated metabolic analysis of nano-sized copper paticle-induced hepatotoxicity and nephrotoxicity in rats:A rapid in vivo screening method for nanotoxicity. Toxicl Appl Pharmol. 2008, 232,(2), 292-301。

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Research ArticleModified Nanoprecipitation Method for Preparation of Cytarabine-Loaded PLGA NanoparticlesKhushwant S.Yadav 1and Krutika K.Sawant 1,2,3Received 5December 2009;accepted 25August 2010;published online 15September 2010Abstract.The present investigation was aimed at developing cytarabine-loaded poly(lactide-coglycolide)(PLGA)-based biodegradable nanoparticles by a modi ﬁed nanoprecipitation which would have sustained release of the drug.Nine batches were prepared as per 32factorial design to optimize volume of the co-solvent (0.22–0.37ml)and volume of non-solvent (1.7–3.0ml).A second 32factorial design was used for optimization of drug:polymer ratio (1:5)and stirring time (30min)based on the two responses,mean particle size (125±2.5nm),and percentage entrapment ef ﬁciency (21.8±2.0%)of the Cyt-PLGA nanoparticles.Optimized formulation showed a zeta potential of −29.7mV indicating good stability;50%w /w of sucrose in Cyt-PLGA NP was added successfully as cryoprotectant during lyophilization for freeze-dried NPs and showed good dispersibility with minimum increase in their mean particle sizes.The DSC thermograms concluded that in the prepared PLGA NP,the drug was present in the amorphous phase and may have been homogeneously dispersed in the PLGA matrix.In vitro drug release from the pure drug was complete within 2h,but was sustained up to 24h from PLGA nanoparticles with Fickian diffusion.Stability studies showed that the developed PLGA NPs should be stored in the freeze-dried state at 2–8°C where they would remain stable in terms of both mean particle size and drug content for 2months.KEY WORDS:cytarabine;factorial design;modi ﬁed nanoprecipitation method;nanoparticles;PLGA;stability studies.INTRODUCTIONCytarabine (CYT)is an antimetabolite used primarily for acute myelogenous leukemia and meningeal leukemia.It is metabolized intracellularly into its active triphosphate form (cytosine arabinoside triphosphate)which damages DNA by multiple mechanisms,including the inhibition of alpha-DNA polymerase,inhibition of DNA repair through an effect on beta-DNA polymerase,and incorporation into DNA (1).Cytarabine is poorly absorbed from gastrointestinal tract with less than 20%bioavailability and has a short half life of 2–4h (2).CYT is usually required to be administered intravenously and is available as multidose vials.The conventional paren-teral therapy is painful to the patients even with as little as effective concentration and cause severe side effects.Thus improvement of treatment modalities for leukemia requires a drug delivery system which can provide sustained release of the drug.The release of cytarabine has been earlier sustained by using different drug delivery systems such as slow releasinghydrogels (3),niosomes (4),liposomes (5),acryloylated polyaspartamide-based nanoparticles in without microemul-sion (6)and comatrices of albumin microspheres (7)but poly (lactide-coglycolide)(PLGA)-based nanoparticles have not been reported.The most widely used polymers for biodegradable nano-particles have been poly(lactic acid)(PLA),poly(glycolic acid)(PGA),and their co polymers,PLGA (8).These polymers are known for their biocompatibility and resorb-ability through natural pathways.Additionally,the degrada-tion rate and accordingly the drug release rate can be manipulated by varying the ratio of PLA to PGA (9).Since the biodegradable polymers are degraded in a certain period of time,they are not harmful to the human body.PLGA is degraded into non-toxic lactic acid and glycolic acid in the body.Therefore,drug delivery systems based on these polymers can be applied in the sustained release of drugs (10).Nanoprecipitation is a simple method used for encapsu-lation of both hydrophilic and hydrophobic drugs in nano-particles (11).The method results in instantaneous formation of nanoparticles,is easy to perform technique,can be easily scaled up and is a one-step procedure.The method requires addition of two solvents that are miscible with each other and results in spontaneous formation of nanoparticles on phase separation.From the two solvents ideally,the ﬁrst one (solvent)is the one in which the polymer and the drug dissolves but not in the second system (the non-solvent)(12).1Pharmacy Department,TIFAC-Centre of Relevance and Excellence in NDDS,The Maharaja Sayajirao University of Baroda,Fatehgunj,Vadodara,390002Gujarat,India.2Pharmacy Department,Faculty of Technology and Engineering,The Maharaja Sayajirao University of Baroda,Kalabhavan,V adodara,390001Gujarat,India.3To whom correspondence should be addressed.(e-mail:dr_krutika sawant@yahoo.co.in)AAPS PharmSciTech,Vol.11,No.3,September 2010(#2010)DOI:10.1208/s12249-010-9519-41530-9932/10/0300-1456/0#2010American Association of Pharmaceutical Scientists1456Fessi and co-workers were theﬁrst to develop and patent the nanoprecipitation method for preparation of nano-particles for use in drug delivery(13,14).A modiﬁed nanoprecipitation method utilizes use of a co-solvent to either increase the entrapment efﬁciency of the drug in nanoparticles or to reduce the mean particle size of the nanoparticles.The present investigation was aimed at developing cytarabine-loaded PLGA-based biodegradable nanoparticles by a modiﬁed nanoprecipitation which would have sustained release of the drug.The prolonged drug release with the PLGA nanoparticles would reduce the side effects associated with the conventional leukemia therapy by reducing dosing frequency and reducing pain at the site of injection.The drug delivery would be given as a single-shot injection by intra-venous route that would release the drug for a sustained period and would be beneﬁcial in better control of leukemia therapy.MATERIALS AND METHODSMaterialsCytarabine was obtained as a gift sample from Biocon Ltd.,Bangalore,poly(DL lactide-co-glycolide)PLGA50:50 (inherent viscosity0.22dl/g)was obtained as a gift sample from Boehringer Ingelheim Limited,Germany,Pluronic F-68 (BASF)was obtained as a gift sample from Alembic Ltd, Vadodara.Chloroform,methanol,acetone,potassium dihy-drogen phosphate,disodium hydrogen phosphate,hydro-chloric acid and sodium hydroxide were obtained from SD ﬁne Chemicals,Mumbai,Synthetic cellulose membrane with molecular weight cut-off(MWCO)of12,000–14,000D was procured from Himedia Labs,Mumbai.Formulation Development of Cytarabine-Loaded PLGA NanoparticlesModiﬁed nanoprecipitation method was used for the preparation of nanoparticles(15).Hydrophilic drug(5mg of CYT)was dissolved in an aqueous phase consisting of a solvent(0.3ml of distilled water)and a co-solvent(0.6ml of methanol).Polymer(25mg of PLGA)was dissolved in an organic phase consisting of a non-solvent(4ml of chloroform).The organic phase was then added drop wise to aqueous phase under stirring.Finally,the above mixture was added drop wise to10ml of distilled water containing 0.5%w/v of Pluronic anic solvent was removed by stirring over night.Nanoparticles were then recovered from the nanodispersion by centrifugation(Sigma centri-fuge)for30min at25,000rpm,washed two times with distilled water to remove unentrapped drug.The dispersion wasﬁnally lyophilized(Heto Dry Winner,Denmark)for 24h to yield freeze-dried nanoparticles.Samples were frozen at−70°C and placed immediately in the freeze-drying chamber.Different concentrations of sucrose in 10%,20%,50%,75%,and100%w/w of the total solid content were used as cryoprotectant.The method wasﬁrst optimized for the choice of a co-solvent.Then a32factorial design was used to investigate the effect of volume of co-solvent and volume of non-solvent.Finally,a second32factorial design was used to investigate the effect of drug:polymer ratio and stirring time on mean particle size(MPS)and percentage of entrapment efﬁciency(%EE).Investigation on Choice of Co-SolventThe Choice of Co-solvent was Based on Least MPS.Three batches in triplicate were taken,ﬁrst without a co-solvent,second with acetone,and third with methanol.Use of32Factorial DesignEffect of Volume of Co-solvent and Non-solvent on MPS Nine batches were prepared as per32factorial design to study the effect of two independent variables,volume of the co-solvent(X1)and volume of non-solvent(X2)on the response,MPS(Y1)of the Cyt-PLGA Nanoparticles.Each factor was tested at three levels designated as−1,0and+1. The regression equation for the response was calculated using Eq.1.Response:Y¼b0þb1X1þb2X2þb3X12þb4X22þb5X1X2ð1ÞThe responses in the above equation Y are the quanti-tative effect of the formulation components or independent variables X1and X2;b is the co-efﬁcient of the term X.Effect of Drug:Polymer Ratio and Stirring Time on MPS and%EEAfter investigation of volume of co-solvent and non-solvent nine batches were further prepared as per32factorial design to study the effect of two independent variables,ratio of drug and polymer(X1),stirring time(X2)on the two responses,MPS(Y2)and percentage entrapment efﬁciency (%EE;Y3)of the Cyt-PLGA Nanoparticles.Each factor was tested at three levels designated as−1,0,and+1.The values of the factors were transformed to allow easy calculation of co-efﬁcient in polynomial equation.To identify the effect of signiﬁcant variables,the reduced model was generated(16).Interactive multiple regression analysis and F statistics was utilized in order to evaluate the response.The multiple regression was applied using Microsoft excel in order to deduce the factors having signiﬁcant effect on the formulation properties.To identify the signiﬁcant variables,the variables having p value>0.05in the full model were discarded and then the reduced model was generated for both the independent variables and each type of formulation.In this mathematical approach,each experimental response(Y)can be represented by a quadratic equation of the response surface.Y is the measured response and b is the estimated co-efﬁcient for the factor X.The coefﬁcients corresponding linear effects(X1and X2),interaction (X1X2),and the quadratic effects(X12and X22)were determined from the results of experiments.1457Cytarabine Loaded PLGA NanoparticlesSurface Response PlotsSurface response plots are diagrammatic representation of the values of the response.They are helpful in explaining the relationship between independent and dependent varia-bles.Response surface methodology(RSM)shows relation-ship between an experimental response and a set of input variables.RSM sets a mathematical trend in the experimental design for determining the optimum level of experimental factors required for a given response(17).The reduced models were used to plot three dimension RSM using STATISTICA software at the values of X1and X2between−1and+1at predetermined values of responses.Evaluation of NanoparticlesMean Particle SizeThe freeze-dried nanoparticles were dispersed in distilled water for particle size analysis using Malvern Zetasizer3000 (Malvern Instruments,UK).The measurement of nano-particle size was based on photon correlation spectroscopy. Polydispersity index was studied to determine the narrowness of the particle size distribution.All the measurements were carried out in triplicate.Surface ChargeZeta potential was studied to determine the surface charge on the nanoparticles using Malvern Zetasizer3000, (Malvern Instruments,UK).The zeta potential of the nano-particles was determined using electrophoretic light scatter-ing.Freeze-dried samples were suspended in distilled water and their zeta potential was determined.All the measure-ments were carried out in triplicate.Differential Scanning Calorimetry ThermogramsDry powder samples of the test samples(CYT,PLGA, and CYT-loaded PLGA NP)weighing2–5mg were placed in aluminum pans and were sealed with aluminum caps. Thermograms were taken on a differential scanning calo-rimeter(Mettler-Toledo,Switzerland)at a heating rate of 10°C/min in nitrogen atmosphere over a temperature range of20–210°C.Entrapment EfficiencyThe entrapment efﬁciency was determined by extract-ing and quantifying the encapsulated drug using UV-spectroscopy;100mg of NPs were added to10ml of1:1mixture of chloroform and methanol.This dispersion was subjected to shaking at room temperature to ensure complete dissolution of the particles,the resulting solution was evaporated to dryness,and the dried residue was reconstituted with5ml of phosphate buffer saline.The reconstituted dispersion was centrifuged at10,000rpm for 15min.In this extraction procedure,the drug was solubi-lised in PBS(pH7.4)and the polymer which was not soluble remained in the pellet.The supernatant was analyzed for drug using UV-spectroscopy atλmax271nm using calibra-tion curve of cytarabine in PBS.The%EE was calculated using the following formula-%EE=(amount of drug in the NPs/drug added in the formulation)×100Redispersibility of Lyophilized NanoparticlesWe used two methods for redispersing the lyophilized NP,manual shaking and sonication(18).First method used was manually shaking a weighed quantity of lyophilized NP (100mg)in a test tube containing5ml of phosphate buffer saline pH7.4.After gentle shaking for2min the nano-suspension was subjected to particle size measurement using Malvern zetasizer.Presences of particles of more than1μm were said to non-dispersible.In the second method,100mg of the lyophilized NP in a test tube containing5ml of phosphate buffer saline pH7.4was subjected to sonication for2min using a bath sonicator and redispersibility was checked as explained above.In Vitro Drug Release StudyThe dialysis bag diffusion technique was used to evaluate the in vitro drug release(19).The NP corresponding to10mg of cytarabine was placed in a dialysis bag with a Synthetic cellulose membrane tied and placed into200ml of phosphate buffer saline(PBS)pH7.4maintained at37°C with continuous magnetic stirring in a beaker.At predetermined time intervals, aliquots were withdrawn from the acceptor compartment and replaced by the same volume of PBS.The drug content of the samples was determined by UV spectrophotometer at271nm. The tests were carried out three times and cumulative percentage drug release was calculated.The data was statisti-cally analyzed using the Sigmastat software(Sigma Stat,USA).Data obtained from in vitro release studies wereﬁtted to Korsmeyer–Peppas equation(log Mt/M∞=nlog t+k)to iden-tify the mechanism of drug release from formulated NPs(20). Where Qt is the amount of drug released at time t and Q0is the initial amount of drug present.Mt/M∞is the fraction of drug released after time t in respect to amount of drug released at inﬁnite time,k is the rate constant and n is the diffusional exponent which characterizes the transport mechanism.Table I.Effect of Co-solvent on Mean Particle Size of Cyt-PLGA NPBatch No.Aqueous Phase(1ml)Organic phase(4ml)MPS(nm)±SD a Drug(mg)V olume of co-solvent Polymer PLGA(mg)V olume of chloroform(ml)CPNP15No co-solvent254250±12.0 CPNP25Acetone,0.3ml254195±6.2 CPNP35Methanol,0.3ml254138±7.8a Standard deviation(n=3)1458Yadav and SawantStability StudiesThe optimized formulations were studied for their stability and their potential to withstand atmospheric/environ-mental changes.The freeze-dried(FD)samples and aqueous dispersion(AD)were sealed in Type-I amber colored glass vials.The samples were stored at2–8°C,25°C,and40°C. Samples were withdrawn at1,2,and3months time interval and analyzed for mean particle size and drug content.Each study was performed in triplicate.RESULTS AND DISCUSSIONSIn the nanoprecipitation method,an organic solution of the polymer is emulsiﬁed in an aqueous solution(with or without a surfactant).Then the organic solvent is removed by stirring(with or without vacuum)and this process allows nanoparticle formation.This method has drawback if the drug to be encapsulated is hydrophilic,because the drug may leak out in the aqueous solution.Hence,we modiﬁed the method and as suggested by Peltonen et al.(15)used a co-solvent in the aqueous phase.Choice of Co-solventFor the optimization of choice of co-solvent,the different formulation conditions and MPS obtained are shown in Table I.With acetone,the particle size achieved was higher compared with methanol because of the tendency of drug substance to precipitate in the presence of acetone.Based on the least MPS(138nm)obtained for batch No. cytarabine-loaded PLGA NP(CPN)P3,methanol was chosen as the co-solvent.Effect of Volume of Co-solvent and Non-solvent on MPS Table II displays the values of factors,their levels and transformed values and values of the response(Y1).Response-Mean Particle SizeThe mean particle size of NP ranged from127±3.1to 148±5.6nm.The lowest MPS was observed in middle level of X1(0.6ml)and middle level of X2(5.0ml)in batch CPNP8.Table III shows model coefﬁcients estimated by multiple linear regression for MPS.The regression coefﬁcients having P value<0.05are highly signiﬁcant.The terms having coefﬁcients with P value>0.05are least contributing in the prediction of mean particle size and hence the factor X1and X2having P value>0.05were removed from the full model to give the reduced model equation.The Eq.2explains the reduced model for Y1(MPS).Y1MPSðÞ¼127:44þ9:33X12þ7:83X22þ3:0X1X2ð2ÞAnalysis of variance(ANOV A)of full and reduced model for MPS is shown in Table IV.Model F value was assessed by the F statistic,which estimates the percentage of the variability in the outcome(21).Full model F value (34.5923)was more than the tabulated F value(F tab=9.01), implying that the model was signiﬁcant.Model F value of theTable II.Formulation of Cyt-PLGA NP for Optimization of V olume of Co-solvent and Non-solventBatch No.Real value Transformed values ResponseV olume of the co-solvent(ml)X1V olume of the non-solvent(ml)X2X1X2X12X22X1X2MPS(nm)±SD a Y1CPNP40.32−1−1111147±7.1 CPNP50.35−10100137±2.3 CPNP60.38−1111−1142±6.2 CPNP70.620−1010137±7.6 CPNP80.6500000127±3.1 CPNP90.6801010134±5.2 CPNP100.921−111−1141±3.4 CPNP110.9510100137±2.5 CPNP120.9811111148±5.6 Batches taken as per32factorial design:factors,their levels,transformed values and response:MPSMPS mean particle sizea Standard deviation(n=3)Table III.Model Coefﬁcients Estimated by Multiple LinearRegression for MPSFull model Reduced model Factor Co-efﬁcient value P value P value Intercept127.444 1.18E-062E-10X101X2−0.1660.787X129.3330.002 6.79E-05X227.8330.0040.000158X1X230.0220.002709Table IV.Regression Analysis of Variance(ANOV A)of Full andReduced Model for MPSFull model regression Reduced model regression F34.59293.348Signiﬁcance f0.007448.25E-05R20.98290.9824Adj R20.95450.97191459Cytarabine Loaded PLGA Nanoparticlesreduced model was 93.34891and the F tab value was 5.41,showing that the model was signi ﬁcant.The R 2value is a measure of total variability explained by the model.The R 2value of 0.98295for the full model indicated that the model was signi ﬁcant.That means the model can explain 98.29%of varibility around the mean.R 2of the reduced model was 0.982459,which was also high but slightly lower than the full model.The numbers of factors in the full model are more than the reduced model,therefore the R 2value increases (22).This explains the higher R 2value of the full model than the reduced model.In such cases,the term R 2adjusted has to be checked.It is called adjusted as the value has been adjusted for the size of the model.The R 2adjusted decreases when non-signi ﬁcant terms are added to the equation.Removal of non-signi ﬁcant terms improves the value of R 2adjusted.In our present model the value of R 2adjusted in the reduced model is 0.982459,which was greater than the R 2adjusted value of the full model (0.95453).Table V shows each of the observed values of Y in both full and reduced model and was compared with the predicted values of Y from each model.The residual value and percent error was calculated to show the correlation between the observed and the predicted values.The low residuals values and the percentage error was less than 5%showed signi ﬁcance of the model used.The response surface curves drawn at −1level to 1level of X 1and X 2for the values of the response is shown in Fig.1to give a diagrammatic representation of the same.The plots were found to be non-linear;therefore non-linear relationship exists between X 1and X 2variables.It was concluded from the non-linear plots that the MPS of 128nm could be obtained with X 1range from 0.22level (0.22ml)to −0.22level (0.37ml)and X 2range from 0.23(1.7ml)to −0.24(3.2ml).Effect of Drug:Polymer Ratio and Stirring Time on MPS and %EENine batches were prepared as per 32factorial design to study the effect of two independent variables,ratio of drug and polymer (X 1),stirring time (X 2)on the two responses,mean particle size (Y 2)and percentage entrapment ef ﬁciency (Y 3)of the Cyt-PLGA Nanoparticles.Table VI displays the values of Factors,their levels and transformed values and values of the responses,MPS and %EE as per 32factorial design.The concentration of drug was kept constant at 5mg/batch,and the concentration of polymer was varied from 25,50,and 75mg to give drug:polymer ratio of 1:5,1:10,and1:15.These three different ratios were tested at three differ-ent stirring rates of 10,20,and 30min and in this way nine batches were prepared as per 32factorial design.Response-Mean Particle SizeThe mean particle size of NP ranged from 125±2.5to 151±2.4.The lowest MPS was observed in lowest level of X 1(1:5)and highest level of X 2(30min)in batch CPNP15.Table VII shows model coef ﬁcients estimated by multiple linear regression for MPS.The factor X 1X 2having P value >0.05was removed from the full model to give the reduced model Eq.3for Y 2(MPS).Y 2MPS ðÞ¼135:222þ4:666X 1À8:5X 2À1:333X 12þ4:166X 22ð3ÞANOV A of full and reduced model for MPS is shown in Table VIII .The F values for both the full model (813.3)and theTable V .Observed Responses and Predicted Values for Full and Reduced Model MPSFull modelReduced modelBatch No.Observed valuePredicted valueResidual value%Error Predicted valueResidual value%Error CPNP4147147.777−0.7770.528147.611−0.6110.415CPNP5137136.7770.2220.162136.7770.2220.162CPNP6142141.4440.5550.390141.6110.3880.273CPNP7137135.444 1.555 1.095135.277 1.722 1.256CPNP8127127.444−0.4440.349127.444−0.4440.349CPNP9134135.111−1.1110.829135.277−1.2770.952CPNP10141141.777−0.7770.551141.611−0.6110.433CPNP11137136.7770.2220.162136.7770.2220.162CPNP12148147.4440.5550.375147.6110.3880.262Fig.1.Surface response plot for optimization of volume of co-solventand non-solvent,response-MPS1460Yadav and Sawantreduced model (1,355.5)were more than their tabulated value (F tab =9.01)suggesting that the models were signi ﬁcant.The R 2value was more than 0.99for both the full and reduced models.The value of R 2adjusted in the reduced model (0.9992)was greater than the R 2adjusted value of the full model (0.9980).From the response surface curves for MPS (Fig.2)it was concluded that the MPS of 125nm could be obtained with X 1range from −0.6level (1:7)to −1.0level (1:5)and X 2range from 0.2(22min)to 1.0(30min).Response-Entrapment EfficiencyThe %EE of CYT in PLGA NP varied from 15.0±2.3%to 22.0±2.1.Highest %EE of 22.0%was observed at the highest levels of X 1(1:15)and X 2(30min)and %EE of 21.8%,was observed at the lowest level of X 1(1:5)and highest level of X 2(30min).We chose the batch which had the lower MPS (Batch No.CPN15,%EE of 21.8%).Table VII shows model coef ﬁcients estimated by multiple linear regression for%EE.The factor X 12,X 22,and X 1X 2having P value >0.05were removed from the full model to give the reduced model Eq.4.Y 3%EE ðÞ¼19:666þ1:166X 1þ1:333X 2ð4ÞThe results of the regression output and are presented in Table VII and ANOVA of the model is presented in Table VIII .Model F value for the full model (15.5142)andreduced model (24.0666)was more than the tabulated F value (F tab =9.01),implying that the models were signi ﬁcant.The value of R 2adjusted in the reduced model (0.9202)was greater than the R 2adjusted value of the full model (0.9007).From the response surface curves for %EE (Fig.3),it was concluded that the %EE of 21%could be achieved with X 1ranging from 1.0(1:15)to 0.6(1:13)and X 2ranging from 0.8(28min)to 1.0(30min).Lyophilization and Optimization of CryoprotectantLyophilization is the process in which freeze-drying is done to remove solvent from the formulation and therefore improve its stability upon storage.The process of freeze-drying is stressful and hence a cryoprotectant is added in the process,which also helps in redispersibility of the freeze-dried nanoparticle in a suitable solvent (23).One of the main challenges during the freeze-drying process is preserving or rather increasing the redispersibility of the nanoparticles upon reconstitution with distilled water or buffer saline.Redisper-sants are generally added to the nanoparticles prior to the drying monly used cryoprotectants such as sugars also act as redispersants.Cryoprotectants such as sorbitol,mannitol,glucose,trehalose can be used to increase the physical stability of nanoparticles during freeze-drying (24).In the present study we have used sucrose in ﬁve different concentrations of 10%,20%,50%,75%,and 100%w /w to act as both a cryoprotectant and a redispersant.Table VI.Formulation of Cyt-PLGA NP for Optimization of Drug:Polymer Ratio and Stirring TimeReal valueTransformed values ResponseBatch No.Drug:polymer ratio (mg)X 1Stirring time (min)X 2X 1X 2X 12X 22X 1X 2MPS (nm)±SD a Y 2%EE ±SD a Y 3CPNP131:510−1−1111142±4.115.0±2.3CPNP141:520−10100129±3.217.8±3.7CPNP151:530−1111−1125±2.521.8±2.0CPNP161:10100−1010148±2.919.6±2.2CPNP171:102000000135±3.220.0±2.1CPNP181:103001010131±4.021.6±1.2CPNP191:15101−111−1151±2.420.0±2.1CPNP201:152010100139±0.921.6±4.2CPNP211:153011111134±2.522.0±2.1Batches taken as per 32factorial design:factors,their levels,transformed values and response:MPS and %EE MPS mean particle size,%EE percentage entrapment ef ﬁciency aStandard deviation (n =3)Table VII.Model Coef ﬁcients Estimated by Multiple Linear Regression for MPS and %EEMPS %EE Full modelReduced modelFull model Reduced modelFactor Co-ef ﬁcient valueP value P value Co-ef ﬁcient valueP value P value Intercept 135.222 2.11E-08 6.84E-1119.777 1.56E-05 1.93E-07X 1 4.6668.38E-05 4.31E-06 1.1660.0111810.00332X 2−8.5 1.39E-05 3.94E-07 1.3330.0076780.002019X 12−1.3330.01620.0048−0.1660.674941X 22 4.16660.0006 6.02E-050.3330.4228260.360051X 1X 21−0.50.1442940.0935991461Cytarabine Loaded PLGA NanoparticlesFreeze-drying has an effect of increasing particle size after lyophilization,probably due to aggregation of nanoparticles during this process;therefore we checked the redispersibility of the particles after lyophilization.If the aggregated particles do not separate during redispersion,then larger particle sizes would be observed which were not desired.Table IX indicates the different concentrations of sucrose used and its effect on particle size after lyophilization.Optimization of the cryoprotectant was based on its ability to give minimum increase in MPS and dispersibility.An increase in size of the NPs was seen following freeze-drying with the use of sucrose as cryoprotectant.All the formulations above 50%w /w sucrose had good dispersibilty and it was seen that use of sucrose in a 50%w /w concentration showed minimum increase in particle size of the Cyt-PLGA e of higher concen-trations of cryoprotectants made the NP dispersible but an increase in MPS was also observed.So higher concentrations of more than 50%w /w for Cyt-PLGA NP were not selected..Effect of Lyophilization on Polydispersity Index and Zeta PotentialThe other two parameters evaluated before and after lyophilization were polydispersity index and zeta potential.Polydispersity index (PdI)is a measure of dispersion homogeneity and usually ranges from 0to 1.Values close to 0indicate a homogeneous dispersion while those greater than 0.3indicate high heterogeneity (25).Table IX shows the effect of lyophilization on MPS and PdI on the formed nanoparticles of the six batches.PdI values were less than 0.3for all batches except the batch in which there was no croprotectant.It was concluded that Batch using 50%sucrose had the least PdI (0.071)and was considered optimum as it also had least increase in MPS.Zeta potential is the potential at the hydrodynamic shear plane and can be determined from the particle mobility under an applied electric ﬁeld.The mobility will depend on the effective charge on the surface.Zeta potential information is helpful in predicting the storage stability of colloidal dis-persions (26).In general,greater the zeta potential value of a nanoparticulate system better is the colloidal suspension stability due to repulsion effect between charged nanopar-ticles.The zeta potential value was −29.6±2.1before lyophi-lization and after lyophilization it was in the range of −30.2±1.1to −27.8±1.4mV .Zeta potential values in the −15to −30mV are common for well-stabilized nanoparticles (27).Hence it was concluded that the NPs would remain stable under storage.The presence of sucrose did not have a signi ﬁcant change on the surface charge of the NPs.Table VIII.Regression Analysis of Variance (ANOV A)of Full and Reduced Model for MPS and %EEMPS%EEFull model regressionReduced model regressionFull model regressionReduced model regressionF813.31,355.515.514224.066Signi ﬁcance f 6.79E-05 1.63E-060.023580.0046R 20.9992630.9992620.9627660.96010Adj R 20.998030.998520.9007090.92021Fig. 2.Surface response plot for MPS for optimization of drug:polymer ratio and stirring time X 1(drug:polymer ratio)and X 2(stirring time)values ranging from −1to+1Fig.3.Surface response of EE for optimization of drug:polymerratio and stirring time1462Yadav and Sawant。