考察配伍前后输液中的不溶性微粒

目的考察配伍前、后输液中不溶性微粒的变化。方法采用自动微粒分析仪测定输液配伍药物前、后的不溶性微粒数。结果与结论输液配伍药物后微粒显著增加,配伍药品数越多,微粒污染越严重。     

折光率对微流成像法检测蛋白制剂不溶性微粒的影响

目的：蛋白制剂中不溶性微粒的含量是衡量样品质量的重要指标之一,为了更为准确地检测不溶性微粒的含量和粒径,本研究探讨了溶液折光率对于微流成像系统检测不溶性微粒的影响。方法：本研究以牛血清白蛋白（BSA）为例,通过常见的外界刺激条件（冷冻-解冻）制备高浓度的蛋白质不溶性微粒,并将此微粒稀释至不同折光率的溶液（由PEG1000、海藻糖制备）中,利用微流成像系统检测不溶性微粒的含量。结果：当溶液的折光率接近蛋白质不溶性微粒折光率时,利用微流成像技术检测的不溶性微粒含量低于实际的微粒含量。此外,随着溶液折光率的增加,采用微流成像技术检测出的不溶性微粒的粒径也随之减小。结论：蛋白质溶液的折光率发生改变,会影响利用微流成像技术检测不溶性微粒的准确性。因此,利用微流成像技术检测蛋白制剂中不溶性微粒时,需要考虑到制剂处方的折光率对于检测不溶性微粒的影响,必要时可以采用稀释的方法降低折光率对蛋白质颗粒的屏蔽作用。     

输液中不溶性微粒检测方法的探讨

本文报告了分别用滤膜-显微镜法和仪器法检测输液中的不溶性微粒。所得数据,经成对比较,方差分析,统计学处理(P>0.05),仪器法具有快速、简便、结果可靠的优点。     

注射液中不溶性微粒检查法的改进

《中国药典》注射液不溶性微粒检查法操作较繁,耗时过长。笔在不改变试验结果的前提下,对其方法做了部分调整,报告如下。     

单抗制剂中典型不溶性微粒的形态特征分析

目的：分析单克隆抗体（简称单抗）中典型不溶性微粒图片特征,建立典型不溶性微粒的图谱特征数据库和相应的预警机制。方法：通过采用微流成像技术检测反复冻融、含气泡或硅油、酶解、氧化等不同条件下的单抗中存在的不溶性微粒,筛选关键特征参数用于区别典型不溶性微粒。结果：不同类型不溶性微粒长宽比、密实度、强度、粗糙度和透明度等参数有明显区别,以此可区分不同类型不溶性微粒。此外,获得了不同种类微粒的关键形态特征,初步建立了图谱特征数据库。结论：利用微流成像技术建立对典型不溶性微粒进行比较和分类的方法,可以建立相应预警机制,对单抗中不溶性微粒进行溯源分析和风险评估。     

不同输液包装容器不溶性微粒比较

考察了聚氯乙烯（PVC）软塑料袋装输液与玻璃瓶装输液不溶性微粒，并对粒度分布情况进行比较。发现PVC软塑料袋装输液中的不溶性微粒与玻璃瓶输液中的不溶性微粒在粒度分布上差别很大，说明输液中不溶性微粒的来源与包装容器关系密切。     

注射剂中不溶性微粒相关研究现状及思考

目的:分析注射剂中不溶性微粒的现状及控制措施,确保安全用药。方法:分析近年有关注射剂中不溶性微粒的相关国家标准和研究文献,探讨各环节可行的有效防范措施。结果:通过严格执行国家标准,控制注射剂生产、配置和使用的各个环节,推广精密输液器等措施,可有效防范不溶性微粒的危害。结论:加强用药指导、提升相关标准、规范和改进输注器材可有效减少注射剂中不溶性微粒的危害。     

静脉注射用粉针剂中不溶性微粒考察

目的:考察临床常用静脉注射用粉针剂复配后注射液中不溶性微粒变化.方法:将临床常用抗生素粉针剂按静注实际用量用0.9%氯化钠注射液溶解后,用微粒分析仪检测直径大于25.0,10.0 μm的粒子数.结果:复配后有29个批次的制剂不含25.0 μm以上粒子,占样品总数的53.70%;所有复配液均含有10.0 μm以上的粒子,占100%;复配后符合药典规定的有23批次,占42.59%.结论:静脉注射药液中的不溶性微粒污染不容忽视,临床须加强控制微粒污染的措施.     

MFI在注射用尿激酶不溶性微粒检测中的应用

目的:研究微流成像颗粒分析技术(micro-flow imaging, MFI)在生化蛋白药物(注射用尿激酶)不溶性微粒检测中的应用,同时考察不同企业注射用尿激酶样品中不溶性微粒的现状和2种不同溶剂复溶后不溶性微粒的差异,探讨不溶性微粒的主要来源。方法:采用注射用水和注射用灭菌生理盐水分别复溶注射用尿激酶,用MFI检测其中的不溶性微粒,进行方法学验证并与药典收载的光阻法比较。结果:MFI测定5μm和15μm微粒标准品的微粒计数的准确度(偏差)分别为11.6%和8.5%,精密度(RSD)分别为1.6%和3.8%,粒径的准确度(偏差)分别为2.5%和0.8%,精密度(RSD)分别为0.59%和0.10%;MFI测定样品不溶性微粒结果中,2～10μm粒径范围的颗粒以蛋白颗粒为主,10μm粒径以上的颗粒以非蛋白颗粒为主,其中非蛋白颗粒结果与光阻法检测的不溶性微粒结果相似;不同企业生产的注射用尿激酶中不溶性微粒存在较大差异,企业A样品的不溶性微粒相对较多;2种不同溶剂复溶样品后企业A和C不溶性微粒的结果基本无差异,企业B和D不溶性微粒的结果存在较大差异,样品采用注射用灭菌生理盐水复溶后测得的不溶...     

药物与输液配伍后引起不溶性微粒变化的探讨

目的：检查输液与药物配伍后不溶性微粒变化。方法：按照注射液中不溶性微粒检查法检查微粒。结果：输液与药物配伍后，微粒数增加，不同剂型增加差异较大，药物配伍品种愈多，微粒增加也愈多。结论：微粒愈多对人体危害愈大，临床医师、临床药师应引起高度重视，并采取有效措施。     

Machine Learning and Accelerated Stress Approaches to Differentiate Potential Causes of Aggregation in Polyclonal Antibody Formulations During Shipping

Therapeutic proteins are among the most widely prescribed medications, with wide distribution and complex supply chains. Shipping exposes protein formulations to stresses that can trigger aggregation, although the exact mechanism(s) responsible for aggregation are unknown. To better understand how shipping causes aggregation, we compared populations of aggregates that were formed in a polyclonal antibody formulation during live shipping studies to populations observed in accelerated stability studies designed to mimic both the sporadic high g-force and continuous low g-force stresses encountered during shipping. Additionally, we compared the effects on aggregation levels generated in two types of secondary packaging, one of which was designed to mitigate the effects of large g-force stresses. Aggregation was quantified using fluorescence intensity of 4,4′-dianilino-1,1′-binaphthyl-5,5′-disulfonic acid (bis-ANS) dye, size exclusion high performance liquid chromatography (SECHPLC), and flow imaging microscopy (FIM). FIM was also combined with machine learning methods to analyze particle morphology distributions. These comparisons revealed that the morphology distributions of aggregates formed during live shipping resemble distributions that result from low g-force events, but not those observed following high g-force events, suggesting that low g-force stresses play a predominant role in shipping-induced aggregation.     

基于风险分析的创面敷料产品分类监管研究

目的：为创面敷料类产品类别属性与分类判定提供参考。方法：本文在梳理产品的基础上,从组成成分是否含药、是否可吸收、是否发挥药理作用、接触创面情况等判定要素风险点进行分析。基于综合判定原则,分别探索分析创面敷料产品成分是否被吸收或发挥药理作用的判断方法,并总结分类界定研判规律。结果与结论：创面敷料类产品由于其类别属性与分类判定的复杂性,一直以来是行业内的关注热点和难点。该研究从判定要素角度出发,介绍具体的判定方法,为创面敷料类产品类别属性与分类判定提供技术支撑,助力其科学监管。     

The Evolution of Commercial Antibody Formulations

It has been nearly four decades since the first therapeutic monoclonal antibodies were approved and made available for widespread human use. Herein, US and EU approved antibody formulations are reviewed, and their nature and compositions are evaluated over time. From 1986 through Jan 2023, significant formulation trends have occurred and to represent this, 165 commercial antibody therapeutic formulations were binned into 5 different periods of time. Overall, we have observed the following: 1) The average formulation pH has decreased in recent years by over 0.5 units along with a decrease in variability that is largely driven by non-high concentration liquid in vial presentations for IV administration, 2) The use of certain excipients and buffers such as histidine, sucrose, metal chelators, arginine and methionine has become significantly more common, whereas formulations that contain phosphate, salt, no sugar or no surfactant have fallen out of favor, 3) Overall formulation space has increasingly become more homogenous and has converged in terms of formulation pH and excipient preferences regardless of formulation concentration, drug product presentation, and route of administration, 4) The average calculated isoelectric point (pI) has decreased 0.26 units, and 5) Overall, the average formulation pH and calculated pI for all commercial antibodies surveyed was 6.0 and 8.4, respectively. These trends and formulation convergence may be driven by multiple factors such as advancements in high-throughput computational and analytical technologies, the increased emphasis and understanding of certain developability attributes and formulation principles during lead selection and formulation development, and the adoption of low-risk development platform approaches.     

Rapid Quantification of Protein Particles in High-Concentration Antibody Formulations

Current technologies for monitoring the subvisible particles that may be generated during fill-finish operations for protein formulations are cumbersome. Measurement times are generally too long for real-time analysis, and the high protein concentrations that are characteristic of many antibody products interfere with common optical techniques for particle analysis. To rapidly monitor protein particle levels in high-concentration protein solutions, we developed a fluorescence-based method that uses extrinsic fluorescent dyes such as 4,4'-dianilino-1,1'-binaphthyl-5,5'-disulfonic acid that are sensitive to the presence of aggregated protein. To test the method, antibody formulations containing various concentrations of protein particles were generated by application of various mechanical and freeze-thaw stresses. After addition of fluorescent dyes, fluorescence intensities were measured and compared to fluorescence intensities in particle-free formulations. The differences in fluorescence intensities were linearly proportional to protein particle levels, which for calibration purposes were measured offline by fluid imaging microscopy and protein assays. Protein particle levels could be measured without requiring sample dilution, even in high-concentration (e.g., 40 mg/mL) antibody formulations.     

Machine Learning Feature Selection for Predicting High Concentration Therapeutic Antibody Aggregation

Protein aggregation can hinder the development, safety and efficacy of therapeutic antibody-based drugs. Developing a predictive model that evaluates aggregation behaviors during early stage development is therefore desirable. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features. We applied this framework to predict aggregation behaviors of 21 approved monospecific monoclonal antibodies at high concentration (150 mg/mL), yielding a correlation coefficient of 0.71 on validation tests with only two features using a linear model. The nearest neighbors and support vector regression models further improved the performance, which have correlation coefficients of 0.86 and 0.80, respectively. This framework can be extended to train other models that predict different physical properties.     

A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process.     

Degradation of Polysorbate 20 by Sialate O-Acetylesterase in Monoclonal Antibody Formulations

Polysorbates (PS) are surfactants commonly added in biologics formulations that can protect proteins from denaturation and aggregation. However, decreases in polysorbate 20 (PS20) content have been observed in some monoclonal antibody formulations, causing the formation of visible and/or subvisible particles that ultimately compromise the quality and stability of the therapeutic protein products. It was determined that the particles are mainly composed of free fatty acid, suggesting enzymatic hydrolysis of PS is responsible for the degradation of PS. Enrichment of host cell proteins (HCPs) by immunoprecipitation followed by shotgun proteomics have been utilized to identify the HCPs that can hydrolyze PS20. One HCP, sialate O-acetylesterase (SIAE), demonstrated strong enzymatic activity for PS20 degradation even at low concentration (<5 ppm level). Incubation of recombinant SIAE with PS20 resulted in a unique degradation pattern where the hydrolysis of monoester with short fatty acid chain (C12, C14) was observed but not the monoester with long fatty acid chain (C16, C18) or higher-order esters. SIAE was detected and quantitated in several formulated mAbs, and the amount of SIAE was positively correlated to PS20 degradation in these mAbs during incubation. Additional experiments also showed that when SIAE was depleted, PS20 degradation was diminished, suggesting a causality between SIAE and PS20 degradation. The lipase activity of SIAE is specific to PS20, but not to PS 80 (PS80), which contains monoesters with long chain fatty acid (C18) and higher-order esters. The specific esterase activity of SIAE on PS20 suggests a possible solution of using PS80 over PS20 to eliminate surfactant degradation in mAb products.     

Evaluation of a Self-Supervised Machine Learning Method for Screening of Particulate Samples: A Case Study in Liquid Formulations

Imaging is commonly used as a characterization method in the pharmaceuticals industry, including for quantifying subvisible particles in solid and liquid formulations. Extracting information beyond particle size, such as classifying morphological subpopulations, requires some type of image analysis method. Suggested methods to classify particles have been based on pre-determined morphological features or use supervised training of convolutional neural networks to learn image representations in relation to ground truth labels. Complications arising from highly complex morphologies, unforeseen classes, and time-consuming preparation of ground truth labels, are some of the challenges faced by these methods. In this work, we evaluate the application of a self-supervised contrastive learning method in studying particle images from therapeutic solutions. Unlike with supervised training, this approach does not require ground truth labels and representations are learned by comparing particle images and their augmentations. This method provides a fast and easily implementable tool of coarse screening for morphological attribute assessment. Furthermore, our analysis shows that in cases with relatively balanced datasets, a small subset of an image dataset is sufficient to train a convolutional neural network encoder capable of extracting useful image representations. It is also demonstrated that particle classes typically observed in protein solutions administered by pre-filled syringes emerge as separated clusters in the encoder's embedding space, facilitating performing tasks such as training weakly-supervised classifiers or identifying the presence of new subpopulations.     

单抗药物的糖化修饰及其影响

目的：对单抗糖化修饰的产生、检测和其对单抗功能活性的影响进行综述,为行业提供参考。方法：通过文献检索,对国内外开展的单抗糖化研究进行梳理和汇总。结果与结论：单抗糖化主要发生在细胞发酵阶段,糖化的程度和速率受多种因素的影响,糖化修饰可以对单抗的异质性、功能活性以及免疫原性产生一定的影响。目前国内研发企业在单抗药学研究中通常忽视对糖化修饰的研究和评价,建议在单抗研发时应充分评估糖化的影响并确定是否为关键质量属性,并对其进行有效的控制和监测。     

Evaluating the Combined Impact of Temperature and Application of Interfacial Dilatational Stresses on Surface-mediated Protein Particle Formation in Monoclonal Antibody Formulations

Formation of submicron and subvisible protein particles (0.1–100 μm) present a major obstacle during processing and storage of therapeutic proteins. While protein aggregation resulting in particle formation is well-understood in bulk solution, the mechanisms of aggregation due to interfacial stresses is less understood. Particularly, in this study, we focus on understanding the combined effect of temperature and application of interfacial dilatational stresses, on interface-induced protein particle formation, using two industrially relevant monoclonal antibodies (mAbs). The surface activity of Molecule C (MC) and Molecule B (MB) were measured at room temperature (RT) and 4 °C in the absence and presence of interfacial dilatation stress using a Langmuir trough. These results were correlated with Micro-flow imaging (MFI) to characterize formation of subvisible protein particles at the interface and in the bulk solution. Our results show that the surface activity for both proteins is temperature dependent. However, the extent of the impact of temperature on the mechanical properties of the monomolecular protein films when subjected to dilatational stresses is protein dependent. Protein particle analysis provided evidence that protein particles formed in bulk solution originate at the interface and are dependent on both application of thermal stresses and interfacial dilatational stresses. In the absence of any interfacial stresses, more and larger protein particles were formed at the interface at RT than at 4 °C. When mAb formulations are subjected to interfacial dilatational stresses, protein particle formation in bulk solution was found to be temperature dependent. Together our results validate that mAb solutions maintained at 4 °C can lower the surface activity of proteins and reduce their tendency to form interface-induced protein particles both in the absence and presence of interfacial dilatational stresses.