Visual identification, analysis and modelling of sterilising grade filtration for liposome enveloped products

该项目寻求解决的生物工艺挑战：无菌过滤对生物药物的生产和安全至关重要。这些过滤系统的性能是一个与工业相关的挑战，随着包括病毒载体和脂质体在内的下一代疗法的发展而不断发展。这些产品在无菌过滤过程中会出现困难，因为它们的尺寸相对较大，导致滞留在过滤器结构中。新冠肺炎基因疫苗的成功突显了脂质体的日益重要，这些疫苗被包裹并以脂质体形式递送。在这个项目中，我们将结合共聚焦显微镜和X射线计算机断层扫描的高分辨率成像技术来测量生物成分在滤膜中的捕获方式。了解这些机制将使确定这些产品的无菌过滤的操作条件、膜类型和配方条件的合理方法成为可能。目的：开发成像方法以可视化脂质体产品、一系列过滤器类型的配方中的进料保留，同时最小化样品干扰-识别复杂配方中的不同污染物，当它们通过或阻塞过滤器时-将信息与补充技术相结合以确定在哪里以及为什么发生污染以开发预测模型-将所获得的知识应用于各种PAL系统，例如，用于展示研究项目稳健性的折叠薄片滤筒和TFF系统项目描述：在无菌过滤脂质包裹的产品(例如，信使核糖核酸疫苗)过程中性能恶化是工业化规模的生物过程中的常见问题[1，2]。在伦敦大学学院和Pall-UCL卓越中心，各种高分辨率成像技术已成功地用于可视化分离介质，包括X射线CT、共聚焦显微镜、电子显微镜和聚焦离子束显微镜。目标是描述这些材料的结构特征，这些结构特征决定了它们的生物处理性能[3-5]。本EngD旨在应用这些方法来了解PAL无菌过滤分离膜系统。每种成像技术都有固有的优势和局限性，我们旨在补充相关方法，以最大限度地提高收集的信息。例如，结合X射线CT测量膜的3D结构的能力和共聚焦显微镜识别荧光标记的生物成分的能力[5]，可以识别这些成分被困在膜内的区域。然后，这些可以与结构特征相关联，例如内部孔径减小或弯曲度增加。这项研究将结合这些不同技术的信息，以建立一个机理模型[6]，以预测脂类的无菌过滤膜性能。在该项目中，将通过调查和比较多种无菌级别的膜和脂质体类型来加深对机理的理解。我们能够在项目中利用最新的微流控脂质体合成技术(由Precision NanoSystems开发)来推动这一目标。

Bioprocess challenge the project seeks to address:Sterile filtration is essential to the manufacture and safety of biological medicines. The performance of these filtration systems is an industrially relevant challenge that continues to evolve along with next generation therapeutics including viral vectors and liposomes. These products can present difficulties during sterile filtration due to their relatively large size leading to retention within the filter structure. The increasing importance of liposomes has been highlighted by the success of Covid-19 mRNA vaccines which are enveloped and delivered in liposomes. In this project we will combine the high-resolution imaging techniques of confocal microscopy and x-ray computed tomography to measure the means of entrapment for biological components within the filter membrane. Understanding these mechanisms will enable a rational approach to determining operating conditions, membrane type and formulation conditions for sterile filtration of these products.Objectives:- Develop imaging approaches to visualise feed material retention for liposome products, formulations in a range of filter types whilst minimising sample interference- Identify different foulants in complex formulations as they pass or block the filter- Combine information with complementary techniques to determine where and why fouling is occurring to develop predictive models- Apply the knowledge gained to various Pall systems, for example pleated sheet cartridges and TFF systems to demonstrate robustness of the research projectProject Description:Deterioration of performance during sterile filtration of lipid enveloped products e.g. mRNA vaccines is a commonplace issue for industrial scale bioprocesses [1, 2]. At UCL and within the Pall-UCL Centre of Excellence various high-resolution imaging techniques have been successfully used to visualise separation media, including X-ray CT, confocal microscopy, electron microscopy and focused ion beam microscopy. The objective is to characterise the structural features of these materials that determine their bioprocess performance [3-5]. This EngD aims to apply these methods in order to understand Pall sterile filtration separation membrane systems.Each imaging technique has inherent advantages and limitations, we aim to complement relevant approaches to maximise information gathered. For example combining the capabilities of X-ray CT to measure the 3D structure of the membrane with confocal microscopy to identify fluorescently tagged biological components [5] enables the identification of the regions where those components are trapped within a membrane. These can then be correlated with structural features such as an internal pore size decrease or tortuosity increase. The research will combine the information from these various techniques in order to build towards a mechanistic model [6] to predict sterile filtration membrane performance for liposomes.Investigating and comparing multiple sterile grade membranes and liposome types during this project will be used to deepen this mechanistic understanding. We are able to make use of the latest microfluidic liposome synthesis technology (developed by Precision NanoSystems) within the project to further this objective.