Use of Raman Spectroscopy in the Evaluation of Materials used in In vitro Diagnostic Devices

体外诊断测试是患者护理的重要组成部分，影响了70%以上的护理路径决策。始终如一地生产具有同等性能的产品的能力是支持医疗保健专业人员做出这一决策的关键。大部分体外诊断是免疫测定法，依赖于抗体与靶分子的高度特异性相互作用。这些测试通常包含生物成分的复杂混合物，包括通常通过添加标记化合物进行修饰的选择抗体。许多抗体偶联物、血清蛋白和动物血清在免疫测定系统内最佳性能所需的性质方面没有得到很好的表征，因此材料批次的变化对试剂的性能构成重大风险。因此，必须评估这些系统的每批新生物材料的性能，以确保保持产品的一致性和质量，并在这些评估中花费大量时间和资源。改进的材料测试方法的发展将会导致材料选择的有效性提高，通过减少废料和返工活动提高生产率，并提高客户满意度。通过以前的博士研究生，拉曼光谱的应用已被证明：1。区分不同等级的BSA，并定义这些等级之间的结构差异2。确定暴露于不同的pH值、温度和混合条件后蛋白质结构中是否存在结构变化3。结合FTIR，分析BSA与小分子结合后发生的结构变化以及随着结合蛋白质老化而发生的进一步微小变化。该项目旨在扩展上述工作，以进一步开发方法学和机器学习分析，用作免疫测定制造中的质量控制和/或故障排除工具。最终目标是能够在新材料的质量控制过程中使用拉曼等分析工具，以减少识别和鉴定合适的新批次所需的时间和资源。该项目的范围将包括：扩大表征的蛋白质范围，使用机器学习分析来确定免疫测定中哪些特定特征与阳性或阴性性能特征相关，以及在复杂试剂混合中检查蛋白质，作为潜在的故障排除工具，以应对意外的试剂性能挑战。

In-vitro diagnostics tests are an important part of patient care, influencing greater than 70% of the decision made on the care pathway to follow. The ability to consistently produce product with equivalent performance is key to supporting healthcare professionals in this decision making. A large proportion of in-vitro diagnostics are immunoassay, relying on the highly specific interaction of antibodies with the target molecule. These tests are commonly contain complex mixes of biological components, including the antibody of choice often modified by addition of a marker compound. Many of the antibody conjugates, serum proteins and animal sera are not well characterised in terms of the properties required for optimum performance within the immunoassay system and so a change in material lot represents a significant risk to the performance of the reagent. Each new lot of the biological materials for these systems therefore has to be assessed as to its performance to ensure the product consistency and quality is maintained, with significant time and resources investigated in these evaluations. Development of improved material testing methodology will result in improved effectiveness of material selection, increased productivity through reduced scrap and rework activities and increased customer satisfaction.Through a previous PhD studentship, the application of Raman spectroscopy has been demonstrated to:1. distinguish different grades of BSA and define the structural differences between these grades2. identify the presence of structural changes in protein structure following exposure to different conditions of pH, temperature and mixing3. in combination with FTIR, characterise the structural changes occurring following conjugation of BSA to a small molecule and the further minute changes that occur as the conjugated protein agesThis project seeks to expand on the work described above to further the develop the methodology and machine learning analytics for use as a quality control and/or troubleshooting tool within immunoassay manufacturing. The ultimate aim is to be able to use analytical tools such as Raman within the quality control process for new materials, to reduce the time and resources taken to identify and qualify suitable new lots. The project scope will include:expanding the range of proteins characterised, the use of machine learning analytics to determine which specific characteristic are linked to positive or negative performance characteristics in immunoassays and examination of proteins within complex reagent mixing as a potential troubleshooting tool for unexpected reagent performance challenges