Adaptive advanced modelling approaches in the evolution of vector/cell manufacturing processes and analytics for patient-tailored cell-gene therapy pr

细胞基因治疗是一种利用载体将外源基因表达单位引入生物体的体细胞以预防、停止或逆转病理状况的技术。近年来，随着载体安全性和有效性的不断提高，基因治疗领域的研究又有了新的进展，许多危及生命的疾病患者已被基因治疗产品成功治疗，GSK公司也于2016年5月获得了首个商业化基因治疗产品的批准。载体和细胞制造过程由大量的手工任务组成，这些手工任务将显著的可变性引入到生产方法中，从而引入到产品质量中。很可能的是，数据融合的方法与过程相关的信息，从载体和细胞的扩增和处理将带来显着的好处，在过程的理解，并增加新的治疗方法的开发速度。该项目将利用先进的数据分析和建模技术来确定患者数据和载体/细胞制造过程之间的关键关系，以及使用患者一生中可用的数据进行重新评估。先进的建模方法，如多变量数据分析（MVDA），随机和混合建模技术，这被证明是非常宝贵的，在提高生物制剂生产的有效性，将探讨的适用性。由Professey小组开发的新颖的半定量方法以及贝叶斯方法已被证明在数据稀疏环境中是有效的，并且也将在本研究中进行研究和进一步开发。结合第一原理知识的过程与MVDA过程表示在半参数混合模型的可能性，此外将探讨，以提供一个有效的工具，快速的过程开发和优化。

Cell gene therapy is a technique whereby an exogenous gene expression unit is introduced, using a vector, into the somatic cells of an organism in order to prevent, halt or reverse a pathological condition. Recent developments in vector safety and efficacy have brought about significant renewed interest in gene therapy, a number of patients with life threatening diseases have been successfully treated with gene therapy products, and GSK were granted approval for the first commercial gene therapy product in May 2016.The development of gene therapy products is hindered by process complexity and variability; vector and cell manufacturing processes consist of a large number of manual tasks which introduce significant variability into the production methods and thereby product quality. It is likely that methods of data fusion with process-related information from vector and cell expansion and processing will bring significant benefits in process understanding, and increase the rate of development of new treatments. This project will make use of advanced data analytics and modelling techniques to identify critical relationships between patient data and vector/cell manufacturing processes, as well as their re-evaluation using data that becomes available over the patient's lifetime. The suitability of advanced modelling approaches, such as multivariate data analysis (MVDA), stochastic, and hybrid modelling techniques, which proved to be invaluable in increasing the effectiveness of biologics manufacturing, will be explored. The novel semi-quantitative methods developed by the group of Prof Glassey as well as Bayesian approaches have proven to be effective in the context of data sparse environment and also will be investigated and further developed in this studentship. The possibility of combining first principles knowledge of the processes with MVDA process representation in semi-parametric hybrid models will in addition be explored in order to provide an effective tool for rapid process development and optimisation.