Towards rapid, high-throughput and cost-effective evaluation of viral vector efficacy: Rapid image pattern analysis from microfluidic cell cultures us

慢病毒载体(LV)制造的特点是开发成本高昂，导致高达每名患者治疗500,000美元的治疗成本。在病毒载体制造过程中监测关键质量属性，如媒介的传染性，是至关重要的。目前病毒载体滴定的黄金标准依赖于传染性分析。它们可以通过细胞感染来估计样本中包含的感染性颗粒的数量。目前，这些分析是在微孔板中进行的，因此仅限于通过昂贵的过程进行终点监测。此外，用于高通量分析的液体体积仍然很大，自动化能力有限。为了促进个性化的基因治疗和分散生产，我们的实验室开发了一种微流控设备，它以微小的体积运行，并提供实时分析数据。在这个项目中，微流控装置将被用来进行传染性分析，并以工业黄金标准为基准。到目前为止开发的技术使用荧光方法来检测LV滴度，但可以实施新的模式。将开发人工智能算法，使其能够进行自动化的传染性分析。这将把微流控技术与先进的图像分析例程和机器学习算法相结合，以开发高通量传染性分析，为该平台能够以最佳方式评估患者对患者的病毒载体摄取变异性的未来工作奠定基础。

Lentiviral Vector (LV) manufacture is characterized by expensive development, resulting in high treatment costs of up to $500,000 per patient treatment. Monitoring of critical quality attributes during viral vector manufacture, such as the infectivity of the vector, is vital. Current gold standards for viral vector titering rely on infectivity assays. They allow estimating the number of infectious particles contained in a sample through the infection of cells. Currently, these assays are carried out in micro well plates, thus limited to end-point monitoring through expensive processes. Furthermore, the liquid volumes are still large for high-throughput analysis, and automation capabilities are limited. To facilitate personalized gene therapy treatments and decentralizing manufacturing, a microfluidic device which operates with tiny volumes and which provides real time assay data was developed in our labs. In this project, the microfluidic device will be used to perform infectivity assays and bench-mark them against the industrial gold standard. The technology developed thus far uses a fluorescence approach to detect LV titer but new modalities can be implemented. Artificial Intelligence algorithms will be developed enabling automated infectivity assays. This will combine microfluidic technology with advanced image analysis routines and machine learning algorithms to allow the development of high throughput infectivity assays, laying the foundations for future work where this platform can optimally assess patient to patient variability of viral vector uptake.