基于无监督机器学习的过程分析工具,用于CAR-T细胞制造过程中的近实时细胞形态分析。

IF 3.6 2区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Nidhi G. Thite, Michael Yarnell, Terry J. Fry, Matthew Seefeldt, Christopher P. Calderon, Theodore W. Randolph
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引用次数: 0

摘要

嵌合抗原受体(CAR)-T细胞疗法等细胞疗法将活细胞作为活性药物成分输送给患者。这些电池的制造是复杂的,经常产生异质产品和高故障率。CAR-T细胞生产中使用的质量控制(QC)分析主要提供终端产品测试。实时过程监控是降低故障率和确保最终产品质量的理想选择。然而,由于CAR-T细胞产品的异质性及其对工艺变化的敏感性,目前的分析工具往往不足。在这项研究中,我们展示了无监督的基于图像的机器学习作为一种过程分析工具(PAT),用于在CAR-T细胞生产过程中进行近乎实时的过程监控。从9个健康供体中收集的T细胞在激活、慢病毒转导(表达CD19 CAR蛋白)和CAR-T细胞扩增阶段的流动成像显微镜(FIM)图像被记录下来。这些图像用于训练变分自编码器(VAE),允许定量跟踪每个供体在CAR-T细胞生产的各个阶段细胞形态的变化。研究结果包括观察到一个新的,瞬时群体在T细胞转导表达CAR蛋白。这个群体在未转导的T细胞中不存在。新群体的密度与传统的基于染色的流式细胞术测定的转导效率成正比。总之,本研究证明了使用VAEs作为PAT工具用于监测患者之间的差异和早期发现过程偏差/异常的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T Cell Manufacturing

Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T Cell Manufacturing

Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T Cell Manufacturing

Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T Cell Manufacturing

Unsupervised Machine Learning-Based Process Analytical Tools for Near Real-Time Cell Morphology Analysis During CAR-T Cell Manufacturing

Cell therapies like Chimeric Antigen Receptor (CAR)-T cell therapy deliver living cells to patients as active pharmaceutical ingredients. Manufacturing of these cells is complex, often yielding, heterogeneous products and high failure rates. Quality control (QC) assays used in CAR-T cell production primarily provide end-point product testing. Real-time process monitoring would be ideal to reduce failure rates and ensure final product quality. However, current analytical tools often fall short due to the heterogeneity of CAR-T cell products and their sensitivity to process changes. In this study, we showcase unsupervised image-based machine learning as a process analytical tool (PAT) for near real-time process monitoring during the production of CAR-T cells. Flow imaging microscopy (FIM) images of T cells collected from nine healthy donors were recorded during the activation, lentiviral-based transduction (expressing CD19 CAR protein), and expansion stages of CAR-T cell production. These images were used to train a Variational Autoencoder (VAE), allowing quantitative tracking of changes in cell morphologies during the various stages of production of CAR-T cells from each donor. Findings include observation of a new, transient population in T cells transduced to express CAR protein. This population was absent in T cells that were not transduced. The density of the new population was proportional to the transduction efficiency determined by traditional stain-based flow cytometry assays. Together, this study demonstrates the utility of using VAEs as a PAT tool for monitoring patient-to-patient variability and early detection of process deviations/upsets.

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来源期刊
Biotechnology and Bioengineering
Biotechnology and Bioengineering 工程技术-生物工程与应用微生物
CiteScore
7.90
自引率
5.30%
发文量
280
审稿时长
2.1 months
期刊介绍: Biotechnology & Bioengineering publishes Perspectives, Articles, Reviews, Mini-Reviews, and Communications to the Editor that embrace all aspects of biotechnology. These include: -Enzyme systems and their applications, including enzyme reactors, purification, and applied aspects of protein engineering -Animal-cell biotechnology, including media development -Applied aspects of cellular physiology, metabolism, and energetics -Biocatalysis and applied enzymology, including enzyme reactors, protein engineering, and nanobiotechnology -Biothermodynamics -Biofuels, including biomass and renewable resource engineering -Biomaterials, including delivery systems and materials for tissue engineering -Bioprocess engineering, including kinetics and modeling of biological systems, transport phenomena in bioreactors, bioreactor design, monitoring, and control -Biosensors and instrumentation -Computational and systems biology, including bioinformatics and genomic/proteomic studies -Environmental biotechnology, including biofilms, algal systems, and bioremediation -Metabolic and cellular engineering -Plant-cell biotechnology -Spectroscopic and other analytical techniques for biotechnological applications -Synthetic biology -Tissue engineering, stem-cell bioengineering, regenerative medicine, gene therapy and delivery systems The editors will consider papers for publication based on novelty, their immediate or future impact on biotechnological processes, and their contribution to the advancement of biochemical engineering science. Submission of papers dealing with routine aspects of bioprocessing, description of established equipment, and routine applications of established methodologies (e.g., control strategies, modeling, experimental methods) is discouraged. Theoretical papers will be judged based on the novelty of the approach and their potential impact, or on their novel capability to predict and elucidate experimental observations.
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