DataSheet1_Bioprocess feeding optimization through in silico dynamic experiments and hybrid digital models—a proof of concept.docx

The development of cell cultures to produce monoclonal antibodies is a multi-step, time-consuming, and labor-intensive procedure which usually lasts several years and requires heavy investment by biopharmaceutical companies. One key aspect of process optimization is improving the feeding strategy. This step is typically performed though design of experiments (DoE) during process development, in such a way as to identify the optimal combinations of factors which maximize the productivity of the cell cultures. However, DoE is not suitable for time-varying factor profiles because it requires a large number of experimental runs which can last several weeks and cost tens of thousands of dollars. We here suggest a methodology to optimize the feeding schedule of mammalian cell cultures by virtualizing part of the experimental campaign on a hybrid digital model of the process to accelerate experimentation and reduce experimental burden. The proposed methodology couples design of dynamic experiments (DoDE) with a hybrid semi-parametric digital model. In particular, DoDE is used to design optimal experiments with time-varying factor profiles, whose experimental data are then utilized to train the hybrid model. This will identify the optimal time profiles of glucose and glutamine for maximizing the antibody titer in the culture despite the limited number of experiments performed on the process. As a proof-of-concept, the proposed methodology is applied on a simulated process to produce monoclonal antibodies at a 1-L shake flask scale, and the results are compared with an experimental campaign based on DoDE and response surface modeling. The hybrid digital model requires an extremely limited number of experiments (nine) to be accurately trained, resulting in a promising solution for performing in silico experimental campaigns. The proposed optimization strategy provides a 34.9% increase in the antibody titer with respect to the training data and a 2.8% higher antibody titer than the optimal results of two DoDE-based experimental campaigns comprising different numbers of experiments (i.e., 9 and 31), achieving a high antibody titer (3,222.8 mg/L) —very close to the real process optimum (3,228.8 mg/L).

用于生产单克隆抗体的细胞培养开发是一项多步骤、耗时且劳动密集型的流程，通常耗时数年，且需要生物制药公司投入巨额资金。工艺优化的核心环节之一在于优化补料策略。该环节在工艺开发阶段通常通过实验设计（Design of Experiments，DoE）开展，以识别可最大化细胞培养生产力的最优因子组合。然而，实验设计无法适用于时变因子分布的优化场景，因其需要开展大量实验，耗时可达数周，且成本高达数万美元。本文提出一种方法，通过在工艺的混合数字模型上虚拟化部分实验工作，以加速实验进程并降低实验负担，从而优化哺乳动物细胞培养的补料计划。所提方法将动态实验设计（Design of Dynamic Experiments，DoDE）与混合半参数化数字模型相结合。具体而言，动态实验设计用于设计包含时变因子分布的最优实验，所得实验数据随后用于训练该混合模型。即便针对该工艺开展的实验数量有限，该方法仍可识别出可最大化培养体系中抗体效价的葡萄糖与谷氨酰胺最优时间分布。作为概念验证，所提方法被应用于1L摇瓶规模的单克隆抗体生产模拟工艺，并将结果与基于动态实验设计与响应面建模的实验工作进行对比。该混合数字模型仅需极少量实验（9次）即可实现精准训练，为开展虚拟（in silico）实验工作提供了极具前景的解决方案。相较于训练数据集，所提优化策略可使抗体效价提升34.9%；相较于两组基于动态实验设计的实验工作（分别包含9次与31次实验）的最优结果，抗体效价仍高出2.8%，最终实现3222.8 mg/L的抗体效价——该结果非常接近该工艺的实际最优效价（3228.8 mg/L）。