Bader Johannes, Narayanan Harini, Arosio Paolo, Leroux Jean-Christophe
Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.
Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.
Eur J Pharm Biopharm. 2023 Jan;182:103-114. doi: 10.1016/j.ejpb.2022.12.004. Epub 2022 Dec 13.
With the growing demand and diversity of biological drugs, developing optimal processes for their accelerated production with minimal resource utilization is a pressing challenge. Typically, such optimization involves multiple target properties, such as production yield, biological activity, and product purity. Therefore, strategic experimental design techniques that can characterize the parameter space while simultaneously arriving at the optimal process satisfying multiple target properties are required. To achieve this, we propose the use of a multi-objective batch Bayesian optimization (MOBBO) algorithm and illustrate its successful application for the production of extracellular vesicles (EVs) from a 3D culture of mesenchymal stem cells (MSCs) considering three objectives, namely to maximize the vesicle-to-protein ratio, maximize the enzymatic activity of the MSC-EV protein CD73, and minimize the amount of calregulin impurities. We show that the optimal combination of the process parameters to address the intended objectives could be achieved with only 32 experiments. For the four parameters considered (i.e., microcarrier concentration, seeding density, centrifugation time, and impeller speed), this number of experiments is comparable to or lower than the classical design of experiments (DoE) and the traditional one-factor-at-a-time (OFAT) approach. We illustrate how the algorithm adaptively samples in the process parameter space, selectively excluding unfavorable regions, thus minimizing the number of experiments required to reach optimal conditions. Finally, we compare the obtained solutions to the literature data and present possible applications of the collected data for other modeling activities such as Quality by Design, process monitoring, control, and scale-up.
随着生物药物需求的不断增长和种类的日益多样化，开发以最少资源利用实现加速生产的优化工艺是一项紧迫的挑战。通常，这种优化涉及多个目标特性，如产量、生物活性和产品纯度。因此，需要能够表征参数空间同时达成满足多个目标特性的最优工艺的战略实验设计技术。为实现这一目标，我们提出使用多目标批次贝叶斯优化（MOBBO）算法，并说明其在从间充质干细胞（MSC）的3D培养物生产细胞外囊泡（EV）中的成功应用，该应用考虑了三个目标，即最大化囊泡与蛋白质的比率、最大化MSC-EV蛋白CD73的酶活性以及最小化钙调节蛋白杂质的量。我们表明，仅通过32次实验就能实现用于实现预期目标的工艺参数的最佳组合。对于所考虑的四个参数（即微载体浓度、接种密度、离心时间和叶轮速度），这个实验次数与经典实验设计（DoE）和传统的一次一因素（OFAT）方法相当或更低。我们说明了该算法如何在工艺参数空间中自适应采样，有选择地排除不利区域，从而最小化达到最佳条件所需的实验次数。最后，我们将获得的解决方案与文献数据进行比较，并展示所收集数据在其他建模活动（如设计质量、过程监测、控制和放大）中的可能应用。
