Design of formulation processes for improved thermostability of viral vaccines

病毒载体疫苗是目前在新冠肺炎3期临床试验中评估的最先进的候选疫苗之一。在制造后，热稳定性是确保这些救生产品在需要的地方有效和安全交付的关键质量属性。基于对细胞培养产生的病毒的广泛科学知识和技术诀窍，我们建议这项研究计划引导我们的努力，在过程开发周期中研究和整合病毒疫苗的液体和固体配方，这是一个主要仍处于经验阶段的过程开发领域。因此，一种新颖的混合模型将使冻干机实时优化运行。这项研究计划的长期目标是确定关键参数并揭示它们的作用机制，以整合专家知识，以数字辅助设计具有更高热稳定性和成本效益的有效病毒疫苗的液体和固体配方。我们的目标是1)设计增强的液体配方，并揭示包膜病毒如水疱性口炎病毒(VSV)和慢病毒在辅料存在下的形成机制，以保持其生物活性；2)设计固体配方并开发机制模型，用于VSV和腺病毒载体疫苗等包膜和非包膜病毒的冷冻干燥，为机器学习产生大量的高质量数据；3)通过结合机制和数据驱动模型，使用数字孪生方法开发冷冻干燥过程的混合模型。数字双胞胎通过传感器和反馈控制连接到物理冻干机，以加快病毒疫苗冷冻干燥工艺的设计和优化操作。在与该领域的专家(来自McGill的联合主管)的合作下，我们建议结合机器学习(ML)算法的最新进展来帮助设计实验，并加速设计健壮的疫苗配方和过程。冷冻干燥过程的实验、结果验证和数字双基优化将在我们已经具备功能的生物安全Level-2+实验室中进行，并由我们经过验证的过程分析技术提供支持。这项研究将促进对包膜和非包膜病毒配方的了解，从而发现新的赋形剂和增强的工艺。通过将设计方法集中在提高VSV载体埃博拉疫苗的热稳定性上，我们将促进这一救命产品以及使用相同VSV抗原传递平台(HIV-1、SARS-CoV-2)的其他产品的传递和储存。至关重要的是，这项研究计划将有助于培训16名HQP，以应对加拿大疫苗生物制造行业对合格人员的高需求。

Viral vectored vaccines are among the most advanced vaccine candidates currently evaluated in COVID-19 phase-3 clinical trials. Following their manufacturing, thermostability is a critical quality attribute to ensure the efficacy and safe delivery of these life-saving products where needed. Building on an extensive scientific knowledge and know-how of cell-culture produced viruses, we propose for this research program to direct our efforts to study and integrate in the process development cycle, liquid and solid formulations of viral vaccines, an area of process development that remained largely empirical. As a result, a novel hybrid model will enable the freeze-dryer optimal operations in real-time. The long-term objective of this research program is to identify the critical parameters and uncover their mechanisms of action towards integration of expert knowledge for digitally assisted design of liquid and solid formulations of effective viral vaccines with improved thermostability and cost-effectiveness. We aim to 1) Design enhanced liquid formulations and uncover mechanisms involved in the formulation of enveloped viruses such as Vesicular Stomatitis Virus (VSV) and lentivirus in presence of excipients to preserve their biological activity; 2) Design solid formulations and develop mechanistic model for freeze-drying of enveloped and non-enveloped viruses such as VSV and adenovirus vectored vaccines generating a large pool of quality data for Machin Learning; 3) Develop a Hybrid model for freeze-drying process using a digital twin approach by combining mechanistic and data-driven models. The digital twin is connected to the physical freeze-dryer by sensors and feed-back control to accelerate the design and optimal operation of freeze-drying processes of viral vaccines. In collaboration with experts in the field (co-supervisors from McGill), we propose to combine recent advancements in Machine Learning (ML) algorithms to assist the design of experiments and accelerate the design of robust vaccine formulations and processes. Experimentation, validation of results and digital twin-based optimization of freeze-drying process will be executed in our already functional biosafety level-2+ laboratory and supported by our validated process analytical technologies. This research will advance knowledge on formulation of enveloped and non-enveloped viruses leading to discovery of novel excipients and enhanced processes. By focusing the design approach on improving the thermostability of the VSV-vectored Ebola vaccine, we will facilitate the delivery and stockpiling of this life-saving product as well as other products using the same VSV delivery platform of antigens (HIV-1, SARS-CoV-2). Critically, this research program will contribute to training 16 HQPs to respond to the high demand of qualified personnel expressed by the vaccine biomanufacturing industry in Canada.