Harnessing Technological Advances to Re-engineer Polymer Particles for Drug Delivery

新冠肺炎疫情对全球科学实验室的运营提出了挑战。具有讽刺意味的是，与此同时，正是科学和医学带领我们走出了这场流行病。事实上，先进的药物配方是在对抗新冠肺炎的斗争中发挥关键作用的关键领域之一。这两种临床批准的信使核糖核酸疫苗依赖于脂基纳米粒的配方。这一成功重新引起了人们对基于脂质和聚合物颗粒的配方策略的兴趣。药物配方包括将药物与非活性物质相结合，以生产具有理想性能的产品。通过配方，制药科学家可以设计出新药的重要特性，如减少副作用和提高治疗效果。聚合物是可用于药物制剂的材料。由于具有合成创新材料的能力，聚合物带来了无与伦比的多样性。然而，到目前为止，基于聚合物的纳米和微米颗粒在药物制剂中的应用潜力还没有得到充分的开发。这在一定程度上是因为，在开发基于纳米或微粒的药物配方时，必须考虑广泛的聚合物特性和制造参数。此外，与纳米和微粒子开发相关的设计-建造-测试循环在很大程度上依赖于实验。因此，充分考虑每种聚合物的性质或制造参数以及它们的组合对药物制剂的特性和性能的影响几乎是不可能的。拟议研究计划的目标是利用人工智能、机器学习、微流体和实验自动化方面的最新技术进步，为聚合物纳米和微粒子药物配方探索数据驱动的开发路径。在过去的十年里，人工智能使制药和医疗保健领域取得了突破性的进展，包括在药物发现和癌症诊断方面的改进。机器学习是人工智能的一个分支，它使计算机能够学习任务，而不需要显式编程。拟议的研究旨在利用这项技术，更快、更便宜地设计出更有效的药物，用于治疗女性发病率较高的慢性疾病，包括卵巢癌和乳腺癌、骨关节炎和痴呆症。这项研究建立在加拿大的研究实力和最近的经济投资领域，即人工智能和机器学习、制造业和生物技术/制药行业。因此，该项目将为学员提供必要的知识和跨学科技能，以确保在这些不断增长的行业部门的交叉点获得领导职位。

The COVID-19 pandemic has challenged the operations of scientific laboratories around the globe. Ironically, at the same time it is science and medicine that are leading us out of this pandemic. In fact, advanced pharmaceutical formulation is amongst the key areas that have played a critical role in the fight against COVID-19. The two clinically approved mRNA vaccines are reliant on formulation in lipid-based nanoparticles. This success has brought renewed interest in lipid and polymer particle-based formulation strategies. Pharmaceutical formulation involves combining drugs with inactive materials to produce products with desirable properties. Through formulation, pharmaceutical scientists can engineer important properties of new medicines, such as reduced side effects and improved therapeutic efficacy. Polymers are materials that can be used for drug formulation. Polymers bring unparalleled diversity owing to the ability to synthesize innovative materials. Yet, to date the potential of polymer-based nano and microparticles for applications in drug formulation has not been fully exploited. This is in part due to the fact that there are a wide range of polymer properties and manufacturing parameters that must be considered in the development of nano or microparticle-based drug formulations. Further, the design - build - test loop associated with nano and microparticle development is largely reliant on hands on experimentation. Thus, full consideration of the impact of each polymer property or manufacturing parameter, and their combinations, on the characteristics and performance of drug formulations has been near to impossible to fully explore. The goal of the proposed research program is to capitalize on recent technological advancements in artificial intelligence, machine learning, microfluidics and experimental automation to pursue data driven development pathways for polymeric nano and microparticle-based drug formulations. Over the past decade AI has enabled ground-breaking advances in the pharmaceutical and healthcare sectors, including improvements in drug discovery and cancer diagnosis. Machine learning is a branch of artificial intelligence, that enables computers to learn tasks without the need to be explicitly programmed. The proposed research aims to harness this technology to enable faster and cheaper design of more effective medicines for the treatment of chronic diseases with a high prevalence in women, including ovarian and breast cancer, osteoarthritis, and dementia. This research builds on areas of research strength and recent economic investment in Canada, namely, artificial intelligence and machine learning, manufacturing, and the biotech/pharmaceutical industry. As a result, this program will provide trainees with the knowledge and interdisciplinary skills necessary to secure leadership positions at the intersection between these growing industry sectors.