EPSRC-SFI: Table Top Manufacturing of Tailored Silica for Personalised Medicine [SiPM]

近年来，个性化医疗(PM)受到了极大的关注，因为它有可能改变全球的医疗保健，摆脱“一刀切”的模式，利用个人情况、病史和提供个性化适当治疗的需求。当前的批量制造技术无法满足大多数这些需求，因为它们对变化的响应速度很慢，资本密集型，使用不可持续的方法，并且不能灵活地满足PM需求。EPSRC资助的医疗再分布式制造中心最近发布的一份白皮书指出，实现PM迫切需要小规模、本地化、高速和自动化的制造平台他们认为，这种“盒式工厂”应该：-能够按需制造，-灵活地提供具有所需性能的多种产品，-可持续(高能效和使用温和的条件)，以及-能够使用数据科学工具整合各种单元操作。考虑到对PM的未来需求，最近的研究努力致力于重新定义活性药物成分(API)的制造及其配方，例如使用微流体、热熔融挤压或3D打印等先进方法制成口服剂量的片剂。然而，由于药物是含有辅料或药物输送系统(DDS)等非活性成分的原料药的精心设计的配方，制造PM的非活性成分的挑战也同样重要，但尚未得到解决。非活性成分改善了原料药的理化性质和生物利用度。在许多形式中，二氧化硅是当前和未来许多原料药配方中最常用的成分之一，但它们的制造不符合PM要求。具体地说，尽管在药物配方中使用二氧化硅取得了巨大的进步，但目前还不存在按需、自动化和灵活的制造方法来生产具有所需性能的粉末冶金用二氧化硅。这其中的一个关键原因是，绝大多数有希望的二氧化硅需要合成条件，而这些条件对于任何有意义的扩大和‘盒子里的工厂’平台的实施来说都是禁忌的。因此，尽管原料药和配方的制造最近取得了进展，但这一缺失的部分为PM的实现制造了一个巨大的障碍。我们已经展示了生物灵感二氧化硅(BIS)作为一种替代药物输送系统的潜力，它是可扩展、经济和可持续的--是按需和灵活制造的理想候选者。这项研究将依赖于计算建模和实验合成之间的密切协同。申请者将以绿色合成工艺和强化反应堆研究为起点。将使用一系列强化反应堆和基于高斯过程的建模来实现颗粒制造过程的过程强化。将使用全面的模型来创建数字孪生流体设备和使用这些设备绿色合成二氧化硅颗粒的配方。将开发基于反应器模拟结果的机器学习方法，将生产的二氧化硅的质量属性与关键工艺和操作参数联系起来。将对设备几何形状和工艺参数进行操作，以实现所需的关键质量属性(CQA)。这项工作将有助于PM的革命性变革，并有助于在医院和药店提供桌面制药设备。归根结底，影响将包括治疗和生活质量的显著改善，以及成立新的公司来建造这样的单元。

Personalised medicine (PM) is gaining significant attention in recent years as it has the potential to transform healthcare across the globe by moving away from the "one-size-fits-all" model to utilise personal circumstances, medical history and needs to deliver individually suitable treatment. Current bulk manufacturing technologies are unable to meet most of these demands as they are slow in responding to changes, capital intensive, use unsustainable methods and are not flexible to meet PM needs. A recent white paper from the EPSRC funded Redistributed Manufacturing in Healthcare has identified that small-scale, localised, high-speed and automated manufacturing platforms are urgently needed to realise PM. They identified that such "factory-in-a-box" should be: - able to manufacture on-demand,- flexible to deliver multiple products with desired properties, - sustainable (energy efficient and using mild conditions) and - able to integrate various unit operations using data science tools. Given the future needs for PM, recent research efforts have been directed towards redefining the manufacturing of active pharmaceutical ingredient (API) and their formulations into e.g. tablets for oral dosages using advanced methods such as microfluidics, Hot Melt Extrusion or 3D printing. However, as a medicine is a carefully designed formulation of an API with non-active components such as excipients or drug delivery systems (DDS), challenges in manufacturing of the non-active components for PM are also equally important, but have not been addressed. The non-active components improve physicochemical properties and bioavailability of APIs. In its many forms silica is one of the most commonly used component of many current and future API formulations, yet their manufacturing to meet the PM requirements do not exist. Specifically, despite tremendous progress made on the use of silica in pharmaceutical formulations, currently, their on-demand, automated and flexible manufacture to produce silica of desired properties for PM is non-existent. A key reason for this is that the vast majority of promising silicas require synthesis conditions that are prohibitive for any meaningful scale-up and for implementation in a 'factory in a box' platform. Hence, this missing piece, despite the recent developments in manufacturing of API and formulations, creates a significant barrier to making PM a reality. We have shown the potential of bioinspired silica (BIS) as an alternate drug delivery system, which is scalable, economical and sustainable - an ideal candidate for on-demand and flexible manufacturing. This research will rely on a close synergy between computational modelling and experimental synthesis. Green synthesis processes and research on intensified reactors by the applicants will be used as a starting point. A range of intensified reactors and Gaussian Process-based modelling will be used to achieve process intensification of particulate manufacturing processes. Comprehensive models will be used to create digital twins of fluidic devices and recipes of green synthesis of silica particles using those devices. Machine learning approaches based on results of simulations of reactors will be developed to relate quality attributes of silica produced with key process and operating parameters. Device geometry and process parameters will be manipulated to achieve the desired Critical Quality Attributes (CQAs). The work will contribute to revolutionising PM and help deliver table top pharmaceutical manufacturing equipment in hospitals and pharmacies. Ultimately, the impact will include significant improvements in treatments and quality of life as well as the formation of new companies to build such units.