Fully Automated Platforms for Drug Nanocrystals Manufacturing via Continuous-Flow, Data-Driven Antisolvent Crystallization

制药行业在产品开发方面正经历着前所未有的变革时期，数字化程度不断提高，更加强调连续生产，新型治疗模式（如个性化药物）的迅速出现变得越来越重要。这一变化因设计质量考虑因素和目前常规使用目标产品特征方法设计以患者为中心的剂型而扩大。最近在一系列可用的治疗策略方面的进展，以及现在可以成功治疗的疾病的广度，导致了对新剂型和制造方法的需求。至关重要的是，已经从大批量、低成本制造转向更专业、更高价值的产品开发。因此，越来越复杂的方法，不仅生产医药产品，而且在生产过程的每个阶段控制其质量，已成为至关重要的。基于人工智能和机器学习技术的新兴技术将大大促进这些工作，以加强在线过程分析和实时响应过程控制。这些技术是特别重要的产品的财政和实际利润率的制造错误是低的，因为是一个新的therapeutic.In这个提案中，我们专注于筛选，制造和质量控制药物的纳米晶体的形式，即，药物制备为纳米尺寸的晶体颗粒稳定的表面活性剂的新方法。特别是，我们将结合联合收割机连续流处理，在线先进的过程分析技术，实时过程控制和质量保证，实验设计，先进的数据分析和人工智能，提供全自动，自我优化的平台，通过反溶剂沉淀筛选和制造纳米晶体药物。这些剂型已经吸引了大量的兴趣，作为一种手段，提供水溶性差（因此生物利用度差）的药物，一个持久的和日益增长的问题，制药industrial.While纳米晶体提供了一个合适的测试系统，我们的方法和制造平台，我们打算提供可以应用到其他药物输送系统。我们专注于纳米晶体，因为它们在国内和国际上都具有相当大的治疗和商业意义。我们打算使用连续流动的小规模（即毫流体）系统。这些提供了出色的工艺可控性，可以生成几乎均匀尺寸的晶体，并且由于该工艺是连续的，因此产品特性比间歇系统更稳定。毫微流体系统是灵活的（一个平台可以生产更多种类的产品）和敏捷的-对市场需求的变化迅速作出反应;它们减少了制造时间，加快了供应链，并且体积更小，可以携带。这些系统还加快了筛选，减少了所需材料的数量，实验设计将放大这些好处。这种数据驱动的技术可以识别信息量最大的实验，最大限度地提高学习能力，同时最大限度地减少时间和成本，这些优势尚未被制药行业充分利用。这些技术加上在线先进工艺分析方法、实时工艺控制、尖端数据分析和机器学习方法，有可能打破现状，加速工艺开发，并为固体剂型活性药物成分的成本效益和可持续生产提供变革性平台，缩短从药物发现到患者的时间轴，并致力于将英国置于制药行业创新的前沿。

The pharmaceutical industry is undergoing a period of unprecedented change in terms of product development, with increased digitization, greater emphasis on continuous manufacture and the rapid advent of novel therapeutic paradigms, such as personalized medicines, becoming more and more business critical. This change is amplified by Quality by Design considerations and the now routine use of the Target Product Profile approach to the design of patient-centred dosage forms. The recent advances in the range of available therapeutic strategies, alongside the breadth of diseases that can now be successfully treated, has resulted in the need for both new dosage forms and manufacturing approaches. Crucially, there has been a shift from high volume, low cost manufacture towards a more specialized, higher value product development. Consequently, ever more sophisticated approaches, not merely to producing medicinal products, but also to controlling their quality at every stage of the manufacturing process, have become paramount. These would be greatly facilitated by the emerging technologies, based on artificial intelligence and machine learning techniques, for enhancing online process analysis as well as real-time responsive process control. These technologies are particularly important for products where the financial and practical margins for manufacturing error are low, as is the case for an increasing proportion of new therapies.In this proposal, we focus on a new way of screening, manufacturing and quality controlling drugs in the form of nanocrystals, that is, drugs prepared as nanosized crystalline particles stabilized by surface-active agents. In particular, we will combine continuous-flow processing, online advanced process analytical technology, real-time process control and quality assurance, design of experiments, advanced data analysis and artificial intelligence to deliver fully automated, self-optimizing platforms for screening and manufacturing drugs as nanocrystals via antisolvent precipitation. These dosage forms have attracted substantial interest as a means of delivering poorly water-soluble (and thus poorly bioavailable) drugs, a persistent and increasing problem for the pharmaceutical industry.While nanocrystals offer a suitable test system for our approach, our methodology and the manufacturing platform we intend to deliver can be applied to other drug delivery systems. We focus on nanocrystals because they are of considerable therapeutic and commercial significance both nationally and internationally.We intend to use continuous-flow small-scale (i.e. millifluidic) systems. These offer excellent process controllability, can generate crystals of nearly uniform size, and as the process is continuous, the product characteristics are more stable than in batch systems. Millifluidic systems are flexible (one platform can produce a larger variety of products) and agile - reacting rapidly to changes in market demands; they reduce the manufacturing time, speed up the supply chain and, being smaller, can be portable. These systems also expedite screening, curtailing the quantities of material required, benefits that design of experiments will amplify. This data-driven technique allows identifying the most informative experiments, maximizing learning while minimizing time and costs, advantages not fully exploited by the pharmaceutical industry. These technologies, coupled with online advanced process analytical methods, real-time process control, cutting-edge data analysis and machine learning methods, have the potential to disrupt the status quo, accelerate process development and deliver transformative platforms for the cost-effective and sustainable manufacturing of active pharmaceutical ingredients in solid dosage form, reducing the timeline from drug discovery to patient, and contributing to placing the UK at the forefront of innovation in the pharmaceutical sector.