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

就产品开发而言，制药业正在经历一个前所未有的变革时期，数字化程度的提高，对连续生产的更加重视，以及个性化药物等新型治疗模式的迅速出现，变得越来越关键。这一变化因设计考虑的质量和现在常规使用目标产品简介方法来设计以患者为中心的剂型而得到放大。可用治疗策略的最新进展，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、低成本的制造转向更专业化、更高价值的产品开发。因此，更复杂的方法，不仅是生产医药产品，而且是在生产过程的每个阶段控制其质量，已经变得至关重要。以人工智能和机器学习技术为基础的用于加强在线过程分析和实时反应过程控制的新兴技术将极大地促进这方面的工作。这些技术对于制造错误的财务和实际利润率较低的产品尤其重要，就像越来越多的新疗法的情况一样。在这项建议中，我们专注于以纳米晶形式筛选、制造和质量控制药物的新方法，即通过表面活性物质稳定的纳米晶颗粒制备的药物。特别是，我们将结合连续流程处理、在线先进流程分析技术、实时流程控制和质量保证、实验设计、先进数据分析和人工智能，为通过抗溶剂沉淀筛选和制造纳米药物提供全自动化、自我优化的平台。这些剂型已经引起了人们的极大兴趣，作为一种输送难溶于水(因此生物利用度很低)的药物的方法，这是制药业一个持续且日益严重的问题。虽然纳米晶体为我们的方法提供了一个合适的测试系统，但我们打算提供的方法和制造平台可以应用于其他药物输送系统。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(即微流控)系统。它们提供了极佳的工艺可控性，可以生成尺寸接近均匀的晶体，并且由于工艺是连续的，因此产品特性比间歇系统更稳定。Millifluidic系统灵活(一个平台可以生产更多种类的产品)和敏捷-对市场需求的变化做出快速反应；它们减少了制造时间，加快了供应链，而且体积小，可以携带。这些系统还加快了筛选，减少了所需材料的数量，这些好处将被实验设计放大。这种数据驱动的技术允许识别信息量最大的实验，最大限度地增加学习，同时最大限度地减少时间和成本，这是制药业没有充分利用的优势。这些技术与在线先进的工艺分析方法、实时工艺控制、尖端数据分析和机器学习方法相结合，有可能打破现状，加快工艺开发，并提供变革性的平台，以经济高效和可持续的方式生产固体剂型的活性药物成分，缩短从药物发现到患者的时间，并有助于使英国处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.