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

通过连续流、数据驱动的反溶剂结晶制造药物纳米晶体的全自动平台

• 批准号: EP/V050796/1
• 负责人: Luca Mazzei
• 金额: $ 150.4万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV050796%2F1
• 关键词: Fully; Automated; Platforms; Drug; Nanocrystals

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.

在产品开发方面，制药行业正在经历前所未有的变化，数字化增加，更加重视连续制造以及新型治疗范式的快速出现，例如个性化药物，变得越来越重要。通过设计注意事项质量和现在常规使用目标产品概况方法来设计以患者为中心的剂型的方法，这种变化会扩大。最新的可用治疗策略范围的进步，以及现在可以成功治疗的疾病的广度，导致了新剂型和制造方法的需求。至关重要的是，从大量，低成本制造到更专业，更高的价值产品开发的转变。因此，不仅是生产药品，而且在制造过程的每个阶段都控制其质量的方法都变得至关重要。基于人工智能和机器学习技术的新兴技术将极大地促进这些问题，以增强在线过程分析以及实时响应迅速的过程控制。这些技术对于制造错误的财务和实用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，我们专注于一种新的筛查，制造和质量控制药物的纳米晶体形式的新方法，也是由纳米化的nanosized Crystalline颗粒稳定的药物制备的药物，由表面稳定性稳定。特别是，我们将结合连续流处理，在线高级过程分析技术，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，以通过反冒险沉淀提供全自动，自动化的平台，以筛查和制造药物作为纳米晶体。这些剂型形式引起了极大的兴趣，作为提供水溶性较差（且因此生物利用性较差）药物的一种手段，对于制药行业来说是一个持久而越来越多的问题。纳米晶体为我们的方法提供了合适的测试系​​统，但我们的方法论和我们的制造平台我们注定要运送其他药物交付系统。我们专注于纳米晶体，因为它们在国内和国际上具有相当大的治疗和商业意义。我们打算使用连续流的小规模（即millifluidic）系统。这些提供了出色的过程可控性，可以产生几乎均匀尺寸的晶体，并且由于过程是连续的，因此产品特性比批处理系统更稳定。 Millifluidic系统是灵活的（一个平台可以生产更多的产品）和敏捷 - 对市场需求的变化迅速反应；它们减少了制造时间，加快供应链的速度，并且较小，可以便携。这些系统还加快了筛选，减少所需材料的数量，实验设计的好处将放大。这种数据驱动的技术允许确定最有用的实验，最大程度地提高学习，同时最大程度地减少时间和成本，这是制药行业无法完全利用的优势。这些技术，再加上在线高级流程分析方法，实时过程控制，尖端数据分析和机器学习方法，有可能破坏现状，加速过程开发并提供变革性的平台，以实现成本效益和可持续性的生产，以实现固体剂量的积极成分，从而使其从Innov中降低，并降低了Innove for Inner的促进，并在公共发现的时间内促进了该公司，并构成了杂物，并撰写了贡献的杂物。药物领域。

项目成果

Non-fouling flow reactors for nanomaterial synthesis

• DOI: 10.1039/d2re00412g
• 发表时间: 2023
• 期刊: Reaction Chemistry & Engineering
• 作者: M. Besenhard;Sayan Pal;G. Gkogkos;A. Gavriilidis