Accelerated Development of Pharmaceutical Processes Through Digitally Coupled Reaction Screening and Process Optimisation

合成的开发和反应的优化仍然是制药过程开发中的速度限制因素，通常依赖于昂贵、耗时和浪费的资源密集型反复试验方法。这突显了开发能够快速应对新出现的卫生挑战的新的数字方法的必要性。为了实现这一目标，我们将创建一个跨越多个地点的数字耦合反应堆网络，能够进行高通量筛选和自我优化制造工艺。这项提议独特地结合了不同的流动反应器技术、分析技术和自动化工作流程，以提供增强的化学空间测绘和生成强大的高质量数据集。机器人技术将被用于设计灵活的实验系统，能够探索连续(例如，时间、温度)和分类(例如，催化剂、配体)变量，以及不同的反应器类型。值得注意的是，将开发并行液滴流动反应器，并将其与智能优化算法相结合，以减少制药开发活动期间所需的材料量。将建立一个多站点反应堆网络，并由下一代机器学习算法驱动，该算法将使用以前实验活动中的知识来提高库合成成功率，并加快化学相关过程的开发和优化。在发现过程中进行的实验比在过程开发过程中进行的实验多几个数量级；在这个早期阶段收集的高质量自动化数据将对加速、降低成本和可持续制造至关重要。与制药行业的合作伙伴合作，我们将利用这一新的工作流程来简化通往未来药物的途径。我们非本地化的人工智能网络产生的能力和结果将可以在不同的化学制造行业之间转移。这项研究的目标是：开发用于合成与药物相关的化合物文库的自主高通量微流控流动反应器。图书馆合成的成功率将通过整合最先进的混合变量优化算法来提高。实时在线分析将用于量化每个反应，从而提供稳健和标准化的数据集，用于预测性机器学习模型，使其能够应用于目前未被开发的化学物质。创建跨多个地点的数字耦合反应堆，以探索广阔的过程空间。为了实现这一点，将利用互补的分析技术和不同的反应堆技术来生成不同尺度的数据集。并行优化将考虑多个目标之间的权衡，使制造的可持续性能够从制药开发的一开始就被考虑。结合多个实验实验室的不同类型的数据，为新的库合成和过程优化活动生成假设。下一代机器学习算法将被设计为使用上下文相似的化学系统的先验知识，目的是加速从发现到制造的过渡。中试制造过程的演示。我们的数字耦合反应堆网络将用于执行并行文库合成和选定过程的自我优化。将利用利兹iPRD内现有的设施对扩大规模进行评估。

Development of synthesis and optimisation of reactions remain rate-limiting factors in pharmaceutical process development, often relying on resource-intensive trial-and-error approaches that are costly, time-consuming, and wasteful. This highlights the need to develop new digital methods that are capable of rapidly responding to emerging health challenges. To achieve this, we will create a network of digitally coupled reactors across multiple sites capable of high-throughput screening and self-optimising manufacturing processes. This proposal uniquely combines different flow reactor technologies, analytical techniques, and automated workflows to provide enhanced mapping of chemical space and generation of robust high-quality datasets. Robotics will be used to design flexible experimental systems capable of exploring continuous (e.g., time, temperature) and categorical (e.g., catalyst, ligand) variables, as well as different reactor types. Notably, parallelised droplet flow reactors will be developed and combined with intelligent optimisation algorithms to reduce the amount of material required during pharmaceutical development campaigns. A multisite reactor network will be established and driven by next generation machine learning algorithms, which will use knowledge from prior experimental campaigns to increase library synthesis success rates and accelerate the development and optimisation of chemically related processes. Orders of magnitude more experiments are performed during discovery than during process development; the high-quality automated data collected at this early stage will be essential for accelerated, lower cost and sustainable manufacturing. In collaboration with our partners in the pharmaceutical industry, we will leverage this novel workflow to streamline the pathway to future medicines. The capabilities and results generated from our delocalised artificially intelligent network will be transferable across different chemical manufacturing sectors. The objectives of this research are:Development of autonomous high-throughput microfluidic flow reactors for the synthesis of pharmaceutically relevant compound libraries. Library synthesis success rates will be increased by integration of state-of-the-art mixed variable optimisation algorithms. Real-time online analytics will be used to quantify each reaction, thus providing robust and standardised datasets for use in predictive machine learning models, enabling their application towards currently underexplored chemistries.Creation of digitally coupled reactors across multiple sites for the exploration of wide process spaces. To achieve this, complementary analytical techniques and different reactor technologies will be leveraged to generate datasets across different scales. Parallelised optimisations will consider the trade-offs between multiple objectives, enabling the sustainability of manufacturing to be considered from the outset of pharmaceutical development.Combination of different types of data across multiple experimental labs to generate hypotheses for new library synthesis and process optimisation campaigns. Next generation machine learning algorithms will be designed to use prior knowledge of contextually similar chemical systems, with the aim of accelerating the transition from discovery to manufacturing.Demonstration of a pilot-scale manufacturing process. Our network of digitally coupled reactors will be used to perform parallelised library synthesis and self-optimisation of a selected process. Scale-up will be evaluated using the facilities available within the iPRD at Leeds.