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

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.