This project proposes developing a new approach to chemical synthesis by constructing and demonstrating a software-based toolkit aimed specifically at synthetic chemists, allowing them to easily digitise and democratise their synthetic procedures in the form of code used to create multistage reactor systems and proto-type them using 3D printing. We aim to explore and validate this concept for a range of targets, from organic to inorganic and nano-scale materials. In preliminary studies published in Science (Science 2018, 359, 314-319) earlier this year we have shown that the digitisation of chemical synthesis is possible. In this grant we propose to expand this methodology that is currently allowing individual reaction steps to be be embodied in parametrically defined reactor 'modules'. The modules are then combined into extended, multi-step sequences, enabling us to turn the complex processes of batch chemical synthesis into small scale, on demand, synthesis cartridges. These cartridges can then be accompanied by a validated set of operating instructions which can be carried out either manually or via an automated interface, minimising the time and skill required to effect the synthesis whilst simultaneously maximising the reproducibility. Using 3D printed reactionware, developed by us in a £10 K 'creativity at home EPSRC project', in conjunction with robotic interfaces for liquid handling, this project will explore how to chemicals can be made in low-resource / limited skill environment after digitisation, aiming at lower costs, greater reproducibility, and vastly expanding the variety of materials available to the end user. Further, we aim that this toolkit can be used to enable mechanistic and material discovery studies by allowing the manipulation of the physical structure of the reactors to constrain the synthetic and reaction parameters vastly decreasing the timescales for customisation and further development.

Improving our current lifestyle and ensuring health of a growing population is reliant on the development of more advanced consumer products. Many of these engineered products have advanced functionality delivered by particles with nanometre dimensions, many thousands of times smaller than the width of a human hair. The exact size of these nanoparticles determines the mechanism of action and performance for the specific application. In healthcare, many drugs require encapsulation within polymer nanoparticles for several reasons, including for dissolving insoluble drugs, protecting drugs from unwanted degradation (e.g. mRNA vaccines) and providing efficient delivery (anti-cancer drugs). In electronics, the colour and intensity of light produced can be finely tuned by controlling the size of quantum dot nanoparticles, thus resulting in much higher quality displays, ultra-thin smart coatings (e.g. for wearable technologies), advanced diagnostics, high intensity medical imaging or high efficiency solar panels. The accuracy required to produce these materials is phenomenal and often only achieved reproducibly in dedicated research laboratories by specialist scientists. There has therefore been little progress on scaling up in a cost-effective or sustainable manner. In this project we will build platform technologies, comprising advanced chemical reactors underpinned by computational intelligence, which can scale up production of advanced nanoparticle products without loss in the precise control over structural dimensions which are achieved in research laboratories. We will build laboratory reactors which can be programmed to monitor the nanoparticle formation process in real time and relate conditions to the particle properties. Throughout the manufacturing process the machine learning algorithms will direct the reactors towards achieving the desired specification through 'self-optimisation' of conditions. A critical part of the project is then using the data obtained in the lab experiments to build a relationship between process and product which can be transferred onto equipment which can make the materials on a commercially relevant scale in a process called augmented lossless scale-up. We will take the optimised laboratory nanoparticle formation processes and demonstrate scale in several manufacturing environments, including R&D process laboratories and Commercial manufacturing facilities at our partners sites. Such demonstration will encourage further innovation beyond the lifetime of the project which can work towards realising advanced materials currently confined to research laboratories.

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.

Improving our current lifestyle and ensuring health of a growing population is reliant on the development of more advanced consumer products. Many of these engineered products have advanced functionality delivered by particles with nanometre dimensions, many thousands of times smaller than the width of a human hair. The exact size of these nanoparticles determines the mechanism of action and performance for the specific application. In healthcare, many drugs require encapsulation within polymer nanoparticles for several reasons, including for dissolving insoluble drugs, protecting drugs from unwanted degradation (e.g. mRNA vaccines) and providing efficient delivery (anti-cancer drugs). In electronics, the colour and intensity of light produced can be finely tuned by controlling the size of quantum dot nanoparticles, thus resulting in much higher quality displays, ultra-thin smart coatings (e.g. for wearable technologies), advanced diagnostics, high intensity medical imaging or high efficiency solar panels. The accuracy required to produce these materials is phenomenal and often only achieved reproducibly in dedicated research laboratories by specialist scientists. There has therefore been little progress on scaling up in a cost-effective or sustainable manner. In this project we will build platform technologies, comprising advanced chemical reactors underpinned by computational intelligence, which can scale up production of advanced nanoparticle products without loss in the precise control over structural dimensions which are achieved in research laboratories. We will build laboratory reactors which can be programmed to monitor the nanoparticle formation process in real time and relate conditions to the particle properties. Throughout the manufacturing process the machine learning algorithms will direct the reactors towards achieving the desired specification through 'self-optimisation' of conditions. A critical part of the project is then using the data obtained in the lab experiments to build a relationship between process and product which can be transferred onto equipment which can make the materials on a commercially relevant scale in a process called augmented lossless scale-up. We will take the optimised laboratory nanoparticle formation processes and demonstrate scale in several manufacturing environments, including R&D process laboratories and Commercial manufacturing facilities at our partners sites. Such demonstration will encourage further innovation beyond the lifetime of the project which can work towards realising advanced materials currently confined to research laboratories.

Our aim is to develop an approach to make and discover molecules using a chemical programming language that is run in a modular Chemical-Robot or Chemobot. To do this we need to develop a 'Universal Chemical Synthesis Machine' architecture which we will refer to here as 'the Chemputer'. The Chemputer represents a new architecture for running chemical synthesis, and will be realised by the development of a portable and modular approach to chemistry. To do this we must establish the ontological relationships and abstractions to allow the development of a code that will drive machine-independent universal synthesis. This ontology will connect a high-level chemical programming language we will develop to the low-level machine code to run the modular Chemobots. The Chemobots will be designed and built around batch 'flask' synthesis and can be networked together allowing the molecules to be made in steps. By establishing the framework and building the underlying firmware, software, and abstractions, we will demonstrate the Chemputer by developing modular robots capable of chemistry, Chemobots. These will be built around batch 'flask' synthesis and can be networked together allowing the molecules to be made discretely in steps. Although synthetic chemistry is complex and demanding, a chemical reaction only requires five operations: i) addition of reagents; ii) reaction process; iii) work-up; iv) separation; v) purification. We will take our Chemputer standard, comprising five modules for batch operations, and enlist our expert pioneer collaborators and industrial stake holders, to test and validate our approach. Importantly, we have already validated the concept of chemical digitization, and the platform approach highlighted by our recent publications in Science and Nature earlier this year. Also, this work builds on our previous programme grant 'digital-synthesis' in terms of our technical abilities to build platforms and write software. However, the vision of the Chemputer architecture represents a step change, resulting in practical Chemobots. We will use the systems of modular Chemobots to also explore reproducibility, and to improve the environment for the chemist from a workflow, safety, and pedagogical point of view. In addition, the ability to individually validate and digitize reactions one by one should allow for the ability to synthesize very complex molecules autonomously as the stability and usability of the systems improve. We will start using our preliminary platform as a 'generation 0' to enable the development of the abstraction, architecture, and ontologies for digital chemistry. As the Chemobots are developed we will explore new reactions using sensors and statistics driven design of experiments to target unknown molecules with target-assay driven search algorithms.