The work in this thesis has focused on exploring the nature of existing self-optimising continuous flow systems, and how the technology could be improved to optimise more challenging and industrially relevant problems. The main areas that needed to be addressed were highlighted at the start of this work, and included: (i) application of more data efficient algorithms; (ii) development of suitable laboratory-scale flow reactors; (iii) optimisation of downstream unit operations. This thesis has contributed to each of the areas above, and demonstrated improvements in the form of relevant case studies.
When designing a chemical process, it is important to consider multiple relevant performance criteria. Work in Chapter 2 described the application of TSEMO, a recently developed Bayesian multi-objective optimisation algorithm.96 Initial work highlighted the a priori selection of suitable objectives as a significant challenge. This was overcome by empirical modelling of the initial dataset, which subsequently enabled successful optimisation of two conflicting economic and environmental performance criteria. This approach provided the optimum values for two objectives, and highlighted the complete trade-off curve between them.69 Therefore, this presents a significantly more data efficient methodology compared to single objective optimisation, and the scalarisation of multiple objectives.66 Despite this, the amount of material required would still be a significant bottleneck for more expensive-to-evaluate systems, such as those involving APIs. It was hypothesised that the number of experiments could be reduced by predicting the Pareto front from the empirical models of the initial dataset. Although the simulations were in fair agreement with the experimental data, the models were at risk of overfitting in some regions of the variable space. Rather, future work should focus on the development of nanomole-scale high throughput flow equipment, which when combined with multi-objective optimisation, would synergistically increase the efficiency of self-optimising systems.32
In Chapter 3, a continuous flow Sonogashira reaction towards the synthesis of API lanabecestat (AZD3293) was self-optimised. Lanabecestat is a BACE1 inhibitor used for the treatment of Alzheimer’s disease which entered phase III clinical trials in July 2016, and was developed by AstraZeneca and Eli Lilly & Co.113 Optimisation of the continuous flow Sonogashira reaction started in April 2019, where results
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were due at the time of the interim analysis in June 2019. To achieve this, the multi-objective self-optimisation approach, developed in Chapter 2, was integrated with a traditional design of experiments workflow. The combination of these methods provided all the desired information within the time constraints of a late-stage pharmaceutical development project. Notably, the trade-off between conversion and productivity was identified, which could be re-evaluated with the dynamic downstream work-up specifications in the active learning process. Hence, data obtained from the multi-objective self-optimisation played a key role in the design of a multi-step process.
In this case, the continuous variables (residence time, temperature, equivalents) were included in the self-optimisation, and the discrete variables (catalyst, ligand, solvent) were screened in preliminary work. Ideally, both continuous and discrete variables would be simultaneously optimised to account for any underlying interactions.49 Therefore, the future development of multi-objective algorithms for mixed-variable optimisations will play a crucial role in the application of self-optimisation to more complex catalytic systems. Furthermore, optimisation of the Sonogashira reaction required 80 experiments carried out over a period of 35 hours. To facilitate a wider uptake of this technology in the pharmaceutical industry, a further reduction in the number of experiments is required, which is directly related to the efficiency of the algorithm. A kinetic-based reaction simulator was developed to assess the performance of multi-objective optimisation algorithms. By assessing new algorithms in this way, it can be ensured that self-optimising platforms are kept up-to-date with the latest advances in computer science. Of the algorithms tested, EIM-EGO was found to outperform TSEMO, and should therefore form the basis of future work in this area.134
In Chapter 4, a miniature CSTR cascade was developed to decouple flow rate and mixing performance. This provided a laboratory-scale reactor suitable for mass transfer limited reactions with longer residence times, thus broadening the scope of self-optimisation to a wider range of chemistries. In addition, the design enabled the incorporation of LEDs for photochemical applications, and was found to have a 10×
greater absorbed photon flux density compared to photochemical batch reactors.162 A biphasic continuous flow process for the site-selective aerobic oxidation of C(sp3)-H bonds was self-optimised using a new hybrid algorithm, which combined global optimisation with local response surface mapping around the optimum. This
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successfully located an optimum with milder reaction conditions compared to previous work, and provided important details regarding process stability around the optimum.171 Further development of the reactor is underway to enable light intensity to be included as a variable during optimisation, which can have a significant impact on processes involving photodecomposition pathways. In this case, the developed reactor was suitable for the self-optimisation of a gas-liquid biphasic reaction. However, there still remains a lack of techniques for sampling and on-line analysis of slurries, which is a challenge that requires future attention to enable the automated optimisation of multiphasic reactions involving solids.
The end-to-end continuous flow synthesis of APIs involves multiple reaction and work-up unit operations, all of which need to be optimised to yield an efficient overall process.183 Work in Chapter 5 addressed for the first time the self-optimisation of in-line liquid-liquid extractions and multi-step reaction-extraction processes. Initially, a pH-based LLE of structurally similar impurities was optimised, and the SNOBFIT algorithm found to outperform traditional statistical methods for response surfaces containing cliff edges. This approach was then adopted for the optimisation of a multi-step reaction-extraction process, where the effect of both the reaction and work-up steps on the purity were simultaneously considered. The ability of the multi-objective TSEMO algorithm to optimise a multi-step process was also explored. A biphasic Claisen-Schmidt condensation reaction-extraction process was successfully optimised with respect to three objectives, utilising a temperature controlled version of the miniature CSTR cascade described in Chapter 4. The trade-off curve between the three objectives was identified in 65 hours without any human intervention, and thus presents an efficient method for multi-step process optimisation. As such, this work will likely be extended for the optimisation of complete end-to-end syntheses of APIs in the future. However, challenges which will need to addressed include the deconvolution of factor effects on each step, and the individual control of residence time in telescoped reactors.
In summary, self-optimising continuous flow reactors provide an automated method for intelligent exploration of experimental space, thus rapidly identifying optimum conditions.50 This thesis has focused on improving self-optimisation to further align with the interests of industry, which has been achieved by introducing multi-objective optimisation algorithms and applying them towards the synthesis of
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APIs, developing a new multiphasic CSTR cascade reactor with photochemical capabilities and including downstream work-up operations in the optimisation of multi-step processes. During this work, areas of interest for future research in the field of self-optimisation have been identified. These include: (i) saving material by using nanomole-scale high throughput flow equipment; (ii) developing multi-objective algorithms for mixed variable optimisations; (iii) introducing sampling techniques for multiphasic reactions involving solids; (iv) optimisation of end-to-end total syntheses of APIs. Furthermore, as not all reactions are suitable for continuous flow, there would be a significant interest in the application of self-optimising technology to batch systems. As the rise of automation in chemistry continues, the production of commercially available self-optimising platforms is inevitable. Therefore, providing a user-friendly end product, that does not require specialist knowledge to operate, will be crucial for the widespread adoption of these systems by general chemists and synthetic-based laboratories.
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