Autonomous AI Assisted Salt Selection for Stability and Performance

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This project will deliver an AI-driven decision-making system for predictive salt selection for pharmaceutical APIs. In addition, the project will explore the pharmaceutical materials science (structure-process-property-performance) relationships that inform our Crystallisation Classification System development (CCS). The work will build our industry demand led programme aligned to CMAC's Lab of the Future vision for to accelerate product and process design by incorporating off-line measurement, collaborative robotics, analysis and decisions into an autonomous smart development platform or DataFactory.BackgroundIn the UK the pharmaceutical industry in 2018 was responsible for a £49bn turnover of goods, with a gross revenue from retail sales of £5bn (Office for National Statistics (UK), 2018). Also, in 2018, it was identified that artificial intelligence (AI) applications could add an additional £630bn to the UK economy by 2035 (Deep Knowledge Analytics, 2018, AI in the UK). This has been stimulated by advances in AI in a wide range of sectors, e.g. materials discovery (Schmidt, et al., 2019, Nature Computational Materials), and has provoked responses in government strategy, e.g. Scotland's AI Strategy Open Call. Most recently the emergence of Industry 5.0 principles that seek to build in human centricity, resilience and sustainability to manufacturing: these goals are especially important in the medicines manufacturing area.The pharmaceutical industry is facing increasing demands to deliver a higher variety of products more quickly, economically and sustainably. To achieve this, the sector seeks to make better use of cyber-physical production systems with a specific focus on digital twins and how they can be used to simulate manufacturing processes during development and hence reduce material requirements to validate processes before operation (Kemppainen, 2017, European Pharmaceutical Review). As a method of purification and forming the pharmaceutically relevant polymorph (Lee, 2014, Asian Journal of Pharmaceutical Sciences; Kesisoglou, et al., 2008, The AAPS Journal), crystallisation of the active pharmaceutical ingredient (API) is a key step in pharmaceutical manufacturing that impacts downstream processes. Determining an industrial-relevant approach for API crystallisation can be resource-intensive as a candidate crystallisation process is constrained by and assessed against industrial relevant solubilities, downstream processing practicalities, and regulator-determined Critical Quality Attributes (CQA) of the API (Chen, et al.,2011, Crystal growth & design; Brown et al., 2018, Molecular Systems Design & Engineering). Critically as ca. 40% of marketed APIs are practically insoluble, salt (and/or co-crystal) selection is a key step in the API form selection process which can provide up to 1000 fold increase in aqueous solubility. Yet suitable counterion selection remains largely trial and error and the resultant form stability and performance (disproportionation) can be impacted by salt crystal structure; impact of excipients in the formulated product on microenvironmental pH and disproportionation; buffer capacity of salts and excipients. The existing DataFactory at CMAC funded via EPSRC Hub and UK RPIF Net Zero Pilot support, uses high throughput small-scale crystallisation experiments coupled with machine learning to reduce the time and material costs associated with exploring the crystallisation design space for APIs. In addition the project is developing a unique Crystallisation Parameter Database that will fuel further basic and applied research moving towards the development of a Crystallisation Classification System (CCS) that will autonomously identifies industrially-relevant experimental conditions for any given API to achieve required performance, stability or manufacturability objectives.