Public Funding for Simomics Limited

This project will develop a cloud based risk modelling application to formalise, streamline and more accurately and transparently calculate environmental and antimicrobial resistance risks associated with discharging pharmaceutical manufacturing effluent into water catchments. The mass production and consumption of pharmaceuticals has resulted in active pharmaceutical ingredients (APIs), antimicrobials and other chemical compounds being present in surface waters worldwide. Although the majority of APIs detected are predominantly due to the consumption and excretion of APIs from normal patient use, pharmaceutical manufacturing sites also contribute to the problem by emitting effluent, containing APIs, into neighbouring water catchments. With pharmaceutical manufacturing scaling up production of APIs in response to the Covid-19 pandemic, rapid increases in production could create potential for localised 'hot spots' in the water catchment where effluent is released if it is not managed effectively. This has highlighted a need to assess and mitigate risks quickly, minimise environmental impact and prevent future adverse human health outcomes that could arise from increased APIs and chemical compounds in the environment, such as increased antimicrobial resistance (AMR) which could have dire consequences for future global human health. While voluntary initiatives already exist for managing the risks of manufacturing emissions, for example the AMR Industry Alliance and Pharmaceutical Supply Chain Initiative (PSCI) safe level targets for antimicrobials, these initiatives lack standardisation across the industry and calculations lack some catchment specific context required to effectively predict and mitigate 'hot spots' around sites. Current calculations considering 'safe' levels of discharge from manufacturing sites don't accurately represent catchment complexities including background patient use in the catchment, temporal river flow data and the potential for multiple manufacturing sites discharging into one catchment. This could lead to localised API levels above the safe threshold, or falsely failed assessments. Furthermore, where safe limits are being requested of manufacturers, audits and calculations are not standardised and are ultimately hard to validate for correctness. Pharmaceutical companies often have many suppliers to audit and it is hard to ensure consistency and be confident in the accuracy of results. With this project, Simomics will address the unique needs of Pharmaceutical manufacturing sites, developing a cloud-based risk prediction and mitigation application, modelling exposure, effects, risk and factors like treatability and re-use. Working in collaboration with subject matter experts at the University of York, this application will enable environmental risk assessors and site/contract management teams to more accurately understand and predict the environmental or AMR risk from effluent containing APIs including antimicrobials being re-used or released in a water catchment area, incorporating the context of background patient use and other sources of effluent including co-located sites. The software solution we develop will enable pharmaceutical manufacturers to streamline, standardise, support and create more confidence in the accuracy of effluent ecotoxicology and AMR risk assessments, facilitate collaboration between closely located manufacturing sites and enable teams to consider water wastage and treatability while minimising environmental and AMR risk prior to production changes or even prior to site planning or manufacturing contract agreements.

Computer models are useful tools for generating predictions that help understand a real world system, for example in determining how effective a potential new drug treatment could be before trial in laboratory or human studies. Building a computer model of a real system that we don't yet completely understand leads developers to make decisions and simplifications that will impact the meaning of these predictions. It is important that the model developer and their collaborating partners document these decisions and the evidence for them in a format that is accessible by teams of differing expertise (for example biologists, computer programmers, statisticians). If documentation is not created, or those working on a project leave the organisation, this information is lost, and it may no longer be sustainable to continue the use and development of this model. This project examines the creation of an infrastructure that generates a traceable history of the creation of a model, including the evidence for each decision, complementing previously created evidencing resources from SimOmics Ltd. Model curation will ensure the generation of high quality models that are sustainable for future studies, while reducing the effort of documenting key design decisions.

All new active pharmaceutical ingredients must undergo an environmental risk assessment (ERA) before being authorised. Currently tens of thousands of fish are used worldwide as part of API ERAs. Development of predictive in silico models has the potential to significantly reduce animal use (3Rs) and reduce R&D costs around the ERA of pharmaceuticals. These models, when combined with recently developed in vitro bioassays, can be used to determine up front risk. Evidence based, in silico approaches that predict the movement of an API from the patient to aquatic systems and the subequent impacts on the ecosystems. The "Virtual Fish EcoToxicology Laboratory" will be a transparent, evidence-based system of interlinked mathematical models, combined with extensive datasets, that will determine risk to both apical end-points (e.g. impacts on fish reproduction and growth) and non-apical end-points (e.g. effects on behaviour).