CEDAR CDT 2025-2026 prospective topic titles 

Primary Supervisor: Yvonne Perrie (Strathclyde)

Secondary Supervisor: Clare Hoskins (Strathclyde)

Research challenges addressed in this project:

This project addresses critical challenges in the synthesis and characterisation of nanoparticles, focusing on enhancing their precision and functionality for advanced biomedical applications. The primary research challenges lie in optimising nanoparticle production methods to ensure reproducibility and scalability while maintaining consistent physicochemical properties such as size, charge, and stability. Variability in these properties often leads to unpredictable biological performance, hindering their clinical translation. Another major challenge is mapping the formulation to the payload, ensuring controlled release and targeted delivery to specific tissues or cells. The project also seeks to bridge the gap between laboratory-scale research and patient-appropriate-scale production by developing scalable processes that align with regulatory and industrial requirements.

Key aspects of Cyber-Physical Systems to be developed:

The project will develop cyber-physical systems to advance nanoparticle synthesis and characterisation by integrating process modelling and performance mapping. These systems will utilise computational models to simulate nanoparticle formation and predict their physicochemical properties based on manufacturing parameters. By correlating key production factors such as mixing speed, flow rate, and temperature to nanoparticle characteristics like size, charge, and encapsulation efficiency, the project will create a framework for optimising manufacturing processes. Additionally, advanced characterisation tools, including dynamic light scattering (DLS), zeta potential analysis, and cryo-electron microscopy, will generate detailed data on nanoparticle properties, which will be integrated into modelling workflows. This data-driven approach will allow for the development of predictive models that map the relationship between production parameters and functional performance, such as RNA encapsulation efficiency and targeted delivery. The insights gained will bridge the gap between laboratory-scale methods and industrial-scale production, ensuring that optimised processes deliver consistent and functional nanoparticles at scale.

Industrial challenge addressed:

This project addresses the critical challenge of scaling nanoparticle production to meet diverse disease-driven demands, from personalised therapies for individual patients to global deployment of vaccines. Current production methods often lack scalability, reproducibility, and sustainability, resulting in variability in nanoparticle quality, high production costs, and significant waste. These factors hinder clinical translation and limit the widespread adoption of nanoparticle-based therapeutics. The project will focus on developing scalable, optimised manufacturing protocols that ensure consistent nanoparticle properties, such as size, charge, and encapsulation efficiency, while aligning with regulatory and quality standards. Through the integration of process modelling and performance mapping, it will create a framework to predict and control functional outcomes across different production scales, reducing variability and improving efficiency. Sustainability will be a core focus, with efforts to incorporate green chemistry principles and resource-efficient practices. By minimising solvent use, reducing energy consumption, and optimising processes to decrease waste, the project will develop environmentally responsible production methods. These innovations will enable cost-effective, scalable, and sustainable nanoparticle production, supporting both high-value personalised therapies and large-scale vaccine manufacturing to meet global healthcare needs.

Primary Supervisor: Clare Hoskins (Strathclyde)

Secondary Supervisor: Yvonne Perrie (Strathclyde)

Research challenges addressed in this project:

Exploring the impact of continuous manufacture processes on the synthesis of metallic nanoparticles with multifunctional components for both detection and therapy in a pancreatic cancer model. The project will focus on translating existing knowledge of microfluidic manufacture from lipid-based systems, translating this into the more complex conditions required for metallic nanoparticle fabrication. Looking at the challenges associated with nanoparticle core formation and coating steps and comprising of multiple analytical methods to ensure product formation, uniformity of batch and dose, before testing in vitro in pancreatic cell lines and in vivo if the data proves favourable. Methods including inductively coupled plasma-optical emission spectroscopy, superconducting quantum interference device, dynamic light scattering, transmission electron microscopy, high performance liquid chromatography, etc.

Key aspects of Cyber-Physical Systems to be developed:

Translation of knowledge from one continuous manufacture process to another more complicated one. Guide experimental strategies to avoid problematic down points, guided by mechanistic understanding of solution components on outcomes. Feed into a digital twin, and open avenues for in line testing in future studies.

Industrial challenge addressed:

Continuous manufacture of metallic multicomponent particles for Theranostic application.

Primary Supervisor: Clare Hoskins (Strathclyde)

Secondary Supervisor: Alison Nordon (Strathclyde)

Research challenges addressed in this project:

Exploring the impact of impregnating polymeric nanoparticles with a metallic nanocomponent conferring additional imaging capabilities to track them real time after administration. Building on the design of experiments in cohort 1 we will adapt the parameters to allow inclusion of a metallic seed into the synthetic procedure and understand effect on particle size, drug loading capacity, stability etc. Once an optimum system has been developed this will be trailed for continuous microfluidic manufacture. The project will focus on translating existing knowledge of polymeric micelle and microfluidic manufacture, translating this into the more complex conditions required for polymer based Theranostic fabrication. Looking at the challenges associated with inclusion of an additional component into the system and comprising of multiple analytical methods, before testing in vitro in pancreatic cell lines and in vivo if the data proves favourable. Methods including inductively coupled plasma-optical emission spectroscopy, superconducting quantum interference device, dynamic light scattering, transmission electron microscopy, high performance liquid chromatography etc. The outcomes of these experiments may feed into a digital twin, in order to form a predictive tool for future development.

Key aspects of Cyber-Physical Systems to be developed:

Guide experimental strategies to avoid problematic down points, guided by mechanistic, understanding of solution components on outcomes. Translation of knowledge from one continuous manufacture process to another more complicated one. Feed into a digital twin, and open avenues for in line testing in future studies.

Industrial challenge addressed:

Precision synthesis and continuous manufacture of polymeric based Theranostic particles.

Primary Supervisor: Yvonne Perrie (Strathclyde)

Secondary Supervisors: Daniel Markl & Jan Sefcik (Strathclyde)

Research challenges addressed in this project:

Cancer therapies require highly specific and efficient delivery systems to target tumours while minimising off-target effects. Lipid nanoparticles (LNPs) have shown promise in addressing these requirements, but current design and manufacturing processes face significant limitations. The first challenge lies in optimising nanoparticle formulations for encapsulating complex therapeutic payloads, such as small molecules, RNA, or combinatory drugs, while ensuring stability and effective tumour-specific delivery. A major hurdle is the lack of predictive frameworks linking production conditions to nanoparticle performance, especially for the intricate targeting demands of cancer therapy. Another challenge is achieving consistent, scalable production, as slight variations in manufacturing conditions can lead to significant differences in therapeutic efficacy. Additionally, manufacturing methods must be adapted to meet the sustainability needs of modern industries, reducing waste and resource use while maintaining high precision in nanoparticle design. This project addresses these challenges through a model-driven approach that integrates computational and experimental methodologies to optimise both the design and production of nanoparticles tailored for cancer therapies.

Key aspects of Cyber-Physical Systems to be developed:

The project will establish a model-driven, cyber-physical framework that bridges the gap between nanoparticle design, manufacture, and therapeutic performance. The development of mechanistic models will be central to this framework, enabling the simulation of key manufacturing parameters—such as lipid mixing dynamics, solvent composition, and process times—and their influence on critical nanoparticle characteristics. These models will be combined with experimental data generated using advanced manufacturing systems like microfluidics and high-throughput screening platforms. A particular focus will be on coupling computational fluid dynamics with mechanistic models to refine process understanding at varying production scales. This framework will incorporate machine learning to map experimental outcomes to process variables, allowing iterative improvement in both formulation and manufacturing workflows.

Industrial challenge addressed:

This project addresses the critical industrial challenge of developing scalable, cost-effective, and reproducible manufacturing processes for nanoparticles tailored to cancer therapeutics that offers targeting.

Primary Supervisor: Javier Cardona (Strathclyde)

Secondary Supervisor: Leo Lue (Strathclyde)

Research challenges addressed in this project:

Mass and heat transport mechanisms in solvent-induced phase separation processes are complex and poorly understood, and yet they play a crucial role in controlling critical quality attributes. This project will explore the impact of non-ideal interactions between solute(s) and solvent(s) on mixing-driven crystal nucleation and growth. The aim is to enhance process understanding and achieve more robust crystallisation in multi-solvent systems while investigating the role of impurities in conjunction with the research challenge team.

Key aspects of Cyber-Physical Systems to be developed:

The project will exploit and extend the capabilities of the existing automated platforms (e.g., Crystallisation Screening, Scale-up DataFactories) to systems formed by multiple solvents, including the selective and controlled addition of solutions and solvents throughout the crystallisation process. Based on model-driven design of experiments, a FAIR dataset will be built to inform and validate multi-scale models for solvent-induced nucleation and growth. Expands upon the solvent selection for impurity control in cohort 1 and contributes to the development of cohort 2 project 4.

Industrial challenge addressed:

Solvent-induced nucleation and growth is still difficult to model and control due to the complex interplay of mixing dynamics and non-ideal interactions between solute(s) and solvent(s). A better understanding of mixing-driven processes will lead to more robust and sustainable crystallisation processes, while avoiding or controlling unexpected outcomes such as undesired particle attributes and liquid-liquid phase separation.

Primary Supervisor:  Róisín O'Connell (Leeds) 

Secondary Supervisors: Sven L M Schroeder (Leeds), Richard Bourne (Leeds) 

Research challenges addressed in this project:  This project addresses several critical challenges in the characterization, stability, and manufacturing of lipid nanoparticles (LNPs). Its key focus is the development of X-ray and electron techniques sensitive to the internal molecular structure in LNPs, including total scattering (pair distribution functions, PDFs) and advanced imaging techniques (X-ray microscopy phase contrast, diffraction and SAXS modalities, cryo-electron microscopy). The main aim is to establish experimental protocols that localize the active pharmaceutical ingredient (API), such as mRNA, within individual particles. Understanding how the API is distributed is essential for optimizing therapeutic efficacy. Additionally, the project will investigate interactions between LNPs, aiming to assess aggregation tendencies and their implications for stability and performance. Especially of interest would be temperature-dependent studies exploring how structural and functional changes occur in LNPs during storage, transport, and delivery, identifying critical thermal thresholds that influence their stability. Methods for the controlled preparation of LNPs will be based on microfluidic platforms, which will be combined with inert gas and cryo-stream technologies. Thereby practical challenges in handling and transporting LNPs will be tackled, ensuring stability and integrity during transit to facilitate global collaboration and sample distribution. By overcoming these challenges, the project will advance both the fundamental understanding and practical utility of LNPs as delivery systems for therapeutic applications. 

Key aspects of Cyber-Physical Systems to be Developed (see Figure 1): This project will develop an integrated cyber-physical approach for advanced LNP characterization and manufacturing, seeking to establish workflows and a database for advance structure characterisation and imaging platforms, such as X-ray microscopy, working towards a vision of real-time data processing to map internal structures and API localization. Establishing feedback to operational parameters of microfluidic LNP preparation will close the loop to facilitating precise LNP preparation with optimal properties for storage and delivery.  

Industrial challenge addressed: This project addresses the critical industrial challenge of achieving precise and scalable manufacturing of lipid nanoparticles (LNPs) while ensuring consistent quality and stability across production batches. Current LNP production methods often suffer from variability and limited characterization capabilities, hindering their widespread application in therapeutics. By developing cyber-physical microfluidic systems and advanced analytical tools, this project aims to overcome these limitations, enabling more reliable and reproducible LNP formulations tailored to industrial-scale applications. 

Primary Supervisor: Iain Oswald (Strathclyde)

Secondary Supervisor: Katharina Edkins (Strathclyde)

Research challenges addressed in this project:

Exploring the impact of impurities on solution structure and self-association to identify high risk systems with respect to solid solution formation and/or solvate formation. Will focus on analysis and modelling of solution composition, structure and nucleation outcomes including solution NMR, X-ray.

Key aspects of Cyber-Physical Systems to be developed:

Predictive CCS tools for impurity rejection where impurities predicted to present a challenge to purge through solid solution formation and incorporation into lattice. Guide experimental strategies to avoid problematic purification and simplify purification guided by mechanistic understanding of solution components on outcomes. In additional, this project will link with the cohort 1 project ‘End-to-end process optimisation for impurity rejection’ to leverage existing impurity rejection models. This will also share a common compound/impurity list between cohort 2 projects 2 and 3.

Industrial challenge addressed:

Quantifying the impact of impurities on crystal nucleation, form control and crystal growth.

Primary Supervisor: Alastair Florence (Strathclyde)

Secondary Supervisor: Cameron Brown (Strathclyde)

Research challenges addressed in this project:

Exploiting the Crystallisation Screening DataFactory (CSDF) for material sparing data acquisition across range of solution compositions (solvent(s), solutes (API, impurities)) and crystallisation conditions to identify factors that impact outcomes (solubility, nucleation/growth kinetics, particle size, shape & form) significantly cf. pure system. Will benefit from developments in CSDF extended workflow from Hub Platform 1 and CEDAR Cohort 1 for ex situ image analysis, Raman and X-ray analysis and underpinning data. This will enable comprehensive view of the sensitivity to different crystallisation systems to different major/minor impurities and their impact on crystallisation process outcomes. Complements project 1 in terms of connecting solution with crystal form outcomes.

Key aspects of Cyber-Physical Systems to be developed:

Exploit automated platform and explore optimisation routes to identify significant factors that impact product quality and predict components that will have a negative impact on particle attributes (e.g. purity, shape) and ideally autonomously identify optimal strategies for control (e.g. solvent system change; impurity removal – link to Leeds). Will extend current phase of CSDF capability (specifically cohort 1’s screening framework) to dimension of multiple solutes; impurity entrainment/rejection mechanisms; develop predictive model for identification of problematic impurities writ crystallisation outcomes to inform synthetic process objectives. This will also share a common compound/impurity list between cohort 2 projects 1 and 3.

Industrial challenge addressed:

Deliver digital tools to assist in rapid process development at the synthesis crystallisation interface. Establishing framework and methods to establish integrated tools and data assets for optimisation of purification, crystal, and particle engineering. Ambition to enable rapid prediction and optimisation within screening DataFactory of solvent selection and process conditions to achieve purification (via controlled cooling and mixed or anti-solvent) and desired process (yield / solids loading) and particle attributes (purity, form, shape, size) or feedback to synthetic route modification to remove problematic impurities. Data and models developed will contribute to the CCS toolbox development and QbDD framework including solubility, nucleation, and growth kinetics as well as morphology and particle engineering tools. Also extend the application of the CSDF platform exploring flexibility with solvent, solute composition and extended analytical workflow. Option to integrate CSDF workflow with HPLC DataFactory (in development) to gain quantitative compositional data. Contribute to development of transfer rules from CSDF to Scale-Up DataFactory.

Primary Supervisor: Cameron Brown (Strathclyde)

Secondary Supervisor: Katharina Edkins (Strathclyde)

Research challenges addressed in this project:

Currently it is not possible to take an API and solvent and predict whether agglomeration will occur, furthermore if agglomeration does occur how to control and model it is also challenging. This project will build a systematic dataset of APIs in solvents with impurities and their extent of agglomeration. In turn this dataset will be used to investigate quantitative structure activity relationships between the agglomeration extent, experimental and computational descriptors.

Key aspects of Cyber-Physical Systems to be developed:

Exploits automated platforms and will develop new operating procedures for investigating agglomeration. Resulting in a FAIR database for agglomeration extent. This in turn will be used to train models to drive experiment design and aid in process understanding. To achieve this, this project will work closely with cohort 1’s characterisation and control project, as well as the ex-situ analysis. This will also share a common compound/impurity list between cohort 2 projects 1 and 2.

Industrial challenge addressed:

Of all the crystallisation sub-processes, agglomeration is perhaps the most poorly characterised but also has a significant impact on quality product attributes.

Primary Supervisor: Jan Sefcik (Strathclyde)

Secondary Supervisor: Cameron Brown (Strathclyde)

Research challenges addressed in this project:

Development of continuous nucleation platforms for generation of crystal seed suspensions with consistent attributes; seed generation informed by systems identified by datafactory screening platform; identify relevant impurity inhibition/promotion but does not need to include impurities for seed generation as pure material can be used.

Key aspects of Cyber-Physical Systems to be developed:

Earlier CMAC core project (McKechnie et al., 2023) has developed workflows for small scale screening to quantitatively assess nucleation kinetics using supersaturation induced by mixing and/or temperature quench. This project aims to 1) implement these workflows within Crystallisation DataFactories to generate a FAIR database of nucleation kinetics and 2) develop and validate model-based digital design of continuous nucleator platforms for scale up of suitable systems and conditions identified through screening workflows, contributing to Scale-up DataFactories in cohort 1’s ‘Crystallisation Scale-up and optimisation’ project.

Industrial challenge addressed:

Having consistent seeding is crucial in industrial-scale crystallization, where crystallization processes are often designed to use seeds of certain size and loading. However, generating consistent seeds repetitively is challenging and this project aims to address those issues by designing nucleator platforms and workflows to screen conditions for producing desired seed load and size characteristics and hence use of nucleator platform suitable for a given type of API.

Primary Supervisor: Omar K Matar (Imperial College London)

Secondary Supervisor: Nausheen Basha (Imperial College London), Cameron Brown (Strathclyde)

Understanding crystallisation in continuous flow systems presents a fundamental challenge due to the complex interplay between solution composition, flow conditions, and particle formation mechanisms. In particular, the spatiotemporal variations in local supersaturation and mixing patterns significantly impact nucleation, growth, and agglomeration behaviour. While computational fluid dynamics (CFD) models can provide detailed insights into these phenomena, their computational expense severely limits design space exploration, optimisation, and real-time monitoring. This challenge is further amplified in multiphase flow systems, such as segmented flows, which show promise for enhanced supersaturation control but remain poorly understood across varying solution compositions and design conditions.

We propose a hybrid framework combining computational fluid dynamics (CFD) with Graph Neural Networks (GNNs) to predict crystallisation outcomes in both single and multiphase flow systems. The framework will also integrate with existing Crystallisation Classification System (CCS) tools. Through this approach, we aim to discover novel flow regime-crystal property relationships and unexpected parameter combinations that enhance product quality that were previously inaccessible using traditional methods.

Research challenges addressed in this project:

This research addresses the computational expense barrier in understanding and predicting crystallisation systems. First, it tackles the challenge of capturing complex spatiotemporal dynamics in crystallisation, particularly the interplay between flow conditions (including multiphase flows) and particle formation mechanisms at varying design conditions. This relationship has remained unclear due to computational limitations in modelling these systems. Second, it addresses the limitation of traditional approaches in exploring design spaces and optimisation routes due to their computational cost.

Key aspects of Cyber-Physical Systems to be developed:

This framework will be integrated with the existing Crystallisation Classification System (CCS) tools for comprehensive crystallisation prediction in real-time. Ideally enabling fine-tuned control over the product quality, enabling rapid digital testing before physical implementation. This project will also include inputs from model being developed in cohort 1 and 2.

Industrial challenges addressed:

Tackles significant computational expense in crystallisation prediction thereby enabling faster and cheaper optimisation and control for improved product quality. Also, reducing the product development time.

Primary Supervisor: John Robertson (Strathclyde)

Secondary Supervisors: Daniel Markl (Strathclyde) & Rachel Smith (Sheffield).

Research challenges addressed in this project: 

This project aims to bridge the existing gap in predicting the manufacturability of commercial-scale tableting equipment using the material-sparing Tableting DataFactory. It will focus on developing new CPS platforms to specifically evaluate scale-up issues, enabling more accurate predictions of performance at larger scales. Additionally, the project will include experiment campaigns on commercial-scale equipment to validate and refine the CPS platforms, providing critical data to inform the development of robust scale-up models. This also links back to work in cohort 1 project S2 that links DC processing to product performance. This integrated approach will ensure reliable and efficient translation from lab-scale to commercial-scale manufacturing.

Key aspects of Cyber-Physical Systems to be developed:

Predictive scale-up models to provide process parameters to achieve equivalent product attributes on rotary tablet press. This project will advance CPS capabilities by incorporating a feed-frame simulator to investigate the impact of feed-frame settings on tablet weight variability and lubrication efficiency. By analysing these effects across a diverse range of formulations, the system will provide valuable insights to predict manufacturability at commercial scale.

Industrial challenge addressed:

This project tackles key industrial challenges in predicting manufacturability during scale-up, focusing on the limited availability of reliable measures and their correlation to performance at commercial scales. Critical issues include accurately evaluating manufacturability and understanding powder behaviour during feeding and compression. These challenges impede efficient scale-up and emphasise the need for advanced predictive models that accurately capture and represent commercial-scale processes.

Primary Supervisor:  Daniel Markl (Strathclyde)

Secondary Supervisors: John Robertson (Strathclyde) & Rachel Smith (Sheffield)

Research challenges addressed in this project:

This project addresses the critical research challenge of material-sparing feedability assessment. The feedability CPS, coupled to a milling and particle size measurement CPS, will enable the generation of large data sets to investigate the relationship between crystal structure, particle properties and feedability. It is likely that better feeder performance will be favoured by larger particle sizes resulting in better bulk flow behaviour. However, this may compromise properties such as dissolution and content uniformity. Therefore, an optimum is required which balances these competing effects, i.e. design of materials which improve feeder performance whilst maintaining appropriate release. This project will also develop a reverse engineering approach to guide particle engineering strategies for improving the feedability of active pharmaceutical ingredients (APIs).

Key aspects of Cyber-Physical Systems to be developed:

Include creating CPS platforms for small-scale feedability assessment, designed to accurately predict feeding performance at commercial scales. A CPS for milling and particle size measurement will allow to grind powder to different particles sizes and enable the automated feedability screening of materials.

Industrial challenge addressed:

This project focuses on accurately predicting the feedability of commercial-scale equipment using minimal material, essential for assessing APIs and enabling efficient, consistent scale-up for continuous manufacturing. It further enables informed decision-making for particle engineering strategies.

Primary Supervisor: Rachel Smith (Sheffield)

Secondary Supervisor: Daniel Markl (Strathclyde)

Research challenges addressed in this project:

This research addresses the challenge of understanding material changes induced during feeding and blending, linking these changes to their effects on tableting and overall product performance. The study will seek to computationally understand and compare the conditions experienced by pharmaceutical powders during feeding and blending, the effects on key particle attributes such as size and shape, and the resultant effects on bulk powder properties such flow and compressibility and finally, drug release. A range of commercially important feeding and blending equipment geometries will be examined, with the aim to establish key differences in flow and stress. The project will utilise advanced modelling approaches, including population balance modelling (potentially coupled with DEM and/or CFD), to establish these connections across process steps. Commercial-scale data will be generated to validate and refine the models, ensuring their applicability to industrial manufacturing.

Key aspects of Cyber-Physical Systems to be developed:

Predicting commercial-scale process-induced changes to material characteristics. Coupled with Project 2, this CPS for feeding and blending will enable material-sparing and predictive development of commercial-scale feeding and blending for DC.

Industrial challenge addressed:

The project addresses the industry challenge of understanding material changes, such as attrition, agglomeration, and fragmentation, during critical processes like feeding, blending, and tableting. These changes are pivotal for accurately predicting manufacturability at commercial scale, where process-induced variations can significantly impact product quality and consistency.

Primary Supervisor: Sven Schroeder (Leeds)

Secondary Supervisors: John Robertson / Iain Oswald (Strathclyde)

Research challenges addressed in this project:

This research challenge focuses on using fast X-Ray CT to investigate material changes during the compression process, specifically exploring fragmentation, elastic deformation, and plastic deformation of pharmaceutical materials. By mapping these physical mechanisms across length scales, the project aims to provide a deeper understanding of manufacturability and mechanical behaviour across a wide range of pharmaceutical formulations, informing advanced modelling approaches. AI and machine learning will be applied to develop autonomous optimisation of product properties to information in the X-ray images.

Key aspects of Cyber-Physical Systems to be developed:

Leveraging fast X-ray CT, this project aims to develop a comprehensive understanding of compaction mechanisms, enabling accurate prediction of compaction behaviour and its effects on product attributes, particularly those related to product performance. AI and ML will be applied to control and target product attributes.

Industrial challenge addressed:

Process variables influence particle structure and surface properties, directly affecting drug release profiles and long-term product stability. Addressing this challenge is essential for optimising manufacturing processes and ensuring consistent product performance.

Primary Supervisor: Hannah Batchelor (Strathclyde)

Secondary Supervisors: John Robertson, Daniel Markl & Rachel Smith

Research challenges addressed in this project:

Improved understanding of the performance of directly compressed tablets within the GI tract. The integration of a PAT probe into TIM-1 coupled with the control to the GI conditions will enable data capture of the exact GI conditions at the time where disintegration is observed. Specifically, we can monitor particle size and potentially look to determine tablet porosity within the luminal contents to determine key attributes that are affecting product performance. This will feedback into formulations and processing design to understand the impact of key DC formulation attributes in vivo.

Key aspects of Cyber-Physical Systems to be developed:

Disintegration under in vivo conditions is complex. A digital model that can replicate the hydrodynamics and fluid composition at the site of disintegration will aid in the prediction of product performance. Once these conditions are known a digital model can be built to replicate the passage of a drug and this model can be used to inform the Critical Quality Attributes (CQAs) for the formulation optimisation. This project will require an initial dataset to inform the model then some additional data for the learn and confirm approach from the cyber-physical system. A further project based on this would be to also incorporate patient variability. Thus, to change the TIM to replicate alternative patient groups, for example those with altered gastric motility and observe the impact of patient variability on product performance for both the observed data and the cyber-physical system.

Industrial challenge addressed:

For both systems the integration of this data on CQAs affecting disintegration of DC tablets into Physiologically Based Pharmacokinetic (PBPK) systems is also critical as this will be the most useful interface for industrial partners for the work conducted. Work will be undertaken using a commercial PBPK platform for further integration and to perform virtual bioequivalence studies based on changes in formulation or population.

Primary Supervisor: Hannah Batchelor (Strathclyde)

Secondary Supervisor:  John Robertson (Strathclyde) & Rachel Smith (Sheffield)

Research challenges addressed in this project:

This project addresses the critical research challenge of developing predictive tools to assess disintegration in vivo. The disintegration CPS, coupled to a porosity and particle size measurement CPS, will enable the generation of large data sets to investigate the relationship between particle properties and disintegration. This project will also develop a reverse engineering approach to guide particle engineering strategies for improving the disintegration of DC products.

Key aspects of Cyber-Physical Systems to be developed:

This project will require DC formulation variants within a conventional disintegration apparatus to generate an initial dataset to inform the digital model then some additional data for the learn and confirm approach from the cyber-physical system. There is potential to adapt the disintegration apparatus to make it more biorelevant to work with project 5 in this cohort to demonstrate the link from the TIM-1 apparatus to this CPS. A CPS for porosity and particle size will enable a range of tablet properties to inform formulation and manufacturing processes.

Industrial challenge addressed:

This project focusses on accurate prediction of the in vivo disintegration of DC tablets. This will feed back into the other student projects to link the manufacturing scale; feedability; blending and material attributes to extend the CPS package into product performance. The integration of this data into Physiologically Based Pharmacokinetic (PBPK) systems is critical as this will be the most useful interface for industrial partners for the work conducted.