Fully Automated Platforms for Drug Nanocrystals Manufacturing via Continuous-Flow, Data-Driven Antisolvent Crystallization
Lead Research Organisation:
University College London
Department Name: Chemical Engineering
Abstract
The pharmaceutical industry is undergoing a period of unprecedented change in terms of product development, with increased digitization, greater emphasis on continuous manufacture and the rapid advent of novel therapeutic paradigms, such as personalized medicines, becoming more and more business critical. This change is amplified by Quality by Design considerations and the now routine use of the Target Product Profile approach to the design of patient-centred dosage forms. The recent advances in the range of available therapeutic strategies, alongside the breadth of diseases that can now be successfully treated, has resulted in the need for both new dosage forms and manufacturing approaches. Crucially, there has been a shift from high volume, low cost manufacture towards a more specialized, higher value product development. Consequently, ever more sophisticated approaches, not merely to producing medicinal products, but also to controlling their quality at every stage of the manufacturing process, have become paramount. These would be greatly facilitated by the emerging technologies, based on artificial intelligence and machine learning techniques, for enhancing online process analysis as well as real-time responsive process control. These technologies are particularly important for products where the financial and practical margins for manufacturing error are low, as is the case for an increasing proportion of new therapies.
In this proposal, we focus on a new way of screening, manufacturing and quality controlling drugs in the form of nanocrystals, that is, drugs prepared as nanosized crystalline particles stabilized by surface-active agents. In particular, we will combine continuous-flow processing, online advanced process analytical technology, real-time process control and quality assurance, design of experiments, advanced data analysis and artificial intelligence to deliver fully automated, self-optimizing platforms for screening and manufacturing drugs as nanocrystals via antisolvent precipitation. These dosage forms have attracted substantial interest as a means of delivering poorly water-soluble (and thus poorly bioavailable) drugs, a persistent and increasing problem for the pharmaceutical industry.
While nanocrystals offer a suitable test system for our approach, our methodology and the manufacturing platform we intend to deliver can be applied to other drug delivery systems. We focus on nanocrystals because they are of considerable therapeutic and commercial significance both nationally and internationally.
We intend to use continuous-flow small-scale (i.e. millifluidic) systems. These offer excellent process controllability, can generate crystals of nearly uniform size, and as the process is continuous, the product characteristics are more stable than in batch systems. Millifluidic systems are flexible (one platform can produce a larger variety of products) and agile - reacting rapidly to changes in market demands; they reduce the manufacturing time, speed up the supply chain and, being smaller, can be portable. These systems also expedite screening, curtailing the quantities of material required, benefits that design of experiments will amplify. This data-driven technique allows identifying the most informative experiments, maximizing learning while minimizing time and costs, advantages not fully exploited by the pharmaceutical industry. These technologies, coupled with online advanced process analytical methods, real-time process control, cutting-edge data analysis and machine learning methods, have the potential to disrupt the status quo, accelerate process development and deliver transformative platforms for the cost-effective and sustainable manufacturing of active pharmaceutical ingredients in solid dosage form, reducing the timeline from drug discovery to patient, and contributing to placing the UK at the forefront of innovation in the pharmaceutical sector.
In this proposal, we focus on a new way of screening, manufacturing and quality controlling drugs in the form of nanocrystals, that is, drugs prepared as nanosized crystalline particles stabilized by surface-active agents. In particular, we will combine continuous-flow processing, online advanced process analytical technology, real-time process control and quality assurance, design of experiments, advanced data analysis and artificial intelligence to deliver fully automated, self-optimizing platforms for screening and manufacturing drugs as nanocrystals via antisolvent precipitation. These dosage forms have attracted substantial interest as a means of delivering poorly water-soluble (and thus poorly bioavailable) drugs, a persistent and increasing problem for the pharmaceutical industry.
While nanocrystals offer a suitable test system for our approach, our methodology and the manufacturing platform we intend to deliver can be applied to other drug delivery systems. We focus on nanocrystals because they are of considerable therapeutic and commercial significance both nationally and internationally.
We intend to use continuous-flow small-scale (i.e. millifluidic) systems. These offer excellent process controllability, can generate crystals of nearly uniform size, and as the process is continuous, the product characteristics are more stable than in batch systems. Millifluidic systems are flexible (one platform can produce a larger variety of products) and agile - reacting rapidly to changes in market demands; they reduce the manufacturing time, speed up the supply chain and, being smaller, can be portable. These systems also expedite screening, curtailing the quantities of material required, benefits that design of experiments will amplify. This data-driven technique allows identifying the most informative experiments, maximizing learning while minimizing time and costs, advantages not fully exploited by the pharmaceutical industry. These technologies, coupled with online advanced process analytical methods, real-time process control, cutting-edge data analysis and machine learning methods, have the potential to disrupt the status quo, accelerate process development and deliver transformative platforms for the cost-effective and sustainable manufacturing of active pharmaceutical ingredients in solid dosage form, reducing the timeline from drug discovery to patient, and contributing to placing the UK at the forefront of innovation in the pharmaceutical sector.
Organisations
- University College London (Lead Research Organisation)
- GSK (UK) (Project Partner)
- CMAC EPSRC Centre (Project Partner)
- Janssen Diagnostics (Project Partner)
- APC Ltd (Project Partner)
- Centre for Process Innovation (Project Partner)
- Arc Trinova Ltd (Arcinova) (Project Partner)
- INNOVATE UK (Project Partner)
Publications
Besenhard M
(2023)
Non-fouling flow reactors for nanomaterial synthesis
in Reaction Chemistry & Engineering
Title | Automated laser diffraction-based online crystal size analysis method |
Description | An online and automated analysis technique was developed and equipped with an optical flow cell for laser diffraction analysis of crystals. The automated platform can automatically collect samples, dilute them to the desired extent, and perform laser diffraction analysis, displaying crystal size distributions in real time. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2023 |
Provided To Others? | No |
Impact | Automated laser diffraction-based online crystal size analysis method is able to rapidly screen the process parameters and identify the conditions suitable for nanonization of API crystals. The platform has been showcased to obtain 500 nm to 5 micron-sized ibuprofen nanocrystals. |
Title | An algorithm for process automation in continuous flow systems |
Description | Combination of flow chemistry, microreaction technology and computational methods for system identification has resulted in the development of automated platforms for rapid development and optimization of processes. One of the major challeneges involved in the development of such platforms is to develop algorithmic frameworks that allow seamless and secure data flow between various componenets of the platform and that establish self-triggered event flow for process automation. One of the objectives of our project is to develop automated platforms for drug nanocrystals synthesis in continuous flow systems. To achieve this, we have developed an algorithmic framework that employs Python programming language, LabVIEW software and open platform communication data access (OPC DA) technology. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | No |
Impact | The methods to develop process automation in continuous flow systems are not commonly used both in academia and industry. One of the reasons for this is the lack of knowledge about the resources needed and the implementation of various methods involved. One of the expected outcome of our research is to make the methods for process automation in continuous flow systems, more accessible to users in academia and industry by developing an algorithmic framework applicable for process automation in continuous flow systems. |
Title | LabVIEW virtual instruments for hardware control |
Description | LabVIEW virtual instruments were developed for different equipment such as pumps, valves, magnetic stirrers, level sensors, and flow sensors enabling external control using a PC. |
Type Of Material | Computer model/algorithm |
Year Produced | 2022 |
Provided To Others? | No |
Impact | LabVIEW virtual instruments was used for automated operation of a crystallization and online particle size measurement platform |
Title | Uncertainty-aware predictive machine learning models to support decision-making in automated drug synthesis platform |
Description | Technological developments have led to the automation of decision-making processes in process plant operations. To use machine learning models (ML) for automated decision-making processes, ML models require to provide reliable predictions with relatively small amount of data. One of the approaches to achieve this feat is to combine ML models with the classical methods of nonlinear parameter estimation based on maximum likelihood framework and optimal experimental design methods. The combined framework allows to solve training of ML models as nonlinear parameter estimation, to quantify the prediction uncertainty of ML models and to produce data for rapid validation of ML models. Our goal is to apply this ML modelling framework to develop autonomous platforms for drug nanocrystals synthesis. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | No |
Impact | The modelling framework developed in this work can be used as a tool to support automated decision-making in smart and responsive manufacturing systems. |
Description | Advisory Board Meetings |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Advisory board meetings with project partners (GSK, Janssen, APC, Quotient Sciences, CPI). The meetings involved a) presentations from industrial partners aimed at clarifying their specific interests in the project and objectives for their involvement, b) presentations from researchers to update the partners about progress, achievements and challenges, c) discussion about progress against deliverables, project direction and possible support, d) discussion about further engagement opportunities (e.g., PDRA and PhD student co-supervision, MEng/MSc projects, secondments, and targeted one-to-one meetings). Impact: various technical suggestions taken on board; targeted one-to-one meetings organized to facilitate progress and overcome technical issues; exploration started for possible activities for further engagement and/or funding. |
Year(s) Of Engagement Activity | 2022,2023 |
Description | CMAC Open day visit |
Form Of Engagement Activity | Participation in an open day or visit at my research institution |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Visit of CMAC open days and visit of project collaborators. |
Year(s) Of Engagement Activity | 2022 |
URL | https://cmac.ac.uk/events-database/cmac-open-day-2022-hub-and-articular-showcase |
Description | Guest lecture at University of Leeds |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Undergraduate students |
Results and Impact | Guest lecture at University of Leeds on "Nano and micro reactors: Flow chemistry for nanomaterial synthesis" |
Year(s) Of Engagement Activity | 2022,2023 |
Description | Poster Presentation in symposium - Responsive Manufacturing Showcase - University of Strathclyde |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Showcase event organised by University of Strathclyde for those who were successful in the 2021 EPSRC Responsive Manufacturing Call with the aim of networking, discussing the research, and identifying new synergies and opportunities for collaboration. In the morning, each PI gave a presentation; in the afternoon, there was a researcher poster session. |
Year(s) Of Engagement Activity | 2022 |
Description | Presentation at the Medical School at the University of Sheffield |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Presenation on "Reproducible and Scalable Synthesis of Nanomaterials" to the Medical School, as well as lab visits and scientific exchange. |
Year(s) Of Engagement Activity | 2023 |
Description | Presentation to industrial board of Sargent Centre for Process Systems Engineering |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | Presentation to industrial board on "Digital manufacturing: From the beaker to machine learning" |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.imperial.ac.uk/process-systems-engineering/industrial-consortium/ |
Description | Responsive Manufacturing Showcase - University of Strathclyde |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Showcase event organised for those who were successful in the 2021 EPSRC Responsive Manufacturing Call. Aim: networking, discussing the research and identifying new synergies and opportunities for collaboration. In the morning, each PI gave a presentation; in the afternoon, there was a researcher poster presentation. |
Year(s) Of Engagement Activity | 2022 |