Intensified approaches and scale-up strategies for continuous automated manufacturing of pharmaceutical products

ABSTRACT

Nanotechnology represents a rapidly growing area of research and has emerged as a new revolution in the field of medical and health sciences. The global nanotechnology market is projected to exceed multiple billions by 2024, as the number of commercially available products continuously increases. Since 1995, approximately 51 nanomedicines have received regulatory approval, while 77 products are in the pre-clinical stage.

Nanoparticles are ideal candidates for detection and treatment of many diseases, having the potential to act, as vectors for gene therapy, multi-drug carriers, and tracking agents. However, in order to meet the increasing global demand, several technology-related bottlenecks need to be resolved. For the synthesis of nanoparticles conventional small-scale batch reactors are utilised, which are poorly controllable and not well-characterised systems and suffer from insufficient mixing and low heat and mass transfer rates. This can lead to low reproduction efficiency, upscaling issues, and inability to decouple in real time the manufacturing stages.

Microfluidic devices have demonstrated enormous potential to generate reproducible nanoparticle formulation and address challenges with existing formulation methods. Microfluidic systems are mainly characterised by controllability, flexibility, and portability, whilst they reduce the manufacturing time, minimise uncertainties, and increase efficiency.

In this project, we aim to formulate predictive platforms for optimal design and operation control of microfluidic platforms for nanoparticle synthesis in manufacturing. The characteristics of the nanoparticles will be evaluated using various techniques such as optical microscopy, scanning and transmission electron microscopes (SEM, TEM). Results will be also compared with CFD simulations to develop and rigorously validate numerical tools that can predict individual flow and mixing phenomena. Combining experimental work, CFD simulations and machine learning methods for process optimization, this platform will provide a sustainable and cost-effective technology for manufacturing nanomaterials.