Computational modelling of separation processes for green continuous pharmaceutical manufacturing

The pharmaceutical industry has traditionally implemented batch manufacturing for the production of a wide range of products due to its mature technological development and ability for recall of products where necessary. However, several demonstrations of Continuous Pharmaceutical Manufacturing (CPM) in the past two decades have drawn significant attention from academia, industry and regulatory bodies due to its potential for smaller equipment, enhanced efficiencies, access to difficult or hazardous process conditions with greater ease and safety and reduced costs and waste. While continuous processing is not new in other manufacturing sectors, its application to pharmaceutical production has only drawn significant attention in recent years due to the numerous demonstrations of continuous flow syntheses of complex molecules and functional groups inherent of Active Pharmaceutical Ingredients (APIs), which is the foundation of any end-to-end CPM plant. The literature to date has predominantly focussed on design and optimisation of flow synthesis routes; however, the development of efficient continuous separation processes is a major bottleneck to CPM and are often challenging and materially intensive unit operations. The design of effective continuous separation processes for societally important APIs amenable to continuous production is essential for CPM success. Mathematical modelling is a viable and useful tool in the elucidation of promising designs prior to pilot plant studies that can allow rapid screening of multiple candidate configurations and can circumvent expensive and laborious experimental campaigns. Moreover, they allow optimisation of process design configurations to maximise their operational and economic benefits. This PhD thesis aims to elucidate cost-optimal upstream CPM plant and continuous separation process designs for a range of APIs. Steady-state process models for upstream CPM plants for different APIs are constructed, using published data for reaction rate law elucidation and kinetic parameter estimation, activity coefficient and group contribution models for non-ideal multicomponent mixture phase equilibria prediction and pharmaceutical process costing methodologies. The constructed models are then used for process simulation, design and optimisation of CPM plants, using Nonlinear Programming (NLP) for individual case-based process optimisation and Mixed Integer Nonlinear Programming (MINLP) for CPM process synthesis to optimality. The systematic frameworks and methods used in this work can be expanded to other APIs amenable to CPM with similar processes. This work highlights the immense value in systematic and rigorous model-based simulation and optimisation campaigns for CPM process development

Investigating the Trade-Off between Design and Operational Flexibility in Continuous Manufacturing of Pharmaceutical Tablets: A Case Study of the Fluid Bed Dryer

Market globalisation, shortened patent lifetimes and the ongoing shift towards personalised medicines exert unprecedented pressure on the pharmaceutical industry. In the push for continuous pharmaceutical manufacturing, processes need to be shown to be agile and robust enough to handle variations with respect to product demands and operating conditions. In this paper we examine the use of operational envelopes to study the trade-off between the design and operational flexibility of the fluid bed dryer at the heart of a tablet manufacturing process. The operating flexibility of this unit is key to the flexibility of the full process and its supply chain. The methodology shows that for the fluid bed dryer case study there is significant effect on flexibility of the process at different drying times with the optimal obtained at 700 s. The flexibility is not affected by the change in volumetric flowrate, but only by the change in temperature. Here the method used a black box model to show how it could be done without access to the full model equation set, as this often needs to be the case in commercial settings

Dynamic modelling and optimisation of the batch enzymatic synthesis of amoxicillin

Amoxicillin belongs to the β-lactam family of antibiotics, a class of highly consumed pharmaceutical products used for the treatment of respiratory and urinary tract infections, and is listed as a World Health Organisation (WHO) “Essential Medicine”. The demonstrated batch enzymatic synthesis of amoxicillin is composed of a desired synthesis and two undesired hydrolysis reactions of the main substrate (6-aminopenicillanic acid (6-APA)) and amoxicillin. Dynamic simulation and optimisation can be used to establish optimal control policies to attain target product specification objectives for bioprocesses. This work performed dynamic modelling, simulation and optimisation of the batch enzymatic synthesis of amoxicillin. First, kinetic parameter regression at different operating temperatures was performed, followed by Arrhenius parameter estimation to allow for non-isothermal modelling of the reaction network. Dynamic simulations were implemented to understand the behaviour of the design space, followed by the formulation and solution of a dynamic non-isothermal optimisation problem subject to various product specification constraints. Optimal reactor temperature (control) and species concentration (state) trajectories are presented for batch enzymatic amoxicillin synthesis

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems