Quality by Design Procedure for Continuous Pharmaceutical Manufacturing: An Integrated Flowsheet Model Approach

Pharmaceutical manufacturing is crucial to global healthcare and requires a higher, more consistent level of quality than any other industry. Yet, the traditional pharmaceutical batch manufacturing has remained largely unchanged in the last fifty years due to high R&D costs, shorter patent durations, and regulatory uncertainty. This has led regulatory bodies to promote modernization of manufacturing process to continuous pharmaceutical manufacturing (CPM) by introducing new methodologies including quality by design, design space, and process analytical technology (PAT). This represents a shift away from the traditional pharmaceutical manufacturing way of thinking towards a risk based approach that promotes increased product and process knowledge through a data-rich environment. While both literature and regulatory bodies acknowledge the need for modernization, manufacturers have been slow to modernize due to uncertainty and lack of confidence in the applications of these methodologies. This paper aims to describe the current applications of QbD principles in literature and the current regulatory environment to identify gaps in literature through leveraging regulatory guidelines and CPM literature. To aid in closing the gap between QbD theory and QbD application, a QbD algorithm for CPM using an integrated flowsheet models is also developed and analyzed. This will help to increase manufacturing confidence in CPM by providing answers to questions about the CPM business case, applications of QbD tools, process validation and sensitivity, and process and equipment characteristics. An integrated flowsheet model will aid in the decision-making process and process optimization, breaking away from ex silico methods extensively covered in literature

Modeling, optimization, and sensitivity analysis of a continuous multi-segment crystallizer for production of active pharmaceutical ingredients

We have investigated the simulation-based, steady-state optimization of a new type of crystallizer for the production of pharmaceuticals. The multi-segment, multi-addition plug-flow crystallizer (MSMA-PFC) offers better control over supersaturation in one dimension compared to a batch or stirred-tank crystallizer. Through use of a population balance framework, we have written the governing model equations of population balance and mass balance on the crystallizer segments. The solution of these equations was accomplished through either the method of moments or the finite volume method. The goal was to optimize the performance of the crystallizer with respect to certain quantities, such as maximizing the mean crystal size, minimizing the coefficient of variation, or minimizing the sum of the squared errors when attempting to hit a target distribution. Such optimizations are all highly nonconvex, necessitating the use of the genetic algorithm. Our results for the optimization of a process for crystallizing flufenamic acid showed improvement in crystal size over prior literature results. Through the use of a novel simultaneous design and control (SDC) methodology, we have further optimized the flowrates and crystallizer geometry in tandem.^ We have further investigated the robustness of this process and observe significant sensitivity to error in antisolvent flowrate, as well as the kinetic parameters of crystallization. We have lastly performed a parametric study on the use of the MSMA-PFC for in-situ dissolution of fine crystals back into solution. Fine crystals are a known processing difficulty in drug manufacture, thus motivating the development of a process that can eliminate them efficiently. Prior results for cooling crystallization indicated this to be possible. However, our results show little to no dissolution is used after optimizing the crystallizer, indicating the negative impact of adding pure solvent to the process (reduced concentration via dilution, and decreased residence time) outweighs the positive benefits of dissolving fines. The prior results for cooling crystallization did not possess this coupling between flowrate, residence time, and concentration, thus making fines dissolution significantly more beneficial for that process. We conclude that the success observed in hitting the target distribution has more to do with using multiple segments and having finer control over supersaturation than with the ability to go below solubility. Our results showed that excessive nucleation still overwhelms the MSMA-PFC for in-situ fines dissolution when nucleation is too high

The use of statistics in understanding pharmaceutical manufacturing processes

D.Eng.Industrial manufacturing processes for pharmaceutical products require a high level of understanding and control to demonstrate that the final product will be of the required quality to be taken by the patient. A large amount of data is typically collected throughout manufacture from sensors located around reaction vessels. This data has the potential to provide a significant amount of information about the variation inherent within the process and how it impacts on product quality. However to make use of the data, appropriate statistical methods are required to extract the information that is contained. Industrial process data presents a number of challenges, including large quantities, variable sampling rates, process noise and non-linear relationships. The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. Individual case studies are presented of investigations into API manufacture. The first considers prediction methods to estimate the drying times of a batch process using data collected early in the process. Good predictions were achieved by selecting a small number of variables as inputs, rather than data collected throughout the process. A further study considers the particle size distribution (PSD) of a product. Multivariate analysis techniques proved efficient at summarising the PSD data, to provide an understanding of the sources of variation and highlight the difference between two processing plants. Process capability indices (PCIs) are an informative tool to estimate the risk of a process failing a specification limit. PCIs are assessed and developed to be applied to data that does not follow a standard normal distribution. Calculating the capability from the percentiles of the data or the proportion of data outside of the specification limits has the potential to generate information about the capability of the process. Finally, the application of Bayesian statistical methods in pharmaceutical process development are investigated, including experimental design, process validation and process capability. A novel Bayesian method is developed to sequentially calculate the process capability when data is collected in blocks over time, thereby reducing the level of noise caused by small sample sizes