Chemical and Bioprocess Engineering Theses

Continuous crystallization has been identified as a means of reducing costs and improving product consistency in the pharmaceutical industry. Withdrawal classification has been identified as a complex topic with broad relevance to the use of continuous stirred-tank crystallizers in the industry and in the laboratory.This thesis begins by summarising recent research into various forms of continuous crystallization, contextualising the main body of work and identifying relevant gaps in the literature. Withdrawal classification constitutes one such gap, with little research data available. The review is used as justification for the research following.Preliminary experiments are detailed, the purpose of which was to compare and assess operational strategies. This work also confirms that controllable classified withdrawal is possible with the experimental setup decided upon. The findings were applied to the main body of work following and used to optimise experimental methods.Two main sets of experiments are detailed in the principal experimental portion of the thesis. The first focuses on withdrawal velocity as a means of directly controlling classified behaviour. This was found to be a significant predictor of certain key system characteristics. The second experimental set focuses on the influence of mixing, by changing agitation rate and withdrawal location. These were also found to significantly affect classification behaviour.Finally, the experimental data was combined to produce a set of empirical equations which allow for qualitative modelling of the system.Two separate approaches to modelling classification behaviour were developed. Experimental data was used to fit kinetics to the models, allowing for simple simulation and comparison. A method for meaningful comparison of model outputs was developed and simulated results compared.Finally, more sophisticated simulation was performed by combining the models with the empirical data gathered. It was demonstrated that particle size data can be predicted based only on system settings and characteristics using this method. Crystallizer responses to independent variables were assessed. The work concludes with a discussion of the possible mechanisms of classification, the applicability of the different models to these, and the relative strengths and weaknesses of the models.

Bacterial adhesion and the subsequent biofilm formation is a complex phenomenon which has many consequences in water filtration. This aggregation of microorganisms can be difficult to remove from nanofiltration and reverse osmosis membrane surfaces, causing damage and eventual replacement of the membrane. In order to elucidate the cause of this biofilm formation, three influential factors were studied: surface topography, nutrient concentration and shear stress. Analysis was performed on the surface topographical heterogeneities in order to examine the influence of surface topography. Image analysis of the adhesion of Pseudomonas fluorescens (Ps. fluorescens) and Staphylococcus epidermidis (S. epidermidis) to the surface topographical heterogeneities was determined for two commercial membranes, NF270 and BW30, using a flow-cell system. Membrane area analysis, using AFM and SEM, showed up to 13% of topographical heterogeneities on the membrane surface with up to 30% of total adhered cells that were discovered within these topographical heterogeneities. For the analysis of the nutrient availability and shear stress on the structural formation of Ps. fluorescens biofilm under two different dynamic conditions, an air-liquid interface biofilm and a flow cell grown biofilm were assessed by confocal scanning laser microscopy (CLSM). The analysis showed a three-fold increase in the EPS biovolume of the high nutrient air-liquid interface grown biofilm. However, the flow cell biofilm increased the biovolume for low nutrient and higher shear stress conditions, suggesting harsher growth conditions of the biofilm results in greater biofilm development. Finally, the adhesive and viscoelastic properties of the Ps. fluorescens air-interface grown biofilm for two different nutrient dilution factors was determined by nanoindentation. The low nutrient availability showed higher adhesion force and work of adhesion with distributed colonies across the surface, while the high nutrient grown biofilm led to a reduction in the adhesive and elastic nature of the biofilm.

The dissolution processes of active pharmaceutical ingredient (API) crystals have been extensively studied in the pharmaceutical industry, as they have a significant impact upon the bioavailability of drugs within the body. Although much experimental work has been conducted and many models have been established, none of the models comprehensively describe the dissolution process of the API. Moreover, the impact of physiochemical properties on the dissolution of the API is not well understood. A deeper understanding of dissolution behaviour and of the factors affecting the dissolution process is critical to the design, evaluation, control and therapeutic efficacy of solid dosage forms. Hence in this thesis, the dissolution processes of API crystals were investigated using both experimental and modelling approaches. A BCS Class II drug, ibuprofen, characterized by a relatively low solubility but high permeability compared to other drugs was used as a model compound to investigate dissolution kinetics. The effect of physicochemical properties of the particles on the dissolution kinetics was also investigated. Firstly, an ibuprofen preparation protocol for dissolution was established. Different ibuprofen crystals with tailored solid-state characteristics (such as crystal morphology and size distribution) were prepared by cooling crystallization. The crystallization process was monitored in situ by process analytical techniques (PATs) such as FBRM, PVM and ATR-FTIR. The properties of the obtained crystalline products were also characterized by off-line techniques such as high performance liquid chromatography (HPLC), microscope, scanning electron microscope (SEM), powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), and Malvern Mastersizer, etc. A laboratory methodology for the dissolution processes of the obtained IBU crystals was then developed. UV and ATR-FTIR spectroscopic techniques were employed to measure the solute concentration and a FBRM probe was applied to track the change of the particle size and population profile during dissolution. The influence of the initial undersaturation ratio, agitation rate, crystal morphology and particle size on the dissolution were examined. Variations in the dissolution rate were observed, from which two distinct dissolution mechanisms during the dissolution process were proposed. Eight mathematical models which have been typically employed to quantify the dissolution of immediate and modified release dosage forms, including the zero-order, first-order kinetics, Weibull, Higuchi, Hixson-Crowell, Korsmeyer-Peppas, Baker-Lonsdale and Hopfenberg models, were used to correlate the dissolution profiles of ibuprofen crystals. The dissolution parameters of each model were determined and the simulation accuracies of the different models were evaluated by comparing simulated and experimental results. It was found that the Weibull, Korsmeyer-Peppas and first-order kinetics models provided the most accurate results, suggesting that these models may be successfully applied to the dissolution of API powders in both API processing and drug product performance analysis. A diffusion-based model which can be used to predict non-sink circumstances was next devised to study the dissolution kinetics of ibuprofen. Variations of the model were created to compare the accuracy of simulation results when applied to spherical and cylindrical particle geometries, with and without the inclusion of a size-dependent diffusion layer thickness component in the model. Experimental data was fitted to the model to obtain the diffusion layer thickness and post-dissolution particle size distribution predictions. The comparison between simulated and experimental results demonstrated that both size-dependent and size-independent models can give good simulation results.

In this thesis, various systems containing interfaces of titanium dioxide (TiO2) and haematite (a-Fe2O3) with water are examined using a number of atomistic simulation methodologies. These systems include large-scale anatase (101) and rutile (110) surface slabs modelled using force-field molecular dynamics (MD); smaller haematite (001) and rutile (110) surface slabs modelled using density functional theory MD; and a large scale anatase nanoparticle and smaller anatase (101) and rutile (110) surface slabs, modelled using density functional tight-binding MD. As part of these studies a variety of analyses are presented, aimed at providing a quantitative understanding of the effects that each surface or nanoparticle has on the properties of water molecules near the interface; and thereby assessing, in a qualitative way, how these effects are manifested using the different methodologies. These analyses include established techniques in the field of atomistic simulations, such as hydrogen bond analysis and electronic density of states calculations. Also employed are techniques novel to the field of atomistic simulation, such as the coherence spectrum. Two points of emphasis are present throughout this thesis: firstly, to improve the understanding of the materials examined towards the development of photoelectrochemical catalysts; and secondly, to explore the current state-of-the-art in atomistic simulations, and "push the boundaries" of the available techniques.

The volatility of renewable energies poses challenges to power system reliability and calls for more flexible electricity resources, both on the supply and the demand side. Energy-intensive water services such as wastewater treatment offer great demand flexibility potential in that regard. However, current demand response modelling approaches are insufficient for assessing this potential accurately. This study aims to fill the knowledge gap in industrial demand response modelling by introducing an integrated energy-water system model, which takes into account the constraints of the wastewater treatment process on power system scheduling in a joint system dispatch problem. The model is applied to a case study of the Irish wastewater treatment sector and power system. The objective of this study is to identify the benefits of energy demand and supply flexibility of wastewater treatment plants for power system operation, wastewater treatment operators and electricity consumers. The findings indicate that the wastewater treatment sector can be a valuable demand response resource for the power system. Wastewater treatment operators, electricity consumers and power system operators benefit from more flexible electricity demand from wastewater treatment plants, even in the presence of other flexibility measures in the system. Furthermore, it decreases the carbon intensity of domestic power generation. There is also a benefit for the power system operator in harnessing the flexibility of demand response and biogas production simultaneously. However, this can result in temporarily high electricity prices in the model, leading to increased electricity costs for consumer and wastewater treatment plants. Two main conclusions can be drawn from the findings of this study. First, wastewater treatment plants have untapped potential for demand response and utilising it for power system flexibility benefits wastewater treatment operators, electricity consumers and power system operators. The results inform policy makers on how to evaluate and support the electricity demand and supply flexibility of wastewater treatment plants. Given the benefits and minimal capital costs, policy makers should incentivise WWTP operators to tap into this readily available flexibility potential. Further, policy makers should carefully select the appropriate support schemes. In particular, smart demand response schemes should take into account possible interactions with electricity supply flexibility from biogas generation. Second, including wastewater treatment constraints in the system dispatch problem is crucial in order to estimate the flexibility potential accurately and uncover bottlenecks, which would probably be concealed by a black-box approach. Thus, this study provides a valuable case study for investigating the demand response potential of highly complex industrial processes, such as wastewater treatment.

Chinese hamster ovary (CHO) cells are the dominant mammalian expression host for recombinant therapeutic protein production. In terms of manufacturing efficiency, much has been accomplished in areas such as optimised transgene design and cell line development. Since the publication of the Chinese hamster genome the field has gained a more refined understanding of the relationship between the CHO biological system and desirable bioprocess traits. Despite the central importance of protein synthesis, few studies to date have focussed on characterising translation in CHO cells. The goal of this thesis is to evaluate the utility of ribosome footprint profiling (Ribo-seq) to further improve our understanding of CHO cell biology and highlight routes towards enhanced biopharmaceutical manufacturing. A key aspect of this work is the combination of multiple translation inhibitors for Ribo-seq to enable the simultaneous analysis of translation initiation and elongation for the first time. The availability of these data enabled the identification of previously uncharacterised open reading frames (ORFs) including those non-AUG start codons. Novel ORFs comprised of N-terminal extensions of canonical proteins, ORFs found in genes previously thought to be non-coding and those found in the 5’ leader sequence of mRNAs (i.e. upstream ORFs). Through the use of Ribo-seq and RNA-seq data, these upstream ORFs were found to have a repressive effect on the translation efficiency of the main ORF. In addition, following comparison of CHO cells at day 4 and day 7 of cell culture as well upon a reduction of cell culture temperature, genes undergoing differential translation were identified. A number of these genes did not have a corresponding change in gene expression, confirming that Ribo-seq can provide an additional dimension compared to using RNA-seq in isolation. Ribosome profiling has further enabled the computation of transcriptome wide decoding times for each codon, and revealed influence of codon context on translational rate. These data provide a potential route towards more efficient codon optimised transgene sequences. Perhaps the most striking finding of this work is the identification of thousands of novel small open reading frames (sORFs) predicted to encode microproteins (i.e. proteins < 100aa). Host cell protein analysis, revealed that 8 microproteins were present in adalimumab, confirming that microproteins are a novel class of potential process related impurity. In summary, ribosome footprint profiling is a powerful analytical method for improving the annotation of the CHO cell genome, understanding CHO cell biology and identification of routes to improve not only the upstream process but also enhance the characterisation of the final drug product.

Monoclonal antibodies (mAbs) are the most dominant selling class of biotherapeutics in the global market. These complex biomolecules are produced through mammalian cell culture and are prone to structural heterogeniety. This heterogeniety can have an adverse effect on the overall stability and efficacy of the drug product and must be closely monitored through product characterisation. Characterisation of mAbs can be carried out on the intact, subunit and peptide level. The drawback with traditional characterisation techniques include high sample requirement, high level of expertise needed to produce reporducible data and the methods involved are usually time consuming. The objective of this work was develop characterisation strategies at all three levels using novel instrumentation to overcome these drawbacks and improve our understanding of product characteristics at all stages of the mAb production cycle.

Photoelectrochemical (PEC) water splitting cells, used to create hydrogen from solar energy, are crucial to the implementation of solar-to-fuel technology. Research in science and technology is focused on improving the functionality and efficiency of these devices while also ensuring that they will last for a long time and will be affordable. Machine-learning based computer-aided simulation is one of the methods exploited to design the next generation of photoelectrochemical cells. Such simulation techniques have filled the gap between conventional force fields, which are fast and inaccurate, and electronic structure simulations, which are slow and accurate. In this thesis, we developed and/or improved machine-learning interatomic potentials (MLPs) for all the chemical systems involved in the design of PEC cells. Having established the reliability of such potentials in describing the many-body share of energy for argon clusters, we then utilize neural networks as a powerful machine-learning method to construct potential energy surfaces for the other studied systems. High-dimensional neural-network (HDNN) model developed by Behler et.al (Behler, 2007) was used to obtain the coordinates of a chemical system as input and output its total energy. We used atom-centered symmetry functions (Behler, 2011) to encode the chemical environment into a fixed-length input vector. As part of this thesis, various sampling schemes are examined and improved to construct the training set needed for model development, as well as a new technique to manipulate publicly available data for model development is implemented using machine-learning. In addition, the network's weights and biases are adjusted using different optimization techniques. We also implemented a molecular dynamics code that includes long-range electrostatic energy via Ewald sums and delta-machine-learning, and we proposed and developed a technique based on checkpoint ensembles in machine-learning to improve the accuracy of a neural network model for total energy prediction.

Understanding effective energy-conversion systems and dealing with the problem of intermittency through scalable energy-storage systems are the two major difficulties in renewable energy. At the Grid size, relatively little progress has been done, and two considerable issues remain: (i) minimizing environmental harm, and (ii) the issue of ecologically friendly energy conversion. Light-driven photoelectrochemical (PEC) water-splitting can create hydrogen, but it is inefficient; instead, we focus on how electric fields can be applied to metal-oxide/water systems to adjust the interplay with their intrinsic electric fields, and how this can change and increase PEC activity, drawing both on experiment and non-equilibrium molecular simulation. Non-equilibrium molecular-dynamics simulations of liquid water were carried out in the canonical ensemble in the presence of both external static and oscillating electric fields of(r.m.s.) intensities 0.05 V/Å and 0.10 V/Å, with oscillating-field frequencies 50, 100 and 200 GHz. The rigid potential model TIP4P/2005 was used, and NEMD simulations were performed, including in the supercooled region, at temperatures ranging from 200 to 310 K. Significant changes in the percentage dipole alignment and self-diffusion constant were found vis-à-vis zero-field conditions, as well as shifting of the probability distribution of individual molecular self- diffusivities. The application of static fields was typically found to reduce the self-diffusion of liquid water, effectively due to some extent of "dipole-locking", or suppression of rotational motion, whereas diffusivity was found to be enhanced in oscillating fields, especially at high frequencies and outside the supercooled region. Classical molecular-dynamics techniques were used to evaluate the distribution of individual water molecules’ self-diffusivities in adsorbed layers at TiO2 surfaces anatase (101) and rutile (110) at 300 K for inner and outer adsorbed layers. Using local order parameters, the layered-water structure was identified and classed in layers, which proved to be an equally viable way of "self-ordering" molecules in layers. Anatase and rutile differed significantly in disrupting these molecular distributions, particularly in the adsorbed outer layer. Anatase (101) had much greater self-diffusivity values, owing to its "corrugated" structure, which allows for increased hydrogen bonding interaction with adsorbed molecules beyond the initial hydration layer. On the contrary, rutile (110) has more securely "trapped" water molecules in the region between Ob atoms, resulting in less mobile adsorbed layers. Finally, the dynamical properties of physically and chemically adsorbed water molecules on pristine hematite-(001) surfaces were investigated using non-equilibrium ab-initio molecular dynamics (NE -AIMD) in the NV T ensemble at room temperature, in the presence of externally applied, uniform static electric fields of increasing intensity. Significant changes in the dipole moment and self-diffusion constant were observed in comparison to zero-field circumstances, as well as a shift in the probability distribution of individual molecule self-diffusivities. For example, static fields were shown to promote the self-diffusion of water molecules at the a-Fe2O3 surface, owing to some degree of ’dipole-locking’ in the applied direction of the field.

The viability of mammalian cells is primarily tested by dye exclusion assays to examine the integrity of the outer membrane. Precursor events to the onset of cell death are detectable using a combination of online and offline technologies. This work explores the use of dielectric spectroscopy and impedance flow cytometry to characterize changes in the biophysical properties of cells as they progress through batch cultures. At-line single cell imaging was examined in tandem with these methods to prove further insight into the identification of morphological changes in the cell culture. This information was collated to better understand at what point cells can no longer be classified as recoverable prior to the loss in membrane integrity. Autophagic activity such as the increased presence of lysosomes was identified using digital holographic imaging. An earlier decline in the online capacitance signal relative to offline counts occurred in tandem with the onset of autophagy due the shifting dynamics of the cell population. Schwan modelling gave insight on the changes in the bulk membrane capacitance and intracellular conductivity of the cells during this period. Single cell impedance measurements were used to examine the population dynamics with greater accuracy. Opacity and phase parameters were derived at suitable frequencies and compared to the online models. Multifrequency data from the capacitance probe proved useful in the identification of apoptotic activity which followed autophagy. The Cole-Cole a and critical frequency of the changing ß-dispersion curve properties were examined relative to these starvation events. A feeding strategy was employed to delay the onset of autophagy in batch cultures, through the introduction of amino acids. Controlled refeeding experiments were shown to affect both the presence of lysosomes and shifts in opacity trends, suggesting that cells could be recovered during autophagy. The effects of such a feed on the online modelling data was examined to see if a real time parameter from the multifrequency trends could be used as an indicator for culture refeeding.

Many countries are working on transitioning towards a carbon-neutral economy by 2050. This has predominantly driven investments towards renewable electricity technologies such as solar and wind generators, driving research to increase energy systems' interconnection. To investigate the role of gas-based vectors in the transition of the energy system, the following sectors are considered, i.e., gas for energy storage, gas for heating, and gas for power generation. Maximizing green hydrogen production in Power to Gas (P2G) systems by understanding 1) stack performance of low and high-temperature electrolyzers and 2) operational modes in response to curtailed renewable electricity could aid potential investors to achieve maximum return on their investment. Progressively increased intermittent operational modes in electrolyzers showed that low-temperature electrolyzers were more resilient to flexible operation (< 7.7% difference in total H2 production) when compared to high-temperature electrolyzers (< 67%). These results indicated that a stack-level understanding of electrolyzers in the integration of the energy system, needs to be incorporated to maximize green hydrogen production. Utilizing the present gas infrastructure for flexible power generation is foreseen as a primary role for natural gas/hydrogen in a low carbon future. A least-cost optimization for systems operational cost in a one-node multi-renewable electricity systems model increased operational costs when large coal generators were progressively replaced with smaller gas generators. The role of reciprocating engines and industrial gas turbines was primarily in providing fast frequency response and reserve regulation. Another pathway for natural gas and hydrogen in a low-carbon energy system is in the heating sector. A detailed investigation of potential decarbonization achievable in Irish building stock that still uses traditional gas boilers for domestic and space heating was analyzed. The study concluded that thermal retrofits and blending of 20% H2 in the gas network alone would contribute to a maximum decarbonization of 34% in Ireland’s heating sector. The studies presented in the thesis provide a novel approach to understanding the use of P2G systems, analyzing the role of gas for heating and flexible gas generators in a future low carbon energy system.

In this thesis, molecular dynamics (MD) simulations were employed to investigate the effects of electric fields on the dynamic processes of proteins, including protein folding, hydration, and agglomeration dynamics. By comparing the simulation results with and without external electric fields, the influence of electric fields on folding pathways of a mini protein named chignolin, free-energy landscapes, state transitions, and hydrogenbond dynamics were explored. For larger proteins like eotaxin and prion, an enhancedsampling methodology referred to as ratchet-and-pawl molecular dynamics (rpMD) was utilized to facilitate protein folding of larger proteins, specifically eotaxin and prion, within a computationally feasible simulation timescale. The current investigation also encompassed an examination of the hydration dynamics of Hen Egg White Lysozyme (HEWL) in the presence of external electric fields, wherein the dipolar response of water molecules on the protein surface was scrutinized. The outcomes thereof yielded discernment into the rotational response of water molecules in the hydration layer, along with the impact from the protein surface, under varying frequencies and intensities of electric fields. In addition, the agglomeration formation process of chignolin and prion was investigated, revealing that effective agglomeration is predominantly driven by the intricate interplay of multiple residues interactions within larger protein structures.

In order to enhance and bridge the lacuna of knowledge relating to the fundamental approaches and techniques that contribute industrially towards physico-chemical of photoelectrochemical (PEC) water splitting processes, in this thesis, various molecular-dynamics (MD) simulation techniques and machine-learning force-fields have been applied at liquid-solid interfacial systems and for water exposed to external electric fields. The current state-of-the-art of molecular simulations and techniques implemented in the study of the behaviour of bulk or interfacial water in contact with titanium dioxide (TiO2) and hematite (α-Fe2O3) serve primarily to improve our understanding of materials required for the development of photo-energy conversion devices. Photoelectrochemical water splitting has been proven to be a great potential for low-cost, environmentally-friendly solar-hydrogen production required to support the future Hydrogen Economy; therefore, properties studied such as; behavioural pattern, dielectric response, emergence of vibrational modes, translational-librational motion, hydrogen bond kinetics, H-bonds lifetime, atomistic surface contours of semiconductor surface, structural, and dynamical properties. Indeed, both water-induced externally-applied electric fields either in bulk environment or interfacial phase play extensive roles in the improvement of photo-energy conversion devices, and, in particular, towards expanding the “boundaries of knowledge” that would indelibly contribute towards addressing the development of new functions required in ‘interfacial engineering’ of the local morphology of the semiconductor layer. In particular, the greater understanding gleaned from elucidating mechanisms of energy transfer at interfaces serves to improve the industrial processing of semiconductors materials or multifunctional optoelectronic devices.

The optimisation of manufacturing processes using CHO cells has been achieved through improvements in cell line development and the increased knowledge of biological characteristics associated with desirable bioprocess phenotypes such as sustained growth, productivity and product quality. Advances in next generation sequencing (NGS) technologies have paved the way for a greater understanding of CHO cell biology, accompanied by the publication of the Chinese hamster genome and subsequent analysis of omics data. Although beneficial, a critical limitation of many of these studies is the use of bulk methods to generate the data, therefore capturing average information across a population of cells. Recently emerged single cell sequencing methods permit the acquisition of omics data from thousands of individual cells in a single experiment and the ability to delineate intra-population heterogeneity. The overall aim of this work is to explore the utility of single cell sequencing approaches in different CHO cell populations to gain a deeper understanding of the underlying biology. A key novel aspect of the work outlined in this thesis is the first application of single cell ATAC sequencing (scATAC-seq) to study chromatin accessibility in CHO cell populations. Optimisation of an experimental workflow enabled efficient nuclei isolation and the generation of high-quality sequencing data in a non-transfected CHO K1-GS cell line. The resulting scATAC-seq data was used to identify genomic regions with favourable characteristics for potential transgene integration. Regions were compared to a previous publication using bulk ATAC-seq and the method was used to create a refined list of integration sites with homogeneous accessibility across the population. The identification of heterogeneous regions demonstrated the utility of scATAC-seq to provide additional insight and the ability to filter integration sites detected in only a proportion of the total cells in the population. Further application of scATAC-seq was used to perform a more comprehensive analysis and compare epigenetic changes associated with temperature shift in CHO cells at single cell resolution. Thousands of differentially accessible regions were found between temperature shifted and non-temperature shifted cells and specific transcription factors involved in the regulation of key biological processes were determined. Integration of scATAC-seq with single cell RNA sequencing (scRNA-seq) allowed additional insight into the regulatory mechanisms involved in production instability in a mAb-producing CHO cell line over long-term cultivation. A subpopulation of cells in an early passage was found to have comparable chromatin accessibility and gene expression to a later passage with significantly reduced mAb production – indicating early instability in the cell line. Key biological processes associated with the loss of mAb productivity were revealed and used to highlight potential routes for precise genetic engineering strategies. The identification of intra-population heterogeneity was permitted through the use of single cell sequencing and would not have been possible using traditional bulk sequencing techniques. Moreover, scRNA-seq was also used to study transcriptional changes in a CHO cell line with inducible mAb expression and generate a small list of genes linked directly to the burden of mAb production. Overall, single cell sequencing is a powerful tool for profiling chromatin accessibility and gene expression in individual cells and can provide an additional layer of understanding into CHO cell biology. This research demonstrates the utility of these methods and provides a benchmark for future research in biopharmaceutical manufacturing through the implementation of an optimised experimental approach and development of bioinformatic workflows for data analysis.

Breast cancer is the most common cancer and the second leading cause of cancer-related death in women in Ireland. Due to the luck of a clinically relevant cell line model of breast cancer progression, our lab previously established an isogenic invasive variant, Hs578T(i)8 cell line from the original Hs578T cell line produced by Hackett et al 1977. Analysis of gene expression profiles in the parental Hs578T and Hs578T(i)8 cells using microarrays identified a gene called placenta specific 8 (PLAC8) as being the most upregulated gene in the Hs578T(i)8 cells. Gene expression data was confirmed at the mRNA level, but protein expression could not be confirmed due to the lack of availability of an antibody to PLAC8. In this current study the expression and localisation of PLAC8 protein was investigated in the parental cell line, Hs578T and the invasive variant Hs578T(i)8 cells. Using immune-histochemistry PLAC8 was shown to localise in the cytoplasm and nucleus. Western blot analysis showed a 14-fold increase in PLAC8 protein expression in the Hs578T(i)8 cells relative to the Hs578T cells. Analysis of the growth rate in batch culture showed a shorter doubling time of 26.5 hours in the Hs578T(i)8 in comparison to 32 hours in Hs578T cells. In addition, this study highlighted a potential role of PLAC8 overexpression in modulating apoptosis and autophagy following treatment with pharmacological inducers, doxorubicin and rapamycin. Preliminary results indicated that induction of apoptosis following treatment with doxorubicin showed an increase in PLAC8 expression in the Hs578T(i)8 cells relative to the nontreated control with reducing PLAC8 expression during treatment. On the other hand, there was a correlated decrease in PLAC8 relative expression determined from the rapamycin induced treatment in the Hs578T(i)8 cells. A study examining the effect of serum starvation was carried out to examine the role of PLAC8 in cell survival. Serum starvation demonstrated a negative correlation with reducing PLAC8 expression plus a reduction in apoptosis and autophagy through p62 expression. This thesis offers novel methodologies including live cell assessment of apoptosis using flow cytometry and a defined procedure for the measurement of autophagy using western blotting with LC3I/II turnover and p62. The results obtained from these experiments supports a role for PLAC8 in modulating both apoptosis and autophagy processes in the invasive cell line model of breast cancer.

The objective of this study is to develop an operation that can conduct separations based on diafiltration using semipermeable nanofiltration or ultrafiltration membranes in a fully continuous manner in a single stage configuration. To this end a Continuous Spatially Distributed Diafiltration (CSDD) operation is proposed, that aims to conduct continuous single stage diafiltration in a manner that would yield equivalent or better purification efficiency than both batch and incumbent single stage continuous diafiltration configurations. To achieve this goal, diafiltration solvent is introduced spatially across the membrane unit in a highly uniform manner; with the flow guided by a range of 3D-printed static mixers, developed by CFD informed design, to increase localized mixing of retentate and diavolume flows or displacement effects within the membrane channel. Static mixers were 3D-printed using titanium and polyether ether ketone (PEEK) providing high pressure and chemical compatibility, suited for intensive continuous processes. Ibuprofen was selected as a model active pharmaceutical ingredient (API) with methanol and ethanol used in model impurity removal and solvent swap scenarios with an organic solvent nanofiltration membrane used to selectively retain ibuprofen. Significant improvements in solvent consumption and yield were realized with both CSDD based purification and solvent exchange operations compared to batch diafiltration. As such CSDD may present an attractive intermediate purification and solvent swap operation for telescoped flow chemical and continuous processing applications, in addition to a highly compatible platform for use in small scale automated flow-based experimentation.

The vast majority of recombinant therapeutic proteins are produced in Chinese Hamster Ovary (CHO) cells. However, it is still recognised that genetic and phenotypic heterogeneity arises over time. The key role of energy for recombinant protein production has led teams to focus on the mitochondria; which contain their own genome in the form of mitochondrial DNA (mtDNA). Previous bulk analysis of CHO cell populations revealed considerable mtDNA variation (heteroplasmy) which could contribute to the metabolic variability often observed. However, bulk analysis neglects the differences between individual cells within the population. By analysing the mtDNA differences of single-cells, this heterogeneity can be characterised with greater specificity. Here, we developed a protocol to sequence single CHO cells from cell culture through to bioinformatic analysis. Then, we expanded our analysis to 84 single CHO cells. We also exploited scRNAseq for multi-modal analysis of mtDNA from thousands of single CHO cells. We observed great variability in allele frequency among single-cells, with possible contributions to phenotypic changes. We attempted to apply our method to single mitochondria but were not able to amplify from the tiny starting mass of DNA. We also used our scRNAseq datasets to compare CHO cells in “high-producing” and “low-producing” populations. Previous scRNAseq analysis of “instability” focused on a mAb-producing CHO cell line which progressively lost productivity over time. However, comparisons were hindered by the comparatively longer time the “low-producing” CHO cell population spent in culture. Here, we exploited the inducible nature of the CHO-PVZ and CHO-RTX cell lines. When cumate was added, mAb production was “switched-on” leading to over 6x greater titre after 3 days in both cell lines; while maintaining control of other environmental factors. We generated lists of genes whose expression was impacted upon induction of antibody expression, finding 18 that were common to both cell lines. This list represents genes whose expression is impacted by or supports the process of producing an antibody but not specific to the mAb being produced. Gene ontology tests converged to suggest a key role of the Unfolded Protein Response and Endosomal Reticulum Associated Degradation pathways in high-producing CHO cell lines.

The biopharmaceutical industry, a cornerstone of the global economy, witnessed monoclonal antibodies (mAbs) generating a staggering $217 billion in revenue in 2021 (80.2% of total biopharmaceutical product sales). Notably, amid the spotlight on COVID-19 vaccine research and production, over 90 approved mAbs continue to address critical diseases such as cancer, immune disorders, infectious diseases, and genetic disorders. With an annual rate of nearly 4 new mAb products gaining approval, their economic significance and life-saving potential have become increasingly apparent. This study aims to establish robust methods for producing mAbs with optimal and consistent galactosylation profiles. Two strategies were devised and implemented. The first strategy focused on gaining transcriptional control over the β1,4-galactosyltransferase gene through a synthetic gene circuit. The second strategy involved a metabolic engineering approach, employing dose-dependent galactosylation inhibition in a hypergalactosylated mAb. In conclusion, this research generates insights into optimizing mAb galactosylation profiles through innovative strategies. These findings have broad implications for enhancing product quality and aid product and process development in mAb manufacturing.

Oral delivery of active pharmaceutical ingredients (APIs) is the most predominant route of drug administration. The bioavailability of orally administered drugs is mainly dependent on their aqueous solubility and membrane permeability properties. Ionic liquid forms of drugs, known as API-ILs, have emerged as a promising solution to address challenges posed by poorly bioavailable drugs and solid-state stability. These organic salts, typically liquid at room temperature, have demonstrated the ability to enhance both aqueous solubility and membrane permeability of APIs. To further optimize their properties, lipidic excipients were successfully incorporated into API-ILs in this study. However, for the routine integration of liquid API-ILs into oral solid dosage forms, challenges related to ease of handling and manufacturing need to be addressed. Therefore, a comprehensive framework has been developed for transforming API-ILs and their lipidic multi-component solutions into solid presentations using spray encapsulation with various polymers. Multiple APIs spanning all BCS Classes were successfully transformed into ionic liquids and solidified, with subsequent evaluation of the solid API-IL products for dissolution, membrane permeability, stability, and powder flow properties, gauging their suitability for incorporation into oral solid dosage forms. Finally, two distinct isolation-free manufacturing processes were devised for the synthesis, purification, and solidification of API-ILs, with the first method focusing on hydrophilic API-ILs using ion exchange resins and the second method targeting lipophilic, self-emulsifying API-ILs through liquid-liquid extraction via a concentric annular separator.

Of utmost importance in the production of any biological medicine is the selection of an optimal producer cell line, such that high-quality, high titer, safe and functional treatments are produced. Volumetric yield remains a challenge in the manufacture of certain recombinant proteins and the drive to engineer mammalian cells towards higher productivity, faster growth and higher product quality is of major interest to the biopharma industry. CRISPR is a revolutionary gene editing tool that can be utilized to knock-out a single gene or, by using a genome-scale guide RNA library, to generate a population of cells with, theoretically, a knockout representing every gene in the genome. A CRISPR/Cas9 genome-wide knockout library was utilized in this work to explore the link between genotype and phenotype in an IgG producing CHO-K1 cell line with the aim of identifying potential gene engineering targets that would be beneficial to cellular productivity. Cells within this library with the highest levels of productivity (top 10%) underwent two rounds of selection by fluorescence-activated cell sorting (FACS) and sequenced to identify guide RNAs that were enriched (525) or depleted (6,290) compared to the unsorted, parental population. From this list, 5 were chosen for validation based on their ranking on the list (extent of depletion or enrichment) and any previous links to productivity-related cellular processes. While no obvious impact on relevant phenotypes was observed in the resulting clones, optimization of critical RNP and FACS selection conditions was achieved resulting in a protocol that can now be used to perform further single knock-out validation studies. The second avenue of host cell line engineering examined was the exploration of HEK293 cell line engineering strategies to optimize rAAV production for gene therapy applications. A combination of directed evolution approaches and physiochemical parameter optimization were explored in a HEK293 suspension cell line producing rAAV. In one particular approach HEK293 cells were transiently transfected with GFP and cells selected based on highest level of GFP expression. These cells were selected, expanded and then used for the production of rAAV in standard triple-transfection process. The resulting virus displayed 30% improved transduction efficiency when applied to target cells. It was also apparent in the various approaches tested that batch to batch variation is a significant challenge in the transient rAAV production system, particularly compared to traditional stable cell line generation for the production of, for example, recombinant proteins. Finally, single cell RNAseq was implemented in a pilot experiment to assess the level of heterogeneity within a population of AAV producing HEK293 cells. The aim was to identify the impact of rAAV production on the HEK293 transcriptome in individual cells within a transiently transfected population, and also to try to measure the level of expression of the various plasmid-derived AAV and helper transcripts in those cells. The most striking learning from this work was that the level of viral genome-encoded transgene, in this case GFP, was so high that the saturation rate achieved in the (short read) library sequencing was only 8%, making it impossible to reliably measure the expression of other transcripts in the samples. Future investigations should either adopt alternative sequencing technologies or establish a means to suppress the levels of GFP present.