The cost-effectiveness of EndoPredict to inform adjuvant chemotherapy decisions in early breast cancer

Background Adjuvant chemotherapy in breast cancer patients post resection has been estimated to reduce mortality rates by up to 30%. However, the heterogeneous nature of the disease and patients implies that not all patients should receive the treatment. Many existing prognostic tools, may not definitively estimate the most effective treatment strategy, resulting in an indeterminate risk classification. In such cases gene expression profiling tests can aid the treatment decision. Methods This study evaluated the cost-effectiveness of EndoPredict in patients with indeterminate risk classification. A mathematical model was constructed to determine how the change in treatment decisions impacted the long term health of the population, and the future cost implications to the NHS. Results EndoPredict was found to lead to 36.9% of patients having a change in treatment decision. As a result its use was found to result in an increase in population health but also in total costs, resulting in an incremental cost-effectiveness ratio of £26,836 per quality adjusted life year. This was subject to significant parametric and structural uncertainty. Conclusion While EndoPredict was found to be more expensive overall, its ability to affect a more optimal allocation of chemotherapy, resulted in long term health gains, however, they were insufficient to justify the high cost of EndoPredict

Molecular engineering improves antigen quality and enables integrated manufacturing of a trivalent subunit vaccine candidate for rotavirus

Background Vaccines comprising recombinant subunit proteins are well-suited to low-cost and high-volume production for global use. The design of manufacturing processes to produce subunit vaccines depends, however, on the inherent biophysical traits presented by an individual antigen of interest. New candidate antigens typically require developing custom processes for each one and may require unique steps to ensure sufficient yields without product-related variants. Results We describe a holistic approach for the molecular design of recombinant protein antigens—considering both their manufacturability and antigenicity—informed by bioinformatic analyses such as RNA-seq, ribosome profiling, and sequence-based prediction tools. We demonstrate this approach by engineering the product sequences of a trivalent non-replicating rotavirus vaccine (NRRV) candidate to improve titers and mitigate product variants caused by N-terminal truncation, hypermannosylation, and aggregation. The three engineered NRRV antigens retained their original antigenicity and immunogenicity, while their improved manufacturability enabled concomitant production and purification of all three serotypes in a single, end-to-end perfusion-based process using the biotechnical yeast Komagataella phaffii. Conclusions This study demonstrates that molecular engineering of subunit antigens using advanced genomic methods can facilitate their manufacturing in continuous production. Such capabilities have potential to lower the cost and volumetric requirements in manufacturing vaccines based on recombinant protein subunits

Genome-scale metabolic modelling of Salmonella and Lactobacillus species

Salmonella Typhimurium is a major cause of morbidity and mortality in humans. It is also a commonly used model organism for intracellular Gram negative pathogens, a group of bacteria that is becoming increasingly resistant to available antibiotics. Systemic Salmonella infection involves proliferation in the small intestine followed by infection of epithelial and later macrophage host cells. In order to advance the understanding of the r^ole of metabolism in virulence, a genome-scale metabolic model of S. Typhimurium was constructed, based on genomic and biochemical data obtained from public databases. A method for modelling metabolic interactions between cells was developed and applied to models of S. Typhimurium and the probiotic Lactobacillus plan-tarum, in order to simulate the intestinal stage of infection. The analysis indicated that interactions, involving the transfer of glycolate from L. plantarum to S. Typhimurium, that favour growth of S. Typhimurium, are possible, by unlikely to occur in vivo. Data from Phenotype Microarray (PM), as well as DNA microarray data obtained during infection of cultured macrophage cells, was integrated with the S. Typhimurium model. The PM data was largely in agreement with model results for growth on carbon and nitrogen sources, and indicated moderate agreement for sulphur and phosphorus sources. A model-based method for analysis of nutrient availability during growth inside host cells, based on PM and DNA microarray data, was developed. This environment is poorly characterised and direct experimental methods for obtaining this information are not available. The analysis indicated a nutritionally complex host environment, dominated by glycerol 3-phosphate and certain nucleosides and amino acids. Owing to the complexity of the host environment, a method for identication of a sub-network of the model, required for viability on all growth supporting carbon sources was developed. The impact of sequentially removing combinations of reactions in the sub-network from the genome-scale model was evaluated. This analysis suggested approximately 60 reactions that in various combinations could be of relevance for designing antimicrobial intervention strategies, including antimicrobial agents and live attenuated vaccines

Methodology for identifying alternative solutions in a population based data generation approach applied to synthetic biology

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDesign is an essential component of sustainable development. Computational modelling has become a useful technique that facilitates the design of complex systems. Variables that characterises a complex system are encoded into a computational model using mathematical concepts and through simulation each of these variables alone or in combination are modified to observe the changes in the outcome. This allows the researchers to make predictions on the behaviour of the real system that is being studied in response to the changes. The ultimate goal of any design process is to come up with the best design; as resources are limited, to minimize the cost and resource consumption, and to maximize the performance, profits and efficiency. To optimize means to find the best solution, the best compromise among several conflicting demands subject to predefined requirements. Therefore, computational optimization, modelling and simulation forms an integrated part of the modern design practice. This thesis defines a data analytics driven methodology which enables the identification of alternative solutions of computational design by analysing the generational history of the population based heuristic search used to generate the templates. While optimisation is focused on obtaining the optimal solution this methodology focuses on alternative solutions which are sub optimal by fitness or solutions with similar fitness but different structures. When the optimal design solution is less robust, alternative solutions can offer a sufficiently good accuracy and an achievable resource requirement. The main advantage of the methodology is that it exploits the exploration process of the solution space during a single run, by focusing also on suboptimal solutions, which usually get neglected in the search for an optimal one. The history of the heuristic search is analysed for the emergence of alternative solutions and evolving of a solution. By examining how an initial solution converts to an optimal solution core design patterns are identified, and these were used to improve the design process. Further, this method limits the number of runs of the heuristic search as more solution space is covered. The methodology is generic because it can be used to any instance where a population based heuristic search is applied to generate optimal designs. The applicability of the methodology is demonstrated using three case studies from mathematics (building of a mathematical function for a set target) and biology (obtaining alternative designs for genomic metabolic models [GEM] and DNA walker circuits). In each case a different heuristic search method was used: Gene expression programming (mathematical expressions), genetic algorithms (GEM models) and simulated annealing (DNA walker circuits). Descriptive analytics, visual analytics and clustering was mainly used to build the data analytics driven approach in identifying alternative solutions. This data analytics driven methodology is useful in optimising the computational design of complex systems

STRUCTURAL AND MECHANISTIC STUDIES OF NUCLEIC ACID DEMETHYLASE FTO

Ph.DDOCTOR OF PHILOSOPH

A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen

SynthSys is a Centre for Integrative Systems Biology (CISB) funded by BBSRC and EPSRC, reference BB/D019621/1.Background: Estrogen receptors alpha (ER) are implicated in many types of female cancers, and are the common target for anti-cancer therapy using selective estrogen receptor modulators (SERMs, such as tamoxifen). However, cell-type specific and patient-to-patient variability in response to SERMs (from suppression to stimulation of cancer growth), as well as frequent emergence of drug resistance, represents a serious problem. The molecular processes behind mixed effects of SERMs remain poorly understood, and this strongly motivates application of systems approaches. In this work, we aimed to establish a mathematical model of ER-dependent gene expression to explore potential mechanisms underlying the variable actions of SERMs. Results: We developed an equilibrium model of ER binding with 17 beta-estradiol, tamoxifen and DNA, and linked it to a simple ODE model of ER-induced gene expression. The model was parameterised on the broad range of literature available experimental data, and provided a plausible mechanistic explanation for the dual agonism/antagonism action of tamoxifen in the reference cell line used for model calibration. To extend our conclusions to other cell types we ran global sensitivity analysis and explored model behaviour in the wide range of biologically plausible parameter values, including those found in cancer cells. Our findings suggest that transcriptional response to tamoxifen is controlled in a complex non-linear way by several key parameters, including ER expression level, hormone concentration, amount of ER-responsive genes and the capacity of ER-tamoxifen complexes to stimulate transcription (e. g. by recruiting co-regulators of transcription). The model revealed non-monotonic dependence of ER-induced transcriptional response on the expression level of ER, that was confirmed experimentally in four variants of the MCF-7 breast cancer cell line. Conclusions: We established a minimal mechanistic model of ER-dependent gene expression, that predicts complex non-linear effects in transcriptional response to tamoxifen in the broad range of biologically plausible parameter values. Our findings suggest that the outcome of a SERM's action is defined by several key components of cellular micro-environment, that may contribute to cell-type-specific effects of SERMs and justify the need for the development of combinatorial biomarkers for more accurate prediction of the efficacy of SERMs in specific cell types.Publisher PDFPeer reviewe

Investigating the effects of venom peptides on canine mammary cancer

Mammary Cancer is the most prevalent form of malignancy to occur in female dogs. With metastasised malignancies representing 50% of diagnosis, current treatments produce little efficacy towards survival and induce harsh adverse side effects, thus there is need for novel therapeutics. Venoms have been shown to exploit anti-cancer properties with specific selective effects towards many forms of human cancers, thus, the prospect of anti-cancer inhibition towards Canine Mammary Cancer is a feasible hypothesis. Utilising in-vitro cell viability assays, panels of venoms from snake, scorpions and spiders were profiled against canine mammary cancer cells lines, CMT28 and CMM26, and an immortalised normal canine kidney cell line, MDCK. Screening of these venom fractions identified selectivity towards the cancerous cells utilising venoms from the Naja genus by >70% inhibition. Mass spectrometry data of 5 fractions identified them as 3-finger toxins with 3 of the fractions identifying as novel cytotoxins and 2 matched to sequence in the database of the same species. Epidermal Growth factor receptor- 2 (HER2) is a key antigenic target in Human breast cancer and has been shown to be as a potential therapeutic target for Canine Mammary Cancer. Utilising computational modelling and molecular docking simulations, the identified cytotoxins obtained from mass spectrometry have been predicted to bind to the dimerisation loop of the extracellular domain of HER2, that is hypothesised to inhibit dimer formation. In practice Canine HER2 demonstrated to have a high binding affinity for proteins in whole snake venoms, signifying the potential of HER2 being a therapeutic target for the treatment of Canine Mammary Cancer

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly

Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach

This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study

Immune-Mediated Drug Induced Liver Injury: A Multidisciplinary Approach

This thesis presents an approach to expose relationships between immune mediated drug induced liver injury (IMDILI) and the three-dimensional structural features of toxic drug molecules and their metabolites. The series of analyses test the hypothesis that drugs which produce similar patterns of toxicity interact with targets within common toxicological pathways and that activation of the underlying mechanisms depends on structural similarity among toxic molecules. Spontaneous adverse drug reaction (ADR) reports were used to identify cases of IMDILI. Network map tools were used to compare the known and predicted protein interactions with each of the probe drugs to explore the interactions that are common between the drugs. The IMDILI probe set was then used to develop a pharmacophore model which became the starting point for identifying potential toxicity targets for IMDILI. Pharmacophore screening results demonstrated similarities between the probe IMDILI set of drugs and Toll-Like Receptor 7 (TLR7) agonists, suggesting TLR7 as a potential toxicity target. This thesis highlights the potential for multidisciplinary approaches in the study of complex diseases. Such approaches are particularly helpful for rare diseases where little knowledge is available, and may provide key insights into mechanisms of toxicity that cannot be gleaned from a single disciplinary study