CAMS Industry Member: NML at LGC
Project Title: Identification and quantification of engineered organisms.
Project Summary: Tracking, identifying and quantifying organisms released into the environment (e.g. engineered microbes or pathogens in, for example, water and wastewater).
Project aims: Quantitative portable methods (NA/ protein/synthetic barcodes) for tracking an engineered microbe or pathogen. Use of unique genetic barcodes engineered into organism for identity purposes. Improved methods to (quantitatively) recover analyte of interest from natural (complex) backgrounds.
CAMS Industry Member: NML at LGC
Project Title: Identity and purity determination of synthesized oligonucleotides.
Project Summary: Identity and purity determination of synthesized oligonucleotides (e.g. therapeutics, miRNA etc) to shape standards for future regulation
Project aims: Novel, homogeneous oligonucleotide synthesis methods. Method comparison assessment through purity (e.g. sequence) characterization and functional activity determination.
CAMS Industry Member: NML at LGC
Project Title: Single cell identification in complex mixtures.
Project Summary: Novel strategies for single cell identification and image data analysis in complex mixtures.
Impact & Benefit: Reference methods and/or materials. Data analytics and uncertainty approaches for single cell imaging.
Project aims: Strategies for differential cell counting and i.d. of biomarkers of cell morphology using multi-technique approaches. Data analytics and uncertainty approaches for single cell imaging.
CAMS Industry Member: NML at LGC
Project Title: Lipid-based delivery systems.
Project Summary: Quantifying cell uptake and functional activity of lipid-based delivery systems.
Impact & Benefit:Enhance characterization of lipid-based delivery systems. Identification of CQAs for such systems. Support emerging regulations relating to use of lipid-based delivery systems for nanomedicine.
Project aims: Methods for size, size distribution, mass concentration and number concentration of lipid-based delivery systems in biological media. Strategies for drug loading and lipid composition determination (e.g. LC/AF4-MS/light scattering). Identification and characterisation of molecular biomarkers of cell expression. Models for correlation of agreed CQAs of nanomedicines with biological activity.
CAMS Industry Member: Syngenta
Project Title: Live assay High-resolution mass spec analysis (General/Broad research idea of capturing HRMS data for live assays)
Project Summary: Generation of small molecule high-resolution mass spectrometry data from Biological samples. Assays samples are live in-vitro assays and benefit from continuous sampling for semi-continuous kinetic data.
PhD research could initially explore 1 technique for multiple assays or multiple techniques with a narrower assay scope. Next stage would be to explore the limitations of the technique (quantitation, interferences, assay components, throughput). Likely project would have no barriers to publication, as it does not focus on specific assays/products but would have some limitations imposed by instrumentation that is available to the project.
CAMS Industry Member: Syngenta
Project Title: Understanding fermentation process in cows
Project Summary: Investigation of corn silage fermentation processes, ruminant biochemistry and detection methods that evaluate value added effects of new products. PhD Studentship to use analytical methods to investigate the digestion of products such as fodder maize silage. The output of the project will be an understanding of how the different compositions of animal feeds result in different levels of digestion of starch materials within the gut of cows. We would expect the student to work with simulation of digestion conditions perhaps with synthetic materials or extracts of gut juice obtained from live animals. Analytical methods deployed are likely to be a mixture of optical measurements and separation technologies. We are open to innovative proposals in this area.
CAMS Industry Member: Syngenta
Project Title: Macromolecular complex mixtures
Project Summary: Focus is on characterization of high MW (>5K) ethoxylated polymers and their blends (we are interested in detection and full characterisation of possible by-products e.g. free PEG – average chain length, distribution, and its amount within the dispersant sample. Characterisation of polymers blends in formulations – linking blend composition to formulation properties. PhD founded collaboration may be more suitable, but we are also open for Postdoc applications. The project does not prioritise any specific characterisation technique yet, therefore the initial work would be reviewing known characterisation techniques, which do not require chromophore present within the molecule (ethoxylated polymers are not detected by UV) and which give the understanding of the entire population (and not only of the low MW fraction). In the next step one or two techniques should be selected for deeper evaluation. The limitation of these techniques for quantitative studies should be evaluated as part of the project. The project may focus on commercially available samples so will have not barriers for publications.
CAMS Industry Member: Syngenta
Project Title: Combination of Electron Diffraction with the Crystalline Sponge Method to determine chemical structures of low-level impurities and metabolites from matrices
Project Summary: In this project, we will exploit the power of Electron Diffraction (ED) to overhaul the Crystal Sponge method. ED will facilitate the targeted discovery of new sponges, expand the scope of compatible analytes, optimise and simplify sample handling and analyte loading, improve data quality and reproducibility, examine the selectivity of sponge-analyte binding, and enable high throughput analytical techniques.
Work package 1: Reconfiguring Existing Crystal Sponges for Electron Diffraction (ED) to be performed due to novel instrumentation for this method.
Work package 2: Discovering and Understanding New Crystal Sponges for ED. Discovery of new Metal Organic Frameworks (MOF), development of soaking conditions and analysis.
Work Package 3. An In-Depth Comparison of ED and SCXRD Crystal Sponge Methods. Optimise conditions using library of historic data aligned with Data analytics
Work package 4: Pushing the Boundaries of the Crystal Sponge Method by reducing quantities, using complex mixtures for separation, using automation to improve throughput.
CAMS Industry Member: Syngenta
Project Title: Direct detection of Active Ingredient. Volatilization and Movement (as gas or aerosol) AFTER its application.
Project Summary: Direct detection of low-level active ingredients in the gas phase (either as gas, or aerosol particle). Building understanding, and characterisation portfolio to study molecular vapour /molecular diffusion of volatile-liquid vapours into air. Ideally technique can be used to quantitate amount of active ingredient, or at least allow relative comparisons. Complexity of the project would require PhD founded collaboration. Part of the project will need to take place on Syngenta site. Project may results in some publications.
CAMS Industry Member: Syngenta
Project Title: Stability of compounds in DMSO solubilised samples for long term storage in Central Research Dispensaries.
Project Summary: To develop a predictive algorithm that will assess compound stability in DMSO and other solvents. We routinely use LC/MS to determine compound stability in DMSO solubilised samples that are stored for 2-20 years in the Central Research Dispensary. The idea here is to feed the machine learning algorithm with LC/MS data results for Syngenta compounds. A model can then be generated to predict chemical moieties that are prone to degradation or other instabilities based on chemical structure and real analytical data.
The project would require a 3-year PhD founded collaboration, after which a successful algorithm and/or software package that predicts compound instability can be published for use by the scientific community, Syngenta should have access to a free licence to use the potentially successful product.
Syngenta has carried out some limited computational work on approximately 200,000 samples (for which we have collected LC/MS and DMSO concentration data) and have identified chemical clusters that exhibit structural instability. However, we would be very interested in forming a data collection consortium with other interested parties to increase the number of chemicals tested for instability/stability. The consortium can form a comprehensive data set for training the prediction model.
CAMS Industry Member: Syngenta
Project Title: Streamlining metabolite annotations from untargeted metabolomics LC-MS/MS datasets.
Project Summary: Use state-of-the art structure mining and MS/MS spectral mining tools to annotate untargeted metabolomics data. Enable the quick annotation of batches of MS/MS spectra from one or several samples.
Objective: To develop an integrated approach to annotate challenging metabolite features from LC-MS metabolomics datasets, using batches of MS/MS spectra from one or several samples.
Target candidate:
The project is particularly suited for a post-doc with some background knowledge in LC-MS data processing and analysis and/or bioinformatics. It can be also adapted for a PhD project.
CAMS Industry Member: Syngenta
Project Title: Rapid detection and quantification of bacteria within product formulations
Project Summary: Syngenta is seeking solutions that allow the rapid detection and quantification of bacteria within their product formulations. Bacterial contamination can lead to compromised product, bulging or ruptured packaging and subsequent product recalls and waste. Therefore, it is key that proposed solutions can both detect and quantify the amount of bacterial contamination within a product so that appropriate action can be taken.
CAMS Industry Member: GSK
Project Title: High Throughput Automated Imaging and Processing of Solid/Liquid Samples For Pharmaceutical Development
Project Summary: In development of novel pharmaceutical compounds and medicines, pharmaceutical companies and associated industrial partners regularly conduct the determination of solubility of drug substances, drug substance intermediates, and pharmaceutical excipients in both organic and aqueous solvents. The solubility data is then subsequently employed in a myriad of processes including for example analytical method development, formulation development, chemical process development and Physiologically based pharmacokinetic (PBPK) modelling. Quantitative measurements of solubility can be conducted either in real-time using suitable process analytical technologies or by sampling and subsequent quantitative analytical techniques. In addition to quantitative data, qualitative data is often gathered via visual observation and imaging. This may include capture of, for example, clarity of sample, colour change, formation of agglomerates, wetting of solids, formation of multiphase systems etc. Obtaining a data set of observations through these processes can be time-consuming and the resulting data set complex. When automated analysis and automated solid dispensing and liquid handling systems are employed, then many hundreds of qualitative observations may be generated concurrently to quantitative measurements. In these instances, processing and interpretation of observations via Humans may lead to a) inconsistency / ambiguity in description and interpretation b) large amounts of time spent in conducting observations and c) physical intervention being required in an automated workstream. GSK would like to discuss the potential of leveraging the CAMS network for academic / industry collaboration to research the application of image collection, image processing, data collation and interpretation of qualitative observations in a fully automated, time sensitive, High Throughput manner.
CAMS Industry Member: Shimadzu
Project Title: Developing a Mitochondrial Analysis Pipeline
Project Summary: Mitochondria is an organelle that is essential in human health and disease. To date no-one has developed targeted methodology that can comprehensively detect all of the mitochondrial proteins and metabolites in reasonable times scales. Thus, there is a huge opportunity to develop new analytical pipelines that can revolutionise our understanding mitochondrial health/disease.
There are several elements:
- High resolution MS technology (qToF) with DIA capability will be used for this study.
- Identify mitochondrial peptides for inclusion in a QconCAT targeted proteomics method.
- Develop a DIA MS method of mitochondrial proteomics. Use this method to look for predictive biomarkers of mitochondrial disease.
- Combine DIA MS data with metabolomics data to develop a holistic model of energy metabolism in mitochondria.
- Apply all methods (targeted, semi targeted, metabolomics) to clinical trial.
