Angela is interested in the development of highly sensitivity, high throughput, well validated mass spectrometry assays for steroid analysis. Application of these assays to answer important clinical questions is critical to her research. She feels the truly translational aspect of the work conducted at the Steroid Metabolome Analysis Core (SMAC) means the work is both analytically enjoyable and socially beneficial.
Angela has an analytical chemistry master’s degree from Swansea University (2005). In 2009 she received her PhD from Swansea University investigating steroid metabolism in the human endometrium using mass spectrometry. This was followed by post-doctoral work at the University of Birmingham working for Prof Wiebke Arlt, which cumulated in setting up the SMAC facility in 2016. The SMAC lab now conducts steroid research from wet lab based projects to clinical trials.

Translation of a test from the initial hypothesis to routine clinical use is a long and challenging journey. In this session I will discuss my experiences in the research laboratory focusing on the development of ‘urinary steroids metabolomics’ a combination of mass spectrometry and machine learning for diagnosis of adrenocortical carcinoma (ACC).

The journey starts with the hypothesis developed by Wiebke Arlt and Cedric Shackleton that urinary steroid excretion in patients with adrenal cancers would differ from those with benign adrenal adenomas (ACA). Initial studies were undertaken investigating 32 steroid metabolites using gas chromatography mass spectrometry (GC-MS). Through collaboration with computer scientists Michael Biehl and statisticians Alice Sitch and John Deeks, we were able to identify a malignant steroid fingerprint that differentiated ACC from ACA. When compared to current available imaging technologies, this urine steroid metabolomic assay demonstrated improved diagnostic sensitivity and specificity.

GC-MS is technically challenging and time consuming making it an expensive assay unsuitable for routine clinical use. Therefore, we selected the most relevant steroids when distinguishing between ACC and ACA and developed and validated a liquid chromatography tandem mass spectrometry (LC-MS/MS) assay for a subset of 15 steroids. Firstly, this was cross-validated to GC-MS to compare quantitation and diagnostic performance. Secondly, we prospectively validated the assay in a cohort of approximately 2000 unbiasedly recruited patients with adrenal tumours. Finally, we were able to determine the optimal stage in the patient pathway to place our assay to aid the diagnosis of ACC.
We are currently working towards the implementation of this assay into routine clinical biochemistry. It has taken us 10 years from the initial concept to its implementation, only possible through the work of a multi-disciplinary team. There have been many challenges on this pathway, I will discuss how we overcame these in this session.

Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors. Arlt W, et al. J Clin Endocrinol Metab. 2011 96(12):3775-84.

Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: a prospective test validation study. Bancos I Taylor AE, et al. Lancet Diabetes Endocrinol. 2020 8(9):773-781.

Gwen Wark is a consultant clinical biochemist employed within the Blood Sciences department of Berkshire and Surrey Pathology Services. She has worked at the Royal Surrey County Hospital in Guildford for over 22 years where she is now Director of the Supraregional Assay Service (SAS) Peptide Hormones Laboratory which specialises in the investigation of hypoglycaemia (includes a forensic case load) and the role of insulin-like growth factors and their binding proteins. Since 2002, she has also been the Scheme Director of the UK National External Quality Assessment Service (UK NEQAS) Guildford Peptide Hormones Scheme which covers the analytes insulin, C-peptide, gastrin, insulin-like growth factor-I (IGF-I) and insulin-like growth factor binding protein-3 (IGFBP-3). Since 2019, she has also been Director of the UK NEQAS Trace Elements schemes. She is involved in developing accuracy-based proficiency testing for all scheme analytes.

Gwen is also involved in promoting the development of reference methods/measurement systems. She is a member of the British Standards Institution (BSI) CH/212 committee which is responsible for standardisation in the field of in vitro diagnostics and reviewing/developing ISO standards. She is a member of the IFCC working group (collaboration with ADA and EASD) for the standardisation of insulin assays and a guest member of the NIDDK led C-peptide standardisation committee. In collaboration with the National Institute for Biological Standards and Control (NIBSC), she has been involved in the development of the WHO International Standards for IGF-I, insulin, C-peptide and proinsulin. She collaborated in the workshop that culminated in the ‘Consensus Statement on the Standardisation and Evaluation of Growth Hormone and Insulin-like Growth Factor Assays’ and also development of mass spectrometric assays for the measurement of IGF-I. Current LC-MS/MS assay development is focussed on developing further LC-MS/MS peptide assays for implementation into the routine laboratory e.g. insulin and C-peptide.

In the routine laboratory, the use of LC-MS/MS is becoming more widespread and is often used to overcome issues encountered with immunoassays. In this presentation, issues with peptide hormone analyses by LC-MS/MS and consensus statement recommendations will be discussed. Insulin and IGF-I will be used to highlight analytical approaches that be adopted to enable implementation of LC-MS/MS into the routine laboratory.

Jo Adaway is a Consultant Clinical Scientist at Manchester University NHS Foundation Trust. She is interested in developing LC-MS/MS assays for small molecules for endocrinology and neuroendocrine tumour applications and has recently branched out into protein quantification. She was awarded her PhD on Proteomic analysis of stem cell commitment in 2005 from the University of Manchester. She is Associate Editor for Annals of Clinical Biochemistry and honorary senior lecturer at the University of Manchester, where she lectures on chromatography, mass spectrometry and endocrinology.

You’ve successfully purchased a mass spectrometer for your clinical lab – what on earth do you do now? In this session, we will discuss the practicalities of what to do next, from training your staff and developing your first method, to integrating the technology into your lab. We will focus on basics that are really important to successfully running a mass spec service but which may not be included in other training programmes, including how to choose solvents, useful peripheral equipment and sources of information and support.

Steph graduated from the University of Lincoln, UK with a BSc (Hons) Forensic Science. She has worked at the LGC Fordham site since 2013 and is working as a Senior Scientist, in a Technical Specialist role. Her role includes method development of LC-MS/MS methods, training and mentoring others through method development, and instrument troubleshooting/maintenance. Steph is also a member of the Chromatographic Society. Outside of work Steph’s main passions are guinea-pigs and mountain biking.

When analysing patient samples the concentration of the analyte measured should mirror the concentration during sample collection; therefore consideration should be given to the pre analytical conditions of the samples. One such consideration in multi-analyte studies is the potential for interconversion between analytes, which can compromise this quantification, leading to potential overestimation of one analyte and underestimation of another. In this session the root causes of interconversion shall be discussed, from the reversible hydrolysis of lactones e.g. statin drugs to cleavage of ester bonds by esterases. We will be examining the entire life-cycle of a patient sample to allow identification of problem areas in a methodology. We shall examine how to create a robust and accurate method with particular focus on stabilisation protocols and practicalities for the clinical laboratory. How to calculate levels of interconversion shall be demonstrated and the ability to assess impact upon data discussed.

Dr Catarina Horro Pita possesses a BSc in Chemistry and Environmental Chemistry and a PhD in Synthetic Organic Chemistry. After concluding her PhD, she initiated her professional career as a Synthetic Chemist, prior to moving into Analytical Chemistry and Bioanalysis. She has been working in the field of Bioanalysis for 15 years, holding both scientific and managerial positions. Catarina currently works as a PK Project Manager at A4P Consulting. During her career, she has gained significant experience in the development, validation and management of LC-MS/MS assays for the analysis of both xenobiotic compounds and small molecule biomarkers. In addition, due to her expertise in Organic Chemistry, she also has an extensive knowledge of derivatization techniques for the improvement of LC-MS/MS methods.

Derivatization is a simple chemical reaction, often used in Bioanalysis to transform a “not so ideal” candidate for LC-MS/MS analysis into a molecule with enhanced phys-chem properties, which leads to improved detectability of the compound.

The objective of this presentation is to describe some of the most commonly used derivatization procedures and explain the benefits and drawbacks of these techniques.

When performing analysis by LC-MS/MS, the ideal analyte should be chemically stable and have moderate retention on the LC column. The molecule should also be ionisable under ESI or APCI conditions and have an efficient fragmentation upon collision. In addition, the target compound should be selectively separated from other xenobiotics or endogenous products present in the extracted matrix. When the analyte does not meet all of the above criteria, derivatisation can be used to modify its chemical structure and address these issues.

A range of derivatization processes will be reviewed, indicating their bioanalytical applications and type of chemical reactions involved. In addition, examples of derivatization reagents and reaction conditions will be described.

Although the application of derivatization procedures can lead to an improved bioanalytical performance, these techniques also present a range of disadvantages, such as extended method development times, lengthy extraction procedures and increased method variability.

Despite all the recent technological improvements in the field of Bioanalysis, derivatization remains a very powerful tool to overcome problems associated with analyte instability, poor chromatographic performance, inadequate analyte ionization or unacceptable method selectivity. However, the application of derivatization procedures can often lead to an increase in assay complexity and may reduce method robustness. Therefore, both the advantages and disadvantages of these procedures should always be considered before their application to a bioanalytical method.

Ivayla Roberts is a doctoral researcher at the University of Liverpool in Prof. Kell system biology group. Originally coming from computer science background (MSc, Montpellier France) Iva transitioned to molecular biology research via a Translational Molecular Medicine MRes (Manchester, UK).
Iva’s research interests are metabolomics, mass-spectrometry, and the application of statistical and machine learning computational approaches to metabolomics.
Iva’s PhD is focused on metabolomics in COVID-19.

The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites (‘targeted metabolomics’), compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.

We here provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict patient’s infection severity (i.e. mild or severe) and potential outcome (i.e. discharged or deceased).

High resolution untargeted LC-MS/MS analysis was performed on patient serum using both positive and negative ionization. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model. The predictors were selected for their relevant biological function and include cytosine and ureidopropionic acid (reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others.

Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on additional 90 patients predicted outcome and severity at ROC_AUC of 0.83 (CI 0.74 – 0.91) and 0.76 (CI 0.67 – 0.86). Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

B.S. in Analytical Chemistry, Masaryk University, Brno, Czech Republic.
Ph.D. in Bioanalytical Chemistry, Brigham Young University, Provo, Utah.
Nearly three decades of HPLC, CE, CE-MS, and LC-MSMS method development and validation experience in academic, pharmaceutical, and clinical laboratory environments.

The popularity of LC-MS/MS-based methods for clinical testing continues to rise. However, despite their superior analytical specificity, these methods may still suffer from interference affecting method accuracy and precision, and hence negatively impacting patient care.

The following topics and issues will be addressed: