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Margrét Thorsteinsdottir (Presenter) University of Iceland
Presenter Bio: I am a professor of Pharmaceutical Analytical Chemistry at the Faculty of Pharmaceutical Sciences, University of Iceland and the R&D director of ArcticMass a spin-off company from a Bioanalytical Laboratory at deCode Genetics. I built up and directed a bioanalytical laboratory, supporting the entire compound development process from drug discovery through post-marketing trials. My current research interest includes development of analytical methods for quantification of clinical biomarkers utilizing liquid chromatography coupled to mass spectrometry (LC-MS/MS). I have extensive experience in the field of mass spectrometry, especially in development of assays for metabolite profiling and quantification of biomarkers in different cell lines and other biofluids utilizing chemometric approaches. I have been using design of experiments for many years and was co-chairman of the Scandinavian Symposium on Chemometrics (SSC) and a member of the scientific committee for SSC. I was chairmen of the board of the Icelandic Chemical Society and I am a scientific advisor in the Rare Kidney Stone Consortium (RKSC), responsible for implementation of UPLC-MS/MS assays for routine diagnostics. I am a principal investigator of several EU projects including a Horizon 2020 project, MossTech; Advanced uses of mosses for biotechnological solutions and a Marine Biotechnology ERA-net project, CYNOBESITY. I have supervised several Ph.D. students as well as M.Sc. students at the University of Iceland.
Relevant Financial Disclosures
(within past 24 months)
No relevant financial relationship(s) to disclose.
Abstract
INTRODUCTION: Method development for a quantitative LC-MS/MS method consists of several integrated steps involving many experimental factors which need to be simultaneously optimized to obtain maximum selectivity and sensitivity at minimum retention time. Optimization of experimental conditions for LC-MS/MS methods is usually performed by changing one-factor-at-time (OFAT) experiments. However, a much more effective optimization strategy for discovering important experimental factors and to optimize the responses is to utilize design of experiment (DoE). DoE is a systematic approach to data collection where all relevant experimental factors are studied simultaneously according to predefined plan. DoE can determine the individual and interactive effects of factors that can influence the output results of the measurements and estimate the optimum operating conditions for the LC-MS/MS method.
OBJECTIVES: The objective of this work is to illustrate how design of experiments (DoE) can be implemented for optimization of quantitative LC-MS/MS clinical diagnostic method.
METHODS: The chemometric approach DoE was used for optimization of an ultra-performance liquid chromatography (UPLC) coupled to an electrospray tandem mass spectrometry (MS/MS) platform for simultaneous quantification of cortisol, cortisone and glycyrrhetinic acid in urine and plasma. A fractional factorial design was used for experimental screening to reveal the most influential experimental factors. When multi-levels qualitative factors were included in the screening experiments D-optimal design was applied. Significant factors were studied via central composite design and related to sensitivity, resolution and retention time utilizing partial least square (PLS)-regression.
RESULTS: Results showed that many interaction factors were significant and therefore these variables could not be independently controlled to obtain optimal conditions. Baseline separation was achieved between the biomarkers and the method was implemented for analyses of human plasma samples from individuals with and without liquorices consumption for support of clinical diagnosis of liquorice induced hypertension and evaluation of 11 β-hydroxysteroid dehydrogenase type 2 (11βHSD2) enzyme activity.
CONCLUSION: This selected study shows that design of experiments (DoE) can be used to optimize the LC-MS/MS quantification method efficiently with only fraction of the experiments that would be required by changing one-factor-at-time (OFAT) experiments.
= Discovery stage.
= Translation stage.
= Clinically available.
