Assurance is good. Control is better. MxP® Quality Control Plasma: New Metabolic Assay for Pre‐Analytical Quality Control of Human Plasma Samples Beate Kamlage1#, Arndt Schmitz3#, Bianca Bethan1, Oliver Schmitz1, and Philipp Schatz2# metanomics GmbH, Tegeler Weg 33, 10589 Berlin, Germany, www.metanomics.de; 2 Metanomics Health GmbH, Tegeler Weg 33, 10589 Berlin, Germany, www.metanomics‐health.de Bayer Pharma AG, Müllerstr. 178, 13342 Berlin, Germany, www.bayerhealthcare.com #These authors contributed equally to this study. Background and Objective Results – Assay Performance: MxP Quality Control Plasma Research in healthcare areas is often based on existing blood samples from biobanks or other sample collections. The quality of these biospecimens is impacted by pre‐analytical processing steps that confound the analytical results and decrease the credibility of the research outcomes (Table 1). Metabolites are well‐suited as biomarkers for the quality control of biobank samples due to the response of the metabolome to physiological and chemical processes. Metabolite levels in plasma allow very comprehensive and sensitive read‐out of human plasma sample quality (quality control) MxP Quality Control Plasma controls EDTA plasma quality at three checkpoints (Fig. 3) MxP Quality Control Plasma is a novel metabolomics‐based profiling assay to control for pre‐analytical quality of human EDTA plasma. This enables you to upgrade your QM system from quality assurance to quality control, safe‐guarding your investment in the right human plasma samples. Table 1: Pre‐analytical confounders Figure 3: Checkpoints of MxP Quality Control Assay MxP Quality Control Plasma Performance Results Blinded study by Bayer Pharma AG (Table 2, Fig. 4a) • Blood storage at RT for 6 h • Plasma storage at RT for 6, 24 and 48 h, 5x freeze‐thaw • Different blood collection tubes Clinical Performance test by Metanomics Health (Fig. 4b, c) • Blood storage at 0°C and RT for 6 h • Plasma storage at room temperature for 6 and 24 h Methods Based on our previously reported metabolomics results, a targeted GC‐MS assay was developed for quantitative analysis of quality markers in human EDTA plasma samples. Human EDTA plasma samples obtained after applying defined pre‐analytical confounding factors were subjected to mass‐spectrometry based metabolomics as well as to the quality control assay (Fig. 1). a) Result 1: MxP QC Collection Tube Control Table 2: Performance data from a blinded study b) Result 2: MxP QC Blood Processing Control c) Result 3: MxP QC Plasma Processing Control a) Various pre‐analytical confounders and conditions MxP Focus on main drivers affecting sample quality ASSAY DEVELOPMENT VALIDATION CLINICAL PERFORMANCE TEST b) Ultrafiltration Solid phase extraction Solid phase extraction Derivatization GC‐MS LC‐MS/MS SPE‐UPLC‐MS/MS MxP MxP MxP Broad Profiling Catecholamines Eicosanoids c) PLASMA SAMPLE UPLC‐MS/MS MxP Lipids (Sphingoid part) MxP Quality Control Assay EXTRACTION & CLEAN‐UP DERIVATIZATION METABOLITE QUALITY MARKER PANEL ANALYSIS USING GC‐ MS Blood processing Sample Protein precipitation EDTA plasma citrate plasma heparin plasma serum Clinical Performance Test d) MxP Metabolite Profiling Platforms Phase separation (polar + lipid) BETA‐TEST MxP Quality Control Assay Metabolite Profiling Platforms Experimental design 2h 0 °C 6h 15 min RT 2h hemolysis shear‐force induced 6h Control RT 6h 24h Figure 1: Quality marker validation a) Development process b) MxP Methods applied c) MxP QC assay d) Set‐up of Clinical Performance Test DATA ANALYSIS AND REPORTING AUC = Area under curve of ROC versus control. * P‐value of linear mixed (middle) or simple (right) model relative to control. Figure 4: Results from QC Assay: 3D scatter plot of compounds X, Y, Z (µmol/L) showing the separation of different matrix processed to plasma IDENTIFICATION n = 20 subjects 3 Plasma processing 1 • Addressing time from blood processing to plasma as well as hemolysis • Addressing impact of time from plasma to analytics (RT = room temperature) Results – Examples of Affected Metabolites: MxP Metabolite Profiling Results derived from MxP technologies applied within identification and validation phase revealed multiple metabolites comprising different chemical and biological classes to be affected by different pre‐analytical parameters such as time, temperature and hemolysis. Examples of metabolites changed with respect to pre‐analytical processes are given in Fig. 2. types (a), boxplots of metabolic quality markers in plasma increasing when blood (b) or plasma (c) is incubated for prolonged time. Understanding the MxP Quality Score (QS) Metabolic effects of prolonged blood and plasma processing are different • metabolic activity of erythrocytes in blood • activation of platelets in blood • different anti‐oxidative capacities MxP Quality Control Plasma detects pre‐analytical issues both from the blood processing and the plasma processing phase by calculating two different scores MxP Quality Control Plasma tells you where a pre‐analytical deviation from the SOP happened and enables for identification of focused training needs Quality Scores: high medium low MxP QC Plasma Processing Control Figure 5: MxP Quality Scores Benefits of MxP Quality Control Plasma PHARMA & DIAGNOSTICS: Maximize success in pharma R&D and any biomarker development • Upgrading Quality Management (QM) from quality assurance to quality control • Monitoring of Standard Operating Procedure (SOP) compliance • Evidence‐based decisions for sample selection BIOBANKS: Become quality‐controlled • Delivery of quality‐controlled human plasma specimens • Establishment of active longitudinal QM • Monitoring of SOP compliance and assessment of training needs Figure 2: Examples of metabolite response to pre‐analytical variations • Serotonin and sphingosine‐1‐phosphate levels in plasma are influenced by pre‐analytical blood processing • Effects can be biochemically interpreted by platelet and erythrocyte metabolism CLINICAL RESEARCH ORGANIZATIONS: Offer superior human plasma sample quality to your customers • Delivery of superior sample quality • Evidence‐based qualification of clinical centers • Monitoring of SOP compliance
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