Development of a generic sample preparation method using dispersive liquid-liquid microextraction for the monitoring of leachable compounds in hospital pharmacy-prepared prefilled drug products.

Performant sample preparation is mandatory in any leachable study to clean and preconcentrate analytes within the sample to offer the best possible extraction recovery as well the best precision for any given substance. The aim consists in developing a sample preparation method for hospital pharmacy-prepared drug products such as long-term storage prefilled syringes, vials and IV bags for the screening of leachable compounds. The Quality Control Laboratory of the Pharmacy of the Lausanne University Hospital (Switzerland) has developed a time- and cost-effective, highly sensitive, robust, and fast method using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) for the analysis of 205 plastic additives. An innovative setup, based on postcolumn infusion (PCI) using 2% ammonium hydroxide in methanol was used to boost the signal intensity of the analytes in MS detection. A database for extractable and leachable trace assessment (DELTA) was built to assist in the screening process of 205 plastic packaging-related compounds. The development of the sample preparation was based on 33 plastic additive candidates in different hospital pharmacy compounding solutions, and their extraction recovery rates as well as their relative standard deviation were taken into consideration. In conclusion, the developed DLLME was assigned with ultrasound assistance and triple extraction, which brought about extraction recovery rates between 67% and 92%, a good RSD <10%, and a preconcentration factor of 50×. Therefore, DLLME could be considered suitable for the semiquantitative screening of leachable additives in simple hospital pharmacy-prepared prefilled drug products.

Development of a generic approach for monitoring leachable compounds in hospital pharmacy-prepared prefilled plastic packaging by ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry with postcolumn infusion.

Prefilled plastic packaging is time- and cost-effective in hospital pharmacy because it prevents waste, preparation errors, dosage errors, microbial contamination and accidents. This packaging mostly includes prefilled syringes (PFS), intravenous (IV) bags and vials intended for long-term storage that can be used for immediate treatment. There is a rising availability in the market for prefilled drug products due to their practical approach. Leachable compounds could be evaluated in hospital pharmacy-prepared prefilled drug solutions. The Pharmacy Department at the Lausanne University Hospital has developed an innovative, highly sensitive, and generic method by postcolumn infusion based on ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) for the analysis of plastic additives in hospital pharmacies. The postcolumn infusion solution was developed with 2% ammonium hydroxide in methanol on a representative set of 30 candidate compounds with different physical-chemical properties, such as log P and molecular structure, to represent the most important categories of additives. The LODs obtained for all compounds ranged from 0.03 to 7.91 ng/mL with linearity up to 250 ng/mL. Through this screening method, plastic additives can be rapidly identified due to the combined use of retention time, exact mass (including isotopic pattern) and MS/MS spectra. In addition, the users can screen for vast categories of plastic additives, including plasticizers, epoxy monomers, antioxidants, UV stabilizers, and others. The screening is facilitated by assessments of a complex in-house-built database for extractable and leachable trace assessment (DELTA), containing 205 compounds for unambiguous identification. Relative response factors were established for all analytes to obtain a semiquantitation of compounds. Moreover, the database also contains valuable estimative toxicology information, which was obtained through calculating their permissible dose exposure threshold; thus, estimative toxicology assessment can be performed for identified compounds in prefilled drug products. This method and the database were applied to a hospital pharmacy-prepared prefilled vancomycin syringe for paediatric use. Ultrasound-assisted dispersive liquid-liquid microextraction (UA-DLLME) was used to prepare the samples for leachable analysis. As a result, 17 plastic additives were formally identified, and their concentrations were estimated. A toxicology assessment was performed by comparing their concentrations with their theoretical PDE thresholds. In conclusion, the prefilled drug solution released a negligible amount of known leachables that appeared to be safe for use in neonates and children.

Ion mobility-high resolution mass spectrometry in doping control analysis. Part II: Comparison of acquisition modes with and without ion mobility.

In the second part of this study, a systematic comparison was made between two ion fragmentation acquisition modes, namely data-independent acquisition (DIA) and DIA with ion mobility spectrometry (IMS) technology. These two approaches were applied to the analysis of 192 doping agents in urine. Group I included 102 compounds such as stimulants, diuretics, narcotics, and ß2-agonists, while Group II contained 90 compounds included steroids, glucocorticoids, and hormone and metabolic modulators. Important method parameters were examined and compared, including the fragmentation, sensitivity, and assignment capability with the minimum occurrence of false positive hits. The results differed between Group I and II in number of detected fragments when exploring the MS/MS spectra. In Group I only 13%, while in the Group II 64% of the substances had a higher number of fragments in DIA-IMS mode vs. DIA. In terms of sensitivity, the performance of the two modes with and without activated IMS dimension was identical for about 50% of the doping agents. The sensitivity was higher without IMS, i.e. in simple DIA mode, for 20-40% of remaining doping agents. Despite this sensitivity reduction with IMS, 82% of compounds from both Groups met the minimum required performance level (MRPL) criteria of the World Anti-Doping Agency (WADA) when the DIA-IMS mode was applied. Automated data processing is important in routine doping analysis. Therefore, processing methods were optimized and evaluated for the prevalence of false peak assignments by analysing the target substances at different concentrations in urine samples. Overall, a significantly higher number of misidentified compounds was observed in Group II, with an almost 2-fold higher number of misidentifications in DIA compared to DIA-IMS. This result highlights the benefit of the IMS dimension to reduce the rate of false positive in screening analysis. The optimized UHPLC-IM-HRMS method was finally applied to the analysis of urine samples from administration studies including nine doping agents from both Groups. However, to limit the number of interferences from the biological matrix, an emphasis is needed on the adequate settings of the data processing method.

Ion mobility-high resolution mass spectrometry in anti-doping analysis. Part I: Implementation of a screening method with the assessment of a library of substances prohibited in sports.

In this series of two papers, 192 doping agents belonging to the classes of stimulants, narcotics, cannabinoids, diuretics, ß2-agonists, ß-blockers, anabolic agents, and hormone and metabolic modulators were investigated, with the aim to assess the benefits and limitations of ion mobility spectrometry (IMS) in combination with ultra-high performance liquid chromatography (UHPLC) and high resolution mass spectrometry (HRMS) in anti-doping analysis. In this first part, a generic UHPLC-IM-HRMS method was successfully developed to analyze these 192 doping agents in standard solutions and urine samples, and an exhaustive database including retention times, TWCCSN2 values, and m/z ratios was constructed. Urine samples were analyzed using either a simple "dilute and shoot" procedure or a supported liquid-liquid extraction (SLE) procedure, depending on the physicochemical properties of the compounds and sensitivity criteria established by the World Anti-Doping Agency (WADA) as the minimum required performance levels (MRPL). Then, the precision of the generic UHPLC-IM-HRMS method was assessed as intraday, interday as well as interweek variation of UHPLC retention times and TWCCSN2 values, for which RSD the values were always lower than 2% in urine samples. The possibility to filter MS data using IMS dimension was also investigated, and in average, the application of IMS filtration provided low energy MS spectra with 86% less interfering peaks in both standard and urine samples. Therefore, the filtered MS spectra allowed for an easier interpretation and a lower risk of false positive result interpretations. Finally, IMS also offers additional selectivity to the UHPLC-HRMS enabling to separate isobaric and isomeric substances. Among the selected set of 192 doping agents, there were 30 pairs of isobaric or isomeric compounds, and only two pairs could not be resolved under the developed conditions. This illustrates the potential of adding ion mobility to UHPLC-HRMS in anti-doping analyses.

Evaluation of Different Tandem MS Acquisition Modes to Support Metabolite Annotation in Human Plasma Using Ultra High-Performance Liquid Chromatography High-Resolution Mass Spectrometry for Untargeted Metabolomics.

Ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is a powerful and essential technique for metabolite annotation in untargeted metabolomic applications. The aim of this study was to evaluate the performance of diverse tandem MS (MS/MS) acquisition modes, i.e., all ion fragmentation (AIF) and data-dependent analysis (DDA), with and without ion mobility spectrometry (IM), to annotate metabolites in human plasma. The influence of the LC separation was also evaluated by comparing the performance of MS/MS acquisition in combination with three complementary chromatographic separation modes: reversed-phase chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) with either an amide (aHILIC) or a zwitterionic (zHILIC) stationary phase. RPLC conditions were first chosen to investigate all the tandem MS modes, and we found out that DDA did not provide a significant additional amount of chemical coverage and that cleaner MS/MS spectra can be obtained by performing AIF acquisitions in combination with IM. Finally, we were able to annotate 338 unique metabolites and demonstrated that zHILIC was a powerful complementary approach to both the RPLC and aHILIC chromatographic modes. Moreover, a better analytical throughput was reached for an almost negligible loss of metabolite coverage when IM-AIF and AIF using ramped instead of fixed collision energies were used.

Applicability of Supercritical fluid chromatography-Mass spectrometry to metabolomics. II-Assessment of a comprehensive library of metabolites and evaluation of biological matrices.

In this work, the impact of biological matrices, such as plasma and urine, was evaluated under SFCHRMS in the field of metabolomics. For this purpose, a representative set of 49 metabolites were selected. The assessment of the matrix effects (ME), the impact of biological fluids on the quality of MS/MS spectra and the robustness of the SFCHRMS method were each taken into consideration. The results have highlighted a limited presence of ME in both plasma and urine, with 30% of the metabolites suffering from ME in plasma and 25% in urine, demonstrating a limited sensitivity loss in the presence of matrices. Subsequently, the MS/MS spectra evaluation was performed for further peak annotation. Their analyses have highlighted three different scenarios: 63% of the tested metabolites did not suffer from any interference regardless of the matrix; 21% were negatively impacted in only one matrix and the remaining 16% showed the presence of matrix-belonging compounds interfering in both urine and plasma. Finally, the assessment of retention times stability in the biological samples, has brought into evidence a remarkable robustness of the SFCHRMS method. Average RSD (%) values of retention times for spiked metabolites were equal or below 0.5%, in the two biological fluids over a period of three weeks. In the second part of the work, the evaluation of the Sigma Mass Spectrometry Metabolite Library of Standards containing 597 metabolites, under SFCHRMS conditions was performed. A total detectability of the commercial library up to 66% was reached. Among the families of detected metabolites, large percentages were met for some of them. Highly polar metabolites such as amino acids (87%), nucleosides (85%) and carbohydrates (71%) have demonstrated important success rates, equally for hydrophobic analytes such as steroids (78%) and lipids (71%). On the negative side, very poor performance was found for phosphorylated metabolites, namely phosphate-containing compounds (14%) and nucleotides (31%).

Interlaboratory and Interplatform Study of Steroids Collision Cross Section by Traveling Wave Ion Mobility Spectrometry.

Collision cross section (CCS) databases based on single-laboratory measurements must be cross-validated to extend their use in peak annotation. This work addresses the validation of the first comprehensive TWCCSN2 database for steroids. First, its long-term robustness was evaluated (i.e., a year and a half after database generation; Synapt G2-S instrument; bias within ±1.0% for 157 ions, 95.7% of the total ions). It was further cross-validated by three external laboratories, including two different TWIMS platforms (i.e., Synapt G2-Si and two Vion IMS QToF; bias within the threshold of ±2.0% for 98.8, 79.9, and 94.0% of the total ions detected by each instrument, respectively). Finally, a cross-laboratory TWCCSN2 database was built for 87 steroids (142 ions). The cross-laboratory database consists of average TWCCSN2 values obtained by the four TWIMS instruments in triplicate measurements. In general, lower deviations were observed between TWCCSN2 measurements and reference values when the cross-laboratory database was applied as a reference instead of the single-laboratory database. Relative standard deviations below 1.5% were observed for interlaboratory measurements (<1.0% for 85.2% of ions) and bias between average values and TWCCSN2 measurements was within the range of ±1.5% for 96.8% of all cases. In the context of this interlaboratory study, this threshold was also suitable for TWCCSN2 measurements of steroid metabolites in calf urine. Greater deviations were observed for steroid sulfates in complex urine samples of adult bovines, showing a slight matrix effect. The implementation of a scoring system for the application of the CCS descriptor in peak annotation is also discussed.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial.

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

Bacterial cell cycle control by citrate synthase independent of enzymatic activity.

Proliferating cells must coordinate central metabolism with the cell cycle. How central energy metabolism regulates bacterial cell cycle functions is not well understood. Our forward genetic selection unearthed the Krebs cycle enzyme citrate synthase (CitA) as a checkpoint regulator controlling the G1→S transition in the polarized alpha-proteobacterium Caulobacter crescentus, a model for cell cycle regulation and asymmetric cell division. We find that loss of CitA promotes the accumulation of active CtrA, an essential cell cycle transcriptional regulator that maintains cells in G1-phase, provided that the (p)ppGpp alarmone is present. The enzymatic activity of CitA is dispensable for CtrA control, and functional citrate synthase paralogs cannot replace CitA in promoting S-phase entry. Our evidence suggests that CitA was appropriated specifically to function as a moonlighting enzyme to link central energy metabolism with S-phase entry. Control of the G1-phase by a central metabolic enzyme may be a common mechanism of cellular regulation.

Combining the advantages of multilevel and orthogonal partial least squares data analysis for longitudinal metabolomics: Application to kidney transplantation.

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.

Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches.

Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using Caulobacter crescentus as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H2O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.

Toward a better understanding of chronic kidney disease with complementary chromatographic methods hyphenated with mass spectrometry for improved polar metabolome coverage.

The prevalence of chronic kidney disease (CKD) is increasing worldwide. New technical approaches are needed to improve early diagnosis, disease understanding and patient monitoring, and to evaluate new therapies. Metabolomics, as a prime candidate in the field of CKD research, aims to comprehensively analyze the metabolic complexity of biological systems. An extensive analysis of the metabolites contained in biofluids is therefore needed, and the combination of data obtained from multiple analytical platforms constitutes a promising methodological approach. This study presents an original workflow based on complementary chromatographic conditions, reversed-phase and hydrophilic interaction chromatography hyphenated to mass spectrometry to improve the polar metabolome coverage coupled with a univocal metabolite annotation strategy enabling a rapid access to the biological interpretation. This multiplatform workflow was applied in a CKD cohort study to assess plasma metabolic profile modifications related to renal disease. Multivariate analysis of 278 endogenous annotated metabolites enabled patient stratification with respect to CKD stages and helped to generate new biological insights, while also confirming the relevance of tryptophan metabolism pathway in this condition.

An Integrative Multi-Omics Workflow to Address Multifactorial Toxicology Experiments.

Toxicology studies can take advantage of omics approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial omics studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.

A scoring approach for multi-platform acquisition in metabolomics.

Since the ultimate goal of untargeted metabolomics is the analysis of the broadest possible range of metabolites, some new metrics have to be used by researchers to evaluate and select different analytical strategies when multi-platform analyses are considered. In this context, we aimed at developing a scoring approach allowing to compare the performance of different LC-MS conditions for metabolomics studies. By taking into account both chromatographic and MS attributes of the analytes' peaks (i.e. retention, signal-to-noise ratio, peak intensity and shape), the newly proposed score reflects the potential of a set of LC-MS operating conditions to provide useful analytical information for a given compound. A chemical library containing 597 metabolites was used as a benchmark to apply this approach on two RPLC and three HILIC methods hyphenated to high resolution mass spectrometry (HRMS) in positive and negative ionization modes. The scores not only allowed to evaluate each analytical platform, but also to optimize the number of analytical methods needed for the analysis of metabolomics samples. As a result, the most informative combination of three LC methods and ionization modes was found, leading to a coverage of nearly 95% of the detected compounds. It was therefore demonstrated that the overall performance reached with three selected methods was almost equivalent to the performance reached when five LC-MS conditions were used.

Effective mobility as a robust criterion for compound annotation and identification in metabolomics: Toward a mobility-based library.

Capillary electrophoresis (CE) presents many advantageous features as an analytical technique in metabolomics, such as very low consumption of a sample or the possibility to easily detect very polar and ionizable compounds. However, CE remains an approach only used by a few research groups due to a relatively lower sensitivity and, higher analysis time compared to liquid chromatography. To circumvent these drawbacks, herein we propose a generic CE-mass spectrometry (MS) approach using positive electrospray ionization mode and performing normal- and reverse-polarity CE separations to analyze anionic and acidic compounds. Preliminary experiments showed better sensitivity using the ESI positive mode compared to the ESI negative mode on a set of representative anionic compounds from different biochemical families. This approach was applied to the investigation of an available library of metabolites. More than 450 compounds out of the 596 in the library were detected, with the possibility to monitor negatively ionizable compounds through their ammonium adducts. Migration time of each data point was converted to an effective mobility (µeff) scale and used for peak alignment in data pre-processing; µeff features were used as a robust migration index for peak annotation and identification criterion. For the first time, a large database based on experimental µeff was built, allowing for the straightforward annotation of detected features in biological samples and demonstrating how CE-MS can complement other analytical techniques commonly used in metabolomics.

Applicability of supercritical fluid chromatography - mass spectrometry to metabolomics. I - Optimization of separation conditions for the simultaneous analysis of hydrophilic and lipophilic substances.

The aim of this study was to evaluate the suitability of SFC-MS for the analysis of a wide range of compounds including lipophilic and highly hydrophilic substances (log P values comprised between -6 and 11), for its potential application toward human metabolomics. For this purpose, a generic unified chromatography gradient from 2 to 100% organic modifier in CO2 was systematically applied. In terms of chemistry, the best stationary phases for this application were found to be the Agilent Poroshell HILIC (bare silica) and Macherey-Nagel Nucleoshell HILIC (silica bonded with a zwitterionic ligand). To avoid system overpressure at very high organic modifier proportion, columns of 100 × 3 mm I.D. packed with sub-3 µm superficially porous particles were selected. In terms of organic modifier, a mixture of 95% MeOH and 5% water was selected, with 50 mM ammonium formate and 1 mM ammonium fluoride, to afford good solubility of analytes in the mobile phase, limited retention for the most hydrophilic metabolites and suitable peak shapes of ionizable species. A sample diluent containing 50%ACN/50% water was employed as injection solvent. These conditions were applied to a representative set of metabolites belonging to nucleosides, nucleotides, small organic acids, small bases, sulfated/sulfonated metabolites, poly-alcohols, lipid related substances, quaternary ammonium metabolites, phosphate-based substances, carbohydrates and amino acids. Among all these metabolites, 65% of the compounds were adequately analyzed with excellent peak shape, 23% provided distorted peak shapes, while only 12% were not detected (mostly metabolites having several phosphate or several carboxylic acid groups).

Unravelling the effects of multiple experimental factors in metabolomics, analysis of human neural cells with hydrophilic interaction liquid chromatography hyphenated to high resolution mass spectrometry.

This work introduces a strategy for decomposing variable contributions within the data obtained from structured metabolomic studies. This approach was applied in the context of an in vitro human neural model to investigate biochemical changes related to neuroinflammation. Neural cells were exposed to the neuroinflammatory toxicant trimethyltin at different doses and exposure times. In the frame of an untargeted approach, cell contents were analysed using HILIC hyphenated with HRMS. Detected features were annotated at level 1 by comparison against a library of standards, and the 126 identified metabolites were analysed using a recently proposed chemometric tool dedicated to multifactorial Omics datasets, namely, ANOVA multiblock OPLS (AMOPLS). First, the total observed variability was decomposed to highlight the contribution of each effect related to the experimental factors. Both the dose of trimethyltin and the exposure time were found to have a statistically significant impact on the observed metabolic alterations. Cells that were exposed for a longer time exhibited a more mature and differentiated metabolome, whereas the dose of trimethyltin was linked to altered lipid pathways, which are known to participate in neurodegeneration. Then, these specific metabolic patterns were further characterised by analysing the individual variable contributions to each effect. AMOPLS was highlighted as a useful tool for analysing complex metabolomic data. The proposed strategy allowed the separation, quantitation and characterisation of the specific contribution of the different factors and the relative importance of every metabolite to each effect with respect to the total observed variability of the system.