Metabolomic Insight into Polycystic Ovary Syndrome-An Overview.

Searching for the mechanisms of the polycystic ovary syndrome (PCOS) pathophysiology has become a crucial aspect of research performed in the last decades. However, the pathogenesis of this complex and heterogeneous endocrinopathy remains unknown. Thus, there is a need to investigate the metabolic pathways, which could be involved in the pathophysiology of PCOS and to find the metabolic markers of this disorder. The application of metabolomics gives a promising insight into the research on PCOS. It is a valuable and rapidly expanding tool, enabling the discovery of novel metabolites, which may be the potential biomarkers of several metabolic and endocrine disorders. The utilization of this approach could also improve the process of diagnosis and therefore, make treatment more effective. This review article aims to summarize actual and meaningful metabolomic studies in PCOS and point to the potential biomarkers detected in serum, urine, and follicular fluid of the affected women.

Determination of tocopherols and tocotrienols in human breast adipose tissue with the use of high performance liquid chromatography-fluorescence detection.

Tocopherols and tocotrienols have been extensively studied owing to their anticancer potential, especially against breast cancer. Therefore, the aim of this study was to quantitatively determine tocochromanols in human breast adipose tissue with the use of HPLC-FLD. The sample preparation procedure included homogenization and solvent extraction with isopropanol-ethanol-0.1% formic acid mixture prior to solid-phase extraction. After implementation of central composite design, satisfactory separation of all eight target compounds was achieved within 10.5 min. Chromatographic runs were carried out with the use of a naphthylethyl chromatographic column with methanol-water mixture (89:11, v/v) as the mobile phase. Fluorescence detection of tocochromanols was performed with excitation and emission wavelengths 298 and 330 nm, respectively. The method was validated in terms of linearity, carryover, recovery, precision, accuracy and stability. Extraction yield was also determined for accurate evaluation of vitamin E content in human breast adipose tissue samples. Finally, concentrations of particular tocochromanols compounds were assessed in human breast adipose tissue samples obtained from 99 patients, including women with breast cancer, healthy volunteers and deceased women who had died as a result of accidents. The raw data was transformed according to the newly developed equation for accurate estimation of the concentrations of tocochromanols in breast adipose tissue samples. Results obtained in the study indicated that the proposed analytical assay could be useful in breast cancer research.

How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective.

Analysis of time series data addresses the question on mechanisms underlying normal physiology and its alteration under pathological conditions. However, adding time variable to high-dimension, collinear, noisy data is a challenge in terms of mining and analysis. Here, we used Bayesian multilevel modeling for time series metabolomics in vivo study to model different levels of random effects occurring as a consequence of hierarchical data structure. A multilevel linear model assuming different treatment effects with double exponential prior, considering major sources of variability and robustness to outliers was proposed and tested in terms of performance. The treatment effect for each metabolite was close to zero suggesting small if any effect of cancer on metabolomics profile change. The average difference in 964 signals for all metabolites varied by a factor ranging from 0.8 to 1.25. The inter-rat variability (expressed as a coefficient of variation) ranged from 3-30% across all metabolites with median around 10%, whereas the inter-occasion variability ranged from 0-30% with a median around 5%. Approximately 36% of metabolites contained outlying data points. The complex correlation structure between metabolite signals was revealed. We conclude that kinetics of metabolites can be modeled using tools accepted in pharmacokinetics type of studies.

Evaluation of sample injection precision in respect to sensitivity in capillary electrophoresis using various injection modes.

A comparative study was conducted to assess the injection precision in capillary electrophoresis for cationic analytes (arecoline, codeine, papaverine). The precision was measured in respect to methods sensitivity in various injection modes in capillary electrophoresis: standard hydrodynamic injection (3.45 kPa for 6 s), large volume sample stacking (3.45 kPa for 40 s), and field-amplified sample injection (10 kV for 65 s). All measurements were conducted for aqueous solutions of standards to minimize the errors linked to the sample preparation step. The methods were submitted to precision assessment at three concentration levels: at the limit of quantification, three-fold and ten-fold of limit of quantification. The results were compared to those from high-performance liquid chromatography as a reference technique. The field-amplified sample injection method was shown to provide greatest sensitivity (quantification limits down to 4 ng/mL for all three tested compounds) but the lowest precision. High-performance liquid chromatography was established as the most reliable technique (coefficient of variation in all intraday experiments was below 1%). It was also shown that with a use of large volume sample injection technique, similar sensitivity as in high-performance liquid chromatography can be easily reached.

Development of the thin film solid phase microextraction (TF-SPME) method for metabolomics profiling of steroidal hormones from urine samples using LC-QTOF/MS.

In the present study, the development and optimization of a thin film solid phase microextraction method (TF-SPME) was conducted for metabolomics profiling of eight steroid compounds (androsterone, dihydrotestosterone, dihydroepiandrosterone, estradiol, hydroxyprogesterone, pregnenolone, progesterone and testosterone) from urine samples. For optimization of extraction method, two extraction sorbents (PAN-C18 and PS-DVB) were used as they are known to be effective for isolation of low-polarity analytes. The stages of sample extraction and analyte desorption were considered as the most crucial steps in the process. Regarding the selection of the most suitable desorption solution, six different mixtures were analyzed. As a result, the mixture of ACN: MeOH (1:1, v/v) was chosen in terms of the highest analytes' abundances that were achieved using the chosen solvent. Besides other factors were examined such as the volume of desorption solvent and the time of both extraction and desorption processes. The analytical determination was carried out using the ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry detection in electrospray ionization and positive polarity in a scan mode (UHPLC-ESI-QTOF/MS). The developed and optimized TF-SPME method was validated in terms of such parameters as extraction efficiency, recovery as well as matrix effect. As a result, the extraction efficiency and recovery were in a range from 79.3% to 99.2% and from 88.8% to 111.8%, respectively. Matrix effect, calculated as coefficient of variation was less than 15% and was in a range from 1.4% to 11.1%. The values of both validation parameters (recovery and matrix effect) were acceptable in terms of EMA criteria. The proposed TF-SPME method was used successfully for isolation of steroids hormones from pooled urine samples before and after enzymatic hydrolysis of analytes.

Common Strategies and Factors Affecting Off-Line Breath Sampling and Volatile Organic Compounds Analysis Using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS).

An analysis of exhaled breath enables specialists to noninvasively monitor biochemical processes and to determine any pathological state in the human body. Breath analysis holds the greatest potential to remold and personalize diagnostics; however, it requires a multidisciplinary approach and collaboration of many specialists. Despite the fact that breath is considered to be a less complex matrix than blood, it is not commonly used as a diagnostic and prognostic tool for early detection of disordered conditions due to its problematic sampling, analysis, and storage. This review is intended to determine, standardize, and marshal experimental strategies for successful, reliable, and especially, reproducible breath analysis.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation.

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords "forensic" and "metabolomics" were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics' applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics' assays.

GC-MS-based untargeted metabolomics of plasma and urine to evaluate metabolic changes in prostate cancer.

Prostate cancer (CaP) is a common cancer in men. Its late detection and inefficient diagnosis are a challenge for researchers who are currently searching for new cancer-related indicators that would facilitate better detectability of CaP and explain its pathogenesis. In the present preliminary study, endogenous volatile metabolites were detected in plasma and urine samples by using the metabolic fingerprinting approach. The analyses were performed using the GC-QqQ/MS technique in the scan mode. The detected and putatively identified metabolites were statistically analyzed using advanced univariate and multivariate statistical methods. Eleven urinary and three plasma metabolites were selected as statistically significant in patients with CaP as compared to those in healthy controls. Supervised methods such as logistic regression and quadratic support vector machine were applied to obtain the classification models. The accuracy, sensitivity, and specificity of the models were above 83%, 85%, and 81%, respectively. The putatively identified metabolites were associated with biochemical pathways such as tricarboxylic acid cycle, glycolysis, carbohydrate conversion, and steroidal lipid metabolism that are mainly involved in energy production for cell growth and proliferation.

The Correlation between Physical Crosslinking and Water-Soluble Drug Release from Chitosan-Based Microparticles.

Microparticles containing water-soluble zidovudine were prepared by spray-drying using chitosan glutamate and beta-glycerophosphate as an ion crosslinker (CF). The Box-Behnken design was applied to optimize the microparticles in terms of their drug loading and release behavior. Physicochemical studies were undertaken to support the results from dissolution tests and to evaluate the impact of the crosslinking ratio on the microparticles' characteristics. The zidovudine dissolution behavior had a complex nature which comprised two phases: an initial burst effect followed with a prolonged release stage. The initial drug release, which can be modulated by the crosslinking degree, was primarily governed by the dissolution of the drug crystals located on the microparticles' surfaces. In turn, the further dissolution stage was related to the drug diffusion from the swollen polymer matrix and was found to correlate with the drug loading. Differential Scanning Calorimetry (DSC) studies revealed the partial incorporation of a non-crystallized drug within the polymer matrix, which correlated with the amount of CF. Although CF influenced the swelling capacity of chitosan glutamate microparticles, surprisingly a higher amount of CF did not impact the time required for 80% of the drug to be released markedly. The formulation with the lowest polymer:CF ratio, 3:1, was selected as optimal, providing satisfactory drug loading and displaying a moderate burst effect within the first 30 min of the study, followed with a prolonged drug release of up to 210 min.

Bayesian multilevel model of micro RNA levels in ovarian-cancer and healthy subjects.

In transcriptomics, micro RNAs (miRNAs) has gained much interest especially as potential disease indicators. However, apart from holding a great promise related to their clinical application, a lot of inconsistent results have been published. Our aim was to compare the miRNA expression levels in ovarian cancer and healthy subjects using the Bayesian multilevel model and to assess their potential usefulness in diagnosis. We have analyzed a case-control observational data on expression profiling of 49 preselected miRNA-based ovarian cancer indicators in 119 controls and 59 patients. A Bayesian multilevel model was used to characterize the effect of disease on miRNA levels controlling for differences in age and body weight. The difference between the miRNA level and health status of the patient on the scale of the data variability were discussed in the context of their potential usefulness in diagnosis. Additionally, the cross-validated area under the ROC curve (AUC) was used to assess the expected out-of-sample discrimination index of a different sets of miRNAs. The proposed model allowed us to describe the set of miRNA levels in patients and controls. Three highly correlated miRNAs: miR-101-3p, miR-142-5p, miR-148a-3p rank the highest with almost identical effect sizes that ranges from 0.45 to 1.0. For those miRNAs the credible interval for AUC ranged from 0.63 to 0.67 indicating their limited discrimination potential. A little benefit in adding information from other miRNAs was observed. There were several miRNAs in the dataset (miR-604, hsa-miR-221-5p) for which inferences were uncertain. For those miRNAs more experimental effort is needed to fully assess their effect in the context of new hits discovery and usefulness as disease indicators. The proposed multilevel Bayesian model can be used to characterize the panel of miRNA profile and to assess the difference in expression levels between healthy and cancer individuals.

Metabolomic Heterogeneity of Urogenital Tract Cancers Analyzed by Complementary Chromatographic Techniques Coupled with Mass Spectrometry.

BACKGROUND: In regard to urogenital tract cancer studies, an estimated 340,650 new cases and 58,360 deaths from genital system cancer and about 141,140 new cases and 29330 deaths from urinary system were projected to occur in the United States in 2012. The main drawbacks of currently available diagnostic tests constitute the low specificity, costliness and quite high invasiveness. OBJECTIVE: The main goal of this pilot study was to determine and compare urine metabolic fingerprints in urogenital tract cancer patients and healthy controls. METHOD: A comparative analysis of the metabolic profile of urine from 30 patients with cancer of the genitourinary system (bladder (n=10), kidney (n=10) and prostate (n=10)) and 30 healthy volunteers as a control group was provided by LC-TOF/MS and GCQqQ/ MS. The data analysis was performed by the use of U-Mann Whitney test or Student's t-test, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: As a result, 33, 43, and 22 compounds were identified as statistically significant in bladder, prostate and kidney cancer, respectively, compared to healthy groups. CONCLUSION: Diverse compounds such as purine, sugars, amino acids, nucleosides, organic acids which play a role in purine metabolism, in tricarboxylic acid cycle, in amino acid metabolism or in gut microbiota metabolism were identified. Only two metabolites namely glucocaffeic acid and lactic acid were found to be in common in studied three types of cancer.

Identification of the metabolic fingerprints in women with polycystic ovary syndrome using the multiplatform metabolomics technique.

In addition to chronic anovulation and clinical signs of hyperandrogenism women with polycystic ovary syndrome (PCOS) are insulin resistant and therefore, develop central obesity with its long term consequences such as dyslipidaemia, hypertension, atherosclerosis and type 2 diabetes mellitus (T2DM), which all lead to the development of cardiovascular disease (CVD). Due to the polysymptomatic nature of this syndrome and lack of consensus on its diagnostic criteria there is a strong need of finding a reliable biochemical or molecular marker, which would facilitate making the accurate diagnosis of PCOS. Therefore, the aim of our study was to perform a metabolomics analysis with the use of two complementary techniques: gas chromatography and liquid chromatography coupled with mass spectrometry, of the serum samples from women with PCOS (n = 30) and to compare them with healthy age and BMI matched controls (n = 30). Obtained results were subjected to one-dimensional statistical analysis (student's t-test or its non-parametric equivalent U Mann-Whitney test) and multivariate statistical analysis (the principal component analysis [PCA], variable importance into projection [VIP] and selectivity ratio [SR]). The results of our study showed that women with PCOS are characterised by metabolic disorders of the amino acids, carbohydrates, steroid hormones, lipids and purines. Compared to control subjects, women with PCOS had increased serum levels of phospholipids, aromatic amino acids, organic acids, hormones and sphinganine and decreased total cholesterol. Among the identified compounds, total cholesterol, phenylalanine and dehydroepiandrosterone sulfate, uric and lactic acid were the compounds with the strongest discriminating power.

The application of gradient reversed-phase high-performance liquid chromatography to the pK(a) and log k(w) determination of polyprotic analytes.

The purpose of this work was to propose a theoretical model of the combined pH/organic modifier gradient in reversed-phase high-performance liquid chromatography (RP HPLC) with special emphasis on its applicability to polyprotic analytes. The model was developed and approximated to be useful for a data set comprising organic modifier gradients obtained at different pH changes and different gradient durations. It was evaluated regarding its ability to describe experimental data. The chromatographic pK(a) and lipophilicity parameter, log k(w), were obtained by fitting to the proposed model and comparing to the literature values.

Targeted metabolomics in bladder cancer: From analytical methods development and validation towards application to clinical samples.

Bladder cancer constitutes the ninth most common cancer worldwide and, despite continuous development of new diagnostic approaches, the thirteenth leading cause of global cancer mortality. In our previous untargeted urine metabolomic investigation, seventeen metabolites were found to be statistically differentiating bladder cancer patients and healthy volunteers. Therefore, the main goal of this study was to develop and validate an analytical method for simultaneous quantitative determination of those metabolites using reversed phase high-performance liquid chromatography coupled with triple quadrupole mass spectrometry technique (RP-HPLC-QQQ/MS). Different chromatographic conditions, as well as various sample treatment procedures were tested in order to provide the best separation and the lowest limit of quantification (LOQ) values for studied compounds. The validation was performed according to the Food and Drug Administration guidelines (FDA). The limit of determination (LOD) and the LOQ values were in the range of 0.21-10.51 ng/ml and 0.69-35.02 ng/ml, respectively. The concentration range of compounds was developed between 2.5 and 12500 ng/ml. Only one compound (trimethyllysine) showed a significant matrix effect (61%) and consequently low process efficiency (64%). Overall, developed method presented recovery and precision values within the ranges proposed by FDA guidelines. The optimized and validated method was applied to urine samples obtained from 40 patients with bladder cancer and 40 healthy volunteers matched according to ones of the most important risk factors for developing urinary bladder tumors, e.i. age, gender and BMI. Afterwards, statistical analysis was provided by the use of Student's t-test or U-Mann Whitney test. The developed method was sensitive, selective and reproducible to be applied for the quantification of metabolites in the investigation of urine samples. As a consequence, ten out of previously chosen seventeen compounds, participating in different metabolites' pathways (gut floral metabolism, RNA degradation, purine metabolism, etc.), were found to be statistically significantly different in the urine concentration (p < 0.05) between cancer and control groups.

Development and validation of urinary nucleosides and creatinine assay by capillary electrophoresis with solid phase extraction.

For the analysis of metabolite nucleoside profiles, capillary electrophoretic (CE) methods preceded by appropriate solid phase extraction procedures have been developed. The approach has been proposed for the determination of 13 nucleosides and creatinine in human urine. A background solution composed of 100 mM borate-72 mM phosphate-160 mM SDS and a fused silica capillary of 70 cm length to detector and 50 microm i.d. were used. The methods developed were statistically validated for their linearity, trueness, precision and selectivity. Stability of the analyzed nucleoside profiles in urine during storage was checked. Validation parameters of solid phase extraction procedures for urinary nucleosides were evaluated. The developed analytical methods were employed for the analysis of 22 urine samples from healthy patients and cancer patients from the urological ward. Nucleoside profiles were compared among the subjects. It was proved that the methods proposed were suitable for a fast and reliable determination of urinary creatinine and modified nucleoside profiles, which can be further submitted for the metabonomic analysis of cancer patients.

Multilevel pharmacokinetics-driven modeling of metabolomics data.

INTRODUCTION: Multilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge. OBJECTIVES: In this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients. METHODS: A Bayesian multilevel model was proposed to describe the nucleoside and creatinine concentration ratio considering age, sex and health status as covariates. The predictive performance of the proposed model was summarized via area under the ROC, sensitivity and specificity using external validation. RESULTS: Cancer was associated with an increase in methylthioadenosine/creatinine excretion rate by a factor of 1.42 (1.09-2.03) which constituted the highest increase among all nucleosides. Age influenced nucleosides/creatinine excretion rates for all nucleosides in the same direction which was likely caused by a decrease in creatinine clearance with age. There was a small evidence of sex-related differences for methylthioadenosine. The individual a posteriori prediction of patient classification as area under the ROC with 5th and 95th percentile was 0.57(0.5-0.67) with sensitivity and specificity of 0.59(0.42-0.76) and 0.57(0.45-0.7), respectively suggesting limited usefulness of 13 nucleosides/creatinine urine concentration measurements in predicting disease in this population. CONCLUSION: Bayesian multilevel pharmacokinetics-driven modeling in metabolomics may be useful in understanding the data and may constitute a new tool for searching towards potential candidates of disease indicators.

HPLC-MS/MS method for dexmedetomidine quantification with Design of Experiments approach: application to pediatric pharmacokinetic study.

AIM: The purpose of this work was to develop and validate a rapid and robust LC-MS/MS method for the determination of dexmedetomidine (DEX) in plasma, suitable for analysis of a large number of samples. METHOD: Systematic approach, Design of Experiments, was applied to optimize ESI source parameters and to evaluate method robustness, therefore, a rapid, stable and cost-effective assay was developed. The method was validated according to US FDA guidelines. LLOQ was determined at 5 pg/ml. The assay was linear over the examined concentration range (5-2500 pg/ml), Results: Experimental design approach was applied for optimization of ESI source parameters and evaluation of method robustness. The method was validated according to the US FDA guidelines. LLOQ was determined at 5 pg/ml. The assay was linear over the examined concentration range (R2 > 0.98). The accuracies, intra- and interday precisions were less than 15%. The stability data confirmed reliable behavior of DEX under tested conditions. CONCLUSION: Application of Design of Experiments approach allowed for fast and efficient analytical method development and validation as well as for reduced usage of chemicals necessary for regular method optimization. The proposed technique was applied to determination of DEX pharmacokinetics in pediatric patients undergoing long-term sedation in the intensive care unit.

Separation of nicotinic acid and its structural isomers using 1-ethyl-3-methylimidazolium ionic liquid as a buffer additive by capillary electrophoresis.

The growing interest in application of ionic liquids (ILs) in analytical chemistry has been observed. The aim of presented investigation was to verify whether ILs would be a suitable modifier of the background electrolyte (BGE) for pharmaceutical analysis of the closely related drug analogues. The study demonstrates the use of 1-ethyl-3-methylimidazolium tetrafluoroborate (1E-3MI-TFB) ionic liquid as modifiers in the separation of nicotinic acid and its structural isomers by capillary electrophoresis. Dependences of the ionic liquid concentration in a BGE on the separation parameters like migration time, resolution factor and width at peak's baseline have been compared. The separation mechanism involves the free imidazolium ions, which can interact with inner surface of the capillary wall. Increased 1E-3MI-TFB concentration to 150 mmol/L caused decrease of migration times of analytes, improve peaks shape and increase of separation performances. At this ionic liquid concentration in a BGE resolution factor between nicotinic and isonicotinic acids increased to 1.86. The proposed CE separation procedure is highly reproducible and can be applied in qualitative and quantitative analysis of carboxylic acids.

Statistical-based approach in potential diagnostic application of urinary nucleosides in urogenital tract cancer.

AIM: We aimed at evaluation the potential diagnostic role of urinary nucleosides in urogenital tract cancer. MATERIALS & METHODS: Concentrations of 12 nucleosides determined by LC-MS/MS were subjected to correlation, association and interaction analyses. RESULTS: We identified six pairs of nucleosides differently correlated in the group of patients and controls (p < 0.05). N-2-methylguanosine (odds ratio: 4.82; 95% CI: 1.78-12.93; p = 0.002) and N,N-dimethylguanosine (odds ratio: 5.45; 95% CI: 1.78-16.44; p = 0.003), were significantly associated with the disease risk (p-corrected = 0.004). Interaction between N-2-methylguanosine and adenosine (p-interaction = 0.019) suggested their multiplicative effect on the outcome. CONCLUSION: Urinary nucleosides, namely N,N-dimethylguanosine and N-2-methylguanosine may have the potential to serve as prognostic biomarkers. Gender-specific differences in urogenital tract cancer are likely to occur.

Least absolute shrinkage and selection operator and dimensionality reduction techniques in quantitative structure retention relationship modeling of retention in hydrophilic interaction liquid chromatography.

The objective of this study was to model the retention of nucleosides and pterins in hydrophilic interaction liquid chromatography (HILIC) via QSRR-based approach. Two home-made (Amino-P-C18, Amino-P-C10) and one commercial (IAM.PC.DD2) HILIC stationary phases were considered. Logarithm of retention factor at 5% of acetonitrile (logkACN) along with descriptors obtained for 16 nucleosides and 11 pterins were used to develop QSRR models. We used and compared the predictive performance of three regression techniques: partial least square (PLS), the least absolute shrinkage and selection operator (LASSO), and the LASSO followed by stepwise multiple linear regression. The highest predictive squared correlation coefficient (QLOOCV(2)) in PLS analysis was found for Amino-P-C10 (QLOOCV(2)=0.687) and IAM.PC.DD2 (QLOOCV(2)=0.506) and the lowest for IAM.PC.DD2 (QLOOCV(2)=-0.01). Much higher values were obtained for the LASSO model. The QLOOCV(2) equaled 0.9 for Amino-P-C10, 0.66 for IAM.PC.DD2 and 0.59 for Amino-P-C18. The combination of LASSO with stepwise regression provided models with comparable predictive performance as the LASSO, however with possibility of calculating the standard error of estimates. The use of LASSO itself and in combination with classical stepwise regression may offer greater stability of the developed models thanks to more smooth change of coefficients and reduced susceptibility towards chance correlation. Application of QSRR-based approach, along with the computational methods proposed in this work, may offer a useful approach in the modeling of retention of nucleoside and pterin compounds in HILIC.