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.

Long-term stability of cellulose acetate butyrate thin films for nuclear certified reference materials.

Characterization of cellulose acetate butyrate (CAB) thin films with 17, 35 and 52 wt% butyryl is carried out to select the most suitable matrix material for the U and Pu containing large-sized dried spike reference material. The virgin CAB samples were aged by vibrations, heat, humidity, UV light and X-rays. Characterization was done by thermo-analytical techniques, gel permeation chromatography, mechanical tests and via Rayleigh and Compton scattering. The results show that CAB with lower butyryl content can withstand higher operational temperatures and has greater mechanical strength while CAB with higher butyryl content seems to be more resistant to radiation.

New Biochemical Insights into the Mechanisms of Pulmonary Arterial Hypertension in Humans.

Diagnosis of pulmonary arterial hypertension (PAH) is difficult due to the lack of specific clinical symptoms and biomarkers, especially at early stages. We compared plasma metabolic fingerprints of PAH patients (n = 20) with matched healthy volunteers (n = 20) using, for the first time, untargeted multiplatform metabolomics approach consisting of high-performance liquid and gas chromatography coupled with mass spectrometry. Multivariate statistical analyses were performed to select metabolites that contribute most to groups' classification (21 from liquid in both ionization modes and 9 from gas chromatography-mass spectrometry). We found metabolites related to energy imbalance, such as glycolysis-derived metabolites, as well as metabolites involved in fatty acid, lipid and amino acid metabolism. We observed statistically significant changes in threitol and aminomalonic acid in PAH patients, which could provide new biochemical insights into the pathogenesis of the disease. The results were externally validated on independent case and control cohorts, confirming up to 16 metabolites as statistically significant in the validation study. Multiplatform metabolomics, followed by multivariate chemometric data analysis has a huge potential for explaining pathogenesis of PAH and for searching potential and new more specific and less invasive markers of the disease.

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data.

Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study). The orthogonal projections to latent structures-discriminant analysis (OPLS-DA) without and with multiple testing correction as well as the least absolute shrinkage and selection operator (LASSO) with bootstrapping, were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction selected 46 and 218 variables based on the VIP criteria using Pareto and UV scaling, respectively. For the PH study, 217 and 320 variables were selected based on the VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built after correcting for multiple testing, selected 4 and 19 variables as in terms of Pareto and UV scaling, respectively. For the PH study, 14 and 18 variables were selected based on the VIP criteria in terms of Pareto and UV scaling, respectively. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3 and 100%, respectively. In the light of PLS-based models, the larger the search space the higher the probability of developing models that fit the training data well with simultaneous poor predictive performance on the validation set. The LASSO offers potential improvements over standard linear regression due to the presence of the constrain, which promotes sparse solutions. This paper is the first one to date utilizing the LASSO penalized logistic regression in untargeted metabolomics studies.

Selective determination of cocaine and its metabolite benzoylecgonine in environmental samples by newly developed sorbent materials.

Sewage epidemiology, as compared to crime statistics, health, medical reports or population surveys, is becoming the most objective and realistic approach to estimate drug consumption and trends in local communities. In this study we proposed newly synthesized sorbent materials for selective extraction of cocaine and benzoylecgonine from wastewater samples. The molecular modeling calculations were conducted to provide the choice of proper template and functional monomer for synthesis of extraction materials. The physicochemical properties of synthesized sorbents were studied using various techniques. The newly developed sorbent materials were applied for selective extraction of cocaine and benzoylecgonine from wastewater samples collected from different wastewater treatment plants in Poland. The obtained recoveries values in wastewater samples were 83.6(±7.1)% and 72.1(±4.8)%, for cocaine and benzoylecgonine, respectively. The newly developed sorbents comprise an alternative to conventional ones, which are not entirely suitable for highly efficient purification of environmental samples due to the presence of contaminants.

New sorbent materials for selective extraction of cocaine and benzoylecgonine from human urine samples.

An increase in cocaine consumption has been observed in Europe during the last decade. Benzoylecgonine, as a main urinary metabolite of cocaine in human, is so far the most reliable marker of cocaine consumption. Determination of cocaine and its metabolite in complex biological samples as urine or blood, requires efficient and selective sample pretreatment. In this preliminary study, the newly synthesized sorbent materials were proposed for selective extraction of cocaine and benzoylecgonine from urine samples. Application of these sorbent media allowed to determine cocaine and benzoylecgonine in urine samples at the concentration level of 100ng/ml with good recovery values as 81.7%±6.6 and 73.8%±4.2, respectively. The newly synthesized materials provided efficient, inexpensive and selective extraction of both cocaine and benzoylecgonine from urine samples, which can consequently lead to an increase of the sensitivity of the current available screening diagnostic tests.

Blood-brain barrier permeability mechanisms in view of quantitative structure-activity relationships (QSAR).

The goal of the present paper was to develop a quantitative structure-activity relationship (QSAR) method using a simple statistical approach, such as multiple linear regression (MLR) for predicting the blood-brain barrier (BBB) permeability of chemical compounds. The "best" MLR models, comprised logP and either molecular mass (M) or isolated atomic energy (E(isol)), tested on a structurally diverse set of 66 compounds, is characterized the by correlation coefficients (R) around 0.8. The obtained models were validated using leave-one-out (LOO) cross-validation technique and the correlation coefficient of leave-one-out- R(LOO)(2) (Q(2)) was at least 0.6. Analysis of a case from legal medicine demonstrated informative value of our QSAR model. To best authors' knowledge the present study is a first application of the developed QSAR models of BBB permeability to case from the legal medicine. Our data indicate that molecular energy-related descriptors, in combination with the well-known descriptors of lipophilicity may have a supportive value in predicting blood-brain distribution, which is of utmost importance in drug development and toxicological studies.

Urine metabolic fingerprinting using LC-MS and GC-MS reveals metabolite changes in prostate cancer: A pilot study.

Prostate cancer (CaP) is a leading cause of cancer deaths in men worldwide. The alarming statistics, the currently applied biomarkers are still not enough specific and selective. In addition, pathogenesis of CaP development is not totally understood. Therefore, in the present work, metabolomics study related to urinary metabolic fingerprinting analyses has been performed in order to scrutinize potential biomarkers that could help in explaining the pathomechanism of the disease and be potentially useful in its diagnosis and prognosis. Urine samples from CaP patients and healthy volunteers were analyzed with the use of high performance liquid chromatography coupled with time of flight mass spectrometry detection (HPLC-TOF/MS) in positive and negative polarity as well as gas chromatography hyphenated with triple quadruple mass spectrometry detection (GC-QqQ/MS) in a scan mode. The obtained data sets were statistically analyzed using univariate and multivariate statistical analyses. The Principal Component Analysis (PCA) was used to check systems' stability and possible outliers, whereas Partial Least Squares Discriminant Analysis (PLS-DA) was performed for evaluation of quality of the model as well as its predictive ability using statistically significant metabolites. The subsequent identification of selected metabolites using NIST library and commonly available databases allows for creation of a list of putative biomarkers and related biochemical pathways they are involved in. The selected pathways, like urea and tricarboxylic acid cycle, amino acid and purine metabolism, can play crucial role in pathogenesis of prostate cancer disease.

Metabolomics for laboratory diagnostics.

Metabolomics is an emerging approach in a systems biology field. Due to continuous development in advanced analytical techniques and in bioinformatics, metabolomics has been extensively applied as a novel, holistic diagnostic tool in clinical and biomedical studies. Metabolome's measurement, as a chemical reflection of a current phenotype of a particular biological system, is nowadays frequently implemented to understand pathophysiological processes involved in disease progression as well as to search for new diagnostic or prognostic biomarkers of various organism's disorders. In this review, we discussed the research strategies and analytical platforms commonly applied in the metabolomics studies. The applications of the metabolomics in laboratory diagnostics in the last 5 years were also reviewed according to the type of biological sample used in the metabolome's analysis. We also discussed some limitations and further improvements which should be considered taking in mind potential applications of metabolomic research and practice.

GC/MS technique and AMDIS software application in identification of hydrophobic compounds of grasshoppers' abdominal secretion (Chorthippus spp.).

The automated mass spectral deconvolution and identification system (AMDIS) is a modern analytical tool, mostly used as a data processing method in environmental studies. The most attractive feature of that software is a fast, automatic data processing, which includes removal of interferences from the overlapping peaks and purification of the obtained mass spectra. The identification of analytes is based on their retention time and retention index and on comparison of the spectra obtained in GC/MS analysis with the spectra from the library of the National Institute of Standards and Technology (NIST). The main aim of the study reported was to elaborate and test a new data processing method with the use of AMDIS software for identification of lipidomic compounds present in the grasshopper's abdominal secretion. For the first time to the best of our knowledge, we have demonstrated the usage of AMDIS in a lipidomic study concerning a complex insect matrix. The samples processed with AMDIS software were analyzed with the use of GC/MS in order to determine the main fatty components of grasshoppers' abdominal secretion. The purification, concentration and fractionation of compounds present in a complex insect matrix were investigated with the use of liquid-liquid extraction as a pretreatment procedure. Moreover, a double-step derivatization process was carried out in order to obtain more volatile and stable derivatives of polar, non-volatile components of insects' secretion. This process, necessary for GC/MS analysis, was performed with the use of methoxyamine hydrochloride dissolved in pyridine and a mixture of bis-N-O-trimethylsilyl trifluoroacetamide (BSTFA) and chlorotrimethylsilane (TMCS). As a result, we obtained a fast, automatic method based on the use of AMDIS software, which enabled identification of 28 analytes, mainly fatty compounds. Moreover, 10 compounds out of 28 were determined to appear with 100% frequency in the tested samples, namely: seven fatty acids, one sterol, one organic acid and one alkaloid. The last part of our study was statistical analysis of average intensities of signals of compounds identified in grasshopper's abdominal secretion in order to differentiate insects collected at two distant locations in Poland: Starogard Gdanski and Lubianka meadows.

Metabolomic evaluation of Mitomycin C and rapamycin in a personalized treatment of pancreatic cancer.

In a personalized treatment designed for a patient with pancreatic cancer resistant to other treatments, the success of Mitomycin C (MMC) has been highlighted. This was revealed in a murine xenograft tumor model encompassing pancreatic adenocarcinoma cells extracted from the patient. The patient was found to exhibit a biallelic inactivation of the PALB2 gene, involved in DNA repair in addition to another mutation in the TSC2 gene that induces susceptibility of the tumor to therapeutic targets of the PI3K-mTOR pathway. The aim of the study was to apply metabolomics to elucidate the modes of action of each therapy, suggesting why MMC was so successful in this patient and why it could be a more popular choice in future pancreatic cancer treatment. The effectiveness of MMC compared to rapamycin (RM), another relevant therapeutic agent has been evaluated through liquid- and gas-chromatography mass spectrometry-based metabolomic analyses of the xenograft tumors. The relative concentrations of many metabolites in the xenograft tumors were found to be increased by MMC relative to other treatments (RM and a combination of both), including a number that are involved in central carbon metabolism (CCM). Metabolic fingerprinting revealed statistically significantly altered pathways including, but not restricted to, the pentose phosphate pathway, glycolysis, TCA cycle, purine metabolism, fatty acid biosynthesis, in addition to many significant lipid and amino acid alterations. Given the genetic background of the patient, it was expected that the combined therapy would be most effective; however, the most effective was MMC alone. It is proposed that the effectiveness of MMC is owed to its direct effect on CCM, a vital region of tumor metabolism.

Determination of Water-Soluble Components of Abdominal Secretion of Grasshopper (Chorthippus spp.) by GC/MS/MS in Search for Potential Wound Healing Agents.

Wound healing is still a serious medical problem due to process complexity and lack of effective medicaments. This is particularly true in the treatment of wounds arising in the course of such diseases as AIDS or diabetes. Therefore, scientific efforts are focused on the search for new compounds of natural origin, which could be used as medicines or evaluated for subsequent drug design. In folk medicine, grasshopper (Chorthippus spp.) abdominal secretion has been used to accelerate the wound healing process. In this context, the knowledge of the composition of grasshopper abdominal secretion is crucial. The aim of this study was to determine the main water-soluble components of grasshopper abdominal secretion with the use of GC/MS/MS. Liquid-liquid extraction was used as a pretreatment method to clean up, concentrate and fractionate compounds from the complex insect matrix. To obtain more stable and volatile compounds, necessary for GC/MS/MS analysis, a double-step derivatization process was carried out with the use of methoxyamine hydrochloride and a mixture of bis-N,O-trimethylsilyl trifluoroacetamide and chlorotrimethylsilane. As a result, 2,108 compounds were identified, mainly as amino acids, carbohydrates and organic acids. Some of the identified compounds are emphasized due to antimicrobial, antifungal or antioxidant activities reported in the literature. Moreover, a set of compounds characteristic for Chorthippus spp. samples has been selected. In the last part of the study, a statistical analysis was performed to demonstrate differences in composition of aqueous fractions of abdominal secretions from grasshoppers collected at two distant locations: Starogard Gdanski and Lubianka meadows.

Metabolomics reveals metabolite changes in acute pulmonary embolism.

Pulmonary embolism (PE) is a common cardiovascular emergency which can lead to pulmonary hypertension (PH) and right ventricular failure as a consequence of pulmonary arterial bed occlusion. The diagnosis of PE is challenging due to nonspecific clinical presentation, which results in relatively high mortality. Moreover, the pathological factors associated with PE are poorly understood. Metabolomics can provide new highlights which can help in the understanding of the processes and even propose biomarkers for its diagnosis. In order to obtain more information about PE and PH, acute PE was induced in large white pigs and plasma was obtained before and after induction of PE. Metabolic fingerprints from plasma were obtained with LC-QTOF-MS (positive and negative ionization) and GC-Q-MS. Data pretreatment and statistical analysis (uni- and multivariate) were performed in order to compare metabolic fingerprints and to select the metabolites that showed higher loading for the classification (28 from LC and 19 from GC). The metabolites found differentially distributed among groups are mainly related to energy imbalance in hypoxic conditions, such as glycolysis-derived metabolites, ketone bodies, and TCA cycle intermediates, as well as a group of lipidic mediators that could be involved in the transduction of the signals to the cells such as sphingolipids and lysophospholipids, among others. Results presented in this report reveal that combination of LC-MS- and GC-MS-based metabolomics could be a powerful tool for diagnosis and understanding pathophysiological processes due to acute PE.

Metabolic profiling of pteridines for determination of potential biomarkers in cancer diseases.

Cancer disease is the second leading cause of death in the world. Epidemiology data indicate that early diagnosis of a tumour increases a patient's chance of recovery. Biomarkers are effective instruments which can potentially lead to precancer screening or precancer diagnosis and may provide useful information on the cancer type and the disease's stage of progression. The analysis of new biomarkers for cancer is currently a popular area of study in clinical and cancer research. Pteridines are a class of potential cancer biomarkers. The monitoring levels of pteridines may have prospective value for controlling the course of the malignant process. This review describes the functional employment of pteridines, as biomarkers, in cancer diagnosis. It also contains the description of application of analytical techniques such as high-performance liquid chromatography (HPLC) and capillary electrophoresis (CE) used for pteridine analysis.

Metabolomics in urogenital cancer.

Although in recent decades the development of many drugs against cancer has been witnessed, the morbidity and mortality for the most prevalent urogenital cancer have not been significantly reduced. A key task in cancer medicine is to detect the disease as early as possible. In order to achieve this, many new technologies have been developed for cancer biomarker discovery. Monitoring fluctuations of certain metabolite levels in body fluids, such as urine, has become an important way to detect early stages in carcinogenesis. Moreover metabolomic approaches are likely to be used to screen for potential diagnostic and prognostic biomarkers of urogenital cancer. In future work, these potential biomarkers should be further validated with a large enough patient cohort to achieve earlier diagnosis not only of urogenital cancer, but also other malignancies. Moreover, the improvement of patient prognosis will be another aim of such investigations. This novel metabolomic approach has the potential to provide more information about the pathophysiological status of an organism and distinguish precancerous and cancerous stages.