Quantitative structure retention relationship (QSRR) modelling for Analytes' retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance.

In metabolomics, retention prediction methods have been developed based on the structural and physicochemical characteristics of analytes. Such methods employ regression models, harnessing machine learning algorithms mapping experimentally derived retention time (tR) analytes with various structural and physicochemical descriptors, known as Quantitative Structure Retention Relationships (QSRR) models. In the present study, QSRR models have been developed by applying four Machine Learning regression algorithms, i.e. Bayesian Ridge Regression (BRidgeR), Extreme Gradient Boosting Regression (XGBR) and Support Vector Regression (SVR) using both linear and non-linear kernels, all tested and compared for their retention prediction ability on experimentally derived and on publicly available chromatographic data, using Molecular Descriptors to describe the physical, chemical or structural properties of molecules. Various configurations of the available datasets, in terms of the highly-correlated features levels (defined as the maximum absolute value of the Pearson's correlation coefficient calculated between any pair of features) they contained, were analyzed in parallel. This is the first study, to the best of our knowledge, of the effect of collinearity on the performance of QSRR predictive models. In the vast majority of cases studied there was no statistically significant difference in the performance of the generated QSRR predictive models among the specified dataset configurations, indicative of the ability of the selected regression algorithms to effectively handle collinearity. In terms of the individual performance of the selected regression algorithms, no pattern was found where one algorithm (or class of algorithms) stood out significantly relative to the others among the study datasets.

Population Pharmacokinetics of Teicoplanin in Preterm and Term Neonates: Is It Time for a New Dosing Regimen?

Our objective was to develop a population pharmacokinetic (PK) model in order to evaluate the currently recommended dosing regimen in term and preterm neonates. By using an optimal design approach, a prospective PK study was designed and implemented in 60 neonates with postmenstrual ages (PMA) of 26 to 43 weeks. A loading dose of 16 mg/kg was administered at day 1, followed by a maintenance dose of 8 mg/kg daily. Plasma concentrations were quantified by high-pressure liquid chromatography-mass spectrometry. Population PK (popPK) analysis was performed using NONMEM software. Monte-Carlo (MC) simulations were performed to evaluate currently recommended dosing based on a pharmacodynamic index of area under the concentration-time curve (AUC)/MIC ratio of ≥400. A two-compartment model with linear elimination best described the data by the following equations: clearance (CL) = 0.0227 × (weight [wt]/1,765)0.75 × (estimated creatinine clearance [eCRCL]/22)0.672, central compartment volume of distribution (V1) = 0.283 (wt/1,765), intercompartmental clearance (Q) = 0.151 (wt/1,765)0.75, and peripheral compartment volume (V2) = 0.541 (wt/1,765). The interindividual variability estimates for CL, V1, and V2 were 36.5%, 45.7%, and 51.4%, respectively. Current weight (wt) and estimated creatinine clearance (eCRCL) significantly explained the observed variability. MC simulation demonstrated that, with the current dosing regimen, an AUC/MIC ratio of ≥400 was reached by only 68.5% of neonates with wt of <1 kg when the MIC was equal to 1 mg/kg, versus 82.2%, 89.7%, and 92.7% of neonates with wt of 1 to <2, 2 to <3, or ≥3 kg, respectively. Augmentation of a maintenance dose up to 10 or 11 mg/kg for preterm neonates with wt of 1 to <2 or <1 kg, respectively, increases the probability of reaching the therapeutic target; the recommended doses seem to be adequate for neonates with wt of ≥2 kg. Teicoplanin PK are variable in neonates, with wt and eCRCL having the most significant impact. Neonates with wt of <2 kg need higher doses, especially for Staphylococcus spp. with an MIC value of ≥1 mg/liter.

Targeted urine metabolomics in preterm neonates with intraventricular hemorrhage.

Intraventricular hemorrhage (IVH) is a major cause of morbidity and mortality in preterm neonates. Elucidation of the mechanisms underlying IVH and/or development of disease biomarkers is essential. The aim of the study was to investigate the urine metabolic profile of preterm neonates (gestational age < 32 weeks) IVH and explore the role of metabolomics in understanding pathophysiological mechanisms of the disease from which novel biomarkers could be derived. In this single-center, prospective, case-control study, urine samples were collected from seven preterm infants with early IVH (IVH group) and from 11 preterm ones without IVH (control group) on days 1, 3 and 9 of life. Urine metabolites were evaluated using targeted liquid chromatography-tandem mass spectrometry. Demographic and perinatal-clinical characteristics were recorded. Univariate and multivariate statistical analyses were performed. Orthogonal Partial Least Squares-Discriminant Analysis showed that the study groups differed significantly due to alternation in 20 out of the 40 metabolites detected in the urine. Elevated differentiated metabolites included energy intermediates and other important compounds, whereas reduced ones various amino acids, hypoxanthine and nicotinamide. A set of metabolites showed high performance as indicators of IVH, especially during day 1. As evidenced by metabolomics, preterm neonates with IVH demonstrate significant metabolism perturbations. Potentially, a selected panel of metabolites could be used as urine biomarkers of IVH development and/or progression in high-risk preterm infants.

Hyphenated MS-based targeted approaches in metabolomics.

While global metabolic profiling (untargeted metabolomics) has been the center of much interest and research activity in the past few decades, more recently targeted metabolomics approaches have begun to gain ground. These analyses are, to an extent, more hypothesis-driven, as they focus on a set of pre-defined metabolites and aim towards their determination, often to the point of absolute quantification. The continuous development of the technological platforms used in these studies facilitates the analysis of large numbers of well-characterized metabolites present in complex matrices. The present review describes recent developments in the hyphenated chromatographic methods most often applied in targeted metabolomic/lipidomic studies (LC-MS/MS, CE-MS/MS, and GC-MS/MS), highlighting applications in the life and food/plant sciences. The review also underlines practical challenges-limitations that appear in such approaches.

An ultra-high pressure liquid chromatography-tandem mass spectrometry method for the quantification of teicoplanin in plasma of neonates.

The development and validation of an ultra-high pressure liquid chromatography (UHPLC) tandem mass spectrometry (MS/MS) method was performed with the aim to be applied for the quantification of plasma teicoplanin concentrations in neonates. Pharmacokinetic data of teicoplanin in the neonatal population is very limited, therefore, a sensitive and reliable method for the determination of all isoforms of teicoplanin applied in a low volume of sample is of real importance. Teicoplanin main components were extracted by a simple acetonitrile precipitation step and analysed on a C18 chromatographic column by a triple quadrupole MS with electrospray ionization. The method provides quantitative data over a linear range of 25-6400ng/mL with LOD 8.5ng/mL and LOQ 25ng/mL for total teicoplanin. The method was applied in plasma samples from neonates to support pharmacokinetic data and proved to be a reliable and fast method for the quantification of teicoplanin concentration levels in plasma of infants during therapy in Intensive Care Unit.

Urine metabolomic profile in neonates with hypoxic-ischemic encephalopa-thy.

BACKGROUND: Metabolomics could provide valuable insights into hypoxemic-ischemic encephalopathy (HIE) revealing new disease-associated biochemical derangements. The study aimed to investigate urine metabolic changes in neonates with HIE compared to healthy controls, using targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). PATIENTS AND METHODS: In this prospective, single-center study we enrolled neonates born at ≥ 36 weeks gestation with HIE (HIE group) and healthy controls (control group). We collected urine samples for metabolomic analysis on days one, three, and nine of life. RESULTS: Twenty-one full-term newborns were studied, 13 in the HIE group and eight in the control group. Six of the affected neonates had moderate/severe HIE and seven mild HIE. Therapeutic hypothermia was applied only in four neonates with moderate/severe HIE. Multivariate and univariate statistical analysis showed a clear separation between the HIE and the control groups. Discriminant metabolites involved pyruvic acid, amino acids, acylcarnitines, inositol, kynurenine, hippuric acid, and vitamins. CONCLUSIONS: We have identified a specific metabolic profile in neonates with HIE, adding to the existing knowledge on the disease biochemistry that may potentially help in biomarker development. HIPPOKRATIA 2017, 21(2): 80-84.