Early nutrition has an enormous influence on a child's physiological function, immune system maturation, and cognitive development. Human breast milk (HBM) is recognized as the gold standard for human infant nutrition. According to a WHO report, breastfeeding is considered as an unequaled way of providing ideal food to the infant, which is required for his healthy growth and development. HBM contains various macronutrients (carbohydrates, proteins, lipids, and vitamins) as well as numerous bioactive compounds and interactive elements (growth factors, hormones, cytokines, chemokines, and antimicrobial compounds. The aim of this review is to summarize and discuss the current knowledge about metabolites, which are the least understood components of HBM, and their potential role in infant development. We focus on small metabolites (<1500 Da) and characterize the chemical structure and biological function of polar metabolites such as human milk oligosaccharides, nonprotein molecules containing nitrogen (creatine, amino acids, nucleotides, polyamines), and nonpolar lipids. We believe that this manuscript will provide a comprehensive insight into a HBM metabolite composition, chemical structure, and their role in a child's early life nutrition.

Staphylococcus aureus resistance to antibiotics is a significant clinical problem worldwide. In this study, an untargeted lipidomics approach was used to compare the lipid fingerprints of S. aureus clinical isolates that are resistant and sensitive to antibiotics. High-performance liquid chromatography coupled with time-of-flight mass spectrometry was employed to rapidly and comprehensively analyze bacterial lipids. Chemometric and statistical analyses of the obtained lipid fingerprints revealed variations in several lipid groups between S. aureus strains resistant and sensitive to tested antibiotics including methicillin, gentamicin, ciprofloxacin, erythromycin, and fusidic acid. The levels of identified monoglycosyldiacylglycerol, phosphatidylglycerol, and diglycosyldiacylglycerol lipid groups were found to be upregulated in antibiotic-resistant S. aureus strains, whereas the levels of diacylglycerol lipid groups were downregulated. Differences in the lipid patterns between sensitive and resistant S. aureus strains suggest that antibiotic susceptibility may be associated with the lipid composition of bacterial cells. The lipids that were found to significantly differ between antibiotic-resistant and antibiotic-sensitive clinical isolates are involved in the biosynthesis of major S. aureus membrane lipids and lipoteichoic acid. This study indicates that S. aureus lipid biosynthesis pathways should be explored further to better understand the mechanism of antibiotic resistance in S. aureus strains.

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In this report, we present a detailed comparison of the lipid composition of human milk (HM) and formula milk (FM) targeting different lactation stages and infant age range. We studied HM samples collected from 26 Polish mothers from colostrum to 19 months of lactation, along with FM from seven brands available on the Polish market (infant formula, follow-on formula and growing-up formula). Lipid extracts were analysed using liquid chromatography coupled to high-resolution mass spectrometry (LC–Q-TOF–MS). We found that the lipid composition of FM deviates significantly from the HM lipid profile in terms of qualitative and quantitative differences. FM had contrasting lipid profiles mostly across brands and accordingly to the type of fat added but not specific to the target age range. The individual differences were dominant in HM; however, differences according to the lactation stage were also observed, especially between colostrum and HM collected in other lactation stages. Biologically and nutritionally important lipids, such as long-chain polyunsaturated fatty acids (LC-PUFAs) containing lipid species, sphingomyelines or ether analogues of glycerophosphoethanoloamines were detected in HM collected in all studied lactation stages. The observed differences concerned all the major HM lipid classes and highlight the importance of the detailed compositional studies of both HM and FM.

Human breast milk (HBM) is a biofluid consisting of various biomolecules such as proteins, lipids, carbohydrates, minerals and bioactive substances. Due to its unique and complex composition, HBM provides not only nutritional components required for the growth of the infant, but also additional protection against infections. Global insight into the composition of HBM is crucial to understanding the health benefits infants receive from breastfeeding and could be used to improve the composition of milk formula for babies that cannot be breastfed. To improve global profiling of the HBM lipidome, a new analytical approach based on solid-phase microextraction (SPME) and liquid chromatography-mass spectrometry (LC-MS) was developed. The new extraction method allows for the rapid and simple extraction of a broad range of lipids directly from HBM samples. Moreover, the optimized two-step lipid extraction protocol ensures high lipidome coverage without using toxic solvents such as chloroform. The use of liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) and an automated search of a lipid database allows comprehensive identification of the lipids contained in HBM. The demonstrated analytical approach based on SPME sample preparation and LC-Q-TOF-MS is rapid, free of toxic solvents, and suited for the qualitative analysis of the HBM lipid composition.

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Commercial formula milk (FM) constitutes the best alternative to fulfill the nutritional requirements of infants when breastfeeding is precluded. Here, we present the comparative study of polar metabolite composition of human breast milk (HBM) and seven different brands of FM by nuclear magnetic resonance spectroscopy. The results of the multivariate data analysis exposed qualitative and quantitative differences between HBM and FM composition as well as within FM of various brands and in HBM itself (between individual mothers and lactation period). Several metabolites were found exclusively in HBM and FM. Statistically significant higher levels of isoleucine and methionine in their free form were detected in FM samples based on caprine milk, while FM samples based on bovine milk showed a higher level of glucose and galactose in comparison to HBM. The results suggest that the amelioration of FM formulation is imperative to better mimic the composition of minor nutrients in HBM.

Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model and predict metabolic stability quantitatively is still lacking. This study proposes a workflow for developing quantitative metabolic stability-structure relationships, taking a set of 30 arylpiperazine derivatives as an example. The metabolic stability of the compounds was assessed in in vitro incubations in the presence of human liver microsomes and NADPH and subsequently quantified by liquid chromatography-mass spectrometry (LC-MS). Density functional theory (DFT) calculations were used to obtain 30 models of the molecules, and Dragon software served as a source of structure-based molecular descriptors. For modeling structure-metabolic stability relationships, Support Vector Machines (SVM), a non-linear machine learning technique, were found to be more effective than a regression technique, based on the validation parameters obtained. Moreover, for the first time, general sites of metabolism for arylpiperazines bearing the 4-aryl-2H-pyrido[1,2-c]pyrimidine-1,3-dione system were defined by analysis of Q-TOF-MS/MS spectra. The results indicated that the application of one of the most advanced chemometric techniques combined with a simple and quick in vitro procedure and LC-MS analysis provides a novel and valuable tool for predicting metabolic half-life values. Given the reduced time and simplicity of analysis, together with the accuracy of the predictions obtained, this is a valid approach for predicting metabolic stability using structural data. The approach presented provides a novel, comprehensive and reliable tool for investigating metabolic stability, factors that affect it, and the proposed structures of metabolites at the same time. The performance of the DFT-SVM-based approach provides an opportunity to implement it in a standard drug development pipeline.