Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolomics and molecular dynamics unveil the therapeutic potential of epalrestat in diabetic nephropathy

Diabetic nephropathy (DN) is one of the leading clinical causes of end-stage renal failure. The classical aldose reductase (AR) inhibitor epalrestat shows beneficial effect on renal dysfunction induced by DN, with metabolic profile and molecular mechanisms remains to be investigated further. In the current study, integrated untargeted metabolomics, network pharmacology and molecular dynamics approaches were applied to explore the therapeutic mechanisms of epalrestat against DN. Firstly, untargeted serum and urine metabolomics analysis based on UPLC-Q-TOF-MS was performed, revealed that epalrestat could regulate the metabolic disorders of amino acids metabolism, arachidonic acid metabolism, pyrimidine metabolism and citrate cycle metabolism pathways after DN. Subsequently, metabolomics-based network analysis was carried out to predict potential active targets of epalrestat, mainly involving AGE-RAGE signaling pathway, TNF signaling pathway and HIF-1 signaling pathway. Moreover, a 100 ns molecular dynamics approach was employed to validate the interactions between epalrestat and the core targets, showing that epalrestat could form remarkable tight binding with GLUT1 and NFκB than it with AR. Surface-plasmon resonance assay further verified epalrestat could bind GLUT1 and NFκB proteins specifically. Overall, integrated system network analysis not only demonstrated that epalrestat could attenuate DN induced metabolic disorders and renal injuries, but also revealed that it could interact with multi-targets to play a synergistic regulatory role in the treatment of DN.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Corneal metabolic biomarkers for moderate and high myopia in human

This study aimed to identify the corneal metabolic biomarkers for moderate and high myopia in human. We enrolled 221 eyes from 221 subjects with myopia to perform the femtosecond laser small incision lenticule extraction (SMILE) surgery. Among these, 71 eyes of 71 subjects were enrolled in the low myopic group, 75 eyes of 75 subjects in the moderate myopic group and 75 eyes of 75 subjects in the high myopic group. The untargeted metabolomics analysis was performed to analyze the corneal tissues extracted during the SMILE surgery using an ultra-high-performance liquid chromatography (UHPLC) coupled to a quadrupole time-of-flight (Q-TOF) mass spectrometry (MS). The one-way analysis of variance (ANOVA) was used to identify the different metabolites among the three myopic groups, the orthogonal partial least-squares discriminant analysis (OPLS-DA) model was used to reveal the different metabolites between moderate myopia and low myopia, and between high myopia and low myopia. The Venn gram was used to find the overlapped metabolites of the three datasets of the different metabolites. The stepwise multiple linear regression analysis was used to determine the metabolic molecules associated with manifest refractive spherical equivalents (MRSE). The Receiver Operating Characteristics (ROC) analysis was performed to reveal the corneal biomarkers for moderate and high myopia. The hub biomarker was further selected by the networks among different metabolites created by the Cytoscape software. A total of 1594 metabolites were identified in myopic corneas. 321 metabolites were different among the three myopic groups, 106 metabolites were different between high myopic corneas and low myopic corneas, 104 metabolites were different between moderate myopic corneas and low myopic corneas, and 30 metabolic molecules overlapped among the three datasets. The multivariate linear regression analysis revealed the myopic degree was significantly influenced by the corneal levels of azelaic acid, arginine-proline (Arg-Pro), 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine, and hypoxanthine. The ROC curve analysis showed that azelaic acid, Arg-Pro and hypoxanthine were effective in discriminating low myopia from moderate to high myopia with the area under the curve (AUC) values as 0.982, 0.991 and 0.982 for azelaic acid, Arg-Pro and hypoxanthine respectively. The network analysis suggested that Arg-Pro had the maximum connections among these three biomarkers. Thus, this study identified azelaic acid, Arg-Pro and hypoxanthine as corneal biomarkers to discriminate low myopia from moderate to high myopia, with Arg-Pro serving as the hub biomarker for moderate and high myopia.

Machine learning identifying peripheral circulating metabolites associated with intraocular pressure alterations

AIMS

To identify blood metabolite markers associated with intraocular pressure (IOP) in a population-based cross-sectional study.

METHODS

This study was conducted in a multiethnic Asian population (Chinese, n=2805; Indians, n=3045; Malays, n=3041 aged 40-80 years) in Singapore. All subjects underwent standardised systemic and ocular examinations, and biosamples were collected. Selected metabolites (n=228) in either serum or plasma were analysed and quantified using nuclear magnetic resonance spectroscopy. Least absolute shrinkage and selection operator regression was used for metabolites selection. Multivariable linear regression was used to evaluate the relationship between metabolites and IOP in each of the three ethnic groups, followed by a meta-analysis combining the three cohorts.

RESULTS

Six metabolites, including albumin, glucose, lactate, glutamine, ratio of saturated fatty acids to total fatty acids (SFAFA) and cholesterol esters in very large high-density lipoprotein (HDL), were significantly associated with IOP in all three cohorts. Higher levels of albumin (per SD, beta=0.24, p=0.002), lactate (per SD, beta=0.27, p=0.008), glucose (per SD, beta=0.11, p=0.010) and cholesterol esters in very large HDL (per SD, beta=0.47, p=0.006), along with lower levels of glutamine (per SD, beta=0.17, p<0.001) and SFAFA (per SD, beta=0.21, p=0.008) were associated with higher IOP levels.

CONCLUSION

We identify several novel blood metabolites associated with IOP. These findings may provide insight into the physiological and pathological processes underlying IOP control.

Plasma metabolomics identifies metabolic alterations associated with the growth and development of cat

BACKGROUND

The purpose of our study was to study the composition and content of the feline plasma metabolome revealing the critical metabolites and metabolic pathways associated with age during growth and development.

METHODS

Blood samples were collected from juvenile and adult groups for blood routine tests and serum biochemistry tests. Non-targeted metabolomics analyses of plasma were also performed to investigate changes in metabolites and metabolic pathways.

RESULTS

In this study, we found that the red blood cell counts, liver function indexes (albumin and gamma-glutamyl transpeptidase), and the concentration of triglyceride and glucose changed significant with growth and development. The metabolomics results revealed that 1427 metabolites were identified in the plasma of young and adult cats. Most of these metabolites belong to major classes of lipids and lipid-like molecules. The most obvious age-related metabolites include reduced levels of chenodeoxycholate, taurocholate, cholate, and taurochenodeoxycholate but increased levels of L-cysteine and taurocyamine in the adult cat's serum. These metabolites are mainly involved in the primary bile acid biosynthesis pathway, the bile secretion pathway, and the taurine and hypotaurine metabolism pathway.

CONCLUSION

This study revealed many age-related metabolite alterations in the feline plasma. These age-varying metabolites, especially in the bile acid biosynthesis and secretion metabolism pathways, indicate that the regulation of these pathways is involved in the growth and development of cats. This study promotes our understanding of the mechanism of feline growth and provides new insights into nutrition and medicine for cats of different ages.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Comprehensive Profiling of Rapamycin Interacting Proteins with Multiple Mass Spectrometry-Based Omics Techniques

Profiling drug-protein interactions is critical for understanding a drug's mechanism of action and predicting the possible adverse side effects. However, to comprehensively profile drug-protein interactions remains a challenge. To address this issue, we proposed a strategy that integrates multiple mass spectrometry-based omics analysis to provided global drug-protein interactions, including physical interactions and functional interactions, with rapamycin (Rap) as a model. Chemoproteomics profiling reveals 47 Rap binding proteins including the known target protein FKBP12 with high confidence. Gen Ontology enrichment analysis suggested that the Rap binding proteins are implicated in several important cellular processes, such as DNA replication, immunity, autophagy, programmed cell death, aging, transcription modulation, vesicle-mediated transport, membrane organization, and carbohydrate and nucleobase metabolic processes. The phosphoproteomics profiling revealed 255 down-regulated and 150 up-regulated phosphoproteins responding to Rap stimulation; they mainly involve the PI3K-Akt-mTORC1 signaling axis. Untargeted metabolomic profiling revealed 22 down-regulated metabolites and 75 up-regulated metabolites responding to Rap stimulation; they are mainly associated with the synthesis processes of pyrimidine and purine. The integrative multiomics data analysis provides deep insight into the drug-protein interactions and reveals Rap's complicated mechanism of action.

NMR-Based Metabolomics to Analyze the Effects of a Series of Monoamine Oxidases-B Inhibitors on U251 Cells

Alzheimer’s disease (AD) is a typical progressive neurodegenerative disorder, and with multiple possible pathogenesis. Among them, coumarin derivatives could be used as potential drugs as monoamine oxidase-B (MAO-B) inhibitors. Our lab has designed and synthesized coumarin derivatives based on MAO-B. In this study, we used nuclear magnetic resonance (NMR)-based metabolomics to accelerate the pharmacodynamic evaluation of candidate drugs for coumarin derivative research and development. We detailed alterations in the metabolic profiles of nerve cells with various coumarin derivatives. In total, we identified 58 metabolites and calculated their relative concentrations in U251 cells. In the meantime, the outcomes of multivariate statistical analysis showed that when twelve coumarin compounds were treated with U251cells, the metabolic phenotypes were distinct. In the treatment of different coumarin derivatives, there several metabolic pathways changed, including aminoacyl-tRNA biosynthesis, D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glutathione metabolism and valine, leucine and isoleucine biosynthesis. Our work documented how our coumarin derivatives affected the metabolic phenotype of nerve cells in vitro. We believe that these NMR-based metabolomics might accelerate the process of drug research in vitro and in vivo.

Rapid detection and quantification of the adulteration of orange juice with grapefruit juice using handheld Raman spectroscopy and multivariate analysis

Detecting food adulteration has always been an important task for food safety, especially when grapefruit is the adulterant as components in the juice have undesired interactions with many medicines. In this study we employed a handheld Raman device to detect adulteration of orange juices with grapefruit juices. Fresh fruits of orange and grapefruit were purchased from five different sources and fruit juices were made using a handheld juicer. The extracted juices were then mixed in a way that concentrations of grapefruit juices varied from 0% to 100% in 5% increments. In order to study the impact of the different sources of the fruits, three different sets of mixtures were prepared based on their spectral similarity and dissimilarity. Raman spectra were collected using a handheld instrument with an excitation laser at 785 nm and data analysed using principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least squares regression (PLS-R). PLS-R models were trained and validated on: (i) the full data set from the three different mixture sets, and (ii) each set of the three mixtures separately. The results showed that a good calibration model was obtained using full data which had a coefficient of determination (Q²) of 0.81 and a root mean square error of prediction (RMSEP) of 12.5%. Such results were improved when the PLS-R model was trained and validated on the three separate mixture combinations, where the Q² varied from 0.85 to 0.89 and RMSEP varied from 9.9% to 11.6%. Finally, we adopted a two step approach in which a partial least squares for discriminant analysis (PLS-DA) was trained first to classify the three sample sources and then three different PLS-R models were subsequently trained on samples from the same source. This resulted in a Q² of 0.83 and RMSEP of 12.0%. In conclusion, we have demonstrated that Raman spectroscopy can be used as a portable and rapid analytical tool for detecting adulteration of grapefruit juice added to orange juice.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Characterizing patterns of dietary exposure using metabolomic profiles of human biospecimens: a systematic review

CONTEXT

Establishing diet-disease associations requires reliable assessment of dietary intake. With the rapid advancement of metabolomics, its use in identifying objective biomarkers of dietary exposure has substantially increased.

OBJECTIVE

The aim of our review was to systematically combine all observational studies linking dietary intake patterns with metabolomic profiles of human biospecimens.

DATA SOURCES

Five databases were searched - MEDLINE, Embase, Scopus, Web of Science, and Cochrane CENTRAL - to March 2020.

DATA EXTRACTION

Of the 14 328 studies initially screened, 35 observational studies that met the specified inclusion criteria were included.

DATA ANALYSIS

All reviewed studies indicated that metabolomic measures were significantly correlated with dietary patterns, demonstrating the potential for using objective metabolomic measures to characterize individuals' dietary intake. However, similar dietary patterns did not always result in similar metabolomic profiles across different study populations.

CONCLUSION

Metabolomic profiles reflect a multitude of factors, including diet, genetic, phenotypic, and environmental influences, thereby providing a more comprehensive picture of the impact of diet on metabolism and health outcomes. Further exploration of dietary patterns and metabolomic profiles across different population groups is warranted.

Using measures of metabolic flux to align screening and clinical development: Avoiding pitfalls to enable translational studies

Screening campaigns, especially those aimed at modulating enzyme activity, often rely on measuring substrate→product conversions. Unfortunately, the presence of endogenous substrates and/or products can limit one's ability to measure conversions. As well, coupled detection systems, often used to facilitate optical readouts, are subject to interference. Stable isotope labeled substrates can overcome background contamination and yield a direct readout of enzyme activity. Not only can isotope kinetic assays enable early screening, but they can also be used to follow hit progression in translational (pre)clinical studies. Herein, we consider a case study surrounding lipid biology to exemplify how metabolic flux analyses can connect stages of drug development, caveats are highlighted to ensure reliable data interpretations. For example, when measuring enzyme activity in early biochemical screening it may be enough to quantify the formation of a labeled product. In contrast, cell-based and in vivo studies must account for variable exposure to a labeled substrate (or precursor) which occurs via tracer dilution and/or isotopic exchange. Strategies are discussed to correct for these complications. We believe that measures of metabolic flux can help connect structure-activity relationships with pharmacodynamic mechanisms of action and determine whether mechanistically differentiated biophysical interactions lead to physiologically relevant outcomes. Adoption of this logic may allow research programs to (i) build a critical bridge between primary screening and (pre)clinical development, (ii) elucidate biology in parallel with screening and (iii) suggest a strategy aimed at in vivo biomarker development.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

INTRODUCTION

The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.

OBJECTIVES

Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased).

METHODS

High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created.

RESULTS

The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86).

CONCLUSION

Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer

Simple Summary

Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.

Abstract

The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5′-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.

Evaluation of Biological Mechanisms of Eucommiae Folium in Hypertensive Kidney Injury by Integration of Untargeted Metabolomics and Network Pharmacology

Hypertensive kidney injury (Hki) is one of the most common complications of hypertension. Early prevention and treatment of renal injury in patients with hypertension is great significance. The study, which used an integrated ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS) analysis, network pharmacology approach, and plasma metabolomics, aimed to discover the active ingredients and therapeutic mechanisms of Eucommiae folium (Ef) in treating Hki. The chemical components of Ef were analyzed by UPLC-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS), and the "compound-target-disease" network was constructed by screening the closely related drug targets from the drug-target database, then the signaling pathways related to Hki were analyzed. Finally, the enzyme-linked immunosorbent assay (ELISA) and real-time quantitative reverse-transcription polymerase chain reaction were used to test and verify the key targets in the common pathways of metabolomics and network pharmacology. The results indicated that Eucommiae folium might play an excellent role in treating Hki, likely through regulating the vascular endothelial growth factor signaling pathway, hypoxia inducible factor 1 (HIF-1) signaling pathway, and glycerophospholipid metabolism pathway, which were validated by increasing levels of nitric oxide, endothelial nitric oxide synthase and reducing levels of endothelin 1, angiotensin II, renin, cyclic guanosine monophosphate, blood urea nitrogen, and serum creatinine, as well as the reduced gene expression of Ache, Ddah2, Egfr, Lcat, Pla2g2a, Stat3 and Vegfa. The study systematically explored the protective mechanisms of Ef against Hki and also provided the practical treatment strategies of Hki from the Chinese herb.

An Analysis of Mechanisms for Cellular Uptake of miRNAs to Enhance Drug Delivery and Efficacy in Cancer Chemoresistance

Up until recently, it was believed that pharmaceutical drugs and their metabolites enter into the cell to gain access to their targets via simple diffusion across the hydrophobic lipid cellular membrane, at a rate which is based on their lipophilicity. An increasing amount of evidence indicates that the phospholipid bilayer-mediated drug diffusion is in fact negligible, and that drugs pass through cell membranes via proteinaceous membrane transporters or carriers which are normally used for the transportation of nutrients and intermediate metabolites. Drugs can be targeted to specific cells and tissues which express the relevant transporters, leading to the design of safe and efficacious treatments. Furthermore, transporter expression levels can be manipulated, systematically and in a high-throughput manner, allowing for considerable progress in determining which transporters are used by specific drugs. The ever-expanding field of miRNA therapeutics is not without its challenges, with the most notable one being the safe and effective delivery of the miRNA mimic/antagonist safely to the target cell cytoplasm for attaining the desired clinical outcome, particularly in miRNA-based cancer therapeutics, due to the poor efficiency of neo-vascular systems revolting around the tumour site, brought about by tumour-induced angiogenesis. This acquisition of resistance to several types of anticancer drugs can be as a result of an upregulation of efflux transporters expression, which eject drugs from cells, hence lowering drug efficacy, resulting in multidrug resistance. In this article, the latest available data on human microRNAs has been reviewed, together with the most recently described mechanisms for miRNA uptake in cells, for future therapeutic enhancements against cancer chemoresistance.

Metabolic changes in the brain and blood of rats following acoustic trauma, tinnitus and hyperacusis

It has been increasingly recognized that tinnitus is likely to be generated by complex network changes. Acoustic trauma that causes tinnitus induces significant changes in multiple metabolic pathways in the brain. However, it is not clear whether those metabolic changes in the brain could also be reflected in blood samples and whether metabolic changes could discriminate acoustic trauma, hyperacusis and tinnitus. We analyzed brain and serum metabolic changes in rats following acoustic trauma or a sham procedure using metabolomics. Hearing levels were recorded before and after acoustic trauma and behavioral measures to quantify tinnitus and hyperacusis were conducted at 4 weeks following acoustic trauma. Tissues from 11 different brain regions and serum samples were collected at about 3 months following acoustic trauma. Among the acoustic trauma animals, eight exhibited hyperacusis-like behavior and three exhibited tinnitus-like behavior. Using Gas chromatography-mass spectrometry and multivariate statistical analysis, significant metabolic changes were found in acoustic trauma animals in both the brain and serum samples with a number of metabolic pathways significantly perturbated. Furthermore, metabolic changes in the serum were able to differentiate sham from acoustic trauma animals, as well as sham from hyperacusis animals, with high accuracy. Our results suggest that serum metabolic profiling in combination with machine learning analysis may be a promising approach for identifying biomarkers for acoustic trauma, hyperacusis and potentially, tinnitus.

A network pharmacology-integrated metabolomics strategy for clarifying the action mechanisms of Schisandrae Chinensis Fructus for treating drug-induced liver injury by acetaminophen

Schisandrae Chinensis Fructus (SCF) was a Traditional Chinese Medicine (TCM) for protecting liver. However, underlying therapeutic mechanisms of SCF for drug-induced liver injury (DILI) by acetaminophen (APAP) are still unclear. This study aims to discover the potential regulation mechanisms of SCF in the treatment of DILI by APAP using the integrated network pharmacology, plasma metabolomics profiling with UPLC-Q-TOF-MS approach. The key targets in the shared pathways of network pharmacology and metabolomics were screened and experimentally validated by Quantitative Real-time PCR analysis. The results showed that SCF could exert excellent effects on DILI by APAP probably through regulating ErbB signaling pathway and Arachidonic acid metabolism pathway, which was reflected by the reduced gene expression of TNF-α, IL-6, IL-1β, COX-2 and EGFR, as well as the increased gene expression of Nrf2, HO-1, MDM2, MAPK8, SRC, PLD1, CYP2E1, CYP1A2, CYP3A1. This study systematically explored the pharmacological mechanisms of SCF in the treatment of DILI, meanwhile, metabolomics combine with network pharmacology approach might be a useful strategy for early diagnosis of DILI by APAP.

In Vivo Targeted Metabolomic Profiling of Prostanit, a Novel Anti-PAD NO-Donating Alprostadil-Based Drug

Prostanit is a novel drug developed for the treatment of peripheral arterial diseases. It consists of a prostaglandin E1 (PGE1) moiety with two nitric oxide (NO) donor fragments, which provide a combined vasodilation effect on smooth muscles and vascular spastic reaction. Prostanit pharmacokinetics, however, remains poorly investigated. Thus, the object of this study was to investigate the pharmacokinetics of Prostanit-related and -affected metabolites in rabbit plasma using the liquid chromatography-mass spectrometry (LC-MS) approach. Besides, NO generation from Prostanit in isolated rat aorta and human smooth muscle cells was studied using the Griess method. In plasma, Prostanit was rapidly metabolized to 1,3-dinitroglycerol (1,3-DNG), PGE1, and 13,14-dihydro-15-keto-PGE1. Simultaneously, the constant growth of amino acid (proline, 4-hydroxyproline, alanine, phenylalanine, etc.), steroid (androsterone and corticosterone), and purine (adenosine, adenosine-5 monophosphate, and guanosine) levels was observed. Glycine, aspartate, cortisol, and testosterone levels were decreased. Ex vivo Prostanit induced both NO synthase-dependent and -independent NO generation. The observed pharmacokinetic properties suggested some novel beneficial activities (i.e., effect prolongation and anti-inflammation). These properties may provide a basis for future research of the effectiveness and safety of Prostanit, as well as for its characterization from a clinical perspective.

Approaching sites of action of drugs in clinical pharmacology: New analytical options and their challenges

Clinical pharmacology is an important discipline for drug development aiming to define pharmacokinetics (PK), pharmacodynamics (PD) and optimum exposure to drugs, i.e. the concentration-response relationship and its modulators. For this purpose, information on drug concentrations at the anatomical, cellular and molecular sites of action is particularly valuable. In pharmacological assays, the limited accessibility of target cells in readily available samples (i.e. blood) often hampers mass spectrometry-based monitoring of the absolute quantity of a compound and the determination of its molecular action at the cellular level. Recently, new sample collection methods have been developed for the specific capture of rare circulating cells, especially for the diagnosis of circulating tumour cells. In parallel, new advances and developments in mass spectrometric instrumentation now allow analyses to be scaled down to the cellular level. Together, these developments may permit the monitoring of minute drug quantities and show their effect at the cellular level. In turn, such PK/PD associations on a cellular level would not only enrich our pharmacological knowledge of a given compound but also expand the basis for PK/PD simulations. In this review, we describe novel concepts supporting clinical pharmacology at the anatomical, cellular and molecular sites of action, and highlight the new challenges in mass spectrometry-based monitoring. Moreover, we present methods to tackle these challenges and define future needs.

FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM

Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Serum metabolic signatures of high myopia among older Chinese adults

PURPOSE

High myopia is associated with blinding ocular morbidities. Identifying novel biomarkers may provide clues on pathogenic pathways that are currently unknown. We aimed to identify serum metabolic biomarkers and investigate the metabolic alterations in relation to high myopia.

METHODS

Forty adults with high myopia and 40 with low myopia aged 60 years or older from the Weitang Geriatric Diseases study were included in the case-control study. Refractive error was determined by autorefraction followed by subjective refraction. We performed the metabolomic analysis of serum samples from patients with high myopia and age- and sex- matched controls with low myopia, using a nontargeted gas chromatography coupled to time-of-flight mass spectrometer. The area under the receiver operating characteristic curve (AUC) was computed to assess the discrimination capacities of each metabolite marker. Databases including KEGG and MetaboAnalyst were utilized to search for the potential pathways of metabolites.

RESULTS

Serum metabolomic profiles could well distinguish high myopia from low myopia. Twenty metabolic biomarkers were identified as potential serum biomarkers for high myopia, yielding AUC values of 0.59-0.71. Metabolic pathways in relation to high myopia, mainly characterized by increased energy metabolism, increased oxidative stress, abnormal amino acid metabolism, and altered biotin metabolism, provide a foundation to support myopia progression.

CONCLUSIONS

This study identified valuable metabolic biomarkers and pathways that may facilitate an improved understanding of the disease pathogenesis. The finding holds translational value in the development of new therapeutic measures for high myopia-related complications.

Differential metabolic markers associated with primary open-angle glaucoma and cataract in human aqueous humor

Background

We aimed to identify metabolic biomarkers and investigate the metabolic alterations in relation to primary open-angle glaucoma (POAG) and cataract in human aqueous humor.

Methods

Sixteen POAG patients undergoing surgical treatments and 24 patients undergoing cataract surgeries were included in this case-control study. We performed the metabolomic analysis of aqueous humor samples using a non-targeted gas chromatography coupled to time-of-flight mass spectrometer. The area under the receiver operating characteristic curve (AUC) was computed to assess the discrimination capacities of each metabolite marker. Databases including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were utilized to search for the potential pathways of metabolites.

Results

Aqueous humor metabolomic profiles could well distinguish POAG from controls. Fourteen metabolic biomarkers were identified as potential aqueous humor biomarkers for POAG, yielding AUC values from 0.62 to 0.86. In pathway analysis, Biotin metabolism was highly impacted, implying that these metabolic markers play important roles in the regulation of this pathway.

Conclusions

This study identified valuable metabolic biomarkers and pathways that may facilitate an improved understanding of the POAG pathogenesis. The finding holds translational value in the development of new therapeutic measures for POAG.

Thymus Regeneration and Future Challenges

Thymus regenerative therapy implementation is severely obstructed by the limited number and expansion capacity in vitro of tissue-specific thymic epithelial stem cells (TESC). Current solutions are mostly based on growth factors that can drive differentiation of pluripotent stem cells toward tissue-specific TESC. Target-specific small chemical compounds represent an alternative solution that could induce and support the clonal expansion of TESC and reversibly block their differentiation into mature cells. These compounds could be used both in the composition of culture media designed for TESC expansion in vitro, and in drugs development for thymic regeneration in vivo. It should allow reaching the ultimate objective - autologous thymic tissue regeneration in paediatric patients who had their thymus removed in the course of cardiac surgery.

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.

Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics

Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Pharmacological Evaluation of Drugs in Animal Models of Tinnitus

Despite the pressing need for effective drug treatments for tinnitus, currently, there is no single drug that is approved by the FDA for this purpose. Instead, a wide range of unproven over-the-counter tinnitus remedies are available on the market with little or no benefit for tinnitus but with potential harm and adverse effects. Animal models of tinnitus have played a critical role in exploring the pathophysiology of tinnitus, identifying therapeutic targets and evaluating novel and existing drugs for tinnitus treatment. This review summarises and compares the studies on pharmacological evaluation of tinnitus treatment in different animal models based on the pharmacological properties of the drug and provides insights into future directions for tinnitus drug discovery.

A novel approach based on metabolomics coupled with network pharmacology to explain the effect mechanisms of Danggui Buxue Tang in anaemia

Danggui Buxue Tang (DBT) is a famous Chinese medicinal decoction. Mechanism of DBT action is wide ranging and unclear. Exploring new ways of treatment with DBT is useful. Sprague-Dawley(SD) rats were randomly divided into 3 groups including control (NC, Saline), the DBT (at a dose of 8.10 g^-1), and blood deficiency(BD) (Cyclophosphamide (APH)-andCyclophosphamide(CTX)-induced anaemia). A metabolomics approach using Liquid Chromatography-Quadrupole-Time-of-Flight/Mass Spectrometry (LC/Q-TOFMS) was developed to perform the plasma metabolic profiling analysis and differential metaboliteswerescreened according to the multivariate statistical analysiscomparing the NC and BD groups, andthe hub metabolites were outliers with high scores of the centrality indices. Anaemia disease-related protein target and compound of DBT databases were constructed. The TCMSP, ChemMapper and STITCH databases were used to predict the protein targets of DBT. Using the Cytoscape 3.2.1 to establish a phytochemical component-target protein interaction network and establish a component, protein and hub metabolite protein-protein interaction (PPI) network and merging the three PPI networks basing on BisoGenet. The gene enrichment analysis was used to analyse the relationship between proteins based on the relevant genetic similarity by ClueGO. The results shown DBT effectively treated anaemia in vivo. 11 metabolic pathways are involved in the therapeutic effect of DBT in vivo; S-adenosyl-l-methionine, glycine, l-cysteine, arachidonic acid (AA) and phosphatidylcholine(PC) were screened as hub metabolites in APH-and CTX-induced anaemia. A total of 288 targets were identified as major candidates for anaemia progression. The gene-set enrichment analysis revealed that the targets are involved in iron ion binding, haemopoiesis, reactive oxygen species production, inflammation and apoptosis. The results also showed that these targets were associated with iron ion binding, haemopoiesis, ROS production, apoptosis, inflammation and related signalling pathways. DBT can promote iron ion binding and haemopoiesis activities, restrain inflammation, production of reactive oxygen, block apoptosis, and contribute significantly to the DBT treat anaemia.

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

Generation of a Small Library of Natural Products Designed to Cover Chemical Space Inexpensively

Natural products space includes at least 200,000 compounds and the structures of most of these compounds are available in digital format. Previous analyses showed (i) that although they were capable of taking up synthetic pharmaceutical drugs, such exogenous molecules were likely the chief ‘natural’ substrates in the evolution of the transporters used to gain cellular entry by pharmaceutical drugs, and (ii) that a relatively simple but rapid clustering algorithm could produce clusters from which individual elements might serve to form a representative library covering natural products space. This exploited the fact that the larger clusters were likely to be formed early in evolution (and hence to have been accompanied by suitable transporters), so that very small clusters, including singletons, could be ignored. In the latter work, we assumed that the molecule chosen might be that in the middle of the cluster. However, this ignored two other criteria, namely the commercial availability and the financial cost of the individual elements of these clusters. We here develop a small representative library in which we to seek to satisfy the somewhat competing criteria of coverage (‘representativeness’), availability and cost. It is intended that the library chosen might serve as a testbed of molecules that may or may not be substrates for known or orphan drug transporters. A supplementary spreadsheet provides details, and their availability via a particular supplier.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

UPLC-Q-TOF/MS-Based Plasma Metabolomics to Evaluate the Effects of Aspirin Eugenol Ester on Blood Stasis in Rats

Aspirin eugenol ester (AEE) is a novel compound that is formed from the esterification of aspirin (acetylsalicylic acid (ASA)) and eugenol. This study aimed to investigate the effects of AEE on blood stasis in rats and to characterize the underlying mechanisms using a plasma metabolomic study. The results indicate that AEE and ASA could modulate whole blood viscosity (WBV), plasma viscosity (PV), blood coagulation parameters, platelet count, platelet aggregation, lactate dehydrogenase (LDH), creatinine (CR) and the levels of thromboxane A2 (TXA₂) and 6-keto prostaglandin F1α (6-keto-PGF_1α). The metabolic profiles of the plasma samples from all groups were clearly separated in the score plots. Nineteen potential metabolites were selected and identified, and disordered levels of these metabolites could be regulated by AEE and ASA. Pathway analysis showed that the mechanism of action of AEE on blood stasis might be principally related to the metabolism of amino acid, fatty acid, energy and glycerophospholipid. The above results indicate that AEE protected the rats against blood stasis, and that this effect might have been caused by the anticoagulation activity of AEE and its abilities to maintain a balance between TXA₂ and PGI₂, reduce blood viscosity, inhibit platelet aggregation and normalize the plasma metabolic profile.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

Metabolic Alterations in Cardiopulmonary Vascular Dysfunction

Cardiovascular diseases (CVD) are the leading cause of death worldwide. CVD comprise a range of diseases affecting the functionality of the heart and blood vessels, including acute myocardial infarction (AMI) and pulmonary hypertension (PH). Despite their different causative mechanisms, both AMI and PH involve narrowed or blocked blood vessels, hypoxia, and tissue infarction. The endothelium plays a pivotal role in the development of CVD. Disruption of the normal homeostasis of endothelia, alterations in the blood vessel structure, and abnormal functionality are essential factors in the onset and progression of both AMI and PH. An emerging theory proposes that pathological blood vessel responses and endothelial dysfunction develop as a result of an abnormal endothelial metabolism. It has been suggested that, in CVD, endothelial cell metabolism switches to higher glycolysis, rather than oxidative phosphorylation, as the main source of ATP, a process designated as the Warburg effect. The evidence of these alterations suggests that understanding endothelial metabolism and mitochondrial function may be central to unveiling fundamental mechanisms underlying cardiovascular pathogenesis and to identifying novel critical metabolic biomarkers and therapeutic targets. Here, we review the role of the endothelium in the regulation of vascular homeostasis and we detail key aspects of endothelial cell metabolism. We also describe recent findings concerning metabolic endothelial cell alterations in acute myocardial infarction and pulmonary hypertension, their relationship with disease pathogenesis and we discuss the future potential of pharmacological modulation of cellular metabolism in the treatment of cardiopulmonary vascular dysfunction. Although targeting endothelial cell metabolism is still in its infancy, it is a promising strategy to restore normal endothelial functions and thus forestall or revert the development of CVD in personalized multi-hit interventions at the metabolic level.

Metabolomics reveals the role of acetyl-l-carnitine metabolism in γ-Fe2O3 NP-induced embryonic development toxicity via mitochondria damage

Iron oxides nanoparticles (FeO_X NPs), including α-Fe₂O₃, γ-Fe₂O₃, and Fe₃O₄, are employed in many technological applications. However, very few studies have investigated the embryonic developmental toxicity of FeO_X NPs. In this study, metabolomics analysis were used to uncover the potential mechanisms of FeO_X NPs developmental toxicity on embryo-larval zebrafish and mice. Our results indicated that γ-Fe₂O₃ NP treatment could cause increased mortality, dropped hatching rate, etc., while α-Fe₂O₃ and Fe₃O₄ NPs showed no obvious effect. Through metabolomics analysis, a total of 42 metabolites were found to be significantly changed between the γ-Fe₂O₃ NP-treated group and the control group (p < 0.05). Pathway enrichment analysis indicated the impairment of mitochondria function. γ-Fe₂O₃ NP treatment caused abnormal mitochondrion structure and a decrease in mitochondrial membrane potential in zebrafish embryos. Meanwhile, ATP synthesis was decreased while oxidative stress levels were affected. It is noteworthy that acetyl-l-carnitine (ALCAR) (p = 6.79E - 04) and l-carnitine (p = 1.43E - 03) were identified with minimal p values, the relationship between the two counter-balance was regulated by acetyltransferase (crata). Subsequently, we performed rescue experiments with ALCAR on zebrafish embryos, and found that the mortality rates reduced and hatching rates raised significantly in the γ-Fe₂O₃ NP-treated group. Additionally, γ-Fe₂O₃ exposure could lead to increased absorbed fetus rate, decreased placental weight, lower expression of acetyltransferase (Crat), reduced ATP synthesis as well as increased oxidative stress (p < 0.05). Our findings demonstrated that γ-Fe₂O₃ NP might affect the mitochondrial membrane potential and ATP synthesis by affecting the metabolism of ALCAR, thereby stimulating oxidative stress, cell apoptosis, and causing embryonic development toxicity.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

Metabolomics Study on the Effects of Salvianolic Acid B and Borneol for Treating Cerebral Ischemia in Rats by Ultra-Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry

Salviae miltiorrliza-borneol Jun-Shi coupled-herbs have been widely used for treatment of ischemia stroke. Salvianolic acid B was the most abundant and bioactive compound of Salviae miltiorrliza and used for prevention and treatment of cerebrovascular diseases. However, the scientific intension and compatible mechanism of Salvianolic acid B - borneol combination were still unknown. A metabolomics study approach based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) combined with a pathological study has been applied to study the metabolic disturbances of cerebral ischemia and evaluate the efficacies of Sal B and Sal B/borneol against cerebral ischemia in middle cerebral artery occlusion (MCAO) rats. The neuroprotection of Sal B and Sal B/borneol was reversed through the evaluation of neurological deficits, infarct volume, and neuronal apoptosis in MCAO model. The metabonomic analysis revealed that the MCAO-induced cerebral ischemia could be ameliorated by Sal B through improving the energy metabolism, lipids metabolism, inflammatory responses, and oxidant stress. Borneol could enhance the neuroprotective effects, was associated with the increased concentration of Sal B, and attenuate the function of sphingolipid metabolism pathway in cerebral ischemia rats. These findings perhaps clarify the mechanism of neuroprotective effects of treating ischemia stroke by Sal B or Sal B/borneol preliminarily through metabolomics and push the quality promotion and the composition of borneol/Sal B in secondary development of prescription.