Emerging Applications of Metabolomics in Clinical Pharmacology

Pharmaco-Multiomics: A New Frontier in Precision Psychiatry

The landscape of psychiatric care is poised for transformation through the integration of pharmaco-multiomics, encompassing genomics, proteomics, metabolomics, transcriptomics, epigenomics, and microbiomics. This review discusses how these approaches can revolutionize personalized treatment strategies in psychiatry by providing a nuanced understanding of the molecular bases of psychiatric disorders and individual pharmacotherapy responses. With nearly one billion affected individuals globally, the shortcomings of traditional treatments, characterized by inconsistent efficacy and frequent adverse effects, are increasingly evident. Advanced computational technologies such as artificial intelligence (AI) and machine learning (ML) play crucial roles in processing and integrating complex omics data, enhancing predictive accuracy, and creating tailored therapeutic strategies. To effectively harness the potential of pharmaco-multiomics approaches in psychiatry, it is crucial to address challenges such as high costs, technological demands, and disparate healthcare systems. Additionally, navigating stringent ethical considerations, including data security, potential discrimination, and ensuring equitable access, is essential for the full realization of this approach. This process requires ongoing validation and comprehensive integration efforts. By analyzing recent advances and elucidating how different omic dimensions contribute to therapeutic customization, this review aims to highlight the promising role of pharmaco-multiomics in enhancing patient outcomes and shifting psychiatric treatments from a one-size-fits-all approach towards a more precise and patient-centered model of care.

Metabolomic profiles in allergic rhinitis: A systematic review and meta-analysis

BACKGROUND

Allergic rhinitis (AR) is a prevalent chronic inflammatory condition that significantly affects patient quality of life and poses a substantial public health burden. Recent advancements in metabolomics have facilitated a deeper understanding of the metabolic pathways involved in AR, offering potential for new biomarkers and therapeutic targets.

OBJECTIVE

To conduct a systematic review and meta-analysis of clinical studies summarizing the metabolomic profiles of AR to gain deeper insights into the metabolic changes and pathologic processes underlying AR.

METHODS

A comprehensive literature search was conducted across PubMed, Embase, Scopus, and Web of Science databases up to October 2024. A qualitative review of the screened studies was performed, followed by meta-analyses of metabolites reported in at least 2 studies. High-impact targets, pathways, and their associations were identified using bioinformatic analyses.

RESULTS

A total of 21 studies, encompassing 84 metabolites associated with AR, met the inclusion criteria. There were 7 metabolites that consistently exhibited up-regulation in AR across multiple studies and were included in the meta-analysis. Pathway enrichment analyses revealed significant involvement of pathways such as "valine, leucine, and isoleucine biosynthesis" and "linoleic acid metabolism" in AR pathogenesis. The metabolite-pathway-gene network analysis highlighted key functional connections between metabolites, pathways, and immune response genes.

CONCLUSION

This comprehensive analysis indicates that differential metabolites may play pivotal roles in AR pathogenesis, offering potential biomarkers and therapeutic targets. Further studies are necessary to validate these findings and elucidate the complex metabolic pathways involved in AR.

Early diagnostics of fibromyalgia: an overview of the challenges and opportunities

INTRODUCTION

Fibromyalgia is a common pain disorder with features of widespread musculoskeletal pain, fatigue, disrupted sleep, cognitive dysfunction, autonomic dysfunction, and mood disorders. Despite its high prevalence and significant impact on quality of life, the diagnosis and management of fibromyalgia remain challenging. Advancements in classification and diagnostics in broad areas have improved our understanding and treatment approach for this condition. We culminate with a discussion of future directions for research into early diagnostics in fibromyalgia.

AREAS COVERED

This perspective examines the current landscape of fibromyalgia biomarker discovery, highlighting challenges that must be addressed and opportunities that are presented as the field evolves.

EXPERT OPINION

Advances in fibromyalgia diagnostics provide an opportunity to dramatically reduce the cost burden placed on health resources for fibromyalgia once we have discovered a reliable reproducible biomarker that is widely accepted among practitioners and patients. Promising results in a number of fields may lead to point of care technologies that will be applicable in the office or bedside without the need for transport to specialized centers. Future research should focus on integrating these various diagnostic approaches to develop a comprehensive, multi-modal diagnostic tool for fibromyalgia.

Matrix effect evaluation using multi-component post-column infusion in untargeted hydrophilic interaction liquid chromatography-mass spectrometry plasma metabolomics

Metabolomics based on hydrophilic interaction liquid chromatography (HILIC) coupled with mass spectrometry (MS) is a powerful tool for polar metabolite identification and quantification to further contribute to biomarker discovery and disease mechanism elucidation. However, matrix effect (ME), which may lead to altered ionization efficiency due to co-eluting compounds, is a significant challenge during biological analysis. Therefore, ME evaluation plays a crucial role during method development. Two approaches to evaluate ME are using stable isotope labelled-internal standards (SIL-IS) and post-column infusion (PCI) of standards. In this study, we developed an untargeted HILIC-MS method by applying four PCI standards for ME evaluation. We found PCI is a compelling approach for ME assessment compared to SIL-IS method due to its advantage in untargeted analysis. Through the ME evaluation and chromatographic performance comparison of 18 SIL standards across three columns and three different mobile phase pH conditions, our findings revealed that the BEH-Z-HILIC column operated at pH 4 with 10 mM ammonium formate exhibited minimal ME and superior performance. The method showed exceptional linearity (R² > 0.98), reliable repeatability (RSD < 15 %), good inter-day precision (RSD < 30 %), and acceptable recovery (>75 %) for all SIL standards. Absolute matrix effect (AME) and relative matrix effect (RME) assessment in three plasma donors revealed a high consistency between PCI and SIL-IS approaches. Finally, this method coupled with the PCI approach was applied to 40 plasma samples. Fifty endogenous compounds were detected and their AME and RME were evaluated. Results showed that many compounds experienced severe ion suppression, though their ME variation between 40 samples is low. In conclusion, PCI method is a robust alternative for monitoring ME and evaluating ME of endogenous compounds during untargeted method optimization and biological analysis.

Evaluation of metabolite stability in dried blood spot stored at different temperatures and times

Dried blood spot (DBS) sampling offers significant advantages over conventional blood collection methods, such as reduced sample volume, minimal invasiveness, suitability for home-based sampling, and ease of transport. However, understanding the effects of variable storage temperatures and times on metabolite stability is crucial due to varying intervals and delivery conditions between sample collection and metabolomics analysis. To minimize biological variances, all samples were collected from the same individual simultaneously and stored at three different temperatures (4 °C, 25 °C, and 40 °C) for diverse time points (3, 7, 14, and 21 days). Metabolic profiling was conducted an untargeted gas chromatography-mass spectrometry (GC-MS) and ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS)-based multi-platform metabolomics. Principal component analysis (PCA) showed alterations in metabolite stability at different temperatures, with phosphatidylcholines (PCs) and triglycerides (TAGs) as the first principal component (PC1). Specifically, we identified 69 metabolites that remained stable across all three temperatures over the 21-day period, while 78 metabolites exhibited instability. Furthermore, linear correlations between metabolite intensity and storage time were observed. Overall, our study elucidated the influence of storage temperature and time on specific metabolite stability in DBS samples, providing valuable insights for study design, biomarker selection, and data improvement.

Altered serum metabolome is associated with disease activity and immune responses in rheumatoid arthritis

Rheumatoid arthritis (RA) is widespread globally, with the emergence of metabolites derived from both the host and microbes playing a pivotal role in its pathogenesis. This study aims to elucidate the relationships between serum metabolites and the immunological and clinical features of RA. Serum samples were collected from 35 RA patients and 37 healthy controls (HC). Metabolite profiling was performed using gas chromatography-mass spectrometry (GC/MS). Principal component analysis revealed a significant distinction between the RA and HC cohorts. Employing univariate statistical analysis, we identified 36 differential metabolites. Among these, 9 metabolites, including galactose and glucose, were found to be enriched, while the remaining metabolites, such as citric acid, fumaric acid, and inosine, were depleted in RA. These diverse metabolites encompassed various metabolic processes, including the biosynthesis of fatty acids, amino acids, and glucose. The enrichment of glucose and galactose in RA exhibited a substantial correlation with elevated IgG levels, as determined through correlation analysis. Conversely, the depletion of citric acid was correlated with elevated levels of C3 and CRP. Methionine, which also declined in RA patients, displayed a negative correlation with ESR. Furthermore, galactose and glucose exhibited significant positive correlations with naïve B cells, while the decreased eicosanoic acid level in RA was significantly associated with an increase in natural killer cells. Our findings suggest that the altered serum metabolite profile in RA is closely linked to disease severity and the dysregulated immune responses observed in RA patients. Key Points • Identified nine metabolites with upregulated expression and twenty-seven metabolites with downregulated expression. • Established a correlation between alterations in serum metabolite levels and inflammatory markers in RA patients. • Discovered a significant association between changes in serum metabolites and immune cell profiles in RA patients.

Metabolomic biomarkers of multiple myeloma: A systematic review

Multiple myeloma (MM) is an incurable malignancy of clonal plasma cells. Various diagnostic methods are used in parallel to accurately determine stage and severity of the disease. Identifying a biomarker or a panel of biomarkers could enhance the quality of medical care that patients receive by adopting a more personalized approach. Metabolomics utilizes high-throughput analytical platforms to examine the levels and quantities of biochemical compounds in biosamples. The aim of this review was to conduct a systematic literature search for potential metabolic biomarkers that may aid in the diagnosis and prognosis of MM. The review was conducted in accordance with PRISMA recommendations and was registered in PROSPERO. The systematic search was performed in PubMed, CINAHL, SciFinder, Scopus, The Cochrane Library and Google Scholar. Studies were limited to those involving people with clinically diagnosed MM and healthy controls as comparators. Articles had to be published in English and had no restrictions on publication date or sample type. The quality of articles was assessed according to QUADOMICS criteria. A total of 709 articles were collected during the literature search. Of these, 436 were excluded based on their abstract, with 26 more removed after a thorough review of the full text. Finally, 16 articles were deemed relevant and were subjected to further analysis of their data. A number of promising candidate biomarkers was discovered. Follow-up studies with large sample sizes are needed to determine their suitability for clinical applications.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Investigating the Impact of SN-38 on Mouse Brain Metabolism Based on Metabolomics

Purpose

SN-38 (7-ethyl-10-hydroxycamptothecin), the active metabolite of irinotecan, has been extensively studied in drug delivery systems. However, its impact on neural metabolism remains unclear. This study aims to investigate the toxic effects of SN-38 on mouse brain metabolism.

Methods

Male mice were divided into an SN-38 group and a control group. The SN-38 group received SN-38 (20 mg/kg/day) via intraperitoneal injection, while the control group was given an equal volume of a blank solvent mixture (DMSO and saline, ratio 1:9). Gas chromatography-mass spectrometry (GC-MS) was employed to analyze differential metabolites in the cortical and hippocampal regions of the SN-38-treated mice.

Results

SN-38 induced metabolic disturbances in the central nervous system. Eighteen differential metabolites were identified in the hippocampus and twenty-four in the cortex, with six common to both regions. KEGG pathway enrichment analysis revealed statistically significant alterations in six metabolic pathways in the hippocampus and ten in the cortex (P<0.05).

Conclusion

This study is the first to demonstrate the neurotoxicity of SN-38 in male mice through metabolomics. Differential metabolites in the hippocampal and cortical regions were closely linked to purine metabolism, pyrimidine metabolism, amino acid metabolism, and glyceride metabolism, indicating disruptions in the blood-brain barrier, energy metabolism, and central signaling pathways.

Pioglitazone-induced alterations of purine metabolism in healthy male subjects

Pioglitazone is class of thiazolidinediones that activates peroxisome proliferator-activated receptors (PPARs) in adipocytes to improve glucose metabolism and insulin sensitivity and has been used as a treatment for type 2 diabetes. However, the underlying mechanisms of associated pioglitazone-induced effects remain unclear. Our study aimed to investigate endogenous metabolite alterations associated with pioglitazone administration in healthy male subjects using an untargeted metabolomics approach. All subjects received 30 mg of pioglitazone once daily in the assigned sequence and period. Urine samples were collected before pioglitazone administration and for 24 h after 7 days of administration. A total of 1465 compounds were detected and filtered using a coefficient of variance below 30% and 108 metabolites were significantly altered upon pioglitazone administration via multivariate statistical analysis. Fourteen significant metabolites were identified using authentic standards and public libraries. Additionally, pathway analysis revealed that metabolites from purine and beta-alanine metabolisms were significantly altered after pioglitazone administration. Further analysis of quantification of metabolites from purine metabolism, revealed that the xanthine/hypoxanthine and uric acid/xanthine ratios were significantly decreased at post-dose. Pioglitazone-dependent endogenous metabolites and metabolic ratio indicated the potential effect of pioglitazone on the activation of PPAR and fatty acid synthesis. Additional studies involving patients are required to validate these findings.

Multi-omics technologies and molecular biomarkers in brain tumor-related epilepsy

BACKGROUND

Brain tumors are one of the leading causes of epilepsy, and brain tumor-related epilepsy (BTRE) is recognized as the major cause of intractable epilepsy, resulting in huge treatment cost and burden to patients, their families, and society. Although optimal treatment regimens are available, the majority of patients with BTRE show poor resolution of symptoms. BTRE has a very complex and multifactorial etiology, which includes several influencing factors such as genetic and molecular biomarkers. Advances in multi-omics technologies have enabled to elucidate the pathophysiological mechanisms and related biomarkers of BTRE. Here, we reviewed multi-omics technology-based research studies on BTRE published in the last few decades and discussed the present status, development, opportunities, challenges, and prospects in treating BTRE.

METHODS

First, we provided a general review of epilepsy, BTRE, and multi-omics techniques. Next, we described the specific multi-omics (including genomics, transcriptomics, epigenomics, proteomics, and metabolomics) techniques and related molecular biomarkers for BTRE. We then presented the associated pathogenetic mechanisms of BTRE. Finally, we discussed the development and application of novel omics techniques for diagnosing and treating BTRE.

RESULTS

Genomics studies have shown that the BRAF gene plays a role in BTRE development. Furthermore, the BRAF V600E variant was found to induce epileptogenesis in the neuronal cell lineage and tumorigenesis in the glial cell lineage. Several genomics studies have linked IDH variants with glioma-related epilepsy, and the overproduction of D2HG is considered to play a role in neuronal excitation that leads to seizure occurrence. The high expression level of Forkhead Box O4 (FOXO4) was associated with a reduced risk of epilepsy occurrence. In transcriptomics studies, VLGR1 was noted as a biomarker of epileptic onset in patients. Several miRNAs such as miR-128 and miRNA-196b participate in BTRE development. miR-128 might be negatively associated with the possibility of tumor-related epilepsy development. The lncRNA UBE2R2-AS1 inhibits the growth and invasion of glioma cells and promotes apoptosis. Quantitative proteomics has been used to determine dynamic changes of protein acetylation in epileptic and non-epileptic gliomas. In another proteomics study, a high expression of AQP-4 was detected in the brain of GBM patients with seizures. By using quantitative RT-PCR and immunohistochemistry assay, a study revealed that patients with astrocytomas and oligoastrocytomas showed high BCL2A1 expression and poor seizure control. By performing immunohistochemistry, several studies have reported the relationship between D2HG overproduction and seizure occurrence. Ki-67 overexpression in WHO grade II gliomas was found to be associated with poor postoperative seizure control. According to metabolomics research, the PI3K/AKT/mTOR pathway is associated with the development of glioma-related epileptogenesis. Another metabolomics study found that SV2A, P-gb, and CAD65/67 have the potential to function as biomarkers for BTRE.

CONCLUSIONS

Based on the synthesized information, this review provided new research perspectives and insights into the early diagnosis, etiological factors, and personalized treatment of BTRE.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

Shizao decoction for cirrhotic ascites: assessing potential targets based on network analysis combined with pharmacokinetics and metabolomics

Introduction: Shizao decoction (SZD) is a traditional Chinese medicine decoction that has therapeutic effects on cirrhotic ascites (CAS). Because of the unclear treatment mechanism, in the current study, the anti-CAS activity of SZD and molecular mechanisms were analyzed by network analysis combined with pharmacokinetics and metabolomics. Methods: Firstly, we assessed the anti-CAS efficacy of SZD by hematoxylin-eosin (H&E), liver function tests, NO and ET-1 levels, and portal venous pressure. Secondly, network analysis was applied to dig out the metabolites, targets, and pathways related to SZD and CAS. Then, the pharmacokinetics of the pharmacokinetically relevant metabolites (PRM) were analyzed. Thirdly, the serum and urine metabolic biomarkers of rats with CAS were identified using metabolomics by comparing them with the SZD treatment group. In addition, MetaboAnalyst was utilized to conduct metabolic pathway analysis. Finally, the correlation analysis established a dynamic connection between absorbed PRM from SZD and CAS-associated endogenous metabolites. Results: Pharmacodynamic analysis indicated that SZD effectively mitigated liver injury symptoms by ameliorating inflammatory cell infiltration in CAS rats. The network analysis results indicated that twelve RPM contribute to the therapeutic efficacy of SZD against CAS; the key signaling pathways involved might be hepatitis B and PI3K-Akt. Pharmacokinetics results showed that the 12 RPM were efficiently absorbed into rat plasma, ensuring desirable bioavailability. The metabolomic analysis yielded 21 and 23 significantly distinct metabolites from the serum and urine, respectively. The 12 bioavailable SZD-PRM, such as luteolin, apigenin, and rutin, may be associated with various CAS-altered metabolites related to tryptophan metabolism, alpha-linolenic acid metabolism, glycine metabolism, etc. Discussion: A novel paradigm was provided in this study to identify the potential mechanisms of pharmacological effects derived from a traditional Chinese medicine decoction.

Huangqi Guizhi Wuwu Decoction Improves Inflammatory Factor Levels in Chemotherapy-induced Peripheral Neuropathy by Regulating the Arachidonic Acid Metabolic Pathway

BACKGROUND

Chemotherapy-induced Peripheral Neuropathy (CIPN) is a common complication that arises from the use of anticancer drugs. Huangqi Guizhi Wuwu Decoction (HGWWD) is an effective classic prescription for treating CIPN; however, the mechanism of the activity is not entirely understood.

OBJECTIVE

This study aimed to investigate the remedial effects and mechanisms of HGWWD on CIPN.

METHODS

Changes in behavioral, biochemical, histopathological, and biomarker indices were used to evaluate the efficacy of HGWWD treatment. Ultra-high-performance liquid chromatography/mass spectrometry combined with the pattern recognition method was used to screen biomarkers and metabolic pathways related to CIPN. The results of pathway analyses were verified by protein blotting experiments.

RESULTS

A total of 29 potential biomarkers were identified and 13 metabolic pathways were found to be involved in CIPN. In addition HGWWD reversed the levels of 19 biomarkers. Prostaglandin H2 and 17α,21-dihydroxypregnenolone were targeted as core biomarkers.

CONCLUSION

This study provides scientific evidence to support the finding that HGWWD mainly inhibits the inflammatory response during CIPN by regulating arachidonic acid metabolism.

Stratification of hypertension and SARS-CoV-2 infection by quantitative NMR spectroscopy of human blood serum

BACKGROUND

Diagnostic approaches like the nuclear magnetic resonance spectroscopy (NMR) based quantification of metabolites, lipoproteins, and inflammation markers has helped to identify typical alterations in the blood serum of COVID-19 patients. However, confounders such as sex, and comorbidities, which strongly influence the metabolome, were often not considered. Therefore, the aim of this NMR study was to consider sex, as well as arterial hypertension (AHT), when investigating COVID-19-positive serum samples in a large age-and sex matched cohort.

METHODS

NMR serum data from 329 COVID-19 patients were compared with 305 healthy controls. 134 COVID-19 patients were affected by AHT. These were analyzed together with NMR data from 58 hypertensives without COVID-19. In addition to metabolite, lipoprotein, and glycoprotein data from NMR, common laboratory parameters were considered. Sex was considered in detail for all comparisons.

RESULTS

Here, we show that several differences emerge from previous NMR COVID-19 studies when AHT is considered. Especially, the previously described triglyceride-rich lipoprotein profile is no longer observed in COVID-19 patients, nor an increase in ketone bodies. Further alterations are a decrease in glutamine, leucine, isoleucine, and lysine, citric acid, HDL-4 particles, and total cholesterol. Additionally, hypertensive COVID-19 patients show higher inflammatory NMR parameters than normotensive patients.

CONCLUSIONS

We present a more precise picture of COVID-19 blood serum parameters. Accordingly, considering sex and comorbidities should be included in future metabolomics studies for improved and refined patient stratification. Due to metabolic similarities with other viral infections, these results can be applied to other respiratory diseases in the future.

Portosystemic shunt placement reveals blood signatures for the development of hepatic encephalopathy through mass spectrometry

Elective transjugular intrahepatic portosystemic shunt (TIPS) placement can worsen cognitive dysfunction in hepatic encephalopathy (HE) patients due to toxins, including possible microbial metabolites, entering the systemic circulation. We conducted untargeted metabolomics on a prospective cohort of 22 patients with cirrhosis undergoing elective TIPS placement and followed them up to one year post TIPS for HE development. Here we suggest that pre-existing intrahepatic shunting predicts HE severity post-TIPS. Bile acid levels decrease in the peripheral vein post-TIPS, and the abundances of three specific conjugated di- and tri-hydroxylated bile acids are inversely correlated with HE grade. Bilirubins and glycerophosphocholines undergo chemical modifications pre- to post-TIPS and based on HE grade. Our results suggest that TIPS-induced metabolome changes can impact HE development, and that pre-existing intrahepatic shunting could be used to predict HE severity post-TIPS.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Precision Medicine: Disease Subtyping and Tailored Treatment

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

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.

6-Benzylaminopurine causes lipid dyshomeostasis via disruption of glycerophospholipid metabolism in zebrafish

6-Benzylaminopurine (6-BA) is ubiquitous in agricultural production and is accessible to humans through diets. The modulation of lipid metabolism by 6-BA has been previously demonstrated in plants and oleaginous microorganisms. Therefore, whether it alters lipid homeostasis in other living organisms requires further investigation. In this study, doses ≥10 mg 6-BA/L caused malformation of the yolk sac, steatosis, and other hepatopathies in zebrafish larvae. Exposure to 25 mg 6-BA/L resulted in increased levels of triglyceride and total cholesterol. Results of transcriptomic analysis indicated that 6-BA alters genes associated with fatty acid and glycerophospholipid metabolism. Among them, the expression levels of hmgcra, elovl7b, and apobb.2 were downregulated, whereas those of lpcat3, bco1l, cyp7al, fabp1b.1, elp6, pde6ha, apoa4b.2_2, sgk1, dgkaa, and mogat2 were upregulated. Correspondingly, a study of the metabolome identified lysophosphatidylcholine (LPC) as the major differentially expressed metabolite in response to 6-BA treatment. Therefore, abnormal accumulation of LPCs and dyshomeostasis of glycerophospholipid metabolism were identified as potential mechanisms causing the toxicity of 6-BA, which should be assessed to understand the risks of 6-BA and the products contaminated by it. ENVIRONMENTAL IMPLICATION: 6-Benzylaminopurine (6-BA), an important residue in "toxic bean sprouts," is ubiquitous in agricultural production and is common in typical diets. Its regulation of lipid metabolism has been demonstrated in plants and oleaginous microorganisms. Whether it alters lipid homeostasis in other organisms and the underlying mechanisms remain largely unknown. The worldwide use of 6-BA and the potential exposure of humans have aroused public attention owing to its hazardous effects; thus, its hazardous effects, particularly those on lipid homeostasis, deserve careful clarification.

Integrated pharmacokinetics and pharmacometabolomics to reveal the synergistic mechanism of a multicomponent Chinese patent medicine, Mailuo Shutong pills against thromboangiitis obliterans

BACKGROUND

Mailuo Shutong Pills (MLST) have displayed pharmacological activity against thromboangiitis obliterans (TAO). However, the active ingredients and therapeutic mechanism of MLST against TAO remained to be further clarified.

PURPOSE

The aim of this study was to explore the active components of MLST and their synergistic mechanism against TAO by integrating pharmacokinetics (PK) and pharmacometabolomics (PM).

METHODS

TAO model rats were established by sodium laurate solution. Firstly, the efficacy of MLST was evaluated by gangrene score, blood flow velocity, and hematoxylin-eosin (H&E) staining. Secondly, PK research was conducted on bioavailable components to characterize their dynamic behaviors under TAO. Thirdly, multiple plasma and urine metabolic biomarkers for sodium laurate-induced TAO rats were found by untargeted metabolomics, and then variations in TAO-altered metabolites following MLST treatment were analyzed utilizing multivariate and bioinformatic analysis. Additionally, metabolic pathway analysis was performed using MetaboAnalyst. Finally, the dynamic link between absorbed MLST-compounds and TAO-associated endogenous metabolites was established by correlation analysis.

RESULTS

MLST significantly alleviated gangrene symptoms by improving the infiltration of inflammatory cells and blood supply in TAO rats. Significant differences in metabolic profiles were found in 17 differential metabolites in plasma and 24 in urine between Sham and TAO rats. The 10 bioavailable MLST-compounds, such as chlorogenic acid and paeoniflorin, showed positive or negative correlations with various TAO-altered metabolites related to glutamate metabolism, histidine metabolism, arachidonic acid metabolism and so on.

CONCLUSION

This study originally investigated the dynamic interaction between MLST and the biosystem, providing unique insight for disclosing the active components of MLST and their synergistic mechanisms against TAO, which also shed light on new therapeutic targets for TAO and treatment.

Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications

In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.

Pre- and Post-Portosystemic Shunt Placement Metabolomics Reveal Molecular Signatures for the Development of Hepatic Encephalopathy

Hepatic encephalopathy (HE) is a common complication of advanced liver disease causing brain dysfunction. This is likely due to the accumulation of unfiltered toxins within the bloodstream. A known risk factor for developing or worsening HE is the placement of a transjugular intrahepatic portosystemic shunt (TIPS), which connects the pre-hepatic and post-hepatic circulation allowing some blood to bypass the dysfunctional liver and decreases portal hypertension. To better understand the pathophysiology of post-TIPS HE, we conducted a multi-center prospective cohort study employing metabolomic analyses on hepatic vein and peripheral vein blood samples from participants with cirrhosis undergoing elective TIPS placement, measuring chemical modifications and changes in concentrations of metabolites resulting from TIPS placement. In doing so, we identified numerous alterations in metabolites, including bile acids, glycerophosphocholines, and bilirubins possibly implicated in the development and severity of HE.

Plasma metabolomic profiling reveals factors associated with dose-adjusted trough concentration of tacrolimus in liver transplant recipients

Inter- and intrapatient variability of tacrolimus exposure is a vital prognostic risk factor for the clinical outcome of liver transplantation. New factors or biomarkers characterizing tacrolimus disposition is essential for optimal dose prediction in recipients of liver transplant. The aim of the study was to identify potential plasma metabolites associated with the dose-adjusted trough concentration of tacrolimus in liver transplant recipients by using a global metabolomic approach. A total of 693 plasma samples were collected from 137 liver transplant recipients receiving tacrolimus and regular therapeutic drug monitoring. Untargeted metabolomic analysis was performed by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. Univariate and multivariate analyses with a mixed linear model were conducted, and the results showed that the dose-adjusted tacrolimus trough concentration was associated with 31 endogenous metabolites, including medium- and long-chain acylcarnitines such as stearoylcarnitine (β = 0.222, p = 0.001), microbiota-derived uremic retention solutes such as indolelactic acid (β = 0.194, p = 0.007), bile acids such as taurohyodeoxycholic acid (β = -0.056, p = 0.002), and steroid hormones such as testosterone (β = 0.099, p = 0.001). A multiple linear mixed model including 11 metabolites and clinical information was established with a suitable predictive performance (correlation coefficient based on fixed effects = 0.64 and correlation coefficient based on fixed and random effects = 0.78). These data demonstrated that microbiota-derived uremic retention solutes, bile acids, steroid hormones, and medium- and long-chain acylcarnitines were the main metabolites associated with the dose-adjusted trough concentration of tacrolimus in liver transplant recipients.

Metabolomic and Lipidomic Profiling of Gliomas-A New Direction in Personalized Therapies

Simple Summary

Gliomas comprise an extremely diverse category of brain tumors that are difficult to diagnose and treat. As a result, scientists continue to search for new treatment solutions, with personalized medicine having emerged as a particularly promising therapeutic approach. Metabolomics and its sub-discipline, lipidomics, are two scientific fields well-suited to support this search. Metabolomics focuses on the physicochemical changes in the metabolome, which include all of the small endogenous and exogenous compounds in a biological system. As such, metabolic analysis can help identify important biochemical pathways which could be the targets for new therapeutic approaches. This review examines the new directions of personalized therapies for gliomas and how metabolomic and lipidomic analysis assists in developing these strategies and monitoring their effectiveness. The discussion of new strategies is preceded by a brief overview of the current “gold standard” treatment for gliomas and the obstacles that new treatment approaches must overcome.

Abstract

In addition to being the most common primary brain tumor, gliomas are also among the most difficult to diagnose and treat. At present, the “gold standard” in glioma treatment entails the surgical resection of the largest possible portion of the tumor, followed by temozolomide therapy and radiation. However, this approach does not always yield the desired results. Additionally, the ability to cross the blood-brain barrier remains a major challenge for new potential drugs. Thus, researchers continue to search for targeted therapies that can be individualized based on the specific characteristics of each case. Metabolic and lipidomic research may represent two of the best ways to achieve this goal, as they enable detailed insights into the changes in the profile of small molecules in a biological system/specimen. This article reviews the new approaches to glioma therapy based on the analysis of alterations to biochemical pathways, and it provides an overview of the clinical results that may support personalized therapies in the future.

Omics profiling identifies the regulatory functions of the MAPK/ERK pathway in nephron progenitor metabolism

Nephron endowment is defined by fetal kidney growth and crucially dictates renal health in adults. Defects in the molecular regulation of nephron progenitors contribute to only a fraction of reduced nephron mass cases, suggesting alternative causative mechanisms. The importance of MAPK/ERK activation in nephron progenitor maintenance has been previously demonstrated, and here, we characterized the metabolic consequences of MAPK/ERK deficiency. Liquid chromatography/mass spectrometry-based metabolomics profiling identified 42 reduced metabolites, of which 26 were supported by in vivo transcriptional changes in MAPK/ERK-deficient nephron progenitors. Among these, mitochondria, ribosome and amino acid metabolism, together with diminished pyruvate and proline metabolism, were the most affected pathways. In vitro cultures of mouse kidneys demonstrated a dosage-specific function for pyruvate in controlling the shape of the ureteric bud tip, a regulatory niche for nephron progenitors. In vivo disruption of proline metabolism caused premature nephron progenitor exhaustion through their accelerated differentiation in pyrroline-5-carboxylate reductases 1 (Pycr1) and 2 (Pycr2) double-knockout kidneys. Pycr1/Pycr2-deficient progenitors showed normal cell survival, indicating no changes in cellular stress. Our results suggest that MAPK/ERK-dependent metabolism functionally participates in nephron progenitor maintenance by monitoring pyruvate and proline biogenesis in developing kidneys.

Mechanism-driven strategies for prevention of rheumatoid arthritis

In seropositive rheumatoid arthritis (RA), the onset of clinically apparent inflammatory arthritis (IA) is typically preceded by a prolonged period of autoimmunity manifest by the presence of circulating autoantibodies that can include antibodies to citrullinated protein antigens (ACPA) and rheumatoid factor (RF). This period prior to clinical IA can be designated preclinical RA in those individuals who have progressed to a clinical diagnosis of RA, and an 'at-risk' status in those who have not developed IA but exhibit predictive biomarkers of future clinical RA. With the goal of developing RA prevention strategies, studies have characterized immune phenotypes of preclinical RA/at-risk states. From these studies, a model has emerged wherein mucosal inflammation and dysbiosis may lead first to local autoantibody production that should normally be transient, but instead is followed by systemic spread of the autoimmunity as manifest by serum autoantibody elevations, and ultimately drives the development of clinically identified joint inflammation. This model can be envisioned as the progression of disease development through serial 'checkpoints' that in principle should constrain or resolve autoimmunity; however, instead the checkpoints 'fail' and clinical RA develops. Herein we review the immune processes that are likely to be present at each step and the potential therapeutic strategies that could be envisioned to delay, diminish, halt or even reverse the progression to clinical RA. Notably, these prevention strategies could utilize existing therapies approved for clinical RA, therapies approved for other diseases that target relevant pathways in the preclinical/at-risk state, or approaches that target novel pathways.

A GC-MS-based untargeted metabolomics approach for comprehensive metabolic profiling of vancomycin-induced toxicity in mice

Background

Vancomycin is a glycopeptide antibiotic that is commonly used for severe drug-resistant infections treatment. Application of vancomycin frequently leads to severe ototoxicity, hepatotoxicity, and nephrotoxicity; however, the comprehensive metabolic analysis of vancomycin-induced toxicity is lacking.

Purpose

This study attempted to investigate the metabolic changes after vancomycin administration in mice.

Methods

Experimental mice (n = 9) received continuous intraperitoneal injection of vancomycin (400 mg/kg) every day for 7 days, and mice in control group (n = 9) were treated with the same amount of normal saline. Pathological changes of the kidney were examined using haematoxylin and eosin (HE) staining. A gas chromatography-mass spectrometry (GC-MS) approach was used to identify discriminant metabolites in serum and various organs including the heart, liver, kidney, spleen, cerebral cortex, hippocampus, inner ear, lung, and intestine. The potential metabolites were identified using orthogonal partial least squares discrimination analysis (OPLS-DA). Subsequently, the MetaboAnalyst 5.0 (http://www.metaboanalyst.ca) and Kyoto Encyclopedia of Genes and Genomes database (KEGG, http://www.kegg.jp) were employed to depict the metabolic pathways.

Results

Compared with the control group, the vancomycin induced 13, 17, 27, 22, 16, 10, 17, 11, 10, and 7 differential metabolites in the serum, liver, kidney, heart, cerebral cortex, lung, spleen, intestine, hippocampus, and inner ear, respectively. Further pathway analyses identified that amino acids metabolism, fatty acids biosynthesis, energy metabolism, and lipid metabolism were disrupted after VCM exposure.

Conclusion

Vancomycin affects the metabolism in various organs in mice, which provides new insights for identification of vancomycin-induced toxicity, and facilitate to better understanding of the metabolic pathogenesis of vancomycin.

Metabolites comparison in post-fermentation stage of manual (mechanized) Chinese Huangjiu (yellow rice wine) based on GC–MS metabolomics

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The differential metabolites of manual (mechanized) Huangjiu were determined during post-fermentation stage.

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The metabolic pathways associated with the differential metabolites were identified.

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The contribution of different metabolites to the flavor of Huangjiu was analyzed.

In order to understand the differences of metabolites and their key metabolic pathways between traditional manual and mechanized Huangjiu, gas chromatography-mass spectrometry (GC–MS) combined with non targeted metabolomics was used to track and monitor Huangjiu in the whole post-fermentation stage. The results showed that 25 metabolites and 14 metabolites were identified as differential metabolites in manual and mechanized Huangjiu, respectively (VIP > 1, P < 0.05); three metabolic pathways had significant effects on differential metabolites (−log (P) > 1, impact > 0.01). Compared with the two kinds of Huangjiu, 21 kinds of metabolites were identified as differential metabolites (VIP > 1, P < 0.05); four metabolic pathways had significant effects on differential metabolites (−log (P) > 1, impact > 0.01). This study is helpful to gain insight into the underlying mechanism of flavor formation during the post-fermentation process of Huangjiu and provide a theoretical basis for the industrial development.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7–12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The monozygotic (MZ) and dizygotic (DZ) correlations for the urinary metabolites indicated a role for non-additive genetic factors for 50 amines, 13 organic acids, and 6 steroids. The average broad-sense heritability for these amines, organic acids, and steroids was 0.49 (range: 0.25–0.64), 0.50 (range: 0.33–0.62), and 0.64 (range: 0.43–0.81), respectively. For 6 amines, 7 organic acids, and 4 steroids the twin correlations indicated a role for shared environmental factors and the average narrow-sense heritability was 0.50 (range: 0.37–0.68), 0.50 (range; 0.23–0.61), and 0.47 (range: 0.32–0.70) for these amines, organic acids, and steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data

Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.

Metabolic characterization of hypertrophic cardiomyopathy in human heart

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease with heterogeneous clinical presentations, governed by multiple molecular mechanisms. Metabolic perturbations underlie most cardiovascular diseases; however, the metabolic alterations and their function in HCM are unknown. Here, we describe the metabolome and lipidome of heart and plasma samples from individuals with and without HCM. Correlation analyses showed strong association between metabolic alterations and cardiac function and prognosis of patients with HCM. Using machine learning we identified metabolite panels as potential HCM diagnostic markers or predictors of survival. Clustering based on metabolome and lipidome of heart enabled stratification of patients with HCM into three subgroups with distinct cardiac function and survival. Integration of metabolomics and proteomics data identified metabolic pathways significantly altered in patients with HCM, with the pentose phosphate pathway and oxidative stress being particularly upregulated. Thus, targeting the pentose phosphate pathway and oxidative stress may serve as potential therapeutic strategies for HCM.

Antibacterial Activity of the Essential Oil From Litsea cubeba Against Cutibacterium acnes and the Investigations of Its Potential Mechanism by Gas Chromatography-Mass Spectrometry Metabolomics

Cutibacterium acnes (C. acnes) is an anaerobic Gram-positive bacterium generally considered as a human skin commensal, but is also involved in different infections, such as acne and surgical infections. Although there are a variety of treatments, the side effects and the problem of bacterial drug resistance still limit their clinical usage. In this study, we found that essential oil (EO) distilled from fresh mature Litsea cubeba possessed promising antibacterial activity against C. acnes. In order to elucidate its potential mechanism, bacteriostatic activity test, Live/Dead kit assay, scanning electron microscope (SEM), transmission electron microscope (TEM), and metabolomics were employed. In addition, the content of adenosine triphosphate (ATP) in bacterium and the activities of key enzymes involved in critical metabolic pathways were detected using a variety of biochemical assays. The results showed that EO exhibited significant antibacterial activity against C. acnes at a minimum inhibitory concentration (MIC) of 400 μg/mL and a minimum bactericidal concentration (MBC) of 800 μg/mL, and EO could destroy C. acnes morphology and inhibit its growth. Moreover, results from our study showed that EO had a significant effect on the C. acnes normal metabolism. In total, 86 metabolites were altered, and 34 metabolic pathways related to the carbohydrate metabolism, energy metabolism, amino acid metabolism, as well as cell wall and cell membrane synthesis were perturbed after EO administration. The synthesis of ATP in bacterial cells was also severely inhibited, and the activities of key enzymes of the glycolysis and Wood-Werkman cycle were significantly affected (Pyruvate Carboxylase, Malate Dehydrogenase and Pyruvate kinase activities were decreased, and Hexokinase was increased). Taken together, these results illustrated that the bacteriostatic effect of EO against C. acnes by breaking the bacterial cell morphology and perturbing cell metabolism, including inhibition of key enzyme activity and ATP synthesis. The results from our study may shed new light on the discovery of novel drugs with more robust efficacy.

Elucidating a Complicated Enantioselective Metabolic Profile: A Study From Rats to Humans Using Optically Pure Doxazosin

Doxazosin (DOX) is prescribed as a racemic drug for the clinical treatment of benign prostatic hyperplasia and hypertension. Recent studies found that the two enantiomers of DOX exhibit differences in blood concentration and pharmacological effects. However, the stereoselective metabolic characteristics and mechanisms for DOX are not yet clear. Herein, we identified 34 metabolites of DOX in rats based on our comprehensive and effective strategy. The relationship among the metabolites and the most discriminative metabolites between (-)-DOX and (+)-DOX administration was analyzed according to the kinetic parameters using state-of-the-art multivariate statistical methods. To elucidate the enantioselective metabolic profile in vivo and in vitro, we carefully investigated the metabolic characteristics of metabolites after optically pure isomers administration in rat plasma, rat liver microsomes (RLMs) or human liver microsomes (HLMs), and recombinant human cytochrome P450 (CYP) enzymes. As a result, the differences of these metabolites were found based on their exposure and elimination rate, and the metabolic profile of (±)-DOX was more similar to that of (+)-DOX. Though the metabolites identified in RLMs and HLMs were the same, the metabolic profiles of the metabolites from (-)-DOX and (+)-DOX were greatly different. Furthermore, four human CYP enzymes could catalyze DOX to produce metabolites, but their preferences seemed different. For example, CYP3A4 highly specifically and selectively catalyzed the formation of the specific metabolite (M22) from (-)-DOX. In conclusion, we established a comprehensive metabolic system using pure optical isomers from in vivo to in vitro, and the complicated enantioselectivity of the metabolites of DOX was clearly shown. More importantly, the comprehensive metabolic system is also suitable to investigate other chiral drugs.

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

How paediatric drug development and use could benefit from OMICs: a c4c expert group white paper

The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates, and infants, is limited by a paucity of good quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts, by complementary information about targeted and non-targeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a sub-division into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as well as type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, non-invasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (e.g. liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, like artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to identify complex phenotypes and subpopulations of patients to improve development of medicines for children with potential economic advantages.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Mass spectrometry based metabolomics for small molecule metabolites mining and confirmation as potential biomarkers for schistosomiasis - case of the Okavango Delta communities in Botswana

INTRODUCTION

Metabolomics for identifying schistosomiasis biomarkers in noninvasive samples at various infection stages is being actively explored. The literature on the traditional detection of schistosomiasis in human specimens is well documented. However, state-of-the-art technologies based on mass spectrometry have simplified the use of biomarkers for diagnostics. This review examines methods currently in use for the metabolomics of small molecules using separation science and mass spectrometry.

AREA COVERED

This article highlights the evolution of traditional diagnostic methods for schistosomiasis based on inter alia microscopy, immunology, and polymerase chain reaction. An exhaustive literature search of metabolite mining, focusing on separation science and mass spectrometry, is presented. A comparative analysis of mass spectrometry methods was undertaken, including a projection for the future.

EXPERT COMMENTARY

Mass spectrometry metabolomics for schistosomiasis will lead to biomarker discovery for noninvasive human samples. These biomarkers, together with those from other neglected tropical diseases, such as malaria and sleeping sickness, could be incorporated as arrays on a single biosensor chip and inserted into smartphones, in order to improve surveillance, monitoring, and management.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

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

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

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Metabolomics Signature of Plasma Renin Activity and Linkage with Blood Pressure Response to Beta Blockers and Thiazide Diuretics in Hypertensive European American Patients

Plasma renin activity (PRA) is a predictive biomarker of blood pressure (BP) response to antihypertensives in European–American hypertensive patients. We aimed to identify the metabolic signatures of baseline PRA and the linkages with BP response to β-blockers and thiazides. Using data from the Pharmacogenomic Evaluation of Antihypertensive Responses-2 (PEAR-2) trial, multivariable linear regression adjusting for age, sex and baseline systolic-BP (SBP) was performed on European–American individuals treated with metoprolol (n = 198) and chlorthalidone (n = 181), to test associations between 856 metabolites and baseline PRA. Metabolites with a false discovery rate (FDR) < 0.05 or p < 0.01 were tested for replication in 463 European–American individuals treated with atenolol or hydrochlorothiazide. Replicated metabolites were then tested for validation based on the directionality of association with BP response. Sixty-three metabolites were associated with baseline PRA, of which nine, including six lipids, were replicated. Of those replicated, two metabolites associated with higher baseline PRA were validated: caprate was associated with greater metoprolol SBP response (β = −1.7 ± 0.6, p = 0.006) and sphingosine-1-phosphate was associated with reduced hydrochlorothiazide SBP response (β = 7.6 ± 2.8, p = 0.007). These metabolites are clustered with metabolites involved in sphingolipid, phospholipid, and purine metabolic pathways. The identified metabolic signatures provide insights into the mechanisms underlying BP response.

Insights into the molecular profiles of adult and paediatric acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a clinically and molecularly heterogeneous disease characterised by uncontrolled proliferation, block in differentiation and acquired self-renewal of hematopoietic stem and myeloid progenitor cells. This results in the clonal expansion of myeloid blasts within the bone marrow and peripheral blood. The incidence of AML increases with age, and in childhood, AML accounts for 20% of all leukaemias. Whilst there are many clinical and biological similarities between paediatric and adult AML with continuum across the age range, many characteristics of AML are associated with age of disease onset. These include chromosomal aberrations, gene mutations and differentiation lineage. Following chemotherapy, AML cells that survive and result in disease relapse exist in an altered chemoresistant state. Molecular profiling currently represents a powerful avenue of experimentation to study AML cells from adults and children pre- and postchemotherapy as a means of identifying prognostic biomarkers and targetable molecular vulnerabilities that may be age-specific. This review highlights recent advances in our knowledge of the molecular profiles with a focus on transcriptomes and metabolomes, leukaemia stem cells and chemoresistant cells in adult and paediatric AML and focus on areas that hold promise for future therapies.

Predicting Range of Initial Warfarin Dose Based on Pharmacometabolomic and Genetic Inputs

Anticoagulation response to warfarin during the initial stage of therapy varies among individuals. In this study, we aimed to combine pharmacometabolomic and pharmacogenetic data to predict interindividual variation in warfarin response, and, on this basis, suggest an initial daily dose range. The baseline metabolic profiles, genotypes, and clinical information of 160 patients with heart valve disease served as the variables of the function of the last international normalized ratio measured before a patient's discharge (INR_day7 ) to screen for potential biomarkers. The partial least-squares model showed that two baseline metabolites (uridine and guanosine), one single-nucleotide variation (VKORC1), and four clinical parameters (weight, creatinine level, amiodarone usage, and initial daily dose) had good predictive power for INR_day7 (R²  = 0.753 for the training set, 0.643 for the test set). With these biomarkers, a machine learning algorithm (two-dimensional linear discriminant analysis-multinomial logit model) was used to predict the subgroups with extremely warfarin-sensitive or less warfarin-sensitive patients with a prediction accuracy of 91% for the training set and 90% for the test set, indicating that individual responses to warfarin could be effectively predicted. Based on this model, we have successfully designed an algorithm,"IniWarD," for predicting an effective dose range in the initial 7-day warfarin therapy. The results indicate that the daily dose range suggested by the IniWarD system is more appropriate than that of the conventional genotype-based method, and the risk of bleeding or thrombus due to warfarin could thus be avoided.

Discovery and validation of peritoneal endometriosis biomarkers in peritoneal fluid and serum

RESEARCH QUESTION

What are the potential biomarkers for peritoneal endometriosis in peritoneal fluid and serum?

DESIGN

Case-control studies composed of independent discovery and validation sets were conducted. In the discovery set, untargeted liquid chromatography-mass spectrometry (LC-MS/MS) metabolomics, multivariable and univariable analyses were conducted to generate global metabolomic profiles of peritoneal fluid for endometriosis and to identify potential metabolites that could distinguish peritoneal endometriosis (n = 10) from controls (n = 31). The identified metabolites from the discovery set were validated in independent peritoneal fluid (n =19 peritoneal endometriosis and n = 20 controls) and serum samples (n = 16 peritoneal endometriosis and n = 19 controls) using targeted metabolomics. The area under the receiver-operating characteristics curve (AUC) analysis was used to evaluate the diagnostic performance of peritoneal endometriosis metabolites.

RESULTS

In the discovery set, peritoneal fluid phosphatidylcholine (34:3) and phenylalanyl-isoleucine were significantly increased in peritoneal endometriosis groups compared with control groups, with AUC 0.77 (95% CI 0.61 to 0.92; P = 0.018) and AUC 0.98 (95% CI 0.95 to 1.02; P < 0.001), respectively. In the validation set, phenylalanyl-isoleucine retained discriminatory performance to distinguish peritoneal endometriosis from controls in both peritoneal fluid (AUC 0.77, 95% CI 0.61 to 0.92; P = 0.006) and serum samples (AUC 0.81, 95% CI 0.64 to 0.99; P = 0.004), with notably stronger discrimination between peritoneal endometriosis and controls in proliferative phase.

CONCLUSION

Our preliminary results propose phenylalanyl-isoleucine as a potential biomarker of peritoneal endometriosis, which may be used as a minimally invasive diagnostic biomarker of peritoneal endometriosis.