Pharmacometabonomics as an effector for personalized medicine

Detection of pharmacolipidodynamic effects following the intravenous and oral administration of gefitinib to C57Bl/6JRj mice by rapid UHPLC-MS analysis of plasma

Omics-based biomarker technologies, including metabolic profiling (metabolomics/metabonomics) and lipidomics, are making a significant impact on disease understanding, drug development, and translational research. A wide range of patho-physiological processes involve lipids and monitoring changes in lipid abundance can give valuable insights into mechanisms of drug action, off target pharmacology and toxicity. Here we report changes, detected by untargeted LC-MS, in the plasma lipid profiles of male C57Bl/6JRj mice following the PO and IV administration of the epidermal growth factor receptor (EGFR) inhibitor gefitinib. Statistical analysis of the data obtained for both the IV and PO samples showed time-related changes in the amounts of lipids from several different classes. The largest effects were associated with a rapid onset of these changes following gefitinib administration followed by a gradual return by 24 h post dose to the type of lipid profile seen in predose samples. Investigation of the lipids responsible for the variance observed in the data showed that the PI, PC, LPC, PE and TG were subject to the largest disruption with both transient increases and decreases in relative amounts seen in response to administration of the drug. The pattern of the changes in the relative abundances of those lipids subject to variation appeared to be correlated to the pharmacokinetics of gefitinib (and its major metabolites). These observations support the concept of a distinct pharmacolipidodynamic relationship between drug exposure and plasma lipid abundance.

Drug-microbiota interactions: an emerging priority for precision medicine

Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.

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.

A Preliminary Nuclear Magnetic Resonance Metabolomics Study Identifies Metabolites that Could Serve as Diagnostic Markers of Major Depressive Disorder

BACKGROUND

The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome.

METHODOLOGY

Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models.

RESULTS

In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful.

CONCLUSION

Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

Pharmacometabolomics by NMR in Oncology: A Systematic Review

Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.

Individualizing pharmacogenomic test results in the context of the microbiome

The field of pharmacogenetic testing was hailed as one of the early successful clinical applications arising from the personalized (or precision) medicine revolution. Substantial progress has been made to identify genes and genetic variants involved in drug response and establish clinical implementation programs. Yet, drug response is a complex trait and recent work has highlighted the key role played by the gut microbiome. As the study of the gut microbiome and pharmacogenetics converge, it may be possible to generate more precise predictions of drug response and improve health outcomes to treatments. Substantial effort will be needed to understand the dynamic impact of the microbiome and the interplay with host genetics and how to implement expanded pharmacogenetic testing.

Disorders of Calcium and Phosphorus Metabolism and the Proteomics/Metabolomics-Based Research

Since calcium and phosphorus play vital roles in a multitude of physiologic systems, disorders of calcium and phosphorus metabolism always lead to severe consequences such as skeletal-related and cardiovascular morbidity, or even life-threatening. Physiologically, the maintenance of calcium and phosphorus homeostasis is achieved via a variety of concerted actions of hormones such as parathyroid hormone (PTH), vitamin D, and fibroblast growth factor (FGF23), which could be regulated mainly at three organs, the intestine, kidney, and bone. Disruption of any organ or factor might lead to disorders of calcium and phosphorus metabolism. Currently, lacking of accurate diagnostic approaches and unknown molecular basis of pathophysiology will result in patients being unable to receive a precise diagnosis and personalized treatment timely. Therefore, it is urgent to identify early diagnostic biomarkers and develop therapeutic strategies. Fortunately, proteomics and metabolomics offer promising tools to discover novel indicators and further understanding of pathological mechanisms. Therefore, in this review, we will give a systematic introduction on PTH-1,25(OH)₂D-FGF23 axis in the disorders of calcium and phosphorus metabolism, diagnostic biomarkers identified, and potential altered metabolic pathways involved.

Plasma metabolomics study of Vogt-Koyanagi-Harada disease identifies potential diagnostic biomarkers

Vogt-Koyanagi-Harada (VKH) disease is a common type of uveitis in China, but the diagnosis criteria of VKH disease is controversial. The aim of this study was to investigate potential diagnostic plasma biomarkers for VKH disease. A case-control study including 55 VKH patients (28 active patients and 27 inactive VKH patients) and 30 healthy controls in a tertiary referral center was performed. The metabolic phenotype of VKH patients showed a significant difference compared to healthy controls. Fifteen differentially expressed metabolites (DEMs) were identified between active VKH patients and healthy controls and nine DEMs were found between inactive VKH patients and healthy controls after controlling variable importance in the projection (VIP) value > 1 and false discovery rate (FDR) < 0.05. D-mannose, stearic acid and L-lysine were shown to be potential diagnostic biomarkers which can discriminate active VKH patients from healthy controls with a diagnostic performance with AUC = 0.965, 0.936 and 0.910 respectively in independent diagnosis and an AUC = 0.999 when combined. Sarcosine was recognized as an independent potential biomarker which could distinguish inactive VKH patients from healthy controls. This study reveals a significant difference of plasma metabolic phenotype and identifies diagnostic biomarkers for VKH disease. Changes in the metabolic profile may provide clues towards the pathophysiology of VKH disease.

Integration analysis of metabolites and single nucleotide polymorphisms improves the prediction of drug response of celecoxib

INTRODUCTION

Pharmacogenetics and pharmacometabolomics are the common methods for personalized medicine, either genetic or metabolic biomarkers have limited predictive power for drug response.

OBJECTIVES

In order to better predict drug response, the study attempted to integrate genetic and metabolic biomarkers for drug pharmacokinetics prediction.

METHODS

The study chose celecoxib as study object, the pharmacokinetic behavior of celecoxib was assessed in 48 healthy volunteers based on UPLC-MS/MS platform, and celecoxib related single nucleotide polymorphisms (SNPs) were also detected. Three mathematic models were constructed for celecoxib pharmacokinetics prediction, the first one was mainly based on celecoxib-related SNPs; the second was based on the metabolites selected from a pharmacometabolomic analysis by using GC-MS/MS method, the last model was based on the combination of the celecoxib-related SNPs and metabolites above.

RESULTS

The result proved that the last model showed an improved prediction power, the integration model could explain 71.0% AUC variation and predict 62.3% AUC variation. To facilitate clinical application, ten potential celecoxib-related biomarkers were further screened, which could explain 68.3% and predict 54.6% AUC variation, the predicted AUC was well correlated with the measured values (r = 0.838).

CONCLUSION

This study provides a new route for personalized medicine, the integration of genetic and metabolic biomarkers can predict drug response with a higher accuracy.

Untargeted Metabolomics to Go beyond the Canonical Effect of Acetylsalicylic Acid

Given to its ability to irreversibly acetylate the platelet cyclooxygenase-1 enzyme, acetylsalicylic acid (ASA) is successfully employed for the prevention of cardiovascular disease. Recently, an antitumoral effect of ASA in colorectal cancer has been increasingly documented. However, the molecular and metabolic mechanisms by which ASA exerts such effect is largely unknown. Using a new, untargeted liquid chromatography–mass spectrometry approach, we have analyzed urine samples from seven healthy participants that each ingested 100 mg of ASA once daily for 1 week. Of the 2007 features detected, 25 metabolites differing after ASA ingestion (nominal p < 0.05 and variable importance in projection (VIP) score > 1) were identified, and pathway analysis revealed low levels of glutamine and of metabolites involved in histidine and purine metabolisms. Likewise, consistent with an altered fatty acid β-oxidation process, a decrease in several short- and medium-chain acyl-carnitines was observed. An abnormal β-oxidation and a lower than normal glutamine availability suggests reduced synthesis of acetyl-Co-A, as they are events linked to one another and experimentally related to ASA antiproliferative effects. While giving an example of how untargeted metabolomics allows us to explore new clinical applications of drugs, the present data provide a direction to be pursued to test the therapeutic effects of ASA—e.g., the antitumoral effect—beyond cardiovascular protection.

Serum metabonomics study of pregnant women with gestational diabetes mellitus based on LC-MS

OBJECTIVE

Through metabolomics method, the objective of the paper is to differentially screen serum metabolites of GDM patients and healthy pregnant women, to explore potential biomarkers of GDM and analyze related pathways, and to explain the potential mechanism and biological significance of GDM.

METHODS

The serum samples from 30 GDM patients and 30 healthy pregnant women were selected to conduct non-targeted metabolomics study by liquid chromatography-mass spectrometry. The differential metabolites between the two groups were searched and the metabolic pathway was analyzed by KEGG database.

RESULTS

Multivariate statistical analysis found that serum metabolism in GDM patients was different significantly from healthy pregnant women, 36 differential metabolites and corresponding metabolic pathways were identified in serum, which involved several metabolic ways like, fatty acid metabolism, butyric acid metabolism, bile secretion, and amino acid metabolism.

CONCLUSION

The discovery of these biomarkers provided a new theoretical basis and experimental basis for further study of the early diagnosis and pathogenesis of GDM. At the same time, LC-MS-based serum metabolomics methods also showed great application values in disease diagnosis and mechanism research.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Serum metabolomic alterations in Beagle dogs experimentally infected with Toxocara canis

Background

Toxocara canis, a globally distributed roundworm, can cause debilitating disease in dogs and humans; however, little is known about the metabolomic response of the hosts to T. canis infection. There is an increasing need to understand the metabolic mechanisms underlying the pathogenesis of T. canis infection in dogs. Here, we examined the metabolomic changes in Beagle dogsʼ serum following T. canis infection using LC-MS/MS.

Results

The metabolic profiles of Beagle dogsʼ serum were determined at 12 h, 24 h, 10 d and 36 d after oral infection with 300 infectious T. canis eggs by LC-MS/MS. We tested whether the T. canis-associated differentially abundant metabolites could distinguish the serum of infected dogs from controls, as measured by the area under the receiver operating characteristic (ROC) curve (AUC). The differentially expressed metabolites were further evaluated by principal components analysis and pathway enrichment analysis. A total of 5756 and 5299 ions were detected in ESI+ and ESI− mode, respectively. ROC curve analysis revealed nine and five metabolite markers, at 12 hpi and 24 hpi to 36 dpi, respectively, with potential diagnostic value for toxocariasis. The levels of taurocholate, estradiol, prostaglandins and leukotriene were significantly changed. Primary bile acid biosynthesis pathway, steroid hormone biosynthesis pathway and biosynthesis of unsaturated fatty acids pathway were significantly altered by T. canis infection.

Conclusions

These findings show that T. canis infection can induce several changes in the dog serum metabolome and that the metabolic signature associated with T. canis infection in dogs has potential for toxocariasis diagnosis.

A Two-Week Treatment with Plant Extracts Changes Gut Microbiota, Caecum Metabolome, and Markers of Lipid Metabolism in ob/ob Mice

SCOPE

Targeting gut microbiota dysbiosis by prebiotics is effective, though side effects such as abdominal bloating and flatulence may arise following high prebiotic consumption over weeks. The aim is therefore to optimize the current protocol for prebiotic use.

METHODS AND RESULTS

To examine the prebiotic properties of plant extracts, two independent studies are conducted in ob/ob mice, over two weeks. In the first study, Porphyra umbilicalis and Melissa officinalis L. extracts are evaluated; in the second study, a high vs low dose of an Emblica officinalis Gaertn extract is assessed. These plant extracts affect gut microbiota, caecum metabolome, and induce a significant lower plasma triacylglycerols (TG) following treatment with P. umbilicalis and significantly higher plasma free fatty acids (FFA) following treatment with the low-dose of E. officinalis Gaertn. Glucose- and insulin-tolerance are not affected but white adipose tissue and liver gene expression are modified. In the first study, IL-6 hepatic gene expression is significantly (adjusted p = 0.0015) and positively (r = 0.80) correlated with the bacterial order Clostridiales in all mice.

CONCLUSION

The data show that a two-week treatment with plant extracts affects the dysbiotic gut microbiota and changes both caecum metabolome and markers of lipid metabolism in ob/ob mice.

Breaking the cycle: Reversal of flux in the tricarboxylic acid cycle by dimethyl fumarate

Objective

To infer molecular effectors of therapeutic effects and adverse events for dimethyl fumarate (DMF) in patients with relapsing-remitting MS (RRMS) using untargeted plasma metabolomics.

Methods

Plasma from 27 patients with RRMS was collected at baseline and 6 weeks after initiating DMF. Patients were separated into discovery (n = 15) and validation cohorts (n = 12). Ten healthy controls were also recruited. Metabolomic profiling using ultra-high-performance liquid chromatography mass spectrometry (UPLC-MS) was performed on the discovery cohort and healthy controls at Metabolon Inc (Durham, NC). UPLC-MS was performed on the validation cohort at the National Phenome Centre (London, UK). Plasma neurofilament concentration (pNfL) was assayed using the Simoa platform (Quanterix, Lexington, MA). Time course and cross-sectional analyses were performed to identify pharmacodynamic changes in the metabolome secondary to DMF and relate these to adverse events.

Results

In the discovery cohort, tricarboxylic acid (TCA) cycle intermediates fumarate and succinate, and TCA cycle metabolites succinyl-carnitine and methyl succinyl-carnitine increased 6 weeks following treatment (q < 0.05). Methyl succinyl-carnitine increased in the validation cohort (q < 0.05). These changes were not observed in the control population. Increased succinyl-carnitine and methyl succinyl-carnitine were associated with adverse events from DMF (flushing and abdominal symptoms). pNfL concentration was higher in patients with RRMS than in controls and reduced over 15 months of treatment.

Conclusion

TCA cycle intermediates and metabolites are increased in patients with RRMS treated with DMF. The results suggest reversal of flux through the succinate dehydrogenase complex. The contribution of succinyl-carnitine ester agonism at hydroxycarboxylic acid receptor 2 to both therapeutic effects and adverse events requires investigation.

Multiplex assay for multiomics advances in personalized-precision medicine

Building the future of precision medicine is the main focus in cancer domain. Clinical trials are moving toward an array of studies that are more adapted to precision medicine. In this domain, there is an enhanced need for biomarkers, monitoring devices, and data-analysis methods. Omics profiling using whole genome, epigenome, transcriptome, proteome, and metabolome can offer detailed information of the human body in an integrative manner. Omes profiles reflect more accurately real-time physiological status. Personalized omics analyses both disease as a whole and the main disease processes, for a better understanding of the individualized health. Through this, multi-omic approaches for health monitoring, preventative medicine, and personalized treatment can be targeted simultaneously and can lead clinicians to have a comprehensive view on the diseasome.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor

When using an appropriate data analysis pipeline, protease inhibitor (PI)-containing urine samples are fit-for-purpose for metabolic phenotyping of patients with nephrotic syndrome and proteinuria.

Pharmacometabolomic prediction of individual differences of gastrointestinal toxicity complicating myelosuppression in rats induced by irinotecan

Pharmacometabolomics has been already successfully used in toxicity prediction for one specific adverse effect. However in clinical practice, two or more different toxicities are always accompanied with each other, which puts forward new challenges for pharmacometabolomics. Gastrointestinal toxicity and myelosuppression are two major adverse effects induced by Irinotecan (CPT-11), and often show large individual differences. In the current study, a pharmacometabolomic study was performed to screen the exclusive biomarkers in predose serums which could predict late-onset diarrhea and myelosuppression of CPT-11 simultaneously. The severity and sensitivity differences in gastrointestinal toxicity and myelosuppression were judged by delayed-onset diarrhea symptoms, histopathology examination, relative cytokines and blood cell counts. Mass spectrometry-based non-targeted and targeted metabolomics were conducted in sequence to dissect metabolite signatures in predose serums. Eventually, two groups of metabolites were screened out as predictors for individual differences in late-onset diarrhea and myelosuppression using binary logistic regression, respectively. This result was compared with existing predictors and validated by another independent external validation set. Our study indicates the prediction of toxicity could be possible upon predose metabolic profile. Pharmacometabolomics can be a potentially useful tool for complicating toxicity prediction. Our findings also provide a new insight into CPT-11 precision medicine.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Drug pharmacomicrobiomics and toxicomicrobiomics: from scattered reports to systematic studies of drug-microbiome interactions

INTRODUCTION

Pharmacomicrobiomics and toxicomicrobiomics study how variations within the human microbiome (the combination of human-associated microbial communities and their genomes) affect drug disposition, action, and toxicity. These emerging fields, interconnecting microbiology, bioinformatics, systems pharmacology, and toxicology, complement pharmacogenomics and toxicogenomics, expanding the scope of precision medicine. Areas covered: This article reviews some of the most recently reported pharmacomicrobiomic and toxicomicrobiomic interactions. Examples include the impact of the human gut microbiota on cardiovascular drugs, natural products, and chemotherapeutic agents, including immune checkpoint inhibitors. Although the gut microbiota has been the most extensively studied, some key drug-microbiome interactions involve vaginal, intratumoral, and environmental bacteria, and are briefly discussed here. Additionally, computational resources, moving the field from cataloging to predicting interactions, are introduced. Expert opinion: The rapid pace of discovery triggered by the Human Microbiome Project is moving pharmacomicrobiomic research from scattered observations to systematic studies focusing on screening microbiome variants against different drug classes. Better representation of all human populations will improve such studies by avoiding sampling bias, and the integration of multiomic studies with designed experiments will allow establishing causation. In the near future, pharmacomicrobiomic testing is expected to be a key step in screening novel drugs and designing precision therapeutics.

A Metabolomics Approach to Pharmacotherapy Personalization

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

LC-HRMS Metabolomics for Untargeted Diagnostic Screening in Clinical Laboratories: A Feasibility Study

Today’s high-resolution mass spectrometers (HRMS) allow bioanalysts to perform untargeted/global determinations that can reveal unexpected compounds or concentrations in a patient’s sample. This could be performed for preliminary diagnosis attempts when usual diagnostic processes and targeted determinations fail. We have evaluated an untargeted diagnostic screening (UDS) procedure. UDS is a metabolome analysis that compares one sample (e.g., a patient) with control samples (a healthy population). Using liquid chromatography (LC)-HRMS full-scan analysis of human serum extracts and unsupervised data treatment, we have compared individual samples that were spiked with one xenobiotic or a higher level of one endogenous compound with control samples. After the use of different filters that drastically reduced the number of metabolites detected, the spiked compound was eventually revealed in each test sample and ranked. The proposed UDS procedure appears feasible and reliable to reveal unexpected xenobiotics (toxicology) or higher concentrations of endogenous metabolites. HRMS-based untargeted approaches could be useful as preliminary diagnostic screening when canonical processes do not reveal disease etiology nor establish a clear diagnosis and could reduce misdiagnosis. On the other hand, the risk of overdiagnosis of this approach should be reduced with mandatory biomedical interpretation of the patient’s UDS results and with confirmatory targeted and quantitative determinations.

Pharmacometabolomics analysis of plasma to phenotype clopidogrel high on treatment platelets reactivity in coronary artery disease patients

Dual antiplatelet therapy (DAPT) of clopidogrel and aspirin is crucial for coronary artery disease (CAD) patients undergoing percutaneous coronary intervention (PCI). However, some patients may endure clopidogrel high on treatment platelets reactivity (HTPR) which may cause thromboembolic events. Clopidogrel HTPR is multifactorial with some genetic and non-genetic factors contributing to it. We aimed to use nuclear magnetic resonance (1H NMR) pharmacometabolomics analysis of plasma to investigate this multifactorial and identify metabolic phenotypes and pathways associated with clopidogrel HTPR. Blood samples were collected from 71 CAD patients planned for interventional angiographic procedure (IAP) before the administration of clopidogrel 600 mg loading dose (LD) and 6 h after the LD. Platelets function testing was done 6 h post-LD using VerifyNow® P2Y12 assay. Pre-dose and post-dose plasma samples were analysed using 1H NMR. Multivariate statistical analysis was used to indicate the discriminating metabolites. Two metabotypes, each with 34 metabolites (pre-dose and post-dose) were associated with clopidogrel HTPR. Pathway analysis of these metabotypes revealed that aminoacyl-tRNA biosynthesis, nitrogen metabolism and glycine-serine-threonine metabolism are the most perturbed metabolic pathways associated with clopidogrel HTPR. Furthermore, the identified biomarkers indicated that clopidogrel HTPR is multifactorial where the metabolic phenotypes of insulin resistance, type two diabetes mellitus, obesity, gut-microbiota and heart failure are associated with it. Pharmacometabolomics analysis of plasma revealed new insights on the implicated metabolic pathways and the predisposing factors of clopidogrel HTPR.

Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer

Defining biomarkers that predict therapeutic effects and adverse events is a crucial mandate to guide patient selection for personalized cancer treatments. In the present study, we applied a pharmacometabolomics approach to identify biomarkers potentially associated with pathological complete response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer patients. Based on histological response the 34 patients enrolled in the study were subdivided into two groups: good responders (n = 15) and poor responders (n = 19). The pre-treatment serum targeted metabolomics profile of all patients were analyzed by liquid chromatography tandem mass spectrometry and the differences in the metabolomics profile between the two groups was investigated by multivariate partial least squares discrimination analysis. The most relevant metabolites that differentiate the two groups of patients were spermidine and tryptophan. The Good responders showed higher levels of spermidine and lower amounts of tryptophan compared with the poor responders (p < 0.001, q < 0.05). The serum level of these two metabolites identified patients who achieved a pathological complete response with a sensitivity of 90% [0.79–1.00] and a specificity of 0.87% [0.67–1.00]. These preliminary results support the role played by the individual patients' metabolism in determining the response to cancer treatments and may be a useful tool to select patients that are more likely to benefit from the trastuzumab-paclitaxel treatment.

Metabolomic prediction of treatment outcome in pancreatic ductal adenocarcinoma patients receiving gemcitabine

PURPOSE

Resistance to gemcitabine remains a key challenge in the treatment of pancreatic ductal adenocarcinoma (PDAC), necessitating the constant search for effective strategies for a priori prediction of clinical outcome. While the existing studies focused on aberration of drug disposition genes and proteins as molecular predictors of gemcitabine treatment outcomes, the metabolic aberration associated with chemoresistance in clinical PDAC has been neglected. This exploratory study investigated the potential role of tissue metabolomics in characterizing the clinical treatment outcome of gemcitabine therapy.

METHODS

Surgically resected tumors from PDAC patients who underwent gemcitabine-based adjuvant chemotherapy (n = 25) were subjected to metabotyping using gas chromatography/time-of-flight mass spectrometry (GC/TOFMS).

RESULTS

A partial least-squares discriminant analysis (PLS-DA) model clearly distinguished patients who had favorable survival [overall survival (OS) > 24 months] from those who exhibited poorer survival (OS < 16 months) (Q ² = 0.302). Receiver-operating characteristic analysis demonstrated the robustness of the PLS-DA model with an area under the curve of 1. PLS-DA revealed 19 marker metabolites (e.g., lactic acid, proline, and pyroglutamate) that shed insights into the chemoresistance of gemcitabine in PDAC. Particularly, tissue levels of lactic acid complemented transcript expression levels of human equilibrative nucleoside transporter 1 in distinguishing patients according to their overall survival.

CONCLUSION

This work established proof-of-principle for GC/TOFMS-based global metabotyping of PDAC and laid the foundation for future discovery of metabolic biomarkers predictive of gemcitabine resistance in PDAC chemotherapy.

1H NMR based pharmacometabolomics analysis of urine identifies metabolic phenotype of clopidogrel high on treatment platelets reactivity in coronary artery disease patients

Clopidogrel high on treatment platelets reactivity (HTPR) has burdened achieving optimum therapeutic outcome. Although there are known genetic and non-genetic factors associated with clopidogrel HTPR, which explain in part clopidogrel HTPR, yet, great portion remains unknown, often hindering personalizing antiplatelet therapy. Nuclear magnetic resonance (1H NMR) pharmacometabolomics analysis is useful technique to phenotype drug response. We investigated using 1H NMR analysis to phenotype clopidogrel HTPR in urine. Urine samples were collected from 71 coronary artery disease (CAD) patients who were planned for interventional angiographic procedure prior to taking 600mg clopidogrel loading dose (LD) and 6h post LD. Patients' platelets function testing was assessed with the VerifyNow® P2Y12 assay at 6h after LD. Urine samples were analysed using 1H NMR. Multivariate statistical analysis was used to identify metabolites associated with clopidogrel HTPR. In pre-dose samples, 16 metabolites were associated with clopidogrel HTPR. However, 18 metabolites were associated with clopidogrel HTPR in post-dose samples. The pathway analysis of the identified biomarkers reflected that multifactorial conditions are associated with clopidogrel HTPR. It also revealed the implicated role of gut microbiota in clopidogrel HTPR. Pharmacometabolomics not only discovered novel biomarkers of clopidogrel HTPR but also revealed implicated pathways and conditions.

Personalized medicine-a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

Pharmacometabolomics for predicting variable busulfan exposure in paediatric haematopoietic stem cell transplantation patients

Owing to its narrow therapeutic range and high pharmacokinetic variability, optimal dosing for busulfan is important to minimise overexposure-related systemic toxicity and underexposure-related graft failure. Using global metabolomics, we investigated biomarkers for predicting busulfan exposure. We analysed urine samples obtained before busulfan administration from 59 paediatric patients divided into 3 groups classified by area under the busulfan concentration-time curve (AUC), i.e., low-, medium-, and high-AUC groups. In the high-AUC group, deferoxamine metabolites were detected. Phenylacetylglutamine and two acylcarnitines were significantly lower in the high-AUC group than in the low-AUC group. Deferoxamine, an iron-chelating agent that lowers serum ferritin levels, was detected in the high-AUC group, indicating that those patients had high ferritin levels. Therefore, in a retrospective study of 130 paediatric patients, we confirmed our hypothesis that busulfan clearance (dose/AUC) and serum ferritin level has a negative correlation (r = −0.205, P = 0.019). Ferritin, acylcarnitine, and phenylacetylglutamine are associated with liver damage, including free radical formation, deregulation of hepatic mitochondrial β-oxidation, and hyperammonaemia. Our findings reveal potential biomarkers predictive of busulfan exposure and suggest that liver function may affect busulfan exposure.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Pharmacometabonomics – the novel way to personalized drug therapy

The review is devoted to pharmacometabonomics - a new branch of science focused on personalization of drug therapy through the comprehensive analysis of metabolites of patient's biological fluids. It considers the history of pharmacometabonomic, positioning to other “-omic” sciences, and system approach, realized by this science, in determination of individual therapeutic dose of the drugs and also a technical implementation of pharmacometabonomic based on direct mass spectrometry of blood plasma metabolites. Special attention is paid to a comparative analysis of pharmacometabonomics and other main approaches to personalized therapy in the clinic, such as pharmacogenetics and therapeutic drug monitoring. Finally, prospects of pharmacometabonomics applications in clinical practice were also discussed.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

INTRODUCTION

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

OBJECTIVES

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs.

METHODS

A literature search of the current relevant primary research was performed.

RESULTS

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

CONCLUSION

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

Feasibility Study of NMR Based Serum Metabolomic Profiling to Animal Health Monitoring: A Case Study on Iron Storage Disease in Captive Sumatran Rhinoceros (Dicerorhinus sumatrensis)

A variety of wildlife species maintained in captivity are susceptible to iron storage disease (ISD), or hemochromatosis, a disease resulting from the deposition of excess iron into insoluble iron clusters in soft tissue. Sumatran rhinoceros (Dicerorhinus sumatrensis) is one of the rhinoceros species that has evolutionarily adapted to a low-iron diet and is susceptible to iron overload. Hemosiderosis is reported at necropsy in many African black and Sumatran rhinoceroses but only a small number of animals reportedly die from hemochromatosis. The underlying cause and reasons for differences in susceptibility to hemochromatosis within the taxon remains unclear. Although serum ferritin concentrations have been useful in monitoring the progression of ISD in many species, there is some question regarding their value in diagnosing hemochromatosis in the Sumatran rhino. To investigate the metabolic changes during the development of hemochromatosis and possibly increase our understanding of its progression and individual susceptibility differences, the serum metabolome from a Sumatran rhinoceros was investigated by nuclear magnetic resonance (NMR)-based metabolomics. The study involved samples from female rhinoceros at the Cincinnati Zoo (n = 3), including two animals that died from liver failure caused by ISD, and the Sungai Dusun Rhinoceros Conservation Centre in Peninsular Malaysia (n = 4). Principal component analysis was performed to visually and statistically compare the metabolic profiles of the healthy animals. The results indicated that significant differences were present between the animals at the zoo and the animals in the conservation center. A comparison of the 43 serum metabolomes of three zoo rhinoceros showed two distinct groupings, healthy (n = 30) and unhealthy (n = 13). A total of eighteen altered metabolites were identified in healthy versus unhealthy samples. Results strongly suggest that NMR-based metabolomics is a valuable tool for animal health monitoring and may provide insight into the progression of this and other insidious diseases.

Precision medicine for psychopharmacology: a general introduction

INTRODUCTION

Precision medicine is an emerging medical model that can provide accurate diagnoses and tailored therapeutic strategies for patients based on data pertaining to genes, microbiomes, environment, family history and lifestyle.

AREAS COVERED

Here, we provide basic information about precision medicine and newly introduced concepts, such as the precision medicine ecosystem and big data processing, and omics technologies including pharmacogenomics, pharamacometabolomics, pharmacoproteomics, pharmacoepigenomics, connectomics and exposomics. The authors review the current state of omics in psychiatry and the future direction of psychopharmacology as it moves towards precision medicine. Expert commentary: Advances in precision medicine have been facilitated by achievements in multiple fields, including large-scale biological databases, powerful methods for characterizing patients (such as genomics, proteomics, metabolomics, diverse cellular assays, and even social networks and mobile health technologies), and computer-based tools for analyzing large amounts of data.

Engineering Human Microbiota: Influencing Cellular and Community Dynamics for Therapeutic Applications

The complex relationship between microbiota, human physiology, and environmental perturbations has become a major research focus, particularly with the arrival of culture-free and high-throughput approaches for studying the microbiome. Early enthusiasm has come from results that are largely correlative, but the correlative phase of microbiome research has assisted in defining the key questions of how these microbiota interact with their host. An emerging repertoire for engineering the microbiome places current research on a more experimentally grounded footing. We present a detailed look at the interplay between microbiota and host and how these interactions can be exploited. A particular emphasis is placed on unstable microbial communities, or dysbiosis, and strategies to reestablish stability in these microbial ecosystems. These include manipulation of intermicrobial communication, development of designer probiotics, fecal microbiota transplantation, and synthetic biology.

Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease

Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches.

Capillary Electrophoresis-Mass Spectrometry for Clinical Metabolomics

In clinical metabolomics, capillary electrophoresis-mass spectrometry (CE-MS) has become a very useful technique for the analysis of highly polar and charged metabolites in complex biologic samples. A comprehensive overview of recent developments in CE-MS for metabolic profiling studies is presented. This review covers theory, CE separation modes, capillary coatings, and practical aspects of CE-MS coupling. Attention is also given to sample pretreatment and data analysis strategies used for metabolomics. The applicability of CE-MS for clinical metabolomics is illustrated using samples ranging from plasma and urine to cells and tissues. CE-MS application to large-scale and quantitative clinical metabolomics is addressed. Conclusions and perspectives on this unique analytic strategy are presented.

LC-MS based metabolomics identification of novel biomarkers of tobacco smoke-induced chronic bronchitis

Tobacco smoke (TS) is a major causative agent to lead to chronic bronchitis (CB). However the mechanisms of CB induced by TS are unclear. In this report, rats were exposed to different concentrations of TS and the metabolic features of CB were characterized by using a nontargeted metabolic profiling method based on liquid chromatography-mass spectrometry (LC-MS) to detect the altered metabolic patterns in serum from CB rats and investigate the mechanisms of CB. 11 potential biomarkers were identified in serum of rats. Among them, the levels of lysophosphatidylethanolamine (18:1), lysophosphatidic acid (18:1), lysophosphatidylethanolamine (18:0), lysophosphatidylethanolamine (16:0), lysophosphatidylethanolamine (20:4), docosahexaenoic acid, 5-hydroxyindoleacetic acid and 5'-carboxy-γ-tocopherol were higher in TS group compared to control group. Conversely, the levels of 4-imidazolone-5-propionic acid, 12-hydroxyeicosatetraenoic acid and uridine were lower in TS group. The results indicated that the mechanism of CB was related to amino acid metabolism and lipid metabolism, particularly lipid metabolism. In addition, lysophosphatidylethanolamines were proved to be important mediators, which could be used as biomarkers to diagnose CB. These results also suggested that metabolomics was suitable for diagnosing CB and elucidating the possible metabolic pathways of TS-induced CB.

Metabonomics and diagnostics

Metabonomic techniques have considerable potential in the field of clinical diagnostics, typifying the application of a translational research paradigm. Care must be taken at all stages to apply appropriate methodology with accurate patient selection and profiling, and rigorous data acquisition and handling, to ensure clinical validity.An ever-increasing number of publications in a wide range of diseases and diverse patient groups suggest a variety of potential clinical uses; prospective studies in large validation cohorts are required to bring metabonomics into routine clinical practice. In this chapter, the utility of metabonomics as a diagnostic tool will be discussed.

Gastrointestinal Symptoms and Altered Intestinal Permeability Induced by Combat Training Are Associated with Distinct Metabotypic Changes

Physical and psychological stress have been shown to modulate multiple aspects of gastrointestinal (GI) physiology, but its molecular basis remains elusive. We therefore characterized the stress-induced metabolic phenotype (metabotype) in soldiers during high-intensity combat training and correlated the metabotype with changes in GI symptoms and permeability. In a prospective, longitudinal study, urinary metabotyping was conducted on 38 male healthy soldiers during combat training and a rest period using gas chromatography-mass spectrometry. The urinary metabotype during combat training was clearly distinct from the rest period (partial least-squares discriminant analysis (PLSDA) Q(2) = 0.581), confirming the presence of a unique stress-induced metabotype. Differential metabolites related to combat stress were further uncovered, including elevated pyroglutamate and fructose, and reduced gut microbial metabolites, namely, hippurate and m-hydroxyphenylacetate (p < 0.05). The extent of pyroglutamate upregulation exhibited a positive correlation with an increase in IBS-SSS in soldiers during combat training (r = 0.5, p < 0.05). Additionally, the rise in fructose levels was positively correlated with an increase in intestinal permeability (r = 0.6, p < 0.005). In summary, protracted and mixed psychological and physical combat-training stress yielded unique metabolic changes that corresponded with the incidence and severity of GI symptoms and alteration in intestinal permeability. Our study provided novel molecular insights into stress-induced GI perturbations, which could be exploited for future biomarker research or development of therapeutic strategies.

"Omics" in pharmaceutical research: overview, applications, challenges, and future perspectives

In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.

Translational biomarkers of acetaminophen-induced acute liver injury

Acetaminophen (APAP) is a commonly used analgesic drug that can cause liver injury, liver necrosis and liver failure. APAP-induced liver injury is associated with glutathione depletion, the formation of APAP protein adducts, the generation of reactive oxygen and nitrogen species and mitochondrial injury. The systems biology omics technologies (transcriptomics, proteomics and metabolomics) have been used to discover potential translational biomarkers of liver injury. The following review provides a summary of the systems biology discovery process, analytical validation of biomarkers and translation of omics biomarkers from the nonclinical to clinical setting in APAP-induced liver injury.