From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

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.

Pharmacometabolomics of trabectedin in metastatic soft tissue sarcoma patients

Objective: Trabectedin is an anti-cancer drug commonly used for the treatment of patients with metastatic soft tissue sarcoma (mSTS). Despite its recognized efficacy, significant variability in pharmacological response has been observed among mSTS patients. To address this issue, this pharmacometabolomics study aimed to identify pre-dose plasma metabolomics signatures that can explain individual variations in trabectedin pharmacokinetics and overall clinical response to treatment. Methods: In this study, 40 mSTS patients treated with trabectedin administered by 24 h-intravenous infusion at a dose of 1.5 mg/m2 were enrolled. The patients' baseline plasma metabolomics profiles, which included derivatives of amino acids and bile acids, were analyzed using multiple reaction monitoring LC-MS/MS together with their pharmacokinetics profile of trabectedin. Multivariate Partial least squares regression and univariate statistical analyses were utilized to identify correlations between baseline metabolite concentrations and trabectedin pharmacokinetics, while Partial Least Squares-Discriminant Analysis was employed to evaluate associations with clinical response. Results: The multiple regression model, derived from the correlation between the AUC of trabectedin and pre-dose metabolomics, exhibited the best performance by incorporating cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio. This model demonstrated a bias of 4.6% and a precision of 17.4% in predicting drug AUC, effectively accounting for up to 70% of the inter-individual pharmacokinetic variability. Through the use of Partial least squares-Discriminant Analysis, cystathionine and hemoglobin were identified as specific metabolic signatures that effectively distinguish patients with stable disease from those with progressive disease. Conclusions: The findings from this study provide compelling evidence to support the utilization of pre-dose metabolomics in uncovering the underlying causes of pharmacokinetic variability of trabectedin, as well as facilitating the identification of patients who are most likely to benefit from this treatment.

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.

Differential Glycosylation Levels in Saliva from Patients with Lung or Breast Cancer: A Preliminary Assessment for Early Diagnostic Purposes

Glycans play a fundamental role in several biological processes, such as cell-cell adhesion, signaling, and recognition. Similarly, abnormal glycosylation is involved in many pathological processes, among which include tumor growth and progression. Several highly glycosylated proteins found in blood are currently used in clinical practice as cancer biomarkers (e.g., CA125, PSA, and CA19-9). The development of novel non-invasive diagnostic procedures would greatly simplify the screening and discovery of pathologies at an early stage, thus also allowing for simpler treatment and a higher success rate. In this observational study carried out on 68 subjects diagnosed with either breast or lung cancer and 34 healthy volunteers, we hydrolyzed the glycoproteins in saliva and quantified the obtained free sugars (fucose, mannose, galactose, glucosamine, and galactosamine) by using high-performance anion-exchange chromatography with pulsed-amperometric detection (HPAEC-PAD). The glycosidic profiles were compared by using multivariate statistical analysis, showing differential glycosylation patterns among the three categories. Furthermore, Receiver Operating Characteristics (ROC) analysis allowed obtaining a reliable and minimally invasive protocol able to discriminate between healthy and pathological subjects.

Pharmacokinetics, Urinary Excretion, and Pharmaco-Metabolomic Study of Tebipenem Pivoxil Granules After Single Escalating Oral Dose in Healthy Chinese Volunteers

Tebipenem pivoxil (TBPM-PI), an oral carbapenem antibiotic, has shown special advantages in pediatric infections and was in urgent need in China. Although pharmacokinetics, urinary excretion, and metabolite information of its active form tebipenem (TBPM) has been reported, ethnic differences may exist among the Chinese and Japanese population. By now, no systematic pharmacokinetics, urinary excretion, metabolites, or safety information has been revealed to the Chinese population. The purpose of the present work was to investigate abovementioned information of TBPM-PI granules after oral single ascending doses of 100, 200, and 400 mg in Chinese volunteers. Based on the pharmacokinetic study, the urine pharmaco-metabolomic analysis was conducted to reveal metabolomic interruptions and metabolite information. The study design was a single-center, open-label, randomized, single-dose pharmacokinetic study of 36 healthy volunteers (with half of them being male and the other half female). Time to maximum concentration (T max) was reached at 0.50, 0.50, or 0.67 h for 100, 200, or 400 mg, respectively. The linear pharmacokinetic characteristic of maximum plasma concentration (C max) was detected over 100-200 mg. The area under the concentration time curve (AUC) was proportional to the dose in the range of 100-400 mg. The maximum urinary excretion rate was detected at 0-1 or 1-2 h for dose of 100 or 200-400 mg. Cumulative amount of TBPM excreted in urine by 24 h accounted up to 90, 95, and 80% of dose administered for three groups, respectively. The pharmaco-metabolomic analysis revealed urine metabolic trajectory of deviation at 0-1 or 1-2 h and gradually regressing back to the pre-dose group at the following time periods. Urine metabolites from M1 to M4 were identified, indicating ethnic difference in metabolites among the Chinese or Japanese population. The current work proved safety and tolerance of single-dose administration of oral TBPM-PI in Chinese healthy volunteers over doses of 100-400 mg. All these results provide pharmacokinetics, urine excretion, urine metabolomics, urine metabolites, and safety information in healthy Chinese volunteers after oral single ascending doses of TBPM-PI, benefitting further development and clinical utilities.

Research progress and perspective in metabolism and metabolomics of psoriasis

ABSTRACT

Psoriasis is considered a systemic disease associated with metabolic abnormalities, and it is important to understand the mechanisms by which metabolism affects pathophysiological processes both holistically and systematically. Metabolites are closely related to disease phenotypes, especially in systemic diseases under multifactorial modulation. The emergence of metabolomics has provided information regarding metabolite changes in lesions and circulation and deepened our understanding of the association between metabolic reprogramming and psoriasis. Metabolomics has great potential for the development of effective biomarkers for clinical diagnosis, therapeutic monitoring, prediction of the efficacy of psoriasis management, and further discovery of new metabolism-based therapeutic targets.

Longitudinal Pharmacometabonomics for Predicting Malignant Tumor Patient Responses to Anlotinib Therapy: Phenotype, Efficacy, and Toxicity

Anlotinib is an oral small molecule inhibitor of multiple receptor tyrosine kinases (RTKs), which was approved by the National Medical Products Administration (NMPA) of China in 2018 for the third-line treatment of non-small cell lung cancer (NSCLC). Here, for the first time, the longitudinal pharmacometabonomics was explored for predicting malignant tumor patient responses to anlotinib, including the metabolic phenotype variation, drug efficacy, and toxicity. A total of 393 plasma samples from 16 subjects collected from a phase I additional study of anlotinib (NCT02752516) were submitted to targeted metabolomics analysis. The orthogonal partial least-squares discriminant analysis (OPLS-DA) models were constructed for the predication of anlotinib efficacy and toxicity based on the longitudinal pharmacometabonomics data. Statistical results showed that 38 metabolites, mainly involved in aminoacyl-tRNA biosynthesis, alanine, aspartate, and glutamate metabolism, and steroid hormone biosynthesis, were all significantly upregulated attributing to anlotinib treatment. The anti-tumor efficacy and occurrence of proteinuria after anlotinib administration can be predicted with 100% accuracy using the established OPLS-DA models. Glycodeoxycholic acid and glycocholic acid possessed the most excellent sensitivity and specificity in predicting the efficacy of anlotinib, with area under the receiver operating characteristic curve (AUC of ROC curve) 0.847 and 0.828, respectively. NG, NG-dimethylarginine was the most promising biomarker for the prediction of proteinuria occurrence after anlotinib administration, with AUC of ROC curve 0.814. In conclusion, this work developed efficient and convenient discriminant models that can accurately predict the efficacy and toxicity of anlotinib based on longitudinal pharmacometabonomics study.

Solid phase microextraction chemical biopsy tool for monitoring of doxorubicin residue during in vivo lung chemo-perfusion

Development of a novel in vivo lung perfusion (IVLP) procedure allows localized delivery of high-dose doxorubicin (DOX) for targeting residual micrometastatic disease in the lungs. However, DOX delivery via IVLP requires careful monitoring of drug level to ensure tissue concentrations of this agent remain in the therapeutic window. A small dimension nitinol wire coated with a sorbent of biocompatible morphology (Bio-SPME) has been clinically evaluated for in vivo lung tissue extraction and determination of DOX and its key metabolites. The in vivo Bio-SPME-IVLP experiments were performed on pig model over various (150 and 225 mg/m²) drug doses, and during human clinical trial. Two patients with metastatic osteosarcoma were treated with a single 5 and 7 μg/mL (respectively) dose of DOX during a 3-h IVLP. In both pig and human cases, DOX tissue levels presented similar trends during IVLP. Human lung tissue concentrations of drug ranged between 15 and 293 μg/g over the course of the IVLP procedure. In addition to DOX levels, Bio-SPME followed by liquid chromatography-mass spectrometry analysis generated 64 metabolic features during endogenous metabolite screening, providing information about lung status during drug administration. Real-time monitoring of DOX levels in the lungs can be performed effectively throughout the IVLP procedure by in vivo Bio-SPME chemical biopsy approach. Bio-SPME also extracted various endogenous molecules, thus providing a real-time snapshot of the physiology of the cells, which might assist in the tailoring of personalized treatment strategy.

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.

Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients

Gemcitabine (GEM)-based chemotherapy is the standard regimen for the treatment of pancreatic cancer (PC). However, chemoresistance is a major challenge in PC treatment. Reliable biomarkers are urgently needed to predict the response to GEM-based therapies. GEM-sensitive (GEM-S) and GEM-resistant (GEM-R) pancreatic carcinoma xenograft models were established, and GEM monotherapy and GEM plus nanoparticle albumin-bound paclitaxel (nab-PTX) doublet therapy were administered to GEM-S/R tumor-bearing mice. Metabolomic mass spectrometry (MS) analysis of serum, liver, and tumor samples was performed using an ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometer. The results showed that both GEM monotherapy and combination therapy significantly inhibited the tumor growth in GEM-S subgroup. However, in the GEM-R subgroup, tumor growth was not significantly inhibited by GEM monotherapy, but was significantly suppressed by GEM combination therapy. Metabolic profiling analysis by hierarchical cluster analysis and partial least squares discriminant analysis showed that the differences in metabolites were most significant in serum of three types of samples in the GEM-S/R subgroups, regardless of the administration of GEM monotherapy or combination therapy. The differential metabolite analysis of serum samples revealed 38 and 26 differential metabolites between the GEM-R and GEM-S subgroups treated with GEM monotherapy or combination therapy, and four common discriminating metabolites were investigated: 3-hydroxyadipic acid, d-galactose, lysophosphatidylcholine (LysoPC) (P-16:0), and tetradecenoyl-l-carnitine. The relative amounts of the four metabolites changed significantly and consistently after GEM monotherapy or combination therapy. The levels of these four metabolites were significantly different in the GEM-S and GEM-R pancreatic carcinoma xenograft models; thus, these metabolites could be effective predictive indicators of the efficacy of chemotherapy in PC patients, regardless of the administration of GEM alone or GEM plus nab-PTX.

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

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

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Lipidomic characteristics and clinical findings of epileptic patients treated with valproic acid

Our early study has found valproic acid (VPA)-induced lipid dysmetabolism in animal model, however, the details of lipid profiling of VPA-treated epileptic patients remain unknown. Therefore, in this study, the blood samples of VPA-treated epileptic patients and VPA-free controls were collected for lipidomic and biochemical assays. As results, clinical data showed the changes of some blood lipid molecules in VPA-treated epileptic patients. In lipidomic assays, all 3797 annotated positive ions were identified prior to the data validation. In addition, the number of differentially expressed lipids were identified. And the 133 lipid molecules in VPA-treated cases were significantly up-regulated when compared to those in controls, while other 250 lipid metabolites were down-regulated. Further, these lipid metabolites were mainly constituted with glycerolipids, glycerophopholipids, fatty acyls, sterol lipids. In addition, the most significant elevations of metabolite molecules of triglyceride, sphingomyelin, phosphorylcholine, ceramides, phenolic phthiocerol, as well as topped reductions of phosphoethanolamines, diradylglycerols, 1α,25-dihydroxy-24-oxo-22-oxavitamin D3, 2-deoxy-20-hydroxy-5alpha-ecdysone 3-acetate, dolichyl-4 phosphate were identified respectively. Taken together, these clinical findings demonstrate that negative impacts of exposure to VPA on expression of lipid mediators, progressively disrupting the functions of lipid molecules. Interestingly, these differentially expressed metabolites may be potential biomarkers for screening VPA-induced dyslipidemia.

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.

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.

Sepsis Diagnostics in the Era of "Omics" Technologies

Sepsis is a multifactorial clinical syndrome with an extremely dynamic clinical course and with high diverse clinical phenotype. Early diagnosis is crucial for the final clinical outcome. Previous studies have not identified a biomarker for the diagnosis of sepsis which would have sufficient sensitivity and specificity. Identification of the infectious agents or the use of molecular biology, next gene sequencing, has not brought significant benefit for the patient in terms of early diagnosis. Therefore, we are currently searching for biomarkers, through "omics" technologies with sufficient diagnostic specificity and sensitivity, able to predict the clinical course of the disease and the patient response to therapy. Current progress in the use of systems biology technologies brings us hope that by using big data from clinical trials such biomarkers will be found.

The role of metabolomic markers for patients with infectious diseases: implications for risk stratification and therapeutic modulation

INTRODUCTION

Metabolomics is a rapidly growing area of research. Metabolomic markers can provide information about the interaction of different organ systems, and thereby improve the understanding of physio-pathological processes, disease risk, prognosis and therapy responsiveness in a variety of diseases. Areas covered: In this narrative review of recent clinical studies investigating metabolomic markers in adult patients presenting with acute infectious disease, we mainly focused on patients with sepsis and lower respiratory tract infections. Currently, there is a growing body of literature showing that single metabolites from distinct metabolic pathways, as well as more complex metabolomic signatures are associated with disease severity and outcome in patients with systemic infections. These pathways include, among others, metabolomic markers of oxidative stress, steroid hormone and amino acid pathways, and nutritional markers. Expert commentary: Metabolic profiling has great potential to optimize patient management, to provide new targets for individual therapy and thereby improve survival of patients. At this stage, research mainly focused on the identification of new predictive signatures and less on metabolic determinants to predict treatment response. The transition from observational studies to implementation of novel markers into clinical practice is the next crucial step to prove the usefulness of metabolomic markers in patient care.

Metabolomics as a Tool to Understand Pathophysiological Processes

Multiple diseases have a strong metabolic component, and metabolomics as a powerful phenotyping technology, in combination with orthogonal biological and clinical approaches, will undoubtedly play a determinant role in accelerating the understanding of mechanisms that underlie these complex diseases determined by a set of genetic, lifestyle, and environmental exposure factors. Here, we provide several examples of valuable findings from metabolomics-led studies in diabetes and obesity metabolism, neurodegenerative disorders, and cancer metabolism and offer a longer term vision toward personalized approach to medicine, from population-based studies to pharmacometabolomics.

Genetic and biochemical changes of the serotonergic system in migraine pathobiology

Migraine is a brain disorder characterized by a piercing headache which affects one side of the head, located mainly at the temples and in the area around the eye. Migraine imparts substantial suffering to the family in addition to the sufferer, particularly as it affects three times more women than men and is most prevalent between the ages of 25 and 45, the years of child rearing. Migraine typically occurs in individuals with a genetic predisposition and is aggravated by specific environmental triggers. Attempts to study the biochemistry of migraine began as early as the 1960s and were primarily directed at serotonin metabolism after an increase of 5-hydroxyindoleacetic acid (5-HIAA), the main metabolite of serotonin was observed in urine of migraineurs. Genetic and biochemical studies have primarily focused on the neurotransmitter serotonin, considering receptor binding, transport and synthesis of serotonin and have investigated serotonergic mediators including enzymes, receptors as well as intermediary metabolites. These studies have been mainly assayed in blood, CSF and urine as the most accessible fluids. More recently PET imaging technology integrated with a metabolomics and a systems biology platform are being applied to study serotonergic biology. The general trend observed is that migraine patients have alterations of neurotransmitter metabolism detected in biological fluids with different biochemistry from controls, however the interpretation of the biological significance of these peripheral changes is unresolved. In this review we present the biology of the serotonergic system and metabolic routes for serotonin and discuss results of biochemical studies with regard to alterations in serotonin in brain, cerebrospinal fluid, saliva, platelets, plasma and urine of migraine patients.

Targeted and global pharmacometabolomics in everolimus-based immunosuppression: association of co-medication and lysophosphatidylcholines with dose requirement

INTRODUCTION

The immunosuppressive therapy with everolimus (ERL) after heart transplantation is characterized by a narrow therapeutic window and a substantial variability in dose requirement. Factors explaining this variability are largely unknown.

OBJECTIVES

Our aim was to evaluate factors affecting ERL metabolism and to identify novel metabolites associated with the individual ERL dose requirement to elucidate mechanisms underlying ERL dose response variability.

METHOD

We used liquid chromatography coupled with mass spectrometry for quantification of ERL metabolites in 41 heart transplant patients and evaluated the effect of clinical and genetic factors on ERL pharmacokinetics. Non-targeted plasma metabolic profiling by ultra-performance liquid chromatography and high resolution quadrupole-time-of-flight mass spectrometry was used to identify novel metabolites associated with ERL dose requirement.

RESULTS

The determination of ERL metabolites revealed differences in metabolite patterns that were independent from clinical or genetic factors. Whereas higher ERL dose requirement was associated with co-administration of sodium-mycophenolic acid and the CYP3A5 expressor genotype, lower dose was required for patients receiving vitamin K antagonists. Global metabolic profiling revealed several novel metabolites associated with ERL dose requirement. One of them was identified as lysophosphatidylcholine (lysoPC) (16:0/0:0). Subsequent targeted analysis revealed that high levels of several lysoPCs were significantly associated with higher ERL dose requirement.

CONCLUSION

For the first time, this study describes distinct ERL metabolite patterns in heart transplant patients and detected potentially new drug-drug interactions. The global metabolic profiling facilitated the discovery of novel metabolites associated with ERL dose requirement that might represent new clinically valuable biomarkers to guide ERL therapy.

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.

Metabolomics as a Driver in Advancing Precision Medicine in Sepsis

The objective of this review is to explain the science of metabolomics-a science of systems biology that measures and studies endogenous small molecules (metabolites) that are present in a single biological sample-and its application to the diagnosis and treatment of sepsis. In addition, we discuss how discovery through metabolomics can contribute to the development of precision medicine targets for this complex disease state and the potential avenues for those new discoveries to be applied in the clinical environment. A nonsystematic literature review was performed focusing on metabolomics, pharmacometabolomics, and sepsis. Human (adult and pediatric) and animal studies were included. Metabolomics has been investigated in the diagnosis, prognosis, and risk stratification of sepsis, as well as for the identification of drug target opportunities. Metabolomics elucidates a new level of detail when compared with other systems biology sciences, with regard to the metabolites that are most relevant in the pathophysiology of sepsis, as well as highlighting specific biochemical pathways at work in sepsis. Metabolomics also highlights biochemical differences between sepsis survivors and nonsurvivors at a level of detail greater than that demonstrated by genomics, transcriptomics, or proteomics, potentially leading to actionable targets for new therapies. The application of pharmacometabolomics and its integration with other systems pharmacology to sepsis therapeutics could be particularly helpful in differentiating drug responders and nonresponders and furthering knowledge of mechanisms of drug action and response. The accumulated literature on metabolomics suggests it is a viable tool for continued discovery around the pathophysiology, diagnosis and prognosis, and treatment of sepsis in both adults and children, and it provides a greater level of biochemical detail and insight than other systems biology approaches. However, the clinical application of metabolomics in sepsis has not yet been fully realized. Prospective validation studies are needed to translate metabolites from the discovery phase into the clinical utility phase.

Chemotherapy-induced gastrointestinal toxicity is associated with changes in serum and urine metabolome and fecal microbiota in male Sprague-Dawley rats

Purpose

Chemotherapy-induced gastrointestinal toxicity (CIGT) is a complex process that involves multiple pathophysiological mechanisms. We have previously shown that commonly used chemotherapeutics 5-fluorouracil, oxaliplatin, and irinotecan damage the intestinal mucosa and increase intestinal permeability to iohexol. We hypothesized that CIGT is associated with alterations in fecal microbiota and metabolome. Our aim was to characterize these changes and examine how they relate to the severity of CIGT.

Methods

A total of 48 male Sprague–Dawley rats were injected intraperitoneally either with 5-fluorouracil (150 mg/kg), oxaliplatin (15 mg/kg), or irinotecan (200 mg/kg). Body weight change was measured daily after drug administration and the animals were euthanized after 72 h. Blood, urine, and fecal samples were collected at baseline and at the end of the experiment. The changes in the composition of fecal microbiota were analyzed with 16S rRNA gene sequencing. Metabolic changes in serum and urine metabolome were measured with 1 mm proton nuclear magnetic resonance (¹H-NMR).

Results

Irinotecan increased the relative abundance of Fusobacteria and Proteobacteria, while 5-FU and oxaliplatin caused only minor changes in the composition of fecal microbiota. All chemotherapeutics increased the levels of serum fatty acids and N(CH₃)₃ moieties and decreased the levels of Krebs cycle metabolites and free amino acids.

Conclusions

Chemotherapeutic drugs, 5-fluorouracil, oxaliplatin, and irinotecan, induce several microbial and metabolic changes which may play a role in the pathophysiology of CIGT. The observed changes in intestinal permeability, fecal microbiota, and metabolome suggest the activation of inflammatory processes.

Electronic supplementary material

The online version of this article (doi:10.1007/s00280-017-3364-z) contains supplementary material, which is available to authorized users.

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age

Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the "normal" metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.