The use of metabolomics for the discovery of new biomarkers of effect

Ravenzwaay, Bennard van; Cunha, G. Coelho-Palermo; Leibold, Edgar; Looser, Ralf; Mellert, W.; Prokoudine, A.; Walk, T.; Wiemer, J.

doi:10.1016/j.toxlet.2007.05.021

Cited by 189 publications

(109 citation statements)

References 11 publications

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“…Metabolomics is complementary to the other "omic" sciences, such as genomics, transcriptomics, and proteomics, but has less limitation because of technical and biological advantages (22). Metabolomics captures the biochemical products of the genes in their environment, and thus can provide fundamental knowledge of the biochemical networks under investigation.…”

Section: Metabolomics and Other "Omics"mentioning

confidence: 99%

Metabolomics: Applications and Promise in Mycobacterial Disease

Mirsaeidi

Banoei²,

Winston

et al. 2015

Annals ATS

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Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

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Section: Metabolomics and Other "Omics"mentioning

confidence: 99%

Metabolomics: Applications and Promise in Mycobacterial Disease

Mirsaeidi

Banoei²,

Winston

et al. 2015

Annals ATS

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“…On the other hand, studies performed by the covalent junctions that form between NAPQI and cellular proteins can be used to identify patients with occult toxicity by paracetamol (26,36) . The metabolic technology (with mass spectrometry and nuclear magnetic resonance) may have the ability to identify specifi c biomarkers of adverse events and toxicity in the early stages (24) . The development of these new biomarkers focuses on the metabolic pathways of paracetamol and the mechanisms responsible for its toxicity.…”

Section: Ethical Aspectsmentioning

confidence: 99%

“…It is important to determine the detection of the biomarkers of liver damage in early stage. Metabolic technology (with mass spectrometry and nuclear magnetic resonance) may have the ability to identify specifi c biomarkers of adverse events and toxicity in the early stages (24) . After low doses of paracetamol, 90% of this drug is metabolized to paracetamol-glucuronide and paracetamol-sulfate in the liver by glucuronyltransferase and sulfotransferase and subsequently eliminated through the urine.…”

Section: Introductionmentioning

confidence: 99%

Acute and chronic paracetamol overdose in paediatric population: Protocol of a prospective study of cohort to evaluate clinic factor and biomarkers to predict development of hepatotoxicity

Tong

Garcia²,

Garcia³

et al. 2017

IBJ Clin Pharmacol

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I., Borobia Pérez AM., Carcas Sansuán AJ., Martí nez Ávila JC., Ramírez García E. Acute and chronic paracetamol overdose in paediatric populati on: Protocol of a prospecti ve study of cohort to evaluate clinic factor and biomarkers to predict development of hepatotoxicity. IBJ Clin Pharmacol 2017 1(1):e0007. Funding: The authors have no fi nancial relati onships relevant to this arti cle to disclose. Competi ng Interests:The authors declare no confl icts of interest. Background:In Spain, 30% of cases of acute liver failure remaining undetermined. Paracetamol is the main drug causing acute liver failure in children of some countries like the United States, UK and other European countries. The key factors to assess in paracetamol toxicity, the ingested dose and the ti me from the poisoning, are diffi cult to assess in children where accidental acute poisoning or medicati on errors are not rare. Metabolomics technology may be able to identi fy specifi c biomarkers of toxicity and adverse events in early stages. The identi fi cati on of paracetamol toxicity biomarkers could have important clinical implicati ons for pati ents who can not apply the Rumack-Matt hew nomogram and could be useful in the evaluati on of children with acute liver failure of unknown eti ology, and to predict liver damage before elevati on of transaminases. Methods and design:This is an observati onal prospecti ve study of case control. The protocol was approved by the Clinical Research Ethics Committ ee of the La Paz University Hospital. It will recruit pati ents who will be att ending in the emergency paediatric at La Paz University Hospital, at Niño Jesus University Hospital, and at Gregorio Marañón University hospital with suspected acute or chronic intake of paracetamol. Likewise, two cohorts of control will be recruited. It is expected to recruit at least 36 cases and 144 controls, matched for age and clinical characteristi cs. Peripheral blood, plasma, serum and urine samples will be collected, paracetamol serum concentrati ons, hepati c and renal functi on, coagulati on and metabolomic analysis. Discussion:It is important to determine the clinical factors and biomarkers that predict the development of hepatotoxicity in paediatric populati on following acute and chronic intake of paracetamol and develop a predicti ve model to assess the risk of hepatotoxicity in both type of intoxicati on by paracetamol suited to paediatric pati ents for use in clinical practi ce. Study registrati on: Editor: Jesús Frías IniestaCite as: Tong HY., Díaz García L., García García S., Ruiz Domínguez JA., Martí n Sánchez J., Storch de Gracias P., Rivas A., Muñoz M., Hernández Zabala R., García García I., Borobia Pérez AM., Carcas Sansuán AJ., Martí nez Ávila JC., Ramírez García E. Acute and chronic paracetamol overdose in paediatric populati on: Protocol of a prospecti ve study of cohort to evaluate clinic factor and biomarkers to predict development of hepatotoxicity. IBJ Clin Pharmacol 2017 1(1):e0007. Funding: The authors have no fi nancial relati onships...

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“…TM broad profiling of plasma by LC/MS/MS and GC/MS MxP TM broad profiling based on GC/MS and LC/MS/MS techniques was used as described previously [20,21]. In brief, polar and non-polar metabolites were separated for both GC/MS and LC/MS/MS analysis by adding water and a mixture of ethanol and dichloromethane (1:2, v:v) to the plasma after protein precipitation.…”

Section: Mxpmentioning

confidence: 99%

Systems Biology Investigation to Discover Metabolic Biomarkers of Acetaminophen-Induced Hepatic Injury Using Integrated Transcriptomics and Metabolomics

2013

J Mol Biomark Diagn

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The use of metabolomics for the discovery of new biomarkers of effect

Cited by 189 publications

References 11 publications

Metabolomics: Applications and Promise in Mycobacterial Disease

Metabolomics: Applications and Promise in Mycobacterial Disease

Acute and chronic paracetamol overdose in paediatric population: Protocol of a prospective study of cohort to evaluate clinic factor and biomarkers to predict development of hepatotoxicity

Systems Biology Investigation to Discover Metabolic Biomarkers of Acetaminophen-Induced Hepatic Injury Using Integrated Transcriptomics and Metabolomics

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