Use of Pharmaco-Metabonomics for Early Prediction of Acetaminophen-Induced Hepatotoxicity in Humans

N-acetyltransferase Gene Variants Involved in Pediatric Idiosyncratic Drug-Induced Liver Injury

Idiosyncratic drug-induced liver injury (DILI) is a complex multifactorial disease in which the toxic potential of the drug, together with genetic and acquired factors and deficiencies in adaptive processes, which limit the extent of damage, may determine susceptibility and make individuals unique in their development of hepatotoxicity. In our study, we sequenced the exomes of 43 pediatric patients diagnosed with DILI to identify important gene variations associated with this pathology. The result showed the presence of two variations in the NAT2 gene: c.590G>A (p.Arg197Gln) and c.341T>C (p.Ile114Thr). These variations could be found separately or together in 41 of the 43 patients studied. The presence of these variations as a risk factor for DILI could confirm the importance of the acetylation pathway in drug metabolism.

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.

Metabolomic Analysis of Pediatric Patients with Idiosyncratic Drug-Induced Liver Injury According to the Updated RUCAM

Hepatotoxicity, a common adverse drug effect, has been extensively studied in adult patients. However, it is equally important to investigate this condition in pediatric patients to develop personalized treatment strategies for children. This study aimed to identify plasma biomarkers that characterize hepatotoxicity in pediatric patients through an observational case-control study. Metabolomic analysis was conducted on 55 pediatric patients with xenobiotic liver toxicity and 88 healthy controls. The results revealed clear differences between the two groups. Several metabolites, including hydroxydecanoylcarnitine, octanoylcarnitine, lysophosphatidylcholine, glycocholic acid, and taurocholic acid, were identified as potential biomarkers (area under the curve: 0.817; 95% confidence interval: 0.696-0.913). Pathway analysis indicated involvement of primary bile acid biosynthesis and the metabolism of taurine and hypotaurine (p < 0.05). The findings from untargeted metabolomic analysis demonstrated an increase in bile acids in children with hepatotoxicity. The accumulation of cytotoxic bile acids should be further investigated to elucidate the role of these metabolites in drug-induced liver injury.

Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges

Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades to investigate the mechanisms of action of these drugs, their pleiotropic effects, and their side effects, aiming to identify novel targets for future personalized medicine with an improvement of the efficacy and safety associated with the treatment. Pharmacometabolomics is a branch of metabolomics that is focused on the study of drug effects on metabolic pathways that are implicated in the variation of response to the treatment considering also the influences from a specific disease, environment, and concomitant pharmacological therapies. In this review, we summarized the most significant metabolomic studies on the effects of lipid-lowering therapies, including the most commonly used statins and fibrates to novel drugs or nutraceutical approaches. The integration of pharmacometabolomics data with the information obtained from the other "omics" approaches could help in the comprehension of the biological mechanisms underlying the use of lipid-lowering drugs in view of defining a precision medicine to improve the efficacy and reduce the side effects associated with the treatment.

Metabolomic Evaluation of N-Acetyl-p-Benzoquinone Imine Protein Adduct Formation with Therapeutic Acetaminophen Administration: Sex-based Physiologic Differences

BACKGROUND

Acetaminophen (APAP)-associated transaminase elevation, induced by N-acetyl-p-benzoquinone imine (NAPQI) protein adduction, remains an area of research interest. Distinct from known genetic, physiologic, and dosage associations dictating severity of hepatic injury, no known factors predict an absence of protein adduct formation at therapeutic APAP dosing.

HYPOTHESIS

Sex-based physiology is predictive of APAP-induced protein adduct formation and differential metabolite expression at therapeutic doses.

METHODS

This retrospective study interrogated serum samples collected for a prior study investigating fluctuations of alanine aminotransferase (ALT) over time with 4G daily APAP dosing for ≥ 16 days in subjects from Denver, Colorado. Subjects were grouped by adduct formation (n = 184) vs no adducts (n = 20). Samples were run on ultra-high-performance liquid chromatography mass spectrometry from study days 0, 7, 16, and 31. Significant metabolite expressions were identified using t-tests with false discovery rate correction (FDR), partial least squares discriminant, and ANOVA simultaneous comparison analyses. Demographic and clinical data were explored using t-tests with FDR (age, weight, BMI, ALT) and Chi-square (sex, ethnicity, race) analyses.

RESULTS

In pre-treatment samples, relative quantitation caprylic acid was expressed ninefold higher and 6-carboxyhexanoate was expressed threefold lower in subjects who did not develop adducts. Lactate had greater expression in the no adducts group (p = 0.001). Using absolute quantitation, glutathione was expressed 2.6-fold greater among no adduct subjects. Odds of males developing NAPQI protein adducts at therapeutic APAP dosing were 5.91 times lower than females (95% CI = 2.3-14.9; p = 0.0001).

CONCLUSION

Multiple metabolites were differentially expressed based on adduct group and sex. Metabolites were identified unique to adduct development independent of sex. At therapeutic APAP dosing, males were less likely to develop APAP protein adducts. Further research into lipid biosynthesis and metabolism may provide further insight into physiology associated with adduct production.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted ¹³C and ¹H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted ¹³C and ¹H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of ¹H and ¹³C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

The Potential Role of Metabolomics in Drug-Induced Liver Injury (DILI) Assessment

Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. Hepatotoxicity is among the major reasons for drug withdrawal during post-market and late development stages, representing a major concern to the pharmaceutical industry. The current biochemical parameters for the detection of DILI are based on enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP)) and bilirubin serum levels that are not specific of DILI and therefore there is an increasing interest on novel, specific, DILI biomarkers discovery. Metabolomics has emerged as a tool with a great potential for biomarker discovery, especially in disease diagnosis, and assessment of drug toxicity or efficacy. This review summarizes the multistep approaches in DILI biomarker research and discovery based on metabolomics and the principal outcomes from the research performed in this field. For that purpose, we have reviewed the recent scientific literature from PubMed, Web of Science, EMBASE, and PubTator using the terms “metabolomics”, “DILI”, and “humans”. Despite the undoubted contribution of metabolomics to our understanding of the underlying mechanisms of DILI and the identification of promising novel metabolite biomarkers, there are still some inconsistencies and limitations that hinder the translation of these research findings into general clinical practice, probably due to the variability of the methods used as well to the different mechanisms elicited by the DILI causing agent.

Novel Lipidomic Signature Associated With Metabolic Risk in Women With and Without Polycystic Ovary Syndrome

CONTEXT

Dyslipidemia is a feature of polycystic ovary syndrome (PCOS) and may augment metabolic dysfunction in this population.

OBJECTIVE

Using comprehensive lipidomic profiling and gold-standard metabolic measures, we examined whether distinct lipid biomarkers were associated with metabolic risk in women with and without PCOS.

METHODS

Using preexisting data and biobanked samples from 76 women (n = 42 with PCOS), we profiled > 700 lipid species by mass spectrometry. Lipids were compared between women with and without PCOS and correlated with direct measures of adiposity (dual x-ray absorptiometry and computed tomography) and insulin sensitivity (hyperinsulinemic-euglycemic clamp), as well as fasting insulin, HbA1c, and hormonal parameters (luteinizing and follicle-stimulating hormones; total and free testosterone; sex hormone-binding globulin [SHBG]; and free androgen index [FAI]). Multivariable linear regression was used with correction for multiple testing.

RESULTS

Despite finding no differences by PCOS status, lysophosphatidylinositol (LPI) species esterified with an 18:0 fatty acid were the strongest lipid species associated with all the metabolic risk factors measured in women with and without PCOS. Across the cohort, higher concentrations of LPI(18:0) and lower concentrations of lipids containing docosahexaenoic acid (DHA, 22:6) n-3 polyunsaturated fatty acids were associated with higher adiposity, insulin resistance, fasting insulin, HbA1c and FAI, and lower SHBG.

CONCLUSION

Our data indicate that a distinct lipidomic signature comprising high LPI(18:0) and low DHA-containing lipids are associated with key metabolic risk factors that cluster in PCOS, independent of PCOS status. Prospective studies are needed to corroborate these findings in larger cohorts of women with varying PCOS phenotypes.

Serum Predose Metabolic Profiling for Prediction of Rosuvastatin Pharmacokinetic Parameters in Healthy Volunteers

Rosuvastatin is a well-known lipid-lowering agent generally used for hypercholesterolemia treatment and coronary artery disease prevention. There is a substantial inter-individual variability in the absorption of statins usually caused by genetic polymorphisms leading to a variation in the corresponding pharmacokinetic parameters, which may affect drug therapy safety and efficacy. Therefore, the investigation of metabolic markers associated with rosuvastatin inter-individual variability is exceedingly relevant for drug therapy optimization and minimizing side effects. This work describes the application of pharmacometabolomic strategies using liquid chromatography coupled to mass spectrometry to investigate endogenous plasma metabolites capable of predicting pharmacokinetic parameters in predose samples. First, a targeted method for the determination of plasma concentration levels of rosuvastatin was validated and applied to obtain the pharmacokinetic parameters from 40 enrolled individuals; then, predose samples were analyzed using a metabolomic approach to search for associations between endogenous metabolites and the corresponding pharmacokinetic parameters. Data processing using machine learning revealed some candidates including sterols and bile acids, carboxylated metabolites, and lipids, suggesting the approach herein described as promising for personalized drug therapy.

A Canadian Study of Cisplatin Metabolomics and Nephrotoxicity (ACCENT): A Clinical Research Protocol

Background:

Cisplatin, a chemotherapy used to treat solid tumors, causes acute kidney injury (AKI), a known risk factor for chronic kidney disease and mortality. AKI diagnosis relies on biomarkers which are only measurable after kidney damage has occurred and functional impairment is apparent; this prevents timely AKI diagnosis and treatment. Metabolomics seeks to identify metabolite patterns involved in cell tissue metabolism related to disease or patient factors. The A Canadian study of Cisplatin mEtabolomics and NephroToxicity (ACCENT) team was established to harness the power of metabolomics to identify novel biomarkers that predict risk and discriminate for presence of cisplatin nephrotoxicity, so that early intervention strategies to mitigate onset and severity of AKI can be implemented.

Objective:

Describe the design and methods of the ACCENT study which aims to identify and validate metabolomic profiles in urine and serum associated with risk for cisplatin-mediated nephrotoxicity in children and adults.

Design:

Observational prospective cohort study.

Setting:

Six Canadian oncology centers (3 pediatric, 1 adult and 2 both).

Patients:

Three hundred adults and 300 children planned to receive cisplatin therapy.

Measurements:

During two cisplatin infusion cycles, serum and urine will be measured for creatinine and electrolytes to ascertain AKI. Many patient and disease variables will be collected prospectively at baseline and throughout therapy. Metabolomic analyses of serum and urine will be done using mass spectrometry. An untargeted metabolomics approach will be used to analyze serum and urine samples before and after cisplatin infusions to identify candidate biomarkers of cisplatin AKI. Candidate metabolites will be validated using an independent cohort.

Methods:

Patients will be recruited before their first cycle of cisplatin. Blood and urine will be collected at specified time points before and after cisplatin during the first infusion and an infusion later during cancer treatment. The primary outcome is AKI, defined using a traditional serum creatinine-based definition and an electrolyte abnormality-based definition. Chart review 3 months after cisplatin therapy end will be conducted to document kidney health and survival.

Limitations:

It may not be possible to adjust for all measured and unmeasured confounders when evaluating prediction of AKI using metabolite profiles. Collection of data across multiple sites will be a challenge.

Conclusions:

ACCENT is the largest study of children and adults treated with cisplatin and aims to reimagine the current model for AKI diagnoses using metabolomics. The identification of biomarkers predicting and detecting AKI in children and adults treated with cisplatin can greatly inform future clinical investigations and practices.

Parallel derivatization strategy for comprehensive profiling of unconjugated and glycine-conjugated bile acids using Ultra-high performance liquid chromatography-tandem mass spectrometry

Bile acids (BAs) are steroidal compounds that play important roles in the occurrence and development of liver injury. However, comprehensive characterization of BAs was rarely reported due to the limitations of both standards access and detection sensitivity. In this study, a parallel derivatization strategy was established for the sensitive and comprehensive profiling of unconjugated and glycine-conjugated BAs by using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Two structural analogues 2-hydrazinyl-4,6-dimethylpyrimidine (DMP) and 2-hydrazinylpyrimidine (DP) were used as the parallel derivatization reagents for BAs labeling, facilitating the improvements of both detection sensitivities and chromatographic performances. The derivatization reactions can be completed in 20 min at room temperature, with derivatization efficacy higher than 99 %. Through derivatization, the sensitivity of BAs increased dozens or hundreds of times compared to their non-derivatized forms. Due to the structural similarities of derivatized BAs, general MS parameters can be forged for the analysis of DMP and DP labeled BAs. In addition, the DP labeled BAs were incorporated into the DMP derivatized biological samples for both the discovery and comprehensive characterization of BAs. Retention time shift (RTS) and peak area ratio (PAR) induced by the parallel DMP and DP labeled BAs were used for the rapid identification of BAs from complex biological samples. More than 200 BAs were profiled in rat serum using this parallel derivatization strategy. Further, the new strategy was successfully implemented in BAs profiling of serum samples from tripterysium glycosides-induced liver injury rat model. The disturbance of the BA metabolism network was further interpreted.

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.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Biomarkers of drug-induced liver injury: a mechanistic perspective through acetaminophen hepatotoxicity

Introduction: Liver injury induced by drugs is a serious clinical problem. Many circulating biomarkers for identifying and predicting drug-induced liver injury (DILI) have been proposed.Areas covered: Biomarkers are mainly predicated on the mechanistic understanding of the underlying DILI, often in the context of acetaminophen overdose. New panels of biomarkers have emerged that are related to recovery/regeneration rather than injury following DILI. We explore the clinical relevance and limitations of these new biomarkers including recent controversies. Extracellular vesicles have also emerged as a promising vector of biomarkers, although the biological role for EVs may limit their clinical usefulness. New technological approaches for biomarker discovery are also explored.Expert opinion: Recent clinical studies have validated the efficacy of some of these new biomarkers, cytokeratin-18, macrophage colony-stimulating factor receptor, and osteopontin for DILI prognosis. Low prevalence of DILI is an inherent limitation to DILI biomarker development. Furthering mechanistic understanding of DILI and leveraging technological advances (e.g. machine learning/omics) is necessary to improve upon the newest generation of biomarkers. The integration of omics approaches with machine learning has led to novel insights in cancer research and DILI research is poised to leverage these technologies for biomarker discovery and development.

Enhancement of acetaminophen-induced chronic hepatotoxicity in spontaneously diabetic torii (SDT) rats

Some patients encounter hepatotoxicity after repeated acetaminophen (APAP) dosing even at therapeutic doses. In the present study, we focused on the diabetic state as one of the suggested risk factors of drug-induced liver injury in humans and investigated the contribution of accelerated gluconeogenesis to the susceptibility to APAP-induced hepatotoxicity using an animal model of type 2 diabetes patients. Sprague-Dawley (SD) rats and spontaneously diabetic torii (SDT) rats were each given APAP at 0 mg/kg, 300 and 500 mg/kg for 35 days by oral gavage. Plasma and urinary glutathione-related metabolites, liver function parameters, and hepatic glutathione levels were compared between the non-APAP-treated SDT and SD rats and between the APAP-treated SDT and SD rats. Hepatic function parameters were not increased at either dose level in the APAP-treated SD rats, but were increased at both dose levels in the APAP-treated SDT rats. Increases in hepatic glutathione levels attributable to the treatment of APAP were noted only in the APAP-treated SD rats. There were differences in the profiles of plasma and urinary glutathione-related metabolites between the non-APAP-treated SD and SDT rats and the plasma/urinary endogenous metabolite profile after treatment with APAP in the SDT rats indicated that hepatic glutathione synthesis was decreased due to accelerated gluconeogenesis. In conclusion, SDT rats were more sensitive to APAP-induced chronic hepatotoxicity than SD rats and the high susceptibility of SDT rats was considered to be attributable to lowered hepatic glutathione levels induced by accelerated gluconeogenesis.

Fatal acetaminophen poisoning with hepatic microvesicular steatosis in a child after repeated administration of therapeutic doses

Acetaminophen is the leading cause of acute liver failure worldwide following massive ingestion. We present here a fatal acute liver failure after repeated administration of four therapeutic doses of acetaminophen at 4-h intervals in a previously healthy 9-year-old female who presented dental pain after a facial trauma during sport practice. A diagnosis of paracetamol-induced hepatitis was deduced from the clinical picture of fulminant hepatitis and tubular necrosis, the encephalopathy with oedema and without signs of trauma. Liver biopsy showed typical acetaminophen-induced necrosis and a microvesicular steatosis in periportal hepatocytes. These injuries might have been favored by pre-existing mitochondrial dysfunction related, for instance, to a deficiency in an enzyme of the mitochondrial β-oxidation pathway, or the respiratory chain. The observation of microvesicular steatosis in the periportal areas suggests an increased vulnerability via pre-existing mitochondrial dysfunction. As the liver status of patients is mostly unknown, the frequency of administration (every six hours) must be respected and the use of pharmaceutical forms allowing to adjust the dose as closely as possible to the child's weight should be promoted.

Metabolomic analysis of acetaminophen induced subclinical liver injury

Introduction: This study examines the metabolomic profile in humans following acetaminophen (APAP) induced subclinical hepatoxicity in the presence and absence of propylene glycol (PG), a cytochrome P450 2E1 inhibitor.Methods: Plasma samples were collected during a previously performed randomized, cross-over trial where 21 subjects received APAP, four grams daily for two weeks in one arm and APAP, four grams daily with 20 mL PG in a second arm. Plasma collected at baseline and at day nine of each arm(time of peak elevation of liver function tests) underwent metabolomic analysis.Results: There were reduced phase two metabolites in subjects who displayed liver injury. There was also decreased sulfonation capacity in all subjects as well as in subjects displaying liver injury relative to subjects not displaying liver injury as evidenced by decreased sulfonation of hepatically derived steroids. There were decreased levels of acylcarnitines in subjects who displayed liver injury relative to subjects not displaying liver injury, indicating inhibition of mitochondrial fatty acid β-oxidation.Conclusions: Daily APAP dosing led to saturation of metabolic pathways and inhibition of mitochondrial function in subjects displaying subclinical liver injury.

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.

Old problem, new solutions: biomarker discovery for acetaminophen liver toxicity

Introduction: Although the hepatotoxicity of acetaminophen is a well-known problem, the search for reliable biomarker of toxicity is still a current issue as clinical tools are missing to assess patients intoxicated following chronic use, sequential ingestion, use of modified release formulations or in case of delayed arrival to hospital. The need for new specific and robust biomarkers for acetaminophen toxicity has prompted many studies exploring the use of blood levels of acetaminophen derivatives, mitochondrial damage markers, liver cell apoptosis and/or necrosis markers and circulating microRNAs. Areas covered: In this review, we present a concise overview of the most promising biomarkers currently under evaluation including descriptions of their properties with respect to exposure type, APAP specificity, and potential clinical application. In addition, we illustrate the power of new technologies for biomarker research and describe their current application to the field of acetaminophen-induced hepatotoxicity. Expert opinion: Recently the use of extracellular vesicles isolation in combination with omics techniques has opened a new perspective to the field of biomarker research. However, the potential of those new technologies for the prediction and monitoring of hepatic diseases and acetaminophen toxicity has not yet been fully taken into consideration.

Antibiotics self-medication among undergraduate pharmacy students in Northern Nigeria

Introduction:

The burden of antibiotic self-medication (ASM) is increasing and becoming a global health threat due to antibiotics resistance. However, little is known about ASM among undergraduate pharmacy students who are the future custodians of medicines including antibiotics. Therefore, this study aims to develop, validate and utilize an online survey tool to investigate the prevalence of ASM among undergraduate pharmacy students in Northern Nigeria.

Methods:

A cross-sectional online survey form was developed, validated by face validity, content validity, and pilot study. The hyperlink to the online survey form was shared with undergraduate pharmacy students in northern Nigeria via WhatsApp, Facebook, and Twitter. Data were collected from eligible participants and analyzed using descriptive statistic.

Results:

A total of 217 students responded to the online survey, with a completion rate of 100%. Of the total number of respondents, 200 (92.2%) reported practicing ASM at least once in their lifetime. The major reasons for ASM were previous knowledge (40.4%) and having no time to see a doctor or pharmacist (27.5%). Amoxicillin (32.6%), Amoxicillin/Clavulanic acid (32.1%), Ampicillin/Cloxacillin (21.7%) and Ciprofloxacin (22.6%) were the most commonly implicated antibiotics in ASM. Cough, diarrhea, typhoid, and wound were the most frequently involved conditions. Patent medicine vendors (75.4%) and community pharmacies (29.4%) were the common source of antibiotics subjected to ASM.

Conclusion:

A research tool to assess ASM among undergraduate pharmacy students has been developed, validated and utilized. The prevalence of ASM is high among undergraduate pharmacy students in Northern Nigeria. Interventions to improve knowledge and awareness on ASM are needed among undergraduate pharmacy students to ensure antibiotic stewardship.

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.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Inhibition of CYP2E1 With Propylene Glycol Does Not Protect Against Hepatocellular Injury in Human Acetaminophen Daily-Dosing Model

Acetaminophen (APAP)-induced liver injury is initiated by metabolism of APAP by the cytochrome P-450 (CYP) system, primarily CYP2E1. We previously demonstrated CYP inhibition following administration of a liquid APAP formulation containing propylene glycol, a CYP2E1 inhibitor, and other excipients. This study was undertaken to determine if propylene glycol specifically inhibits production of CYP-derived metabolites and if propylene glycol reduces the rise in alanine aminotransferase (ALT) seen following prolonged APAP dosing. Human subjects were randomized to receive 4 g of APAP daily in one arm of the study or 4 g of APAP with 5 mL of 99% propylene glycol in the other arm, both for 14 days. After a washout period of at least 14 days, subjects were crossed over between arms. Outcomes were rise of ALT greater than 2 times baseline (responders) and proportion of randomly sampled CYP-derived metabolites relative to total metabolites produced. There was no difference in percentage of responders between treatment groups: 6 of 21 in the APAP group (29%) compared with 8 of 20 in the APAP + propylene glycol group (40%); chi-square, P = .59. For all subjects, the mean percentage of CYP-derived metabolites produced was 5.8% (APAP) versus 4.3% (APAP + propylene glycol); P = .018. This effect was solely attributable to the responders: the mean percentage of CYP metabolites of responders was 7.7% (APAP) versus 4.6% (APAP + propylene glycol), P = .050, whereas there was no difference for the nonresponders. Five subjects were responders in both arms (2% probability of random occurrence). Our data indicates that propylene glycol inhibits CYP2E1 metabolism of APAP in some subjects but does not effect hepatocellular indury at the dose given.

Pharmacometabolomics Reveals Irinotecan Mechanism of Action in Cancer Patients

The purpose of this study was to identify early circulating metabolite changes implicated in the mechanism of action of irinotecan, a DNA topoisomerase I inhibitor, in cancer patients. A liquid chromatography-tandem mass spectrometry-based targeted metabolomic platform capable of measuring 254 endogenous metabolites was applied to profile circulating metabolites in plasma samples collected pre- and post-irinotecan treatment from 13 cancer patients. To gain further mechanistic insights, metabolic profiling was also performed for the culture medium of human primary hepatocytes (HepatoCells) and 2 cancer cell lines on exposure to SN-38 (an active metabolite of irinotecan). Intracellular reactive oxygen species (ROS) was detected by dihydroethidium assay. Irinotecan induced a global metabolic change in patient plasma, as represented by elevations of circulating purine/pyrimidine nucleobases, acylcarnitines, and specific amino acid metabolites. The plasma metabolic signature was well replicated in HepatoCells medium on SN-38 exposure, whereas in cancer cell medium SN-38 induced accumulation of pyrimidine/purine nucleosides and nucleobases while having no impact on acylcarnitines and amino acid metabolites. SN-38 induced ROS in HepatoCells, but not in cancer cells. Distinct metabolite signatures of SN-38 exposure in HepatoCells medium and cancer cell medium revealed different mechanisms of drug action on hepatocytes and cancer cells. Elevations in circulating purine/pyrimidine nucleobases may stem from nucleotide degradation following irinotecan-induced DNA double-strand breaks. Accumulations of circulating acylcarnitines and specific amino acid metabolites may reflect, at least in part, irinotecan-induced mitochondrial dysfunction and oxidative stress in the liver. The plasma metabolic signature of irinotecan exposure provides early insights into irinotecan mechanism of action in patients.

Dietary supplementation with cysteine prevents adverse metabolic outcomes of repeated cures with paracetamol in old rats

Cysteine (Cys), a conditionally indispensable amino acid, is required for the detoxification of paracetamol (acetaminophen, N-acetyl-para-aminophenol, 4-hydroxy-acetanilide, APAP), a drug of widespread use in older persons. We recently reported that repeated APAP cures could worsen sarcopenia in old rats, likely to be due to the impairment of Cys/GSH homoeostasis. The aim of the study was to evaluate whether a dietary Cys supplementation during APAP cures could improve Cys/GSH homoeostasis and thus preserve skeletal muscle. Male 21·5-month-old Wistar rats received three 2-week-long cures of APAP (1 % of diet) alone or with extra Cys (0·5 % of diet), intercalated with washout periods of 2 weeks (APAP and APAP-Cys groups, respectively). They were compared with untreated control rats (CT group). CT and APAP-Cys groups were pair-fed to the APAP group. Dietary Cys supplementation was efficient to prevent increase in liver mass (P<0·0001), decrease in liver GSH (P<0·0001), increase in blood GSH concentration (P<0·0001), and to some extent, decrease in plasma free Cys concentration (P<0·05), all induced by repeated APAP cures. The addition of Cys to APAP cures decreased plasma alanine transaminase (P<0·05), the fractional synthesis rate of liver proteins (P<0·01), and increased masses of extensor digitorum longus (P<0·01), and soleus (P<0·05), compared with the APAP group. Cys supplementation prevented alteration in Cys/GSH homoeostasis and increased some muscle masses in old rats under repeated cures with a non-toxic dose of APAP.

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.

Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study

Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.

Metabolomics Analysis of Urine Samples from Children after Acetaminophen Overdose

Acetaminophen (APAP), a commonly used over-the-counter analgesic, accounts for approximately fifty percent of the cases of acute liver failure (ALF) in the United States due to overdose, with over half of those unintentional. Current clinical approaches for assessing APAP overdose rely on identifying the precise time of overdose and quantitating acetaminophen alanine aminotransferase (ALT) levels in peripheral blood. Novel specific and sensitive biomarkers may provide additional information regarding patient status post overdose. Previous non-clinical metabolomics studies identified potential urinary biomarkers of APAP-induced hepatotoxicity and metabolites involved pathways of tricarboxylic acid cycle, ketone metabolism, and tryptophan metabolism. In this study, biomarkers identified in the previous non-clinical study were evaluated in urine samples collected from healthy subjects ( N = 6, median age 14.08 years) and overdose patients ( N = 13, median age 13.91 years) as part of an IRB-approved multicenter study of APAP toxicity in children. The clinical results identified metabolites from pathways previously noted, and pathway analysis indicated analogous pathways were significantly altered in both the rats and humans after APAP overdose. The results suggest a metabolomics approach may enable the discovery of specific, translational biomarkers of drug-induced hepatotoxicity that may aid in the assessment of patients.

21st century toolkit for optimizing population health through precision nutrition

Scientific, technological, and economic progress over the last 100 years all but eradicated problems of widespread food shortage and nutrient deficiency in developed nations. But now society is faced with a new set of nutrition problems related to energy imbalance and metabolic disease, which require new kinds of solutions. Recent developments in the area of new analytical tools enable us to systematically study large quantities of detailed and multidimensional metabolic and health data, providing the opportunity to address current nutrition problems through an approach called Precision Nutrition. This approach integrates different kinds of "big data" to expand our understanding of the complexity and diversity of human metabolism in response to diet. With these tools, we can more fully elucidate each individual's unique phenotype, or the current state of health, as determined by the interactions among biology, environment, and behavior. The tools of precision nutrition include genomics, metabolomics, microbiomics, phenotyping, high-throughput analytical chemistry techniques, longitudinal tracking with body sensors, informatics, data science, and sophisticated educational and behavioral interventions. These tools are enabling the development of more personalized and predictive dietary guidance and interventions that have the potential to transform how the public makes food choices and greatly improve population health.

Paracetamol: overdose-induced oxidative stress toxicity, metabolism, and protective effects of various compounds in vivo and in vitro

Paracetamol (APAP) is one of the most widely used and popular over-the-counter analgesic and antipyretic drugs in the world when used at therapeutic doses. APAP overdose can cause severe liver injury, liver necrosis and kidney damage in human beings and animals. Many studies indicate that oxidative stress is involved in the various toxicities associated with APAP, and various antioxidants were evaluated to investigate their protective roles against APAP-induced liver and kidney toxicities. To date, almost no review has addressed the APAP toxicity in relation to oxidative stress. This review updates the research conducted over the past decades into the production of reactive oxygen species (ROS), reactive nitrogen species (RNS), and oxidative stress as a result of APAP treatments, and ultimately their correlation with the toxicity and metabolism of APAP. The metabolism of APAP involves various CYP450 enzymes, through which oxidative stress might occur, and such metabolic factors are reviewed within. The therapeutics of a variety of compounds against APAP-induced organ damage based on their anti-oxidative effects is also discussed, in order to further understand the role of oxidative stress in APAP-induced toxicity. This review will throw new light on the critical roles of oxidative stress in APAP-induced toxicity, as well as on the contradictions and blind spots that still exist in the understanding of APAP toxicity, the cellular effects in terms of organ injury and cell signaling pathways, and finally strategies to help remedy such against oxidative damage.

Metabolomic approaches in the discovery of potential urinary biomarkers of drug-induced liver injury (DILI)

Drug-induced liver injury (DILI) is a major safety issue during drug development, as well as the most common cause for the withdrawal of drugs from the pharmaceutical market. The identification of DILI biomarkers is a labor-intensive area. Conventional biomarkers are not specific and often only appear at significant levels when liver damage is substantial. Therefore, new biomarkers for early identification of hepatotoxicity during the drug discovery process are needed, thus resulting in lower development costs and safer drugs. In this sense, metabolomics has been increasingly playing an important role in the discovery of biomarkers of liver damage, although the characterization of the mechanisms of toxicity induced by xenobiotics remains a huge challenge. These new-generation biomarkers will offer obvious benefits for the pharmaceutical industry, regulatory agencies, as well as a personalized clinical follow-up of patients, upon validation and translation into clinical practice or approval for routine use. This review describes the current status of the metabolomics applied to the early diagnosis and prognosis of DILI and in the discovery of new potential urinary biomarkers of liver injury.

Liver Effects of Clinical Drugs Differentiated in Human Liver Slices

Drugs with clinical adverse effects are compared in an ex vivo 3-dimensional multi-cellular human liver slice model. Functional markers of oxidative stress and mitochondrial function, glutathione GSH and ATP levels, were affected by acetaminophen (APAP, 1 mM), diclofenac (DCF, 1 mM) and etomoxir (ETM, 100 μM). Drugs targeting mitochondria more than GSH were dantrolene (DTL, 10 μM) and cyclosporin A (CSA, 10 μM), while GSH was affected more than ATP by methimazole (MMI, 500 μM), terbinafine (TBF, 100 μM), and carbamazepine (CBZ 100 μM). Oxidative stress genes were affected by TBF (18%), CBZ, APAP, and ETM (12%–11%), and mitochondrial genes were altered by CBZ, APAP, MMI, and ETM (8%–6%). Apoptosis genes were affected by DCF (14%), while apoptosis plus necrosis were altered by APAP and ETM (15%). Activation of oxidative stress, mitochondrial energy, heat shock, ER stress, apoptosis, necrosis, DNA damage, immune and inflammation genes ranked CSA (75%), ETM (66%), DCF, TBF, MMI (61%–60%), APAP, CBZ (57%–56%), and DTL (48%). Gene changes in fatty acid metabolism, cholestasis, immune and inflammation were affected by DTL (51%), CBZ and ETM (44%–43%), APAP and DCF (40%–38%), MMI, TBF and CSA (37%–35%). This model advances multiple dosing in a human ex vivo model, plus functional markers and gene profile markers of drug induced human liver side-effects.

Nuclear magnetic resonance metabolomics and human liver diseases: The principles and evidence associated with protein and carbohydrate metabolism

During the last decade, metabolomics has become widely used in the field of human diseases. Numerous studies have demonstrated that this is a powerful technique for improving the understanding, diagnosis and management of various types of liver disease, such as acute and chronic liver diseases, and liver transplantation. Nuclear magnetic resonance (NMR) spectroscopy is one of the two most commonly applied methods for metabolomics. The aim of the present review was to investigate the results from recent key publications focusing on aspects of protein and carbohydrate metabolism. The review includes existing procedures, which are currently used for NMR data acquisition and statistical analysis. In addition, notable results obtained by these studies on protein and carbohydrate metabolism concerning human liver diseases are presented.

Role of nonalcoholic fatty liver disease as risk factor for drug-induced hepatotoxicity

BACKGROUND

Obesity is often associated with nonalcoholic fatty liver disease (NAFLD), which refers to a large spectrum of hepatic lesions including fatty liver, nonalcoholic steatohepatitis (NASH) and cirrhosis. Different investigations showed or suggested that obesity and NAFLD are able to increase the risk of hepatotoxicity of different drugs. Some of these drugs could induce more frequently an acute hepatitis in obese individuals whereas others could worsen pre-existing NAFLD.

AIM

The main objective of the present review was to collect the available information regarding the role of NAFLD as risk factor for drug-induced hepatotoxicity. For this purpose, we performed a data-mining analysis using different queries including drug-induced liver injury (or DILI), drug-induced hepatotoxicity, fatty liver, nonalcoholic fatty liver disease (or NAFLD), steatosis and obesity. The main data from the collected articles are reported in this review and when available, some pathophysiological hypotheses are put forward.

RELEVANCE FOR PATIENTS

Drugs that could pose a potential risk in obese patients include compounds belonging to different pharmacological classes such as acetaminophen, halothane, methotrexate, rosiglitazone, stavudine and tamoxifen. For some of these drugs, experimental investigations in obese rodents confirmed the clinical observations and unveiled different pathophysiological mechanisms which could explain why these pharmaceuticals are particularly hepatotoxic in obesity and NAFLD. Other drugs such as pentoxifylline, phenobarbital and omeprazole might also pose a risk but more investigations are required to determine whether this risk is significant or not. Because obese people often take several drugs for the treatment of different obesity-related diseases such as type 2 diabetes, hyperlipidemia and coronary heart disease, it is urgent to identify the main pharmaceuticals that can cause acute hepatitis on a fatty liver background or induce NAFLD worsening.

A metabolomics approach for predicting the response to intravenous iron therapy in peritoneal dialysis patients with anemia

Anemia is an almost universal complication of chronic kidney disease (CKD), and nearly all patients with end-stage renal disease (ESRD) and approximately 70% of those with earlier stages of CKD receive treatment for anemia.

Effect of Metformin on Metabolites and Relation With Myocardial Infarct Size and Left Ventricular Ejection Fraction After Myocardial Infarction

Background—

Left ventricular ejection fraction (LVEF) and infarct size (ISZ) are key predictors of long-term survival after myocardial infarction (MI). However, little is known about the biochemical pathways driving LV dysfunction after MI. To identify novel biomarkers predicting post-MI LVEF and ISZ, we performed metabolic profiling in the GIPS-III randomized clinical trial (Glycometabolic Intervention as Adjunct to Primary Percutaneous Intervention in ST Elevation Myocardial Infarction). We also investigated the metabolic footprint of metformin, a drug associated with improved post-MI LV function in experimental studies.

Methods and Results—

Participants were patients with ST-segment–elevated MI who were randomly assigned to receive metformin or placebo for 4 months. Blood samples were obtained on admission, 24 hours post-MI, and 4 months post-MI. A total of 233 metabolite measures were quantified using nuclear magnetic resonance spectrometry. LVEF and ISZ were assessed 4 months post-MI. Twenty-four hours post-MI measurements of high-density lipoprotein (HDL) triglycerides (HDL-TG) predicted LVEF (β=1.90 [95% confidence interval (CI), 0.82 to 2.98]; P =6.4×10 −4 ) and ISZ (β=−0.41 [95% CI, −0.60 to −0.21]; P =3.2×10 −5 ). In addition, 24 hours post-MI measurements of medium HDL-TG (β=−0.40 [95% CI, −0.60 to −0.20]; P =6.4×2×10 −5 ), small HDL-TG (β=−0.34 [95% CI, −0.53 to −0.14]; P =7.3×10 −4 ), and the triglyceride content of very large HDL (β=−0.38 [95% CI, −0.58 to −0.18]; P =2.7×10 −4 ) were associated with ISZ. After the 4-month treatment, the phospholipid content of very large HDL was lower in metformin than in placebo-treated patients (28.89% versus 38.79%; P =7.5×10 −5 ); alanine levels were higher in the metformin group (0.46 versus 0.44 mmol/L; P =2.4×10 −4 ).

Conclusions—

HDL triglyceride concentrations predict post-MI LVEF and ISZ. Metformin increases alanine levels and reduces the phospholipid content in very large HDL particles.

Clinical Trial Registration—

URL: https://clinicaltrials.gov/ct2/show/NCT01217307 . Unique Identifier: NCT01217307.

Circulating acetaminophen metabolites are toxicokinetic biomarkers of acute liver injury

Acetaminophen (paracetamol-APAP) is the most common cause of drug-induced liver injury in the Western world. Reactive metabolite production by cytochrome P450 enzymes (CYP-metabolites) causes hepatotoxicity. We explored the toxicokinetics of human circulating APAP metabolites following overdose. Plasma from patients treated with acetylcysteine (NAC) for a single APAP overdose was analyzed from discovery (n = 116) and validation (n = 150) patient cohorts. In the discovery cohort, patients who developed acute liver injury (ALI) had higher CYP-metabolites than those without ALI. Receiver operator curve (ROC) analysis demonstrated that at hospital presentation CYP-metabolites were more sensitive/specific for ALI than alanine aminotransferase (ALT) activity and APAP concentration (optimal CYP-metabolite receiver operating characteristic area under the curve (ROC-AUC): 0.91 (95% confidence interval (CI) 0.83-0.98); ALT ROC-AUC: 0.67 (0.50-0.84); APAP ROC-AUC: 0.50 (0.33-0.67)). This enhanced sensitivity/specificity was replicated in the validation cohort. Circulating CYP-metabolites stratify patients by risk of liver injury prior to starting NAC. With development, APAP metabolites have potential utility in stratified trials and for refinement of clinical decision-making.

Exploration of Biomarkers for Amoxicillin/Clavulanate-Induced Liver Injury: Multi-Omics Approaches

To explore potential biomarkers for amoxicillin/clavulanate‐induced liver injury (AC‐DILI), we conducted a clinical trial in 32 healthy subjects based on multi‐omics approaches. Every subject was administered amoxicillin/clavulanate for 14 days. The liver‐specific microRNA‐122 (miR‐122) level increased prior to and correlated well with the observed alanine aminotransferase (ALT) level increase. This result indicates its potential as a sensitive early marker for AC‐DILI. We also identified urinary metabolites, such as azelaic acid and 7‐methylxanthine, with levels that significantly differed among the groups classified by ALT elevation level on day 8 after drug administration (P < 0.05). Lymphocyte proliferation in response to the drug was also observed. These findings demonstrate sequential changes in the process of AC‐DILI, including metabolic changes, increased miR‐122 level, increased liver enzyme activity, and enhanced lymphocyte proliferation after drug administration. In conclusion, this study provides potential biomarkers for AC‐DILI based on currently known mechanisms using comprehensive multi‐omics approaches.

The 100 most influential publications in paracetamol poisoning treatment: a bibliometric analysis of human studies

BACKGROUND

Analysis of the most influential publications within paracetamol poisoning treatment can be helpful in recognizing main and novel treatment issues within the field of toxicology. The current study was performed to recognize and describe the most highly cited articles related to paracetamol poisoning treatment.

METHODS

The 100 most highly cited articles in paracetamol poisoning treatment were identified from the Scopus database in November 2015. All eligible articles were read for basic information, including total number of citations, average citations per year, authors' names, journal name, impact factors, document types and countries of authors of publications.

RESULTS

The median number of citations was 75 (interquartile range 56-137). These publications were published between 1974 and 2013. The average number of years since publication was 17.6 years, and 45 of the publications were from the 2000s. A significant, modest positive correlation was found between years since publication and the number of citations among the top 100 cited articles (r = 0.316; p = 0.001). A total of 55 journals published these 100 most cited articles. Nine documents were published in Clinical Toxicology, whereas eight documents were published in Annals of Emergency Medicine. Citations per year since publication for the top 100 most-cited articles ranged from 1.5 to 42.6 and had a mean of 8.5 citations per year and a median of 5.9 with an interquartile range of 3.75-10.35. In relation to the origin of the research publications, they were from 8 countries. The USA had the largest number of articles, 47, followed by the UK and Australia with 38 and nine articles respectively.

CONCLUSIONS

This study is the first bibliometric assessment of the top 100 cited articles in toxicology literature. Interest in paracetamol poisoning as a serious clinical problem continues to grow. Research published in high-impact journals and from high income countries is most likely to be cited in published paracetamol research.

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

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

Metabolomics enables precision medicine: "A White Paper, Community Perspective"

INTRODUCTION BACKGROUND TO METABOLOMICS

Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.

OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE

We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.

CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE

Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses

We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r(2) value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r(2) values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r(2) values. The Q(2)X value for each PCA model is extracted. K-means clustering is then applied to the Q(2)X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q(2)X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q(2)X values is then further subclustered into two groups based on the r(2) values. We considered the cluster with high Q(2)X but low r(2) values as nondiscriminatory, while the cluster with high Q(2)X and r(2) values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r(2) values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated (1)H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the sample size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini-Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data.

Blood transcript immune signatures distinguish a subset of people with elevated serum ALT from others given acetaminophen

The diagnosis of drug-induced liver injury is hindered by the limited utility of clinical chemistries. We have shown that hepatotoxicants can produce peripheral blood transcriptome "signatures" (PBTS) in rodents and humans. In this study, 42 adults were treated with acetaminophen (APAP; 1 g every 6 hours) for seven days, followed by three days of placebo. Eleven subjects received only placebo. After five days, 12 subjects (30%) had increases in serum alanine aminotransferase (ALT) levels ("responders"). PBTS of 707 and 760 genes, respectively, could distinguish responders and nonresponders from placebos. Functional analysis of the responder PBTS revealed increased expression of genes involved in TH2-mediated and innate immune responses, whereas the nonresponders demonstrated increased gene expression consistent with a tolerogenic immune response. Taken together, these observations suggest that the clinical subjects with transient increases in serum ALT failed to maintain or intensify a hepatic tolerogenic immune response.