Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study

Lipidomics and cardiovascular disease

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating innovative approaches for early detection and personalized interventions. Lipidomics, leveraging advanced mass spectrometry techniques, has become instrumental in deciphering lipid-mediated mechanisms in CVDs. This review explores the application of lipidomics in identifying biomarkers for myocardial infarction, heart failure, stroke, and calcific aortic valve stenosis (CAVS). This review examines the technological advancements in shotgun lipidomics and LC/MS, which provide unparalleled insights into lipid composition and function. Key lipid biomarkers, including ceramides and lysophospholipids, have been linked to disease progression and therapeutic outcomes. Integrating lipidomics with genomic and proteomic data reveals the molecular underpinnings of CVDs, enhancing risk prediction and intervention strategies. This review positions lipidomics as a transformative tool in reshaping cardiovascular research and clinical practice.

Systematic Review on Efficacy, Effectiveness, and Safety of Pitavastatin in Dyslipidemia in Asia

Objectives: Dyslipidemia, a significant risk factor for cardiovascular disease (CVD), is marked by abnormal lipid levels, such as the elevated lowering of low-density lipoprotein cholesterol (LDL-C). Statins are the first-line treatment for LDL-C reduction. Pitavastatin (PIT) has shown potential in lowering LDL-C and improving high-density lipoprotein cholesterol (HDL-C). This review assesses pitavastatin's efficacy, effectiveness, and safety in dyslipidemia management in Asia. Methods: A systematic review was conducted using PubMed, Cochrane, and Embase databases up to November 2024, adhering to Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Seventeen studies (12 RCTs and 5 non-RCTs) were analyzed, focusing on LDL-C reduction, safety profiles, and adverse events. The quality of the studies was assessed using checklists to ensure the selection of the best studies and to limit bias. Results: Pitavastatin doses (1-4 mg) reduced LDL-C by 28-47%, comparable to atorvastatin, rosuvastatin, and simvastatin. The 2 mg dose matched atorvastatin's 10 mg dose in efficacy for both short-term (35-42%) and long-term (28-36%) use. LDL-C target achievement rates were 75-95%. Adverse events, including mild myalgia and elevated liver enzymes, were rare, and discontinuation rates were low. Conclusions: Pitavastatin is an effective and safe alternative to traditional statins for dyslipidemia management in Asia. Further research on long-term outcomes and high-risk groups is warranted.

Perspectives on the success of plasma lipidomics in cardiovascular drug discovery and future challenges

INTRODUCTION

Plasma lipidomics has emerged as a powerful tool in cardiovascular drug discovery by providing insights into disease mechanisms, identifying potential biomarkers for diagnosis and prognosis, and discovering novel targets for drug development. Widespread application of plasma lipidomics is hampered by technological limitations and standardization and requires a collaborative approach to maximize its use in cardiovascular drug discovery.

AREAS COVERED

This review provides an overview of the utility of plasma lipidomics in cardiovascular drug discovery and discusses the challenges and future perspectives of this rapidly evolving field. The authors discuss the role of lipidomics in understanding the molecular mechanisms of CVD, identifying novel biomarkers for diagnosis and prognosis, and discovering new therapeutic targets for drug development. Furthermore, they highlight the challenges faced in data analysis, standardization, and integration with other omics approaches and propose future directions for the field.

EXPERT OPINION

Plasma lipidomics holds great promise for improving the diagnosis, treatment, and prevention of CVD. While challenges remain in standardization and technology, ongoing research and collaboration among scientists and clinicians will undoubtedly help overcome these obstacles. As lipidomics evolves, its impact on cardiovascular drug discovery and clinical practice is expected to grow, ultimately benefiting patients and healthcare systems worldwide.

Effect of statin treatment on metabolites, lipids and prostanoids in patients with Statin Associated Muscle Symptoms (SAMS)

BACKGROUND

Between 5-10% of patients discontinue statin therapy due to statin-associated adverse reactions, primarily statin associated muscle symptoms (SAMS). The absence of a clear clinical phenotype or of biomarkers poses a challenge for diagnosis and management of SAMS. Similarly, our incomplete understanding of the pathogenesis of SAMS hinders the identification of treatments for SAMS. Metabolomics, the profiling of metabolites in biofluids, cells and tissues is an important tool for biomarker discovery and provides important insight into the origins of symptomatology. In order to better understand the pathophysiology of this common disorder and to identify biomarkers, we undertook comprehensive metabolomic and lipidomic profiling of plasma samples from patients with SAMS who were undergoing statin rechallenge as part of their clinical care.

METHODS AND FINDINGS

We report our findings in 67 patients, 28 with SAMS (cases) and 39 statin-tolerant controls. SAMS patients were studied during statin rechallenge and statin tolerant controls were studied while on statin. Plasma samples were analyzed using untargeted LC-MS metabolomics and lipidomics to detect differences between cases and controls. Differences in lipid species in plasma were observed between cases and controls. These included higher levels of linoleic acid containing phospholipids and lower ether lipids and sphingolipids. Reduced levels of acylcarnitines and altered amino acid profile (tryptophan, tyrosine, proline, arginine, and taurine) were observed in cases relative to controls. Pathway analysis identified significant increase of urea cycle metabolites and arginine and proline metabolites among cases along with downregulation of pathways mediating oxidation of branched chain fatty acids, carnitine synthesis, and transfer of acetyl groups into mitochondria.

CONCLUSIONS

The plasma metabolome of patients with SAMS exhibited reduced content of long chain fatty acids and increased levels of linoleic acid (18:2) in phospholipids, altered energy production pathways (β-oxidation, citric acid cycle and urea cycles) as well as reduced levels of carnitine, an essential mediator of mitochondrial energy production. Our findings support the hypothesis that alterations in pro-inflammatory lipids (arachidonic acid pathway) and impaired mitochondrial energy metabolism underlie the muscle symptoms of patients with statin associated muscle symptoms (SAMS).

Lipidomic analysis identified potential predictive biomarkers of statin response in subjects with Familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a disorder of lipid metabolism that causes elevated low-density lipoprotein cholesterol (LDL-c) and increased premature atherosclerosis risk. Statins inhibit endogenous cholesterol biosynthesis, which reduces LDL-c plasma levels and prevent from cardiovascular events. This study aimed to explore the effects of statin treatment on serum lipidomic profile and to identify biomarkers of response in subjects with FH. Seventeen adult FH patients underwent a 6-week washout followed by 4-week treatment with atorvastatin (80 mg/day) or rosuvastatin (40 mg/day). LDL-c response was considered good (40-70 % reduction, n = 9) or poor (3-33 % reduction, n = 8). Serum lipidomic profile was analyzed by ultra-high-performance liquid chromatography combined with electrospray ionization tandem time-of-flight mass spectrometry, and data were analyzed using MetaboAnalyst v5.0. Lipidomic analysis identified 353 lipids grouped into 16 classes. Statin treatment reduced drastically 8 of 13 lipid classes, generating a characteristic lipidomic profile with a significant contribution of phosphatidylinositols (PI) 16:0/18:2, 18:0/18:1 and 18:0/18:2; and triacylglycerols (TAG) 18:2x2/18:3, 18:1/18:2/18:3, 16:1/18:2x2, 16:1/18:2/18:3 and 16:1/18:2/Arachidonic acid (p-adjusted <0.05). Biomarker analysis implemented in MetaboAnalyst subsequently identified PI 16:1/18:0, 16:0/18:2 and 18:0/18:2 as predictors of statin response with and receiver operating characteristic (ROC) areas under the curve of 0.98, 0.94 and 0.91, respectively. In conclusion, statins extensively modulate the overall serum lipid composition of FH individuals and these findings suggest that phosphatidyl-inositol molecules are potential predictive biomarkers of statin response.

Effects of Atorvastatin and Rosuvastatin on Blood Lipids in Quaker Parrots (Myiopsitta monachus)

Statin drugs are the most effective class of hypolipidemic and antiatherosclerotic drugs, with atorvastatin and rosuvastatin being the most effective. While the use of statins would be a tremendous asset in the treatment of dyslipidemia and lipid-accumulation disorders in birds, there are only limited data available regarding their use and effectiveness in psittacine species. Two consecutive randomized crossover trials on Quaker parrots (Myiopsitta monachus) were performed to study the effect of atorvastatin and rosuvastatin. Ten birds were used in an initial balanced crossover experiment with 5 oral treatments (control; atorvastatin 10 mg/kg q12h and q24h; rosuvastatin 10 mg/kg q12h and q24h) for 2 weeks each. Plasma lipidomics and lipoprotein profiling were performed after each treatment. Twelve birds were used in a second experiment consisting of 2 parallel crossover studies, each with 6 birds either fed their regular diet or a 0.3% cholesterol diet. In the 2 parallel crossover studies, the treatment group was administered atorvastatin 20 mg/kg orally q12h and the control group a placebo suspension orally q12h. Plasma lipidomics, lipoprotein profiles, and 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase activity were subsequently measured. Results were analyzed with serial linear mixed models and trends were assessed graphically. No statistically significant effect of any statin treatment was detected on plasma lipids, lipoproteins, creatinine kinase, or HMG-CoA reductase activity. In the first trial, all the rosuvastatin treatments led to some nonsignificant decreases in several triacylglycerol species, while in the second trial this was only observed in the birds on atorvastatin 20 mg/kg q12h being fed their regular diet. Quaker parrots may require much higher doses of statin drugs to show significant and clinically useful lipid-lowering effects.

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.

Platelet Redox Imbalance in Hypercholesterolemia: A Big Problem for a Small Cell

The imbalance between reactive oxygen species (ROS) synthesis and their scavenging by anti-oxidant defences is the common soil of many disorders, including hypercholesterolemia. Platelets, the smallest blood cells, are deeply involved in the pathophysiology of occlusive arterial thrombi associated with myocardial infarction and stroke. A great deal of evidence shows that both increased intraplatelet ROS synthesis and impaired ROS neutralization are implicated in the thrombotic process. Hypercholesterolemia is recognized as cause of atherosclerosis, cerebro- and cardiovascular disease, and, closely related to this, is the widespread acceptance that it strongly contributes to platelet hyperreactivity via direct oxidized LDL (oxLDL)-platelet membrane interaction via scavenger receptors such as CD36 and signaling pathways including Src family kinases (SFK), mitogen-activated protein kinases (MAPK), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. In turn, activated platelets contribute to oxLDL generation, which ends up propagating platelet activation and thrombus formation through a mechanism mediated by oxidative stress. When evaluating the effect of lipid-lowering therapies on thrombogenesis, a large body of evidence shows that the effects of statins and proprotein convertase subtilisin/kexin type 9 inhibitors are not limited to the reduction of LDL-C but also to the down-regulation of platelet reactivity mainly by mechanisms sensitive to intracellular redox balance. In this review, we will focus on the role of oxidative stress-related mechanisms as a cause of platelet hyperreactivity and the pathophysiological link of the pleiotropism of lipid-lowering agents to the beneficial effects on platelet function.

Statin therapy upregulates arachidonic acid status via enhanced endogenous synthesis in patients with plaque psoriasis

Circulating fatty acids (FA) may be important in the psoriatic pro-inflammatory phenotype. FADS1 converts linoleic acid (LA) to arachidonic acid (AA), a precursor to potent signaling molecules. HMG-CoA reductase inhibitors (statins) increase FADS1/2 expression in vitro. Psoriasis patients (42 ± 14 years/age, 47% male) were randomized to 40 mg of atorvastatin (n = 20) or nothing (n = 10) for two weeks and plasma FA measured pre and post treatment. After treatment, LDL-C was 44% lower in the statin compared to the no-treatment group. Statins increased FADS1/2 expression, and lowered LA 12% (33% - > 29%, p<0.001) and raised AA 14% (7.7% - > 9.0%, p<0.01) with no change in the no-treatment group. In psoriasis, statins enhance AA and decrease LA, consistent with the action of enhanced FADS expression in vivo. Therapies intended to blunt the effects of AA on platelet aggregation, such as aspirin or omega-3 fatty acids, may require dose adjustment when co-administered with atorvastatin. NCT: NCT03228017.

Lipidomics in Understanding Pathophysiology and Pharmacologic Effects in Inflammatory Diseases: Considerations for Drug Development

The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease.

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.

The Novel Interplay between Commensal Gut Bacteria and Metabolites in Diet-Induced Hyperlipidemic Rats Treated with Simvastatin

Hyperlipidemia is one kind of metabolic syndrome for which the treatment commonly includes simvastatin (SV). Individuals vary widely in statin responses, and growing evidence implicates gut microbiome involvement in this variability. However, the associated molecular mechanisms between metabolic improvement and microbiota composition following SV treatment are still not fully understood. In this study, combinatory approaches using ultrahigh-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS/MS)-based metabolomic profiling, PCR-denaturing gradient gel electrophoresis (PCR-DGGE), quantitative PCR (qPCR), and 16S rRNA gene sequencing-based gut microbiota profiling were performed to investigate the interplay of endogenous metabolites and the gut microbiota related to SV treatment. A total of 6 key differential endogenous metabolites were identified that affect the metabolism of amino acids (phenylalanine and tyrosine), unsaturated fatty acids (linoleic acid and 9-hydroxyoctadecadienoic acid (9-HODE)), and the functions of gut microbial metabolism. Moreover, a total of 22 differentially abundant taxa were obtained following SV treatment. Three bacterial taxa were identified to be involved in SV treatment, namely, Bacteroidaceae, Prevotellaceae, and Porphyromonadaceae. These findings suggested that the phenylalanine and tyrosine-associated amino acid metabolism pathways, as well as the linoleic acid and 9-HODE-associated unsaturated fatty acid metabolism pathways, which are involved in gut flora interactions, might be potential therapeutic targets for improvement in SV hypolipidemic efficacy. The mass spectrometric data have been deposited to MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp). Username: MSV000087842_reviewer. Password: hardworkingzsr.

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.

A pilot randomized trial of atorvastatin as adjunct therapy in patients with acute venous thromboembolism

Venous thromboembolism (VTE) is the third most common cardiovascular disease and optimizing treatment is essential. In this single-center pilot study, we sought to investigate the effects of statins in addition to anticoagulation in patients with acute VTE. We enrolled patients over 18 with an acute proximal lower extremity deep vein thrombosis with or without pulmonary embolism. Patients were randomized to anticoagulation alone (with either warfarin or rivaroxaban) or anticoagulation and atorvastatin 40 mg daily and followed for 9 months. The primary objective was to determine if adjunct atorvastatin reduced thrombin generation, measured by endogenous thrombin potential and/or peak thrombin concentration. Secondary endpoints included recurrent VTE, arterial thrombosis, bleeding events, lipidomic profiles, and symptoms of post thrombotic syndrome. A total of 21 patients were enrolled (11 anticoagulation only and 10 anticoagulation and atorvastatin) over 3.5 years. Endogenous thrombin potential or peak thrombin was not significantly recued with the addition of atorvastatin. Atorvastatin did significantly reduce the mean LDLs at 3 months, without reduction of either d-dimer or high-sensitivity-C reactive protein. Given the low recruitment rate, continuation of the study was deemed futile and the study was terminated early. Barriers to enrollment and completion of study included the many ineligible patients by exclusion criteria (e.g., preexisting statin use, active malignancy, etc.) and high rate of lost follow-up. The pilot study was terminated early but could inform obstacles for future studies investigating the effects of statins in the management of patients with VTE.

Plasma Lipidomics Reveals Insights into Anti-Obesity Effect of Chrysanthemum morifolium Ramat Leaves and Its Constituent Luteolin in High-Fat Diet-Induced Dyslipidemic Mice

The Chrysanthemum morifolium Ramat (CM) is widely used as a traditional medicine and herbal tea by the Asian population for its health benefits related to obesity. However, compared to the flowers of CM, detailed mechanisms underlying the beneficial effects of its leaves on obesity and dyslipidemia have not yet been elucidated. Therefore, to investigate the lipidomic biomarkers responsible for the pharmacological effects of CM leaf extract (CLE) in plasma of mice fed a high-fat diet (HFD), the plasma of mice fed a normal diet (ND), HFD, HFD plus CLE 1.5% diet, and HFD plus luteolin 0.003% diet (LU) for 16 weeks were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with multivariate analysis. In our analysis, the ND, HFD, CLE, and LU groups were clearly differentiated by partial least-squares discriminant analysis (PLS-DA) score plots. The major metabolites contributing to this differentiation were cholesteryl esters (CEs), lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), ceramides (CERs), and sphingomyelins (SMs). The levels of plasma CEs, LPCs, PCs, SMs, and CERs were significantly increased in the HFD group compared to those in the ND group, and levels of these lipids recovered to normal after administration of CLE or LU. Furthermore, changes in hepatic mRNA expression levels involved in the Kennedy pathway and sphingolipid biosynthesis were also suppressed by treatment with CLE or LU. In conclusion, this study examined the beneficial effects of CLE and LU on obesity and dyslipidemia, which were demonstrated as reduced synthesis of lipotoxic intermediates. These results may provide valuable insights towards evaluating the therapeutic effects of CLE and LU and understanding obesity-related diseases.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Lipid Profiling of Serum and Lipoprotein Fractions in Response to Pitavastatin Using an Animal Model of Familial Hypercholesterolemia

Statins are widely used for the treatment of atherosclerotic cardiovascular diseases. They inhibit cholesterol biosynthesis in the liver and cause pleiotropic effects, including anti-inflammatory and antioxidant effects. To develop novel therapeutic drugs, the effect of blood-borne lipid molecules on the pleiotropic effects of statins must be elucidated. Myocardial infarction-prone Watanabe heritable hyperlipidemic (WHHLMI) rabbits, an animal model for hypercholesterolemia, are suitable for the determination of lipid molecules in the blood in response to statins because their lipoprotein metabolism is similar to that of humans. Herein, lipid molecules were investigated by lipidome analysis in response to pitavastatin using WHHLMI rabbits. Various lipid molecules in the blood were measured using a supercritical fluid chromatography triple quadrupole mass spectrometry. Cholesterol and cholesterol ester blood concentrations decreased by reducing the secretion of very low density lipoproteins from the liver. Independent of the inhibition effects of cholesterol biosynthesis, the concentrations of some lipids with anti-inflammation and antioxidant effects (phospholipid molecules with n-6 fatty acid side chains, lysophosphatidylcholines, phosphatidylethanolamine plasmalogens, and ceramide molecules) were significantly altered. These findings may lead to further investigation of the mechanism of statin action.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues

The importance of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis is expected to grow substantially due to recent failures in detecting severe toxicity issues of new chemical entities during preclinical/clinical development. Traditionally, safety risk assessment studies for humans have been conducted in animals during advanced preclinical or clinical phase of drug development. However, potential drug toxicity in humans now needs to be detected in the drug discovery process as soon as possible without reliance on animal studies. The "omics", such as genomics, proteomics, and metabolomics, have recently entered pharmaceutical research in both drug discovery and drug development, but to the best of our knowledge, no applications in high-throughput safety risk assessment have been attempted so far. This paper reports an innovative method to anticipate adverse drug effects in an early discovery phase based on lipid fingerprints using human three-dimensional microtissues. The risk of clinical hepatotoxicity potential was evaluated for a data set of 22 drugs belonging to five different therapeutic chemical classes and with various drug-induced liver injury effect. The treatment of microtissues with repeated doses of each drug allowed collecting lipid fingerprints for five time points (2, 4, 7, 9, and 11 days), and multivariate statistical analysis was applied to search for correlations with the hepatotoxic effect. The method allowed clustering of the drugs based on their hepatotoxic effect, and the observed lipid impairments for a number of drugs was confirmed by literature sources. Compared to traditional screening methods, here multiple interconnected variables (lipids) are measured simultaneously, providing a snapshot of the cellular status from the lipid perspective at a molecular level. Applied here to hepatotoxicity, the proposed workflow can be applied to several tissues, being tridimensional microtissues from various origins.

Pharmacometabolomics Informs About Pharmacokinetic Profile of Methylphenidate

Carboxylesterase 1 (CES1) metabolizes methylphenidate and other drugs. CES1 gene variation only partially explains pharmacokinetic (PK) variability. Biomarkers predicting the PKs of drugs metabolized by CES1 are needed. We identified lipids in plasma from 44 healthy subjects that correlated with CES1 activity as determined by PK parameters of methylphenidate including a ceramide (q value = 0.001) and a phosphatidylcholine (q value = 0.005). Carriers of the CES1 143E allele had decreased methylphenidate metabolism and altered concentration of this phosphatidylcholine (q value = 0.040) and several high polyunsaturated fatty acid lipids (PUFAs). The half‐maximal inhibitory concentration (IC₅₀) values of chenodeoxycholate and taurocholate were 13.55 and 19.51 μM, respectively, consistent with a physiological significance. In silico analysis suggested that bile acid inhibition of CES1 involved both binding to the active and superficial sites of the enzyme. We initiated identification of metabolites predicting PKs of drugs metabolized by CES1 and suggest lipids to regulate or be regulated by this enzyme.

On the road to unraveling the molecular functions of ether lipids

Ether lipids are glycerolipids further classified into alkyl-ether and alkenyl-ether (also termed plasmalogens) lipids. The two ether lipid subclasses share the first steps of their synthesis. However, alkyl-ether and alkenyl-ether lipids differ in their structure and physico-chemical properties (featuring different head groups) and, thus, probably in their functions. Ether lipids have intermittent distribution across the evolutionary tree and defects in their synthesis have been shown to perturb cellular homeostasis and lead to disease in humans. Here, we review their structure, their interactions with other lipids, and their potential roles in cellular functions, such as membrane homeostasis and membrane trafficking. Moreover, we discuss still unclear aspects of these lipids such as their subcellular distribution, and the need to unravel their molecular functions as well as how novel tools to study lipid biology will help clarify these aspects.

Perturbed Lipidomic Profiles in Rats With Chronic Cerebral Ischemia Are Regulated by Xiao-Xu-Ming Decoction

Chronic cerebral ischemia (CCI) is a serious human health condition with lacking therapeutic agents. Moreover, its mechanism of action remains elusive, and thus novel treatment options are required. Lipid metabolism disorder are closely related to CCI. In this study, a CCI-rats model was established by the permanent occlusion of rat bilateral common carotid arteries, and then the rats were treated with a Xiao-Xu-Ming decoction (XXMD). Lipidomic profiling was conducted in both plasma and brain o determine the effects of the injury and therapy on lipid metabolism. Sphingolipid (particularly long acyl chain and total ceramides), glyceryl phosphatide, and glyceride profiles significantly changed in the brain after model induction and again after dosing. A total of 35 potential biomarkers were found in the brain and four were found in the plasma, representing both CCI injury and XXMD action. Correlations between endogenous lipids and exogenous XXMD compounds were analyzed using linear regression. Two exogenous compounds (cimifugin and 5-O-methylvisamminol) in the brain and 17 exogenous compounds in the plasma, which may represent the active constituents in XXMD, were significantly associated with lipid metabolism. This study provides a new perspective on the potential mechanism of CCI and its treatment with XXMD, as well as on discovering effective components in traditional Chinese medicines.

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.

A Metabolomics Approach to Pharmacotherapy Personalization

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

Sphingolipid Metabolic Pathway Impacts Thiazide Diuretics Blood Pressure Response: Insights From Genomics, Metabolomics, and Lipidomics

BACKGROUND

Although hydrochlorothiazide (HCTZ) is a well-established first-line antihypertensive in the United States, <50% of HCTZ treated patients achieve blood pressure (BP) control. Thus, identifying biomarkers that could predict the BP response to HCTZ is critically important. In this study, we utilized metabolomics, genomics, and lipidomics to identify novel pathways and biomarkers associated with HCTZ BP response.

METHODS AND RESULTS

First, we conducted a pathway analysis for 13 metabolites we recently identified to be significantly associated with HCTZ BP response. From this analysis, we found the sphingolipid metabolic pathway as the most significant pathway (P=5.8E-05). Testing 78 variants, within 14 genes involved in the sphingolipid metabolic canonical pathway, with the BP response to HCTZ identified variant rs6078905, within the SPTLC3 gene, as a novel biomarker significantly associated with the BP response to HCTZ in whites (n=228). We found that rs6078905 C-allele carriers had a better BP response to HCTZ versus noncarriers (∆SBP/∆DBP: -11.4/-6.9 versus -6.8/-3.5 mm Hg; ∆SBP P=6.7E-04; ∆DBP P=4.8E-04). Additionally, in blacks (n=148), we found genetic signals in the SPTLC3 genomic region significantly associated with the BP response to HCTZ (P<0.05). Last, we observed that rs6078905 significantly affects the baseline level of 4 sphingomyelins (N24:2, N24:3, N16:1, and N22:1; false discovery rate <0.05), from which N24:2 sphingomyelin has a significant correlation with both HCTZ DBP-response (r=-0.42; P=7E-03) and SBP-response (r=-0.36; P=2E-02).

CONCLUSIONS

This study provides insight into potential pharmacometabolomic and genetic mechanisms underlying HCTZ BP response and suggests that SPTLC3 is a potential determinant of the BP response to HCTZ.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00246519.

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.

Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review

Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.

Gut Microbiota Modulation Attenuated the Hypolipidemic Effect of Simvastatin in High-Fat/Cholesterol-Diet Fed Mice

The hypolipidemic effect of simvastatin varies greatly among patients. In the current study, we investigated the gut microbial-involved mechanisms underlying the different responses to simvastatin. Male C57BL/6J mice were divided into control (Con), high-fat/cholesterol diet (HFD), antibiotic (AB), simvastatin (SV) and antibiotic_simvastatin (AB_SV) groups, respectively. At the end of the experiment, serum samples were collected for lipids and metabolomic analysis, and liver tissues for histology, gene and protein expression analysis. The results showed that antibiotic treatment not only altered the composition of gut microbiota, but attenuated the hypolipidemic effect of SV. A total of 16 differential metabolites between SV and HFD groups were identified with metabolomics, while most of them showed no statistical differences between AB_SV and HFD groups, and similar changes were also observed in bile acids profile. The expressions of several genes and proteins involved in regulating bile acids synthesis were significantly reversed by SV, but not AB_SV in HFD fed mice. In summary, our current study indicated that the hypolipidemic effect of SV was correlated with the composition of the gut microbiota, and the attenuated hypolipidemic effect of SV by gut microbiota modulation was associated with a suppression of bile acids synthesis from cholesterol.

Pharmacometabolomic signature links simvastatin therapy and insulin resistance

Introduction

Statins, widely prescribed drugs for treatment of cardiovascular disease, inhibit the biosynthesis of low density lipoprotein cholesterol (LDL-C). Despite providing major benefits, sub populations of patients experience adverse effects, including muscle myopathy and development of type II diabetes mellitus (T2DM) that may result in premature discontinuation of treatment. There are no reliable biomarkers for predicting clinical side effects in vulnerable individuals. Pharmacometabolomics provides powerful tools for identifying global biochemical changes induced by statin treatment, providing insights about drug mechanism of action, development of side effects and basis of variation of response.

Objective

To determine whether statin-induced changes in intermediary metabolism correlated with statin-induced hyperglycemia and insulin resistance; to identify pre-drug treatment metabolites predictive of post-drug treatment increased diabetic risk.

Methods

Drug-naïve patients were treated with 40 mg/day simvastatin for 6 weeks in the Cholesterol and Pharmacogenetics (CAP) study; metabolomics by gas chromatography-time-of-flight mass-spectrometry (GC-TOF-MS) was performed on plasma pre and post treatment on 148 of the 944 participants.

Results

Six weeks of simvastatin treatment resulted in 6.9% of patients developing hyperglycemia and 25% developing changes consistent with development of pre-diabetes. Altered beta cell function was observed in 53% of patients following simvastatin therapy and insulin resistance was observed in 54% of patients. We identified initial signature of simvastatin-induced insulin resistance, including ethanolamine, hydroxylamine, hydroxycarbamate and isoleucine which, upon further replication and expansion, could be predictive biomarkers of individual susceptibility to simvastatin-induced new onset pre-type II diabetes mellitus. No patients were clinically diagnosed with T2DM.

Conclusion

Within this short 6 weeks study, some patients became hyperglycemic and/or insulin resistant. Diabetic markers were associated with decarboxylated small aminated metabolites as well as a branched chain amino acid directly linked to glucose metabolism and fatty acid biosynthesis. Pharmacometabolomics provides powerful tools for precision medicine by predicting development of drug adverse effects in sub populations of patients. Metabolic profiling prior to start of drug therapy may empower physicians with critical information when prescribing medication and determining prognosis.

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.

Pharmacometabonomics – the novel way to personalized drug therapy

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

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

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

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

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

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 to identify biomarkers and as a predictive tool in inflammatory diseases

There is an overwhelming need for a simple, reliable tool that aids clinicians in diagnosing, assessing disease activity and treating rheumatic conditions. Identification of biomarkers in partially understood inflammatory disorders has long been sought after as the Holy Grail of Rheumatology. Given the complex nature of inflammatory conditions, it has been difficult to earmark the potential biomarkers. Metabolomics, however, is promising in providing new insights into inflammatory conditions and also identifying such biomarkers. Metabolomic studies have generally revealed increased energy requirements for by-products of a hypoxic environment, leading to a characteristic metabolic fingerprint. Here, we discuss the significance of such studies and their potential as a biomarker.

Oxylipid Profile of Low-Dose Aspirin Exposure: A Pharmacometabolomics Study

Background

While aspirin is a well‐established and generally effective anti‐platelet agent, considerable inter‐individual variation in drug response exists, for which mechanisms are not completely understood. Metabolomics allows for extensive measurement of small molecules in biological samples, enabling detailed mapping of pathways involved in drug response.

Methods and Results

We used a mass‐spectrometry‐based metabolomics platform to investigate the changes in the serum oxylipid metabolome induced by an aspirin intervention (14 days, 81 mg/day) in healthy subjects (n=156). We observed a global decrease in serum oxylipids in response to aspirin (25 metabolites decreased out of 30 measured) regardless of sex. This decrease was concomitant with a significant decrease in serum linoleic acid levels (−19%, P=1.3×10⁻⁵), one of the main precursors for oxylipid synthesis. Interestingly, several linoleic acid‐derived oxylipids were not significantly associated with arachidonic‐induced ex vivo platelet aggregation, a widely accepted marker of aspirin response, but were significantly correlated with platelet reactivity in response to collagen.

Conclusions

Together, these results suggest that linoleic acid‐derived oxylipids may contribute to the non‐COX1 mediated variability in response to aspirin. Pharmacometabolomics allowed for more comprehensive interrogation of mechanisms of action of low dose aspirin and of variation in aspirin response.

Lipidomics to Assess Omega 3 Bioactivity

How can we resolve the conflict between the strong epidemiological evidence pointing to the usefulness of fish—and, thus, omega 3—consumption with the debacle of supplementation trials? One potential explanation is that the null results obtained thus far are the consequences of ill-contrived investigations that do not allow us to conclude on the effects (or lack thereof) of omega 3 fatty acid supplementation. One potential solution is through the use of lipidomics, which should prove very useful to screen suitable patients and to correlate plasma (or red blood cells, or whole blood, or phospholipid) fatty acid profile with outcomes. This has never been done in omega 3 trials. The wise use of lipidomics should be essential part of future omega 3 trials and would help in untangling this current riddle.

Lipidomics: quest for molecular lipid biomarkers in cardiovascular disease

Lipidomics is the comprehensive analysis of molecular lipid species, including their quantitation and metabolic pathways. The huge diversity of native lipids and their modifications make lipidomic analyses challenging. The method of choice for sensitive detection and quantitation of molecular lipid species is mass spectrometry, either by direct infusion (shotgun lipidomics) or coupled with liquid chromatography. Although shotgun lipidomics allows for high-throughput analysis, low-abundant lipid species are not detected. Previous separation of lipid species by liquid chromatography increases ionization efficiency and is better suited for quantifying low abundant and isomeric lipid species. In this review, we will discuss the potential of lipidomics for cardiovascular research. To date, cardiovascular research predominantly focuses on the role of lipid classes rather than molecular entities. An in-depth knowledge about the molecular lipid species that contribute to the pathophysiology of cardiovascular diseases may provide better biomarkers and novel therapeutic targets for cardiovascular disease.

Lipidomics in vascular health: current perspectives

Identifying the mechanisms that convert a healthy vascular wall to an atherosclerotic wall is of major importance since the consequences may lead to a shortened lifespan. Classical risk factors (age, smoking, obesity, diabetes mellitus, hypertension, and dyslipidemia) may result in the progression of atherosclerotic lesions by processes including inflammation and lipid accumulation. Thus, the evaluation of blood lipids and the full lipid complement produced by cells, organisms, or tissues (lipidomics) is an issue of importance. In this review, we shall describe the recent progress in vascular health research using lipidomic advances. We will begin with an overview of vascular wall biology and lipids, followed by a short analysis of lipidomics. Finally, we shall focus on the clinical implications of lipidomics and studies that have examined lipidomic approaches and vascular health.

Metabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine

The scaling up of data in clinical pharmacology and the merger of systems biology and pharmacology has led to the emergence of a new discipline of Quantitative and Systems Pharmacology (QSP). This new research direction might significantly advance the discovery, development, and clinical use of therapeutic drugs. Research communities from computational biology, systems biology, and biological engineering--working collaboratively with pharmacologists, geneticists, biochemists, and analytical chemists--are creating and modeling large data on drug effects that is transforming our understanding of how these drugs work at a network level. In this review, we highlight developments in a new and rapidly growing field--pharmacometabolomics--in which large biochemical data-capturing effects of genome, gut microbiome, and environment exposures is revealing information about metabotypes and treatment outcomes, and creating metabolic signatures as new potential biomarkers. Pharmacometabolomics informs and complements pharmacogenomics and together they provide building blocks for QSP.

Lipidomics in drug development

Numerous human pathologies, including common conditions such as obesity, diabetes, cardiovascular disease, cancer, inflammatory disease and neurodegeneration, involve changes in lipid metabolism. Likewise, a growing number of drugs are being developed that directly or indirectly affect lipid metabolic pathways. Instead of classical and cumbrous radiochemical analyses, lipid profiling by mass spectrometry (MS)-based lipidomics holds great potential as companion diagnostic in several steps along the drug development process. In this review we describe some typical lipidomics set-ups and illustrate how these technologies can be implemented in target discovery, compound screening, in vitro and in vivo preclinical testing, toxicity testing of drugs, and prediction and monitoring of response.

Lipidome and transcriptome profiling of pneumolysin intoxication identifies networks involved in statin-conferred protection of airway epithelial cells

Pneumonia remains one of the leading causes of death in both adults and children worldwide. Despite the adoption of a wide variety of therapeutics, the mortality from community-acquired pneumonia has remained relatively constant. Although viral and fungal acute airway infections can result in pneumonia, bacteria are the most common cause of community-acquired pneumonia, with Streptococcus pneumoniae isolated in nearly 50% of cases. Pneumolysin is a cholesterol-dependent cytolysin or pore-forming toxin produced by Streptococcus pneumonia and has been shown to play a critical role in bacterial pathogenesis. Airway epithelium is the initial site of many bacterial contacts and its barrier and mucosal immunity functions are central to infectious lung diseases. In our studies, we have shown that the prior exposure to statins confers significant resistance of airway epithelial cells to the cytotoxicity of pneumolysin. We decided to take this study one step further, assessing changes in both the transcriptome and lipidome of human airway epithelial cells exposed to toxin, statin or both. Our current work provides the first global view in human airway epithelial cells of both the transcriptome and the lipid interactions that result in cellular protection from pneumolysin.