Purine pathway implicated in mechanism of resistance to aspirin therapy: pharmacometabolomics-informed pharmacogenomics

Genetic factors related to aspirin resistance using the Multiplate® device in Hong Kong Chinese patients with stable coronary heart disease

Objective

Associations between single nucleotide polymorphisms (SNPs) and aspirin resistance (AR) have been studied with variable results. The associations of genetic variants with AR may be helpful to explain why some individuals demonstrate aspirin insensitivity with this anti-platelet therapy. The purpose of this research was to investigate the effect of different genotypes in candidate genes on aspirin response in patients taking long-term aspirin therapy by measuring the serum thromboxane B2 (TXB2) and platelet function using the Multiplate® analyser.

Methods

A total of 266 patients with stable coronary heart disease (CHD) taking low-dose aspirin for long periods of time and without any other anti-platelet drugs medications were enrolled into the study. They were required to take 80 mg of aspirin every morning for a week including the day before blood tests. Blood samples were collected 24 h after the last dose. The 80 mg dose of aspirin was taken orally and blood samples were collected again 1 h later. The serum TXB2 levels were measured in samples at 24 h post-dose and 1 h post-dose using the EIA kit and platelet activity was determined using the Multiplate® Impedance Platelet Aggregometry (ASPI) assay. Genotyping assays were performed by the TaqMan SNP genotyping technique.

Results

Of the 266 patients, only 251 patients were enrolled in the present study. The PTGS1/COX1-1676 A > G (rs1330344) and the PTGS2/COX2-765 G > C (rs20417) SNPs showed significant associations with the ASPI measurements in samples taken at 24 h post-dose, but not with the values at 1 h post-dose or with the TXB2 levels (P < 0.05).

Conclusions

Our results suggest that polymorphisms in the PTGS1/COX1 and the PTGS2/COX2 genes may be associated with reduced anti-aggregatory effects and increased the risk of AR, but future larger-scale cohort studies are necessary for further validation.

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.

Transcriptome- and metabolome-based candidate mechanism of BCR-ABL-independent resistance to olverembatinib in Philadelphia chromosome-positive acute lymphoblastic leukemia

Olverembatinib represents the third-generation breakpoint cluster region protein-Abelson-murine leukemia 1 (BCR-ABL1) tyrosine kinase inhibitor with oral bioavailability, which can be used to overcome the T315I mutation in Philadelphia chromosome-positive (Ph +) leukemia. BCR-ABL-independent resistance to olverembatinib has been reported among patients in various clinical cases. However, the mechanism of olverembatinib resistance has rarely been reported. This study has illustrated bone marrow cell transcriptome and metabolome profiles among Ph + acute lymphoblastic leukemias (ALL) cases pre- and post-olverembatinib resistance. The transcriptome studies demonstrated that PI3K/AKT, purine metabolism, and other signaling pathways could play a vital role in olverembatinib resistance. As suggested by metabolomics, olverembatinib resistance in Ph + ALL was associated with purine metabolism alterations. Subsequently, high-performance liquid chromatography along with real-time quantitative PCR was utilized to measure purine metabolism-related mRNA levels and metabolism expression levels between olverembatinib resistance and sensitive cell lines. Our results elucidate the mechanism of olverembatinib resistance in Ph + ALL at transcriptome and metabolome levels, which facilitate a better understanding of olverembatinib resistance and hence may prove crucial in identifying novel drugs to tackle this conundrum.

Distinct transcriptomic and metabolomic profiles characterize NSAID-induced urticaria/angioedema patients undergoing aspirin desensitization

BACKGROUND

There is limited data on the mechanisms of aspirin desensitization in patients with nonsteroidal anti-inflammatory drug (NSAID)-induced urticaria/angioedema (NIUA).

OBJECTIVES

We sought to characterize the transcriptomic and metabolomic profiles of patients with NIUA undergoing aspirin desensitization.

METHODS

PBMCs and plasma were separated from the blood of patients with NIUA undergoing aspirin desensitization for coronary artery disease and NSAID-tolerant controls. RNA was isolated from PBMCs and subjected to messenger RNA (mRNA)- and long noncoding RNA (lncRNA)-sequencing. Plasma samples were analyzed using LC-MS/MS for metabolite shifts using a semitargeted metabolomics panel.

RESULTS

Eleven patients with NIUA and 10 healthy controls were recruited. The mRNA gene profiles of predesensitization versus postdesensitization and healthy control versus postdesensitization did not differ significantly. However, we identified 739 mRNAs and 888 lncRNAs as differentially expressed from preaspirin desensitization patients and controls. A 12-mRNA gene signature was trained using a machine learning algorithm to distinguish between controls, postdose, and predose samples. Ingenuity Pathway Analysis identified 5 canonical pathways that were significantly enriched in preaspirin desensitization samples. IL-22 was the most upregulated pathway. To investigate the potential regulatory roles of the differentially expressed lncRNA on the mRNAs, 9 lncRNAs and 12 mRNAs showed significantly correlated expression patterns in the IL-22 pathway. To validate the transcriptomics data, IL-22 was measured in the plasma samples of the subjects using ELISA. IL-22 was significantly higher in preaspirin desensitization patients compared with controls. In parallel, metabolomic analysis revealed stark differences in plasma profiles of preaspirin desensitization patients and healthy controls. In particular, 2-hydroxybenzoic acid (salicylic acid) was significantly lower in preaspirin desensitization patients compared with healthy controls.

CONCLUSIONS

This is the first study to combine both transcriptomic and metabolomic approaches in patients with NIUA, which contributes to a deeper understanding about the pathogenesis of NIUA and may potentially pave the way toward a molecular diagnosis of NSAID hypersensitivity.

Challenges and opportunities associated with rare-variant pharmacogenomics

Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.

Plasma Metabolomics Analysis of Aspirin Treatment and Risk of Colorectal Adenomas

Despite substantial observational and experimental evidence that aspirin use can provide protection against the development of colorectal neoplasia, our understanding of the molecular mechanisms involved is inadequate and limits our ability to use this drug effectively and safely for chemoprevention. We employed an untargeted plasma metabolomics approach using liquid chromatography with high-resolution mass spectroscopy to explore novel metabolites that may contribute to the chemopreventive effects of aspirin. Associations between levels of metabolic features in plasma and aspirin treatment were investigated among 523 participants in a randomized placebo-controlled clinical trial of two doses of aspirin (81 or 325 mg/day) and were linked to risk of colorectal adenoma occurrence over 3 years of follow-up. Metabolic pathways that were altered with aspirin treatment included linoleate and glycerophospholipid metabolism for the 81-mg dose and carnitine shuttle for both doses. Metabolites whose levels increased with 81 mg/day aspirin treatment and were also associated with decreased risk of adenomas during follow-up included certain forms of lysophosphatidylcholine and lysophosphatidylethanolamine as well as trihydroxyoctadecenoic acid, which is a derivative of linoleic acid and is upstream of cyclooxygenase inhibition by aspirin in the linoleate and arachidonic acid metabolism pathways. In conclusion, our findings regarding lysophospholipids and metabolites in the linoleate metabolism pathway may provide novel insights into the chemopreventive effects of aspirin in the colorectum, although they should be considered hypothesis-generating at this time.

PREVENTION RELEVANCE

This research used metabolomics, an innovative discovery-based approach, to identify molecular changes in human blood that may help to explain how aspirin use reduces the risk of colorectal neoplasia in some individuals. Ultimately, this work could have important implications for optimizing aspirin use in the prevention of colorectal cancer.

Predicting Range of Initial Warfarin Dose Based on Pharmacometabolomic and Genetic Inputs

Anticoagulation response to warfarin during the initial stage of therapy varies among individuals. In this study, we aimed to combine pharmacometabolomic and pharmacogenetic data to predict interindividual variation in warfarin response, and, on this basis, suggest an initial daily dose range. The baseline metabolic profiles, genotypes, and clinical information of 160 patients with heart valve disease served as the variables of the function of the last international normalized ratio measured before a patient's discharge (INR_day7 ) to screen for potential biomarkers. The partial least-squares model showed that two baseline metabolites (uridine and guanosine), one single-nucleotide variation (VKORC1), and four clinical parameters (weight, creatinine level, amiodarone usage, and initial daily dose) had good predictive power for INR_day7 (R²  = 0.753 for the training set, 0.643 for the test set). With these biomarkers, a machine learning algorithm (two-dimensional linear discriminant analysis-multinomial logit model) was used to predict the subgroups with extremely warfarin-sensitive or less warfarin-sensitive patients with a prediction accuracy of 91% for the training set and 90% for the test set, indicating that individual responses to warfarin could be effectively predicted. Based on this model, we have successfully designed an algorithm,"IniWarD," for predicting an effective dose range in the initial 7-day warfarin therapy. The results indicate that the daily dose range suggested by the IniWarD system is more appropriate than that of the conventional genotype-based method, and the risk of bleeding or thrombus due to warfarin could thus be avoided.

UPLC-MS-Based Serum Metabolic Profiling Reveals Potential Biomarkers for Predicting Propofol Responsiveness in Females

Although previous studies have shown that certain factors interfere with the sensitivity of propofol, the mechanisms for interindividual variability in response to propofol remain unclear. This study aimed to screen the metabolites to predict patients' sensitivity to propofol and to identify metabolic pathways to explore possible mechanisms associated with propofol resistance. Sera from 40 female patients undergoing elective hysteroscopic surgery in a prospective cohort propofol study were obtained before the administration of propofol. The patients' responsiveness to propofol was differentiated based on propofol effect-site concentration. Serum samples from two sets, a discovery set (n = 24) and an independent validation set (n = 16), were analyzed using ultraperformance liquid chromatography coupled with mass spectrometry based untargeted metabolomics. In the discovery set, 494 differential metabolites were screened out, and then 391 potential candidate biomarkers with the area under receiver operating characteristic curve >0.80 were selected. Pathway analysis showed that the pathway of glycerophospholipid metabolism was the most influential pathway. In the independent validation set, six potential biomarkers enabled the discrimination of poor responders from good and intermediate responders, which might be applied to predict propofol sensitivity. The mass spectrometry data are available via MetaboLights (http://www.ebi.ac.uk/metabolights/login) with the identifier MTBLS2311.

Effective Aspirin Treatment of Women at Risk for Preeclampsia Delays the Metabolic Clock of Gestation

Preeclampsia, characterized by the onset of hypertension with significant proteinuria after 20 weeks' gestation, is one of the leading causes of maternal and perinatal morbidity and mortality. Prophylactic low-dose aspirin treatment reduces the rate of preterm preeclampsia in high-risk women, but a significant proportion still develops preeclampsia. The mechanism of the prophylactic response is unknown. Here, the untargeted metabolomics analysis of 144 plasma samples from high-risk pregnant women before (11-13 weeks) and after (20-23 weeks) aspirin/placebo treatment elucidated metabolic effects of aspirin and metabolic differences potentially associated with the variation of the treatment response. We demonstrated that aspirin treatment resulted in a strong drug-associated metabolomics signature and that the preeclamptic or nonpreeclamptic outcome in response to treatment was significantly associated with the level of internal aspirin exposure ascertained from metabolomics data (t test, P=0.0083). Comparing women with and without preeclampsia after aspirin treatment, differences in 73 metabolites were detected, some of which involve pathways whose regulation is of importance in pregnancy and placental functions, such as glycerophospholipids metabolism, polyunsaturated fatty acid metabolism, and steroid hormone biosynthesis. To further examine the hypothesis that aspirin delays gestational age advancement and thus the onset of preeclampsia, we constructed a metabolic clock on pretreatment and placebo-treated samples that estimated gestational age with high accuracy and found that aspirin significantly decelerated metabolic gestational age by 1.27 weeks (95% CI, 0.66-1.88 weeks), and partially reversed one-fourth of the metabolites changed over gestational age advancement, suggesting that aspirin treatment slowed down the metabolic clock of gestation.

Cellular Metabolomics Profiles Associated With Drug Chemosensitivity in AML

Background

Acute myeloid leukemia (AML) is a hematological malignancy with a dismal prognosis. For over four decades, AML has primarily been treated by cytarabine combined with an anthracycline. Although a significant proportion of patients achieve remission with this regimen, roughly 40% of children and 70% of adults relapse. Over 90% of patients with resistant or relapsed AML die within 3 years. Thus, relapsed and resistant disease following treatment with standard therapy are the most common clinical failures that occur in treating this disease. In this study, we evaluated the relationship between AML cell line global metabolomes and variation in chemosensitivity.

Methods

We performed global metabolomics on seven AML cell lines with varying chemosensitivity to cytarabine and the anthracycline doxorubicin (MV4.11, KG-1, HL-60, Kasumi-1, AML-193, ME1, THP-1) using ultra-high performance liquid chromatography - mass spectrometry (UHPLC-MS). Univariate and multivariate analyses were performed on the metabolite peak intensity values from UHPLC-MS using MetaboAnalyst to identify cellular metabolites associated with drug chemosensitivity.

Results

A total of 1,624 metabolic features were detected across the leukemic cell lines. Of these, 187 were annotated to known metabolites. With respect to doxorubicin, we observed significantly greater abundance of a carboxylic acid (1-aminocyclopropane-1-carboxylate) and several amino acids in resistant cell lines. Pathway analysis found enrichment of several amino acid biosynthesis and metabolic pathways. For cytarabine resistance, nine annotated metabolites were significantly different in resistance vs. sensitive cell lines, including D-raffinose, guanosine, inosine, guanine, aldopentose, two xenobiotics (allopurinol and 4-hydroxy-L-phenylglycine) and glucosamine/mannosamine. Pathway analysis associated these metabolites with the purine metabolic pathway.

Conclusion

Overall, our results demonstrate that metabolomics differences contribute toward drug resistance. In addition, it could potentially identify predictive biomarkers for chemosensitivity to various anti-leukemic drugs. Our results provide opportunity to further explore these metabolites in patient samples for association with clinical response.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Metabolomics applications in coronary artery disease personalized medicine

Coronary artery disease (CAD), the most common cardiovascular disease (CVD), contributes to significant mortality worldwide. CAD is a multifactorial disease wherein various factors contribute to its pathogenesis often complicating management. Biomarker based personalized medicine may provide a more effective way to individualize therapy in multifactorial diseases in general and CAD specifically. Systems' biology "Omics" biomarkers have been investigated for this purpose. These biomarkers provide a more comprehensive understanding on pathophysiology of the disease process and can help in identifying new therapeutic targets and tailoring therapy to achieve optimum outcome. Metabolomics biomarkers usually reflect genetic and non-genetic factors involved in the phenotype. Metabolomics analysis may provide better understanding of the disease pathogenesis and drug response variation. This will help in guiding therapy, particularly for multifactorial diseases such as CAD. In this chapter, advances in metabolomics analysis and its role in personalized medicine will be reviewed with comprehensive focus on CAD. Assessment of risk, diagnosis, complications, drug response and nutritional therapy will be discussed. Together, this chapter will review the current application of metabolomics in CAD management and highlight areas that warrant further investigation.

Cytokine and lipid metabolome effects of low-dose acetylsalicylic acid in critically ill patients with systemic inflammation: a pilot, feasibility, multicentre, randomised, placebo-controlled trial

OBJECTIVE

The systemic inflammatory response syndrome (SIRS) is a dysregulated response that contributes to critical illness. Adjunctive acetylsalicylic acid (ASA) treatment may offer beneficial effects by increasing the synthesis of specialised proresolving mediators (a subset of polyunsaturated fatty acid-derived lipid mediators).

DESIGN

Pilot, feasibility, multicentre, double-blind, randomised, placebo-controlled trial.

SETTING

Four interdisciplinary intensive care units (ICUs) in Australia.

PARTICIPANTS

Critically ill patients with SIRS.

INTERVENTIONS

ASA 100 mg 12-hourly or placebo, administered within 24 hours of ICU admission and continued until ICU day 7, discharge or death, whichever came first.

MAIN OUTCOME MEASURES

Interleukin-6 (IL-6) serum concentration at 48 hours after randomisation and, in a prespecified subgroup of patients, serum lipid mediator concentrations measured by mass spectrometry.

RESULTS

The trial was discontinued in December 2017 due to slow recruitment and after the inclusion of 48 patients. Compared with placebo, ASA did not decrease IL-6 serum concentration at 48 hours. In the 32 patients with analysis of lipid mediators, low-dose ASA increased the concentration of 15-hydroxyeicosatetraenoic acid, a proresolving precursor of lipoxin A4, and reduced the concentration of the proinflammatory cytochrome P-dependent mediators 17-HETE (hydroxyeicosatetraenoic acid), 18-HETE and 20-HETE. In the eicosapentaenoic acid pathway, ASA significantly increased the concentration of the anti-inflammatory mediators 17,18-DiHETE (dihydroxyeicosatetraenoic acid) and 14,15-DiHETE.

CONCLUSIONS

In ICU patients with SIRS, low-dose ASA did not significantly alter serum IL-6 concentrations, but it did affect plasma concentrations of certain lipid mediators. The ability to measure lipid mediators in clinical samples and to monitor the effect of ASA on their levels unlocks a potential area of biological investigation in critical care.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ACTRN 12614001165673).

FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM

Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.

Cytokine and lipid metabolome effects of low-dose acetylsalicylic acid in critically ill patients with systemic inflammation: a pilot, feasibility, multicentre, randomised, placebo-controlled trial

OBJECTIVE

The systemic inflammatory response syndrome (SIRS) is a dysregulated response that contributes to critical illness. Adjunctive acetylsalicylic acid (ASA) treatment may offer beneficial effects by increasing the synthesis of specialised proresolving mediators (a subset of polyunsaturated fatty acid-derived lipid mediators).

DESIGN

Pilot, feasibility, multicentre, double-blind, randomised, placebo-controlled trial.

SETTING

Four interdisciplinary intensive care units (ICUs) in Australia.

PARTICIPANTS

Critically ill patients with SIRS.

INTERVENTIONS

ASA 100 mg 12-hourly or placebo, administered within 24 hours of ICU admission and continued until ICU day 7, discharge or death, whichever came first.

MAIN OUTCOME MEASURES

Interleukin-6 (IL-6) serum concentration at 48 hours after randomisation and, in a prespecified subgroup of patients, serum lipid mediator concentrations measured by mass spectrometry.

RESULTS

The trial was discontinued in December 2017 due to slow recruitment and after the inclusion of 48 patients. Compared with placebo, ASA did not decrease IL-6 serum concentration at 48 hours. In the 32 patients with analysis of lipid mediators, low-dose ASA increased the concentration of 15-hydroxyeicosatetraenoic acid, a proresolving precursor of lipoxin A4, and reduced the concentration of the proinflammatory cytochrome P-dependent mediators 17-HETE (hydroxyeicosatetraenoic acid), 18-HETE and 20-HETE. In the eicosapentaenoic acid pathway, ASA significantly increased the concentration of the anti-inflammatory mediators 17,18-DiHETE (dihydroxyeicosatetraenoic acid) and 14,15-DiHETE.

CONCLUSIONS

In ICU patients with SIRS, low-dose ASA did not significantly alter serum IL-6 concentrations, but it did affect plasma concentrations of certain lipid mediators. The ability to measure lipid mediators in clinical samples and to monitor the effect of ASA on their levels unlocks a potential area of biological investigation in critical care.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ACTRN 12614001165673).

Metabolomics Analysis of Aspirin's Effects in Human Colon Tissue and Associations with Adenoma Risk

Although substantial evidence supports aspirin's efficacy in colorectal cancer chemoprevention, key molecular mechanisms are uncertain. An untargeted metabolomics approach with high-resolution mass spectrometry was used to elucidate metabolic effects of aspirin treatment in human colon tissue. We measured 10,269 metabolic features in normal mucosal biopsies collected at colonoscopy after approximately 3 years of randomized treatment with placebo, 81 or 325 mg/day aspirin from 325 participants in the Aspirin/Folate Polyp Prevention Study. Linear regression was used to identify aspirin-associated metabolic features and network analysis was used to identify pathways and predict metabolite identities. Poisson regression was used to examine metabolic features associations with colorectal adenoma risk. We detected 471 aspirin-associated metabolic features. Aside from the carnitine shuttle, aspirin-associated metabolic pathways were largely distinct for 81 mg aspirin (e.g., pyrimidine metabolism) and 325 mg (e.g., arachidonic acid metabolism). Among aspirin-associated metabolic features, we discovered three that were associated with adenoma risk and could contribute to the chemopreventive effect of aspirin treatment, and which have also previously been associated with colorectal cancer: creatinine, glycerol 3-phosphate, and linoleate. The last two of these are in the glycerophospholipid metabolism pathway, which was associated with 81 mg aspirin treatment and provides precursors for the synthesis of eicosanoids from arachidonic acid upstream of cyclooxygenase inhibition by aspirin. Conversely, carnitine shuttle metabolites were increased with aspirin treatment and associated with increased adenoma risk. Thus, our untargeted metabolomics approach has identified novel metabolites and pathways that may underlie the effects of aspirin during early colorectal carcinogenesis.

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.

Progress in understanding hypersensitivity reactions to nonsteroidal anti-inflammatory drugs

Nonsteroidal anti-inflammatory drugs (NSAIDs), the medications most commonly used for treating pain and inflammation, are the main triggers of drug hypersensitivity reactions. The latest classification of NSAIDs hypersensitivity by the European Academy of Allergy and Clinical Immunology (EAACI) differentiates between cross-hypersensitivity reactions (CRs), associated with COX-1 inhibition, and selective reactions, associated with immunological mechanisms. Three phenotypes fill into the first group: NSAIDs-exacerbated respiratory disease, NSAIDs-exacerbated cutaneous disease and NSAIDs-induced urticaria/angioedema. Two phenotypes fill into the second one: single-NSAID-induced urticaria/angioedema/anaphylaxis and single-NSAID-induced delayed reactions. Diagnosis of NSAIDs hypersensitivity is hampered by different factors, including the lack of validated in vitro biomarkers and the uselessness of skin tests. The advances achieved over recent years recommend a re-evaluation of the EAACI classification, as it does not consider other phenotypes such as blended reactions (coexistence of cutaneous and respiratory symptoms) or food-dependent NSAID-induced anaphylaxis. In addition, it does not regard the natural evolution of phenotypes and their potential interconversion, the development of tolerance over time or the role of atopy. Here, we address these topics. A state of the art on the underlying mechanisms and on the approaches for biomarkers discovery is also provided, including genetic studies and available information on transcriptomics and metabolomics.

Metabolomic signature of exposure and response to citalopram/escitalopram in depressed outpatients

Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD17) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway, we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD17 scores. In the tyrosine pathway, changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway, significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine-homogentisic acid and methionine-tyrosine interactions associated with HRSD17. Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.

Personalized medicine into health national services: barriers and potentialities

Research and innovation in personalized medicine (PM) are extensive and expanding, with several pharmacogenetic/pharmacogenomic (PGx) testing options currently available for a wide range of health problems. However, PGx-guided therapy faces many barriers to full integration into clinical practice and acceptance by practitioner/patient: utilization and uptake by payers in real-world practice are being discussed, and the criteria to guide clinicians and policy makers in PGx test selection are not fully incorporated. This review focuses on the advances of pharmacogenomics to individualize treatments, the relationship between pharmacogenetics and pharmacometabolomics, the new paradigm of the Big Data, the needs and barriers facing PGx clinical application and the situation of PGx testing in health national services. It is based on lectures presented by speakers of the European Society of Pharmacogenomics and Personalised Therapy (ESPT) Fourth Conference, held in Catania, October 4th, 2017.

Understanding the effect of repeated administration of meloxicam on feline renal cortex and medulla: A lipidomics and metabolomics approach

Repeated administration of meloxicam can cause kidney damage in cats by mechanisms that remain unclear. Metabolomics and lipidomics are powerful, noninvasive approaches used to investigate tissue response to drug exposure. Thus, the objective of this study was to assess the effects of meloxicam on the feline kidney using untargeted metabolomics and lipidomics approaches. Female young-adult purpose-breed cats were allocated into the control (n = 4) and meloxicam (n = 4) groups. Cats in the control and meloxicam groups were treated daily with saline and meloxicam at 0.3 mg/kg subcutaneously for 17 days, respectively. Renal cortices and medullas were collected at the end of the treatment period. Random forest and metabolic pathway analyses were used to identify metabolites that discriminate meloxicam-treated from saline-treated cats and to identify disturbed metabolic pathways in renal tissue. Our results revealed that the repeated administration of meloxicam to cats altered the kidney metabolome and lipidome and suggest that at least 40 metabolic pathways were altered in the renal cortex and medulla. These metabolic pathways included lipid, amino acid, carbohydrate, nucleotide and energy metabolisms, and metabolism of cofactors and vitamins. This is the first study using a pharmacometabonomics approach for studying the molecular effects of meloxicam on feline kidneys.

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.

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.

Augmented reality for personalized nanomedicines

As our understanding of onset and progress of diseases at the genetic and molecular level rapidly progresses, the potential of advanced technologies, such as 3D-printing, Socially-Assistive Robots (SARs) or augmented reality (AR), that are applied to personalized nanomedicines (PNMs) to alleviate pathological conditions, has become more prominent. Among advanced technologies, AR in particular has the greatest potential to address those challenges and facilitate the translation of PNMs into formidable clinical application of personalized therapy. As AR is about to adapt additional new methods, such as speech, voice recognition, eye tracing and motion tracking, to enable interaction with host response or biological systems in 3-D space, a combination of multiple approaches to accommodate varying environmental conditions, such as public noise and atmosphere brightness, will be explored to improve its therapeutic outcomes in clinical applications. For instance, AR glasses still being developed by Facebook or Microsoft will serve as new platform that can provide people with the health information they are interested in or various measures through which they can interact with medical services. This review has addressed the current progress and impact of AR on PNMs and its application to the biomedical field. Special emphasis is placed on the application of AR based PNMs to the treatment strategies against senior care, drug addiction and medication adherence.

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.

Mass spectrometry imaging shows major derangements in neurogranin and in purine metabolism in the triple-knockout 3×Tg Alzheimer mouse model

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) can simultaneously measure hundreds of biomolecules directly from tissue. Using different sample preparation strategies, proteins and metabolites have been profiled to study the molecular changes in a 3×Tg mouse model of Alzheimer's disease. In comparison with wild-type (WT) control mice MALDI-MSI revealed Alzheimer's disease-specific protein profiles, highlighting dramatic reductions of a protein with m/z 7560, which was assigned to neurogranin and validated by immunohistochemistry. The analysis also revealed substantial metabolite changes, especially in metabolites related to the purine metabolic pathway, with a shift towards an increase in hypoxanthine/xanthine/uric acid in the 3×Tg AD mice accompanied by a decrease in AMP and adenine. Interestingly these changes were also associated with a decrease in ascorbic acid, consistent with oxidative stress. Furthermore, the metabolite N-arachidonyl taurine was increased in the diseased mouse brain sections, being highly abundant in the hippocampus. Overall, we describe an interesting shift towards pro-inflammatory molecules (uric acid) in the purinergic pathway associated with a decrease in anti-oxidant level (ascorbic acid). Together, these observations fit well with the increased oxidative stress and neuroinflammation commonly observed in AD. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

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

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

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.

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.

TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics

Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P<0.0001). Baseline and plasma serotonin concentration changes were associated with clinical outcomes (P<0.05). Therefore, baseline and serotonin concentration changes were used as phenotypes for genome-wide association studies (GWAS). GWAS for baseline plasma serotonin concentrations revealed a genome-wide significant (P=7.84E-09) SNP cluster on chromosome four 5' of TSPAN5 and a cluster across ERICH3 on chromosome one (P=9.28E-08) that were also observed during GWAS for change in serotonin at 4 (P=5.6E-08 and P=7.54E-07, respectively) and 8 weeks (P=1.25E-06 and P=3.99E-07, respectively). The SNPs on chromosome four were expression quantitative trait loci for TSPAN5. Knockdown (KD) and overexpression (OE) of TSPAN5 in a neuroblastoma cell line significantly altered the expression of serotonin pathway genes (TPH1, TPH2, DDC and MAOA). Chromosome one SNPs included two ERICH3 nonsynonymous SNPs that resulted in accelerated proteasome-mediated degradation. In addition, ERICH3 and TSPAN5 KD and OE altered media serotonin concentrations. Application of a pharmacometabolomics-informed pharmacogenomic research strategy, followed by functional validation, indicated that TSPAN5 and ERICH3 are associated with plasma serotonin concentrations and may have a role in SSRI treatment outcomes.

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.

Pharmacometabolomics informs Pharmacogenomics

INTRODUCTION

The initial decades of the 21^st century have witnessed striking technical advances that have made it possible to detect, identify and quantitatively measure large numbers of plasma or tissue metabolites. In parallel, similar advances have taken place in our ability to sequence DNA and RNA. Those advances have moved us beyond studies of single metabolites and single genetic polymorphisms to the study of hundreds or thousands of metabolites and millions of genomic variants in a single cell or subject. It is now possible to merge and integrate large data sets generated by the use of different "-omics" techniques to increase our understanding of the molecular basis for variation in disease risk and/or drug response phenotypes.

OBJECTIVES

This "Brief Review" will outline some of the challenges and opportunities associated with studies in which metabolomic data have been merged with genomics in an attempt to gain novel insight into mechanisms associated with variation in drug response phenotypes, with an emphasis on the application of a pharmacometabolomics-informed pharmacogenomic research strategy and with selected examples of the application of that strategy.

METHODS

Studies that used pharmacometabolomics to inform and guide pharmacogenomics were reviewed. Clinical studies that were used as the basis for pharmacometabolomics-informed pharmacogenomic studies, published in five independent manuscripts, are described briefly.

RESULTS

Within these five manuscripts, both pharmacokinetic and pharmacodynamic metabolomics approaches were used. Candidate gene and genome-wide approaches that were used in concert with these metabolomic data identified novel metabolite-gene relationships that were associated with drug response phenotypes in these pharmacometabolomics-informed pharmacogenomics studies.

CONCLUSION

This "Brief Review" outlines the emerging discipline of pharmacometabolomics-informed pharmacogenomics in which metabolic profiles are associated with both clinical phenotypes and genetic variants to identify novel genetic variants associated with drug response phenotypes based on metabolic profiles.

Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling

Gas chromatography-mass spectrometry (GC-MS)-based metabolomics is ideal for identifying and quantitating small-molecule metabolites (<650 Da), including small acids, alcohols, hydroxyl acids, amino acids, sugars, fatty acids, sterols, catecholamines, drugs, and toxins, often using chemical derivatization to make these compounds sufficiently volatile for gas chromatography. This unit shows how GC-MS-based metabolomics allows integration of targeted assays for absolute quantification of specific metabolites with untargeted metabolomics to discover novel compounds. Complemented by database annotations using large spectral libraries and validated standard operating procedures, GC-MS can identify and semiquantify over 200 compounds from human body fluids (e.g., plasma, urine, or stool) per study. Deconvolution software enables detection of more than 300 additional unidentified signals that can be annotated through accurate mass instruments with appropriate data processing workflows, similar to untargeted profiling using liquid chromatography-mass spectrometry. GC-MS is a mature technology that uses not only classic detectors (quadrupole) but also target mass spectrometers (triple quadrupole) and accurate mass instruments (quadrupole-time of flight). This unit covers sample preparation from mammalian samples, data acquisition, quality control, and data processing.

Emerging applications of metabolomics in drug discovery and precision medicine

Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock

RATIONALE

Sepsis therapeutics have a poor history of success in clinical trials, due in part to the heterogeneity of enrolled patients. Pharmacometabolomics could differentiate drug response phenotypes and permit a precision medicine approach to sepsis.

OBJECTIVES

To use existing serum samples from the phase 1 clinical trial of l-carnitine treatment for severe sepsis to metabolically phenotype l-carnitine responders and nonresponders.

METHODS

Serum samples collected before (T0) and after completion of the infusion (T24, T48) from patients randomized to either l-carnitine (12 g) or placebo for the treatment of vasopressor-dependent septic shock were assayed by untargeted (1)H-nuclear magnetic resonance metabolomics. The normalized, quantified metabolite data sets of l-carnitine- and placebo-treated patients at each time point were compared by analysis of variance with post-hoc testing for multiple comparisons. Pathway analysis was performed to statistically rank metabolic networks.

MEASUREMENTS AND MAIN RESULTS

Thirty-eight metabolites were identified in all samples. Concentrations of 3-hydroxybutyrate, acetoacetate, and 3-hydroxyisovalerate were different at T0 and over time in l-carnitine-treated survivors versus nonsurvivors. Pathway analysis of pretreatment metabolites revealed that synthesis and degradation of ketone bodies had the greatest impact in differentiating l-carnitine treatment response. Analysis of all patients based on pretreatment 3-hydroxybutyrate concentration yielded distinct phenotypes. Using the T0 median 3-hydroxybutyrate level (153 μM), patients were categorized as either high or low ketone. l-Carnitine-treated low-ketone patients had greater use of carnitine as evidenced by lower post-treatment l-carnitine levels. The l-carnitine responders also had faster resolution of vasopressor requirement and a trend toward a greater improvement in mortality at 1 year (P = 0.038) compared with patients with higher 3-hydroxybutyrate.

CONCLUSIONS

The results of this preliminary study, which were not readily apparent from the parent clinical trial, show a unique metabolite profile of l-carnitine responders and introduce pharmacometabolomics as a viable strategy for informing l-carnitine responsiveness. The approach taken in this study represents a concrete example for the application of precision medicine to sepsis therapeutics that warrants further study.

Pharmacometabolomic Assessments of Atenolol and Hydrochlorothiazide Treatment Reveal Novel Drug Response Phenotypes

Achieving hypertension (HTN) control and mitigating the adverse health effects associated with HTN continues to be a global challenge. Some individuals respond poorly to current HTN therapies, and mechanisms for response variation remain poorly understood. We used a nontargeted metabolomics approach (gas chromatography time-of-flight/mass spectrometry gas chromatography time-of-flight/mass spectrometry) measuring 489 metabolites to characterize metabolite signatures associated with treatment response to anti-HTN drugs, atenolol (ATEN), and hydrochlorothiazide (HCTZ), in white and black participants with uncomplicated HTN enrolled in the Pharmacogenomic Evaluation of Antihypertensive Responses study. Metabolite profiles were significantly different between races, and metabolite responses associated with home diastolic blood pressure (HDBP) response were identified. Metabolite pathway analyses identified gluconeogenesis, plasmalogen synthesis, and tryptophan metabolism increases in white participants treated with HCTZ (P < 0.05). Furthermore, we developed predictive models from metabolite signatures of HDBP treatment response (P < 1 × 10(-5)). As part of a quantitative systems pharmacology approach, the metabolites identified herein may serve as biomarkers for improving treatment decisions and elucidating mechanisms driving HTN treatment responses.

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.

The pharmacogenetic control of antiplatelet response: candidate genes and CYP2C19

INTRODUCTION

Aspirin, clopidogrel, prasugrel and ticagrelor are antiplatelet agents for the prevention of ischemic events in patients with acute coronary syndromes (ACS), percutaneous coronary intervention (PCI) and other indications. Variability in response is observed to different degrees with these agents, which can translate to increased risks for adverse cardiovascular events. As such, potential pharmacogenetic determinants of antiplatelet pharmacokinetics, pharmacodynamics and clinical outcomes have been actively studied.

AREAS COVERED

This article provides an overview of the available antiplatelet pharmacogenetics literature. Evidence supporting the significance of candidate genes and their potential influence on antiplatelet response and clinical outcomes are summarized and evaluated. Additional focus is directed at CYP2C19 and clopidogrel response, including the availability of clinical testing and genotype-directed antiplatelet therapy.

EXPERT OPINION

The reported aspirin response candidate genes have not been adequately replicated and few candidate genes have thus far been implicated in prasugrel or ticagrelor response. However, abundant data support the clinical validity of CYP2C19 and clopidogrel response variability among ACS/PCI patients. Although limited prospective trial data are available to support the utility of routine CYP2C19 testing, the increased risks for reduced clopidogrel efficacy among ACS/PCI patients that carry CYP2C19 loss-of-function alleles should be considered when genotype results are available.

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.

Parsing interindividual drug variability: an emerging role for systems pharmacology

There is notable interindividual heterogeneity in drug response, affecting both drug efficacy and toxicity, resulting in patient harm and the inefficient utilization of limited healthcare resources. Pharmacogenomics is at the forefront of research to understand interindividual drug response variability, but although many genotype-drug response associations have been identified, translation of pharmacogenomic associations into clinical practice has been hampered by inconsistent findings and inadequate predictive values. These limitations are in part due to the complex interplay between drug-specific, human body and environmental factors influencing drug response and therefore pharmacogenomics, whilst intrinsically necessary, is by itself unlikely to adequately parse drug variability. The emergent, interdisciplinary and rapidly developing field of systems pharmacology, which incorporates but goes beyond pharmacogenomics, holds significant potential to further parse interindividual drug variability. Systems pharmacology broadly encompasses two distinct research efforts, pharmacologically-orientated systems biology and pharmacometrics. Pharmacologically-orientated systems biology utilizes high throughput omics technologies, including next-generation sequencing, transcriptomics and proteomics, to identify factors associated with differential drug response within the different levels of biological organization in the hierarchical human body. Increasingly complex pharmacometric models are being developed that quantitatively integrate factors associated with drug response. Although distinct, these research areas complement one another and continual development can be facilitated by iterating between dynamic experimental and computational findings. Ultimately, quantitative data-derived models of sufficient detail will be required to help realize the goal of precision medicine.

Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.