[Show abstract][Hide abstract] ABSTRACT: CES1 is involved in the hydrolysis of ester group-containing xenobiotic and endobiotic compounds including several essential and commonly used drugs. The individual variation in the efficacy and tolerability of many drugs metabolized by CES1 is considerable. Hence, there is a large interest in individualizing the treatment with these drugs. The present review addresses the issue of individualized treatment with drugs metabolized by CES1. It describes the composition of the gene encoding CES1, reports variants of this gene with focus upon those with a potential effect on drug metabolism and provides an overview of the protein structure of this enzyme bringing notice to mechanisms involved in the regulation of enzyme activity. Subsequently, the review highlights drugs metabolized by CES1 and argues that individual differences in the pharmacokinetics of these drugs play an important role in determining drug response and tolerability suggesting prospects for individualized drug therapies. Our review also discusses endogenous substrates of CES1 and assesses the potential of using metabolomic profiling of blood to identify proxies for the hepatic activity of CES1 that predict the rate of drug metabolism. Finally, the combination of genetics and metabolomics to obtain an accurate prediction of the individual response to CES1-dependent drugs is discussed.
[Show abstract][Hide abstract] ABSTRACT: Module-based analysis (MBA) aims to evaluate the effect of a group of biological elements sharing common features, such as SNPs in the same gene or metabolites in the same pathways, and has become an attractive alternative to traditional single bio-element approaches. Because bio-elements regulate and interact with each other as part of network, incorporating network structure information can more precisely model the biological effects, enhance the ability to detect true associations, and facilitate our understanding of the underlying biological mechanisms. However, most MBA methods ignore the network structure information, which depicts the interaction and regulation relationship among basic functional units in biology system. We construct the connectivity kernel and the topology kernel to capture the relationship among bio-elements in a module, and use a kernel machine framework to evaluate the joint effect of bio-elements. Our proposed kernel machine approach directly incorporates network structure so to enhance the study efficiency; it can assess interactions among modules, account covariates, and is computational efficient. Through simulation studies and real data application, we demonstrate that the proposed network-based methods can have markedly better power than the approaches ignoring network information under a range of scenarios.
PLoS ONE 03/2015; 10(3):e0122309. DOI:10.1371/journal.pone.0122309 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: While aspirin is generally effective for prevention of cardiovascular disease, considerable variation in drug response exists, resulting in some individuals displaying high on-treatment platelet reactivity. We used pharmacometabolomics to define pathways implicated in variation of response to treatment. We profiled serum samples from healthy subjects pre- and postaspirin (14 days, 81 mg/day) using mass spectrometry. We established a strong signature of aspirin exposure independent of response (15/34 metabolites changed). In our discovery (N = 80) and replication (N = 125) cohorts, higher serotonin levels pre- and postaspirin correlated with high, postaspirin, collagen-induced platelet aggregation. In a third cohort, platelets from subjects with the highest levels of serotonin preaspirin retained higher reactivity after incubation with aspirin than platelets from subjects with the lowest serotonin levels preaspirin (72 ± 8 vs. 61 ± 11%, P = 0.02, N = 20). Finally, ex vivo, serotonin strongly increased platelet reactivity after platelet incubation with aspirin (+20%, P = 4.9 × 10(-4), N = 12). These results suggest that serotonin is implicated in aspirin response variability.
CPT: Pharmacometrics and Systems Pharmacology 07/2014; 3(7):e125. DOI:10.1038/psp.2014.22
[Show abstract][Hide abstract] ABSTRACT: Previous research has shown an association between hostility and fasting glucose in African American women. Central nervous system serotonin activity is implicated both in metabolic processes and in hostility related traits.
The purpose of this study is to determine whether central nervous system serotonin influences the association between hostility and fasting glucose in African American women.
The study consisted of 119 healthy volunteers (36 African American women, 27 White women, 21 White males, and 35 African American males, mean age 34 ± 8.5 years). Serotonin related compounds were measured in cerebrospinal fluid. Hostility was measured by the Cook-Medley Hostility Scale.
Hostility was associated with fasting glucose and central nervous system serotonin related compounds in African American women only. Controlling for the serotonin related compounds significantly reduced the association of hostility to glucose.
The positive correlation between hostility and fasting glucose in African American women can partly be explained by central nervous system serotonin function.
Annals of Behavioral Medicine 05/2014; 49(1). DOI:10.1007/s12160-014-9626-7 · 4.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: -The five amino acid (AA) signature including isoleucine (Ile), leucine (Leu), valine (Val), tyrosine (Tyr), and phenylalanine (Phe) has been associated with incident diabetes and insulin resistance. We investigated whether this same AA signature, single nucleotide polymorphisms (SNPs) in genes in their catabolic pathway, were associated with development of impaired fasting glucose (IFG) after atenolol treatment.
-Among 234 European American participants enrolled in the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study and treated with atenolol for 9 weeks, we prospectively followed a nested cohort that had both metabolomics profiling and genotype data available, for the development of IFG. We assessed the association between baseline circulating levels of Ile, Leu, Val, Tyr and Phe, as well as SNPs in BCAT1 and PAH with development of IFG. All baseline AA levels were strongly associated with IFG development. Each increment in standard deviation of the five AAs was associated with the following odds ratio and 95% confidence interval for IFG based on fully adjusted model: Ile 2.29 (1.31-4.01), Leu 1.80 (1.10-2.96), Val 1.77 (1.07-2.92), Tyr 2.13 (1.20-3.78) and Phe 2.04 (1.16-3.59). The composite p value was 2x10(-5). Those with PAH (rs2245360) AA genotype had the highest incidence of IFG (p for trend=0.0003).
-Our data provide important insight into the metabolic and genetic mechanisms underlying atenolol associated adverse metabolic effects. Clinical Trial Registration-clinicaltrials.gov; Unique Identifier: NCT00246519.
[Show abstract][Hide abstract] ABSTRACT: The USA National Science Foundation (NSF) recently funded a Research Coordination Network (RCN) grant (Award # 1340058) in September 2013. The primary goal of the RCN is the development of a USA Plant, Algae and Microbial Metabolomics Research Coordination Network (PAMM-NET) that will promote effective communication, enhance opportunities for collaboration, build community consensus, identify key challenges in metabolomics, and facilitate coordinated community empirical efforts to meet these challenges. Participation in the USA PAMM-NET is open to the public, and PAMM NET will interact with existing and future international metabolomics organizations and communities.The Specific Aims of the USA PAMM-NET are to:Unify and facilitate greater collaboration between the awardees of the USA National Science Foundation and Japanese Science and Technology Agency (NSF-JST) Metabolomics for a Low Carbon Society ProgramBuild a collaborative and cooperative metabolomics research coordination networ ...
[Show abstract][Hide abstract] ABSTRACT: Metabolomics, the study of metabolism at an "omic" level, has the potential to transform our understanding of mechanisms of drug action and the molecular basis for variation in drug response. It is now possible to define metabolic signatures of drug exposure that can identify pathways involved in both drug efficacy and adverse drug reactions. Additionally the "metabotype", the metabolic "signature" of a patient, is a unique identity that contains information about drug response and disease heterogeneity. The application of metabolomics for the study of drug effects and variation in drug response is creating "pharmacometabolomics", a discipline that will contribute to personalized drug therapy and will compliment pharmacogenomics by capturing environmental and microbiome influences on response to drug therapy. This field has the potential to transform pharmacology and clinical pharmacology in significant ways and will contribute to efforts for personalized therapy. This overview highlights developments in the new discipline of pharmacometabolomics.Clinical Pharmacology & Therapeutics (2013); accepted article preview online 5 November 2013 doi:10.1038/clpt.2013.217.
[Show abstract][Hide abstract] ABSTRACT: Statins reduce risk of cardiovascular disease (CVD) by decreasing plasma LDL concentrations, as well as reducing inflammation and improving endothelial function. Despite their documented efficacy, there is considerable interindividual variation in effects of statins on CVD biomarkers. In the studies summarized here we used complementary metabolomics platforms to define global effects of a statin (simvastatin) on metabolism and to identify markers indicative of mechanisms that contribute to variation in plasma LDL response to statin treatment.Clinical Pharmacology & Therapeutics (2013); accepted article preview online 14 August 2013 doi:10.1038/clpt.2013.164.
[Show abstract][Hide abstract] ABSTRACT: We have shown that lithium treatment improves motor coordination in a spinocerebellar ataxia type 1 (SCA1) disease mouse model (Sca1(154Q/+) ). To learn more about disease pathogenesis and molecular contributions to the neuroprotective effects of lithium, we investigated metabolomic profiles of cerebellar tissue and plasma from SCA1-model treated and untreated mice. Metabolomic analyses of wild-type and Sca1(154Q/+) mice, with and without lithium treatment, were performed using gas chromatography time-of-flight mass spectrometry and BinBase mass spectral annotations. We detected 416 metabolites, of which 130 were identified. We observed specific metabolic perturbations in Sca1(154Q/+) mice and major effects of lithium on metabolism, centrally and peripherally. Compared to wild-type, Sca1(154Q/+) cerebella metabolic profile revealed changes in glucose, lipids, and metabolites of the tricarboxylic acid cycle and purines. Fewer metabolic differences were noted in Sca1(154Q/+) mouse plasma versus wild-type. In both genotypes, the major lithium responses in cerebellum involved energy metabolism, purines, unsaturated free fatty acids, and aromatic and sulphur-containing amino acids. The largest metabolic difference with lithium was a 10-fold increase in ascorbate levels in wild-type cerebella (p<0.002), with lower threonate levels, a major ascorbate catabolite. In contrast, Sca1(154Q/+) mice that received lithium showed no elevated cerebellar ascorbate levels. Our data emphasize that lithium regulates a variety of metabolic pathways, including purine, oxidative stress and energy production pathways. The purine metabolite level, reduced in the Sca1(154Q/+) mice and restored upon lithium treatment, might relate to lithium neuroprotective properties.
PLoS ONE 08/2013; 8(8):e70610. DOI:10.1371/journal.pone.0070610 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Interindividual variability in response to antiplatelet therapy results in higher platelet reactivity as well as higher rates of cardiovascular events. Despite substantial effort, the genetic and nongenetic determinants of antiplatelet variability remain poorly understood. Emerging pharmacometabolomic paradigms that integrate systems approaches such as pharmacogenomics have the potential to unveil novel biology regarding disease pathogenesis, reveal the effect of drugs on pathways, and allow better understanding of response variability. Such approaches offer great potential for personalized antiplatelet treatment.Clinical Pharmacology & Therapeutics (2013); 94 5, 570-573. doi:10.1038/clpt.2013.153