Molecules 2009, 14, 1-x manuscripts; doi:10

International Bulletin of Pharmaceutical Sciences 2012, 1 (1-2): 17–29 International Bulletin of Pharmaceutical Sciences
http://www.vufb-publishing.org/Journals/IBPS.aspx Use of Metabolomics for Monitoring Metabolic Responses
Caused by Drug Administration
2. Application of Metabolomics in the Study of Metabolic
Changes Induced by Drugs
Josef Jampilek 1,2,*, Katarina Kralova 3 and Ivan Ostrovsky 3
1 Department of Chemical Drugs, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences Brno, Palackeho 1/3, 612 42 Brno, Czech Republic 2 Research Institute for Pharmacy and Biochemistry, Lidicka 1879/48, 60200 Brno, Czech 3 Institute of Chemistry, Faculty of Natural Sciences, Comenius University, Mlynska dolina CH-2, 842 15 Bratislava, Slovakia * Corresponding author; E-Mail:Tel.: + 420721256271.
Article history: Received: 27 June 2012 / Revised: 9 July 2012 / Accepted: 10 July 2012 / Published: 11 July 2012 Abstract: The aim of the paper is to present metabolomics as an effective tool for
the study of responses of humans to drug treatment or animal models to drug
administration. Generally, metabolomics can be used for monitoring disease status
and drug efficacy, drug metabolism, toxicity and drug-drug interactions. Attention
is focused on different classes of drugs, and metabolite alterations caused by drug
administration are analysed. Individual drugs are classified according to ATC
groups. The selected drugs and pharmacotherapeutic groups were chosen
according to their market share or potential. Antipsychotics, anti-inflammatory
and antipyretic drugs, antihyperlipidemic agents, antiplatelet/anticoagulant drugs,
antimicrobial chemotherapeutics, antineoplastic agents, immunosuppressants and
contraceptives are discussed. In addition, the perspectives of the use of
metabolomics for future drug discovery are outlined.
Keywords:
1. Introduction
Metabolomics deals with investigation of chemical processes involving metabolites, and comprises procedures used by metabolome analysis or metabolome fraction, such as sampling, sample preparation, chemical analysis and chemometric analysis of obtained data. The metabolome represents a complete set of all metabolites that can be found in a cell in J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
relation to its metabolism. It is necessary to distinguish endometabolome (a complete set of all intracellular metabolites) and exometabolome (a complete set of all the metabolites excreted to extracellular fluids). The term “metabolic profiling” is used for identification and semiquantitative or quantitative determination of a selected number of pre-defined metabolites, generally related to a specific metabolic pathway(s), e.g. aminoacids. Metabolic fingerprinting represents a high-throughput rapid global analysis of metabolites that are produced by a cell. On the other hand, targeted analysis of metabolites involves quantification of specific metabolites [1,2]. Metabolome analysis involves identification and quantification of all intracellular and extracellular metabolites using various advanced combined analytical techniques [1,2]. Metabolic profiling yields first physiological information about the condition of organism or its response/reaction to administered drugs. The obtained analytical data are processed chemometrically (by multivariate statistical analyses), and final results can be incorporated into metabolic models. Thus, metabolic fingerprinting provides “a fingerprint” that can be used for investigation and/or comparison of different biosamples. Although the convolution character of cell metabolism (the same metabolite can participate in several metabolic pathways) complicates metabolic data interpretation, the interest in metabolomic and metabolic profiling is continually increasing [1,2]. Below a number of metabolomic/metabonomic approaches used in the study of metabolic changes induced by drugs are discussed. Individual drugs are classified according to ATC groups. The selection of drugs and pharmacotherapeutic groups was made taking into account their market share. 2. Investigation of Metabolic Changes Induced by Drugs Using Metabolomics
Lee et al. [3] compared metabolic profiles of Chinese patients treated with second- generation antipsychotic (SGA) drugs (risperidone, olanzapine and ziprasidone) and first-generation antipsychotic (FGA) drugs (chlorpromazine, haloperidol and trifluoperazine). The most frequent psychiatric diagnosis of these patients was schizophrenia, followed by affective disorder and other psychiatric diagnoses. SGA was associated with higher low-density lipoproteins (LDL) level than FGA. Individual comparison of different antipsychotics showed that patients on olanzapine had the greatest increases in cholesterol and triglycerides among all antipsychotics. The finding suggested that SGA, particularly olanzapine, was associated with more metabolic risk factors than first-generation antipsychotics. Monotherapy by risperidone was investigated by Xuan et al. [4]. They used gas chromatography coupled to mass spectrometry (GC–MS) based metabolomic profiling in serum of unmedicated schizophrenic patients before and after an 8-week risperidone monotherapy, to detect potential biomarkers associated with schizophrenia and risperidone treatment. Twenty-two marker metabolites contributing to the complete separation of schizophrenic patients from matched healthy controls were identified, with citrate, palmitic acid, myoinositol and allantoin exhibiting the best combined classification performance. Twenty marker metabolites contributing to between posttreatment and pretreatment patients were identified, with myo-inositol, uric acid and tryptophan showing the maximum combined classification performance. 2.2. Anti-inflammatory and antipyretic drugs Nonsteroidal anti-inflammatory drugs (NSAIDs), which are commonly used to treat rheumatoid arthritis, osteoarthritis, acute pain and fever, exhibit side effects that include gastric erosions, ulceration, bleeding, perforation, etc. Um et al. [5] performed pattern J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
recognition analysis of the 1H nuclear magnetic resonance (NMR) spectra of urine to develop
surrogate biomarkers related to the gastrointestinal damage induced by NSAIDs in rats. They
analyzed the urine samples collected for 5 h after administration of high doses of NSAIDs
(celecoxib: 133 mg/kg, p.o.; indomethacin: 25 mg/kg, p.o.; ibuprofen: 800 mg/kg, p.o.), using
1H NMR for spectral binning and targeted profiling, and determined the level of gastric
damage in each animal. Indomethacin and ibuprofen (nonselective cyclooxygenase (COX)
inhibitors) caused severe gastric damage, but no lesions were observed in the rats treated with
celecoxid, a COX-2-selective inhibitor. The 1H NMR urine spectra were divided into spectral
bins (0.04 ppm) for global profiling, and 36 endogenous metabolites were assigned for
targeted profiling. There were different clusterings of 1H NMR spectra according to the
gastric damage scores in global profiling. In targeted profiling, a few endogenous metabolites
of allantoine, taurine, and dimethylamine were selected as putative biomarkers for the gastric
damage induced by NSAIDs.
Verhoeckx et al. [6] used a metabolomic approach for screening of new classes of anti- inflammatory compounds. They used dexamethasone, clenbuterol, salbutamol and zilpaterol, i.e. compounds influencing anti-inflammation processes. They used macrophage-like U937 cells which were stimulated with lipopolysaccharide (LPS) in the absence or presence of these compounds, using micro-arrays, 2D gel electrophoresis and liquid chromatography coupled to mass spectroscopy (LC–MS) method for lipids. It was found that different classes of NSAIDs showed distinct and characteristic lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. Moreover, it was shown that zilpaterol, a poorly characterized β2-agonist, gives rise to an almost identical expression pattern as the β2-agonists clenbuterol and salbutamol. Indomethacine-induced modifications of amino acid metabolism, fatty acid metabolism and energetically associated metabolic pathways accounted for metabolic perturbations of rats were studied by Lv et al. [7]. This metabolomic approach integrated with ingenuity pathway analysis (IPA) was applied to deeply analyze the biomarkers and their relations with the metabolic perturbations evidenced by pattern recognition analyses. The IPA correlation of three major biomarkers, identified as creatinine, prostaglandin E2 and guanosine, suggested that the administration of indomethacin induced certain levels of toxicity in the kidneys and liver. Jung et al. [8] investigated dose-dependent perturbations in urinary metabolite concentrations caused by naproxen toxicity using 1H NMR spectroscopy coupled with multivariate statistical analysis. The 1H NMR data from rat urine processed by principal component analysis (PCA) revealed a dose-dependent metabolic shift between the vehicle-treated control rats and rats treated with low-dose (10 mg/kg body weight), moderate-dose (50 mg/kg), and high-dose (100 mg/kg) naproxen, coinciding with their gastric damage scores after naproxen administration. The resultant metabolic profiles demonstrated that the naproxen-induced gastric damage exhibited energy metabolism perturbations that elevatedurinary levels of citrate, cis-aconitate, creatine and creatine phosphate. In addition, naproxen administration decreased the choline level and increased the betaine level, indicating that it depleted the main protective constituent of the gastric mucosa. Moreover, naproxen stimulated the decomposition of tryptophan into kynurenate, which inhibits fibroblast growth factor-1 and delays ulcer healing. Rofecoxib administrated p.o. for 3 months resulted in a greater than 120-fold higher blood level of 20-hydroxyicosatetraenoic acid (20-HETE). Neither 20-HETE biosynthesis nor cytochrome P4A-like immune reactivity was increased by rofecoxib administration, but 20-HETE production increased in vitro with the addition of COX-2 inhibitor. 20-HETE was significantly more potent than its COX-mediated metabolites in shortening clotting time in vitro. Furthermore, 20-HETE but not rofecoxib significantly increased rat platelet aggregation in vitro in a dose-dependent manner [9]. J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
Clayton et al. [10] reported pre-dose prediction of an aspect of the urinary drug metabolite profile and an association between pre-dose urinary composition and the extent of liver damage sustained after paracetamol administration. Sun et al. [11] administated acute and chronic doses of paracetamol to male Sprague-Dawley rats and used NMR and LC–MS to detect drug metabolites and endogenous metabolites simultaneously. Data obtained by liquid chromatography coupled to mass spectrometry (LC–MS) and NMR spectra, both processed by PCA, showed that metabolic changes observed in both acute and chronic dosing of paracetamol were similar. Depletion of antioxidants (e.g., ferulic acid), trigonelline, (S)-adenosyl-L-methionine and energy-related metabolites indicated that oxidative stress was caused by acute and chronic paracetamol administration. Nicholls et al. [12] investigated the metabolism of acetyl-labelled phenacetin-C2H3 in man, following a single (150 mg) oral dose, in urine samples using 1H-NMR spectroscopy. The phenacetin metabolites paracetamol glucuronide, sulphate and N-acetyl-L-cysteinyl conjugate were identified by this method, and all showed a clear evidence of the loss of the original 2H3-acetyl label and its replacement with 1H3 (futile deacetylation). Further profiling was performed using high performance liquid chromatography coupled to mass spectrometry, solid-phase extraction and NMR (HPLC–MS–SPE–NMR), confirming futile deacetylation had taken place as indicated by NMR spectroscopy on both the isolated paracetamol glucuronide and L-cysteinyl-metabolites. A number of non-acetylated metabolites were also detected in the sample via HPLC–MS (TOF, time of flight). These results showed that phenacetin underwent a transient formation via a number of toxic intermediates to a much greater extent than had been observed in similar studies on paracetamol. The hyphenated technique HPLC–MS–SPE–NMR is applicable for investigation of complex mixtures of various origins in order to prove the versatility of individual combined techniques. An SPE device leads to concentrated samples being transferred to the NMR spectrometer. This is a diverse group of pharmaceuticals that are used in the treatment of hyperlipidemias. There are several classes of hypolipidemic drugs. They may differ in their impact on the cholesterol profile and adverse effects; for example, some can lower the LDL level more than others, while others can preferentially increase high density lipoproteins (HDL). Wheelock et al. [13] applied targeted metabolomics to liver, heart, brain and white adipose tissue samples from male Swiss-Webster mice exposed to a 5 day, 500 mg/kg/day regimen of i.p. clofibrate. They quantified tissue concentrations of free fatty acids and the fatty acid content of sphingomyelin, cardiolipin, cholesterol esters, triglycerides and phospholipids and found that responses were tissue-specific, with changes observed in the liver > heart > > brain > adipose. The results indicated that liver saturated fatty acid-rich triglycerides feed the synthesis of clofibrate-induced monounsaturated fatty acids (MUFA), which were incorporated into hepatic phospholipids and sphingomyelin. Selective enrichment of docosahexeneoic acid in the phosphatidylserine of liver (1.7-fold), heart (1.6-fold) and brain (1.5-fold) suggested a clofibrate-dependent systemic activation of phosphatidylserine synthetase 2. About 20% decline in cardiac sphingomyelin was consistent with activation of a sphingomeylinase with a substrate preference for polyunsaturate-containing sphingomyelin. Exposure to clofibrate resulted in elevating brain cholesterol arachidonyl-esters and perturbations in the liver, brain, and adipose cholesterol esters were observed. Fenofibrate, a peroxisome proliferator-activated receptor α (PPAR-α) agonist used to treat dyslipidemia, is known to cause hepatocarcinogenesis in rodents. Ohta et al. [14] analyzed the urine and plasma of male rats dosed with 300 mg/kg/day of fenofibrate for 14 days to evaluate untargeted metabolic profiling. A combination of LC–MS yielded profiles of 486 plasma and 932 urinary metabolites. It was found that tricarboxylic acid cycle intermediates were reduced J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
and energy metabolism homeostasis was altered. Perturbation of the glutathione biosynthesis and elevation of oxidative stress markers were observed. Furthermore, tryptophan metabolism was up-regulated, resulting in accumulation of tryptophan metabolites associated with reactive oxygen species generation, suggesting the possibility of oxidative stress as a mechanism of nongenotoxic carcinogenesis. Moreover, several metabolites related to liver function, kidney function, cell damage and cell proliferation were altered by fenofibrate-induced toxicity at the dose of 300 mg/kg/day. Liu et al. [15] investigated the metabolism of fenofibrate in cynomolgus monkeys by LC–MS (qTOF, quadrupole time of flight) based metabolomics. They collected urine samples before and after oral administration of fenofibrate and analyzed them in positive-ion and negative-ion modes by LC–MS (qTOF), and after data deconvolution the resulting data matrices were subjected to multivariate data analysis. Pattern recognition was performed on the retention time, mass/charge ratio and other metabolite-related variables. Several metabolites were identified, including fenofibric acid, reduced fenofibric acid, fenofibric acid ester glucuronide and reduced fenofibric acid ester glucuronide. Another two metabolites, not previously reported in other species, were characterized in cynomolgus monkeys, and previously unknown metabolites, fenofibric acid taurine conjugate and reduced fenofibric acid taurine conjugate were identified, revealing a previously unrecognized conjugation pathway for fenofibrate. Aura et al. [16] identified novel drug metabolites of simvastatin by using an anaerobic human in vitro colon model at body temperature coupled with systems biology platform, excluding the metabolism of the host liver and intestinal epithelia. They used comprehensive two-dimensional GC–MS (TOF) for the metabolomic analysis. The results suggested that simvastatin is degraded by hydrolytic cleavage of methylbutanoic acid from the simvastatin backbone. hydroxylation/dehydroxylation and β-oxidation resulting in the production of 2-hydroxyisovaleric acid (3-methyl-2-hydroxybutanoic acid), 3-hydroxybutanoic acid and lactic acid (2-hydroxypropanoic acid), and, finally, re-cyclisation of heptanoic acid (possibly de-esterified and cleaved methylpyranyl arm) to produce cyclohexanecarboxylic acid. Kumar et al. [17] performed metabolomic study with hyperlipidemic rats after administration of atorvastatin p.o. (doses administered for a period of 7 days were 70 mg/kg/day or 250 mg/kg/day). Global metabolic profiling was performed using LC–MS (TOF) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using GC–MS and CE–MS. The metabolic differences between control and drug-treated groups were compared using partial least squares (PLS) score plots. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers. 2.4. Antiplatelet/anticoagulant drugs Antiplatelet drugs (anti-aggregants) are members of the class of pharmaceuticals that decrease platelet aggregation and inhibit thrombus formation. They are effective in arterial circulation, where anticoagulants have little effect. Anticoagulant agents are drugs that prevent coagulation of blood and can be used as a medication for thrombotic disorders. Clinical efficacy of clopidogrel is hampered by its variable biotransformation into the active metabolite. Using in vitro metabolomic profiling techniques, Bouman et al. [18] identified paraoxonase-1 (PON1) as the crucial enzyme for clopidogrel bioactivation, with its common Q192R polymorphism determining the rate of active metabolite formation. Ticagrelor is a reversibly binding and selective oral P2Y(12) antagonist, developed for prevention of atherothrombotic events in patients with acute coronary syndromes. Li et al. J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
[19] investigated the disposition and metabolism of 14C ticagrelor (p.o. as well as i.v. administration) in mice, rats and marmosets. Urinary excretion of drug-related material accounted for only 1 to 15% of the total radioactivity administered. Milk samples from lactating rats displayed significantly higher levels of total radioactivity than plasma after oral administration of the drug. Ticagrelor and active metabolite AR-C124910 (loss of hydroxyethyl side chain) were the major components in plasma from all species studied and similar to human plasma profiles. The primary metabolite of ticagrelor excreted in urine across difluorophenylcyclopropyl group). Ticagrelor, AR-C124910 and AR-C133913 were the major components found in feces from the three species examined. Warfarin is a commonly prescribed oral anticoagulant which has two enantiomers, S-(-) and R-(+) and undergoes stereoselective metabolism, with the S-(-)-enantiomer being more effective. Bai et al. [20] reported the intracellular metabolic profile in HepG2 cells incubated with S-(-)- and R-(+)-warfarin, using GC–MS. A total of 80 metabolites belonging to different categories were identified. Glucuronic acid showed a significant decrease in cells incubated with R-(+)-warfarin but not in those incubated with S-(-)-warfarin, what may partially explain the lower bio-activity of R-(+)-warfarin. On the other hand, arachidonic acid showed an increase in cells incubated with S-(-)-warfarin but not in those incubated with R-(+)-warfarin, and a number of small molecules involved in γ-glutamyl cycle displayed ratio variations. Jones et al. [21] reported a multi-mode LC–MS/MS method which combines phenyl based reverse phase chromatography with chiral phase chromatography prior to quantitation by liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS) and enables separation of individual (R)- and (S)-enantiomers of hydroxywarfarin and warfarin. All four possible isomers of 10-hydroxywarfarin were resolved to reveal unprecedented insights into the stereo-specific metabolism of warfarin. 2.5. Antimicrobial chemoterapeutics These are compounds that kill or slow down the growth of various cell pathogens (bacteria, parasites, fungi, etc.). Currently a huge number of these chemotherapeutics, so-called antibiotics, exist. This section deals with only the most common in clinical practice or the new ones. Swann et al. [22] investigated the effects of penicillin and streptomycin sulfate on gut microbial composition and host metabolic phenotype in male Han-Wistar rats after eight days treatment, using fluorescence in situ hybridization analysis of the intestinal contents. Significant reduction in all bacterial groups measured with respect to the control was observed. Bacterial suppression reduced the excretion of mammalian-microbial urinary cometabolites including hippurate, phenylpropionic acid, phenylacetylglycine and indoxyl-sulfate, whereas taurine, glycine, citrate, 2-oxoglutarate and fumarate excretion were elevated. The effects of vancomycin (2×100 mg/kg/day) on the gut microbiota of female mice were studied and the urine and fecal extract samples were analysed by 1H NMR [23]. Vancomycin treatment was associated with fecal excretion of uracil, amino acids and short chain fatty acids. Reduced urinary excretion of gut microbial co-metabolites phenylacetylglycine and hippurate was also observed. Regression of urinary hippurate and phenylacetylglycine concentrations against the fecal metabolite profile showed a strong association between these urinary metabolites and a wide range of fecal metabolites, including amino acids and short chain fatty acids. Fecal choline was inversely correlated with urinary hippurate. The effect of the rifaximin on microbial metabolic profiles, using 1H-NMR and solid phase microextraction coupled with GC–MS, was studied by Maccaferri et al. [24]. The antibiotic did not affect the overall composition of the gut microbiota, whereas it caused an increase in concentration of Bifidobacterium, Atopobium and Faecalibacterium prausnitzii. A shift in microbial metabolism was observed, which was manifested by increases in short-chain fatty J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
acids, propanol, decanol, nonanone and aromatic organic compounds and decreases in ethanol, methanol and glutamate. Romick-Rosendale et al. [25] monitored changes in metabolic profiles of urine and fecal extracts of mice following gut sterilization by the broad-spectrum chemotherapeutic enrofloxacin using 1H NMR. Ten metabolites changed in urine following enrofloxacin treatment. Acetate decreased due to the loss of microbial catabolism of sugars and polysaccharides, lowered level of trimethylamine-N-oxide was connected with the loss of microbial catabolism of choline, while loss of microbial enzyme degradation was reflected in increased creatine and creatinine concentration. Eight metabolites changed in fecal extracts of mice treated with enrofloxacin, including depletion of amino acids produced by microbial proteases, reduction in metabolites generated by lactate-utilizing bacteria and increased urea caused by loss of microbial ureases. Kwon et al. [26] applied LC–MS/MS to explore the effects of the dihydrofolate reductase inhibitor trimethoprim in Escherichia coli by tracking both metabolite concentrations and fluxes throughout the folate pathway. Kinetic flux profiling with 15N-labeled ammonia in Escherichia coli reveals that trimethoprim leads to blockade not only of dihydrofolate reductase but also of another critical enzyme in folate metabolism: folylpoly--glutamate synthetase. Fosmidomyoin is a new broad-spectrum antimicrobial agent, currently in clinical trials of combination therapies for the treatment of malaria. A novel MS method to quantitate six metabolites of non-mevalonate isoprenoid metabolism from complex biological samples developed by Zhang et al. [27] permits to define the in vivo metabolic response to fosmidomycin. Metabolic profiling demonstrated a block of isoprenoid metabolism following fosmidomycin treatment in both Plasmodium falciparum malaria parasites and Escherichia coli. The results validated fosmidomycin as a biological reagent for blocking non-mevalonate isoprenoid metabolism and suggested a second in vivo target for fosmidomycin within isoprenoid biosynthesis, in two evolutionarily diverse pathogens. This is a group of specialized drugs used primarily to treat cancer, the so-called anti- tumour or anticancer drugs. A number of compounds with various mechanisms of actions have been developed. Li et al. [28] profiled metabolites of ifosfamide and cyclophosphamide in mouse by LC–MS (ESI, electrospray ionization, qTOF), and the results were analyzed by multivariate data analysis. Of the total 23 drug metabolites identified by the method for both ifosfamide and cyclophosphamide, five were found to be novel. Ifosfamide preferentially underwent N-dechloroethylation, the pathway yielding 2-chloroacetaldehyde, while cyclophosphamide preferentially underwent ring-opening, the pathway yielding acrolein. Additionally, S-carboxymethylcysteine and thiodiglycolic acid, two downstream ifosfamide and cyclophosphamide metabolites, were produced similarly in both ifosfamide- and cyclophosphamide-treated mice suggesting that other metabolites, perhaps precursors of thiodiglycolic acid, may be responsible for ifosfamide encephalopathy and nephropathy. Morvan and Demidem [29] applied metabolomics, using two-dimensional proton high-resolution magic angle spinning (1H HR-MAS) NMR spectroscopy to investigate metabolite disorders following treatment by chloroethylnitrosourea of murine B16 melanoma and 3LL pulmonary carcinoma in vivo. Although some differences were observed in the metabolite profile of untreated tumour types, the prominent metabolic features of the response to nitrosourea were common to both. During the growth inhibition phase, there was an accumulation of glucose (more than 10-fold), glutamine (more than 3 to 4-fold) and aspartate (more than 2 to 5-fold). This response testified to nucleoside de novo synthesis down-regulation and drug efficacy. However, this phase also involved an increase in alanine, J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
a decrease in succinate and an accumulation of serine-derived metabolites (glycine, phosphoethanolamine and formate). During the growth recovery phase polyunsaturated fatty acid accumulation (1.5 to 2-fold) and reduced utilization of glucose remained comparable with glutamine. Wen et al. [30] treated male Sprague-Dawley rats with cisplatin (10 mg/kg single dose), and the urine samples obtained before and after treatment were analyzed by NMR. Multivariable analysis of NMR data presented clear separation between non-treated and treated groups. The statistical results from orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that 1,3-dimethylurate, taurine, glucose, glycine and branched-chain amino acid (isoleucine, leucine and valine) were significantly elevated in the treated group and that phenylacetylglycine and sarcosine levels were decreased in the treated group. Wang et al. [31] used a metabolomics-based systems toxicology approach to profile urinary metabolites for toxicity related processes and pathogenesis induced by doxorubicin to rats. Endogenous metabolite profiles were obtained with LC–MS for rats receiving different single doses of doxorubicin (5, 10 or 20 mg/kg) prior and at three time points after dosage. Various metabolites involved in the toxic processes were identified and it was confirmed that metabolomics as a systems toxicology approach was able to provide comprehensive information on the dynamic process of drug induced toxicity. Triba et al. [32] investigated the metabolomic profiles of B16 melanoma cells in vitro with 1H HR-MAS NMR and OPLS multivariate statistical analysis. They compared the profiles for untreated melanoma B16-F10 cells and Ca2+ chelating ethylene glycol tetraacetic acid, doxorubicin or BP7033 bisphosphonate treated cells. Untreated and tetraacetic acid treated cells, which had similar profiles, were considered together as control cells. Doxorubicin and BP7033 displayed distinct metabolic profiles. Important changes in neutral lipids and inositol were related to doxorubicin activity, whereas BP7033 affected essentially phospholipids and alanine/lactate metabolism. Serkova and Boros [33] performed stable isotope-based dynamic metabolic profiling (SIDMAP) studies conducted in parallel with the development and clinical testing of imatinib. The results showed that this targeted drug is the most effective in controlling glucose transport, direct glucose oxidation for RNA ribose synthesis in the pentose cycle as well as de novo long-chain fatty acid synthesis. Tracer-based NMR studies revealed a restitution of mitochondrial glucose metabolism and an increased energy state by reversing the Warburg effect, consistent with a subsequent decrease in anaerobic glycolysis. Moreover, it was found that imatinib-resistant cells utilize alternate substrates for macromolecule synthesis to overcome limited glucose transport controlled by imatinib. Klawitter et al. [34] analysed two human imatinib-sensitive CML cell lines, LAMA84-s and K562-s, and their resistant counterparts, LAMA84-r and K562-r, by NMR spectroscopy to assess global metabolic profiling, including energy state, glucose and phospholipid metabolism. They found that phospholipid metabolism and lactate production were highly predictive for cell response to imatinib. Sensitive cells showed significantly decreased glycolytic activity (lactate) and phospholipid synthesis (phosphocholine) as well as increased phospholipid catabolism (glycerophosphocholine) after 24 h of 1 M imatinib treatment, which correlated with inhibition of cell proliferation and induction of apoptosis. In contrast to their sensitive counterparts, the K562-R and LAMA84-r maintained increased phospholipid synthesis and glycolytic lactate production in the presence of 1 M (K562-r and LAMA84-r) and 5 M (K562-R) imatinib. Liu et al. [35] used the LC–MS (qTOF) method to characterize metabolites of icotinib, a novel anti-cancer drug, in human plasma, urine and feces and NMR detection to determine the connection between side-chain and quinazoline groups for some complex metabolites. In total, 29 human metabolites (21 isomer metabolites) were characterized, of which 23 metabolites are novel compared to the metabolites in rats. This metabolic study revealed that icotinib was extensively metabolized at the 12-crown-4 ether moiety (ring-opening and J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
further oxidation), carbon 15 (hydroxylation) and an acetylene moiety (oxidation) to yield 19 oxidized metabolites and to further form 10 conjugates with sulfate acid or glucuronic acid. Ciclosporin (Cyclosporin A) is a widely used immunosuppressant drug in organ transplantation to prevent rejection. It reduces the activity of the immune system by interfering with the activity of T cells. Klawitter et al. [36] conducted an open label, placebo-controlled, crossover study assessing the time-dependent toxicodynamic effects of a single oral ciclosporin dose (5 mg/kg) on the kidney in 13 healthy individuals. They assessed ciclosporin and 15-F2t-isoprostane concentrations in plasma and urine samples using LC–MS and metabolite profiles using 1H-NMR spectroscopy. The maximum ciclosporin concentrations were 1489  425 ng/ml (blood) and 2629  1308 ng/ml (urine). The increase in urinary 15-F2t-isoprostane observed 4 h after administration of ciclosporin indicated an increase in oxidative stress. 15-F2t-Isoprostane concentrations were on average 2.9-fold higher after ciclosporin than after placebo (59.8  31.2 vs. 20.9  19.9 pg/mg creatinine). The major metabolites that differed between the 4 h urine samples after ciclosporin and placebo were citrate, hippurate, lactate, trimethylamine N-oxide, creatinine and phenylalanine. Based on these results it could be concluded that changes in urine metabolite patterns as a molecular marker are sufficiently sensitive for the detection of negative effects of ciclosporin on the kidney after a single oral dose. Samuelsson et al. [37] used 1H NMR metabolomics to compare blood plasma and plasma lipid extracts from rainbow trout exposed to the synthetic contraceptive estrogen ethinylestradiol (EE2) with plasma from control fish. The plasma metabolite profile was affected in fish exposed to 10 ng/L but not 0.87 ng/L of EE2, which was in agreement with an induced vitellogenin synthesis in the high dose group only, as measured by enzyme-linked immunosorbent assay (ELISA). The main affected metabolites were vitellogenin, alanine, phospholipids and cholesterol. The effects of a combined oral contraceptive taken continuously (90 g levonorgestrel/20 g ethinylestradiol) or cyclically (21/7 days pattern; 100 g levonorgestrel/20 g ethinylestradiol) on 30 variables related to haemostasis, lipids, carbohydrates, bone metabolism, and sex hormone-binding globulin was investigated by Rad et al. [38]. After 13 pill packs, changes in total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol differed significantly between the continuous and cyclic regimens, respectively. Enea et al. [39] used metabolomics to investigate biochemical changes resulting from acute and chronic physical exercise in women using identical oral contraceptives. The urine samples were analyzed by 1H NMR spectroscopy, and multivariate statistical techniques were utilized to process the data. Distinguishing characteristics were observed only in the urine profiles of specimens collected before and 30 min after the short-term, intensive exercise test. The metabolites responsible for such changes were creatinine, lactate, pyruvate, alanine, β-hydroxybutyrate, acetate and hypoxanthine. The excretion of lactate, pyruvate, alanine, β-hydroxybutyrate and hypoxanthine increased similarly after the completion of the short-term, intensive exercise test, while acetate excretion increased to a lesser extent in trained than in untrained subjects. 3. Conclusions
The metabolomics approach can provide new and important information and knowledge for drug design, discovery and development. Metabolomics can facilitate discovery of new J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
lead compounds, improve biomarker identification and help to monitor drug metabolism and toxicity. The identification of biomarkers facilitates monitoring of disease status and drug efficacy. This approach represents an effective, relatively inexpensive route which can be used to addressing many of the riskier or more expensive issues associated with discovery, development and monitoring of drug products. Screening strategies allowing detection of the physiological compounds/potential drugs are promising approaches for detection of drug misuse. Profiling of biological matrices used to reveal biological effects of a drug can be performed by targeting a particular class of compounds or in an untargeted way using global strategies, such as transcriptomics, proteomics or metabolomics. Acknowledgements
This publication is a result of implementation of the following project: “Research and development of novel technologies of chemical analysis for metabonomics/metabolomics” (“Výskum a vývoj nových technológií chemickej analýzy pre metabonomiku/metabolomiku”) ITMS: 26240220007 supported by the Research & Development Operational Programme funded by the ERDF and it was partially financially supported by Sanofi Aventis Pharma Slovakia. References
1. Kralova, K.; Jampilek, J.; Ostrovsky, I. Metabolomics in research of phytotherapeutics. Ces. Slov. Farm. 2012, 61, 21–25.
2. Kralova, K.; Jampilek, J.; Ostrovsky, I. Metabolomics – useful tool for study of plant responses to abiotic stresses. Ecol. Chem. Eng. S 2012, 19, 133–161.
3. Lee, E.; Chow, L.Y.; Leung, C.M. Metabolic profile of first and second generation antipsychotics among Chinese patients. Psychiatry Res. 2011, 185, 456–458.
4. Xuan, J.K.; Pan, G.H.; Qiu, Y.P.; Yang, L.; Su, M.M.; Liu, Y.M.; Chen, J.; Feng, G.Y.; Fang, Y.R.; Jia, W.; Xing, Q.H.; He, L. Metabolomic profiling to identify potential serum
biomarkers for schizophrenia and risperidone action. J. Proteome. Res. 2011, 10,
5433–5443.
5. Um, S.Y.; Chung, M.W.; Kim, K.B.; Kim, S.H.; Oh, J.S.; Oh, H.Y.; Lee, H.J.; Choi, K.H. Pattern recognition analysis for the prediction of adverse effects by nonsteroidal
anti-inflammatory drugs using 1H NMR-based metabolomics in rats. Anal. Chem. 2009,
81, 4734–4741.
6. Verhoeckx, K.C.M.; Bijlsma, S.; Jespersen, S.; Ramaker, R.; Verheij, E.R.; Witkamp, R.F.; van der Greef, J.; Rodenburg, R.J.T. Characterization of anti-inflammatory
compounds using transcriptomics, proteomics, and metabolomics in combination with
multivariate data analysis. Int. Immunopharmacol. 2004, 4, 1499–1514.
7. Lv, H.T.; Liu, L.; Palacios, G.; Chen, X. Metabolomic analysis characterizes tissue specific indomethacin-induced metabolic perturbations of rats. Analyst 2011, 136,
2260–2269.
8. Jung, J.; Park, M.; Park, H.J.; Shim, S.B.; Cho, Y.H.; Kim, J.; Lee, H.S.; Ryu, D.H.; Choi, D.; Hwang, G.S. 1H NMR-based metabolic profiling of naproxen-induced toxicity
in rats. Toxicol. Lett. 2011, 200, 1–7.
9. Liu, J.Y.; Li, N.; Yang, J.; Li, N.; Qiu, H.; Ai, D.; Chiamvimonvat, N.; Zhu, Y.; Hammock, B.D. Metabolic profiling of urine plasma reveals an unexpected biomarker in
rofecoxib-mediated cardiovascular events. Proc. Natl. Acad. Sci. 2010, 107,
17017–17022.
J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
10. Clayton, T.A.; Lindon, J.C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J.P.; Le Net, J.L.; Baker, D.; Walley, R.J.; Everett, J.R.; Nicholson, J.K. Pharmaco-
metabonomic phenotyping and personalized drug treatment. Nature 2006, 440,
1073–1077.
11. Sun, J.; Schnackenberg, L.K.; Holland, R.D.; Schmitt, T.C.; Cantor, G.H.; Dragan, Y.R.; Beger, R.D. Metabonomics evaluation of urine from rats given acute and chronic doses
of acetaminophen using NMR and UPLC/MS. J. Chromatogr. B 2008, 871, 328–340.
12. Nicholls, A.W.; Wilson, I.D.; Godejohann. M.; Nicholson, J.K.; Shockcor, J.P. Identification of phenacetin metabolites in human urine after administration of
phenacetin-C2H3: Measurement of futile metabolic deacetylation via HPLC/MS-SPE-
NMR and HPLC-ToF MS. Xenobiotica 2006, 36, 615–629.
13. Wheelock, C.E.; Goto, S.; Hammock, B.D.; Newman, J.W. Clofibrate-induced changes in the liver, heart, brain and white adipose lipid metabolome of Swiss-Webster mice.
Metabolomics 2007, 3, 137–145.
14. Ohta, T.; Masutomi, N.; Tsutsui, N.; Sakairi, T.; Mitchell, M.; Milburn, M.V.; Ryals, J.A.; Beebe, K.D.; Liu, A.M.; Patterson, A.D.; Yang, Z.T.; Zhang, X.Y.; Liu, W.; Qiu,
F.Y.; Sun, H.; Krausz, K.W.; Idle, J.R.; Gonzalez, F.J.; Dai, R. Fenofibrate metabolism
in the cynomolgus monkey using ultraperformance liquid chromatography-quadrupole
time-of-flight mass spectrometry-based metabolomics. Drug Metabol. Dispos. 2009, 37,
1157–1163.
15. Liu, A.M.; Patterson, A.D.; Yang, Z.T.; Zhang, X.Y.; Liu, W.; Qiu, F.Y.; Sun, H.; Krausz, K.W.; Idle, J.R.; Gonzalez, F.J.; Dai, R.K. Fenofibrate metabolism in the
cynomolgus monkey using ultraperformance liquid chromatography-quadrupole time-of-
flight mass spectrometry-based metabolomics. Drug Metabol. Dispos. 2009, 37,
1157–1163.
16. Aura, A.M.; Mattila, I.; Hyotylainen, T.; Gopalacharyulu, P.; Bounsaythip, C.; Oresic, M.; Oksman-Caldentey, K.M. Drug metabolome of the simvastatin formed by human
intestinal microbiota in vitro. Mol. Biosystems 2011, 7, 437–446.
17. Kumar, B.S.; Lee, Y.J.; Yi, H.J.; Chung, B.C.; Jung, B.H. Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics
combined with global and targeted approach. Anal. Chim. Acta 2010, 661, 47–59.
18. Bouman, H.J.; Schomig, E.; van Werkum, J.W.; Velder, J.; Hackeng, C.M.; Hirschhauser, C.; Waldmann, C.; Schmalz, H.G.; ten Berg, J.M.; Taubert, D.
Paraoxonase-1 is a major determinant of clopidogrel efficacy. Nat. Med. 2010, 17,
110–116.
19. Li, Y.; Landqvist, C.; Grimm, S.W. Disposition and metabolism of ticagrelor, a novel P2Y(12) receptor antagonist, in mice, rats, and marmosets. Drug Metabol. Dispos. 2011,
39, 1555–1567.
20. Bai, J.; Wang, M.X.; Chowbay, B.; Ching, C.B.; Chen, W.N. Metabolic profiling of HepG2 cells incubated with S(-) and R(+) enantiomers of anti-coagulating drug warfarin.
Metabolomics 2011, 7, 353–362.
21. Jones, D.R.; Boysen, G.; Miller, G.P. Novel multi-mode ultra performance liquid chromatography-tandem mass spectrometry assay for profiling enantiomeric
hydroxywarfarins and warfarin in human plasma. J. Chromatogr. B 2011, 879,
1056–1062.
22. Swann, J.R.; Tuohy, K.M.; Lindfors, P.; Brown, D.T.; Gibson, G.R.; Wilson, I.D.; Sidaway, J.; Nicholson, J.K.; Holmes, E. Variation in antibiotic-induced microbial
recolonization impacts on the host metabolic phenotypes of rats. J. Proteome. Res. 2011,
10, 3590–3603.
J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
23. Yap, I.K.S.; Li, J.V.; Saric, J.; Martin, F.P.; Davies, H.; Wang, Y.L.; Wilson, I.D.; Nicholson, J.K.; Utzinger, J.; Marchesi, J.R.; Holmes, E. Metabonomic and
microbiological analysis of the dynamic effect of vancomycin-induced gut microbiota
modification in the mouse. J. Proteome. Res. 2008, 7, 3718–3728.
24. Maccaferri, S.; Vitali, B.; Klinder, A.; Kolida, S.; Ndagijimana, M.; Laghi, L.; Calanni, F.; Brigidi, P.; Gibson, G.R.; Costabile, A. Rifaximin modulates the colonic microbiota
of patients with Crohn's disease: An in vitro approach using a continuous culture colonic
model system. J. Antimicrob. Chemother. 2010, 65, 2556–2565.
25. Romick-Rosendale, L.E.; Goodpaster, A.M.; Hanwright, P.J.; Patel, N.B.; Wheeler, E.T.; Chona, D.L.; Kennedy, M.A. NMR-based metabonomics analysis of mouse urine and
fecal extracts following oral treatment with the broad-spectrum antibiotic enrofloxacin
(Baytril). Magn. Reson. Chem. 2009, 47, S36–S46.
26. Kwon, Y.K.; Lu, W.Y.; Melamud, E.; Khanam, N.; Bognar, A.; Rabinowitz, J.D. A domino effect in antifolate drug action in Escherichia coli. Nature Chem. Biol. 2008, 4,
602–608.
27. Zhang, B.C.; Watts, K.M.; Hodge, D.; Kemp, L.M.; Hunstad, D.A.; Hicks, L.M.; Odoms, A.R. A second target of the antimalarial and antibacterial agent fosmidomycin revealed
by cellular metabolic profiling. Biochemistry 2011, 50, 3570–3577.
28. Li, F.; Patterson, A.D.; Hoefer, C.C.; Krausz, K.W.; Gonzalez, F.J.; Idle, J.R. Comparative metabolism of cyclophosphamide and ifosfamide in the mouse using
UPLC-ESI-QTOFMS-based metabolomics. Biochem. Pharmacol. 2010, 80, 1063–1074.
29. Morvan, D.; Demidem, A. Metabolomics by proton nuclear magnetic resonance spectroscopy of the response to chloroethylnitrosourea reveals drug efficacy and tumor
adaptive metabolic pathways. Cancer Res. 2007, 67, 2150–2159.
30. Wen, H.; Yang, H.J.; Choi, M.J.; Kwon, H.N.; Kim, M.A.; Hong, S.S.; Park, S. Identification of urinary biomarkers related to cisplatin-induced acute renal toxicity using
NMR-based metabolomics. Biomol. Ther. 2011, 19, 38–44.
31. Wang, J.S.; Reijmers, T.; Chen, L.J.; van der Heijden, R.; Wang, M.; Peng, S.Q.; Hankemeier, T.; Xu, G.W.; van der Greef, J. Systems toxicology study of doxorubicin on
rats using ultra performance liquid chromatography coupled with mass spectrometry
based metabolomics. Metabolomics 2009, 5, 407–418.
32. Triba, M.N.; Starzec, A.; Bouchemal, N.; Guenin, E.; Perret, G.Y.; Le Moyec, L. Metabolomic profiling with NMR discriminates between biphosphonate and doxorubicin
effects on B16 melanoma cells. NMR Biomed. 2010, 23, 1009–1016.
33. Serkova, N.; Boros, L.G. Detection of resistance to imatinib by metabolic profiling: Clinical and drug development implications. Am. J. Pharmacogenomics 2005, 5,
293–302.
34. Klawitter, J.; Kominsky, D.; Brown, J.L.; Klawitter, J.; Christians, U.; Leibfritz, D.; Melo, J.V.; Eckhardt, S.G.; Serkova, N.J. Metabolic characteristics of imatinib resistance
in chronic myeloid leukaemia cells. British J. Pharmacol. 2009, 158, 588–600.
35. Liu, D.Y.; Jiang, J.; Zhang, L.; Tan, F.L.; Wang, Y.X.; Hu, P. Metabolite characterization of a novel anti-cancer agent, icotinib, in humans through liquid
chromatography/quadrupole time-of-flight tandem mass spectrometry. Rapid Commun.
Mass Spectr. 2011, 25, 2131–2140.
36. Klawitter, J.; Haschke, M.; Kahle, C.; Dingmann, C.; Klawitter, J.; Leibfritz, D.; Christians, U. Toxicodynamic effects of ciclosporin are reflected by metabolite profiles
in the urine of healthy individuals after a single dose. British J. Clin. Pharmacol. 2010,
70, 241–251.
37. Samuelsson, L.M.; Forlin, L.; Karlsson, G.; Adolfsson-Eric, M.; Larsson, D.G.J. Using NMR metabolomics to identify responses of an environmental estrogen in blood plasma
of fish. Aquat. Toxicol. 2006, 78, 341–349.
J. Jampilek et al. / Int. Bull. Pharm. Sci. 2012, 1, 17–29
38. Rad, M.; Kluft, C.; de Kam, M.L.; Meijer, P.; Cohen, A.F.; Grubb, G.S.; Constantine, G.D.; Burggraaf, J. Metabolic profile of a continuous versus a cyclic low-dose combined
oral contraceptive after one year of use. Eur. J. Contracep. Reprod. Health Care 2011,
16, 85–94.
39. Enea, C.; Seguin, F.; Petitpas-Mulliez, J.; Boildieu, N.; Boisseau, N.; Delpech, N.; Diaz, V.; Eugene, M.; Dugue, B. 1H NMR-based metabolomics approach for exploring urinary
metabolome modifications after acute and chronic physical exercise. Anal. Bioanal.
Chem. 2010, 396, 1167–1176.
2012 by the authors; licensee Research Institute for Pharmacy and Biochemistry (VUFB), Brno, Czech Republic. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license

Source: http://vufb-publishing.cz/Portals/0/ibps/vol_1/issue_1-2/Jampilek2_et_al.pdf