NMR Metabolomics Analysis of Parkinson's Disease

Blood-Derived Metabolic Signatures as Biomarkers of Injury Severity in Traumatic Brain Injury: A Pilot Study

Metabolomic biomarkers hold promise in aiding the diagnosis and prognostication of traumatic brain injury. In Canada, over 165,000 individuals annually suffer from a traumatic brain injury (TBI), making it one of the most prevalent neurological conditions. In this pilot investigation, we examined blood-derived biomarkers as proxy measures that can provide an objective approach to TBI diagnosis and monitoring. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined whether (1) blood-derived metabolites change during recovery in male participants with mild to severe TBI; (2) biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery; and (3) changes in metabolites correlate to initial injury severity. Eight male participants with mild to severe TBI (with intracranial lesions) provided morning blood samples within 1-4 days and again 6 months post-TBI. Following NMR analysis, the samples were subjected to multivariate statistical and machine learning-based analyses. Statistical modelling displayed metabolic changes during recovery through group separation, and eight significant metabolic pathways were affected by TBI. Metabolic changes were correlated to injury severity. L-alanine (R= -0.63, p < 0.01) displayed a negative relationship with the Glasgow Coma Scale. This study provides pilot data to support the feasibility of using blood-derived metabolites to better understand changes in biochemistry following TBI.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients

Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress.

Mechanisms of Neurodegeneration in Various Forms of Parkinsonism-Similarities and Differences

Parkinson’s disease (PD), dementia with Lewy body (DLB), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and multiple system atrophy (MSA) belong to a group of neurodegenerative diseases called parkinsonian syndromes. They share several clinical, neuropathological and genetic features. Neurodegenerative diseases are characterized by the progressive dysfunction of specific populations of neurons, determining clinical presentation. Neuronal loss is associated with extra- and intracellular accumulation of misfolded proteins. The parkinsonian diseases affect distinct areas of the brain. PD and MSA belong to a group of synucleinopathies that are characterized by the presence of fibrillary aggregates of α-synuclein protein in the cytoplasm of selected populations of neurons and glial cells. PSP is a tauopathy associated with the pathological aggregation of the microtubule associated tau protein. Although PD is common in the world’s aging population and has been extensively studied, the exact mechanisms of the neurodegeneration are still not fully understood. Growing evidence indicates that parkinsonian disorders to some extent share a genetic background, with two key components identified so far: the microtubule associated tau protein gene (MAPT) and the α-synuclein gene (SNCA). The main pathways of parkinsonian neurodegeneration described in the literature are the protein and mitochondrial pathways. The factors that lead to neurodegeneration are primarily environmental toxins, inflammatory factors, oxidative stress and traumatic brain injury.

Identification of metabolic kinetic patterns in different brain regions using metabolomics methods coupled with various discriminant approaches

Metabolomics is widely used as a powerful technique for identifying metabolic patterns and functions of organs and biological systems. Normally, there are multiple groups/targets involved in data processed by discriminant analysis. This is more common in cerebral studies, as there are always several brain regions involved in neuronal studies or brain metabolic dysfunctions. Furthermore, neuronal activity is highly correlated with cerebral energy metabolism, such as oxidation of glucose, especially for glutamatergic (excitatory) and GABAergic (inhibitory) neuronal activities. Thus, regional cerebral energy metabolism recognition is essential for understanding brain functions. In the current study, ten different brain regions were considered for discrimination analysis. The metabolic kinetics were investigated with ¹³C enrichments in metabolic products of glucose and measured using the nuclear magnetic spectroscopic method. Multiple discriminative methods were used to construct classification models in order to screen out the best method. After comparing all the applied discriminatory analysis methods, the boost-decision tree method was found to be the best method for classification and every cerebral region exhibited its own metabolic pattern. Finally, the differences in metabolic kinetics among these brain regions were analyzed. We, therefore, concluded that the current technology could also be utilized in other multi-class metabolomics studies and special metabolic kinetic patterns could provide useful information for brain function studies.

Holistic Metabolomic Laboratory-Developed Test (LDT): Development and Use for the Diagnosis of Early-Stage Parkinson's Disease

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is developed and used within a single laboratory. The holistic metabolomic LDT integrating the currently available data on human metabolic pathways, changes in the concentrations of low-molecular-weight compounds in the human blood during diseases and other conditions, and their prevalent location in the body was developed. That is, the LDT uses all of the accumulated metabolic data relevant for disease diagnosis and high-resolution mass spectrometry with data processing by in-house software. In this study, the LDT was applied to diagnose early-stage Parkinson's disease (PD), which currently lacks available laboratory tests. The use of the LDT for blood plasma samples confirmed its ability for such diagnostics with 73% accuracy. The diagnosis was based on relevant data, such as the detection of overrepresented metabolite sets associated with PD and other neurodegenerative diseases. Additionally, the ability of the LDT to detect normal composition of low-molecular-weight compounds in blood was demonstrated, thus providing a definition of healthy at the molecular level. This LDT approach as a screening tool can be used for the further widespread testing for other diseases, since 'omics' tests, to which the metabolomic LDT belongs, cover a variety of them.

Changes in the metabolic profiles of the serum and putamen in Parkinson's disease patients - In vitro and in vivo NMR spectroscopy studies

The aim of this study was to investigate the relationship between serum metabolomic biomarkers and brain in vivo magnetic resonance spectroscopy (MRS) biomarkers in patients with Parkinson's disease (PD) as well as to investigate compound concentration changes by comparing the results with healthy control subjects. Univariate statistical analysis of the serum showed significant differences in the levels of phenylalanine, tyrosine, lysine, glutamine, glutamate, acetone, acetate, 3-hydroxybutyrate, and 1-monoacylglycerol (1-MAG) between the PD patient group and the control group. Orthogonal partial least squares discriminant analysis showed significantly different compound concentrations of acetate, 3-hydroxybutyrate, glutamine, tyrosine, 1-MAG and testosterone. In vivo MRS of the putamen showed significantly higher concentrations of glutamine/glutamate complex and glutamine in patients with PD in comparison to control subjects. Following disrupted metabolic pathways in patients with PD were identified: dopamine synthesis, steroid hormone biosynthesis, fatty acid biosynthesis, the synthesis and degradation of ketone bodies, the metabolism of pyruvate, arginine, proline, alanine, aspartate, glutamate, tyrosine and phenylalanine. The obtained results may indicate changes in neurotransmission, disturbances in energy production and an altered cell membrane structure.

Diagnosis of Parkinson's Disease by A Metabolomics-Based Laboratory-Developed Test (LDT)

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is designed, manufactured and used in the same laboratory (i.e., an in-house test). In this study, a metabolomics-based LDT was developed. This test involves a blood plasma preparation, direct-infusion mass spectrometry analysis with a high-resolution mass spectrometer, alignment and normalization of mass peaks using original algorithms, metabolite annotation by a biochemical context-driven algorithm, detection of overrepresented metabolic pathways and results in a visualization in the form of a pathway names cloud. The LDT was applied to detect early stage Parkinson’s disease (PD)—the diagnosis of which currently requires great effort due to the lack of available laboratory tests. In a case–control study (n = 56), the LDT revealed a statistically sound pattern in the PD-relevant pathways. Usage of the LDT for individuals confirmed its ability to reveal this pattern and thus diagnose PD at the early-stage (1–2.5 stages, according to Hoehn and Yahr scale). The detection of this pattern by LDT could diagnose PD with a specificity of 64%, sensitivity of 86% and an accuracy of 75%. Thus, this LDT can be used for further widespread testing.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Chronic sleep restriction in the rotenone Parkinson's disease model in rats reveals peripheral early-phase biomarkers

Parkinson’s disease (PD) is a chronic disorder that presents a range of premotor signs, such as sleep disturbances and cognitive decline, which are key non-motor features of the disease. Increasing evidence of a possible association between sleep disruption and the neurodegenerative process suggests that sleep impairment could produce a detectable metabolic signature on the disease. In order to integrate neurocognitive and metabolic parameters, we performed untargeted and targeted metabolic profiling of the rotenone PD model in a chronic sleep restriction (SR) (6 h/day for 21 days) condition. We found that SR combined with PD altered several behavioural (reversal of locomotor activity impairment; cognitive impairment; delay of rest-activity rhythm) and metabolic parameters (branched-chain amino acids, tryptophan pathway, phenylalanine, and lipoproteins, pointing to mitochondrial impairment). If combined, our results bring a plethora of parameters that represents reliable early-phase PD biomarkers which can easily be measured and could be translated to human studies.

Recent advances and perspectives of metabolomics-based investigations in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease of the central nervous system (CNS), which affects mostly older adults. In recent years, the incidence of PD has been dramatically increasing with the aging population expanding. Due to the lack of effective biomarkers, the accurate diagnosis and precise treatment of PD are currently compromised. Notably, metabolites have been considered as the most direct reflection of the physiological and pathological conditions in individuals and represent attractive candidates to provide deep insights into disease phenotypes. By profiling the metabolites in biofluids (cerebrospinal fluid, blood, urine), feces and brain tissues, metabolomics has become a powerful and promising tool to identify novel biomarkers and provide valuable insights into the etiopathogenesis of neurological diseases. In this review, we will summarize the recent advancements of major analytical platforms implemented in metabolomics studies, dedicated to the improvement and extension of metabolome coverage for in-depth biological research. Based on the current metabolomics studies in both clinical populations and experimental PD models, this review will present new findings in metabolomics biomarkers research and abnormal metabolic pathways in PD, and will discuss the correlation between metabolomic changes and clinical conditions of PD. A better understanding of the biological underpinning of PD pathogenesis might offer novel diagnostic, prognostic, and therapeutic approaches to this devastating disease.

Metabolic Disturbances in the Striatum and Substantia Nigra in the Onset and Progression of MPTP-Induced Parkinsonism Model

Metabolic confusion has been linked to the pathogenesis of Parkinson's disease (PD), while the dynamic changes associated with the onset and progression of PD remain unclear. Herein, dynamic changes in metabolites were detected from the initiation to the development of 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) -induced Parkinsonism model to elucidate its potential metabolic mechanism. Ex vivo¹H nuclear magnetic resonance (NMR) spectroscopy was used to measure metabolite changes in the striatum and substantia nigra (SN) of mice at 1, 7, and 21 days after injection of MPTP. Metabolomic analysis revealed a clear separation of the overall metabolites between PD and control mice at different time points. Glutamate (Glu) in the striatum was significantly elevated at induction PD day 1 mice, which persisted to day 21. N-acetylaspartate (NAA) increased in the striatum of induction PD mice on days 1 and 7, but no significant difference was found in striatum on day 21. Myo-Inositol (mI) and taurine (Tau) were also disturbed in the striatum in induction PD day 1 mice. Additionally, key enzymes in the glutamate-glutamine cycle were significantly increased in PD mice. These findings suggest that neuron loss and motor function impairment in induction PD mice may be linked to overactive glutamate-glutamine cycle and altered membrane metabolism.

Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations

Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.

Neurotoxin-Induced Animal Models of Parkinson Disease: Pathogenic Mechanism and Assessment

Parkinson disease (PD) is the second most common neurodegenerative movement disorder. Pharmacological animal models are invaluable tools to study the pathological mechanisms of PD. Currently, invertebrate and vertebrate animal models have been developed by using several main neurotoxins, such as 6-hydroxydopamine, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, paraquat, and rotenone. These models achieve to some extent to reproduce the key features of PD, including motor defects, progressive loss of dopaminergic neurons in substantia nigra pars compacta, and the formation of Lewy bodies. In this review, we will highlight the pathogenic mechanisms of those neurotoxins and summarize different neurotoxic animal models with the hope to help researchers choose among them accurately and to promote the development of modeling PD.

Biomarker Research in Parkinson's Disease Using Metabolite Profiling

Biomarker research in Parkinson's disease (PD) has long been dominated by measuring dopamine metabolites or alpha-synuclein in cerebrospinal fluid. However, these markers do not allow early detection, precise prognosis or monitoring of disease progression. Moreover, PD is now considered a multifactorial disease, which requires a more precise diagnosis and personalized medication to obtain optimal outcome. In recent years, advanced metabolite profiling of body fluids like serum/plasma, CSF or urine, known as "metabolomics", has become a powerful and promising tool to identify novel biomarkers or "metabolic fingerprints" characteristic for PD at various stages of disease. In this review, we discuss metabolite profiling in clinical and experimental PD. We briefly review the use of different analytical platforms and methodologies and discuss the obtained results, the involved metabolic pathways, the potential as a biomarker and the significance of understanding the pathophysiology of PD. Many of the studies report alterations in alanine, branched-chain amino acids and fatty acid metabolism, all pointing to mitochondrial dysfunction in PD. Aromatic amino acids (phenylalanine, tyrosine, tryptophan) and purine metabolism (uric acid) are also altered in most metabolite profiling studies in PD.

Using an NMR metabolomics approach to investigate the pathogenicity of amyloid-beta and alpha-synuclein

INTRODUCTION

The pathogenicity at differing points along the aggregation pathway of many fibril-forming proteins associated with neurodegenerative diseases is unclear. Understanding the effect of different aggregation states of these proteins on cellular processes is essential to enhance understanding of diseases and provide future options for diagnosis and therapeutic intervention.

OBJECTIVES

To establish a robust method to probe the metabolic changes of neuronal cells and use it to monitor cellular response to challenge with three amyloidogenic proteins associated with neurodegenerative diseases in different aggregation states.

METHOD

Neuroblastoma SH-SY5Y cells were employed to design a robust routine system to perform a statistically rigorous NMR metabolomics study into cellular effects of sub-toxic levels of alpha-synuclein, amyloid-beta 40 and amyloid-beta 42 in monomeric, oligomeric and fibrillar conformations.

RESULTS

This investigation developed a rigorous model to monitor intracellular metabolic profiles of neuronal cells through combination of existing methods. This model revealed eight key metabolites that are altered when neuroblastoma cells are challenged with proteins in different aggregation states. Metabolic pathways associated with lipid metabolism, neurotransmission and adaptation to oxidative stress and inflammation are the predominant contributors to the cellular variance and intracellular metabolite levels. The observed metabolite changes for monomer and oligomer challenge may represent cellular effort to counteract the pathogenicity of the challenge, whereas fibrillar challenge is indicative of system shutdown. This implies that although markers of stress are more prevalent under oligomeric challenge the fibrillar response suggests a more toxic environment.

CONCLUSION

This approach is applicable to any cell type that can be cultured in a laboratory (primary or cell line) as a method of investigating how protein challenge affects signalling pathways, providing additional understanding as to the role of protein aggregation in neurodegenerative disease initiation and progression.

Altered Striatocerebellar Metabolism and Systemic Inflammation in Parkinson's Disease

Parkinson's disease (PD) is the most second common neurodegenerative movement disorder. Neuroinflammation due to systemic inflammation and elevated oxidative stress is considered a major factor promoting the pathogenesis of PD, but the relationship of structural brain imaging parameters to clinical inflammatory markers has not been well studied. Our aim was to evaluate the association of magnetic resonance spectroscopy (MRS) measures with inflammatory markers. Blood samples were collected from 33 patients with newly diagnosed PD and 30 healthy volunteers. MRS data including levels of N-acetylaspartate (NAA), creatine (Cre), and choline (Cho) were measured in the bilateral basal ganglia and cerebellum. Inflammatory markers included plasma nuclear DNA, plasma mitochondrial DNA, and apoptotic leukocyte levels. The Cho/Cre ratio in the dominant basal ganglion, the dominant basal ganglia to cerebellum ratios of two MRS parameters NAA/Cre and Cho/Cre, and levels of nuclear DNA, mitochondrial DNA, and apoptotic leukocytes were significantly different between PD patients and normal healthy volunteers. Significant positive correlations were noted between MRS measures and inflammatory marker levels. In conclusion, patients with PD seem to have abnormal levels of inflammatory markers in the peripheral circulation and deficits in MRS measures in the dominant basal ganglion and cerebellum.

Melatonin and Other Tryptophan Metabolites Produced by Yeasts: Implications in Cardiovascular and Neurodegenerative Diseases

Yeast metabolism produces compounds derived from tryptophan, which are found in fermented beverages, such as wine and beer. In particular, melatonin and serotonin, may be relevant due to their bioactivity in humans. Indeed, the former is a neurohormone related to circadian rhythms, which also has a putative protective effect against degenerative diseases. Moreover, serotonin is a neurotransmitter itself, in addition to being a precursor of melatonin synthesis. This paper summarizes data reported on fermented beverages, to evaluate dietary intake. Additionally, the article reviews observed effects of yeast amino acid metabolites on the prevention of neurodegenerative diseases (Alzheimer’s and Parkinson’s) and angiogenesis, focusing on evidence of the molecular mechanism involved and identification of molecular targets.

LC-MS-based urinary metabolite signatures in idiopathic Parkinson's disease

Increasing evidence has shown that abnormal metabolic phenotypes in body fluids reflect the pathogenesis and pathophysiology of Parkinson's disease (PD). These body fluids include urine; however, the relationship between, specifically, urinary metabolic phenotypes and PD is not fully understood. In this study, urinary metabolites from a total of 401 clinical urine samples collected from 106 idiopathic PD patients and 104 normal control subjects were profiled by using high-performance liquid chromatography coupled to high-resolution mass spectrometry. Our study revealed significant correlation between clinical phenotype and urinary metabolite profile. Metabolic profiles of idiopathic PD patients differed significantly and consistently from normal controls, with related metabolic pathway variations observed in steroidogenesis, fatty acid beta-oxidation, histidine metabolism, phenylalanine metabolism, tryptophan metabolism, nucleotide metabolism, and tyrosine metabolism. In the fruit fly Drosophila melanogaster, the alteration of the kynurenine pathway in tryptophan metabolism corresponded with pathogenic changes in the alpha-synuclein overexpressed Drosophila model of PD. The results suggest that LC-MS-based urinary metabolomic profiling can reveal the metabolite signatures and related variations in metabolic pathways that characterize PD. Consistent PD-related changes across species may provide the basis for understanding metabolic regulation of PD at the molecular level.

The current state of drug discovery and a potential role for NMR metabolomics

The pharmaceutical industry has significantly contributed to improving human health. Drugs have been attributed to both increasing life expectancy and decreasing health care costs. Unfortunately, there has been a recent decline in the creativity and productivity of the pharmaceutical industry. This is a complex issue with many contributing factors resulting from the numerous mergers, increase in out-sourcing, and the heavy dependency on high-throughput screening (HTS). While a simple solution to such a complex problem is unrealistic and highly unlikely, the inclusion of metabolomics as a routine component of the drug discovery process may provide some solutions to these problems. Specifically, as the binding affinity of a chemical lead is evolved during the iterative structure-based drug design process, metabolomics can provide feedback on the selectivity and the in vivo mechanism of action. Similarly, metabolomics can be used to evaluate and validate HTS leads. In effect, metabolomics can be used to eliminate compounds with potential efficacy and side effect problems while prioritizing well-behaved leads with druglike characteristics.