Metabonomics in preclinical drug development

Serum and urine toxicometabolomics following gentamicin-induced nephrotoxicity in male Sprague-Dawley rats

Gentamicin (GM) is an aminoglycoside antibiotic used in treatment of various types of bacterial infections, but the major adverse effect is drug-induced nephrotoxicity. This study aimed to determine biomarkers that might predict nephrotoxicity initiated by GM using serum or urinary proton nuclear magnetic resonance (¹H NMR) spectral data in male Sprague-Dawley rats. GM (0, 30, or 300 mg/kg/d) was intraperitoneally administered for 3 consecutive days. Animals were sacrificed 2 d (D2) or 8 d (D8) after last administration of GM in order to perform analysis of serum biochemistries and histopathologic examination. Urine samples were collected every 24 h from prior to treatment until sacrifice. Serum and urinary ¹H NMR spectral data revealed apparent differential clustering between control and GM-treated groups as evidenced by principal component analysis (PCA) and orthogonal projections to latent structure-discriminant analysis (OPLS-DA) in global and targeted profiling. The concentrations of endogenous serum metabolites including 3-hydroxybutyrate, alanine, citrate, creatine, glucose, and glycine were increased significantly on D2 or D8. Urinary levels of glucose, glycine, and succinate were significantly elevated on D2 or D8, whereas the concentration of hippurate was significantly decreased on D2 and D8. Correlation of serum and urinary ¹H NMR OPLS-DA with serum biochemistry and renal histopathologic changes suggests that ¹H NMR urinalysis may be used to reliably predict or screen for GM-induced nephrotoxicity. In contrast, Western blot analysis of kidney injury molecule-1 (KIM-1) demonstrated that protein expression was not markedly altered indicating this biomarker was not sensitive to detect GM-mediated renal damage. Data suggest that these altered metabolites might serve as specific and sensitive biomarkers for GM-mediated renal damage.

Metabonomics in the preclinical and environmental toxicity field

Preclinical studies assess both efficacy and safety of new drugs through a series of assays used to identify potential target organs and determine safety thresholds. However, despite these efforts, too many drugs prove toxic to humans during clinical phases or later on the market. This paper reviews how metabonomics, one of the key players in systems biology, should be able to assist toxicologists in better predicting the adverse effects of xenobiotics.

Safety biomarkers in preclinical development: translational potential

The identification, application, and qualification of safety biomarkers are becoming increasingly critical to successful drug discovery and development as companies are striving to develop drugs for difficult targets and for novel disease indications in a risk-adverse environment. Translational safety biomarkers that are minimally invasive and monitor drug-induced toxicity during human clinical trials are urgently needed to assess whether toxicities observed in preclinical toxicology studies are relevant to humans at therapeutic doses. The interpretation of data during the biomarker qualification phase should include careful consideration of the analytic method used, the biology, pharmacokinetic and pharmacodynamic properties of the biomarker, and the pathophysiology of the process studied. The purpose of this review is to summarize commonly employed technologies in the development of fluid- and tissue-based safety biomarkers in drug discovery and development and to highlight areas of ongoing novel assay development.

Metabolomic analysis of the toxic effect of chronic low-dose exposure to acephate on rats using ultra-performance liquid chromatography/mass spectrometry

To study the toxic effect of chronic exposure to acephate at low-dose levels, a metabolomics approach based on ultra-performance liquid chromatography/mass spectrometry (UPLC-MS) was applied. Three different doses of 0.5 mg/kg/day, 1.5 mg/kg/day, and 4.5 mg/kg/day acephate were administered to Wistar rats for 24 weeks. Endogenous metabolite profiles were obtained with UPLC-MS for all rats at six time points after treatment. Some metabolites like dimethylthiophosphate and uric acid in urine were detected at week 4. Dimethylthiophosphate, which had the most significant elevations compared with other biomarkers, was considered as an early, sensitive biomarker of exposure to acephate. Moreover, there were some endogenous metabolite changes, which demonstrated that the doses of 1.5 mg/kg/day and 4.5 mg/kg/day of acephate led to renal injury and perturbed the normal metabolic processes of rats, including glucose, nucleic acid, and protein metabolism. A connection between exposure to acephate and the metabolic disturbance has been found and interpreted. Our study indicates that the metabolomics approach based on UPLC-MS of urine provides more information on toxicity than the conventional toxicological evaluation methods in measuring changes and can be considered as a promising technique for the study of the toxic effect of acephate.

Metabolomics and its potential in drug development

Metabolomics is the global and unbiased survey of the complement of small molecules (say, <1 kDa) in a biofluid, tissue, organ or organism and measures the end-products of the cellular metabolism of both endogenous and exogenous substrates. Many drug candidates fail during Phase II and III clinical trials at an enormous cost to the pharmaceutical industry in terms of both time lost and of financial resources. The constantly evolving model of drug development now dictates that biomarkers should be employed in preclinical development for the early detection of likely-to-fail candidates. Biomarkers may also be useful in the preselection of patients and through the subclassification of diseases in clinical drug development. Here we show with examples how metabolomics can assist in the preclinical development phases of discovery, pharmacology, toxicology, and ADME. Although not yet established as a clinical trial patient prescreening procedure, metabolomics shows considerable promise in this regard. We can be certain that metabolomics will join genomics and transcriptomics in lubricating the wheels of clinical drug development in the near future.

Metabonomics analysis of urine and plasma from rats given long-term and low-dose dimethoate by ultra-performance liquid chromatography-mass spectrometry

This study assessed the effects of long-term, low-dose dimethoate administration to rats by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Dimethoate (0.04, 0.12, and 0.36 mg/kg body weight/day) was administered daily to male Wistar rats through their drinking water for 24 weeks. Significant changes in serum clinical chemistry were observed in the middle- and high-dose groups. UPLC-MS revealed evident separate clustering among the different dose groups using global metabolic profiling by supervised partial least squares-discriminant analysis. Metabonomic analysis showed alterations in a number of metabolites (12 from urine and 13 from plasma), such as L-tyrosine, dimethylthiophosphate (DMTP), dimethyldithiophosphate (DMDTP), citric acid, uric acid, suberic acid, glycylproline, allantoin, isovalerylglutamic acid and kinds of lipids. The results suggest that long-term, low-dose exposure to dimethoate can cause disturbances in liver function, antioxidant and nervous systems, as well as the metabolisms of lipids, glucose, fatty acids, amino acids, and collagen in rats. DMTP and DMDTP, which had the most significant changes among all other studied biomarkers, were considered as early, sensitive biomarkers of exposure to dimethoate. The other aforementioned proposed toxicity biomarkers in metabonomic analysis may be useful in the risk assessment of the toxic effects of dimethoate. Metabonomics as a systems toxicology approach was able to provide comprehensive information on the dynamic process of dimethoate induced toxicity. In addition, the results indicate that metabonomic approach could detect systemic toxic effects at an earlier stage compared to clinical chemistry. The combination of metabonomics and clinical chemistry made the toxicity of dimethoate on rats more comprehensive.

Identification and application of biomarkers in molecular and genomic epidemiologic research

Biomarkers are characteristic biological properties that can be detected and measured in a variety of biological matrices in the human body, including the blood and tissue, to give an indication of whether there is a threat of disease, if a disease already exists, or how such a disease may develop in an individual case. Along the continuum from exposure to clinical disease and progression, exposure, internal dose, biologically effective dose, early biological effect, altered structure and/or function, clinical disease, and disease progression can potentially be observed and quantified using biomarkers. While the traditional discovery of biomarkers has been a slow process, the advent of molecular and genomic medicine has resulted in explosive growth in the discovery of new biomarkers. In this review, issues in evaluating biomarkers will be discussed and the biomarkers of environmental exposure, early biologic effect, and susceptibility identified and validated in epidemiological studies will be summarized. The spectrum of genomic approaches currently used to identify and apply biomarkers and strategies to validate genomic biomarkers will also be discussed.

Advances in mass spectrometry applied to pharmaceutical metabolomics

Metabolomics, also referred to in the literature as metabonomics, is a relatively new systems biology tool for drug discovery and development and is increasingly being used to obtain a detailed picture of a drug's effect on the body. Metabolomics is the qualitative assessment and relative or absolute quantitative measurement of the endogenous metabolome, defined as the complement of all native small molecules (metabolites less than 1,500 Da). A metabolomics study frequently involves the comparative analysis of sample sets from a normal state and a perturbed state, where the perturbation can be of any nature, such as genetic knockout, administration of a drug, or change in diet or lifestyle. Advances in mass spectrometry (MS) technologies including direct introduction or in-line chromatographic separation modes, ionization techniques, mass analyzers, and detection methods have provided powerful tools to assess the molecular changes in the metabolome. This review focuses on advances in MS pertaining to the analytical data generation for the main metabolomics methods, namely, fingerprinting, nontargeted, and targeted approaches, as they are applied to pharmaceutical drug discovery and development.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

[Recent development of metabonomics and its applications in clinical research]

In the post-genomic era, systems biology is central to the biological sciences. Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or other intervention. However, these changes can't be coupled directly to changes in biological function. As a result, metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past several years. Metabonomics is a rapidly growing research area and a system approach for comprehensive and quantitative analysis of the global metabolites in a biological matrix. Analytical chemistry approach is necessary for the development of comprehensive metabonomics investigations. Fundamentally, there are two types of metabonomics approaches: mass-spectrometry (MS) based and nuclear magnetic resonance (NMR) methodologies. Metabonomics measurements provide a wealth of data information and interpretation of these data relies mainly on chemometrics approaches to perform large-scale data analysis and data visualization, such as principal and independent component analysis, multidimensional scaling, a variety of clustering techniques, and discriminant function analysis, among many others. In this review, the recent development of analytical and statistical techniques used in metabonomics is summarized. Major applications of metabonomics relevant to clinical and preclinical study are then reviewed. The applications of metabonomics in study of liver diseases, cancers and other diseases have proved useful both as an experimental tool for pathogenesis mechanism re-search and ultimately a tool for diagnosis and monitoring treatment response of these diseases. Next, the applications of metabonomics in preclinical toxicology are discussed and the role that metabonomics might do in pharmaceutical research and development is explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology (COMET), and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Finally, the role of metabonomics in elucidating the function of the unknown or novel enzyme is mentioned.

Beyond clinical phenotype: the biologic integratome

Current classification of medical diagnosis derives from observational correlation between clinical syndromes and pathologic analysis. Limited understanding of the molecular determinants of diseases encountered in the critically ill remains a major obstacle to the rationale selection of therapeutic targets. Indeed, many human diseases reflect a disorder in physiologic processes that are known to involve the interaction of many complex control loops and to respond to a variety of pharmacologic agents and environmental factors. The advent of whole-genome sequencing and other high-throughput technologies have changed biomedical research into a data-rich discipline. "Omics" data sets that describe virtually all biomolecules in the cell are now publicly available. One of the challenges faced by investigators now lies in the interpretation of vast amounts of biological data sets to derive fundamental and applied biological information about whole systems. As mechanistic understanding of disease requires more than an agglomeration of information on the expression and activities of disease-associated molecules, network analysis has been applied to biological problems. Network analysis of the biological integratome promises to identify factors that influence disease phenotype, providing unique insight into disease mechanism. Network analysis also provides a mechanistic basis for defining phenotypic differences through consideration of unique genetic and environmental factors that govern intermediate phenotypes contributing to disease expression. Lastly, network analysis offers a unique method for identifying therapeutic targets that can alter disease expression.

Metabolomics approach to risk assessment: methoxyclor exposure in rats

The primary objective of this study was to develop exposure biomarkers that "correlate with the endocrine-disrupting effects induced by methoxyclor (MTC), an organochlorine pesticide, using" urinary (1)H nuclear magnetic resonance (NMR) spectral data. Exposure biomarkers play an important role in risk assessment. MTC is an environmental endocrine disruptor with estrogenic, anti-estrogenic, and anti-androgenic properties. A new approach of proton nuclear magnetic resonance ((1)H NMR) urinalysis using pattern recognition was proposed for exposure biomarkers of MTC in female rats. The endocrine disruptor was expected to induce estrogenic effects in a dose dependent manner which, was confirmed by the uterotrophic assay. MTC [50, 100, or 200 m g/kg/d, orally (p.o.) or subcutaneously (s.c.)] was administered to ovariectomized female Sprague-Dawley (SD) rats for 3 d consecutively and urine was collected every 24 h. The animals were sacrificed 24 h after the last dose. All animals treated orally with MTC showed a significant increase in uterine and vaginal weight at all doses. However, in the s.c. route, only a high dose of 200 mg MTC/kg induced a significant increase in uterine and vaginal weight. (1)H NMR spectroscopy revealed evident separate clustering between pre- and post-treatment groups using global metabolic profiling through principal component analysis (PCA) and partial least square (PLS) discrimination analysis (DA) after different exposure routes. With targeted profiling, the endogenous metabolites of acetate, alanine, benzoate, lactate, and glycine were selected as putative exposure biomarkers for MTC. Data suggest that the proposed putative exposure biomarkers may be useful in a risk assessment of the endocrine-disrupting effects produced by MTC.

Metabolite profiling in human urine by LC-MS/MS: method optimization and application for glucuronides from dextromethorphan metabolism

Analysis of human urine for specific compounds or metabolites is an established method for biomonitoring occupational or environmental exposures. Modern liquid chromatography-tandem mass spectrometry is not limited to single compounds but can simultaneously analyze whole classes of urine constituents with both high sensitivity and specificity. Individual differences in the composition of urine are very large in humans, which raises a number of problems that are not encountered in animal experimentation. In this report, we investigated whether analysis of glucuronides as a class could reflect differences between human individuals regarding the polymorphic activity of the cytochrome P450 enzyme CYP2D6. From a group of 152 students that had been classified for CYP2D6 activity, urine of 12 "poor metabolizers" and 35 "extensive metabolizers" was collected 90 min after ingestion of 10mg of the antitussive drug dextromethorphan (DEX) and analyzed for glucuronides. Methods development included the following aspects: adjustment of urine samples to equal creatinine concentration to avoid differences between samples in retention times and ion suppression; on-line enrichment of low-level analytes by column switching; precursor ion scan vs. theoretical multiple reaction monitoring; use of quality control samples to check for reproducibility in large sample series; peak extraction and handling of null entries to build the data matrix; logarithmic data transformation and different scaling procedures; principal component analysis (PCA) vs. discriminant analysis. Our results show that an optimized procedure not only identified the known DEX metabolites as predictors of CYP2D6-specific metabolic pathways but also indicated the presence of additional, so far unknown path-specific glucuronide metabolites. We conclude that metabolite profiling of urine and other biofluids by modern mass spectrometric methodology may help characterize individual differences and become useful in drug development and personalized pharmacotherapy.

Pharmacokinetics in drug discovery

The aim of this current review is to summarize the present status of pharmacokinetics in Drug Discovery. The review is structured into four sections. The first section is a general overview of what we understand by pharmacokinetics and the different LADMET aspects: Liberation, Absorption, Distribution, Metabolism, Excretion, and Toxicity. The second section highlights the different computational or in silico approaches to estimate/predict one or several aspects of the pharmacokinetic profile of a discovery lead compound. The third section discusses the most commonly used in vitro methodologies. The fourth and last section examines the various approaches employed towards the pharmacokinetic assessment of discovery molecules; including all the LADME processes, discussing the different mathematical methodologies available to establish the PK profile of a test compound; what the main differences are and what should be the criteria for using one or another mathematical approach. The major conclusion of this review is that the use of the appropriate preclinical assays has a key role in the long-term viability of a pharmaceutical company since applying the right tools early in discovery will play a key role in determining the company's ability to discover novel safe and effective therapeutics to patients as quickly as possible.

Chemometrics in metabonomics

We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature.

Metabonomics in pharmaceutical discovery and development

Metabonomics has emerged as a key technology in pharmaceutical discovery and development, evolving as the small molecule counterpart of transcriptomics and proteomics. In drug discovery laboratories, metabonomics aids in target identification, phenotyping, and the understanding of the biochemical basis of disease and toxicity. This review focuses on three areas where metabonomics is used in the industry: (1) analytical considerations, (2) chemometric and statistical concerns, and (3) biological aspects and applications.

Virtual Chromatographic Resolution Enhancement in Cryoflow LC−NMR Experiments via Statistical Total Correlation Spectroscopy

A new approach to enhancing information recovery from cryogenic probe "on-flow" LC-NMR spectroscopic analyses of complex biological mixtures is demonstrated using a variation on the statistical total correlation spectroscopy (STOCSY) method. Cryoflow probe technology enables sensitive and efficient NMR detection of metabolites on-flow, and the rapid spectral scanning allows multiple spectra to be collected over chromatographic peaks containing several species with similar, but nonidentical, retention times. This enables 1H NMR signal connectivities between close-eluting metabolites to be identified resulting in a "virtual" chromatographic resolution enhancement visualized directly in the NMR spectral projection. We demonstrate the applicability of the approach for structure assignment of drug and endogenous metabolites in urine. This approach is of wide general applicability to any complex mixture analysis problem involving chromatographic peak overlap and with particular application in metabolomics and metabonomics.