Discovery of a novel melanin concentrating hormone receptor 1 (MCHR1) antagonist with reduced hERG inhibition

An initial SAR study resulted in the identification of the novel, potent MCHR1 antagonist 2. After further profiling, compound 2 was discovered to be a potent inhibitor of the hERG potassium channel, which prevented its further development. Additional optimization of this structure resulted in the discovery of the potent MCHR1 antagonist 11 with a dramatically reduced hERG liability. The decrease in hERG activity was confirmed by several in vivo preclinical cardiovascular studies examining QT prolongation. This compound demonstrated good selectivity for MCHR1 and possessed good pharmacokinetic properties across preclinical species. Compound 11 was also efficacious in reducing body weight in two in vivo mouse models. This compound was selected for clinical evaluation and was given the code AMG 076.     

Extraction parameters for metabolomics from cultured cells

The successful extraction of metabolites is a critical step in metabolite profiling. By optimizing metabolite extraction, the range and quantitative capacity of metabolomics studies can be improved. We considered eight separate extraction protocols for the preparation of a metabolite extract from cultured mammalian cells. Parameters considered included temperature, pH, and cell washing before extraction. The effects on metabolite recovery were studied using a liquid chromatography high-resolution mass spectrometry (LC–HRMS) platform that measures metabolites of diverse chemical classes, including amino acids, lipids, and sugar derivatives. The temperature considered during the extraction or the presence of formic acid, a commonly used additive, was shown to have minimal effects on the measured ion intensities of metabolites. However, washing of samples before metabolite extraction, whether with water or phosphate-buffered saline, exhibited dramatic effects on measured intensities of both intracellular and extracellular metabolites. Together, these findings present a systematic assessment of extraction conditions for metabolite profiling.     

Metabolite profiling of recombinant CHO cells: Designing tailored feeding regimes that enhance recombinant antibody production

Chinese hamster ovary (CHO) cells are the primary platform for commercial expression of recombinant therapeutic proteins. Obtaining maximum production from the expression platform requires optimal cell culture medium (and associated nutrient feeds). We have used metabolite profiling to define the balance of intracellular and extracellular metabolites during the production process of a CHO cell line expressing a recombinant IgG4 antibody. Using this metabolite profiling approach, it was possible to identify nutrient limitations, which acted as bottlenecks for antibody production, and subsequently develop a simple feeding regime to relieve these metabolic bottlenecks. This metabolite profiling‐based strategy was used to design a targeted, low cost nutrient feed that increased cell biomass by 35% and doubled the antibody titer. This approach, with the potential for utilization in non‐specialized laboratories, can be applied universally to the optimization of production of commercially important biopharmaceuticals. Biotechnol. Bioeng. 2011;108: 3025–3031. © 2011 Wiley Periodicals, Inc.     

ABP688, a novel selective and high affinity ligand for the labeling of mGlu5 receptors: identification, in vitro pharmacology, pharmacokinetic and biodistribution studies

[(11)C]ABP688 (2) has recently been demonstrated to be a useful PET tracer for in vivo imaging of the metabotropic glutamate receptors type 5 (mGluR5) in rodents. We describe here the identification and preclinical profiling of ABP688 and its tritiated version [(3)H]ABP688, and show that its high affinity (K(d)=2nM), selectivity, and pharmacokinetic properties fulfill all requirements for development as a PET tracer for clinical imaging of the mGlu5 receptor.     

Metabolite profiling of fungi and yeast: from phenotype to metabolome by MS and informatics

Filamentous fungi and yeast from the genera Saccharomyces, Penicillium, Aspergillus, and Fusarium are well known for their impact on our life as pathogens, involved in food spoilage by degradation or toxin contamination, and also for their wide use in biotechnology for the production of beverages, chemicals, pharmaceuticals, and enzymes. The genomes of these eukaryotic micro-organisms range from about 6000 genes in yeasts (S. cerevisiae) to more than 10,000 genes in filamentous fungi (Aspergillus sp.). Yeast and filamentous fungi are expected to share much of their primary metabolism; therefore much understanding of the central metabolism and regulation in less-studied filamentous fungi can be learned from comparative metabolite profiling and metabolomics of yeast and filamentous fungi. Filamentous fungi also have a very active and diverse secondary metabolism in which many of the additional genes present in fungi, compared with yeast, are likely to be involved. Although the 'blueprint' of a given organism is represented by the genome, its behaviour is expressed as its phenotype, i.e. growth characteristics, cell differentiation, response to the environment, the production of secondary metabolites and enzymes. Therefore the profile of (secondary) metabolites--fungal chemodiversity--is important for functional genomics and in the search for new compounds that may serve as biotechnology products. Fungal chemodiversity is, however, equally efficient for identification and classification of fungi, and hence a powerful tool in fungal taxonomy. In this paper, the use of metabolite profiling is discussed for the identification and classification of yeasts and filamentous fungi, functional analysis or discovery by integration of high performance analytical methodology, efficient data handling techniques and core concepts of species, and intelligent screening. One very efficient approach is direct infusion Mass Spectrometry (diMS) integrated with automated data handling, but a full metabolic picture requires the combination of several different analytical techniques.     

Untargeted plasma metabolite profiling reveals the broad systemic consequences of xanthine oxidoreductase inactivation in mice

A major challenge in systems biology is integration of molecular findings for individual enzyme activities into a cohesive high-level understanding of cellular metabolism and physiology/pathophysiology. However, meaningful prediction for how a perturbed enzyme activity will globally impact metabolism in a cell, tissue or intact organisms is precluded by multiple unknowns, including in vivo enzymatic rates, subcellular distribution and pathway interactions. To address this challenge, metabolomics offers the potential to simultaneously survey changes in thousands of structurally diverse metabolites within complex biological matrices. The present study assessed the capability of untargeted plasma metabolite profiling to discover systemic changes arising from inactivation of xanthine oxidoreductase (XOR), an enzyme that catalyzes the final steps in purine degradation. Using LC-MS coupled with a multivariate statistical data analysis platform, we confidently surveyed >3,700 plasma metabolites (50-1,000 Da) for differential expression in XOR wildtype vs. mice with inactivated XOR, arising from gene deletion or pharmacological inhibition. Results confirmed the predicted derangements in purine metabolism, but also revealed unanticipated perturbations in metabolism of pyrimidines, nicotinamides, tryptophan, phospholipids, Krebs and urea cycles, and revealed kidney dysfunction biomarkers. Histochemical studies confirmed and characterized kidney failure in xor-nullizygous mice. These findings provide new insight into XOR functions and demonstrate the power of untargeted metabolite profiling for systemic discovery of direct and indirect consequences of gene mutations and drug treatments.     

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.?Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.     

Metabolomics and metabolite profiling — can we achieve the goal?

Deciphering of the plant metabolome is one of the most difficult analytical tasks in functional genomic research. Studies directed at the gene or protein expression are well established, sequencing analyses of these kinds of biopolymers on genome or proteome level are possible. This is not the case for metabolites, where identification in single sample of many chemical entities of different elemental composition and structures and various physicochemical properties is necessary. Different instrumental methods are applied for identification of metabolites but none of them allows obtaining unambiguous structural information about more than 500 compounds in single mixture (metabolite profiling). This is a much smaller number of metabolites than is predicted for single plant metabolome. However, instrumental approaches were proposed (metabolite fingerprinting) in which biochemical phenotype of an organism may be estimated, but identification of individual compounds is not possible.     

An in-silico method to predict and quantify the effect of gold nanoparticles in X-ray imaging

PurposeOver the last few years studies are conducted, highlighting the feasibility of Gold Nanoparticles (GNPs) to be used in clinical CT imaging and as an efficient contrast agent for cancer research. After ensuring that GNPs formulations are appropriate for in vivo or clinical use, the next step is to determine the parameters for an X-ray system’s optimal contrast for applications and to extract quantitative information. There is currently a gap and need to exploit new X-ray imaging protocols and processing algorithms, through specific models avoiding trial-and-error procedures and provide an imaging prognosis tool. Such a model can be used to confirm the accumulation of GNPs in target organs before radiotherapy treatments with a system easily available in hospitals, as low energy X-rays.MethodsIn this study a complete, easy-to-use, simulation platform is designed and built, where simple parameters, as the X-ray’s specifications and experimentally defined biodistributions of specific GNPs are imported. The induced contrast and images can be exported, and accurate quantification can be performed. This platform is based on the GATE Monte Carlo simulation toolkit, based on the GEANT4 toolkit and the MOBY phantom, a realistic 4D digital mouse.ResultsWe have validated this simulation platform to predict the contrast induction and minimum detectable concentration of GNPs on any given X-ray system. The study was applied to preclinical studies but is also expandable to clinical studies.ConclusionsAccording to our knowledge, no other such validated simulation model currently exists, and this model could help radiology imaging with GNPs to be truly deployed.     

Metabolite profiling: from diagnostics to systems biology

The concept of metabolite profiling has been around for several decades, but only recent technical innovations have allowed metabolite profiling to be carried out on a large scale - with respect to both the number of metabolites measured and the number of experiments carried out. As a result, the power of metabolite profiling as a technology platform for diagnostics, and the research areas of gene-function analysis and systems biology, is now beginning to be fully realized.