Metabolism databases

MetaDrug Metabolism database. Metabolite prediction. Metabolite prioritization, QSAR models for enzymes, transporters and network building algorithms for Systems-ADME/Tox www.genego.com... 

Computational methods including both metabolism databases and predictive metabolism software can be used to aid bioanalytical groups in suggesting all possible potential metabolite masses before identification by mass spectroscopy (MS) [116,117]. This approach can also combine specialized MS spectra feature prediction software that will use the outputs from databases and prediction software and make comparisons with the molecular masses observed... 

Erhardt PW. A human drug metabolism database potential roles in the quantitative predictions of drug metabolism and metabolism-related drug-drug interactions. Curr Drug Metab 2003 4 411-22. 

For a commercial database of known metabolic transformations, Borodina et al. [76] extracted all known sites of aromatic hydroxylations. These observed transformations were used to generate all possible transformations for each molecule, giving an estimate of the probability that each transformation would actually occur. The method was 85% accurate in predicting site of aromatic hydroxylation when tested against a second metabolism database containing 1552 molecules. Boyer et al. [77] took a similar approach using reaction center fingerprints to estimate the occurrence ratio of a particular metabolic transformation. The method successfully predicted the three most probable sites of metabolism in 87% of compounds tested. 

Knowledge-based methods are those based on the application of certain rules to describe the metabolism. These rules could be defined as chemical reactions relating structure and biotransformations to predict the metabolic fate of a query chemical structure, as in the Meteor approach [26], or alternatively they could be obtained by fragment analysis of a metabolic database as performed in the SPORCalc (Substrate Product Occurrence Ratio Calculator) system [27]. 

Having access to metabolism data in the early discovery stage is invaluable. For example, hepatic metabolism data could be used to characterize the pharmacokinetic behavior of a perspective lead. Several studies have reported how metabolism databases and software systems have been used at various settings (272). In this section, we will provide an overview of recent databases, software systems, websites, tools, and services that could be potential starting points for metabolism modeling at various stages in drug discovery process (271,273). 

IUPAC is constructing a human drug metabolism database model on the internet (286). This effort is based on the premise that very few published datasets are available for modeling (287,288), and even fewer sources outside the major pharmaceuticals are providing metabolism databases for various use by modeling groups. A version of this dataset is expected to be available in 2003, and it ultimately will serve as a standard for how new molecules are metabolized in humans. 

Erhardt, P. W., IUPAC. (2003) Human Dmg Metabolism Database http //www. iupac.org/proiects/2000/2000-010-l-70Q.html. 

METABOLIC DATABASES AND SIMULATION 8.3.1. Search for Metabolic Pathways and Information... 

Metabolic databases serve as online reference sources making metabolic information readily accessible via the Internet. These databases typically describe collections of enzymes, reactions, and biochemical pathways with pointers to genetic, sequence, and structural servers. Table 8.2 lists some of metabolic databases. 

In addition to the metabolic databases listed above, some of the enzyme databases described in the previous chapter (Chapter 6) also serve as useful metabolic resources. All of the enzyme and metabolic databases make use of EC (Enzyme Commission) numbers which are available at the International Union of Biochemistry and Molecular Biology (IUBMB) site (http //www.chem.qmw.ac.uk/ iubmb/enzyme/). 

A cellular redox regulator, glutathione, which undergoes NADP(H)-linked interconversion between the oxidized and reduced forms, also interconverts with its constituent amino acids (Glu, Gly, and Cys). Construct (search metabolic databases) the annotated glutathione metabolic cycle including its redox and anabolic/catabolic interconversions. 

Several databases of published biotransformations are commercially available, such as Molecular Design Ltd s Metabolite and the Accelerys Metabolism Database (formerly produced by Synopsis). The former is quite extensive, and contains in vivo and in vitro biotransformation summaries from the literature, while the latter has as its core information based on the U.K. Royal Society of Chemistry s Biotransformations series (Hawkins, 1988-1996) supplemented by additional data from the literature. Both systems are searchable by reaction type. The intelligent use of such databases provides much valuable information on likely metabolic profile. 

Applying metabonomics involves generating metabolic databases for control animals and humans, diseased patients, and animals used in drug testing, and the simultaneous acquisition of multiple biochemical parameters on biological samples. Metabonomics is usually conducted with biofluids, many of which can be obtained noninvasively (urine) or relatively easily (blood), but other more exotic fluids such as cerebrospinal fluid, bile, seminal fluid, cell culture supernatants, tissue extracts, and similar preparations can also be used to determine the metabolites present, both in homeostasis and when the organism has been affected by factors such as environmental exposures. 

Metabolism databases and several predictive software packages also have been reviewed.A knowledge-based expert system for the prediction of phase I and II biotransformations called META was developed on the VAX/VMS platform,META contains over 750 biotransformations based on substrnctnres and qnantnm mechanical calculations that gave excellent predictions on test data and have been optimized using a genetic algorithm to perform better than human experts.However, when used to predict the metabolism of 42 polycyclic aromatic hydrocarbons (PAHs), META overpredicted 29 and missed 8 of 72 experimentally observed epoxidations, and missed 27 of 49 experimentally observed hydroxylations. ... 

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