Databases pharmacokinetics

FUN tool is a new integrated software based on a multimedia model, physiologically based pharmacokinetic (PBPK) models and associated databases. The tool is a dynamic integrated model and is capable of assessing the human exposure to chemical substances via multiple exposure pathways and the potential health risks (Fig. 9) [70]. 2-FUN tool has been developed in the framework of the European project called 2-FUN (Full-chain and UNcertainty Approaches for Assessing Health Risks in FUture ENvironmental Scenarios www.2-fun.org). 

The main pre-clinical species used for pharmacokinetic studies are the rat, mouse and dog. An examination of the Biosys database for 2000 and 2001 shows that of the abstracted papers, 6334 mapped to the subject heading Pharmacokinetics . Of these, the vast majority (70%) were studies on humans. Studies on rats constituted 14% of the reports, mice 7.5% and dogs 3.4% (Table 6.2). Nonhuman primates can also be important pharmacokinetic models, but ethical and practical considerations severely limit studies in these animals such that, within the same period, they represented less than 0.5% of the abstracted reports on PK. 

The initial choice of the rat as the primary species for pharmacokinetic studies arose because of their use in pharmacology and toxicology studies. However, there is now such a large database of information about the relative pharmacokinetics of the same compounds in rats and man that, as described below, useful predictions to man can be made. 

Generating valid in silico models requires high quality databases for model training. True values of VD in human require that the parameters are calculated from pharmacokinetic data measured after intravenous administration. From equation 7 above, calculation of VDSS requires that the dose that enters the bloodstream is known, which can only be guaranteed by intravenous... 

BioPrint consists of a large database and a set of tools with which both the data and the models generated from the data can be accessed. The database contains structural information, in vivo and in vitro data on most of the marketed pharmaceuticals and a variety of other reference compounds. The in vitro data generated consist of panels of pharmacology and early ADME assays. The in vivo data consist of ADR data extracted from drug labels, mechanisms of action, associated therapeutic areas, pharmacokinetic (PK) data and route of administration data. 

Hits on individual assays can be analyzed in a couple of different ways. First, drugs can be identified that have similar strength hits and then the ADR profiles of these drugs can be examined to identify ADRs that maybe associated with these hits. Also, contained in BioPrint are extensive collections of ADR associations [2], which have been identified by querying the database for statistically significant correlations between individual assays and individual ADRs. These ADRs are stored in the database and can be accessed by searching assay or the ADR. It is also useful to consult the pharmacokinetic data to confirm that the strength of the in vitro hit is consistent with in vivo exposure levels. 

To Study interactions between proteins and drugs, an available tool is the Drug Absorption, Distribution, Metabolism, and Excretion (ADME) Associated Protein Database (see Table 1.5). The database contains information about relevant proteins, functions, similarities, substrates and hgands, tissue distributions, and other features of targets. Eor the understanding of pharmacokinetic (PK) and pharmacodynamic (PD) features, some available resources are listed in Table 1.5. For example, the Pharmacokinetic and Pharmacodynamic Resources site provides links to relevant software, courses, textbooks, and journals (see Note 5). For quantitative structure-activity relationship (QSAR), the QSAR Datasets site collects data sets that are available in a structural format (see Table 1.5). 

Ginsberg, G., D. Hattis, B. Sonawane, et al. 2002. Evaluation of child/adult pharmacokinetic differences from a database derived from the therapeutic drug literature. Toxicol. Sci. 66 185-200. 

Here too, Pearson clustering with complete linkage can be applied to identify compounds with similar ADME profiles. Having identified BioPrint drugs with similar ADME profiles, the BioPrint pharmacokinetics database (which contains literature pharmacokinetic data on over 1000 drugs) is queried and predictions for the test compound are made based on the pharmacokinetic profile of the ADME nearest neighbors. 

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). 

Prous Science provides a database for drug discovery and development encompassing all the areas in drug discovery, including metabolism. Integrity enables researchers to combine chemistry and genomics data with pharmacodynamics and pharmacokinetics databases. All the data are cumulated through available public records, literature, conferences, and patents. This database is a very useful system to acquire public information (290). 

Mechanisms involved in chlorine dioxide- and chlorite-induced oxidative stress, such as methemoglobinemia in humans and animals, would be expected to be similar across species. However, the database of pharmacokinetic and health effects information for chlorine dioxide or chlorite does not include studies in which interspecies comparisons were made. 

No ongoing studies pertaining to the toxicity or pharmacokinetics of chlorine dioxide or chlorite were located in a search of the Federal Research in Progress database (FEDRIP 2002). 

However, in recent years the term "in silica expanded its meaning to any type of modeling performed with the use of a computer. This includes the use of experimentally measured in vitra and/or in viva data for the prediction of in viva pharmacokinetics, as well as the construction and analysis of molecular databases. 

In a recent review of pharmacokinetics in drug discovery, Ruiz-Garcia et al. [81] compiled an exhaustive list of software resources for absorption prediction. The main topic in the described databases is transporters, in particular the ATP-binding cassette, of which the efflux transporter P-gp and the peptide transporter PEPTl are well known examples. These examples show that science is moving away from the simplistic passive transport view of permeability and towards an all-inclusive, mechanism-understanding model of absorption, which takes account of all the interactions between the agents involved in the specific permeation process. 

Obach, R.S., Lombardo, F. and Waters, N.J. (2008) Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 compounds. Drug Metabolism and Disposition The Biological Fate of Chemicals, 36, 1385—1405. 

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