BioPrint database

BioPrint ] database, plC50]. Biological assays are ofthe activities of the same set ofcompounds for on the x-axis, and compounds on the y-axis each target. The activities are shown in the form (i.e. a row contains the biological fingerprint of a of a heat map, with red most active and blue-compound as heat map). Hierarchical clustering green inactive. Compounds tend to cluster into has been performed on both axes compounds therapeutic area and this is marked on the left. 

Figure 2.6 shows a histogram of a number of different targets (assays) against which drugs (1388) in the BioPrint database were found to be active (using a cutoff of 50% inhibition at 10 pM). 

To demonstrate this, Figure 2.8 shows the comparison of similarity for Daylight structural and biological fingerprints created from a panel of 154 assays from the BioPrint database (measured by pairwise Tanimoto distance for 347 drugs with MW 200-600 60031 points) [6]. Figure 2.8a shows the overall scatter plot of the... 

The full matrix nature of the BioPrint database also enables an analysis of targets in drug chemical space. In this approach, a target is characterized by a fingerprint of the activities of a fixed set of compounds (the drug and reference compound set) against... 

Most drug labels report incidence for ADRs in categories such as common, frequent, infrequent, rare, or not known. These clinical incidence data were entered into the BioPrint database in text-based groups defined below. Although some drug labels report ADRs with continuous clinical incidence data from 0% to 100%, this data was also entered according to the text-based bins ... 

Cerep, BioPrint Database, http //www.cerep.fr/cerep/users/pages/ Collaborations/Bioprint.asp, 2007. 

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. 

The value of the BioPrint dataset is achieved from a combination of high quality in vitro data generated for each compound, and in vivo data extracted from public medical literature (see below). Relating both types of information supports the bioinformatics applications of the database. Also of value is the diversity of compounds, both chemical and biological, which are indicated for a large array of therapeutic areas. This diversity provides a good training set to develop and test various QSAR methods, and supports the cheminformatics applications of the database (Fig. 1). 

At the level of individual hits, the database can be queried to retrieve either marketed BioPrint drugs that have that same activity, or the ADR associations discussed in the previous section can be queried to identify potential ADRs and their relative risks. At the profile level, compounds with similar profiles can be identified using standard statistical methods such as similarity metrics and hierarchical clustering. This similarity can be assessed using the whole panel of assays or by using selected subsets of those assays as determined by the user. Once compounds with similar profiles have been identified, in vivo data for the similar compoimds can be accessed and examined for information that may permit the user to anticipate in vivo effects. 

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. 

The applications of the database to the exploration of in vitro-in vivo relationships (referred to as bioinformatics applications in Fig. 1) have been the focus of the last sections of this chapter. Applications of BioPrint include predicting biological and pharmaceutical properties of existing... 

Significant statistical relationships between patterns of in vitro activity and in vivo endpoints have been identified and are available in the database to aid in the interpretation of profiling data from new compounds. Some fundamental uses of BioPrint are ... 

Since BioPrint is primarily a contextual tool, and is most useful when several neighbors of a drug candidate are identihed, expanding the dataset itself will logically improve the quality of the tool. Expanding the database can be accomplished by increasing either the number of compoimds, the number of biological data per compound, or both. 

The current development plan for BioPrint calls for information-rich compounds, compounds which have human clinical data available. In 2005, 50 new compounds were added to the database, including 41 recently marketed drugs. Additional information-rich compounds are compounds with available animal data. 

Bioprint (Cerep) Drug-focused pharmacology/ADME profiling database, full matrix 2400 Experimentally determined data, full data matrix Yes... 

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