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BioPrint® profile & services

BioPrint® is a large, homogenous pharmacology and ADME database, which provides a unique resource for supporting decision making in drug discovery. The database is composed of three main data sets:
   chemical descriptors (structures and chemical information, 2D and 3D descriptors),
   in vitro data also considered as compound descriptors,
   collected and curated in vivo effects of drugs.
Chemical and pharmacophoric descriptors together form a data set for QSAR generation supporting organ safety modeling.
BioPrint® positions a new drug candidate in the context of marketed drugs, anticipating potential in vivo liabilities, predicting off-target activities, and ADME characteristics (Drug profile interpretation). In another application, BioPrint® serves to identify secondary targets that are not genetically parented to a test target (Target profile design). Both these applications are available as custom services without need of subscription to BioPrint®.

BioPrint® profile
The BioPrint® profile includes the assays used to explore the properties of about 2,500 BioPrint® compounds (mainly marketed drugs and reference compounds), establishing individual Pharma-ADME fingerprints for each compound.
Now offered on a standard basis, this profile is mainly based on target diversity and includes today 105 binding assays (non-peptide, peptide and nuclear receptors, ion channels and amine transporters), 34 enzyme assays (including 10 kinases, 10 proteases and 5 PDEs), as well as 20 ADME-Tox assays (Solubility, Absorption, LMS and CYP-mediated drug-drug interaction). More than 70% are human targets.
The 159 assays of this profile represent a rationalized panel from a larger assay collection in the database, selected for highest information content.

BioPrint® drug profile interpretation
Any compound in drug development, when run as a test compound on the BioPrint® profile, can be placed in the context of already marketed drugs, when similar in vitro profiles exist in the database. Hypotheses on clinical behavior of the test compounds can be drawn by comparison with data on BioPrint® compounds that have been collected and analyzed over the last ten years, including:

Clinical effects of drugs with a similar profile or sub-profile.

Adverse Drug Reactions (ADRs) correlated with in vitro assays: more than 5,000 significant statistical associations have been identified between the activity of molecules on a specific target and the occurrence of an ADR in man.

Probability of occurrence of clinical effects or ADRs estimated with multivariate models using the complete in vitro profile: today more than 170 ADRs have been modeled.

BioPrint® target profile design
Any target, run as a test target on the BioPrint® compound library, can be compared to any other target in BioPrint®, based on its pharmacological profile (compounds that interact with a given target). Analysis of cross-reacting compounds between targets (number and intensity of common hits), allows to identify targets that are “pharmacophorically related” to the test target. Interestingly, a profile of pharmacophorically related targets can differ significantly to a list of genetically related targets.
BioPrint® target profile design represents highly valuable information, when setting up secondary screening tests, anticipating secondary targets and effectively supporting lead optimization to monitor potential secondary target related liability issues. If the test target is already part of BioPrint® assays, BioPrint® target profile design is limited to data-analysis and establishment of a profile of secondary targets. If the test target is not part of the BioPrint® assay panel, Cerep can develop the assay and run the BioPrint® compounds on an exclusive or shared data basis.

References

The BioPrint® approach for the evaluation of ADMET properties: application to the prediction of cytochrome P450 2D6 inhibition
Gozalbes, R., et al. (2006) in Pharmacokinetic profiling in drug Research, VHCA, Zürich, WILEY-VCH, Weinheim.
Can we rationally design promiscuous drugs?
Hopkins et al. (2006) et al. (2005), Current Opinion in Structural Biology, 16:127-136
Construction of a homogeneous and informative in vitro profiling database for anticipating the clinical effects of drugs
Froloff, N. et al. (2006) in Chemogenomics knowledge-based approaches to Drug discovery, Imperial College Press, London.
QSAR modeling of in vitro inhibition of cytochrome P450 3A4
Mao, B., et al. (2006) J. of Chem. Inf. and Modeling, 46: 2125-2134.
Pharmacological and pharmaceutical profiling: new trends
Hamon, V., et al. (2006) in The Process of New Drug Discovery and Development (2nd Edition), Smith, C.G. & O’Donnell, J.T. (Eds.), Informa Healthcare, New York..
Analysis of drug-induced effect patterns to linj structure and side effects of medicines
Fliri, F. A. et al. (November 2005), Nature Chemical Biology
Biological spectra analysis: Linking biological activity profiles to molecular structure
Fliri, F. A. et al. (2005), PNAS, 102: 261-266
G-protein-coupled receptor affinity prediction based on the use of a profiling dataset: QSAR design, synthesis, and experimental validation
Rolland C., et al. (2005) Journal of Medicinal Chemistry, 48: 6563-6574.
Probing drug action using in vitro pharmacological profiles
Froloff, N. (2005) Trends in Biotechnology, 23: 488-490.
Predicting ADME properties and side effects: The BioPrint approach
Krejsa C.M., et al. (2003) Curr. Opin. Drug. Discov. Devel., 6: 470-480
Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces – A novel understanding of the molecular similarity principle in the context of multiple receptor binding profiles
Horvath D. and Jeandenans C. (2003). J. Chem. Inf. & Comp. Sci., 43: 680-690.
Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces – A benchmark for neighborhood behavior assessment of different in silico similarity metrics
Horvath D. and Jeandenans C. (2003) J. Chem. Inf. & Comp. Sci., 43: 691-698.
Method of identification of leads or active compounds
Jean, T. and Chapelain, B. (1999) - EP 1018008B1.