Biomedical subjects

Theodora M Steindl

Publications and source records attributed to Theodora M Steindl.

6 recordsLinked to original sources

PubMed2006

High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening.

In order to assess bioactivity profiles for small organic molecules we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual molecules. In this proof of principle study, carried out with the new structure-based pharmacophore modelling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their known biological targets could be correctly predicted via an enrichment of corresponding pharmaco-phores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approximately 90% of all input molecules. Additionally, we discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biological activities of a compound or a compound library by screening it against a series of pharmacophore queries.

Antiviral Agents↗

PubMed2006

Predicting drug metabolism induction in silico.

The inducibility of drug-metabolizing enzymes and transporters by numerous xenobiotics has become a vital issue to be considered in the drug development process. Activation of so-called orphan nuclear receptors has been identified to result in increased expression of these detoxifying systems and consequently altered drug levels in the human body. In order to anticipate such mechanisms already in early stages of drug design and to avoid dangerous drug-drug interactions, reliable in silico simulation tools are highly desirable. This review aims to give an introduction into induction of drug metabolism and transport and focuses on computer-assisted molecular modeling prediction techniques, on their applicability and limitations, on recent case studies, and on success stories.

Animals↗

PubMed2006

Development and validation of an in silico P450 profiler based on pharmacophore models.

In today's drug discovery process, the very early consideration of ADME properties is aimed at a reduction of drug candidate drop out rate in later development stages. Apart from in vitro testing, in silico methods are evaluated as complementary screening tools for compounds with unfavorable ADME attributes. Especially members of the cytochrome P450 (P450) enzyme superfamily-- e.g. P450 1A2, P450 2C9, P450 2C19, P450 2D6, and P450 3A4-- contribute to xenobiotic metabolism, and compound interaction with one of these enzymes is therefore critically evaluated. Pharmacophore models are widely used to identify common features amongst ligands for any target. In this study, both structure-based and ligand-based models for prominent drug-metabolizing members of the P450 family were generated employing the software packages LigandScout and Catalyst. Essential chemical ligand features for substrate and inhibitor activity for all five P450 enzymes investigated were determined and analyzed. Finally, a collection of 11 pharmacophores for substrates and inhibitors was evaluated as an in silico P450 profiling tool that could be used for early ADME estimation of new chemical entities.

Cytochrome P-450 Enzyme Inhibitors↗

PubMed2005

Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

Antiviral Agents↗

PubMedDate not supplied

Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors.

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.

Algorithms↗

PubMedDate not supplied

Parallel screening: a novel concept in pharmacophore modeling and virtual screening.

Parallel screening comprises a novel in silico method to predict the potential biological activities of a compound by screening it with a multitude of pharmacophore models. Our aim is to provide a fast, large-scale system that allows for virtual activity profiling. In this proof of principle study, carried out with the software tools LigandScout and Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their biological targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, that is, successful virtual activity profiling, was achieved for approximately 90% of all input molecules. We discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the utilized search modus for screening.

Antiviral Agents↗