Theodora M. Steindl

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.

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.

Structure-based virtual screening for the discovery of natural inhibitors for human rhinovirus coat protein.

Inhibitors of the human rhinovirus (HRV) coat protein are promising candidates to treat and prevent a number of upper respiratory diseases. The aim of this study was to find antiviral compounds from nature, focusing on the HRV coat protein. Through computational structure-based screening of an in-house 3D database containing 9676 individual plant metabolites from ancient herbal medicines, combined with knowledge from traditional use, we selected sesquiterpene coumarins from the gum resin asafetida as promising natural products. Chromatographic separation steps resulted in the isolation of microlobidene (1), farnesiferol C (2), farnesiferol B (3), and kellerin (4). Determination of the inhibition of the HRV-induced cytopathic effect for serotypes 1A, 2, 14, and 16 revealed a dose-dependent and selective antirhinoviral activity against serotype 2 for asafetida (IC50 = 11.0 microg/mL) and its virtually predicted constituents 2 (IC50 = 2.5 microM) and 3 (IC50 = 2.6 microM). Modeling studies helped to rationalize the retrieved results.

Influenza virus neuraminidase inhibitors: Generation and comparison of structure-based and common feature pharmacophore hypotheses and their application in virtual screening

X-ray crystallographic data of the influenza virus neuraminidase in complex with different inhibitors were used to generate chemical feature-based pharmacophore models of the binding site of this enzyme. The models were built using the software package Catalyst. Pharmacophore hypotheses derived from the 3-D structure of ligands cocrystallized with the enzyme were then compared with automatically generated common feature pharmacophore hypotheses for neuraminidase inhibitors. The latter models were found to contain fewer features and exhibited lower selectivity in virtual screening experiments. Some functions of the inhibitors obviously participate in more than one mode of interaction with the enzyme (charge-charge interaction and hydrogen bond) or form hydrogen bonds to several amino acids. Since such multiple interactions of one chemical function cannot be included into the Catalyst data format, strategies are presented to overcome these limitations. Finally, the results of 3-D database searching experiments using these hypotheses are described.

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 pharmacophores 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.

Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models.

Parallel Screening has been introduced as an in silico method to predict the potential biological activities of compounds by screening them with a multitude of pharmacophore models. This study presents an early application example employing a Pipeline Pilot-based screening platform for automatic large-scale virtual activity profiling. An extensive set of HIV protease inhibitor pharmacophore models was used to screen a selection of active and inactive compounds. Furthermore, we aimed to address the usually critically eyed point, whether it is possible in a parallel screening system to differentiate between similar molecules/molecules acting on closely related proteins, and therefore we incorporated a collection of other protease inhibitors including aspartic protease inhibitors. The results of the screening experiments show a clear trend toward most extensive retrieval of known active ligands, followed by the general protease inhibitors and lowest recovery of inactive compounds.

Development and Validation of an In Silico P450 Profiler Based on Pharmacophore Models

In todays 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.

Human rhinovirus 3C protease: generation of pharmacophore models for peptidic and nonpeptidic inhibitors and their application in virtual screening.

Three-dimensional pharmacophore models for peptidic and small organic nonpeptidic inhibitors of the human rhinovirus 3C protease were generated in a structure-based as well as in a ligand-based approach, using the software package Catalyst. The inhibitors possess an electrophilic moiety, often a Michael acceptor function, which covalently binds to a cysteine in the active site of the enzyme. Since this process presents the key step for virus inactivation, the creation of a new function in Catalyst was required in order to include this decisive functionality into the pharmacophore models. In the present study we focus on this feature definition process because it presents an innovative strategy to expand the pharmacophore description ability of the Catalyst software to also include covalent bonds between ligand and binding site. The resulting hypotheses were then used for virtual screening of 3D databases in order to verify their quality and to search for structurally diverse, possible new lead substances.

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.