Biomedical subjects

Thierry Langer

Publications and source records attributed to Thierry Langer.

At least 37 records · Page 2Linked to original sources

PubMed2004

Chemical function based pharmacophore generation of endothelin-A selective receptor antagonists.

Both quantitative and qualitative chemical function based pharmacophore models of endothelin-A (ET(A)) selective receptor antagonists were generated by using the two algorithms HypoGen and HipHop, respectively, which are implemented in the Catalyst molecular modeling software. The input for HypoGen is a training set of 18 ET(A) antagonists exhibiting IC(50) values ranging between 0.19 nM and 67 microM. The best output hypothesis consists of five features: two hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI) function. The highest scoring Hip Hop model consists of six features: three hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI). It is the result of an input of three highly active, selective, and structurally diverse ET(A) antagonists. The predictive power of the quantitative model could be approved by using a test set of 30 compounds, whose activity values spread over 6 orders of magnitude. The two pharmacophores were tested according to their ability to extract known endothelin antagonists from the 3D molecular structure database of Derwent's World Drug Index. Thereby the main part of selective ET(A) antagonistic entries was detected by the two hypotheses. Furthermore, the pharmacophores were used to screen the Maybridge database. Six compounds were chosen from the output hit lists for in vitro testing of their ability to displace endothelin-1 from its receptor. Two of these are new potential lead compounds because they are structurally novel and exhibit satisfactory activity in the binding assay.

Algorithms↗

PubMed2004

In vitro opioid activity profiles of 6-amino acid substituted derivatives of 14-O-methyloxymorphone.

A series of 6-amino acid conjugates (glycine, alanine and phenylalanine) of the highly potent opioid analgesic 14-O-methyloxymorphone was developed in an effort to obtain agonists that would have potentially limited ability to cross the blood-brain barrier. Binding studies revealed that all derivatives displayed high affinities (0.77-2.58 nM) at the mu-opioid receptor in rat brain membranes. They were potent agonists in mouse vas deferens preparation (IC(50)=5.52-26.8 nM). While the alpha-amino acid epimers are favoured by mu-opioid receptors, the beta-epimers proved to have increased interaction with delta-sites. Only the beta-phenylalanine conjugate showed some preference for delta- over mu-opioid receptors and delta-opioid receptor agonist activity. The relatively high delta-opioid receptor affinity of this analogue was also predicted by molecular modelling studies. The newly developed ionizable derivatives could find clinical applications as potent analgesics without the adverse actions of centrally acting opioids.

Analgesics, Opioid↗

PubMed2004

Lead identification for modulators of multidrug resistance based on in silico screening with a pharmacophoric feature model.

Considerable effort has been devoted to the characterization of P-glycoprotein - drug interaction in the past. Systematic quantitative structure-activity relationship (QSAR) studies identified both predictive physicochemical parameters and pharmacophoric substructures within homologous series of compounds. Comparative molecular field analysis (CoMFA) led to distinct 3D-QSAR models for propafenone and phenothiazine analogs. Recently, several pharmacophore models have been generated for diverse sets of ligands. Starting from a training set of 15 propafenone-type MDR-modulators, we established a chemical function-based pharmacophore model. The pharmacophoric features identified by this model were (i) one hydrogen bond acceptor, (ii) one hydrophobic area, (iii) two aromatic hydrophobic areas, and (iv) one positive ionizable group. In silico screening of the Derwent World Drug Index using the model led to identification of 28 compounds. Substances retrieved by database screening are diverse in structure and include dihydropyridines, chloroquine analogs, phenothiazines, and terfenadine. On the basis of its general applicability, the presented 3DQSAR model allows in silico screening of virtual compound libraries to identify new potential lead compounds.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2003

Non-peptide angiotensin II receptor antagonists: chemical feature based pharmacophore identification.

Chemical feature based pharmacophore models were elaborated for angiotensin II receptor subtype 1 (AT(1)) antagonists using both a quantitative and a qualitative approach (Catalyst HypoGen and HipHop algorithms, respectively). The training sets for quantitative model generation consisted of 25 selective AT(1) antagonists exhibiting IC(50) values ranging from 1.3 nM to 150 microM. Additionally, a qualitative pharmacophore hypothesis was derived from multiconformational structure models of the two highly active AT(1) antagonists 4u (IC(50) = 0.2 nM) and 3k (IC(50) = 0.7 nM). In the case of the quantitative model, the best pharmacophore hypothesis consisted of a five-features model (Hypo1: seven points, one hydrophobic aromatic, one hydrophobic aliphatic, a hydrogen bond acceptor, a negative ionizable function, and an aromatic plane function). The best qualitative model consisted of seven features (Hypo2: 11 points, two aromatic rings, two hydrogen bond acceptors, a negative ionizable function, and two hydrophobic functions). The obtained pharmacophore models were validated on a wide set of test molecules. They were shown to be able to identify a range of highly potent AT(1) antagonists, among those a number of recently launched drugs and some candidates presently undergoing clinical tests and/or development phases. The results of our study provide confidence for the utility of the selected chemical feature based pharmacophore models to retrieve structurally diverse compounds with desired biological activity by virtual screening.

Acrylates↗

PubMed2003

In silico screening with benzofurane- and benzopyrane-type MDR-modulators.

Development of inhibitors of the drug efflux pump P-glycoprotein is a versatile approach to overcome multi drug resistance (MDR) in tumor therapy. In an approach to lower the conformational flexibility of the lead compound propafenone, we synthesized a set of dihydrobenzofuranes and benzopyranones. In the case of the 4 diastereomeric dihydrobenzofuranes, no significant differences in activity regarding the configuration on the side-chains at the dihydrofurane moiety (cis or trans) was observed. This may be due to the high flexibility of the side-chains, which still allow mutually overlap of pharmacophores. The benzopyranones showed a good correlation between lipophilicity and activity with gnerally lower logpotency/logP ratios. This decrease may be due to the rigidization of the molecules. In an in silico screening approach, a set of diverse propafenone-type compounds was used to establish a pharmacophore model, which was used to screen the world drug index. Among the hits retrieved there are several compounds, which were previously described as MDR-modulators. This demonstrates the validity of the model.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2003

Chemical feature-based pharmacophores and virtual library screening for discovery of new leads.

During the past years, efforts in the pharmaceutical industry have focused on optimizing the early phase hit-to-lead development of the drug discovery process. In silico-based high-throughput screening (HTS) approaches emerged, with a number of issues arising, such as the need for efficient search algorithms, library design, diversity, drug- and/or lead-likeness. These problems were addressed in numerous publications. This review focuses on the generation and use of virtual compound libraries, and on studies in which chemical feature-based pharmacophore models are used in combination with in silico screening. These procedures are generally used to obtain hits (or leads) that are more likely to give successful clinical candidates.

Combinatorial Chemistry Techniques↗

PubMed2003

Putative dynamics of vasopressin in its V1a receptor binding site.

The molecular architecture of the GPCRs, including the dynamic set of interactions between the receptor and the ligand, is one of the key structural questions of biophysical approaches. In the present study, molecular dynamics (MD) simulations were performed on the well-validated molecular model of the vasopressin V1a receptor applying different parameters (i.e., force fields, time variation, use of constraints) in order to sample the conformational space of the endogenous ligand arginine vasopressin (AVP), to explore different putative binding modes, and to analyze the simulation results with respect to experimental data. Noteworthy, it is to mention that for the first time a model of the vasopressin receptor remained stable in a 500 ps MD simulation run under vacuo boundary conditions using the Kollman all-atom FF even though no constraints were imposed. Conclusively, we determined an optimized experimental procedure for studying the dynamics and structure-functionship of this highly important family of GPCRs: the use of MD simulations with the Kollman all-atom force-field parameters on a constrained receptor. Our simplified model may be used as a basis for structure based design of new GPCR ligands and for in silico screening of virtual combinatorial chemistry libraries.

Arginine↗

PubMed2002

Synthesis and molecular modeling of new 1-aryl-3-[4-arylpiperazin-1-yl]-1-propane derivatives with high affinity at the serotonin transporter and at 5-HT(1A) receptors.

It has been proposed that 5-HT(1A) receptor antagonists augment the antidepressant efficacy of selective serotonin (5-HT) reuptake inhibitors. In a search toward new and efficient antidepressants, 1-(aryl)-3-[4-arylpiperazin-1-yl]-1-propane molecular hybrids were designed, synthesized, and evaluated for 5-HT reuptake inhibition and 5-HT(1A) receptor affinity. The design was based in coupling structural moieties related to inhibition of serotonin reuptake, such as benzo[b]thiophene derivatives to arylpiperazines, typical 5-HT(1A) receptor ligands. In binding studies, several compounds showed affinity at the 5-HT transporter and at 5-HT(1A) receptors. Molecular modeling studies predicted the pharmacophore elements required for high affinity binding and the features that enable to discriminate between agonist, partial agonist, or antagonist action at 5-HT(1A) receptors and 5-HT transporter inhibition. Solvent interactions in desolvation prior to the binding step along with enthalpy and enthropy compensations might be responsible to explain agonist, partial agonist, and antagonist character. Hydrogen-bonding capability seems to be important to break hydrogen interhelical hydrogen bonds or alternatively to form other bonds upon ligand binding. Partial agonists and antagonists are unable to do this as the full agonist, which interacts closely by long-range forces or directly. The compounds showing the higher affinity at both the 5-HT transporter (K(i) < 50 nM) and the 5-HT(1A) receptors (K(i) < 20 nM) were further explored for their ability to stimulate [(35)S]GTPgammaS binding or to antagonize 8-hydroxy-2-di-n-propylamino-tetralin (8-OH-DPAT)-stimulated [(35)]GTPgammaS binding to rat hippocampal membranes, an index of agonist/antagonist action at 5-HT(1A) receptors, respectively. Compound 8g exhibited agonist activity (EC(50) = 30 nM) in this assay, whereas compounds 7g and 8h,i behaved as weak partial agonists and 7h-j and 8j,l antagonized the R(+)-8-OH-DPAT-stimulated GTPgammaS binding. Functional characterization was performed by measuring the antagonism to 8-OH-DPAT-induced hypothermia in mice.

8-Hydroxy-2-(di-n-propylamino)tetralin↗

PubMedDate not supplied

Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example.

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.

Computer Simulation↗

PubMedDate not supplied

Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors.

The docking program LigandFit/Cerius(2) has been used to perform shape-based virtual screening of databases against the aspartic protease renin, a target of determined three-dimensional structure. The protein structure was used in the induced fit binding conformation that occurs when renin is bound to the highly active renin inhibitor 1 (IC(50) = 2 nM). The scoring was calculated using several different scoring functions in order to get insight into the predictability of the magnitude of binding interactions. A database of 1000 diverse and druglike compounds, comprised of 990 members of a virtual database generated by using the iLib diverse software and 10 known active renin inhibitors, was docked flexibly and scored to determine appropriate scoring functions. All seven scoring functions used (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, LUDI) were able to retrieve at least 50% of the active compounds within the first 20% (200 molecules) of the entire test database. A hit rate of 90% in the top 1.4% resulted using the quadruple consensus scoring of LigScore2, PLP1, PLP2, and JAIN. Additionally, a focused database was created with the iLib diverse software and used for the same procedure as the test database. Docking and scoring of the 990 focused compounds and the 10 known actives were performed. A hit rate of 100% in the top 8.4% resulted with use of the triple consensus scoring of PLP1, PLP2, and PMF. As expected, a ranking of the known active compounds within the focused database compared to the test database was observed. Adequate virtual screening conditions were derived empirically. They can be used for proximate docking and scoring application of compounds with putative renin inhibiting potency.

Ligands↗

PubMedDate not supplied

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.

Binding Sites↗

PubMedDate not supplied

The identification of ligand features essential for PXR activation by pharmacophore modeling.

Drug metabolizing enzymes and transporters are often involved in clinically relevant drug-drug interactions. These functional proteins can be induced by a wide range of xenobiotics. The induction is mediated by a group of receptors known as orphan nuclear receptors. The pregnane X receptor (PXR) is a member of this receptor family and regulates the expression of multiple Cytochrome P450 enzyme families (e.g. CYP 3A and 2B), phase II enzymes (e.g. UDP glucuronosyl transferases), and transporters (e.g. multidrug resistance protein 1). The software package Catalyst was employed to derive pharmacophore models for PXR activation. A structure based pharmacophore hypothesis and several ligand based ones were compared in order to identify ligand receptor interactions essential for receptor activation. The results suggest that hydrogen bonding to Gln285 is indispensable for PXR activation. Most ligands were found to form a second hydrogen bond to His407. Hydrophobic interactions are not essential for receptor activation but contribute to ligand affinity. Highly active compounds share up to five hydrophobic features that allow the ligand to occupy large areas of the predominantly hydrophobic binding pocket.

Binding Sites↗

PubMedDate not supplied

Comparative analysis of protein-bound ligand conformations with respect to catalyst's conformational space subsampling algorithms.

We examined the quality of Catalyst's conformational model generation algorithm via a large scale study based on the crystal structures of a sample of 510 pharmaceutically relevant protein-ligand complexes extracted from the Protein Data Bank (PDB). Our results show that the tested algorithms implemented within Catalyst are able to produce high quality conformers, which in most of the cases are well suited for in silico drug research. Catalyst-specific settings were analyzed, such as the method used for the conformational model generation (FAST vs BEST) and the maximum number of generated conformers. By setting these options for higher fitting quality, the average RMS values describing the similarity of experimental and simulated conformers were improved from an RMS of 1.06 with max. 50 FAST generated conformers to an RMS of 0.93 with max. 255 BEST generated conformers, which represents an improvement by 12%. Each method provides best fitting conformers with an RMS value<1.50 in more than 80% of all cases. We analyzed the computing time/quality ratio of various conformational model generation settings and examined ligands in high energy conformations. Furthermore, properties of the same ligands in various proteins were investigated, and the fitting qualities of experimental conformations from the PDB and the Cambridge Structural Database (CSD) were compared. One of the most important conclusions of former studies, the fact that bioactive conformers often have energy high above that of global minima, was confirmed.

Algorithms↗

PubMedDate not supplied

Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa.

Factor Xa inhibitors are innovative anticoagulant agents that provide a better safety/efficacy profile compared to other anticoagulative drugs. A chemical feature-based modeling approach was applied to identify crucial pharmacophore patterns from 3D crystal structures of inhibitors bound to human factor Xa (Pdb entries 1fjs, 1kns, 1eqz) using the software LIGANDSCOUT and CATALYST. The complex structures were selected regarding the criteria of high inhibitory potency (i.e. all ligands show K(i) values against factor Xa in the subnanomolar range) and good resolution (i.e. at least 2.2 A) in order to generate selective and high quality pharmacophore models. The resulting chemical-feature based hypotheses were used for virtual screening of commercial molecular databases such as the WDI database. Furthermore, a ligand-based molecular modeling approach was performed to obtain common-feature hypotheses that represent the relevant chemical interactions between 10 bioactive factor Xa inhibitors and the protein, respectively. In a next step a virtual combinatorial library was designed in order to generate new compounds with similar chemical and spatial properties as known inhibitors. The software tool ILIB DIVERSE was used for this procedure in order to provide new scaffolds of this group of anticoagulants. Finally we present the combination of these two techniques, hence virtual screening was performed with selective pharmacophore models in a focused virtual combinatorial database. De novo derived molecular scaffolds that were able to adequately satisfy the pharmacophore criteria are revealed and are promising templates for candidates for further development.

Anticoagulants↗

PubMedDate not supplied

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.

Crystallography, X-Ray↗

PubMedDate not supplied

LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters.

From the historically grown archive of protein-ligand complexes in the Protein Data Bank small organic ligands are extracted and interpreted in terms of their chemical characteristics and features. Subsequently, pharmacophores representing ligand-receptor interaction are derived from each of these small molecules and its surrounding amino acids. Based on a defined set of only six types of chemical features and volume constraints, three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases. The algorithms for ligand extraction and interpretation as well as the pharmacophore creation technique from the automatically interpreted data are presented and applied to a rhinovirus capsid complex as application example.

Antineoplastic 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

Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors.

The metallopeptidase Angiotensin Converting Enzyme (ACE) is an important drug target for the treatment of hypertension, heart, kidney, and lung disease. Recently, a close and unique human ACE homologue termed ACE2 has been identified and found to be an interesting new cardiorenal disease target. With the recently resolved inhibitor-bound ACE2 crystal structure available, we have attempted a structure-based approach to identify novel potent and selective inhibitors. Computational approaches focus on pharmacophore-based virtual screening of large compound databases. Selectivity was ensured by initial screening for ACE inhibitors within an internal database and the Derwent World Drug Index, which could be reduced to zero false positives and 0.1% hit rate, respectively. An average hit reduction of 0.44% was achieved with a five feature hypothesis, searching approximately 3.8 million compounds from various commercial databases. Seventeen compounds were selected based on high fit values as well as diverse structure and subjected to experimental validation in a bioassay. We show that all compounds displayed an inhibitory effect on ACE2 activity, the six most promising candidates exhibiting IC50 values in the range of 62-179 microM.

Angiotensin-Converting Enzyme 2↗