Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query.

Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A(3), the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These "binding site properties-based" guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available.

Evaluation of a hypertension-based patient education program in a stroke center.

Background The benefits of educational programs are recognized in chronic diseases. An education program was designed in our hospital, for hypertensive patients after an acute episode of stroke to prevent stroke recurrence. Objective Evaluate the effects of such program on patient knowledge and blood pressure management. Setting The 12-bed stroke center of the Groupe Hospitalier Paris Saint-Joseph, France. Method An individual educational session was provided to all the patients by the pharmacist a few days after admission. The effectiveness of the session was evaluated using a questionnaire completed by each patient before and after education. The patients had to identify the correct responses and to judge their answer's self-confidence. The answers were ranked based on their accuracy and the surety of the respondent. Reported medication adherence and self-measurement of blood pressure were analyzed as part of the survey. Patient satisfaction with the intervention was also measured by means of a separate questionnaire. Main outcome measure Evolution of response correctness and self-confidence as well as medication adherence and blood pressure self-measurement practice. Results 64 patients were enrolled. Correct response rate increased from 77.9 to 94.1% and the absolutely sure response rate raised from 52.9 to 80.8%. Patient self-confidence was improved mainly for correct responses. Patients reported a better medication adherence and a more frequent practice of blood pressure self-measurement. They were highly satisfied. A negative correlation was found between knowledge evolution and baseline knowledge. Conclusion Education can improve stroke patient knowledge, which may enhance medication adherence and blood pressure control. Such programs should be developed even early after a stroke.

Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches.

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

Identification of an in vivo orally active dual-binding protein-protein interaction inhibitor targeting TNFα through combined in silico/in vitro/in vivo screening.

TNFα is a homotrimeric pro-inflammatory cytokine, whose direct targeting by protein biotherapies has been an undeniable success for the treatment of chronic inflammatory diseases. Despite many efforts, no orally active drug targeting TNFα has been identified so far. In the present work, we identified through combined in silico/in vitro/in vivo approaches a TNFα direct inhibitor, compound 1, displaying nanomolar and micromolar range bindings to TNFα. Compound 1 inhibits the binding of TNFα with both its receptors TNFRI and TNFRII. Compound 1 inhibits the TNFα induced apoptosis on L929 cells and the TNFα induced NF-κB activation in HEK cells. In vivo, oral administration of compound 1 displays a significant protection in a murine TNFα-dependent hepatic shock model. This work illustrates the ability of low-cost combined in silico/in vitro/in vivo screening approaches to identify orally available small-molecules targeting challenging protein-protein interactions such as homotrimeric TNFα.

NRLiSt BDB, the manually curated nuclear receptors ligands and structures benchmarking database.

Nuclear receptors (NRs) constitute an important class of drug targets. We created the most exhaustive NR-focused benchmarking database to date, the NRLiSt BDB (NRs ligands and structures benchmarking database). The 9905 compounds and 339 structures of the NRLiSt BDB are ready for structure-based and ligand-based virtual screening. In the present study, we detail the protocol used to generate the NRLiSt BDB and its features. We also give some examples of the errors that we found in ChEMBL that convinced us to manually review all original papers. Since extensive and manually curated experimental data about NR ligands and structures are provided in the NRLiSt BDB, it should become a powerful tool to assess the performance of virtual screening methods on NRs, to assist the understanding of NR's function and modulation, and to support the discovery of new drugs targeting NRs. NRLiSt BDB is freely available online at http://nrlist.drugdesign.fr .