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 .
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.
[a] 1Introduction Nuclear receptors (NRs) are transcription factors naturally switched on and off by small-molecule hormones that monitor aw ide range of physiological functions. NRs can be targeted in numerous diseases [1] and synthetic ligands can artificially modulate the action of NRs,m ainly by activating (agonist ligands) or inhibiting (antagonist ligands) its activity.I nt his context, identifying the best ligand of ag iven targeti sn ot sufficient,i ti sn ecessary to find al igand with the suitablep harmacological profilea nd thus to be able to predict the agonist or antagonist behaviour of aN Rl igand. Virtual screening methods are widely used to predict the activity of small compounds [2] and can also be applied to the prediction of the pharmacologicalp rofile of NRs ligands using the knowledgeo ft he molecular bases of NRs agonism and antagonism.[3] However,t he different virtual screening tools evaluated in this purpose lead to mixed results [4] and being able to predict the pharmacological profile of NRs ligands remains ac hallenge.In this study, we review the ability of currently available virtual screeningm ethodst oc haracterize specifically the pharmacologicalp rofile of NRs ligands using the 27 NRLiSt BDB datasets.[5] After at horough description of the results we obtained using molecular descriptors and ar ecall of the results we obtainedu sing ad ocking method [4i] and a3 D ligand-based (LB) and structure-based (SB) pharmacophore modeling method, [6] we will emphasize on the advantages and drawbacks of each approach. 2Material and MethodsNuclearR eceptors Ligands and Structures Benchmarking DataBase (NRLiStB DB). The NRLiSt BDB [5] is ap ublic benchmarkingd atabase dedicated to the NRs and constructed to be used for the evaluation of both SB and LB methods. The NRLiSt BDB includest he 27 NRs (out of the 48 known human NRs) for whichm ore thano ne agonist ligand, one antagonistl igand, and at least one experimental structure were described. For each NR, all of the ligands foundt ob e agonisto ra ntagonist in the scientific literature are provided in two separated datasets and all available human holo PDB structures are provided (except for RXRg,f or which Abstract:N uclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4years, we tackled the pharmacological profilea ssessment of NR ligands for which we constructedt he NRLiSt BDB. We evaluated and compared the performance of different virtual screeninga pproaches:m ean of molecular descriptor distribution values, molecular dockinga nd 3D pharmacophore models. The simple comparison of the distributionp rofiles of 4885 molecular descriptors between the agonista nd antagonist datasets didn'tp rovide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonistl igands. But the availability of PDB structures in the "pharmacological-profile-to-predictbound-state" (agonist-bound or antagonist-bound) and the a...
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.
Background Smoking is a strong risk factor for cancer and atherosclerosis. Cancer mortality, especially from lung cancer, overtakes cardiovascular (CV) death rate in patients with peripheral arterial disease (PAD). Only a few patients with lung cancer after PAD management may benefit from surgical excision. Circulating tumor cells (CTC) associated with low-dose chest CT (LDCT) may improve early cancer detection. This study focuses on a screening strategy that can address not only lung cancer but all tobacco-related cancers in this high-risk population. Methods DETECTOR Project is a prospective cohort study in two French University hospitals. Participants are smokers or former smokers (≥30 pack-years, quitted ≤15 years), aged ≥55 to 80 years, with atherosclerotic PAD or abdominal aortic aneurysm. After the first screening round combining LDCT and CTC search on a blood sample, two other screening rounds will be performed at one-year interval. Incidental lung nodule volume, volume doubling time and presence of CTC will be taken into consideration for adapted diagnostic management. In case of negative LDCT and presence of CTC, a contrast enhanced whole-body PET/CT will be performed for extra-pulmonary malignancy screening. Psychological impact of this screening strategy will be evaluated in population study using a qualitative methodology. Assuming 10% prevalence of smoking-associated cancer in the studied population, a total of at least 300 participants will be enrolled. Discussion Epidemiological data underline an increase incidence in cancer and related death in the follow-up of patients with PAD, compared with the general population, particularly for tobacco-related cancers. The clinical benefit of a special workup for neoplasms in patients with PAD and a history of cigarette smoking has never been investigated. By considering CTCs detection in this very high-risk selected PAD population for tobacco-induced cancer, we expect to detect earlier pulmonary and extra-pulmonary malignancies, at a potentially curable stage. Trial registration The study was registered in the French National Agency for Medicines and Health Products Safety (No N° EUDRACT_ID RCB: 2016-A00657–44) and was approved by the ethics Committee for Persons Protection (IRB number 1072 and n° initial agreement 2016-08-02; ClinicalTrials.gov identifier NCT02849041).
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