New insights in the in vitro characterisation and molecular modelling of the P-glycoprotein inhibitory promiscuity.

The presence of several binding sites for both substrates and inhibitors is yet a poorly explored thematic concerning the assessment of the drug-drug interactions risk due to interactions of multiple drugs with the human transport protein P-glycoprotein (P-gp or MDR1, gene ABCB1). In this study we measured the inhibitory behaviour of a set of known drugs towards P-gp by using three different probe substrates (digoxin, Hoechst 33,342 and rhodamine 123). A structure-based model was built to unravel the different substrates binding sites and to rationalize the cases where drugs were not inhibiting all the substrates. A separate set of experiments was used to validate the model and confirmed its suitability to either detect the substrate-dependent P-gp inhibition and to anticipate proper substrates for in vitro experiments case by case. The modelling strategy described can be applied for either design safer drugs (P-gp as antitarget) or to target specific sub-site inhibitors towards other drugs (P-gp as target).

ADME-Space: a new tool for medicinal chemists to explore ADME properties.

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

Comprehensive Network Map of ADME-Tox Databases.

In the last decade, many statistical-based approaches have been developed to improve poor pharmacokinetics (PK) and to reduce toxicity of lead compounds, which are one of the main causes of high failure rate in drug development. Predictive QSAR models are not always very efficient due to the low number of available biological data and the differences in the experimental protocols. Fortunately, the number of available databases continues to grow every year. However, it remains a challenge to determine the source and the quality of the original data. The main goal is to identify the relevant databases required to generate the most robust predictive models. In this study, an interactive network of databases was proposed to easily find online data sources related to ADME-Tox parameters data. In this map, relevant information regarding scope of application, data availability and data redundancy can be obtained for each data source. To illustrate the usage of data mining from the network, a dataset on plasma protein binding is selected based on various sources such as DrugBank, PubChem and ChEMBL databases. A total of 2,606 unique molecules with experimental values of PPB were extracted and can constitute a consistent dataset for QSAR modeling.

A Computational Selection of Metabolite Biomarkers Using Emerging Pattern Mining: A Case Study in Human Hepatocellular Carcinoma.

The biomarker development in metabolomics aims at discriminating diseased from normal subjects and at creating a predictive model that can be used to diagnose new subjects. From a case study on human hepatocellular carcinoma (HCC), we studied for the first time the potential usefulness of the emerging patterns (EPs) that come from the data mining domain. When applied to a metabolomics data set labeled with two classes (e.g., HCC patients vs healthy subjects), EP mining can capture differentiating combinations of metabolites between the two classes. We observed that the so-called jumping emerging patterns (JEPs), which correspond to the combinations of metabolites that occur in only one of the two classes, achieved better performance than individual biomarkers. Particularly, the implementation of the JEPs in a rules-based diagnostic tool drastically reduced the false positive rate, i.e., the rate of healthy subjects predicted as HCC patients.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6.

MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

Pharmacophore-based discovery of inhibitors of a novel drug/proton antiporter in human brain endothelial hCMEC/D3 cell line.

BACKGROUND AND PURPOSE: An influx drug/proton antiporter of unknown structure has been functionally demonstrated at the blood-brain barrier. This transporter, which handles some psychoactive drugs like diphenhydramine, clonidine, oxycodone, nicotine and cocaine, could represent a new pharmacological target in drug addiction therapy. However, at present there are no known drugs/inhibitors that effectively inhibit/modulate this transporter in vivo. EXPERIMENTAL APPROACH: The FLAPpharm approach was used to establish a pharmacophore model for inhibitors of this transporter. The inhibitory potency of 44 selected compounds was determined against the specific substrate, [(3)H]-clonidine, in the human cerebral endothelial cell line hCMEC/D3 and ranked as good, medium, weak or non-inhibitor. KEY RESULTS: The pharmacophore model obtained was used as a template to screen xenobiotic and endogenous compounds from databases [Specs, Recon2, Human Metabolome Database (HMDB), human intestinal transporter database], and hypothetical candidates were tested in vitro to determine their inhibitory capacity with [(3)H]-clonidine. According to the transporter database, 80% of the proton antiporter inhibitor candidates could inhibit P-glycoprotein/MDR1/ABCB1 and specificity is improved by reducing inhibitor size/shape and increasing water solubility. Virtual screening results using HMDB and Recon2 for endogenous compounds appropriately scored tryptamine as an inhibitor. CONCLUSIONS AND IMPLICATIONS: The pharmacophore model for the proton-antiporter inhibitors was a good predictor of known inhibitors and allowed us to identify new good inhibitors. This model marks a new step towards the discovery of this drug/proton antiporter and will be of great use for the discovery and design of potent inhibitors that could potentially help to assess and validate its pharmacological role in drug addiction in vivo.

Generative topographic mapping-based classification models and their applicability domain: application to the biopharmaceutics Drug Disposition Classification System (BDDCS).

Earlier (Kireeva et al. Mol. Inf. 2012, 31, 301-312), we demonstrated that generative topographic mapping (GTM) can be efficiently used both for data visualization and building of classification models in the initial D-dimensional space of molecular descriptors. Here, we describe the modeling in two-dimensional latent space for the four classes of the BioPharmaceutics Drug Disposition Classification System (BDDCS) involving VolSurf descriptors. Three new definitions of the applicability domain (AD) of models have been suggested: one class-independent AD which considers the GTM likelihood and two class-dependent ADs considering respectively, either the predominant class in a given node of the map or informational entropy. The class entropy AD was found to be the most efficient for the BDDCS modeling. The predominant class AD can be directly visualized on GTM maps, which helps the interpretation of the model.

In silico prediction of aqueous solubility: a multimodel protocol based on chemical similarity.

Aqueous solubility is one of the most important ADMET properties to assess and to optimize during the drug discovery process. At present, accurate prediction of solubility remains very challenging and there is an important need of independent benchmarking of the existing in silico models such as to suggest solutions for their improvement. In this study, we developed a new protocol for improved solubility prediction by combining several existing models available in commercial or free software packages. We first performed an evaluation of ten in silico models for aqueous solubility prediction on several data sets in order to assess the reliability of the methods, and we proposed a new diverse data set of 150 molecules as relevant test set, SolDiv150. We developed a random forest protocol to evaluate the performance of different fingerprints for aqueous solubility prediction based on molecular structure similarity. Our protocol, called a "multimodel protocol", allows selecting the most accurate model for a compound of interest among the employed models or software packages, achieving r(2) of 0.84 when applied to SolDiv150. We also found that all models assessed here performed better on druglike molecules than on real drugs, thus additional improvement is needed in this direction. Overall, our approach enlarges the applicability domain as demonstrated by the more accurate results for solubility prediction obtained using our protocol in comparison to using individual models.

Toward in silico structure-based ADMET prediction in drug discovery.

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.

QSAR Modelling of CYP3A4 Inhibition as a Screening Tool in the Context of DrugDrug Interaction Studies.

Drugdrug interaction potential (DDI), especially cytochrome P450 (CYP) 3A4 inhibition potential, is one of the most important parameters to be optimized before preclinical and clinical pharmaceutical development as regard to the number of marketed drug metabolized mainly by this CYP and potentially co-administered with the future drug. The present study aims to develop in silico models for CYP3A4 inhibition prediction to help medicinal chemists during the discovery phase and even before the synthesis of new chemical entities (NCEs), focusing on NCEs devoid of any inhibitory potential toward this CYP. In order to find a relevant relationship between CYP3A4 inhibition and chemical features of the screened compounds, we applied a genetic-algorithm-based QSAR exploratory tool SQS (Stochastic QSAR Sampler) in combination with different description approaches comprising alignment-independent Volsurf descriptors, ISIDA fragments and Topological Fuzzy Pharmacophore Triplets. The experimental data used to build models were extracted from an in-house database. We derived a model with good prediction ability that was confirmed on both newly synthesized compound and public dataset retrieved from Pubchem database. This model is a promising efficient tool for filtering out potentially problematic compounds.

Mining collections of compounds with Screening Assistant 2.

BACKGROUND: High-throughput screening assays have become the starting point of many drug discovery programs for large pharmaceutical companies as well as academic organisations. Despite the increasing throughput of screening technologies, the almost infinite chemical space remains out of reach, calling for tools dedicated to the analysis and selection of the compound collections intended to be screened. RESULTS: We present Screening Assistant 2 (SA2), an open-source JAVA software dedicated to the storage and analysis of small to very large chemical libraries. SA2 stores unique molecules in a MySQL database, and encapsulates several chemoinformatics methods, among which: providers management, interactive visualisation, scaffold analysis, diverse subset creation, descriptors calculation, sub-structure / SMART search, similarity search and filtering. We illustrate the use of SA2 by analysing the composition of a database of 15 million compounds collected from 73 providers, in terms of scaffolds, frameworks, and undesired properties as defined by recently proposed HTS SMARTS filters. We also show how the software can be used to create diverse libraries based on existing ones. CONCLUSIONS: Screening Assistant 2 is a user-friendly, open-source software that can be used to manage collections of compounds and perform simple to advanced chemoinformatics analyses. Its modular design and growing documentation facilitate the addition of new functionalities, calling for contributions from the community. The software can be downloaded at http://sa2.sourceforge.net/.

Genetic deletion of trace amine 1 receptors reveals their role in auto-inhibiting the actions of ecstasy (MDMA).

"Ecstasy" [3,4-methylenedioxymetamphetamine (MDMA)] is of considerable interest in light of its prosocial properties and risks associated with widespread recreational use. Recently, it was found to bind trace amine-1 receptors (TA(1)Rs), which modulate dopaminergic transmission. Accordingly, using mice genetically deprived of TA(1)R (TA(1)-KO), we explored their significance to the actions of MDMA, which robustly activated human adenylyl cyclase-coupled TA(1)R transfected into HeLa cells. In wild-type (WT) mice, MDMA elicited a time-, dose-, and ambient temperature-dependent hypothermia and hyperthermia, whereas TA(1)-KO mice displayed hyperthermia only. MDMA-induced increases in dialysate levels of dopamine (DA) in dorsal striatum were amplified in TA(1)-KO mice, despite identical levels of MDMA itself. A similar facilitation of the influence of MDMA upon dopaminergic transmission was acquired in frontal cortex and nucleus accumbens, and induction of locomotion by MDMA was haloperidol-reversibly potentiated in TA(1)-KO versus WT mice. Conversely, genetic deletion of TA(1)R did not affect increases in DA levels evoked by para-chloroamphetamine (PCA), which was inactive at hTA(1) sites. The TA(1)R agonist o-phenyl-3-iodotyramine (o-PIT) blunted the DA-releasing actions of PCA both in vivo (dialysis) and in vitro (synaptosomes) in WT but not TA(1)-KO animals. MDMA-elicited increases in dialysis levels of serotonin (5-HT) were likewise greater in TA(1)-KO versus WT mice, and 5-HT-releasing actions of PCA were blunted in vivo and in vitro by o-PIT in WT mice only. In conclusion, TA(1)Rs exert an inhibitory influence on both dopaminergic and serotonergic transmission, and MDMA auto-inhibits its neurochemical and functional actions by recruitment of TA(1)R. These observations have important implications for the effects of MDMA in humans.

[Chemoinformatics and virtual screening of molecules for therapeutic use]. / Apports de la chémo-informatique dans la recherche et l'optimisation des molécules d'intérêt thérapeutique.

Successful identification of new chemical entities with drug-like properties in pharmaceutical and academic research groups involves an early screen and the use of a large number of public and proprietary chemical libraries. Before applying high-throughput experimental screening approaches, virtual screening strategies have been put in place in order to sort and filter this massive amount of compounds and data available at these very early stages. Chemoinformatic tools have a crucial role in this selection process and enable therapeutic chemists to focus very early on promising candidates. Virtual screening has conventionally been based either on models of the target or the ligand (molecule), but today these models include biopharmaceutical filters addressing right from the start of the project the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the molecules. Above all, chemoinformatic tools help chemists understand better the chemical diversity they can work with, especially when comparing chemical libraries. This paper will focus on exemples of the day-to-day use of chemoinformatics in screening programs. A large part will be dedicated to new tools (chemographic and pharmacographic approaches) being developed for the representation and analysis of chemical diversity, but also for combining chemical and biological information to expedite research programs.