The haves, the have-nots, and the health of everyone: the relationship between social inequality and environmental quality

A growing body of literature suggests that more unequal societies have more polluted and degraded environments, perhaps helping explain why more unequal societies are often less healthy. We summarize the mechanisms by which inequality can lead to environmental degradation and their relevance for public health. We review the evidence of a relationship between environmental quality and social inequality along the axes of income, wealth, political power, and race and ethnicity. Our review suggests that the evidence is strongest for air- and water-quality measures that have more immediate health implications; evidence is less strong for more dispersed pollutants that have longer-term health impacts. More attention should be paid in research and in practice to links among inequality, the environment, and health, including more within-country studies that may elucidate causal pathways and points of intervention. We synthesize common metrics of inequality and methodological considerations in an effort to bring cohesion to such efforts.

Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation

Prediction of human Cytochrome P450 (CYP) binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD) simulations and Linear Interaction Energy (LIE) theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE) of 4.1 kJ mol-1 and a standard error in prediction (SDEP) in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units).

Toward a unifying strategy for the structure-based prediction of toxicological endpoints

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.

Ground-Truthing Validation to Assess the Effect of Facility Locational Error on Cumulative Impacts Screening Tools

Researchers and government regulators have developed numerous tools to screen areas and populations for cumulative impacts and vulnerability to environmental hazards and risk. These tools all rely on secondary data maintained by government agencies as part of the regulatory and permitting process. Stakeholders interested in cumulative impacts screening results have consistently questioned the accuracy and completeness of some of these datasets. In this study, three cumulative impacts screening tools used in California were compared, and ground-truth validation was used to determine the effect database inaccuracy. Ground-truthing showed substantial locational inaccuracy and error in hazardous facility databases and statewide air toxics emission inventories of up to 10 kilometers. These errors resulted in significant differences in cumulative impact screening scores generated by one screening tool, the Environmental Justice Screening Method.

Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models

The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project "eTOX" (electronic toxicity) and its application to the in silico models developed within the frame of this project.

Multi-Component Protein - Protein Docking Based Protocol with External Scoring for Modeling Dimers of G Protein-Coupled Receptors

In order to apply structure-based drug design techniques to GPCR complexes, it is essential to model their 3D structure. For this purpose, a multi-component protocol was derived based on protein-protein docking which generates populations of dimers compatible with membrane integration, considering all reasonable interfaces. At the next stage, we applied a scoring procedure based on up to eleven different parameters including shape or electrostatics complementarity. Two methods of consensus scoring were performed: (i) average scores of 100 best scored dimers with respect to each interface, and (ii) frequencies of interfaces among 100 best scored dimers. In general, our multi-component protocol gives correct indications for dimer interfaces that have been observed in X-ray crystal structures of GPCR dimers (opsin dimer, chemokine CXCR4 and CCR5 dimers, κ opioid receptor dimer, β1 adrenergic receptor dimer and smoothened receptor dimer) but also suggests alternative dimerization interfaces. Interestingly, at times these alternative interfaces are scored higher than the experimentally observed ones suggesting them to be also relevant in the life cycle of studied GPCR dimers. Further results indicate that GPCR dimer and higher-order oligomer formation may involve transmembrane helices (TMs) TM1-TM2-TM7, TM3-TM4-TM5 or TM4-TM5-TM6 but not TM1-TM2-TM3 or TM2-TM3-TM4 which is in general agreement with available experimental and computational data.

eTOXlab, an open source modeling framework for implementing predictive models in production environments

Background

Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments.

Results

We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series.

Conclusions

The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by eTOXlab (web services, VM, object-oriented programming) provide an elegant solution to common practical issues; the system can be installed easily in heterogeneous environments and integrates well with other software. Moreover, the system provides a simple and safe solution for building models with confidential structures that can be shared without disclosing sensitive information.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-015-0058-6) contains supplementary material, which is available to authorized users.

A dynamic view of molecular switch behavior at serotonin receptors: implications for functional selectivity

Functional selectivity is a property of G protein-coupled receptors that allows them to preferentially couple to particular signaling partners upon binding of biased agonists. Publication of the X-ray crystal structure of serotonergic 5-HT1B and 5-HT2B receptors in complex with ergotamine, a drug capable of activating G protein coupling and β-arrestin signaling at the 5-HT1B receptor but clearly favoring β-arrestin over G protein coupling at the 5-HT2B subtype, has recently provided structural insight into this phenomenon. In particular, these structures highlight the importance of specific residues, also called micro-switches, for differential receptor activation. In our work, we apply classical molecular dynamics simulations and enhanced sampling approaches to analyze the behavior of these micro-switches and their impact on the stabilization of particular receptor conformational states. Our analysis shows that differences in the conformational freedom of helix 6 between both receptors could explain their different G protein-coupling capacity. In particular, as compared to the 5-HT1B receptor, helix 6 movement in the 5-HT2B receptor can be constrained by two different mechanisms. On the one hand, an anchoring effect of ergotamine, which shows an increased capacity to interact with the extracellular part of helices 5 and 6 and stabilize them, hinders activation of a hydrophobic connector region at the center of the receptor. On the other hand, this connector region in an inactive conformation is further stabilized by unconserved contacts extending to the intracellular part of the 5-HT2B receptor, which hamper opening of the G protein binding site. This work highlights the importance of considering receptor capacity to adopt different conformational states from a dynamic perspective in order to underpin the structural basis of functional selectivity.

Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions

We report a novel method called ADAN (Applicability Domain ANalysis) for assessing the reliability of drug property predictions obtained by in silico methods. The assessment provided by ADAN is based on the comparison of the query compound with the training set, using six diverse similarity criteria. For every criterion, the query compound is considered out of range when the similarity value obtained is larger than the 95th percentile of the values obtained for the training set. The final outcome is a number in the range of 0-6 that expresses the number of unmet similarity criteria and allows classifying the query compound within seven reliability categories. Such categories can be further exploited to assign simpler reliability classes using a traffic light schema, to assign approximate confidence intervals or to mark the predictions as unreliable. The entire methodology has been validated simulating realistic conditions, where query compounds are structurally diverse from those in the training set. The validation exercise involved the construction of more than 1000 models. These models were built using a combination of training set, molecular descriptors, and modeling methods representative of the real predictive tasks performed in the eTOX project (a project whose objective is to predict in vivo toxicological end points in drug development). Validation results confirm the robustness of the proposed assessment methodology, which compares favorably with other classical methods based solely on the structural similarity of the compounds. ADAN characteristics make the method well-suited for estimate the quality of drug predictions obtained in extremely unfavorable conditions, like the prediction of drug toxicity end points.

Novel insights into biased agonism at G protein-coupled receptors and their potential for drug design

G-protein coupled receptors (GPCRs) are the most important class of current pharmacological targets. However, it is now widely acknowledged that their regulation is more complex than previously thought: the evidence that GPCRs can couple to several effector pathways, and the existence of biased agonists able to activate them differentially, has introduced a new level of complexity in GPCR drug research. Considering bias represents a challenge for the research of new GPCR modulators, because it demands a detailed characterization of compound properties for several effector pathways. Still, biased ligands could provide an opportunity to modulate GPCR function in a finer way and to separate therapeutic from side effects. Nowadays, a variety of agonists for GPCRs have been described, which differ in their ability to promote receptor coupling to different Gprotein families or even subunits, recruit signal transducers such as arrestins, activate a variety of downstream molecular pathways and induce certain phosphorylation signatures or gene expression patterns. In this review, we will cover some of the experimental techniques currently used to understand and characterize biased agonism and discuss their strengths and limitations. Additionally, we will comment on the computational efforts that are being devoted to study ligand-induced bias and on the potential they hold for rationalizing its structural determinants. Finally, we will discuss which of these strategies could be used for the rational design of biased ligands and give some examples of the potential therapeutic value of this class of compounds.

The Truth, the Whole Truth, and Nothing but the Ground-Truth: Methods to Advance Environmental Justice and Researcher-Community Partnerships

Environmental justice advocates often argue that environmental hazards and their health effects vary by neighborhood, income, and race. To assess these patterns and advance preventive policy, their colleagues in the research world often use complex and methodologically sophisticated statistical and geospatial techniques. One way to bridge the gap between the technical work and the expert knowledge of local residents is through community-based participatory research strategies. We document how an environmental justice screening method was coupled with "ground-truthing"-a project in which community members worked with researchers to collect data across six Los Angeles neighborhoods-which demonstrated the clustering of potentially hazardous facilities, high levels of air pollution, and elevated health risks. We discuss recommendations and implications for future research and collaborations between researchers and community-based organizations.

SEOM clinical guidelines for the treatment of head and neck cancer (HNC) 2013

Head and neck cancer represents 5 % of oncologic cases in adults. Early stage treatments are local with surgery and/or radiotherapy. For locally advanced stages, treatment requires radiotherapy combined with platinum-based drugs or cetuximab. Induction chemotherapy should be considered for selected cases. In the case of metastatic disease, adjuvant or palliative treatment is based on platinum agents and cetuximab.

SEOM clinical guidelines for the treatment of nasopharyngeal carcinoma 2013

Nasopharyngeal carcinoma cases are not frequently encountered in our environment. Local stages are treated with radiotherapy. For advanced local stages, the association of chemotherapy with radiotherapy improves the rates of survival. In the case of metastatic disease stages, treatment requires platinum-based chemotherapy and patients may achieve a long survival time.

Modeling Complexes of Transmembrane Proteins: Systematic Analysis of ProteinProtein Docking Tools

Proteinprotein docking methodology is frequently used to model complexes of transmembrane proteins, in particular oligomers of G protein-coupled receptors (GPCRs), even if its applicability for these systems has never been fully validated. The aim of this work is to perform a systematic study on the suitability of some widely-used proteinprotein docking software for modeling complexes of transmembrane proteins. In this study we tested the programs ZDOCK, ClusPro, HEX, GRAMM-X, PatchDock, SymmDock, and HADDOCK, using a set of membrane protein oligomers for which the 3D structure has been obtained experimentally, including opsin dimer, the recently published chemokine CXCR4 and kappa opioid receptor dimers. The results show that the docking success depends on the applied docking algorithm and scoring functions, but also on inherent structural features of the transmembrane proteins. Thus, proteins with large interface surfaces, rich in surface cavities, high-order symmetry, and small conformational change upon complex formation are well predicted more often than proteins without these features. The results of this systematic analysis provide guidelines that can be used for obtaining reliable models of transmembrane proteins, including GPCRs. Therefore they can be useful for the application of structure-based methods in drug discovery projects involving these targets.