Chronic obstructive pulmonary disease (COPD) in Spain and the different aspects of its social impact: a multidisciplinary opinion document

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent diseases in the World, and one of the most important causes of mortality and morbidity. In adults 40 years and older, it affects more than 10% of the population and has enormous personal, family and social burden. Tobacco smoking is its main cause, but not the only one, and there is probably a genetic predisposition that increases the risk in some patients. The paradigm of this disease is changing in Spain, with an increase of women that has occurred in recent years. Many of the physio pathological mechanisms of this condition are well known, but the psychological alterations to which it leads, the impact of COPD on relatives and caregivers, the limitation of daily life observed in these patients, and the economic and societal burden that they represent for the health system, are not so well-known. A major problem is the high under-diagnosis, mainly due to difficulties for obtaining, in a systematic way, spirometries in hospitals and health-care centers. For this reason, the Fundación de Ciencias de la Salud and the Spanish National Network Center for Research in Respiratory Diseases (CIBERES) have brought together experts in COPD, patients and their organizations, clinical psychologists, experts in health economics, nurses and journalists to obtain their opinion about COPD in Spain. They also discussed the scientific bibliometrics on COPD that is being carried out from the CIBERES and speculated on the future of this condition. The format of the meeting consisted in the discussion of a series of questions that were addressed by different speakers and discussed until a consensus conclusion was reached.

Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays

Many toxicological test methods, including assays of cell viability and function, require an evaluation of concentration-response data. This often involves curve fitting, and the resulting mathematical functions are then used to determine the concentration at which a certain deviation from the control value occurs (e.g. a decrease of cell viability by 15%). Such a threshold is called the benchmark response (BMR). For a toxicological test, it is often of interest to determine the concentration of test compound at which a pre-defined BMR of e.g. 10, 25 or 50% is reached. The concentration at which the modelled curve crosses the BMR is called the benchmark concentration (BMC). We present a user-friendly, web-based tool (BMCeasy), designed for operators without programming skills and profound statistical background, to determine BMCs and their confidence intervals. BMCeasy allows simultaneous analysis of viability plus a functional test endpoint, and it yields absolute BMCs with confidence intervals for any BMR. Besides an explanation of the algorithm underlying BMCeasy, this article also gives multiple examples of data outputs. BMCeasy was used within the EU-ToxRisk project for preparing data packages that were submitted to regulatory authorities, demonstrating the real-life applicability of the tool.

Corrigendum to A high-throughput approach to identify specific neurotoxicants / developmental toxicants in human neuronal cell function assays

In this manuscript, which appeared in ALTEX 35 , 235-253 ( doi:10.14573/altex.1712182 ), the Acknowledgements should read: This work was supported by the Land BW, the Doerenkamp-Zbinden Foundation, the DFG (RTG1331, KoRS-CB), the BMBF (NeuriTox), and it has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 681002 (EU-ToxRisk).

Development of an Infrastructure for the Prediction of Biological Endpoints in Industrial Environments. Lessons Learned at the eTOX Project

In silico methods are increasingly being used for assessing the chemical safety of substances, as a part of integrated approaches involving in vitro and in vivo experiments. A paradigmatic example of these strategies is the eTOX project http://www.etoxproject.eu, funded by the European Innovative Medicines Initiative (IMI), which aimed at producing high quality predictions of in vivo toxicity of drug candidates and resulted in generating about 200 models for diverse endpoints of toxicological interest. In an industry-oriented project like eTOX, apart from the predictive quality, the models need to meet other quality parameters related to the procedures for their generation and their intended use. For example, when the models are used for predicting the properties of drug candidates, the prediction system must guarantee the complete confidentiality of the compound structures. The interface of the system must be designed to provide non-expert users all the information required to choose the models and appropriately interpret the results. Moreover, procedures like installation, maintenance, documentation, validation and versioning, which are common in software development, must be also implemented for the models and for the prediction platform in which they are implemented. In this article we describe our experience in the eTOX project and the lessons learned after 7 years of close collaboration between industrial and academic partners. We believe that some of the solutions found and the tools developed could be useful for supporting similar initiatives in the future.

Carbon trading, co-pollutants, and environmental equity: Evidence from California's cap-and-trade program (2011-2015)

BACKGROUND

Policies to mitigate climate change by reducing greenhouse gas (GHG) emissions can yield public health benefits by also reducing emissions of hazardous co-pollutants, such as air toxics and particulate matter. Socioeconomically disadvantaged communities are typically disproportionately exposed to air pollutants, and therefore climate policy could also potentially reduce these environmental inequities. We sought to explore potential social disparities in GHG and co-pollutant emissions under an existing carbon trading program-the dominant approach to GHG regulation in the US and globally.

METHODS AND FINDINGS

We examined the relationship between multiple measures of neighborhood disadvantage and the location of GHG and co-pollutant emissions from facilities regulated under California's cap-and-trade program-the world's fourth largest operational carbon trading program. We examined temporal patterns in annual average emissions of GHGs, particulate matter (PM2.5), nitrogen oxides, sulfur oxides, volatile organic compounds, and air toxics before (January 1, 2011-December 31, 2012) and after (January 1, 2013-December 31, 2015) the initiation of carbon trading. We found that facilities regulated under California's cap-and-trade program are disproportionately located in economically disadvantaged neighborhoods with higher proportions of residents of color, and that the quantities of co-pollutant emissions from these facilities were correlated with GHG emissions through time. Moreover, the majority (52%) of regulated facilities reported higher annual average local (in-state) GHG emissions since the initiation of trading. Neighborhoods that experienced increases in annual average GHG and co-pollutant emissions from regulated facilities nearby after trading began had higher proportions of people of color and poor, less educated, and linguistically isolated residents, compared to neighborhoods that experienced decreases in GHGs. These study results reflect preliminary emissions and social equity patterns of the first 3 years of California's cap-and-trade program for which data are available. Due to data limitations, this analysis did not assess the emissions and equity implications of GHG reductions from transportation-related emission sources. Future emission patterns may shift, due to changes in industrial production decisions and policy initiatives that further incentivize local GHG and co-pollutant reductions in disadvantaged communities.

CONCLUSIONS

To our knowledge, this is the first study to examine social disparities in GHG and co-pollutant emissions under an existing carbon trading program. Our results indicate that, thus far, California's cap-and-trade program has not yielded improvements in environmental equity with respect to health-damaging co-pollutant emissions. This could change, however, as the cap on GHG emissions is gradually lowered in the future. The incorporation of additional policy and regulatory elements that incentivize more local emission reductions in disadvantaged communities could enhance the local air quality and environmental equity benefits of California's climate change mitigation efforts.

In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk

Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models ( O'Hara , T. , PLos Comput. Biol. 2011 , 7 , e1002061 ) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current ( I_Ks and I_Kr, respectively) and the L-type calcium current ( I_CaL) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC₅₀ based test.

Generating Modeling Data From Repeat-Dose Toxicity Reports

Over the past decades, pharmaceutical companies have conducted a large number of high-quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models. As the studies were originally conducted to investigate the properties of individual compounds, the experimental conditions across the studies are highly heterogeneous. Consequently, the original data required normalization/standardization, filtering, categorization and integration to make possible any data analysis (such as building predictive models). Additionally, the primary objectives of the RDT studies were to identify toxicological findings, most of which do not directly translate to in vivo endpoints. This article describes a method to extract datasets containing comparable toxicological properties for a series of compounds amenable for building predictive models. The proposed strategy starts with the normalization of the terms used within the original reports. Then, comparable datasets are extracted from the database by applying filters based on the experimental conditions. Finally, carefully selected profiles of toxicological findings are mapped to endpoints of interest, generating QSAR-like tables. In this work, we describe in detail the strategy and tools used for carrying out these transformations and illustrate its application in a data sample extracted from the eTOX database. The suitability of the resulting tables for developing hazard-predicting models was investigated by building proof-of-concept models for in vivo liver endpoints.

Quantitative structure-activity relationships for primary aerobic biodegradation of organic chemicals in pristine surface waters: starting points for predicting biodegradation under acclimatization

Microbial biomass and acclimation can affect the removal of organic chemicals in natural surface waters. In order to account for these effects and develop more robust models for biodegradation, we have compiled and curated removal data for un-acclimated (pristine) surface waters on which we developed quantitative structure-activity relationships (QSARs). Global analysis of the very heterogeneous dataset including neutral, anionic, cationic and zwitterionic chemicals (N = 233) using a random forest algorithm showed that useful predictions were possible (Q_ext² = 0.4-0.5) though relatively large standard errors were associated (SDEP ∼0.7). Classification of the chemicals based on speciation state and metabolic pathway showed that biodegradation is influenced by the two, and that the dependence of biodegradation on chemical characteristics is non-linear. Class-specific QSAR analysis indicated that shape and charge distribution determine the biodegradation of neutral chemicals (R² ∼ 0.6), e.g. through membrane permeation or binding to P450 enzymes, whereas the average biodegradation of charged chemicals is 1 to 2 orders of magnitude lower, for which degradation depends more directly on cellular uptake (R² ∼ 0.6). Further analysis showed that specific chemical classes such as peptides and organic halogens are relatively less biodegradable in pristine surface waters, resulting in the need for the microbial consortia to acclimate. Additional literature data was used to verify an acclimation model (based on Monod-type kinetics) capable of extrapolating QSAR predictions to acclimating conditions such as in water treatment, downstream lakes and large rivers under μg L^-1 to mg L^-1 concentrations. The framework developed, despite being based on multiple assumptions, is promising and needs further validation using experimentation with more standardised and homogenised conditions as well as adequate characterization of the inoculum used.

A high-throughput approach to identify specific neurotoxicants/ developmental toxicants in human neuronal cell function assays

The (developmental) neurotoxicity hazard is still unknown for most chemicals. Establishing a test battery covering most of the relevant adverse outcome pathways may close this gap, without requiring a huge animal experimentation program. Ideally, each of the assays would cover multiple mechanisms of toxicity. One candidate test is the human LUHMES cell-based NeuriTox test. To evaluate its readiness for larger-scale testing, a proof of concept library assembled by the U.S. National Toxicology Program (NTP) was screened. Of the 75 unique compounds, seven were defined as specifically neurotoxic after the hit-confirmation phase and additional ten compounds were generally cytotoxic within the concentration range of up to 20 micromolar. As complementary approach, the library was screened in the PeriTox test, which identifies toxicants affecting the human peripheral nervous system. Of the eight PeriTox hits, five were similar to the NeuriTox hits: rotenone, colchicine, diethylstilbestrol, berberine chloride, and valinomycin. The unique NeuriTox hit, methyl-phenylpyridinium (MPP+) is known from in vivo studies to affect only dopaminergic neurons (which LUHMES cells are). Conversely, the known peripheral neurotoxicant acrylamide was picked up in the PeriTox, but not in the NeuriTox assay. All of the five common hits had also been identified in the published neural crest migration (cMINC) assay, while none of them emerged as cardiotoxicant in a previous screen using the same library. These comparative data suggest that complementary in vitro tests can pick up a broad range of toxicants, and that multiple test results might help to predict organ specificity patterns.

SEOM clinical guideline in nasopharynx cancer (2017)

Nasopharyngeal carcinoma (NPC) is distinct from other cancers of the head and neck in biology, epidemiology, histology, natural history, and response to treatment. Radiation therapy is an essential component of curative-intent of non-disseminated disease and the association of chemotherapy improves the rates of survival. In the case of metastatic disease stages, treatment requires platinum/gemcitabine-based chemotherapy and patients may achieve a long survival time.

Legacy data sharing to improve drug safety assessment: the eTOX project

The sharing of legacy preclinical safety data among pharmaceutical companies and its integration with other information sources offers unprecedented opportunities to improve the early assessment of drug safety. Here, we discuss the experience of the eTOX project, which was established through the Innovative Medicines Initiative to explore this possibility.

Reading, Writing, and Toxics: Children's Health, Academic Performance, and Environmental Justice in Los Angeles

A significant body of previous research on environmental justice has demonstrated a disproportionate burden of environmental hazards on low-income and minority residents. In this paper we evaluate spatially indexed data on estimated respiratory and cancer risks associated with exposures to ambient air toxics to show that children of color in the Los Angeles Unified School District suffer potentially disparate health impacts, and that disparities in environmental risks may be associated with diminished school performance—even after controlling for socioeconomic and demographic covariates that generally explain much of the variation in student scores. Remediating environmental health risks in distressed neighborhoods could, therefore, improve both health and human capital.

Stability study of sodium colistimethate-loaded lipid nanoparticles

In the last decades, the encapsulation of antibiotics into nanoparticulate carriers has gained increasing attention for the treatment of infectious diseases. Sodium colistimethate-loaded solid lipid nanoparticles (Colist-SLNs) and nanostructured lipid carriers (Colist-NLCs) were designed aiming to treat the pulmonary infection associated to cystic fibrosis patients. The nanoparticles were freeze-dried using trehalose as cryoprotectant. The stability of both nanoparticles was analysed over one year according to the International Conference of Harmonisation (ICH) guidelines by determining the minimum inhibitory concentration (MIC) against clinically isolated Pseudomonas aeruginosa strains and by studying their physico-chemical characteristics. The results showed that Colist-SLNs lost their antimicrobial activity at the third month; on the contrary, the antibacterial activity of Colist-NLCs was maintained throughout the study within an adequate range (MIC ≤16 μg/mL). In addition, Colist-NLCs exhibited suitable physico-chemical properties at 5 °C and 25 °C/60% relative humidity over one year. Altogether, Colist-NLCs proved to have better stability than Colist-SLNs.

Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data

The present study applies a systems biology approach for the in silico predictive modeling of drug toxicity on the basis of high-quality preclinical drug toxicity data with the aim of increasing the mechanistic understanding of toxic effects of compounds at different levels (pathway, cell, tissue, organ). The model development was carried out using 77 compounds for which gene expression data for treated primary human hepatocytes is available in the LINCS database and for which rodent in vivo hepatotoxicity information is available in the eTOX database. The data from LINCS were used to determine the type and number of pathways disturbed by each compound and to estimate the extent of disturbance (network perturbation elasticity), and were used to analyze the correspondence with the in vivo information from eTOX. Predictive models were developed through this integrative analysis, and their specificity and sensitivity were assessed. The quality of the predictions was determined on the basis of the area under the curve (AUC) of plots of true positive vs. false positive rates (ROC curves). The ROC AUC reached values of up to 0.9 (out of 1.0) for some hepatotoxicity endpoints. Moreover, the most frequently disturbed metabolic pathways were determined across the studied toxicants. They included, e.g., mitochondrial beta-oxidation of fatty acids and amino acid metabolism. The process was exemplified by successful predictions on various statins. In conclusion, an entirely new approach linking gene expression alterations to the prediction of complex organ toxicity was developed and evaluated.

Integrating public health and community development to tackle neighborhood distress and promote well-being

Recently there have been calls for public health to reconnect to urban planning in ways that emphasize the impact of place on health and that address fundamental causes of poor health, such as poverty, social inequality, and discrimination. Community developers have realized that poor health limits individuals' and communities' economic potential and have begun to integrate into their work such neighborhood health issues as access to fresh food and open space. In this article we review recent shifts in the community development field and give examples of programs that operate at the intersection of community development, public health, and civic engagement. For example, in Sacramento, California, the Building Healthy Communities program successfully promoted the creation of community gardens and bike paths and the redevelopment of brownfields. A major housing revitalization initiative in San Francisco, California, known as Sunnydale-Velasco, is transforming the city's largest public housing site into a mixed-income community that provides existing residents with new housing, infrastructure, services, and amenities. These examples and others illustrate the need to identify and make use of interdisciplinary approaches to ensure that all places are strong platforms for economic mobility, full democratic participation, and community health.