Mapping Potential Population-Level Pesticide Exposures in Ecuador Using a Modular and Scalable Geospatial Strategy.

Human populations and ecosystems are extensively exposed to pesticides. Most nations lack the capacity to control pesticide contamination and have limited availability of pesticide use information. Ecuador is a country with intense pesticide use with high exposure risks to humans and the environment, although relative or combined risks are not well understood. Here, we analyzed the distribution of application rates in Ecuador and identified regions of concern because of high potential exposure. We used a geospatial analysis to identify grid cells (∼8 km × 8 km) where the highest pesticide application rates and density of human populations overlap. Furthermore, we identified other regions of concern based on the number of amphibian species as an indicator of ecosystem integrity and the location of natural protected areas. We found that 28% of Ecuador's population dwelled in areas with high pesticide application rate. We identified an area of ∼512 km2 in the Amazon region where high application rates, large human settlements, and a high number of amphibian species overlapped. Additionally, we distinguished clusters of pesticide application rates and human populations that intersected with natural protected areas. Ecuador exemplifies how pesticides are disproportionately applied in areas with the potential to affect human health and ecosystems' integrity. Global estimates of population dwelling, pesticide application rates, and environmental factors are key in prioritizing locations to conduct further exposure assessments. The modular and scalable nature of the geospatial tools we developed can be expanded and adapted to other regions of the world where data on pesticide use are limited.

Long-term exposure to ambient O3 and PM2.5 is associated with reduced cognitive performance in young adults: A retrospective longitudinal repeated measures study in adults aged 18-90 years.

A growing body of evidence indicates that exposure to air pollution affects cognitive performance; however, few studies have assessed this in the context of repeated measures within a large group of individuals or in a population with a large age range. In this study, we evaluated the associations between long-term exposure to fine particulate matter (PM2.5) and ozone (O3) in large cohort of adults aged 18-90 years. The study cohort included 29,091 Lumosity users in the contiguous US who completed 20 repetitions of the Lost in Migration game between 2017 and 2018. Game scores reflect the ability to filter information and avoid distracting information. Long-term air pollution data included ambient PM2.5 and O3 averaged for the 365-day period before each gameplay date. Generalized linear models were used to examine the associations between long-term PM2.5 and O3 and game score percentile. Co-pollutant models were adjusted for meteorology, time trend, age, gender, device, education, local socioeconomic factors, and urbanicity. Results represent the change in attention game score percentile per 1 µg/m3 increase in PM2.5 or 0.01 ppm increase in O3. In the entire cohort, a -0.10 (95% CI: -0.16, -0.04) change in score percentile was associated with PM2.5, while no significant association was observed with O3. Modification of these associations by age was observed for both PM2.5 and O3, with stronger associations observed in younger users. In users aged 18-29, a -0.25 (-0.45, -0.05) change in score percentile was associated with PM2.5, while no associations were observed in other age groups. With O3, there was a -2.92 (-4.63, -1.19) and -2.81 (-4.29, -1.25) change in score percentile for users aged 18-29 and 30-39, respectively. We observed that elevated long-term PM2.5 and O3 were associated with decreased focus scores in young adults, but follow-up research is necessary to further illuminate these associations.

The Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM): A machine learning approach to estimate national daily fine particulate matter (PM2.5) exposure.

Exposure to biomass smoke has been associated with a wide range of acute and chronic health outcomes. Over the past decades, the frequency and intensity of wildfires has increased in many areas, resulting in longer smoke episodes with higher concentrations of fine particulate matter (PM2.5). There are also many communities where seasonal open burning and residential wood heating have short- and long-term impacts on ambient air quality. Understanding the acute and chronic health effects of biomass smoke exposure requires reliable estimates of PM2.5 concentrations during the wildfire season and throughout the year, particularly in areas without regulatory air quality monitoring stations. We have developed a machine learning approach to estimate PM2.5 across all populated regions of Canada from 2010 to 2019. The random forest machine learning model uses potential predictor variables integrated from multiple data sources and estimates daily mean (24-hour) PM2.5 concentrations at a 5 km × 5 km spatial resolution. The training and prediction datasets were generated using observations from National Air Pollution Surveillance (NAPS) network. The Root Mean Squared Error (RMSE) between predicted and observed PM2.5 concentrations was 2.96 µg/m3 for the entire prediction set, and more than 96 % of the predictions were within 5 µg/m3 of the NAPS PM2.5 measurements. The model was evaluated using 10-fold, leave one-region-out, and leave-one-year-out cross-validations. Overall, CanOSSEM performed well but performance was sensitive to removal of large wildfire events such as the Fort McMurray interface fire in May 2016 or the extreme 2017 and 2018 wildfire seasons in British Columbia. Exposure estimates from CanOSSEM will be useful for epidemiologic studies on the acute and chronic health effects associated with PM2.5 exposure, especially for populations affected by biomass smoke where routine air quality measurements are not available.

Short-Term Exposure to Wildfire Smoke and PM2.5 and Cognitive Performance in a Brain-Training Game: A Longitudinal Study of U.S. Adults.

BACKGROUND: There is increasing evidence that long-term exposure to fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)] may adversely impact cognitive performance. Wildfire smoke is one of the biggest sources of PM2.5 and concentrations are likely to increase under climate change. However, little is known about how short-term exposure impacts cognitive function. OBJECTIVES: We aimed to evaluate the associations between daily and subdaily (hourly) PM2.5 and wildfire smoke exposure and cognitive performance in adults. METHODS: Scores from 20 plays of an attention-oriented brain-training game were obtained for 10,228 adults in the United States (U.S.). We estimated daily and hourly PM2.5 exposure through a data fusion of observations from multiple monitoring networks. Daily smoke exposure in the western U.S. was obtained from satellite-derived estimates of smoke plume density. We used a longitudinal repeated measures design with linear mixed effects models to test for associations between short-term exposure and attention score. Results were also stratified by age, gender, user behavior, and region. RESULTS: Daily and subdaily PM2.5 were negatively associated with attention score. A 10 µg/m3 increase in PM2.5 in the 3 h prior to gameplay was associated with a 21.0 [95% confidence interval (CI): 3.3, 38.7]-point decrease in score. PM2.5 exposure over 20 plays accounted for an estimated average 3.7% (95% CI: 0.7%, 6.7%) reduction in final score. Associations were more pronounced in the wildfire-impacted western U.S. Medium and heavy smoke density were also negatively associated with score. Heavy smoke density the day prior to gameplay was associated with a 117.0 (95% CI: 1.7, 232.3)-point decrease in score relative to no smoke. Although differences between subgroups were not statistically significant, associations were most pronounced for younger (18-29 y), older (≥70y), habitual, and male users. DISCUSSION: Our results indicate that PM2.5 and wildfire smoke were associated with reduced attention in adults within hours and days of exposure, but further research is needed to elucidate these relationships. https://doi.org/10.1289/EHP10498.

Moving Towards Precision Urologic Oncology: Targeting Enzalutamide-resistant Prostate Cancer and Mutated Forms of the Androgen Receptor Using the Novel Inhibitor Darolutamide (ODM-201).

Darolutamide (ODM-201) is a novel androgen receptor (AR) antagonist with a chemical structure distinctly different from currently approved AR antagonists that targets both wild-type and mutated ligand binding domain variants to inhibit AR nuclear translocation. Here, we evaluate the activity of darolutamide in enzalutamide-resistant castration resistant prostate cancer (CRPC) as well as in AR mutants detected in patients after treatment with enzalutamide, abiraterone, or bicalutamide. Darolutamide significantly inhibited cell growth and AR transcriptional activity in enzalutamide-resistant MR49F cells in vitro, and led to decreased tumor volume and serum prostate-specific antigen levels in vivo, prolonging survival in mice bearing enzalutamide-resistant MR49F xenografts. Moreover, darolutamide inhibited the transcriptional activity of AR mutants identified in the plasma of CRPC patients progressing on traditional therapies. In particular, darolutamide significantly inhibited the transcriptional activity of the F877L, H875Y/T878A, F877L/T878A, and the previously unreported T878G AR mutants, that transform enzalutamide into a partial agonist. In silico cheminformatics computer modeling provided atomic level insights confirming darolutamide antagonist effect in F877L and T878G AR mutants. In conclusion, our results provide a rationale for further clinical evaluation of darolutamide in enzalutamide-resistant CRPC, in particular in combination with circulating tumor DNA assays that detect AR mutants sensitive to darolutamide, in a precision oncology setting. PATIENT SUMMARY: In this study we evaluated the novel drug darolutamide in preclinical models of prostate cancer. We found that darolutamide delays growth of enzalutamide-resistant prostate cancer, in particular in cells with mutated forms of the androgen receptor after previous treatment. Our data supports further evaluation of darolutamide in clinical trials.

Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor.

The human androgen receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of prostate cancer (PCa). Many forms of castration-resistant prostate cancer (CRPC) still rely on the AR for survival. Currently used antiandrogens face clinical limitations as drug resistance develops in patients over time since they all target the mutation-prone androgen binding site (ABS), where gain-of-function mutations eventually convert antagonists into agonists. With a significant number of reported distinct mutations located across the ABS, it is imperative to develop a prognostic platform which would equip clinicians with prior knowledge and actionable strategies if cases of previously unreported AR mutations are encountered. The goal of this study is to develop a theoretical approach that can predict such previously unreported AR mutants in response to current treatment options for PCa. The expected drug response by these mutants has been modeled using cheminformatics methodology. The corresponding QSAR pipeline has been created, which extracts key protein-ligand interactions and quantifies them by 4D molecular descriptors. The developed models reported with an accuracy reaching 90% and enable prediction of activation of AR mutants by its native ligand as well as assess whether known antiandrogens will act on them as agonists or antagonists. As a result, a previously uncharacterized mutant, T878G, has been predicted to be activated by the latest antiandrogen enzalutamide, and the corresponding experimental evaluation confirmed this prediction. Overall, the developed cheminformatics pipeline provides useful insights toward understanding the changing genomic landscape of advanced PCa.