Compound Risk Research Articles

Compound Risk of Air Pollution and Heat Days and the Influence of Wildfire by SES across California, 2018-2020: Implications for Environmental Justice in the Context of Climate Change.

Major wildfires and heatwaves have begun to increase in frequency throughout much of the United States, particularly in western states such as California, causing increased risk to public health. Air pollution is exacerbated by both wildfires and warmer temperatures, thus adding to such risk. With climate change and the continued increase in global average temperatures, the frequency of major wildfires, heat days, and unhealthy air pollution episodes is projected to increase, resulting in the potential for compounding risks. Risks will likely vary by region and may disproportionately impact low-income communities and communities of color. In this study, we processed daily particulate matter (PM) data from over 18,000 low-cost PurpleAir sensors, along with gridMET daily maximum temperature data and government-compiled wildfire perimeter data from 2018-2020 in order to examine the occurrence of compound risk (CR) days (characterized by high temperature and high PM2.5) at the census tract level in California, and to understand how such days have been impacted by the occurrence of wildfires. Using American Community Survey data, we also examined the extent to which CR days were correlated with household income, race/ethnicity, education, and other socioeconomic factors at the census tract level. Results showed census tracts with a higher frequency of CR days to have statistically higher rates of poverty and unemployment, along with high proportions of child residents and households without computers. The frequency of CR days and elevated daily PM2.5 concentrations appeared to be strongly related to the occurrence of nearby wildfires, with over 20% of days with sensor-measured average PM2.5 > 35 μg/m3 showing a wildfire within a 100 km radius and over two-thirds of estimated CR days falling on such days with a nearby wildfire. Findings from this study are important to policymakers and government agencies who preside over the allocation of state resources as well as organizations seeking to empower residents and establish climate resilient communities.

Benchmark Dose Analysis of DNA Damage Biomarker Responses Provides Compound Potency and Adverse Outcome Pathway Information for the Topoisomerase II Inhibitor Class of Compounds.

Genetic toxicology data have traditionally been utilized for hazard identification to provide a binary call for a compound's risk. Recent advances in the scientific field, especially with the development of high-throughput methods to quantify DNA damage, have influenced a change of approach in genotoxicity assessment. The in vitro MultiFlow® DNA Damage Assay is one such method which multiplexes γH2AX, p53, phospho-histone H3 biomarkers into a single-flow cytometric analysis (Bryce et al., [2016]: Environ Mol Mutagen 57:546-558). This assay was used to study human TK6 cells exposed to each of eight topoisomerase II poisons for 4 and 24 hr. Using PROAST v65.5, the Benchmark Dose approach was applied to the resulting flow cytometric datasets. With "compound" serving as covariate, all eight compounds were combined into a single analysis, per time point and endpoint. The resulting 90% confidence intervals, plotted in Log scale, were considered as the potency rank for the eight compounds. The in vitro MultiFlow data showed a maximum confidence interval span of 1Log, which indicates data of good quality. Patterns observed in the compound potency rank were scrutinized by using the expert rule-based software program Derek Nexus, developed by Lhasa Limited. Compound sub-classification and structural alerts were considered contributory to the potencies observed for the topoisomerase II poisons studied herein. The Topo II poison Adverse Outcome Pathway was evaluated with MultiFlow endpoints serving as Key Events. The step-wise approach described herein can be considered as a foundation for risk assessment of compounds within a specific mode of action of interest. Environ. Mol. Mutagen. 2020. © 2020 Wiley Periodicals, Inc.

Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs

Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Because the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the Food and Drug Administration-approved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent validation sets, and the 3 validation data sets have a total of 483 unique drugs. We observed that the QSAR model's performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus, the model's applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compound's risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.

Development of a surgical approach for telemetering guinea pigs as a model for screening QT interval effects

Introduction Identifying a compound's risk of potentially producing Torsade de Pointes (TdP)/QT interval prolongation has become a goal early within the drug development process. Our aim was to develop a surgical approach for instrumenting guinea pigs for collecting ECG data. Methods Male Dunkin Hartley guinea pigs (386–661 g) were surgically implanted with telemetry transducers for the measurement of lead II ECG and arterial blood pressure. Using strict aseptic techniques and isofluorane anaesthesia (0.5–5.0%), one of the three implantation procedures was used. In Phase I the animals were either implanted intraperitoneally (abdominal aorta cannulated) or subcutaneously (carotid artery cannulated). In Phase II all animals were implanted subcutaneously and the subcutaneous pocket formed either with a single incision, as in Phase I, or a double incision. Results Phase I—During intraperitoneal implantation, rupture of the aorta occurred in 50% of the animals. The remaining animals were terminated between 2 and 14 days postsurgery on human grounds. All of the animals implanted subcutaneously survived the surgical procedure and the following 28 days. Skin thinning over the implant occurred in 25% of the animals in weeks 5–6. Phase II—Following single incision subcutaneous implantation 72% of the animals were terminated within the 28-day postsurgery due to skin rupture over the implant body, with only 22% of the animals viable after 28 days. Following double incision subcutaneous implantation 86% of animals were viable after 28 days and thus the recommended method for implantation. Discussion The improvements to the surgical approach have improved survival rates and increased the potential for robust, long term use of telemetered guinea pig colonies for screening for QT prolonging potential and additional cardiovascular assessment in vivo.