Better alternatives than normalizing to control: case studies with algae toxicity and dose-response analysis

Application of systematic evidence mapping to assess the impact of new research when updating health reference values: A case example using acrolein

Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population prediction methods

The use of short-term toxicogenomic tests to predict cancer (or other health effects) offers considerable advantages relative to traditional toxicity testing methods. The advantages include increased throughput, increased mechanistic data, and significantly reduced costs. However, precisely how toxicogenomics data can be used to support human health risk assessment (RA) is unclear. In a companion paper (Moffat et al. 2014), we present a case study evaluating the utility of toxicogenomics in the RA of benzo[a]pyrene (BaP), a known human carcinogen. The case study is meant as a proof-of-principle exercise using a well-established mode of action (MOA) that impacts multiple tissues, which should provide a best case example. We found that toxicogenomics provided rich mechanistic data applicable to hazard identification, dose–response analysis, and quantitative RA of BaP. Based on this work, here we share some useful lessons for both research and RA, and outline our perspective on how toxicogenomics can benefit RA in the short- and long-term. Specifically, we focus on (1) obtaining biologically relevant data that are readily suitable for establishing an MOA for toxicants, (2) examining the human relevance of an MOA from animal testing, and (3) proposing appropriate quantitative values for RA. We describe our envisioned strategy on how toxicogenomics can become a tool in RA, especially when anchored to other short-term toxicity tests (apical endpoints) to increase confidence in the proposed MOA, and emphasize the need for additional studies on other MOAs to define the best practices in the application of toxicogenomics in RA.

A framework for fit-for-purpose dose response assessment

A rate for hepatic metabolism (Vmax) determined in vitro must be scaled for in vivo use in a physiologically based pharmacokinetic (PBPK) model. This requires the use of scaling factors such as mg of microsomal protein per gram of liver (MPPGL) and liver mass (FVL). Variation in MPPGL and FVL impacts variation in Vmax, and hence PBPK model-derived estimates of internal dose used in dose response analysis. The impacts of adult human variation in MPPGL and FVL on estimates of internal dose were assessed using a human PBPK model for bromodichloromethane (BDCM), a water disinfection byproduct, for multiple internal dose metrics for two exposure scenarios (single 0.25 liter drink of water or 10 min shower) under plausible (5 μg/L) and high level (20 μg/L) water concentrations. For both concentrations, all internal dose metrics were changed less than 5% for the showering scenario (combined inhalation and dermal exposure). In contrast, a 27-fold variation in area under the curve (AUC) for BDCM in venous blood was observed at both oral exposure concentrations, whereas total amount of BDCM metabolized in liver was relatively unchanged. This analysis demonstrates that variability in the scaling factors used for in vitro to in vivo extrapolation (IVIVE) for metabolic rate parameters can have a significant route-dependent impact on estimates of internal dose under environmentally relevant exposure scenarios. This indicates the need to evaluate both uncertainty and variability for scaling factors used for IVIVE.

A main step for the development and the validation of medical drugs is the screening on whole organisms, which gives the systemic information that is missing when using cellular models. Among the organisms of choice, Caenorhabditis elegansis a soil worm which catches the interest of researchers who study systemic physiopathology (e.g. metabolic and neurodegenerative diseases) because: (1) its large genetic homology with humans supports translational analysis; (2) worms are much easier to handle and grow in large amounts compared to rodents, for which (3) the costs and (4) the ethical concerns are substantial.C. elegansis therefore well suited for large screens, dose-response analysis and target-discovery involving an entire organism. We have developed and tested a microfluidic array for high-content screening, enabling the selection of small populations of its first larval stage in many separated chambers divided into channels for multiplexed screens. With automated protocols for feeding, drug administration and image acquisition, our chip enables the study of the nematodes throughout their entire lifespan. By using a paralyzing agent and a mitochondrial-stress inducer as case studies, we have demonstrated large field-of-view motility analysis, and worm-segmentation/signal-detection for mode-of-action quantification with genetically-encoded fluorescence reporters.

Fipronil, a broad-spectrum insecticide, is widely used in agriculture and veterinary practices. Fipronil-induced neurotoxicity and potential adverse effects on humans and aquatic organisms have raised health concerns. Monitoring programs have been implemented globally to assess fipronil residues in food, including fruits, vegetables, and animal products. However, previous exposure assessments have often focused on specific food categories or subsets of items, resulting in limited insights into the overall health risks. Additionally, the large number of non-detect fipronil residues in food has introduced uncertainties in exposure assessment. To address these issues, a probabilistic exposure assessment and dose-response analysis were adopted in this study, considering the sample distribution below the detection limit to better characterize uncertainties and population variability in health risk assessments. The estimated fipronil exposure to the general public ranges from 6.38 × 10-6 ± 0.00017 mg/kg/day to 9.83 × 10-6 ± 0.00034 mg/kg/day. Only one out of 200,000 simulated individuals had a fipronil dose exceeding the probabilistic reference dose (0.048 mg/kg/day, pRfD), which aims to protect 99% of the population with effects less than 10% extra risk. By incorporating uncertainties in exposure and dose-response data, a more comprehensive understanding of the health risks associated with fipronil exposure in the Taiwanese population has been achieved.

Statistical analyses are an essential part of regulatory toxicological evaluations. While projects would be ideally monitored by both toxicologists and statisticians, this is often not possible in practice. Hence, toxicologists should be trained in some common statistical approaches but also need a tool for statistical evaluations. Due to transparency needed in regulatory processes and standard tests that can be evaluated with template approaches, the freely available open-source statistical software R may be suitable. R is a well-established software in the statistical community. The principal input method is via software code, which is both benefit and weakness of the tool. It is increasingly used by regulating authorities globally and can be easily extended by software packages, e.g., for new statistical functions and features. This manuscript outlines how R can be used in regulatory toxicology, allowing toxicologists to perform all regulatory required data evaluations in a single software solution. Practical applications are shown in case studies on simulated and experimental data. The examples cover a) Dunnett testing of treatment groups against a common control and in relation to a biological relevance threshold, assessing the test's assumptions and plotting the results; b) dose-response analysis and benchmark dose derivation for chronic kidney inflammation as a function of Pyridine; and c) graphical/exploratory data analysis of previously published developmental neurotoxicity data for Chlorpyrifos.

Per- and polyfluoroalkyl substances (PFASs) are a major priority for many federal and state regulatory agencies charged with monitoring levels of emerging contaminants in environmental media and setting health-protective benchmarks to guide risk assessments. While screening levels and toxicity reference values have been developed for numerous individual PFAS compounds, there remain important data gaps regarding the mode of action for toxicity of PFAS mixtures. The present study aims to contribute whole-mixture toxicity data and advance the methods for evaluating mixtures of two key components of aqueous film-forming foams: perfluorooctanesulfonic acid (PFOS), and 6:2 fluorotelomer sulfonic acid (6:2 FTS). Wildtype (AB) zebrafish embryos were exposed to PFOS and 6:2 FTS, both as individual components and as binary mixtures, from 2 to 122 h post-fertilization. Five treatment levels were selected to encompass environmentally relevant exposure levels. Experimental endpoints consisted of mortality, hatching, and developmental endpoints, including swim bladder inflation, yolk sac area, and larval body length. Results from dose–response analysis indicate that the assumption of additivity using conventional points of departure (e.g., NOAEL, LOAEL) is not supported for critical effect endpoints with these PFAS mixtures, and that the interactions vary as a function of the dose range. Alternative methods for quantifying relative potency are proposed, and recommendations for additional investigations are provided to further advance assessments of the toxicity of PFAS mixtures to aquatic organisms.

Background:Naphthalene is a polycyclic aromatic hydrocarbon that has been associated with health effects, including cancer. As the state of the science on naphthalene toxicity continues to evolve, updated toxicity reference value(s) may be required to support human health risk assessment.Objectives:We present a systematic evidence map of studies that could be used to derive toxicity reference value(s) for naphthalene.Methods:Human and animal health effect studies and physiologically based pharmacokinetic (PBPK) models were identified from a literature search based on populations, exposures, comparators, and outcomes (PECO) criteria. Human and animal studies meeting PECO criteria were refined to a smaller subset considered most informative for deriving chronic reference value(s), which are preferred for assessing risk to the general public. This subset was evaluated for risk of bias and sensitivity, and the suitability of each study for dose–response analysis was qualitatively assessed. Lowest observed adverse effect levels (LOAELs) were extracted and summarized. Other potentially relevant studies (e.g., mechanistic and toxicokinetic studies) were tracked as supplemental information but not evaluated further. Existing reference values for naphthalene are also summarized.Results:We identified 26 epidemiology studies and 16 animal studies that were considered most informative for further analysis. Eleven PBPK models were identified. The available epidemiology studies generally had significant risk of bias and/or sensitivity concerns and were mostly found to have low suitability for dose–response analysis due to the nature of the exposure measurements. The animal studies had fewer risk of bias and sensitivity concerns and were mostly found to be suitable for dose–response analysis.Conclusion:Although both epidemiological and animal studies of naphthalene provide weight of evidence for hazard identification, the available animal studies appear more suitable for reference value derivation. PBPK models and mechanistic and toxicokinetic data can be applied to extrapolate these animal data to humans, considering mode of action and interspecies metabolic differences. https://doi.org/10.1289/EHP7381

There are unique challenges in estimating dose-response with chemicals that are associated with multiple health outcomes and numerous studies. Some studies are more suitable than others for quantitative dose-response analyses. For such chemicals, an efficient method of screening studies and endpoints to identify suitable studies and potentially important health effects for dose-response modeling is valuable. Using inorganic arsenic as a test case, we developed a tiered approach that involves estimating study-specific margin of exposure (MOE)-like unitless ratios for two hypothetical scenarios. These study-specific unitless ratios are derived by dividing the exposure estimated to result in a 20% increase in relative risk over the background exposure (RRE20) by the background exposure, as estimated in two different ways. In our case study illustration, separate study-specific ratios are derived using estimates of United States population background exposure (RRB-US) and the mean study population reference group background exposure (RRB-SP). Systematic review methods were used to identify and evaluate epidemiologic studies, which were categorized based on study design (case-control, cohort, cross-sectional), various study quality criteria specific to dose-response analysis (number of dose groups, exposure ascertainment, exposure uncertainty), and availability of necessary dose-response data. Both case-control and cohort studies were included in the RRB analysis. The RRE20 estimates were derived by modeling effective counts of cases and controls estimated from study-reported adjusted odds ratios and relative risks. Using a broad (but not necessarily comprehensive) set of epidemiologic studies of multiple health outcomes selected for the purposes of illustrating the RRB approach, this test case analysis would suggest that diseases of the circulatory system, bladder cancer, and lung cancer may be arsenic health outcomes that warrant further analysis. This is suggested by the number of datasets from adequate dose-response studies demonstrating an effect with RRBs close to 1 (i.e., RRE20 values close to estimated background arsenic exposure levels).

Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods

Market participation and pastoral welfare in drought-prone areas: A dose-response analysis

Dose-dependent transitions in mechanisms of toxicity

Effective hemodynamic management in the intensive care unit requires individualized targets that adapt to dynamic clinical conditions. We developed Dynamic Cohort Ensemble Learning (DynaCEL), a real-time framework that recommends personalized heart rate and systolic blood pressure targets by modeling each time point post-intensive care unit admission as a distinct temporal cohort. Trained on eICU data and validated on MIMIC-IV and Indiana University Health datasets, DynaCEL demonstrated robust predictive performance (AUCs 0.83-0.91). In the MIMIC-IV cohort, proximity to DynaCEL-predicted targets was associated with lower 24-hour mortality compared to fixed targets, after adjustment using propensity score matching. Dose-response and comparative analyses revealed that greater deviations from personalized targets were associated with higher mortality. Case studies illustrated temporal and inter-individual variation in optimal targets. DynaCEL offers interpretable and scalable support for exploring precision hemodynamic management, although its clinical utility remains to be established in prospective trials.