Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects

Despite advances in toxicity testing and the development of new approach methodologies (NAMs) for hazard assessment, the ecological risk assessment (ERA) framework for terrestrial wildlife (i.e., air-breathing amphibians, reptiles, birds, and mammals) has remained unchanged for decades. While survival, growth, and reproductive endpoints derived from whole-animal toxicity tests are central to hazard assessment, nonstandard measures of biological effects at multiple levels of biological organization (e.g., molecular, cellular, tissue, organ, organism, population, community, ecosystem) have the potential to enhance the relevance of prospective and retrospective wildlife ERAs. Other factors (e.g., indirect effects of contaminants on food supplies and infectious disease processes) are influenced by toxicants at individual, population, and community levels, and need to be factored into chemically based risk assessments to enhance the "eco" component of ERAs. Regulatory and logistical challenges often relegate such nonstandard endpoints and indirect effects to postregistration evaluations of pesticides and industrial chemicals and contaminated site evaluations. While NAMs are being developed, to date, their applications in ERAs focused on wildlife have been limited. No single magic tool or model will address all uncertainties in hazard assessment. Modernizing wildlife ERAs will likely entail combinations of laboratory- and field-derived data at multiple levels of biological organization, knowledge collection solutions (e.g., systematic review, adverse outcome pathway frameworks), and inferential methods that facilitate integrations and risk estimations focused on species, populations, interspecific extrapolations, and ecosystem services modeling, with less dependence on whole-animal data and simple hazard ratios. Integr Environ Assess Manag 2024;20:725-748. © 2023 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Population pharmacokinetics of the dual endothelin receptor antagonist aprocitentan in subjects with or without essential or resistant hypertension

Aprocitentan is a novel, potent, dual endothelin receptor antagonist that recently demonstrated efficacy in the treatment of difficult-to-treat (resistant) hypertension. The aim of this study was to develop a population pharmacokinetic (PK) model describing aprocitentan plasma concentration over time, to investigate relationships between subject-specific factors (covariates) and model parameters, and to quantify the influence of the identified covariates on the exposure to aprocitentan via model-based simulations, enabling judgment about the clinical relevance of the covariates.PK data from 902 subjects in ten Phase 1, one Phase 2, and one Phase 3 study were pooled to develop a joint population PK model. The concentration-time course of aprocitentan was described by a two-compartment model with absorption lag time, first-order absorption and elimination, and reduced relative bioavailability following very high doses of 300 and 600 mg.The population PK model described the observed data well. Volume and clearance parameters were associated with body weight. Renal function as reflected by estimated glomerular filtration rate (eGFR), hepatic impairment, and sex were identified as relevant covariates on clearance.The subject-specific characteristics of body weight, eGFR, hepatic impairment, and sex were shown to influence exposure parameters area under the concentration-time curve and maximum concentration in steady state to a limited extent, i.e., not more than 25% different from a reference subject, and therefore do not warrant dose adjustments.

Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma

Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (k_g= 0.130 vs. 0.0551 week^-1, p-value < 0.0001), but similar for both groups (k_g = 0.0624 vs.0.0563 week^-1, p-value = 0.37) with the sequential modeling approach. The predicted TK profiles by joint modeling appeared better aligned with clinical observations. Joint modeling also predicted OS more accurately than the sequential approach according to concordance index and Brier score. The sequential and joint modeling approaches were also compared using additional simulated datasets, and survival was predicted better by joint modeling in the case of a strong association between TK and OS. In conclusion, joint modeling enabled the establishment of a robust association between TK and OS and may represent a better choice for parametric survival analyses over the sequential approach.

Inequalities of spatial primary healthcare accessibility in China

A key step to the establishment of a tiered healthcare system is equitable access to basic primary healthcare services for all. However, no quantitative research on the national status quo of primary healthcare accessibility in China exists. We filled this gap by estimating spatial accessibility to primary healthcare centers (PHCs) and mapping its inequality across the mainland China. Four national datasets during 2015-2018, including administrative boundaries, residential communities, points-of-interest (including PHCs), and road networks, were collected to calculate the distance to the nearest PHC for each community. Five other national datasets including census, elevation, land use, vegetation, and nightlight, were collected to model 100m × 100 m population grids, based on which geographical modeling was used to calculate PHC accessibility of each community. Inequalities in PHC accessibility across China were described with concentration indices. About 44% of communities across China representing approximately 30% of the overall population had no access to PHCs within their 6-km catchment areas; about 78% of communities across China representing approximately 68.4% of the overall population had no access to PHCs within their 1.5-km catchment areas. Some municipalities/provinces like Shanghai, Beijing, Tianjin, Jiangsu, Shandong, and Zhejiang generally had higher proximity to the nearest PHCs, while others like Tibet, Guizhou, and Guangxi had lower proximity to the nearest PHCs. However, assuming similar basic service capacity across all PHCs, Shanghai, Tianjin, and Chongqing showed the lowest PHC accessibility due to high population density. Variations in PHC accessibility existed, with more inequalities observed in the north and northeastern provinces and less inequalities in southwestern and south-central provinces. This study demonstrates primary healthcare accessibility and inequality at province and city levels, and identifies communities with lower proximity and accessibility to PHCs in China. It would serve as a starting point to facilitate precise healthcare planning and preparedness for health emergencies in China.

Predicting Combined Effects of Chemical Stressors: Population-Level Effects of Organic Chemical Mixtures with a Dynamic Energy Budget Individual-Based Model

Most regulatory ecological risk-assessment frameworks largely disregard discrepancies between the laboratory, where effects of single substances are assessed on individual organisms, and the real environment, where organisms live together in populations and are often exposed to multiple simultaneously occurring substances. We assessed the capability of individual-based models (IBMs) with a foundation in the dynamic energy budget (DEB) theory to predict combined effects of chemical mixtures on populations when they are calibrated on toxicity data of single substances at the individual level only. We calibrated a DEB-IBM for Daphnia magna for four compounds (pyrene, dicofol, α-hexachlorocyclohexane, and endosulfan), covering different physiological modes of action. We then performed a 17-week population experiment with D. magna (designed using the DEB-IBM), in which we tested mixture combinations of these chemicals at relevant concentrations, in a constant exposure phase (7-week exposure and recovery), followed by a pulsed exposure phase (3-day pulse exposure and recovery). The DEB-IBM was validated by comparing blind predictions of mixture toxicity effects with the population data. The DEB-IBM accurately predicted mixture toxicity effects on population abundance in both phases when assuming independent action at the effect mechanism level. The population recovery after the constant exposure was well predicted, but recovery after the pulse was not. The latter could be related to insufficient consideration of stochasticity in experimental design, model implementation, or both. Importantly, the mechanistic DEB-IBM performed better than conventional statistical mixture assessment methods. We conclude that the DEB-IBM, calibrated using only single-substance individual-level toxicity data, produces accurate predictions of population-level mixture effects and can therefore provide meaningful contributions to ecological risk assessment of environmentally realistic mixture exposure scenarios. Environ Toxicol Chem 2022;41:2240-2258. © 2022 SETAC.

Comparison of two modeling frameworks for unifying the toxicant-affected percentage of aquatic species, individuals, and time into a single metric

To address time-variable exposure to toxicants, this work compares simple and complex approaches to unifying the affected percentage of aquatic species, individuals, and time into a single metric. The simple approach uses only information on the probability distribution of exposure concentrations, a species sensitivity distribution (SSD) of chronic values, and the distribution of tolerance within species. The complex approach involves time-series simulation with a kinetics-based toxicity model coupled with a population model for each species in the SSD. Unlike the simple approach, this takes into account the exposure duration needed to elicit toxicity, differing sensitivities of life stages within a species, differing effects on survival versus reproduction, and species differences in their model population's response to press disturbance and recovery time from pulse disturbance. The probability distribution approach indicated that, for SSD assemblages challenged with moderately variable toxicant concentrations exceeding the aquatic life criterion a few percent of the time, most of the predicted aggregate effect is usually experienced by the most sensitive 10% of species and individuals. The kinetics-population simulation approach indicated that, for time-variable exposure (but not for constant exposure), the severity of the population effect depended on the type of effect and life stage affected. The results from both approaches suggest that moderately time-variable exposure is best viewed as a fluctuating press disturbance, not as a sporadic pulse disturbance. Integr Environ Assess Manag 2022;18:1364-1374. © 2021 SETAC.

Population pharmacokinetics of crenolanib in children and young adults with brain tumors

PURPOSE

Crenolanib, an oral inhibitor of platelet-derived growth factor receptor, was evaluated to treat children and young adults with brain tumors. Crenolanib population pharmacokinetics and covariate influence were characterized in this patient population.

METHODS

Patients enrolled on this phase I study (NCT01393912) received oral crenolanib once daily. Serial single-dose and steady-state serum pharmacokinetic samples were collected and analyzed using a validated LC-ESI-MS/MS method. Population modeling and covariate analysis evaluating demographics, laboratory values, and comedications were performed. The impact of significant covariates on crenolanib exposure was further explored using model simulations.

RESULTS

Crenolanib serum concentrations were analyzed for 55 patients (2.1-19.2 years-old) and best fitted with a linear two-compartment model, with delayed absorption modeled with a lag time. A typical patient [8-year-old, body surface area (BSA) 1 m2] had an apparent central clearance, volume, and absorption rate of 41 L/h, 54.3 L, and 0.19 /h, respectively. Patients taking acid reducers (histamine H2 antagonists or proton pump inhibitors) concomitantly exhibited about 2- and 1.7-fold lower clearance and volume (p < 0.0001 and p = 0.018, respectively). Crenolanib clearance increased with BSA (p < 0.0001), and absorption rate decreased with age (p < 0.0001). Model simulations showed cotreatment with an acid reducer was the only covariate significantly altering crenolanib exposure and supported the use of BSA-based crenolanib dosages vs flat-dosages for this population.

CONCLUSIONS

Crenolanib pharmacokinetics were adequately characterized in children and young adults with brain tumors. Despite marked increased drug exposure with acid reducer cotreatment, crenolanib therapy was well tolerated. No dosing adjustments are recommended for this population.

Pop-guide: Population modeling guidance, use, interpretation, and development for ecological risk assessment

The assimilation of population models into ecological risk assessment (ERA) has been hindered by their range of complexity, uncertainty, resource investment, and data availability. Likewise, ensuring that the models address risk assessment objectives has been challenging. Recent research efforts have begun to tackle these challenges by creating an integrated modeling framework and decision guide to aid the development of population models with respect to ERA objectives and data availability. In the framework, the trade-offs associated with the generality, realism, and precision of an assessment are used to guide the development of a population model commensurate with the protection goal. The decision guide provides risk assessors with a stepwise process to assist them in developing a conceptual model that is appropriate for the assessment objective and available data. We have merged the decision guide and modeling framework into a comprehensive approach, Population modeling Guidance, Use, Interpretation, and Development for Ecological risk assessment (Pop-GUIDE), for the development of population models for ERA that is applicable across regulatory statutes and assessment objectives. In Phase 1 of Pop-GUIDE, assessors are guided through the trade-offs of ERA generality, realism, and precision, which are translated into model objectives. In Phase 2, available data are assimilated and characterized as general, realistic, and/or precise. Phase 3 provides a series of dichotomous questions to guide development of a conceptual model that matches the complexity and uncertainty appropriate for the assessment that is in concordance with the available data. This phase guides model developers and users to ensure consistency and transparency of the modeling process. We introduce Pop-GUIDE as the most comprehensive guidance for population model development provided to date and demonstrate its use through case studies using fish as an example taxon and the US Federal Insecticide Fungicide and Rodenticide Act and Endangered Species Act as example regulatory statutes. Integr Environ Assess Manag 2021;17:767-784. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Population pharmacodynamic modeling of intramuscular and oral dexamethasone and betamethasone effects on six biomarkers with circadian complexities in Indian women

Population pharmacokinetic/pharmacodynamic (PK/PD) analysis was performed for extensive data for differing dosage forms and routes for dexamethasone (DEX) and betamethasone (BET) in 48 healthy nonpregnant Indian women in a partial and complex cross-over design. Single doses of 6 mg dexamethasone phosphate (DEX-P), betamethasone phosphate (BET-P), or 1:1 mixture of betamethasone phosphate and acetate (BET-PA) were administered orally (PO) or intramuscularly (IM) where each woman enrolled in a two-period cross-over study. Plasma concentrations collected over 96 h were described with a two-compartment model with differing PO and IM first-order absorption inputs. Overall, BET exhibited slower clearance, similar volume of distribution, faster absorption, and longer persistence than DEX with BET acetate producing extremely slow absorption but full bioavailability of BET. Six biomarkers were assessed over a 24-h baseline period with four showing circadian rhythms with complex baselines. These baselines and the strong responses seen after drug dosing were fitted with various indirect response models using the Laplace estimation methods in NONMEM 7.4. Both the PK and six biomarker responses were well-described with modest variability likely due to the homogeneous ages, weights, and ethnicities of the women. The drugs either inhibited or stimulated the influx processes with some models requiring joint inclusion of drug effects on circadian cortisol suppression. The biomarkers and order of sensitivity (lowest IC₅₀/SC₅₀ to highest) were: cortisol, T-helper cells, basophils, glucose, neutrophils, and T-cytotoxic cells. DEX sensitivities were generally greater than BET with corresponding mean ratios for these biomarkers of 2.86, 1.27, 1.72, 1.27, 2.69, and 1.06. Overall, the longer PK (e.g. half-life) of BET, but lesser PD activity (e.g. higher IC₅₀), produces single-dose response profiles that appear quite similar, except for the extended effects from BET-PA. This comprehensive population modeling effort provides the first detailed comparison of the PK profiles and six biomarker responses of five commonly used dosage forms of DEX and BET in healthy women.

Population pharmacokinetic modeling of intramuscular and oral dexamethasone and betamethasone in Indian women

Population analysis of pharmacokinetic data for five differing dosage forms and routes for dexamethasone and betamethasone in 48 healthy nonpregnant Indian women was performed that accounted for a partial and complex cross-over design. Single doses of 6 mg dexamethasone phosphate (DEX-P), betamethasone phosphate (BET-P), or 1:1 mixture of betamethasone phosphate and acetate (BET-PA) were administered orally (PO) or intramuscularly (IM). Plasma concentrations collected for two periods over 96 h were described with a two-compartment model with differing PO and IM first-order absorption inputs. Clearances and volumes were divided by the IM bioavailability [Formula: see text]. The homogeneous ages, body weights, and ethnicity of the women obviated covariate analysis. Parameter estimates were obtained by the Laplace estimation method implemented in NONMEM 7.4. Typical values for dexamethasone were clearance ([Formula: see text] of 9.29 L/h, steady-state volume ([Formula: see text] of 56.4 L, IM absorption constant [Formula: see text] of 0.460 1/h and oral absorption constant ([Formula: see text] of 0.936 1/h. Betamethasone parameters were CL/FIM of 5.95 L/h, [Formula: see text] of 72.4 L, [Formula: see text] of 0.971 1/h, and [Formula: see text] of 1.21 1/h. The PO to IM F values were close to 1.0 for both drugs. The terminal half-lives averaged about 7.5 h for DEX, 17 h for BET, and 78 h for BET from BET-PA with the latter reflecting very slow release of BET from the acetate ester. Overall, BET exhibited slower clearance, larger volume of distribution, faster absorption, and longer persistence than DEX. These data may be useful in considering exposures when substituting one form of corticosteroid for another.

Mechanistic Effect Modeling of Earthworms in the Context of Pesticide Risk Assessment: Synthesis of the FORESEE Workshop

Earthworms are important ecosystem engineers, and assessment of the risk of plant protection products toward them is part of the European environmental risk assessment (ERA). In the current ERA scheme, exposure and effects are represented simplistically and are not well integrated, resulting in uncertainty when the results are applied to ecosystems. Modeling offers a powerful tool to integrate the effects observed in lower tier laboratory studies with the environmental conditions under which exposure is expected in the field. This paper provides a summary of the (In)Field Organism Risk modEling by coupling Soil Exposure and Effect (FORESEE) Workshop held 28-30 January 2020 in Düsseldorf, Germany. This workshop focused on toxicokinetic-toxicodynamic (TKTD) and population modeling of earthworms in the context of ERA. The goal was to bring together scientists from different stakeholder groups to discuss the current state of soil invertebrate modeling and to explore how earthworm modeling could be applied to risk assessments, in particular how the different model outputs can be used in the tiered ERA approach. In support of these goals, the workshop aimed at addressing the requirements and concerns of the different stakeholder groups to support further model development. The modeling approach included 4 submodules to cover the most relevant processes for earthworm risk assessment: environment, behavior (feeding, vertical movement), TKTD, and population. Four workgroups examined different aspects of the model with relevance for risk assessment, earthworm ecology, uptake routes, and cross-species extrapolation and model testing. Here, we present the perspectives of each workgroup and highlight how the collaborative effort of participants from multidisciplinary backgrounds helped to establish common ground. In addition, we provide a list of recommendations for how earthworm TKTD modeling could address some of the uncertainties in current risk assessments for plant protection products. Integr Environ Assess Manag 2021;17:352-363. © 2020 SETAC.

Investigating potential toxic effects of pollutants on population growth rates and probability of extinction for a representative squamate

Chemical contamination has been suggested as an important contributing factor to reptile population declines, but direct links are rarely reported. Population modeling provides a quantitative method to understand the long-term effects of contaminants on population persistence. We created a matrix model for Sceloporus lizards and investigated hypothetical toxic effects by reducing survival and reproductive parameters by 0 to 100% in 10% increments. We report effects on population growth rate (λ) and elasticity values for each stage due to these reductions. We then incorporated stochasticity to the model to simulate the variation seen in demographic data and quantified extinction risk. The deterministic model yielded a λ of 1.07 suggesting stability in some wild Sceloporus populations. A yearly reduction of 20 to 30% in demographic parameters was needed to push λ to decline in both our deterministic and stochastic simulations. Surprisingly, our baseline stochastic simulations had a 30% extinction probability despite a stable deterministic model. We tested three adjustments to the stochastic model, (1) increased survival/fecundity parameters, (2) higher starting densities, and (3) a density-dependent juvenile survival function. The model with density-dependent juvenile growth had the lowest extinction risk. Ultimately, 20 or 30% mortality every year is likely unrealistic, but our results provide insight in linking toxicity to population effects. Ultimately, very little reduction in demographics is needed to cause declines in these populations. Our generalized models provide important tools for screening-level risk assessment of chemical contamination, especially for taxonomic groups that tend to receive less research interest.

Integrating Metapopulation Dynamics into a Bayesian Network Relative Risk Model: Assessing Risk of Pesticides to Chinook Salmon (Oncorhynchus tshawytscha) in an Ecological Context

The population level is often the biological endpoint addressed in ecological risk assessments (ERAs). However, ERAs tend to ignore the metapopulation structure, which precludes an understanding of how population viability is affected by multiple stressors (e.g., toxicants and environmental conditions) at large spatial scales. Here we integrate metapopulation model simulations into a regional-scale, multiple stressors risk assessment (Bayesian network relative risk model [BN-RRM]) of organophosphate (OP) exposure, water temperature, and DO impacts on Chinook salmon (Oncorhynchus tshawytscha). A matrix metapopulation model was developed for spring Chinook salmon in the Yakima River Basin (YRB), Washington, USA, including 3 locally adapted subpopulations and hatchery fish that interact with those subpopulations. Three metapopulation models (an exponential model, a ceiling density-dependent model, and an exponential model without dispersal) were integrated into the BN-RRM to evaluate the effects of population model assumptions on risk calculations. Risk was defined as the percent probability that the abundance of a subpopulation would decline from their initial abundance (500 000). This definition of risk reflects the Puget Sound Partnership's management goal of achieving "no net loss" of Chinook abundance. The BN-RRM model results for projection year 20 showed that risk (in % probability) from OPs and environmental stressors was higher for the wild subpopulations-the American River (50.9%-97.7%) and Naches (39.8%-84.4%) spring Chinook-than for the hatchery population (CESRF 18.5%-46.5%) and the Upper Yakima subpopulation (21.5%-68.7%). Metapopulation risk was higher in summer (58.1%-68.7%) than in winter (33.6%-53.2%), and this seasonal risk pattern was conserved at the subpopulation level. To reach the management goal in the American River spring Chinook subpopulation, the water temperature conditions in the Lower Yakima River would need to decrease. We demonstrate that 1) relative risk can vary across a metapopulation's spatial range, 2) dispersal among patches impacts subpopulation abundance and risk, and 3) local adaptation within a salmon metapopulation can profoundly impact subpopulation responses to equivalent stressors. Integr Environ Assess Manag 2021;17:95-109. © 2020 SETAC.

Relationship between antofloxacin concentration and QT prolongation and estimation of the possible false-positive rate

PURPOSE

To elucidate the relationship between antofloxacin (AT) plasma concentration and QT interval prolongation, compare the effects of different correction and analytical methods on conclusions, and estimate the possible false-positive rate in thorough QT (TQT) studies.

METHODS

Twenty-four healthy Chinese volunteers from a four-period crossover TQT study orally received 200 mg/d AT, 400 mg/d AT, 400 mg/d moxifloxacin, and a placebo in a random order for 5 d for each. QT interval samples were collected on d 1 and d 5. Population models were established describing the relationship between QT and AT concentration. The yardstick from ICH E14 guidelines was used to measure the effect of drugs on QT prolongation both in biostatistical and modeling analyses. A possible false-positive rate was estimated by constructing a 1000-time bootstrap to obtain the rate-of-difference values between d 1 and d 5 over 5 ms in the placebo period.

RESULTS

In the modeling analysis, the QT prolongation estimate at the mean maximal concentration of AT (4.51 μg/mL) was 3.84 ms, and its upper bound of the one-sided 95 % CI was 7.04 ms, which showed a negative effect on QT interval prolongation. The estimation for the false-positive rate was 31 % in this study.

CONCLUSION

The effect of AT on QT interval prolongation may not have been significant at the dosage of 400 mg. Baseline and placebo adjustments were necessary in TQT studies. Population modeling has demonstrated clear superiority in making full use of data to accurately analyze the relationship between drugs and QT intervals.

Confidence in the dynamic spread of epidemics under biased sampling conditions

The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests, which necessitate a bias in testing favoring symptomatic individuals. A thorough understanding of sampling confidence and bias is necessary in order make accurate conclusions. In this manuscript, we provide a stochastic model of sampling for assessing confidence in disease metrics such as trend detection, peak detection and disease spread estimation. Our model simulates testing for a disease in an epidemic with known dynamics, allowing us to use Monte-Carlo sampling to assess metric confidence. This model can provide realistic simulated data which can be used in the design and calibration of data analysis and prediction methods. As an example, we use this method to show that trends in the disease may be identified using under 10,000 biased samples each day, and an estimate of disease spread can be made with additional 1,000-2,000 unbiased samples each day. We also demonstrate that the model can be used to assess more advanced metrics by finding the precision and recall of a strategy for finding peaks in the dynamics.

Pharmacokinetic and pharmacodynamic modeling of levetiracetam: investigation of factors affecting the clinical outcome

This study aimed to evaluate the pharmacokinetics and pharmacodynamics of oral levetiracetam therapy in drug refractory adult epileptic outpatients, as well as factors affecting them. Concentration-time data were collected at steady state, while seizure recurrence was monitored for 13 months. Non-linear mixed effects modeling was applied, and covariates assessed included weight, height, age, daily dose and creatinine clearance.Plasma concentrations of levetiracetam were best described by a one-compartment pharmacokinetic model (V/F = 34.7 L) with first-order absorption (ka = 0.616 h^-1) and clearance (CL/F = 3.26 L/h). Patient's CrCL was found to significantly affect levetiracetam clearance (beta = 0.795). Time to seizure occurrence followed an exponential distribution and the mean time to seizure occurrence was estimated Te = 22.08 days. Seizure rate per month followed a Poisson distribution, while mean seizure rate per month was estimated λ = 1.33. Daily dose significantly affected the mean estimated time to seizure (beta = -2.2) and the mean monthly seizure rate (beta = 2.27) in a reverse way. Using discrete time Markov chains, it was shown that the transition probability from focal seizures to focal to bilateral tonic-clonic is significantly altered in relation to patient's CrCL.Simulations showed that dose should be adjusted in relation to CrCL, while low doses of levetiracetam are more effective for seizure control. Modeling and simulation in every-day clinical practice may provide significant information for the optimization of seizure control using well-known agents.

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT), and dissolved oxygen concentration (DO) in 4 watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle-Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN-RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data from CH toxicity data sets. The completed BN-RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling runs up to 50-y timeframes. There were 3 primary conclusions drawn from the model-building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65% to 85%. Environmental conditions contributed to a larger proportion of the risk compared to CH. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential than CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management goal, we calculated the conditions that would increase juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. Integr Environ Assess Manag 2019;00:1-15. © 2019 SETAC.

Time-Variable Exposure Experiments in Conjunction with Higher Tier Population and Effect Modeling to Assess the Risk of Chlorotoluron to Green Algae

An algae population model was applied to describe measured effects of pulsed exposure to chlorotoluron on populations of Pseudokirchneriella subcapitata in 2 laboratory flow-through chemostat tests with different exposure regimes. Both tests enabled evaluation of adverse effects on algae during the exposure and population recovery afterward. Impacts on population densities after chlorotoluron exposure were directly visible as biomass loss in the chemostats. Recovery was observed after each exposure peak. The test results indicate that P. subcapitata is unlikely to show an increased sensitivity to chlorotoluron after pulsed exposure. No altered response or adaptation of the algae to chlorotoluron was observed, with the exception of the last high peak in flow-through test 2. Therefore, an adaptation to the test substance cannot be excluded after long-term exposure. However, recovery to the steady-state level after this peak indicates that the growth rate (fitness) was not significantly reduced in the population with higher tolerance. No differences in chlorotoluron impact on the populations over time in terms of growth were detected. Model predictions agreed well with the measured data. The tests and modeling results validate the model to simulate population dynamics of P. subcapitata after pulsed exposure to chlorotoluron. Model predictions and extrapolations with different exposure patterns are considered reliable for chlorotoluron. The good reproducibility of the population behavior in the test systems supports this conclusion. An example modeled extrapolation of the experimental results to other (untested) exposure scenarios shows a potential approach to using the validated model as a supportive tool in risk assessment. Environ Toxicol Chem 2019;38:2520-2534. © 2019 SETAC.

An Evaluation of the BEEHAVE Model Using Honey Bee Field Study Data: Insights and Recommendations

A lack of standard and internationally agreed procedures for higher-tier risk assessment of plant protection products for bees makes coherent availability of data, their interpretation, and their use for risk assessment challenging. Focus has been given to the development of modeling approaches, which in the future could fill this gap. The BEEHAVE model, and its submodels, is the first model framework attempting to link 2 processes vital for the assessment of bee colonies: the within-hive dynamics for honey bee colonies and bee foraging in heterogeneous and dynamic landscapes. We use empirical data from a honey bee field study to conduct a model evaluation using the control data set. Simultaneously, we are testing several model setups for the interlinkage between the within-hive dynamics and the landscape foraging module. Overall, predictions of beehive dynamics fit observations made in the field. This result underpins the European Food Safety Authority's evaluation of the BEEHAVE model that the most important in-hive dynamics are represented and correctly implemented. We show that starting conditions of a colony drive the simulated colony dynamics almost entirely within the first few weeks, whereas the impact is increasingly substituted by the impact of foraging activity. Common among field studies is that data availability for hive observations and landscape characterizations is focused on the proportionally short exposure phase (i.e., the phase where colony starting conditions drive the colony dynamics) in comparison to the postexposure phase that lasts several months. It is vital to redistribute experimental efforts toward more equal data aquisition throughout the experiment to assess the suitability of using BEEHAVE for the prediction of bee colony overwintering survival. Environ Toxicol Chem 2019;38:2535-2545. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Comparative Analysis of Plant Demographic Traits Across Species of Different Conservation Concern: Implications for Pesticide Risk Assessment

Pesticide risk assessment for "listed" (threatened and endangered) plant species is hampered by a lack of quantitative demographic information. Demographic information for nonlisted plant species could provide risk-assessment data and inform recovery plans for listed species; however, it is unclear how representative demography of the former would be for the latter. We performed a comparison of plant demographic traits and elasticity metrics to explore how similar these are between listed and nonlisted species. We used transition matrices from the COMPADRE Plant Matrix Database to calculate population growth rate (λ), net reproductive rate (R_o ), generation time (T_g ), damping ratio (ρ), and summed elasticities for survival (stasis), growth, fertility (reproduction), and evenness of elasticity (EE). We compared these across species varying in conservation status and population trend. Phylogenetic generalized least squares (PGLS) models were used to evaluate differences between listed and nonlisted plants. Overall, demographic traits were largely overlapping for listed and nonlisted species. Population trends had a significant impact on most demographic traits and elasticity patterns. The influence of T_g on elasticity metrics was consistent across all data groupings. In contrast, the influence of λ on elasticity metrics was highly variable, and correlated in opposite directions in growing and declining populations. Our results suggested that population models developed for nonlisted plant species may be useful for assessing the risks of pesticides to listed species. Environ Toxicol Chem 2019;38:2043-2052. © 2019 SETAC.

The Unexpected Absence of Nickel Effects on a Daphnia Population at 3 Temperatures is Correctly Predicted by a Dynamic Energy Budget Individual-Based Model

Recent studies have shown that temperature affects chronic nickel (Ni) toxicity to Daphnia magna at the individual (apical) level. However, the effect of temperature on Ni toxicity to D. magna at the population level is unknown. The present study investigated whether the effect of temperature on chronic Ni toxicity to D. magna assessed on apical endpoints can be extrapolated to the population level. The results of the population experiment showed no consistent Ni effects on total D. magna population abundance at 15, 20, and 25 °C, although the Ni concentrations tested were previously reported to significantly reduce reproduction in D. magna individuals. This result supports the idea that ecological risk assessment should not extrapolate as such from apical endpoints to the population level. A dynamic energy budget individual-based model (DEB-IBM) was calibrated using apical Ni toxicity data at 15, 20, and 25 °C. The goal was to investigate whether the calibrated DEB-IBM would be able to predict the unexpected absence of effects at the population level and to further investigate the effect of temperature on Ni toxicity to a D. magna population. At the population level, the calibrated DEB-IBM correctly predicted the unexpected absence of an effect of Ni on a D. magna population. Detailed analysis of simulation output suggests that the predicted lower Ni sensitivity at the population level occurs because Ni-induced mortality is compensated by reduced starvation (less intraspecific competition). Extrapolated median effective concentration (EC50) values for population density predicted that the effect of temperature on Ni toxicity to D. magna populations was smaller (1.9-fold higher at 25 °C than at 15 °C) than on Ni toxicity to D. magna apical reproduction (the EC50 is 6.5-fold higher at 25 °C than at 15 °C). These results show that the DEB-IBM can help to replace population experiments by in silico simulations and to optimize the experimental design of population studies. Environ Toxicol Chem 2019;38:1423-1433. © 2019 SETAC.

Development of a non-human primate model to support CNS translational research: Demonstration with D-amphetamine exposure and dopamine response

BACKGROUND

Challenges specific to the discovery and development of candidate CNS drugs have led to implementation of various in silico, in vitro and in vivo approaches to improve the odds for commercialization of novel treatments.

NEW METHOD

Advances in analytical methodology and microdialysis probe design have enabled development of a non-human primate model capable of measuring concentrations of drugs or endogenous chemicals in brain extracellular fluid (ECF) and cerebrospinal fluid (CSF). Linking these to population modeling reduces animal numbers to support predictive translational sciences in primates. Application to measure D-amphetamine exposure and dopamine response in ECF and CSF demonstrate the approach.

RESULTS

Following a 0.1 mg/kg intravenous bolus dose of D-amphetamine, a population approach was used to build a plasma compartmental-based and brain physiologic-based pharmacokinetic (PK) model linking drug concentrations in plasma to brain ECF and CSF concentrations. Dopamine was also measured in brain ECF. The PK model was used to simulate the relationship between D-amphetamine exposure and dopamine response in ECF over a wide dose range.

COMPARISONS WITH EXISTING METHODS

Ability to co-sample and measure drug and endogenous substances in blood, brain ECF and/or CSF, coupled with population modeling, provides an in vivo approach to evaluate CNS drug penetration and effect in non-human primates.

CONCLUSIONS

A method to measure drug and endogenous neurochemicals in non-human primate brain fluids is demonstrated. Its basis in non-human primates merits improved confidence regarding predictions of drug exposure and target engagement in human CNS.

Integral gene drives for population replacement

A first generation of CRISPR-based gene drives has now been tested in the laboratory in a number of organisms, including malaria vector mosquitoes. Challenges for their use in the area-wide genetic control of vector-borne disease have been identified, including the development of target site resistance, their long-term efficacy in the field, their molecular complexity, and practical and legal limitations for field testing of both gene drive and coupled anti-pathogen traits. We have evaluated theoretically the concept of integral gene drive (IGD) as an alternative paradigm for population replacement. IGDs incorporate a minimal set of molecular components, including drive and anti-pathogen effector elements directly embedded within endogenous genes - an arrangement that in theory allows targeting functionally conserved coding sequences without disrupting their function. Autonomous and non-autonomous IGD strains could be generated, optimized, regulated and imported independently. We performed quantitative modeling comparing IGDs with classical replacement drives and show that selection for the function of the hijacked host gene can significantly reduce the establishment of resistant alleles in the population, while drive occurring at multiple genomic loci prolongs the duration of transmission blockage in the face of pre-existing target site variation. IGD thus has potential as a more durable and flexible population replacement strategy.

Assessing the effects of temperature and salinity oscillations on a key mesopredator fish from European coastal systems

A population dynamics model was developed to assess the short and long-term effects of temperature and salinity variations in the common goby Pomatoschistus microps in a Portuguese estuary (Minho estuary, NW Portugal). The population was divided into juveniles, females and males, which constituted the model's state variables. Linear regressions between the observed and the predicted density of juveniles, females and the total population were significant. Parameter's sensitivity and uncertainty analysis were estimated. The model was able to satisfactory describe the P. microps population dynamics, and thus was used to simulate the effects of climatic changes on the fish population. Simulations indicated that the common goby population is sensitive to both temperature and salinity changes. Overall, scenarios of more than 3 °C increase caused significant population decreases. Similarly, increased salinities led to a population shrinkage, whereas scenarios of salinity decrease generated an opposite variation on the population. According to the IPCC predictions for climatic tendencies, the population of the common goby will tend to decrease in the near future, experiencing marked oscillations (decrease or increase) during climatic extremes, namely droughts and floods, respectively. These results may be a useful for future planning and management of estuarine systems given that the common goby is an important species of estuarine food webs in many temperate ecosystems.

Coupling toxicokinetic-toxicodynamic and population models for assessing aquatic ecological risks to time-varying pesticide exposures

Population modeling evaluations of pesticide exposure time series were compared with aspects of a currently used risk assessment process. The US Environmental Protection Agency's Office of Pesticide Programs models daily aquatic 30-yr pesticide exposure distributions in its risk assessments, but does not routinely make full use of the information in such time series. We used mysid shrimp Americamysis bahia toxicity and demographic data to demonstrate the value of a toxicokinetic-toxicodynamic model coupled with a series of matrix population models in risk assessment refinements. This species is a small epibenthic marine crustacean routinely used in regulatory toxicity tests. We demonstrate how the model coupling can refine current risk assessments using only existing standard regulatory toxicity test results. Several exposure scenarios (each with the same initial risk characterization as determined by a more traditional organism-based approach) were created within which population modeling documented risks different from those of assessments based on the traditional approach. We also present different acute and chronic toxicity data scenarios by which toxicokinetic-toxicodynamic coupled with population modeling can distinguish responses that traditional risk evaluations are not designed to detect. Our results reinforce the benefits of this type of modeling in risk evaluations, especially related to time-varying exposure concentrations. Environ Toxicol Chem 2018;37:2633-2644. Published 2018 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Zinc toxicity to Daphnia magna in a two-species microcosm can be predicted from single-species test data: The effects of phosphorus supply and pH

Ecological interactions and abiotic stress factors may significantly affect species sensitivities to toxicants, and these are not incorporated in standard single-species tests. The present study tests whether a model, calibrated solely on single-species data, can explain abiotic stress factors in a two-species microcosm, a test applied to the effects of nutritional stress (phosphorus [P] limitation) on zinc (Zn) toxicity to Daphnia magna. A population model was developed based on P- and Zn-dependent algal and daphnid growth. Two separate two-species (phytoplankton Pseudokirchneriella subcapitata and consumer D. magna) microcosm experiments with P × Zn factorial combinations and a different pH (7.3 and 7.8) were set up to validate the model. The 21-d daphnid population size was considerably reduced by increased Zn and by decreased P supply, with a significant (p < 0.001) interaction between the 2 factors. The observed median effective concentration (EC50) of Zn on D. magna population size varied 12-fold (25 to 310 μg Zn L^-1 ), with the lowest EC50 values found at the highest pH and high P treatments. For both experiments, Zn toxicity to D. magna was correctly predicted within a factor of 2 for EC50 values, and this is explained by the model through 1) a higher phytoplankton Zn sensitivity at higher pH, affecting food supply to D. magna, and 2) an increased algal P content at higher Zn, offering a nutritional benefit to daphnids that counteracts direct Zn toxicity under P limitation. The present study illustrates that indirect effects of Zn via producer-consumer relationships can outweigh the direct toxic effects and that models calibrated solely on single-species test data can help with interpreting these results in two-species systems. Environ Toxicol Chem 2018;37:2153-2164. © 2018 SETAC.

Population pharmacokinetics of levodopa/carbidopa microtablets in healthy subjects and Parkinson's disease patients

OBJECTIVES

Low dose, dispersible, levodopa/carbidopa microtablets with an automatic dose dispenser have been developed to facilitate individualized levodopa treatment. The aim of this study was to characterize the pharmacokinetics (PK) of levodopa and carbidopa after microtablet administration, and evaluate the impact of potential covariates.

METHODS

The population PK analysis involved data from 18 healthy subjects and 18 Parkinson's disease patients included in two single-dose, open-label levodopa/carbidopa microtablet studies. The analysis was carried out using non-linear mixed effects modeling. Bodyweight was included on all disposition parameters according to allometric scaling. Potential influence of additional covariates was investigated using graphical evaluation and adjusted adaptive least absolute shrinkage and selection operator.

RESULTS

Dispositions of levodopa and carbidopa were best described by a two- and one-compartment model respectively. Double-peak profiles were described using two parallel absorption compartments. Levodopa apparent clearance was found to decrease with increasing carbidopa dose (15% lower with 75 compared to 50 mg of carbidopa) and disease stage (by 18% for Hoehn and Yahr 1 to 4). Carbidopa apparent clearance was found to decrease with age (28% between the age of 60 and 80 years). An external evaluation showed the model to be able to reasonably well predict levodopa concentrations following multiple-dose microtablet administration in healthy subjects.

CONCLUSIONS

The presented models adequately described the PK of levodopa and carbidopa, following microtablet administration. The developed model may in the future be combined with a pharmacokinetic-pharmacodynamic target and used for individualized dose selection, utilizing the flexibility offered by the microtablets.

Assessing and mitigating simulated population-level effects of 3 herbicides to a threatened plant: Application of a species-specific population model of Boltonia decurrens

Extrapolating from organism-level endpoints, as generated from standard pesticide toxicity tests, to populations is an important step in threatened and endangered species risk assessments. We apply a population model for a threatened herbaceous plant species, Boltonia decurrens, to estimate the potential population-level impacts of 3 herbicides. We combine conservative exposure scenarios with dose-response relationships for growth and survival of standard test species and apply those in the species-specific model. Exposure profiles applied in the B. decurrens model were estimated using exposure modeling approaches. Spray buffer zones were simulated by using corresponding exposure profiles, and their effectiveness at mitigating simulated effects on the plant populations was assessed with the model. From simulated exposure effects scenarios that affect plant populations, the present results suggest that B. decurrens populations may be more sensitive to exposures from herbicide spray drift affecting vegetative stages than from runoff affecting early seedling survival and growth. Spray application buffer zones were shown to be effective at reducing effects on simulated populations. Our case study demonstrates how species-specific population models can be applied in pesticide risk assessment to bring organism-level endpoints, exposure assumptions, and species characteristics together in an ecologically relevant context. Environ Toxicol Chem 2018;37:1545-1555. © 2018 SETAC.

A framework for linking population model development with ecological risk assessment objectives

The value of models that link organism-level impacts to the responses of a population in ecological risk assessments (ERAs) has been demonstrated extensively over the past few decades. There is little debate about the utility of these models to translate multiple organism-level endpoints into a holistic interpretation of effect to the population; however, there continues to be a struggle for actual application of these models as a common practice in ERA. Although general frameworks for developing models for ERA have been proposed, there is limited guidance on when models should be used, in what form, and how to interpret model output to inform the risk manager's decision. We propose a framework for developing and applying population models in regulatory decision making that focuses on trade-offs of generality, realism, and precision for both ERAs and models. We approach the framework development from the perspective of regulators aimed at defining the needs of specific models commensurate with the assessment objective. We explore why models are not widely used by comparing their requirements and limitations with the needs of regulators. Using a series of case studies under specific regulatory frameworks, we classify ERA objectives by trade-offs of generality, realism, and precision and demonstrate how the output of population models developed with these same trade-offs informs the ERA objective. We examine attributes for both assessments and models that aid in the discussion of these trade-offs. The proposed framework will assist risk assessors and managers to identify models of appropriate complexity and to understand the utility and limitations of a model's output and associated uncertainty in the context of their assessment goals. Integr Environ Assess Manag 2018;14:369-380. Published 2017. This article is a US Government work and is in the public domain in the USA.

Models with Men and Women: Representing Gender in Dynamic Modeling of Social Systems

Dynamic engineering models have yet to be evaluated in the context of feminist engineering ethics. Decision-making concerning gender in dynamic modeling design is a gender and ethical issue that is important to address regardless of the system in which the dynamic modeling is applied. There are many dynamic modeling tools that operationally include the female population, however, there is an important distinction between females and women; it is the difference between biological sex and the social construct of gender, which is fluid and changes over time and geography. The ethical oversight in failing to represent or misrepresenting gender in model design when it is relevant to the model purpose can have implications for model validity and policy model development. This paper highlights this gender issue in the context of feminist engineering ethics using a dynamic population model. Women are often represented in this type of model only in their biological capacity, while lacking their gender identity. This illustrative example also highlights how language, including the naming of variables and communication with decision-makers, plays a role in this gender issue.

Environmental stochasticity and intraspecific competition influence the population dynamics of Culex quinquefasciatus (Diptera: Culicidae)

Background

Members of the Culex pipiens complex (Cx. pipiens quinquefasciatus in Southern USA) play a critical role in the spillover of urban arboviruses such as West Nile virus or St. Louis encephalitis virus. Field studies have shown strong correlation between the periodicity of rainfall events and larval proliferation. However, mechanistic determinants driving this relationship are poorly understood. We hypothesize that rainfall events decrease strain from intraspecific competition through the associated reduction of immature density and the introduction of detritus.

Results

To address our hypothesis, we used laboratory competition experiments to inform a deterministic matrix projection model consisting of an age-structured larval matrix coupled with a stage-structured adult mosquito matrix. Rain events were simulated in a competition-based metabolic age model and compared to a null model including environmental variability. Variable rain delays in two-event simulations showed optimal proliferation occurring with rain delays between 16 and 21 days when including density-dependent effects.

Conclusions

These results are comparable to the pattern observed in natural populations, indicating that Cx. quinquefasciatus proliferation rates can be modeled mechanistically as a density-dependent system. The empirical understanding of density-dependence as it relates to environmental stochasticity provides a theoretical platform for the study of larval dynamics and the impact of larval control in this medically relevant disease vector.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2711-1) contains supplementary material, which is available to authorized users.

The importance of maintenance conditions of Daphnia magna Straus as a test organism for ecotoxicological analysis

Insufficient attention to the standardization of conditions for test organisms can lead to a distortion of bioassay results by changing the characteristics of the culture. The present study deals with the influence of abiotic factors (temperature of cultivated water, its chemical composition, season of the year) and biotic factors (seeding density) on the culture of Daphnia magna Straus. The natural mortality of D. magna is described by the Gompertz equation. One of the parameters of the equation related to daily increase of mortality is very stable, and we suggest monitoring its constancy as a sign of the health of D. magna culture. Seeding density of crustaceans affects both the fertility of species and their mortality. We observed a high degree of compensation for the natural death of adults with increasing fertility for groups of 10 and 25 species living in 1 L of water; the current population density of D. magna regulates the birth rate of juveniles. If the density is 50 individuals/L, the compensatory effect is reduced. Keeping Daphnia in small groups (for example, 10 individuals) and an increase in temperature from 20 to 25 °C reduces the average life expectancy. Therefore, when choosing a test protocol to determine chronic toxicity, it is important to pay attention to the seeding density of Daphnia, the optimal value being approximately 25 individuals/L. Sensitivity of D. magna depends on the season: it is maximal in winter and minimal in spring and summer. In addition, crustaceans' sensitivity increases when they are kept in water with low content of natural salts. Environ Toxicol Chem 2018;37:376-384. © 2017 SETAC.

Model-based precision dosing of sirolimus in pediatric patients with vascular anomalies

Sirolimus is the first drug to show efficacy in the treatment of patients with complicated vascular anomalies. The current study expands on the evolution of a PK model-based strategy for the precision dosing of sirolimus as part of prospective concentration controlled clinical trials in pediatric patients with vascular anomalies. Twelve month follow up data collected from 52 pediatric patients participating in the Phase 2 clinical trial were analyzed. Target attainment across the age range of 3weeks to 18years after 2-3months of therapy was 94% (49 out of 52 patients). The mean sirolimus dose to achieve the target of ~10ng/mL for patients older than 2years was 1.8mg/m² twice daily (range 0.8-2.9), while it was 0.7 to 1.6mg/m² twice daily for patients 3weeks of age to 2years. A total of 676 blood concentration data were used for the population PK analysis by nonlinear mixed effect modeling using NONMEM. The final model included a maturation function for sirolimus clearance and allometrically scaled body weight to account for size differences. The mean allometrically scaled sirolimus clearance estimates increased from 3.9 to 17.0L/h per 70kg with age from shortly after the birth to 2years of age while the mean estimate for patients older than 2years was 18.5L/h per 70kg. The developed model based dosing strategy provides a foundation for ongoing efforts to define the sirolimus exposure-response and clinical outcome relationships across the pediatric age spectrum from birth to adolescence.

Colony impact of pesticide-induced sublethal effects on honeybee workers: A simulation study using BEEHAVE

Research on neonicotinoids and honeybees have changed focus from direct mortality to sublethal effects. In the present study, a published honeybee model, BEEHAVE, is used to compare induced colony level impact of pesticides including direct mortality, poor brood care, disorientation, and increased handling time in oilseed rape and sunflower crops. Actual effects on individual bees will depend on exposure concentrations, but in the present study large effects were enforced. In oilseed rape, poor brood care had the largest colony impact, because it created a bottleneck for spring build-up of the workforce, and colony impact for all effect types peaked 1 mo after exposure ceased. In sunflower, the later exposure changed the response so colony impact peaked during exposure, and the bottleneck was honey store build-up. In all scenarios, good forage mitigated effects substantially. It is concluded that field studies should continue at least 1 mo after exposure to ensure detection of ecologically relevant sublethal effects. The results indicated that even if a sublethal effect is difficult to detect in the field, subsequent ecologically relevant colony level impacts would be clear if studies are continued for 1 mo after exposure. Guidance for regulatory studies recommends extended observation periods, and published field studies already use extended observation periods, so it is concluded that current methods are adequate for detecting ecologically relevant sublethal effects. Although published laboratory and semifield studies conducted under controlled exposure conditions suggest that sublethal effects may occur, published field studies with neonicotinoid seed treatments, naturally foraging bees, and extended observation periods do not report colony-level effects, suggesting that in these studies no ecologically relevant sublethal effects occurred. Environ Toxicol Chem 2017;36:831-840. © 2016 SETAC.

Using BEEHAVE to explore pesticide protection goals for European honeybee (Apis melifera L.) worker losses at different forage qualities

Losses of honeybee colonies are intensely debated and although honeybees suffer multiple stressors, the main focus has been on pesticides. As a result, the European Food Safety Authority (EFSA) revised the guidance for pesticide risk assessment for honeybees. The European Food Safety Authority reported a protection goal of negligible effect at 7% of colony size and then used the Khoury honeybee colony model to set trigger values for forager losses. However, the Khoury model is very simplistic and simulates colonies in an idealized state. In the present study, the authors demonstrate how a more realistic published honeybee model, BEEHAVE, with a few simple changes, can be used to explore pesticide risks. The results show that forage availability interacts with pesticide-induced worker losses, and colony resilience increases with forage quality. Adding alternative unexposed forage to the landscape also substantially mitigates the effects of pesticide exposure. The results indicate that EFSA's reported protection goal of 7% of colony size and triggers for daily worker losses are overly conservative. The authors conclude that forage availability is critical for colony resilience and that with adequate forage the colonies are resilient to even high levels of worker losses. However, the authors recommend setting protection goals using suboptimal forage conditions to ensure conservatism and for such suboptimal forage, a total of 20% reduction in colony size was safe. Environ Toxicol Chem 2017;36:254-264. © 2016 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

A risk assessment example for soil invertebrates using spatially explicit agent-based models

Current risk assessment methods for measuring the toxicity of plant protection products (PPPs) on soil invertebrates use standardized laboratory conditions to determine acute effects on mortality and sublethal effects on reproduction. If an unacceptable risk is identified at the lower tier, population-level effects are assessed using semifield and field trials at a higher tier because modeling methods for extrapolating available lower-tier information to population effects have not yet been implemented. Field trials are expensive, time consuming, and cannot be applied to variable landscape scenarios. Mechanistic modeling of the toxicological effects of PPPs on individuals and their responses combined with simulation of population-level response shows great potential in fulfilling such a need, aiding ecologically informed extrapolation. Here, we introduce and demonstrate the potential of 2 population models for ubiquitous soil invertebrates (collembolans and earthworms) as refinement options in current risk assessment. Both are spatially explicit agent-based models (ABMs), incorporating individual and landscape variability. The models were used to provide refined risk assessments for different application scenarios of a hypothetical pesticide applied to potato crops (full-field spray onto the soil surface [termed "overall"], in-furrow, and soil-incorporated pesticide applications). In the refined risk assessment, the population models suggest that soil invertebrate populations would likely recover within 1 year after pesticide application, regardless of application method. The population modeling for both soil organisms also illustrated that a lower predicted average environmental concentration in soil (PECsoil) could potentially lead to greater effects at the population level, depending on the spatial heterogeneity of the pesticide and the behavior of the soil organisms. Population-level effects of spatial-temporal variations in exposure were elucidated in the refined risk assessment, using ABMs and population-level endpoints while yielding outputs that directly address the protection goals. We recommend choosing model outputs that are closely related to specific protection goals, using available toxicity data and accepted fate models to the extent possible in parameterizing models to minimize additional data needs and testing, evaluating, and documenting models following recent guidance.

Non-additive response of larval ringed salamanders to intraspecific density

Conditions experienced in early developmental stages can have long-term consequences for individual fitness. High intraspecific density during the natal period can affect juvenile and eventually adult growth rates, metabolism, immune function, survival, and fecundity. Despite the important ecological and evolutionary effects of early developmental density, the form of the relationship between natal density and resulting juvenile phenotype is poorly understood. To test competing hypotheses explaining responses to intraspecific density, we experimentally manipulated the initial larval density of ringed salamanders (Ambystoma annulatum), a pond-breeding amphibian, over 11 densities. We modeled the functional form of the relationship between natal density and juvenile traits, and compared the relative support for the various hypotheses based on their goodness of fit. These functional form models were then used to parameterize a simple simulation model of population growth. Our data support non-additive density dependence and presents an alternate hypothesis to additive density dependence, self-thinning and Allee effects in larval amphibians. We posit that ringed salamander larvae may be under selective pressure for tolerance to high density and increased efficiency in resource utilization. Additionally, we demonstrate that models of population dynamics are sensitive to assumptions of the functional form of density dependence.

Linking feeding inhibition with reproductive impairment in Gammarus confirms the ecological relevance of feeding assays in environmental monitoring

The in situ feeding bioassay in Gammarus fossarum is recognized as a reliable tool for monitoring the toxicity of freshwater contamination. However, whether recorded feeding inhibitions can potentially provoke population-level adverse outcomes remains an open question. In the present study, the authors present an experimental study in G. fossarum, which contributes to the quantitative description of the links between feeding inhibitions and impacts on female reproductive performance. The authors studied the impacts of food deprivation on reproductive endpoints (i.e., fecundity, fertility, molt cycle) during 2 successive molting cycles. Among the main results, the authors found that food deprivation triggered a slowdown of the molting process and a reduction in fertility but no alteration to embryonic development. These reproductive impairments appeared for feeding inhibition values usually recorded in monitoring programs of environmental pollution. Using a population model translating Gammarus life-history, the authors predicted that the observed reproductive alterations predict a strong degradation of population dynamics. The present study underlines the importance of feeding inhibition in population-level risk assessment and discusses how establishing upscaling schemes based on quantitative mechanistic links between impacts at different levels of biological organization can be applied in environmental monitoring to propose an ecotoxicological assessment of water quality, which would be sensitive, specific, and ecologically relevant.

Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development

In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene-behavior associations can inform cognitive theory with respect to effect size, specificity, and timing. The model demonstrates a means by which researchers can undertake multiscale modeling with respect to cognition and develop highly specific and complex hypotheses across multiple levels of description.

A Nagumo-type model for competing populations with nonlocal coupling

We consider a model of two competing species with nonlocal competition for resources. The net birthrate is cubic, so that the model allows simulation of the Allee effect, whereby extinction is stable and intermediate populations promote growth, while saturation occurs via cubic competition terms. The model includes both interspecies and intraspecies nonlocal competition which enters via convolution integrals with a specified asymmetric competition kernel function. We introduce two parameters, δ, describing the extent of the coupling, with δ = 0 corresponding to local coupling, and α, describing the extent of the asymmetry, with α = 0 corresponding to symmetric nonlocal interactions. We consider the case where the local model admits a stable coexistence (populations of both species positive) equilibrium solution. We perform a linear stability analysis and show that this solution can be destabilized by sufficient nonlocality, i.e., when δ increases beyond a critical value. We then consider nonlinear patterns, far from the stability boundary. We show that nonlinear patterns consist of arrays of islands, regions of nonzero population, separated by deadzones, where the populations are essentially extinct, (with the array propagating in the case α ≠ 0). The predominant effect of the cubic model is that the islands for the two species are disjoint, so that each species lives in the deadzone of the other species. In addition, some patterns involve both hospitable and inhospitable deadzones, so that islands form in only some of the deadzones.

Risk assessment considerations with regard to the potential impacts of pesticides on endangered species

Simple, deterministic screening-level assessments that are highly conservative by design facilitate a rapid initial screening to determine whether a pesticide active ingredient has the potential to adversely affect threatened or endangered species. If a worst-case estimate of pesticide exposure is below a very conservative effects metric (e.g., the no observed effects concentration of the most sensitive tested surrogate species) then the potential risks are considered de minimis and unlikely to jeopardize the existence of a threatened or endangered species. Thus by design, such compounded layers of conservatism are intended to minimize potential Type II errors (failure to reject a false null hypothesis of de minimus risk), but correspondingly increase Type I errors (falsely reject a null hypothesis of de minimus risk). Because of the conservatism inherent in screening-level risk assessments, higher-tier scientific information and analyses that provide additional environmental realism can be applied in cases where a potential risk has been identified. This information includes community-level effects data, environmental fate and exposure data, monitoring data, geospatial location and proximity data, species biology data, and probabilistic exposure and population models. Given that the definition of "risk" includes likelihood and magnitude of effect, higher-tier risk assessments should use probabilistic techniques that more accurately and realistically characterize risk. Moreover, where possible and appropriate, risk assessments should focus on effects at the population and community levels of organization rather than the more traditional focus on the organism level. This document provides a review of some types of higher-tier data and assessment refinements available to more accurately and realistically evaluate potential risks of pesticide use to threatened and endangered species.

Estimating the incidence and prevalence of traumatic spinal cord injury in Australia

OBJECTIVES

To determine estimates of the incidence and prevalence of traumatic spinal cord injury (TSCI) in Australia as of June 30, 2011.

DESIGN

Population modeling using cohort survival.

SETTING

Australia.

PARTICIPANTS

Hospital data regarding people with TSCI in Australia.

INTERVENTIONS

Modeling using the following data: 2 population-based databases of hospital separations of patients with TSCI, giving upper and lower estimates of incidence; national population profiles and life tables; levels of TSCI based on Australian Spinal Cord Injury Registry; and life expectancy for persons with spinal cord injury under 3 scenarios--1 constant and 2 with a trend standardized mortality ratio (SMR).

MAIN OUTCOME MEASURES

Age- and sex-specific incidence and prevalence estimates.

RESULTS

The lower estimate of incidence was 21.0 per million population per year, and the upper estimate was 32.3 per million population per year. The derived prevalence rates ranged from 490 per million population (10,944 persons--lower incidence, trend SMR with survival from 1948) up to 886 per million population (19,784 persons--higher incidence, constant SMR). The prevalence was highest in males, persons aged 46 to 60 years, and those with tetraplegia.

CONCLUSIONS

We have reported a method for calculating an estimate of the prevalence of TSCI which provides information that will be vital to optimize health care planning for this group of highly disabled members of society.

Implications of interacting microscale habitat heterogeneity and disturbance events on Folsomia candida (Collembola) population dynamics: a modeling approach

The authors implemented a fractal algorithm in a spatially explicit individual-based model to generate landscapes with different microscale patterns of habitat fragmentation and disturbance events and studied their effects on population dynamics of the collembolan Folsomia candida. Among human activities that may cause habitat destruction, the present study focused on agricultural practices. Soil organisms living in a cultivated field are subjected to habitat loss and fragmentation as well as disturbance events generated by the application of agrochemicals and related activities. In addition, they are exposed to natural stressors, which might influence the effects of chemicals on populations. The authors designed simulation experiments that incorporate these 3 factors and investigated their effects on populations of F. candida in the presence or absence of behavioral avoidance of contaminated habitat. Simulation results show that spatial autocorrelation of contamination has different effects on population growth and equilibrium size according to the percentage of clean habitat. This pattern changes when avoidance behavior is excluded from the model, as does population recovery after a series of disturbance events. The model suggests that a combination of heterogeneous contamination and multiple stressors can lead to unexpected effects of toxicants at the population level. Individual-based models can help to understand these effects and therefore add ecological realism to environmental risk assessment of chemicals and can help to explore the effects of different risk management options.

Competition matters: species interactions prolong the long-term effects of pulsed toxicant stress on populations

Recent empirical studies have revealed the importance of species competition for the effects of toxicants on populations. In the present study, the authors applied a generic individual-based simulation model of 2 competing species to analyze the consequences of interspecific competition for population dynamics under pulsed contamination. The results indicated that competition that causes a density-dependent decrease in reproduction can substantially prolong the long-term effects of the toxicant. In the example investigated, population recovery time increased from approximately 1 generation time without competition to more than 3 generation times under competition. In particular, species with low reproductive capacity exhibited a strongly prolonged recovery time when interspecific competition was included in the model. The authors conclude that toxicant concentrations derived from risk assessments for pesticides that do not consider competition might be under-protective for populations in real-world systems. The consideration of competition is especially relevant for species with low reproductive capacities to enable a realistic estimation of recovery pace.

Effects of 17α-ethynylestradiol, fluoxetine, and the mixture on life history traits and population growth rates in a freshwater gastropod

Pharmaceutical and personal care products (PPCPs), some of which have endocrine-disrupting effects at environmentally relevant concentrations, have been detected in many surface waters. The authors evaluated the effects of 2 common endocrine disrupting PPCPs on the life history traits of the snail, Physa pomilia, using a life table response experiment with snails raised in environmentally relevant concentrations of 17α-ethynylestradiol (EE2), fluoxetine, or their mixture. Exposure to fluoxetine or the mixture reduced snail reproduction, but EE2 did not. Generally, individual life history traits were affected minimally by the PPCPs, but when integrated using a demographic model, all 3 chemical exposure scenarios decreased population growth rates, with the EE2 and fluoxetine mixture causing the most adverse effects. Overall, the results provide additional insight into the effects of PPCPs on freshwater invertebrates and point to the importance of testing simultaneous exposures to multiple PPCPs. In addition, using a demographic model to integrate individual endpoints provided insights into effects that were not apparent from individual life history traits alone and suggest at least a potential for adverse ecological effects under realistic environmental exposures concentrations.

Life-history phenology strongly influences population vulnerability to toxicants: a case study with the mudsnail Potamopyrgus antipodarum

One of the main objectives of ecological risk assessment is to evaluate the effects of toxicants on ecologically relevant biological systems such as populations or communities. However, the effects of toxicants are commonly measured on selected subindividual or individual endpoints due to their specificity against chemical stressors. Introducing these effects into population models is a promising way to predict impacts on populations. The models currently employed are very simplistic, and their environmental relevance needs to be improved to establish the ecological relevance of hazard assessment. The present study with the gastropod Potamopyrgus antipodarum combines a field experimental approach with a modeling framework. It clarifies the role played by seasonal variability of life-history traits in the population's vulnerability to the alteration of individual performance, potentially due to toxic stress. The present study comprised 3 steps: 1) characterization of the seasonal variability in life-history traits of a local population over 1 yr by using in situ experiments on caged snails, coupled with a demographic follow-up; 2) development of a periodic matrix population model that visualizes the monthly variability of population dynamics; and 3) simulation of the demographic consequences of an alteration in life-history traits (i.e., fertility, juvenile, and adult survival). The results revealed that demographic impacts strongly depend on the season when alterations of individual performance occur. Model analysis showed that this seasonal variability in population vulnerability is strongly related to the phenology of the population. The authors emphasize that improving the realism of population models is a major objective for ecological risk assessment, and that taking into account species phenology in modeling approaches should be a priority.