Pharmacokinetics of polymyxin B in different populations: a systematic review

BACKGROUND AND OBJECTIVES

Despite being clinically utilized for the treatment of infections, the limited therapeutic range of polymyxin B (PMB), along with considerable interpatient variability in its pharmacokinetics and frequent occurrence of acute kidney injury, has significantly hindered its widespread utilization. Recent research on the population pharmacokinetics of PMB has provided valuable insights. This study aims to review relevant literature to establish a theoretical foundation for individualized clinical management.

METHODS

Follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, Pop-PK studies of PMB were searched in PubMed and EMBASE database systems from the inception of the database until March 2023.

RESULT

To date, a total of 22 population-based studies have been conducted, encompassing 756 subjects across six different countries. The recruited population in these studies consisted of critically infected individuals with multidrug-resistant bacteria, patients with varying renal functions, those with cystic fibrosis, kidney or lung transplant recipients, patients undergoing extracorporeal membrane oxygenation (ECMO) or continuous renal replacement therapy (CRRT), as well as individuals with obesity or pediatric populations. Among these studies, seven employed a one-compartmental model, with the range of typical clearance (CL) and volume (Vc) being 1.18-2.5L /h and 12.09-47.2 L, respectively. Fifteen studies employed a two-compartmental model, with the ranges of the clearance (CL) and volume of the central compartment (Vc), the volume of the peripheral compartment (Vp), and the intercompartment clearance (Q) were 1.27-8.65 L/h, 5.47-38.6 L, 4.52-174.69 L, and 1.34-24.3 L/h, respectively. Primary covariates identified in these studies included creatinine clearance and body weight, while other covariates considered were CRRT, albumin, age, and SOFA scores. Internal evaluation was conducted in 19 studies, with only one study being externally validated using an independent external dataset.

CONCLUSION

We conclude that small sample sizes, lack of multicentre collaboration, and patient homogeneity are the primary reasons for the discrepancies in the results of the current studies. In addition, most of the studies limited in the internal evaluation, which confined the implementation of model-informed precision dosing strategies.

Application of wastewater-based epidemiology to estimate the usage of beta-agonists in 31 cities in China

The illegal use of beta-agonists could cause severe problems to human health. In this study, the usage of beta-agonists in 31 cities across China was estimated using wastewater-based epidemiology (WBE). The proposed method is based on solid-phase extraction (SPE) and LC-MS/MS and was developed and validated to determine the concentration of seven beta-agonists in wastewater. A population model based on cotinine (COT), NH4-N and the flow volume was constructed to estimate the population equivalents for different wastewater treatment plants (WWTPs). Clenbuterol and ractopamine are banned in China for both animal husbandry and medical use, but were nevertheless detected in some wastewater samples at rates of 6.2 % and 4.7 %, respectively (n = 339). The WBE-based consumption of clenbuterol and ractopamine were compared with the acceptable daily intake (ADI) and the health risks were assessed by their hazard quotients (0.26-6.62 for clenbuterol and 9.27 × 10-4-0.05 for ractopamine). Salbutamol, clorprenaline and terbutaline were observed in practically all wastewater samples at concentrations of up to several ng/L, whereas the formoterol and bambuterol concentrations were below the detection limit in all samples. Salbutamol consumption (7.35 ± 4.14 mg/1000 inh/day) was highest among the examined beta-agonists and varied regionally. Beta-agonist consumption based on WBE was higher in some cities than that based on medical survey data, indicating potential illegal use. These results show that WBE can be a straightforward and supplementary method for monitoring beta-agonist usage at the population level and spatially.

A Multidimensional Matrix Model for Predicting the Effects of Male-Biased Sex Ratios on Fish Populations

Laboratory experiments have established that exposure to certain endocrine-active substances prior to and/or during the period of sexual differentiation can lead to skewed sex ratios in fish. However, the potential long-term population impact of biased sex ratio depends on multiple factors including the life history of the species and whether the ratio is male or female-biased. In the present study, we describe a novel multidimensional, density-dependent matrix model that analyzes age class-structure of both males and females over time, allowing for the quantitative evaluation of the effects of biased sex ratio on population status. This approach can be used in conjunction with field monitoring efforts and/or laboratory testing to link effects on sex ratio due to chemical and/or nonchemical stressors to adverse outcomes in whole organisms and populations. For demonstration purposes, we applied the model to evaluate population trajectories for fathead minnow (Pimephales promelas) exposed to prochloraz, an aromatase inhibitor, during sexual differentiation. The model also was used to explore the population impact in a more realistic exposure scenario in which both adult and early life stages of fish are exposed concurrently to prochloraz, which, in addition to altering sex ratio during development, can decrease vitellogenin and egg production in adult females. For each exposure scenario, the model was used to analyze total population size, numbers of females and of males, and sex specific recruitment of the F1 generation. The present study illustrates the utility of multidimensional matrix population models for ecological risk assessment in terms of integrating effects across a population of an organism even when chemical effects on individuals are manifested via different pathways depending on life stage. Environ Toxicol Chem 2022;41:1066-1077. Published 2022. This article is a U.S. Government work and is in the public domain in the USA.

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression

Objective

To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival.

Methods

We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients’ disease trajectories and predict the probability of functional impairment and survival at different time points.

Results

DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80–0.93 and 0.84–0.89 for the two scenarios, respectively).

Conclusions

Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-022-11022-0.

Seabird meta-Population Viability Model (mPVA) methods

The seabird meta-population viability model (mPVA) uses a generalized approach to project abundance and quasi-extinction risk for 102 seabird species under various conservation scenarios. The mPVA is a stage-structured projection matrix that tracks abundance of multiple populations linked by dispersal, accounting for breeding island characteristics and spatial distribution. Data are derived from published studies, grey literature, and expert review (with over 500 contributions). Invasive species impacts were generalized to stage-specific vital rates by fitting a Bayesian state-space model to trend data from Islands where invasive removals had occurred, while accounting for characteristics of seabird biology, breeding islands and invasive species. Survival rates were estimated using a competing hazards formulation to account for impacts of multiple threats, while also allowing for environmental and demographic stochasticity, density dependence and parameter uncertainty.•The mPVA provides resource managers with a tool to quantitatively assess potential benefits of alternative management actions, for multiple species•The mPVA compares projected abundance and quasi-extinction risk under current conditions (no intervention) and various conservation scenarios, including removal of invasive species from specified breeding islands, translocation or reintroduction of individuals to an island of specified location and size, and at-sea mortality amelioration via reduction in annual at-sea deaths.

Evaluation of pharmacogenomics and hepatic nuclear imaging-related covariates by population pharmacokinetic models of irinotecan and its metabolites

BACKGROUND

Body surface area (BSA)-based dosing of irinotecan (IR) does not account for its pharmacokinetic (PK) and pharmacodynamic (PD) variabilities. Functional hepatic nuclear imaging (HNI) and excretory/metabolic/PD pharmacogenomics have shown correlations with IR disposition and toxicity/efficacy. This study reports the development of a nonlinear mixed-effect population model to identify pharmacogenomic and HNI-related covariates that impact on IR disposition to support dosage optimization.

METHODS

Patients had advanced colorectal cancer treated with IR combination therapy. Baseline blood was analysed by Affymetrix DMET™ Plus Array and, for PD, single nucleotide polymorphisms (SNPs) by Sanger sequencing. For HNI, patients underwent ^99mTc-IDA hepatic imaging, and data was analysed for hepatic extraction/excretion parameters. Blood was taken for IR and metabolite (SN38, SN38G) analysis on day 1 cycle 1. Population modelling utilised NONMEM version 7.2.0, with structural PK models developed for each moiety. Covariates include patient demographics, HNI parameters and pharmacogenomic variants.

RESULTS

Analysis included (i) PK data: 32 patients; (ii) pharmacogenomic data: 31 patients: 750 DMET and 22 PD variants; and (iii) HNI data: 32 patients. On initial analysis, overall five SNPs were identified as significant covariates for CL_SN38. Only UGT1A3_c.31 T > C and ABCB1_c.3435C > T were included in the final model, whereby CL_SN38 reduced from 76.8 to 55.1%.

CONCLUSION

The identified UGT1A3_c.31 T > C and ABCB1_c.3435C > T variants, from wild type to homozygous, were included in the final model for SN38 clearance.

A modelling framework for integrating reproduction, survival and count data when projecting the fates of threatened populations

A key goal of ecological research is to obtain reliable estimates of population demographic rates, abundance and trends. However, a common challenge when studying wildlife populations is imperfect detection or breeding observation, which results in unknown survival status and reproductive output for some individuals. It is important to account for undetected individuals in population models because they contribute to population abundance and dynamics, and can have implications for population management. Promisingly, recent methodological advances provide us with the tools to integrate data from multiple independent sources to gain insights into the unobserved component of populations. We use data from five reintroduced populations of a threatened New Zealand bird, the hihi (Notiomystis cincta), to develop an integrated population modelling framework that allows missing values for survival status, sex and reproductive output to be modelled. Our approach combines parallel matrices of encounter and reproduction histories from marked individuals, as well as counts of unmarked recruits detected at the start of each breeding season. Integrating these multiple data types enabled us to simultaneously model survival and reproduction of detected individuals, undetected individuals and unknown (never detected) individuals to derive parameter estimates and projections based on all available data, thereby improving our understanding of population dynamics and enabling full propagation of uncertainty. The methods presented will be especially useful for management programmes for populations that are intensively monitored but where individuals are still imperfectly detected, as will be the case for most threatened wild populations.

Development and Validation of a Mixture Toxicity Implementation in the Dynamic Energy Budget-Individual-Based Model: Effects of Copper and Zinc on Daphnia magna Populations

Mechanistic population models are gaining considerable interest in ecological risk assessment. The dynamic energy budget approach for toxicity (DEBtox) and the general unified threshold model for survival (GUTS) are well-established theoretical frameworks that describe sublethal and lethal effects of a chemical stressor, respectively. However, there have been limited applications of these models for mixtures of chemicals, especially to predict long-term effects on populations. We used DEBtox and GUTS in an individual-based model (IBM) framework to predict both single and combined effects of copper and zinc on Daphnia magna populations. The model was calibrated based on standard chronic toxicity test results with the single substances. A mixture toxicity implementation based on the general independent action model for mixtures was developed and validated with data from a population experiment with copper and zinc mixtures. Population-level effects of exposure to individual metals were accurately predicted by DEB-IBM. The DEB-IBM framework also allowed us to identify the potential mechanisms underlying these observations. Under independent action the DEB-IBM was able to predict the population dynamics observed in populations exposed to the single metals and their mixtures (R²  > 65% in all treatments). Our modeling shows that it is possible to extrapolate from single-substance effects at the individual level to mixture toxicity effects at the population level, without the need for mixture toxicity data at the individual level from standard mixture toxicity tests. The application of such modeling techniques can increase the ecological realism in risk assessment. Environ Toxicol Chem 2021;40:513-527. © 2020 SETAC.

Robust calibration of hierarchical population models for heterogeneous cell populations

Cellular heterogeneity is known to have important effects on signal processing and cellular decision making. To understand these processes, multiple classes of mathematical models have been introduced. The hierarchical population model builds a novel class which allows for the mechanistic description of heterogeneity and explicitly takes into account subpopulation structures. However, this model requires a parametric distribution assumption for the cell population and, so far, only the normal distribution has been employed. Here, we incorporate alternative distribution assumptions into the model, assess their robustness against outliers and evaluate their influence on the performance of model calibration in a simulation study and a real-world application example. We found that alternative distributions provide reliable parameter estimates even in the presence of outliers, and can in fact increase the convergence of model calibration.

Guidance for Developing Amphibian Population Models for Ecological Risk Assessment

Despite widespread acceptance of the utility of population modeling and advocacy of this approach for a more ecologically relevant perspective, it is not routinely incorporated in ecological risk assessments (ERA). A systematic framework for situation-specific model development is one of the major challenges to broadly adopting population models in ERA. As risk assessors confront the multitude of species and chemicals requiring evaluation, an adaptable stepwise guide for model parameterization would facilitate this process. Additional guidance on interpretation of model output and evaluating uncertainty would further contribute to establishing consensus on good modeling practices. We build on previous work that created a framework and decision guide for developing population models for ERA by focusing on data types, model structure, and extrinsic stressors relevant to anuran amphibians. Anurans have a unique life cycle with varying habitat requirements and high phenotypic plasticity. These species belong to the amphibian class, which is facing global population decline in large part due to anthropogenic stressors, including chemicals. We synthesize information from databases and literature relevant to amphibian risks to identify traits that influence exposure likelihood, inherent sensitivity, population vulnerability, and environmental constraints. We link these concerns with relevant population modeling methods and structure in order to evaluate pesticide effects with appropriate scale and parameterization. A standardized population modeling approach, with additional guidance for anuran ERA, offers an example method for quantifying population risks and evaluating long-term impacts of chemical stressors to populations. Integr Environ Assess Manag 2020;16:223-233. © 2019 SETAC.

Population recovery of peregrine falcons in central Norway in the 4 decades since the DDT-ban

The breeding population of peregrine falcons (Falco peregrinus) in Norway was almost exterminated by the early 1970's. Long-term monitoring of breeding pairs has been conducted since 1976 up to present. Peregrine falcons were first established at breeding sites in coastal habitats, where they remained at stable low numbers until the early 1990's. Starting around 2000, numbers began to increase steadily, and current numbers have now reached historical population levels from the pre-DDT era. We documented a range expansion with increasing numbers of peregrines nesting in the fjords and inland valleys. We found that once a territory was colonized, the probability that a territory remained occupied was high (S > 0.958). During early stages of population recovery, the transitional probabilities of becoming or remaining a breeding territory were high (ψ_N-B > 0.40, ψ_B-B > 0.65) but declined over time, especially in coastal habitats. Moreover, the productivity per nest has also decreased over time at sites in coastal habitats in the former stronghold of the population. The levels of environmental pollutants in eggs of the peregrines have dropped sharply over the last few decades, and contaminant levels now seem to be below critical levels. Eggshells were relatively thin throughout the 1970s, 1980s, and 1990s, but have increased to almost normal levels during the last 2 decades. Reductions in levels of organochlorine pollutants, especially DDT, appear to have been the main factor in explaining the population recovery. The territory dynamics are consistent with density-dependence and the low breeding success of the coastal-breeding peregrines is believed to be caused by declining numbers of colonial seabirds and other prey species.

Population model for the decline of Homalodisca vitripennis (Hemiptera: Cicadellidae) over a ten-year period

The glassy-winged sharpshooter, Homalodisca vitripennis (Germar), is an invasive pest which presents a major economic threat to grape industries in California, because it spreads a disease-causing bacterium, Xylella fastidiosa. In this note we develop a time and temperature dependent mathematical model to analyze aggregate population data for H. vitripennis from a 10-year study consisting of biweekly monitoring of H. vitripennis populations on unsprayed citrus, during which H. vitripennis decreased significantly. This model was fitted to the aggregate H. vitripennis time series data using iterative reweighted weighted least squares (IRWLS) with assumed probability distributions for certain parameter values. Results indicate that the H. vitripennis model fits the phenological and temperature data reasonably well, but the observed population decrease may possibly be attributed to factors other than the abiotic effect of temperature. A key factor responsible for this decline but not analyzed here could be biotic, for example, potentially parasitism of H. vitripennis eggs by Cosmocomoidea ashmeadi. A biological control program targeting H. vitripennis utilizing the mymarid egg parasitoid Cosmocomoidea (formerly Gonatocerus) ashmeadi (Girault) is described.

The Use of Mechanistic Population Models in Metal Risk Assessment: Combined Effects of Copper and Food Source on Lymnaea stagnalis Populations

Environmental risk assessment (ERA) of chemicals aims to protect populations, communities, and ecosystems. Population models are considered more frequent in ERA because they can bridge the gap between the individual and the population level. Lymnaea stagnalis (the great pond snail) is an organism that is particularly sensitive to various metals, including copper (Cu). In addition, the sensitivity of this species to Cu differs between food sources. The first goal of the present study was to investigate whether we could explain the variability in sensitivity between food sources (lettuce and fish flakes) at the individual level with a dynamic energy budget (DEB) model. By adapting an existing DEB model and calibrating it with Cu toxicity data, thereby combining information from 3 studies and 2 endpoints (growth and reproduction), we put forward inhibition of energy assimilation as the most plausible physiological mode of action (PMoA) of Cu. Furthermore, the variation in Cu sensitivity between both food sources was considerably lower at the PMoA level than at the individual level. Higher Cu sensitivity at individual level under conditions of lower food quality or availability appears to emerge from first DEB principles when inhibition of assimilation is the PMoA. This supports the idea that DEB explained Cu sensitivity variation between food sources. Our second goal was to investigate whether this food source effect propagated to the population level. By incorporating DEB in an individual-based model (IBM), population-level effects were predicted. Based on our simulations, the food source effect was still present at the population level, albeit less prominently. Finally, we compared predicted population-level effect concentration, x% (ECx) values with individual-level ECx values for different studies. Using the DEB-IBM, the range of effect concentrations decreased significantly: at the individual level, the difference in chronic EC10 values between studies was a factor of 70 (1.13-78 µg dissolved Cu/L), whereas at the population level the difference was a factor of 15 (2.9-44.6 µg dissolved Cu/L). To improve interstudy comparability, a bioavailability correction for differences in water chemistry was performed with a biotic ligand model. This further decreased the variation, down to a factor of 7.4. Applying the population model in combination with a bioavailability correction thus significantly decreased the variability of chronic effect concentrations of Cu for L. stagnalis. Overall, the results of the present study illustrate the potential usefulness of transitioning to a more modeling-based environmental risk assessment. Environ Toxicol Chem 2019;00:1-16. © 2019 SETAC.

Applying a population model based on hydrochemical parameters in wastewater-based epidemiology

Wastewater-based epidemiology combining with a population model based on hydrochemical parameters was applied in 10 wastewater treatment plants (WWTPs) in Jilin province, China. Population of WWTPs served was calculated by the model including three hydrochemical parameters ammonia nitrogen, chemical oxygen demand, and total phosphorus. The population model was constructed by using analytic hierarchy process to calculate weight factors of each hydrochemical parameters equivalent population. The size of population estimated by the model showed the highest correlations with cotinine mass load (r²=0.91, p<0.001), demonstrating better population estimation. Meanwhile daily excretion of cotinine per capita was first estimated about 0.68mg in China through liner regression analysis. In accessing the viability of the population model, the abuse of methamphetamine (METH) was calculated. Prevalence of METH use in Jilin province was calculated with an average of 0.72% based on the population model, which was similar with the result reported by United Nations World Drug Report in 2018. In assessing uncertainty of different population estimations, population model showed lower uncertainty than single hydrochemical equivalent population. These results indicate the population model based on hydrochemical parameters reduces uncertainty in population estimation and is a useful tool in monitoring illicit drug abuse.

Large-cage assessment of a transgenic sex-ratio distortion strain on populations of an African malaria vector

Background

Novel transgenic mosquito control methods require progressively more realistic evaluation. The goal of this study was to determine the effect of a transgene that causes a male-bias sex ratio on Anopheles gambiae target populations in large insectary cages.

Methods

Life history characteristics of Anopheles gambiae wild type and Ag(PMB)1 (aka ^gfp124L-2) transgenic mosquitoes, whose progeny are 95% male, were measured in order to parameterize predictive population models. Ag(PMB)1 males were then introduced at two ratios into large insectary cages containing target wild type populations with stable age distributions and densities. The predicted proportion of females and those observed in the large cages were compared. A related model was then used to predict effects of male releases on wild mosquitoes in a west African village.

Results

The frequency of transgenic mosquitoes in target populations reached an average of 0.44 ± 0.02 and 0.56 ± 0.02 after 6 weeks in the 1:1 and in the 3:1 release ratio treatments (transgenic male:wild male) respectively. Transgenic males caused sex-ratio distortion of 73% and 80% males in the 1:1 and 3:1 treatments, respectively. The number of eggs laid in the transgenic treatments declined as the experiment progressed, with a steeper decline in the 3:1 than in the 1:1 releases. The results of the experiment are partially consistent with predictions of the model; effect size and variability did not conform to the model in two out of three trials, effect size was over-estimated by the model and variability was greater than anticipated, possibly because of sampling effects in restocking. The model estimating the effects of hypothetical releases on the mosquito population of a West African village demonstrated that releases could significantly reduce the number of females in the wild population. The interval of releases is not expected to have a strong effect.

Conclusions

The biological data produced to parameterize the model, the model itself, and the results of the experiments are components of a system to evaluate and predict the performance of transgenic mosquitoes. Together these suggest that the Ag(PMB)1 strain has the potential to be useful for reversible population suppression while this novel field develops.

Community composition influences the population growth and ecological impact of invasive species in response to climate change

Predicting long-term impacts of introduced species is challenging, since stressors related to global change can influence species-community interactions by affecting both demographic rates of invasive species and the structure of the invaded ecosystems. Invasive species can alter ecosystem structure over time, further complicating interactions between invasive species and invaded communities in response to additional stressors. Few studies have considered how cumulative impacts of species invasion and global change on the structure of invaded ecosystems may influence persistence and population growth of introduced species. Here, we present an empirically based population model for an invasive epiphytic bryozoan that can dramatically alter the structure of its invaded kelp bed ecosystems. We use this model to predict the response of invasive species to climate change and associated changes in the invaded community. Population growth of the bryozoan increased under near-future projections of increasing ocean temperature; however, the magnitude of population growth depended on the community composition of invaded kelp beds. Our results suggest that, in some cases, indirect effects of climate change mediated through changes to the structure of the invaded habitat can modulate direct effects of climate change on invasive species, with consequences for their long-term ecological impact. Our findings have important implications for management of invasive species, as modifying invaded habitats at local to regional scales may be more logistically feasible than addressing stressors related to global climate change.

The trouble with surrogates in environmental risk assessment: a daphniid case study

The use of indicator species to test for environmental stability and functioning is a widespread practice. In aquatic systems, several daphniids (Cladocera: Daphniidae) are commonly used as indicator species; registration of new pesticides are mandated by the Environmental Protection Agency to be accompanied by daphniid toxicity data. This reliance upon a few species to infer ecosystem health and function assumes similar responses to toxicants across species with potentially very different life histories and susceptibility. Incorporating lab-derived life-history data into a simple mathematical model, we explore the reliability of three different daphniid species as surrogates for each other by comparing their responses to reductions in survivorship and fecundity after simulated exposure to toxicants. Our results demonstrate that daphniid species' responses to toxicant exposure render them poor surrogates for one another, highlighting that caution should be exercised in using a surrogate approach to the use of indicator species in risk assessment.

Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data

The distribution of random parameters in, and the input signal to, a distributed parameter model with unbounded input and output operators for the transdermal transport of ethanol are estimated. The model takes the form of a diffusion equation with the input, which is on the boundary of the domain, being the blood or breath alcohol concentration (BAC/BrAC), and the output, also on the boundary, being the transdermal alcohol concentration (TAC). Our approach is based on the reformulation of the underlying dynamical system in such a way that the random parameters are treated as additional spatial variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating a functional diffusivity in a multi-dimensional diffusion equation. The resulting system is referred to as a population model, and well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods can be used to fit it to population data and to analyze the resulting fit. Once the forward population model has been identified or trained based on a sample from the population, the resulting distribution can then be used to deconvolve the BAC/BrAC input signal from the biosensor observed TAC output signal formulated as either a quadratic programming or linear quadratic tracking problem. In addition, our approach allows for the direct computation of corresponding credible bands without simulation. We use our technique to estimate bivariate normal distributions and deconvolve BAC/BrAC from TAC based on data from a population that consists of multiple drinking episodes from a single subject and a population consisting of single drinking episodes from multiple subjects.

Population cost-effectiveness of the Triple P parenting programme for the treatment of conduct disorder: an economic modelling study

Parenting programmes are the recommended treatments of conduct disorders (CD) in children, but little is known about their longer term cost-effectiveness. This study aimed to evaluate the population cost-effectiveness of one of the most researched evidence-based parenting programmes, the Triple P-Positive Parenting Programme, delivered in a group and individual format, for the treatment of CD in children. A population-based multiple cohort decision analytic model was developed to estimate the cost per disability-adjusted life year (DALY) averted of Triple P compared with a 'no intervention' scenario, using a health sector perspective. The model targeted a cohort of 5-9-year-old children with CD in Australia currently seeking treatment, and followed them until they reached adulthood (i.e., 18 years). Multivariate probabilistic and univariate sensitivity analyses were conducted to incorporate uncertainty in the model parameters. Triple P was cost-effective compared to no intervention at a threshold of AU$50,000 per DALY averted when delivered in a group format [incremental cost-effectiveness ratio (ICER) = $1013 per DALY averted; 95% uncertainty interval (UI) 471-1956] and in an individual format (ICER = $20,498 per DALY averted; 95% UI 11,146-39,470). Evidence-based parenting programmes, such as the Triple P, for the treatment of CD among children appear to represent good value for money, when delivered in a group or an individual face-to-face format, with the group format being the most cost-effective option. The current model can be used for economic evaluations of other interventions targeting CD and in other settings.

Genome-Wide Quantitative Fitness Analysis (QFA) of Yeast Cultures

We provide a detailed protocol for robot-assisted, genome-wide measurement of fitness in the model yeast Saccharomyces cerevisiae using Quantitative Fitness Analysis (QFA). We first describe how we construct thousands of double or triple mutant yeast strains in parallel using Synthetic Genetic Array (SGA) procedures. Strains are inoculated onto solid agar surfaces by liquid spotting followed by repeated photography of agar plates. Growth curves are constructed and the fitness of each strain is estimated. Robot-assisted QFA, can be used to identify genetic interactions and chemical sensitivity/resistance in genome-wide experiments, but QFA can also be used in smaller scale, manual workflows.

Modeling individual movement decisions of brown hare (Lepus europaeus) as a key concept for realistic spatial behavior and exposure: A population model for landscape-level risk assessment

Spatial behavior is of crucial importance for the risk assessment of pesticides and for the assessment of effects of agricultural practice or multiple stressors, because it determines field use, exposition, and recovery. Recently, population models have increasingly been used to understand the mechanisms driving risk and recovery or to conduct landscape-level risk assessments. To include spatial behavior appropriately in population models for use in risk assessments, a new method, "probabilistic walk," was developed, which simulates the detailed daily movement of individuals by taking into account food resources, vegetation cover, and the presence of conspecifics. At each movement step, animals decide where to move next based on probabilities being determined from this information. The model was parameterized to simulate populations of brown hares (Lepus europaeus). A detailed validation of the model demonstrated that it can realistically reproduce various natural patterns of brown hare ecology and behavior. Simulated proportions of time animals spent in fields (PT values) were also comparable to field observations. It is shown that these important parameters for the risk assessment may, however, vary in different landscapes. The results demonstrate the value of using population models to reduce uncertainties in risk assessment and to better understand which factors determine risk in a landscape context. Environ Toxicol Chem 2017;36:2299-2307. © 2017 SETAC.

Periodicity computation of generalized mathematical biology problems involving delay differential equations

In this paper, we consider a low initial population model. Our aim is to study the periodicity computation of this model by using neutral differential equations, which are recognized in various studies including biology. We generalize the neutral Rayleigh equation for the third-order by exploiting the model of fractional calculus, in particular the Riemann-Liouville differential operator. We establish the existence and uniqueness of a periodic computational outcome. The technique depends on the continuation theorem of the coincidence degree theory. Besides, an example is presented to demonstrate the finding.

Management of a Dutch resident barnacle goose Branta leucopsis population: How can results from counts, ringing and hunting bag statistics be reconciled?

The resident Dutch Northern Delta barnacle geese Branta leucopsis population expanded steadily since birds first bred in 1982, increasing agricultural conflict. Derogation shooting has been used since 2005 to scare geese from sensitive crops and to reduce population size. Numbers almost doubled to ca. 28 000 individuals during 2007-2014, despite annual removal of 15-25% of geese and adult and juvenile survival rates of 79 and 67% (cf. natural survival of 96% for both age classes). Simple population modelling, using estimated annual survival values and fixed fecundity, predicted a moderate increase to 21 500 individuals in 2014. It is unclear whether current harvest levels are sufficient to halt population growth. Shooting may be less effective because of the disproportionate take of immature post-breeding and of individuals from other populations in winter. Discrepancies between counted and modelled abundance call for caution and improved effect monitoring of derogation shooting before harvest levels are increased further.

Population modeling for pesticide risk assessment of threatened species-A case study of a terrestrial plant, Boltonia decurrens

Although population models are recognized as necessary tools in the ecological risk assessment of pesticides, particularly for species listed under the Endangered Species Act, their application in this context is currently limited to very few cases. The authors developed a detailed, individual-based population model for a threatened plant species, the decurrent false aster (Boltonia decurrens), for application in pesticide risk assessment. Floods and competition with other plant species are known factors that drive the species' population dynamics and were included in the model approach. The authors use the model to compare the population-level effects of 5 toxicity surrogates applied to B. decurrens under varying environmental conditions. The model results suggest that the environmental conditions under which herbicide applications occur may have a higher impact on populations than organism-level sensitivities to an herbicide within a realistic range. Indirect effects may be as important as the direct effects of herbicide applications by shifting competition strength if competing species have different sensitivities to the herbicide. The model approach provides a case study for population-level risk assessments of listed species. Population-level effects of herbicides can be assessed in a realistic and species-specific context, and uncertainties can be addressed explicitly. The authors discuss how their approach can inform the future development and application of modeling for population-level risk assessments of listed species, and ecological risk assessment in general. Environ Toxicol Chem 2017;36:480-491. © 2016 SETAC.

Experiment design for nonparametric models based on minimizing Bayes Risk: application to voriconazole¹

An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a NP model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the NP model. Specifically, the problem of identifying an individual from a NP prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient's behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (multiple-model optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications.

Eave tubes for malaria control in Africa: a modelling assessment of potential impact on transmission

Background

Novel interventions for malaria control are necessary in the face of problems such as increasing insecticide resistance and residual malaria transmission. One way to assess performance prior to deployment in the field is through mathematical modelling. Modelled here are a range of potential outcomes for eave tubes, a novel mosquito control tool combining house screening and targeted use of insecticides to provide both physical protection and turn the house into a lethal mosquito killing device.

Methods

The effect of eave tubes was modelled by estimating the reduction of infectious mosquito bites relative to no intervention (a transmission metric defined as relative transmission potential, RTP). The model was used to assess how RTP varied with coverage when eave tubes were used as a stand-alone intervention, or in combination with either bed nets (LLINs) or indoor residual spraying (IRS).

Results

The model indicated the impact of eave tubes on transmission increases non-linearly as coverage increases, suggesting a community level benefit. For example, based on realistic assumptions, just 30 % coverage resulted in around 70 % reduction in overall RTP (i.e. there was a benefit for those houses without eave tubes). Increasing coverage to around 70 % reduced overall RTP by >90 %. Eave tubes exhibited some redundancy with existing interventions, such that combining interventions within properties did not give reductions in RTP equal to the sum of those provided by deploying each intervention singly. However, combining eave tubes and either LLINs or IRS could be extremely effective if the technologies were deployed in a non-overlapping way.

Conclusion

Using predictive models to assess the benefit of new technologies has great value, and is especially pertinent prior to conducting expensive, large scale, randomized controlled trials. The current modelling study indicates eave tubes have considerable potential to impact malaria transmission if deployed at scale and can be used effectively with existing tools, especially if they are combined strategically with, for example, IRS and eave tubes targeting different houses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1505-1) contains supplementary material, which is available to authorized users.

Prediction of pest pressure on corn root nodes: the POPP-Corn model

A model for the corn rootworm Diabrotica spp. combined with a temporally explicit model for development of corn roots across the soil profile was developed to link pest ecology, root damage and yield loss. Development of the model focused on simulating root damage from rootworm feeding in accordance with observations in the field to allow the virtual testing of efficacy from management interventions in the future. We present the model and demonstrate its applicability for simulating root damage by comparison between observed and simulated pest development and root damage (assessed according to the node injury scale from 0 to 3) for field studies from the literature conducted in Urbana, Illinois (US), between 1991 and 2014. The model simulated the first appearance of larvae and adults to within a week of that observed in 88 and 71 % of all years, respectively, and in all cases to within 2 weeks of the first sightings recorded for central Illinois. Furthermore, in 73 % of all years simulated root damage differed by <0.5 node injury scale points compared to the observations made in the field between 2005 and 2014 even though accurate information for initial pest pressure (i.e. number of eggs in the soil) was not measured at the sites or available from nearby locations. This is, to our knowledge, the first time that pest ecology, root damage and yield loss have been successfully interlinked to produce a virtual field. There are potential applications in investigating efficacy of different pest control measures and strategies.

Dynamics and recovery of a sediment-exposed Chironomus riparius population: A modelling approach

Models can be used to assess long-term risks of sediment-bound contaminants at the population level. However, these models usually lack the coupling between chemical fate in the sediment, toxicokinetic-toxicodynamic processes in individuals and propagation of individual-level effects to the population. We developed a population model that includes all these processes, and used it to assess the importance of chemical uptake routes on a Chironomus riparius population after pulsed exposure to the pesticide chlorpyrifos. We show that particle ingestion is an important additional exposure pathway affecting C. riparius population dynamics and recovery. Models ignoring particle ingestion underestimate the impact and the required recovery times, which implies that they underestimate risks of sediment-bound chemicals. Additional scenario studies showed the importance of selecting the biologically relevant sediment layer and showed population effects in the long term.

Research of population with impulsive perturbations based on dynamics of a neutral delay equation and ecological quality system

This paper studies the global behaviors of a nonlinear autonomous neutral delay differential population model with impulsive perturbation. This model may be suitable for describing the dynamics of population with long larval and short adult phases. It is shown that the system may have global stability of the extinction and positive equilibria, or grow without being bounded under some conditions.

How to use mechanistic effect models in environmental risk assessment of pesticides: Case studies and recommendations from the SETAC workshop MODELINK

Mechanistic effect models (MEMs) are useful tools for ecological risk assessment of chemicals to complement experimentation. However, currently no recommendations exist for how to use them in risk assessments. Therefore, the Society of Environmental Toxicology and Chemistry (SETAC) MODELINK workshop aimed at providing guidance for when and how to apply MEMs in regulatory risk assessments. The workshop focused on risk assessment of plant protection products under Regulation (EC) No 1107/2009 using MEMs at the organism and population levels. Realistic applications of MEMs were demonstrated in 6 case studies covering assessments for plants, invertebrates, and vertebrates in aquatic and terrestrial habitats. From the case studies and their evaluation, 12 recommendations on the future use of MEMs were formulated, addressing the issues of how to translate specific protection goals into workable questions, how to select species and scenarios to be modeled, and where and how to fit MEMs into current and future risk assessment schemes. The most important recommendations are that protection goals should be made more quantitative; the species to be modeled must be vulnerable not only regarding toxic effects but also regarding their life history and dispersal traits; the models should be as realistic as possible for a specific risk assessment question, and the level of conservatism required for a specific risk assessment should be reached by designing appropriately conservative environmental and exposure scenarios; scenarios should include different regions of the European Union (EU) and different crops; in the long run, generic MEMs covering relevant species based on representative scenarios should be developed, which will require EU-level joint initiatives of all stakeholders involved. The main conclusion from the MODELINK workshop is that the considerable effort required for making MEMs an integral part of environmental risk assessment of pesticides is worthwhile, because it will make risk assessments not only more ecologically relevant and less uncertain but also more comprehensive, coherent, and cost effective.

An example of population-level risk assessments for small mammals using individual-based population models

This article presents a case study demonstrating the application of 3 individual-based, spatially explicit population models (IBMs, also known as agent-based models) in ecological risk assessments to predict long-term effects of a pesticide to populations of small mammals. The 3 IBMs each used a hypothetical fungicide (FungicideX) in different scenarios: spraying in cereals (common vole, Microtus arvalis), spraying in orchards (field vole, Microtus agrestis), and cereal seed treatment (wood mouse, Apodemus sylvaticus). Each scenario used existing model landscapes, which differed greatly in size and structural complexity. The toxicological profile of FungicideX was defined so that the deterministic long-term first tier risk assessment would result in high risk to small mammals, thus providing the opportunity to use the IBMs for risk assessment refinement (i.e., higher tier risk assessment). Despite differing internal model design and scenarios, results indicated in all 3 cases low population sensitivity unless FungicideX was applied at very high (×10) rates. Recovery from local population impacts was generally fast. Only when patch extinctions occured in simulations of intentionally high acute toxic effects, recovery periods, then determined by recolonization, were of any concern. Conclusions include recommendations for the most important input considerations, including the selection of exposure levels, duration of simulations, statistically robust number of replicates, and endpoints to report. However, further investigation and agreement are needed to develop recommendations for landscape attributes such as size, structure, and crop rotation to define appropriate regulatory risk assessment scenarios. Overall, the application of IBMs provides multiple advantages to higher tier ecological risk assessments for small mammals, including consistent and transparent direct links to specific protection goals, and the consideration of more realistic scenarios.

A simple, physiologically-based model of sea turtle remigration intervals and nesting population dynamics: Effects of temperature

Variation in the yearly number of sea turtles nesting at rookeries can interfere with population estimates and obscure real population dynamics. Previous theoretical models suggested that this variation in nesting numbers may be driven by changes in resources at the foraging grounds. We developed a physiologically-based model that uses temperatures at foraging sites to predict foraging conditions, resource accumulation, remigration probabilities, and, ultimately, nesting numbers for a stable population of sea turtles. We used this model to explore several scenarios of temperature variation at the foraging grounds, including one-year perturbations and cyclical temperature oscillations. We found that thermally driven resource variation can indeed synchronize nesting in groups of turtles, creating cohorts, but that these cohorts tend to break down over 5-10 years unless regenerated by environmental conditions. Cohorts were broken down faster at lower temperatures. One-year perturbations of low temperature had a synchronizing effect on nesting the following year, while high temperature perturbations tended to delay nesting in a less synchronized way. Cyclical temperatures lead to cyclical responses both in nesting numbers and remigration intervals, with the amplitude and lag of the response depending on the duration of the cycle. Overall, model behavior is consistent with observations at nesting beaches. Future work should focus on refining the model to fit particular nesting populations and testing further whether or not it may be used to predict observed nesting numbers and remigration intervals.

Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada

Recovery of fish and wildlife populations after stressor mitigation serves as a basis for evaluating remediation success. Unfortunately, effectively monitoring population status on a routine basis can be difficult and costly. In the present study, the authors describe a framework that can be applied in conjunction with field monitoring efforts (e.g., through effects-based monitoring programs) to link chemically induced alterations in molecular and biochemical endpoints to adverse outcomes in whole organisms and populations. The approach employs a simple density-dependent logistic matrix model linked to adverse outcome pathways (AOPs) for reproductive effects in fish. Application of this framework requires a life table for the organism of interest, a measure of carrying capacity for the population of interest, and estimation of the effect of stressors on vital rates of organisms within the study population. The authors demonstrate the framework using linked AOPs and population models parameterized with long-term monitoring data for white sucker (Catostomus commersoni) collected from a study site at Jackfish Bay, Lake Superior, Canada. Individual responses of fish exposed to pulp mill effluent were used to demonstrate the framework's capability to project alterations in population status, both in terms of ongoing impact and subsequent recovery after stressor mitigation associated with process changes at the mill. The general approach demonstrated at the Jackfish Bay site can be applied to characterize population statuses of other species at a variety of impacted sites and can account for effects of multiple stressors (both chemical and nonchemical) and dynamics within complex landscapes (i.e., meta-populations including emigration and immigration processes).

Ecological modelling and toxicity data coupled to assess population recovery of marine amphipod Gammarus locusta: Application to disturbance by chronic exposure to aniline

A population agent-based model of marine amphipod Gammarus locusta was designed and implemented as a basis for ecological risk assessment of chemical pollutants impairing life-history traits at the individual level. We further used the model to assess the toxic effects of aniline (a priority hazardous and noxious substance, HNS) on amphipod populations using empirically-built dose-response functions derived from a chronic bioassay that we previously performed with this species. We observed a significant toxicant-induced mortality and adverse effects in reproductive performance (reduction of newborn production) in G. locusta at the individual level. Coupling the population model with the toxicological data from the chronic bioassay allowed the projection of the ecological costs associated with exposure to aniline that might occur in wild populations. Model simulations with different scenarios indicated that even low level prolonged exposure to the HNS aniline can have significant long-term impacts on G. locusta population abundance, until the impacted population returns to undisturbed levels. This approach may be a useful complement in ecotoxicological studies of chemical pollution to transfer individual-collected data to ecological-relevant levels.

Effects of silver nanoparticles (NM-300K) on Lumbricus rubellus earthworms and particle characterization in relevant test matrices including soil

The impact of silver nanoparticles (AgNP; at 0 mg Ag/kg, 1.5 mg Ag/kg, 15.4 mg Ag/kg, and 154 mg Ag/kg soil) and silver nitrate (AgNO3 ; 15.4 mg Ag/kg soil) on earthworms, Lumbricus rubellus, was assessed. A 4-wk exposure to the highest AgNP treatment reduced growth and reproduction compared with the control. Silver nitrate (AgNO3 ) exposure also impaired reproduction, but not as much as the highest AgNP treatment. Long-term exposure to the highest AgNP treatment caused complete juvenile mortality. All AgNP treatments induced tissue pathology. Population modeling demonstrated reduced population growth rates for the AgNP and AgNO3 treatments, and no population growth at the highest AgNP treatment because of juvenile mortality. Analysis of AgNP treated soil samples revealed that single AgNP and AgNP clusters were present in the soil, and that the total Ag in soil porewater remained high throughout the long-term experiment. In addition, immune cells (coelomocytes) of earthworms showed sensitivity to both AgNP and AgNO3 in vitro. Overall, the present study indicates that AgNP exposure may affect earthworm populations and that the exposure may be prolonged because of the release of a dissolved Ag fraction to soil porewater.

Integrative demographic modeling reveals population level impacts of PCB toxicity to juvenile snapping turtles

A significant challenge in ecotoxicology and risk assessment lies in placing observed contaminant effects in a meaningful ecological context. Polychlorinated biphenyls (PCBs) have been shown to affect juvenile snapping turtle survival and growth but the ecological significance of these effects is difficult to discern without a formal, population-level assessment. We used a demographic matrix model to explore the potential population-level effects of PCBs on turtles. Our model showed that effects of PCBs on juvenile survival, growth and size at hatching could translate to negative effects at the population level despite the fact that these life cycle components do not typically contribute strongly to population level processes. This research points to the utility of using integrative demographic modeling approaches to better understand contaminant effects in wildlife. The results indicate that population-level effects are only evident after several years, suggesting that for long-lived species, detecting adverse contaminant effects could prove challenging.