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Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment.

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.

2-Propanol↗

Development of an economic model to assess the cost effectiveness of treatment interventions for chronic obstructive pulmonary disease.

OBJECTIVE: To develop a Markov model that allows the cost effectiveness of interventions in patients with chronic obstructive pulmonary disease (COPD) to be estimated, and to apply the model to investigate the cost effectiveness of an inhaled corticosteroid/long-acting beta(2)-adrenoceptor agonist (beta(2)-agonist) combination (salmeterol/fluticasone propionate) versus usual care. METHODS: A Markov model consisting of four mutually exclusive disease states was constructed (mild, moderate and severe disease, and death). The transition probabilities of disease progression (for smokers and ex-smokers) and death were derived from the published medical literature. The model outputs were costs, exacerbations, survival, QALYs and cost effectiveness. The model was made fully probabilistic to reflect the joint uncertainty in the model parameters. Efficacy data for the combination of inhaled salmeterol/fluticasone propionate 50/500microg twice daily in poorly reversible COPD patients with a history of exacerbations were obtained from the 1-year TRISTAN (TRial of Inhaled STeroids ANd long-acting beta-agonists) study and applied to the model, based on patient profiles representative of COPD clinical trials. RESULTS: According to the model, the mean life expectancy with usual care alone (placebo group) was 8.95 years, which decreased to 4.08 QALYs once adjusted for quality and discounted, at a lifetime discounted cost of Can 16,415 dollars per patient (year 2002 values). Assuming that salmeterol/fluticasone propionate reduced exacerbation frequency only (base case analysis), the estimated mean survival time remained unchanged but there was an increase in the number of QALYs (4.21) for an estimated lifetime cost of Can 25,780 dollars, resulting in a cost-effectiveness ratio of Can 74,887 dollars per QALY (95% CI 21,985, 128,671) versus usual care. If a survival benefit was assumed for salmeterol/fluticasone propionate, the incremental cost per QALY was Can11,125 dollars (95% CI 8710, dominated) versus usual care. If the combination achieved around a 10% improvement in forced expiratory volume in 1 second, leading to delayed progression to more severe disease states, the benefits translated into an incremental cost per QALY of Can 49,928 dollars (95% CI 37 269, 66,006) versus usual care. CONCLUSIONS: This Markov model allows, for the first time, a means of estimating the long-term cost effectiveness and cost utility of interventions for COPD. Initial evidence suggests that for patients with poorly reversible COPD and a documented history of frequent COPD exacerbations, the addition of salmeterol (a long-acting beta(2)-agonist) to fluticasone propionate (an inhaled corticosteroid) is potentially cost effective from the Canadian healthcare payer's perspective. However, the precision of this estimate will be improved when additional data are available from clinical trials such as the ongoing TORCH (TOwards a Revolution in COPD Health) study.

Adrenergic beta-Agonists↗

Cancer and the family: an integrative model.

The stresses and uncertainties of cancer can be overwhelming for families. The Family System Illness model offers a psychosocial map to address these challenges and to make the inevitable strains more manageable. It provides a set of terms and a common language within a framework that attends to the longitudinal, psychosocial pattern of a health condition within a multigenerational, life cycle, and belief system context. Such a psychosocial framework can facilitate collaborative, effective coping and adaptation, thereby enhancing the quality of life for families affected by cancer. The model's utility is discussed for research, preventive screening, family assessment, treatment planning, and service delivery in a wide range of healthcare settings.

Adaptation, Psychological↗

The saturated zone at Yucca Mountain: an overview of the characterization and assessment of the saturated zone as a barrier to potential radionuclide migration.

The US Department of Energy is pursuing Yucca Mountain, Nevada, for the development of a geologic repository for the disposal of spent nuclear fuel and high-level radioactive waste, if the repository is able to meet applicable radiation protection standards established by the US Nuclear Regulatory Commission and the US Environmental Protection Agency (EPA). Effective performance of such a repository would rely on a number of natural and engineered barriers to isolate radioactive waste from the accessible environment. Groundwater beneath Yucca Mountain is the primary medium through which most radionuclides might move away from the potential repository. The saturated zone (SZ) system is expected to act as a natural barrier to this possible movement of radionuclides both by delaying their transport and by reducing their concentration before they reach the accessible environment. Information obtained from Yucca Mountain Site Characterization Project activities is used to estimate groundwater flow rates through the site-scale SZ flow and transport model area and to constrain general conceptual models of groundwater flow in the site-scale area. The site-scale conceptual model is a synthesis of what is known about flow and transport processes at the scale required for total system performance assessment of the site. This knowledge builds on and is consistent with knowledge that has accumulated at the regional scale but is more detailed because more data are available at the site-scale level. The mathematical basis of the site-scale model and the associated numerical approaches are designed to assist in quantifying the uncertainty in the permeability of rocks in the geologic framework model and to represent accurately the flow and transport processes included in the site-scale conceptual model. Confidence in the results of the mathematical model was obtained by comparing calculated to observed hydraulic heads, estimated to measured permeabilities, and lateral flow rates calculated by the site-scale model to those calculated by the regional-scale flow model. In addition, it was confirmed that the flow paths leaving the region of the potential repository are consistent with those inferred from gradients of measured head and those independently inferred from water-chemistry data. The general approach of the site-scale SZ flow and transport model analysis is to calculate unit breakthrough curves for radionuclides at the interface between the SZ and the biosphere using the three-dimensional site-scale SZ flow and transport model. Uncertainties are explicitly incorporated into the site-scale SZ flow and transport abstractions through key parameters and conceptual models.

Forecasting↗

Modelling AIDS reduction strategies.

BACKGROUND: Mathematical models of the AIDS epidemic have not been able to give accurate predictions about the size of the epidemic because it is not possible to obtain sufficiently accurate measurements of the factors that enable HIV transmission. The uncertainties inherent in models of the AIDS epidemic appear to limit their relevance to epidemiologists. However, it is shown here that the uncertainties need not prevent models being used to make reliable decisions about which preventive strategy will be most effective. METHOD: A range of strategies are simulated in a model of the AIDS epidemic. The simulations are repeated as the value of what seems to be the most important uncertain factor, is varied. The effect of this variation on the effectiveness of each strategy is noted. In principle, the process could be repeated whilst all other uncertain factors are varied as well. RESULTS: Although varying one uncertain factor created enormous variation in the size of the epidemic, it is remarkable that for most preventive strategies the relative effectiveness of the strategies was barely altered. Hence for the most part the ranking of strategies in order of effectiveness is not affected by the area of uncertainty explored here. The results also highlight the potential effectiveness not only of general condom promotion, but also the use of circumcision and spermicides and general screening or targetted screening in sexually transmitted disease clinics. CONCLUSIONS: Epidemiological modelling may accurately rank the effectiveness of interventions although it may fail to predict the size of the epidemic.

Acquired Immunodeficiency Syndrome↗

Sensitivity to change for low-ANC eastern US lakes and streams and brook trout populations under alternative sulfate deposition scenarios.

A weight-of-evidence approach was used by the US National Acid Precipitation Assessment Program (NAPAP) to assess the sensitivity of chemistry and biology of lakes and streams to hypothesized changes in sulfate deposition over the next 50 years. The analyses focused on projected effects in response to differences in the magnitude and the timing of changes in sulfate deposition in the north-eastern United States, the Mid-Appalachian Highlands, and the Southern Blue Ridge Province. A number of tools was used to provide the weight of evidence that is required to have confidence in an assessment that has many uncertainties because of the complexity of the systems for which the projections of future conditions were made and because of limited historical data. The MAGIC model provided the projections of chemical changes in response to alternative deposition scenarios. Projected chemical conditions were input into biological models that evaluate effects on fish populations. The sensitivity of water chemistry and brook trout resources to the hypothesized changes in deposition was found to be greatest in the Adirondacks and Mid-Atlantic Highlands. Under the hypothesized sulfur deposition reduction scenarios, chemical conditions suitable for fish were projected to improve 20-30 years sooner than with the scenario that assumed no new legislated controls. Other lines of evidence, e.g. other models, field observations, and paleolimnological findings, were used to evaluate uncertainty in the projections. Model parameter/calibration uncertainty for the chemical models and population sampling uncertainty were explicitly quantified. Model structural uncertainties were bracketed using model comparisons, recent measured changes, and paleolimnological reconstructions of historical changes in lake chemistry.

Journal Article↗

Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO.

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation. Results for simulated percolation were similar for the two soils. Predictions of water volumes percolated were found to be only marginally affected by changes in input parameters and the most influential parameter was the water content defining the boundary between micropores and macropores in this dual-porosity model. In contrast, predictions of pesticide losses were found to be dependent on the scenarios considered and to be significantly affected by variations in input parameters. In most scenarios, predictions for pesticide losses by MACRO were most influenced by parameters related to sorption and degradation. Under specific circumstances, pesticide losses can be largely affected by changes in hydrological properties of the soil. Since parameters were varied within ranges that approximated their uncertainty, a first-step assessment of uncertainty for the predictions of pesticide losses was possible. Large uncertainties in the predictions were reported, although these are likely to have been overestimated by considering a large number of input parameters in the exercise. It appears desirable that a probabilistic framework accounting for uncertainty is integrated into the estimation of pesticide exposure for regulatory purposes.

Adsorption↗

The development and application of a multilevel decision analysis model for the remediation of contaminated groundwater under uncertainty.

A study was initiated which combined elements of stochastic hydrology, risk assessment, simulation modeling, cost analysis and decision making to define the optimum remediation choice(s) for a Superfund site in the southern United States. The effort focused upon the premise that groundwater remediation is inherently complex due to uncertainties in the geological matrix as well as in contaminant concentrations at points of compliance and/or exposure. The technical analyst should supply the decision maker with estimates of these uncertainties as well as the cost penalties required to reduce them to manageable levels. Monte Carlo transport modeling was employed to define the probability of contaminant excursions from the site, while geostatistical simulation identified a joint plume configuration and its attendant probability. Bayesian modeling was used to define the worth of additional data. These individual components were combined within a Decision Model to identify optimum remediation configurations for a given levels of risk tolerance which could be supplied by the decision maker or affected community. Sensitivity analyses were conducted to define ranges over which the decision would not be affected by variation in the respective decision parameter.

Decision Making↗

Model discrimination using data collaboration.

This paper introduces a practical data-driven method to discriminate among large-scale kinetic reaction models. The approach centers around a computable measure of model/data mismatch. We introduce two provably convergent algorithms that were developed to accommodate large ranges of uncertainty in the model parameters. The algorithms are demonstrated on a simple toy example and a methane combustion model with more than 100 uncertain parameters. They are subsequently used to discriminate between two models for a contemporarily studied biological signaling network.

Algorithms↗

Stochastic modelling of landfill processes incorporating waste heterogeneity and data uncertainty.

A landfill is a very complex heterogeneous environment and as such it presents many modelling challenges. Attempts to develop models that reproduce these complexities generally involve the use of large numbers of spatially dependent parameters that cannot be properly characterised in the face of data uncertainty. An alternative method is presented, which couples a simplified microbial degradation model with a stochastic hydrological and contaminant transport model. This provides a framework for incorporating the complex effects of spatial heterogeneity within the landfill in a simplified manner, along with other key variables. A methodology for handling data uncertainty is also integrated into the model structure. Illustrative examples of the model's output are presented to demonstrate effects of data uncertainty on leachate composition and gas volume prediction.

Environmental Pollutants↗

Congener specific transfer of PCDD/Fs from air to cows' milk: an evaluation of current modelling approaches.

Three different approaches have been used to model the transfer of individual PCDD/F congeners from the air to cows' milk. These are: (1) an 'Equilibrium Partitioning' approach, (2) a 'Deposition Velocity' approach and (3) a 'Scavenging' approach. Air-leaf transfers and livestock feed-milk transfers, the two most critical components of the food chain exposure model, are discussed. A representative database for measured PCDD/Fs in UK air, herbage and milk is presented and the performance of each predicted model concentration against this measured dataset is assessed. Weaknesses and uncertainties associated with modelling the complex transfer processes involved are highlighted.

Journal Article↗

HCN formation under electron impact: experimental studies and application to Neptune's atmosphere.

Laboratory experiments simulating organic synthesis in Neptune's atmosphere have been performed. We have submitted to a spark discharge gaseous mixtures containing 9 mbar of molecular nitrogen and 3 mbar of methane (the p(N2)/p(CH4) ratio is compatible with upper limits in Neptune's stratosphere) with varying quantities of molecular hydrogen. The spark discharge is used to model the energetic electrons produced by the impact of cosmic rays on the high atmosphere of Neptune. HCN is synthesized in the described experimental conditions, even with a low mixing ratio of molecular nitrogen. Studying the variation of HCN production with the initial composition of the gas mixture and extrapolating to high mixing ratio of molecular hydrogen allows to estimate HCN production in Neptune's atmosphere. The computed HCN production flux is 7x10(7) m-2 s-1, which is two orders of magnitude lower than the value predicted by chemical models for an internal source of N atoms. The major uncertainty in our extrapolation is the energetic distribution of electrons, implicitly assumed comparable in the discharge and in Neptune's atmosphere. We note that this distribution is also a source of uncertainty in chemical models. The chemical mechanism responsible for the local formation of HCN in the stratosphere probably occurs in the reactor too. We propose a simple characterization of the spark discharge. We thus link the molecular nitrogen dissociation cross section by electron impact to the measured parameters of the experiments (current, voltage, initial partial pressures) and to the resulting HCN partial pressures. However, other laboratory experiments with larger hydrogen pressures, requiring a more powerful electric source, have to be performed to yield a value of the cross section.

Atmosphere↗

A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both "block-like" patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide-polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site.

Calibration↗

Predictions of extreme precipitation and sea-level rise under climate change.

Two aspects of global climate change are particularly relevant to river and coastal flooding: changes in extreme precipitation and changes in sea level. In this paper we summarize the relevant findings of the IPCC Third Assessment Report and illustrate some of the common results found by the current generation of coupled atmosphere-ocean general circulation models (AOGCMs), using the Hadley Centre models. Projections of changes in extreme precipitation, sea-level rise and storm surges affecting the UK will be shown from the Hadley Centre regional models and the Proudman Oceanographic Laboratory storm-surge model. A common finding from AOGCMs is that in a warmer climate the intensity of precipitation will increase due to a more intense hydrological cycle. This leads to reduced return periods (i.e. more frequent occurrences) of extreme precipitation in many locations. The Hadley Centre regional model simulates reduced return periods of extreme precipitation in a number of flood-sensitive areas of the UK. In addition, simulated changes in storminess and a rise in average sea level around the UK lead to reduced return periods of extreme high coastal water events. The confidence in all these results is limited by poor spatial resolution in global coupled models and by uncertainties in the physical processes in both global and regional models, and is specific to the climate change scenario used.

Altitude↗

The diagnosis and management of patients admitted to hospital with acute breathlessness.

The authors have studied 352 emergency medical admissions during a summer period and 355 during the winter months to identify 177 patients who had been treated for breathlessness. Multiple and rapidly changing treatments have been taken to indicate diagnostic uncertainty. Using this model, the authors have shown that there is much more uncertainty in winter than in summer and that the working diagnosis of chest infection or asthma is particularly likely to be associated with multiple treatment. It is questionable whether the stereotyped descriptions of diseases in standard text books provide an adequate basis for emergency treatment decisions and it is considered that the diagnostic value of investigations such as chest radiography needs further careful scrutiny.

Acute Disease↗

vcfgl: a flexible genotype likelihood simulator for VCF/BCF files.

MOTIVATION: Accurate quantification of genotype uncertainty is pivotal in ensuring the reliability of genetic inferences drawn from NGS data. Genotype uncertainty is typically modeled using Genotype Likelihoods (GLs), which can help propagate measures of statistical uncertainty in base calls to downstream analyses. However, the effects of errors and biases in the estimation of GLs, introduced by biases in the original base call quality scores or the discretization of quality scores, as well as the choice of the GL model, remain under-explored. RESULTS: We present vcfgl, a versatile tool for simulating genotype likelihoods associated with simulated read data. It offers a framework for researchers to simulate and investigate the uncertainties and biases associated with the quantification of uncertainty, thereby facilitating a deeper understanding of their impacts on downstream analytical methods. Through simulations, we demonstrate the utility of vcfgl in benchmarking GL-based methods. The program can calculate GLs using various widely used genotype likelihood models and can simulate the errors in quality scores using a Beta distribution. It is compatible with modern simulators such as msprime and SLiM, and can output data in pileup, Variant Call Format (VCF)/BCF, and genomic VCF file formats, supporting a wide range of applications. The vcfgl program is freely available as an efficient and user-friendly software written in C/C++. AVAILABILITY AND IMPLEMENTATION: vcfgl is freely available at https://github.com/isinaltinkaya/vcfgl.

Software↗

Effect of uncertainties in agricultural working schedules and Monte-Carlo evaluation of the model input in basin-scale runoff model analysis of herbicides.

In the prediction of time-series concentrations of herbicides in river water with diffuse-pollution hydrological models, farming schedules (the dates of herbicide application and drainage of irrigation water from rice paddies) greatly affect the runoff behavior of the herbicides. For large catchments, obtaining precise data on farming schedules is impractical, and so the model input inevitably includes substantial uncertainty. This paper evaluates the effectiveness of using the Monte-Carlo method to generate sets of estimated farming schedules to use as input to a GIS-based basin-scale runoff model to predict the concentrations of paddy-farming herbicides in river water. The effects of using the Monte-Carlo method to compensate for uncertainty in the evaluated parameters for herbicide decomposition and sorption were also evaluated.

Acetanilides↗

A simulation study for the analysis of uncertain binary responses: application to first insemination success in beef cattle.

A simulation was carried out to investigate the methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included, herd, year, and month of mating as fixed effects; and unrelated service sire, sire and residual as random effects was used to generate binary data. Binary responses were assigned using the difference between days to calving and average gestation length. Females deviating from average gestation length lead to uncertain binary responses. Thus, the methods investigated were the following: (1) a threshold model fitted to certain (no uncertainty) binary data (M1); (2) a threshold model fitted to uncertain binary data ignoring uncertainty (M2); and (3) analysis of uncertain binary data, accounting for uncertainty from day 16 to 26 (M3) or from day 14 to 28 (M4) after introduction of the bull, using a threshold model with fuzzy logic classification. There was virtually no difference between point estimates obtained from M1, M3, and M4 with true values. When uncertain binary data were analyzed ignoring uncertainty (M2), sire variance and heritability were underestimated by 22 and 24%, respectively. Thus, for noisy binary data, a threshold model contemplating uncertainty is needed to avoid bias when estimating genetic parameters.

Analysis of Variance↗