Simulated concentration data

Wade et al. (1993) simulated concentration data for 100 subjects under a one-compartment steady-state model using either first-or zero-order absorption. Simulated data were then fit using FO-approximation with a first-order absorption model having ka fixed to 0.25-, 0.5-, 1-, 2-, 3-, and 4 times the true ka value. Whatever value ka was fixed equal to, clearance was consistently biased, but was relatively robust with underpredictions of the true value by less than 5% on average. In contrast, volume of distribution was very sensitive to absorption misspecification, but only when there were samples collected in the absorption phase. When there were no concentration data in the absorption phase, significant parameter bias was not observed for any parameter. The variance components were far more sensitive to model misspecification than the parameter estimates with some... 

Figure 3.7. Schematic depiction of the relation between experiment and simulation. The first step is to define the experimental conditions (concentrations, molecular species, etc.), which then form the basis either for the experiment or for simulation. Real data are manually or automatically transferred from the instruments to the data file for further processing. Simulated values are formatted to appear indistinguishable from genuine data.

A new idea has recently been presented that makes use of Monte Carlo simulations [60,61], By defining a range of parameter values, the parameter space can be examined in a random fashion to obtain the best model and associated parameter set to characterize the experimental data. This method avoids difficulties in achieving convergence through an optimization algorithm, which could be a formidable problem for a complex model. Each set of simulated concentration-time data can be evaluated by a goodness-of-fit criterion to determine the models that predict most accurately. 

In comparing the May storms of 1978 and 1976, clearly the simulated concentration values in Figure 3 are more representative of what actually occurred than the observed values. This is not meant to be a criticism of the sampling program but an indication of how errors in observed data can exist and impact the model validation process. 

Now the surface reaction rates alter the gas-phase reactant concentrations. Cutlip (38) has studied CO oxidation over Pt/Al203 in a gradientless reactor under conditions often leading to complete conversion. The feed gas alternated between 2% CO and 3% 02 in argon. Figure 9 shows some typical results. Clearly there is no hope of simulating such data by anthing but a complicated computer model. 

PK models (Section 13.2.4), PD models (Section 13.2.5), and PK/PD models (Section 13.2.6) can be used in two different ways, that is, in simulations (Section 13.2.7) and in data analysis (Section 13.2.8). Simulations can be performed if the model structure and its underlying parameter values are known. In fact, for any arbitrary dose or dosing schedule the drug concentration profile in each part of the model can be calculated. The quantitative measures of the effectiveness of drug targeting (Section 13.4) can also be evaluated. If actual measurements have been performed in in-vivo experiments in laboratory animals or man, the relevant model structure and its parameter values can be assessed by analysis of plasma disappearance curves, excretion rate profiles, tissue concentration data, and so forth (Section 13.2.8). 

This chapter will focus on PM ambient concentrations, which are key variables for exposure models, and are generally obtained by direct measurements in air quality monitoring stations. However, depending on the location and dimension of the region to be studied, monitoring data could not be sufficient to characterise PM levels or to perform population exposure estimations. Numerical models complement and improve the information provided by measured concentration data. These models simulate the changes of pollutant concentrations in the air using a set of mathematical equations that translate the chemical and physical processes in the atmosphere. 

A PBPK model was used to simulate the data but was then analyzed in reverse, starting with blood concentrations but missing the exposure infor-... 

Then, the concentration data have been lumped into the three components A, I, and P defined in Sect. 3.8.1. In conclusion, 9 x 71 = 639 simulated measurements of q and 3x9x71 = 1917 measurements of concentrations have been obtained. It is worth remarking that the above-described simulated measurements are easily available in a real context. In fact, as discussed in Sect. 2.6, the concentrations can be measured by drawing a sample of reacting mixture and analyzing it offline, while the heat released by the reactions can be obtained via calorimetric methods. 

One of the main issues with concentration data is how the non-detectable (ND) values are treated. In many instances the substance(s) of interest is non-detectable in either food simulants or real foodstuffs. In a UK FSA survey (2000) for BADGE (bisphenol A diglycidyl ether) in caimed foodstuffs, in more than 95% (105 of III targeted samples tested) of the foodstuffs tested the levels were non-detectable. Using targeted foodstuffs in any surveillance will always skew any results to a higher level, in that only foodstuffs considered most likely to contain the substance will typically be analysed. 

Food surveillance surveys give concentration values in either p,g/kg (ppb) or mg/kg (ppm). However, concentration data derived using simulants normally give results in pg/dm or mg/dm, therefore in order to relate these values to concentrations in foodstuffs it is necessary to know the actual surface to... 

The USFDA approach to assessing exposure to migrants from FCMs is explained in CFSAN/Office of Food Additive Safety, April 2002 and is available on their web site (http //www.cfsan.fda.gov/). It describes the use of exposure estimates for use in food contact notifications (FCNs) which would normally be based upon simulant rather than food migration data, as is the case for new materials. The USFDA approach is described in more detail in Chapter 2. In the USFDA approach a consumption factor is combined with a food distribution factor and concentration data to derive an estimate of exposure from all food types and all FCMs containing the substance of interest. 

The AQ forecasting system results have been compared with observations over a 8 month period from June 2006 to January 2007. The predicted concentration data have been divided in two time series obtained selecting the first (last) 24 h of each daily forecast cycle that covers the 48 h period. Comparison of these two time series with observations (Eig. 9.7) showed that the 48 h forecast generally obtains higher concentrations and better fits with observations in comparison to the 24 h forecast. This behaviour was common to all pollutants except ozone (which was overestimated for the 24 h simulation). The comparison of initial and 24 h concentration fields showed that differences were due to the influence of initial conditions on the first simulated day. The resolution difference between CHIMERE (50 km) and FARM (4 km) background domains did not allow obtaining a proper initialization. In CHIMERE topography, the city of Torino is located on the slope of the western Alps, at about 800 m asl. This feature clearly favours ozone overestimation and... 

---