What is dphp

A representation of the model structure is given in Figure 2. The model code is available in Supplementary Materials. The main model contained a lymphatic compartment which received a portion of oral dose from the stomach and GI tract.

Urinary excretion of metabolites was ascribed to the sub-model. The baseline model was subsequently refined using an iterative model development process to better represent the trends in BM blood and urine data from the human volunteer study reported in Klein et al. Techniques for uncertainty and sensitivity analysis described in the statistical analysis section were deployed, at each iteration of model development, to establish the bounding behaviour of the model and the key uncertain parameters that the model outputs under study were sensitive to.

The improvements and a brief justification are described in the points below:. The model for lymphatic circulation was modified. A delay term, Lymphlag was introduced to describe a delay between DPHP entering lymphatic circulation and the subsequent appearance in venous blood at the thoracic duct. Mixing into venous blood was modelled as a first order process proportional to the mass in lymphatic circulation. This description of the lymph in the baseline model resulted in slow emptying from the lymph into venous blood.

This modification to the model was necessary in order to approximate the almost complete elimination of DPHP from blood over a 48 h period apparent in BM data. A better representation of the absorption phase of BM data was achieved following this modification. The model was adapted to simulate the transport process of enterohepatic recirculation. A schematic showing pre-systemic metabolism and enterohepatic recirculation, systemic and lymphatic uptake of DPHP and uptake of MPHP from gastrointestinal tract.

Baseline estimates of organ and tissue masses and regional blood flows were taken from Brown et al. The mass of the lymphatic system was obtained from Offman et al. Tissue: blood partition coefficients were estimated using algorithms as described previously. The in-vivo intrinsic clearance of DPHP in the gut was calculated using the in vitro hepatic clearance scaled to in vivo using gut microsomal protein yield and gut volume.

Baseline values for parameters for which there was no prior knowledge such as FracDOSEHep, FracDOSELymph and the various delay terms and uptake and elimination rates were determined during the model development and testing process to provide a reasonable but not optimised fit to BM data. TABLE 3. Physiological and kinetic default values used in PBPK model and probability distributions applied for uncertainty and sensitivity analyses.

Probability distributions for uncertainty and sensitivity analysis of the final PBPK model are listed in Table 3. Anatomical and physiological parameter distributions were obtained from the freely available web-based application PopGen McNally et al. The range of ages, heights and body weights supplied as input to PopGen were chosen to encompass the characteristics of the volunteers who participated in the human volunteer study Klein et al.

Parameter ranges for organ masses and blood flows were modelled by normal or log-normal distributions as appropriate with parameters estimated from the sample and truncated at the 5th and 95th percentiles.

Uniform distributions were ascribed to the various delay terms and uptake and elimination rates. The upper and lower bounds in Table 3 were refined during the model development process. The tabulated values are therefore based upon expert judgement and represent conservative yet credible bounding estimates.

As described above, uncertainty analysis was conducted throughout the model development process in order to efficiently establish the bounding behaviour of the model i.

A point maxi-min Latin Hypercube Design LHD was created based upon the probability distributions ascribed to model parameters and the PBPK model was run for each of these design points; the behaviour of the final model was studied based upon the probability distributions given in Table 3.

The development process followed here was broadly similar to that of McNally et al. The differing units of the outputs under study reflect the different aspects of model outputs that the uncertainty analysis was designed to study. This phase of work is only briefly reported on in results. Sensitivity analysis was conducted throughout the model development process in order to study the key model output sensitivities for each version of the model under development.

Results from sensitivity analysis for the final model were obtained using the probability distributions given in Table 3. A total of 59 parameters were varied in elementary effects screening, with five elementary effects per input computed, leading to a design of runs of the PBPK model.

The model outputs studied are described below. The three output times studied were broadly of representative of the following periods in the concentration-time courses: prior to peak concentration of DPHP and MPHP ; post peak concentration; and returning to baseline zero concentrations.

Rather than studying model output at specific time points, instead the peak concentrations of DPHP and MPHP in plasma; the times that corresponded to these peak concentrations; and the rate of change of DPHP and MPHP in plasma over the hour following the peak concentrations, were extracted from each of the model runs. These measures were chosen since they proved to be more useful metrics for understanding the BM data of Klein et al.

This phase of sensitivity analysis is only briefly described in results. All parameters that were within 0. In this analysis 1, runs per retained parameter were conducted, leading to 31, simulations of the PBPK model.

Calibration of a subset of sensitive model parameters using the BM data of Klein, et al. A Bayesian approach was followed McNally et al. This requires the specification of a joint prior distribution for the parameters under study, which is refined through a comparison of PBPK model predictions and measurements within a statistical model. The resulting refined parameter space that is consistent with the prior specification and measurements is the posterior distribution.

The final calibration model utilised data from five of the six individuals data from individual E were unusual and thus excluded from the BM study of Klein et al. The latter measure i. These measurements were compared to corresponding predictions from the PBPK model using the statistical models depicted in Eqs.

Normal distributions, truncated at zero were assumed for all four relationships. Prior distributions for global and local parameters in the PBPK model were taken from Table 3 for the sensitive parameters that were studied.

Non-informative gamma 0. Inference for model parameters in the final calibration model was made using thermo-dynamic integration TI as described in Bois et al. A single chain of 1,, iterations was run with every 10th retained. The reshape2 package of R was used for reshaping of data for plotting and other processing of results.

In Figure 4 a small subset of results from uncertainty analysis of the final model are shown: each curve shows the predictions of a particular model output corresponding to a design point.

These plots indicate that the model structure remains stable over a wide range parameter values. Figures 4A—C show the mass of DPHP within the plasma, lymph and bowel compartments and were used to study the range of behaviours of specific aspects of the model that could be achieved based upon the form of the PBPK model and through variations in the uncertain parameters.

Through this uncertainty analysis of the mass of DPHP in plasma Figure 4A the effect of protein binding on the mass retained and subsequent elimination from plasma could be studied. Specifically, in this exploratory phase of work we had a particular interest in attempting to replicate the unusual findings from Klein et al.

Through uncertainty analysis of DPHP in the lymph, variability in the uptake, retention and elimination of DPHP in the lymph compartment could be studied. Other checks on a range of outputs or functions of model outputs masses, rates and concentrations were also undertaken in this phase of modelling to ensure behaviour of the model appeared reasonable over the range of parameter space specified through probability distributions.

Uncertainty analysis. Results from this technique are usually obtained at specific time points, i. However sensitivity analysis can be applied to any chosen model outputs calculated from each model run specified through the design.

The parameters with lower overall importance are clustered toward zero of both axes. Unfortunately, we could not prevent the overlapping of some parameter labels in this region Figure 5. Areas with overlapping parameter labels represent clusters of parameters with minimal sensitivity. The first four parameters were significantly more important for variance in urinary excretion than the other parameters over this period.

The trends for the individual shown in Figure 8 are broadly representative of the two individuals whose data are not shown. This is a pointwise credible interval which was derived through running the PBPK model for each retained parameter set, ordering the predictions by magnitude at each time point and reading off the 2. TABLE 4. Summary statistics from marginal posterior distributions for calibrated global parameters. Because plasticisers US: plasticizers are so widely used, they have undergone extensive testing for possible health and environmental effects and are amongst the most widely researched of all chemical substances.

In Europe, the safe use of plasticisers is enabled by REACH, the most comprehensive product safety regulation anywhere in the world. Globally, 7. Ortho-phthalates, due to their high degree of compatibility with PVC, are the most widely consumed plasticisers.

As can be seen from these figures, major plasticisers are high volume commodity chemicals, which take decades and billions of Euros to achieve full commercial development. In Europe, ortho-phthalates — also know simply as phthalates LMW and HMW — make up for the majority of the plasticisers market followed by aliphatics and cyclohexanoates.

Join Industry Group. Interested in joining the High Phthalates Panel? Contact us. View More Resources. Article service life Release to the environment of this substance can occur from industrial use: in the production of articles.

Widespread uses by professional workers This substance is used in the following products: polymers. Formulation or re-packing This substance is used in the following products: polymers. Uses at industrial sites This substance is used in the following products: polymers. Manufacture Release to the environment of this substance can occur from industrial use: manufacturing of the substance.

Properties of concern are calculated at four "levels" of certainty: "Recognised" - meaning that the concern is indicated in an official source. Recognised concerns are illustrated with a dark red icon.

Potential concerns are illustrated with a light red icon. There are no potential Ss or Sr s. Broad agreement concerns are illustratated with a solid outlined circle icon. Minority position concerns are illustrated with a greyed out circle icon. Broad agreement: comes from industry data where a majority of data submitters agree the substance is carcinogenic. Minority position: comes from industry data where a minority of data submitters indicate the substance is carcinogenic.

More information about carcinogenicity here. Broad agreement: comes from industry data where a majority of data submitters agree the substance is mutagenic. Minority position: comes from industry data where a minority of data submitters indicate the substance is mutagenic. More information about mutagenicity here. Broad agreement: comes from industry data where a majority of data submitters agree the substance is toxic to reproduction.

Minority position: comes from industry data where a minority of data submitters indicate the substance is toxic to reproduction. More information about reproductive toxicity here. Broad agreement: comes from industry data where a majority of data submitters agree the substance is a skin sensitiser.

Minority position: comes from industry data where a minority of data submitters indicate the substance is skin sensitising. More information about skin sensitiser here. Broad agreement: comes from industry data where a majority of data submitters agree the substance is a respiratory sensitiser. Minority position: comes from industry data where a minority of data submitters indicate the substance is a respiratory sensitiser. More information about respiratory sensitiser here.

Broad agreement: comes from industry data where a majority of data submitters agree the substance is PBT. Minority position: comes from industry data where a minority of data submitters indicate the substance is PBT.

More information about persistent, bioaccumulative and toxic substances here. At this time there is no industry submitted data taken into account to display an ED property of concern. More information about endocrine disrupting substances here. Potentially a Persistent Organic Pollutant: is shown for substances that have been proposed for their inclusion in the Stockholm Convention and are under assessment, or for which a proposal is under preparation in the European Union.

ED Under assessment as Endocrine Disrupting. The CoRAP list includes substances that could pose a risk to human health or the environment and whose potentially hazardous properties are to be evaluated by the Member States in the next three years. After evaluation, proposals may be made for further regulatory action regarding the substance. Candidate List - indicates if the substance is included in the candidate list of substances of very high concern SVHCs.

The Candidate List includes substances that are subject to additional protocols and reporting obligations and which may eventually be included in the Authorisation List, further limiting their use. These substances cannot be placed on the market or used after a given date, unless an authorisation is granted for their specific use, or the use is exempted from authorisation.