Cancer risk assessment predictive modeling

Sixth, and finally, the adequacy of model structure as well as parameter values should be evaluated based on comparison of mode predictions with experimental data that had not been used for calibration purpose. This process essentially evaluates whether the PBPK model is capable of providing reliable predictions of the various dose metrics of potential use in a cancer risk assessement. The model should not only reprodnce consistently the shape of the pharmacokinetic time-course curve (i.e., including bnmps and valleys) and not jnst provide satisfactory fit only to a portion of the cnrve. Evaluation or validation of PBPK models should be regarded... 

The Corley model (Corley et al. 1990) was the first chloroform PBPK model to describe and ultimately predict the fate of chloroform in several species (including humans) under a variety of exposure conditions. Many subsequent PBPK models for chloroform (Chinery and Gleason 1993 McKone 1993) are based on the Corley model. The Corley model has been used for cancer risk assessment (Reitz et al. 1990). 

Risk Assessment. This model successfully described the disposition of chloroform in rats, mice and humans following various exposure scenarios and developed dose surrogates more closely related to toxicity response. With regard to target tissue dosimetry, the Corley model predicts the relative order of susceptibility to chloroform toxicity consequent to binding to macromolecules (MMB) to be mouse > rat > human. Linking the pharmacokinetic parameters of this model to the pharmacodynamic cancer model of Reitz et al. (1990) provides a biologically based risk assessment model for chloroform. 

The EPA uses the linearized multistage model (LMS)—illustrated in Figure 9.34—to conduct its cancer risk assessments. It yields a cancer slope factor, known as the ql (pronounced Ql-star), which can be used to predict cancer risk at a specific dose. The LMS assumes a linear extrapolation with a zero dose threshold from the upper confidence level of the lowest dose that produced cancer in an animal test or in a human epidemiology study. 

Dose-response models describe a cause-effect relationship. There are a wide range of mathematical models that have been used for this purpose. The complexity of a dose-response model can range from a simple one-parameter equation to complex multicompartment pharmacokinetic/pharmacodynamic models. Many dose-response models, including most cancer risk assessment models, are population models that predict the frequency of a disease in a population. Such dose-response models typically employ one or more frequency distributions as part of the equation. Dose-response may also operate at an individual level and predict the severity of a health outcome as a function of dose. Particularly complex dose-response models may model both severity of outcome and population variability, and perhaps even recognize the influence of multiple causal factors. 

Two assumptions about the surface have been made to determine the effect of natural attenuation on the contaminated groundwater. First, despite the fractured nature of the bedrock, it has been assumed that the subsurface is homogeneous so as to facilitate the evaluation. Second, the potential for reduction in TCE concentrations has been assessed using a hydrogeologic model in which the fact that the cap would reduce existing leachate production by 75% is taken into account. This model is assumed to predict that the concentration of TCE in the groundwater would be reduced to an excess cancer risk level of 28 pg/L in 60 yr and an excess cancer risk level of 5 pg/L, approximately equal to the MCL, in approximately 100 yr. 

Most scientists would hold that these unknowns and uncertainties in the regulatory risk-assessment model would tend to favor risk overestimation rather than underestimation or accurate prediction. While this view seems correct, it must be admitted that there is no epidemiological method available to test the hypothesis of an extra lifetime cancer risk of about 10 per 1000 000 from methylene chloride in drinking water. The same conclusion holds for most environmental carcinogens. It is also the case that more uncertainties attend the risk assessment process than we have indicated above. 

Furthermore, it is critical for physicians to determine which combination of treatment is most suitable for each individual patient. However, it still remains challenging to make an accurate predictive assessment of a patient s risk or response to certain treatment regimens. Advances in microarray technology promise breakthroughs in personalized medicine for breast cancer treatment. To date, the prediction models based on microarray technology for breast cancer have focused mainly on either transcriptional profiles or proteomic profiles, instead of the integrated transcriptional and proteomic profiles. 

In the absence of definitive human data, risk assessment may have to depend on the results of cancer bioassays in laboratory animals, short-term tests, or other experimental methods. Hence the following issues must be addressed under such circumstances the ability of the test system to predict risks for man (quantitatively as well as qualitatively) the reproducibility of test results the influence of species differences in pharmacokinetics, metabolism, homeostasis, repair rates, life span, organ sensitivity, and baseline cancer rates extrapolation across dose and dose rates, and routes of exposure the significance of benign tumors fitting models to the data in order to characterize dose-incidence relationships and the significance of negative results. 

A different approach, called a quantitative risk assessment, is used for nonthreshold effects, such as cancer. Sophisticated statistical models are used to extrapolate the experimental animal data obtained at high doses to the low exposures predicted in humans. The linearized multistage (LMS) model is frequently... 

There is a considerable latent period between radiation exposure and the appearance of cancer. For most cancers in adults, the latent period is at least 10 years, or even longer. The shortest latent period is for leukemia and thyroid cancer (3 to 5 years The appearance of radiation-induced cancers follows additive or multiplicative models of prediction with absolute or relative risks as main parameters. Assessment of the risk coefficients is based on the follow-up of exposed persons through epidemiological studies. 

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