Integrating Quantitative Aspects of Risk Assessment
Quantitative considerations in risk assessment include dose–response assessment, exposure assessment, variation in susceptibility, and characterization of uncertainty.
The fundamental basis of the quantitative relationships between exposure to an agent and the incidence of an adverse response is the dose–response assessment. Analysis of dose–response relationships must start with the determination of the critical effects to be quantitatively evaluated. It is usual practice to choose the data sets with adverse effects occurring at the lowest levels of exposure; the “critical” adverse effect is defined as the significant adverse biological effect that occurs at the lowest exposure level.
Threshold dose–response relationships characterization includes identification of “no or lowest observed adverse effect levels” (NOAELs or LOAELs). On the dose–response curve illustrated in Figure 4–3, the threshold, indicated as T, represents the dose below which no additional increase in response is observed. The NOAEL is identified as the highest nonstatistically significant dose tested; in this example it is point F, at 2 mg/kg body weight. Point G is the LOAEL (2.5 mg/kg body weight), as it is the lowest dose tested with a statistically significant effect.
Dose–response curve. This figure is designed to illustrate a typical dose–response curve with points E to I indicating the biologically determined responses. Statistical significance of these responses is indicated with a “*” symbol. The threshold is shown by T, a dose below which no change in biological response occurs. Point E represents the point of departure (POD), the dose near the lower end of the observed dose–response range, below which extrapolation to lower doses is necessary. Point F is the highest nonstatistical significant response point; hence, it is the “no observed adverse effect level” (NOAEL) for this example. Point G is the “lowest observed adverse effect level” (LOAEL) for this example. Curves A to D show some options for extrapolating the dose–response relationship below the range of biologically observed data points, POD, point E.
In general, animal bioassays are constructed with sufficient numbers of animals to biological responses at the 10 percent response range. Significance usually refers to both biological and statistical criteria and is dependent on the number of dose levels tested, the number of animals tested at each dose, and background incidence of the adverse response in the nonexposed control groups. The NOAEL should not be perceived as risk-free.
As described in Chapter 2, approaches for characterizing dose–response relationships include identification of effect levels such as LD50 (dose producing 50 percent lethality), LC50 (concentration producing 50 percent lethality), ED10 (dose producing 10 percent response), as well as NOAELs.
NOAELs have traditionally served as the basis for risk assessment calculations, such as reference doses or acceptable daily intake (ADI) values. References doses (RfDs) or concentrations (RfCs) are estimates of a daily exposure to an agent that is assumed to be without adverse health impact on the human population. ADI values may be defined as the daily intake of chemical during an entire lifetime, which appears to be without appreciable risk on the basis of all known facts at that time. Reference doses and ADI values typically are calculated from NOAEL values by dividing by uncertainty (UF) and/or modifying factors (MF):
Tolerable daily intakes (TDI) can be used to describe intakes for chemicals that are not “acceptable” but are “tolerable” as they are below levels thought to cause adverse health effects. These are calculated in a manner similar to ADI. In principle, dividing by these factors allows for interspecies (animal-to-human) and intraspecies (human-to-human) variability with default values of 10 each. An additional UF can be used to account for experimental inadequacies—for example, to extrapolate from short-exposure-duration studies to a situation more relevant for chronic study or to account for inadequate numbers of animals or other experimental limitations. If only a LOAEL value is available, then an additional 10-fold factor commonly is used to arrive at a value more comparable to a NOAEL. Traditionally, a safety factor of 100 would be used for RfD calculations to extrapolate from a well-conducted animal bioassay (10-fold factor animal-to-human) and to account for human variability in response (10-fold factor human-to-human variability).
MF can be used to adjust the UF if data on mechanisms, pharmacokinetics, or relevance of the animal response to human risk justify such modification.
Recent efforts have focused on using data-derived factors to replace the 10-fold UF traditionally used in calculating RfDs and ADIs. Such efforts have included reviewing the human pharmacologic literature from published clinical trials and developing human variability databases for a large range of exposures and clinical conditions. Intra- and interspecies UF have two components: toxicokinetic and toxicodynamic aspects. Figure 4–4 shows these distinctions. This approach provides a structure for incorporating scientific information on specific aspects of the overall toxicologic process into the reference dose calculations; thus, relevant data can replace a portion of the overall “uncertainty” surrounding these extrapolations.
Toxicokinetic (TK) and toxicodynamic (TD) considerations inherent in interspecies and interindividual extrapolations.Toxicokinetics refers to the processes of absorption, distribution, elimination, and metabolism of a toxicant. Toxicodynamics refers to the actions and interactions of the toxicant within the organism and describes processes at organ, tissue, cellular, and molecular levels. This figure shows how uncertainty in extrapolation both across and within species can be considered as being due to two key factors: a kinetic component and a dynamic component. Refer to the text for detailed explanations.
The NOAEL approach has been criticized on several points, including that (1) the NOAEL must, by definition, be one of the experimental doses tested; and (2) once this is identified, the rest of the dose–response curve is ignored. Because of these limitations, an alternative to the NOAEL approach, the benchmark dose (BMD) method, was proposed. In this approach, the dose–response is modeled and the lower confidence bound for a dose at a specified response level (benchmark response [BMR]) is calculated. The BMR is usually specified at 1, 5, or 10 percent. The BMDx (with x representing the percent BMR) is used as an alternative to the NOAEL value for reference dose calculations. Thus, the RfD would be:
The proposed values to be used for the UF and MF for BMDs can range from the same factors as for the NOAEL to lower values due to increased confidence in the response level and increased recognition of experimental variability owing to use of a lower confidence bound on dose.
Advantages of the BMD approach can include (1) the ability to take into account the full dose–response curve; (2) the inclusion of a measure of variability (confidence limit); and (3) the use of a consistent BMR level for RfD calculations across studies. Obviously, limitations in the animal bioassays in regard to minimal test doses for evaluation, shallow dose–responses, and use of study designs with widely spaced test doses will limit the utility of these assays for any type of quantitative assessments, whether NOAEL- or BMD-based approaches.
As Figure 4–3 shows, numerous dose–response curves can be proposed in the low-dose region of the dose–response curve if a threshold assumption is not made. Because the risk assessor generally needs to extrapolate beyond the region of the dose–response curve for which experimentally observed data are available, the choice of models to generate curves in this region has received lots of attention. For nonthreshold responses, methods for dose–response assessments have also utilized models for extrapolation to de minimus (10−4 to 10−6) risk levels at very low doses, far below the biologically observed response range and far below the effect levels evaluated for threshold responses. Two general types of dose–response models exist: statistical (or probability distribution models) and mechanistic models.
The distribution models are based on the assumption that each individual has a tolerance level for a test agent and that this response level is a variable following a specific probability distribution function. These responses can be modeled using a cumulative dose–response function. However, extrapolation of the experimental data from 50 percent response levels to a “safe,” “acceptable,” or “de minimus” level of exposure—for example, one in a million risk above background—illustrates the huge gap between scientific observations and highly protective risk limits (sometimes called virtually safe doses, or those corresponding to a 95 percent upper confidence limit on adverse response rates).
Models Derived from Mechanistic Assumptions
This modeling approach designs a mathematical equation to describe dose–response relationships that are consistent with postulated biological mechanisms of response. These models are based on the idea that a response (toxic effect) in a particular biological unit (animal or human) is the result of the random occurrence of one or more biological events (stochastic events).
A series of “hit models” exist for cancer modeling, where a hit is defined as a critical cellular event that must occur before a toxic effect is produced. The simplest mechanistic model is the one-hit (one-stage) linear model in which only one hit or critical cellular interaction is required for a cell to be altered. As theories of cancer have grown in complexity, multihit models have been developed that can describe hypothesized single-target multihit events, as well as multitarget, multihit events in carcinogenesis.
Toxicologic Enhancements of the Models
Three exemplary areas of research that have improved the models used in risk extrapolation are time to tumor information, physiologically based toxicokinetic modeling (described in Chapter 7), and biologically based dose–response (BBDR) modeling. The BBDR model aims to make the generalized mechanistic models discussed in the previous section more clearly reflect specific biological processes. Measured rates are incorporated into the mechanistic equations to replace default or computer-generated values.
Development of BBDR models for endpoints other than cancer is limited; however, several approaches have been explored in developmental toxicity utilizing cell cycle kinetics, enzyme activity, litter effects, and cytotoxicity as critical endpoints. Approaches have been proposed that link pregnancy-specific toxicokinetic models with temporally sensitive toxicodynamic models for developmental impacts. Unfortunately, the lack of specific, quantitative biological information for most toxicants and for most endpoints limits study and utilization of these models.