Graded dose response relationship and plausibility

Graded dose response, - "A response to a drug such that as the dose of drug A graded dose-response relationship can be measured on a continuous scale. Biological Gradient A causal relationship commonly has a graded dose- response relationship, and the objective monitoring of physical activity is helpful. The graded dose–response relationship requires the careful selection of a range plausibility and delineate temporal relationships between cause and effect.

Third, the dose-response model s used to estimate LEDs should also be presented. Fourth, the methods used to derive human-equivalent doses from animal data should be described.

It is important that the summary statistic used for the conversion e. Given the variety of approaches available to derive human-equivalent doses, the results using the different approaches should be presented in tables that allow them to be easily identified and compared. This suggests that multiple dose metrics should be considered for each data set to help inform the selection of the appropriate adjustment methods.

Page Share Cite Suggested Citation: These guidelines were finalized after the agency conducted the risk assessment on trichloroethylene, so EPA will need to update the assessment of trichloroethylene to ensure that it is consistent with the new guidelines. For example, with the exception of its consideration of kidney cancer, EPA b proposed the use of only LED10 values from rodent carcinogenicity studies adjusted to achieve human-equivalent doses as points of departure in the trichloroethylene assessment.

It is important to explain and justify the procedure for selecting the particular response level for the point of departure so that the selection does not seem arbitrary. A different approach may be necessary when most exposed individuals have unique doses common in epidemiologic studies.

Categorization of exposure in quartiles or other groupings may be helpful in that situation, but the results may be sensitive to the arbitrary cut points used to distinguish categories, just as NOAELs are sensitive to cut points Bailer et al. This procedure should be objective and transparent and should yield a point of departure near the lower end of the range of tested nonzero doses in accordance with EPA guidelines.

Other procedures may also be reasonable, so EPA should establish a clear protocol. Under the current cancer guidelines, a variety of dose-response models may be used to estimate effective doses EDs and LEDs from the data.

Although the logit and probit models typically used for these estimates should Page Share Cite Suggested Citation: If the establishment of point-of-departure-based dose-response assessment as a default policy model is intended to avoid the difficulties of choosing from among equally reasonable scientific models, it would be sensible to stipulate a default modeling procedure rather than allowing for a variety of approaches.

The effects of selecting different dose metrics for adjustment to equivalent human doses from animal models may be important for both noncancer and cancer dose-response modeling. For example, EPA notes that subchronic dosing studies indicate that cumulative exposure metrics may not be appropriate for predicting the risk of liver cancer EPA b, p.

When the mode of action is unclear, EPA suggests that linear extrapolation or interpolation 1 be used as a default approach, as it is thought to overestimate the response level for a given dose.

Graded dose response (Keyword Definition)

In the draft risk assessment on trichloroethylene, the low dose-response function was estimated by extrapolating between zero dose and the point of departure. The committee found this approach to be consistent with the current cancer guidelines. Most toxicologic and epidemiologic data sets include a control group or some observations at zero dose, in which case modeling between the point of departure to zero dose is an interpolation between two points.

This is true even after adjusting for background response using excess risk or other risk metrics, although the data point at the origin is sometimes excluded from the dose-response plot e. The committee recommends that a general study of the implications of linear extrapolation from the point of departure for dose-response assessment be performed in support of all human health risk assessments not just for trichloroethylene.

Such study is warranted because the statistical properties of linear extrapolation between zero dose and the point of departure have never been evaluated, unlike the statistical properties of traditional dose-response modeling techniques such as probit and logit regression McCullagh and Nelder Such evaluations typically include mathematical derivations or simulation studies to determine the degree of conservatism compared with hypothetically true dose-response models as well as comparisons among alternative dose-response models using real data sets.

If the true shape of the dose-response curve is sigmoidal, the linear extrapolation will likely overestimate the actual risk at a given dose, as suggested by EPA a; p. Although the linear extrapolation procedure was adopted to avoid the difficulty of choosing from among alternative dose-response models that fit equally well, there appears to be little scientific basis for evaluating its performance. The relevant issue is whether the dose-response curve is linear below the point of departure, but there appear to be insufficient data to evaluate this claim for human exposure to trichloroethylene or trichloroacetic acid.

Although linear extrapolation has been advocated as an intentionally conservative approach to protect public health, there are some theoretical reasons to think that sublinear nonthreshold dose-response models may be more relevant for human exposure to toxicants, regardless of the mode of action.

One basis for judging that dose-response patterns are not linear is related to how population variability affects the dose-response curve for humans. For example, a possible interpretation of mechanistic data on trichloroethylene for renal cancer is that any individual may have an exposure threshold below which the glutathione conjugation pathway may be less utilized; at an exposure below that threshold, there is possibly no excess risk of an individual developing renal cancer. However, the existence of individual dose-response thresholds does not necessarily imply the existence of a population dose-response threshold below which nobody is at excess risk of renal cancer; in fact, most plausible models for variability in individual dose-response thresholds imply a sigmoidal population dose-response curve even in this case.

The classic tolerance model may then be expressed as: One might expect these individual tolerances to vary extensively in humans depending on genetics, coincident exposures, nutritional status, and various other susceptibility factors, producing a continuous distribution with one or more modes and relatively narrow tails describing the population extremes.

In contrast, a uniform distribution of tolerance values is required to produce a linear dose response under this model. Consider a more complicated model that allows for increasing risk with exposure above the individual threshold in an approximately linear fashion, as one might posit for a mode of action that takes effect only at higher doses: Although these dose-response models are just two simple examples, a similar phenomenon of translating individual dose-response functions to population dose-response functions should be considered for any human dose-response Page Share Cite Suggested Citation: It is important to emphasize that it is the population-based dose-response relationships that are generally observable, not individual dose-response relationships, and population dose-response functions form the basis for public health interventions and regulations.

There is epidemiologic evidence for some toxicants other than trichloroethylene suggesting a linear or even a supralinear dose response at low doses in humans Stayner et al. Although these data may simply reflect unusual mechanisms or heavy-tailed population distributions of Page Share Cite Suggested Citation: This is an important difference between epidemiologic and toxicologic studies; the latter tend to have relatively little exposure measurement error because of intentionally administered doses are used.

If background effects can be assumed to be additive in a mechanistic manner, it would shift the dose-response curve so that response to any additional exposure is linear Peto ; Hoel ; Crump et al. This discussion illustrates an important fact: Therefore, it is difficult to draw conclusions about the shape of the dose-response model from the mode of action alone, without any information on response variability among humans.

In fact, any monotonic dose-response model, including the linearized multistage model, can be defined solely in terms of a tolerance distribution without resorting to mechanistic arguments.

These considerations suggest that one must consider both the role of mode of action and the role of response variability among humans in determining the likely shape of the dose-response function. From a scientific perspective, one approach to characterizing doseresponse relationships is to develop models that explain the variability in the available data and, when possible, incorporate known mechanisms of toxicity.

Population variability can be directly incorporated within these models by using hierarchical model structures Allen et al. Although direct measurements of population variation in human susceptibility are rarely available, the relevant parameter s could be statistically estimated along with any other parameters in the dose-response model.

Alternatively, a surrogate such as variation of rates in a key toxicodynamic step could be used to estimate population variation in susceptibility. Formal Bayesian methods similar to those applied for physiologically based pharmacokinetic modeling of trichloroethylene offer a natural unified framework for addressing population variability and uncertainty in dose-response assessment and for incorporating information from multiple sources see Chapter Explicit modeling approaches eventually might replace post hoc applications of uncertainty factors for both cancer and noncancer dose-response assessment.

From a public health perspective, the optimal dose-response model for any toxicant is often unclear, requiring the judicious use of plausible models that adequately protect health. Moreover, typical toxicologic and epidemiologic data rarely provide confirmation for potentially susceptible subpopulations, such as children, the infirm, and other subgroups, suggesting that, in the face of uncertainty, appropriate correction factors should be applied to protect the population from unnecessary risks.

However, exposure ascertainment is weak in many of the epidemiologic studies, as discussed in Chapter 2 and in the EPA assessment, so it may not be worthwhile to conduct more detailed dose-response modeling for many of these data sets.

Compound distribution or over-dispersion may result from two different mechanisms: A 'unit" as detected by the measurement process e.

This is commonly observed for viruses, but may also be the case for other pathogens. The degree of clumping strongly depends on the methods used for preparing the inoculum. In a well-homogenized liquid suspension, unit doses will be more or less randomly distributed. If the inoculum consists of a solid or semisolid food matrix, however, spatial clustering may occur and result in over-dispersion of the inoculum.

This aspect may differ between the data underlying the dose-response model and the actual exposure scenario. The Poisson distribution is generally used to characterize the variability of the individual doses when pathogens are randomly distributed. Microorganisms have a tendency to aggregate in aqueous suspensions.

Looking for other ways to read this?

In such cases, the number of "units" counted is not equal to the number of infectious particles but to the number of aggregates containing one or more infectious particles. In such cases, it is important to know whether the aggregates remain intact during inoculum preparation or in the gastrointestinal tract. Also, different levels of aggregation in experimental samples and in actual water or food products need to be accounted for.

The relation between the actual number of surviving organisms the effective dose and the probability of colonization of the host is a key concept in the derivation of dose-response models, as will be discussed later.

Infection is most commonly defined as a situation in which the pathogen, after ingestion and surviving all barriers, actively grows at its target site Last, Infection may be measured by different methods, such as faecal excretion or immunological response.

Apparent infection rates may differ from actual infection rates, depending on the sensitivity and specificity of the diagnostic assays. Infection is usually measured as a quantal response presence or absence of infection by some criterion. The use of continuous-response variables e. Infections may be asymptomatic, where the host does not develop any adverse reactions to the infection, and clears the pathogens within a limited period of time, but infection may also lead to symptomatic illness.

In general, disease symptoms may result from either the action of toxins or damage to the host tissue. Toxins may have been preformed in the food or water matrix "intoxication" or may be produced in vivo by microorganisms in the gut "toxico-infection"and may operate by different pathogenic mechanisms e. Granum, Tomas and Alouf, Tissue damage may also result from a wide range of mechanisms, including destruction of host cells, invasion and inflammatory responses.

For many foodborne pathogens, the precise pathogenic sequence of events is unknown, and is likely to be complex. Note that health risks of toxins in water e.

Illness can basically be considered as a process of cumulative damage to the host, leading to adverse reactions. There are usually many different and simultaneous signs and symptoms of illness in any individual, and the severity of symptoms varies among pathogens and among hosts infected with the same pathogen.

Dose–response relationship - Wikipedia

Illness is therefore a process that is best measured on a multidimensional, quantitative, continuous scale number of stools passed per day, body temperature, laboratory measurements, etc. In contrast, in risk assessment studies, illness is usually interpreted as a quantal response presence or absence of illnessimplying that the results depend strongly on the case definition.

A wide variety of case definitions for gastrointestinal illness are used in the literature, based on a variable list of symptoms, with or without a specified time window, and sometimes including laboratory confirmation of etiological agents. This lack of standardization severely hampers integration of data from different sources. Some pathogens, such as Salmonella enterica serotype Typhi, are invasive and may cause bacteraemia and generalized infections.

Other pathogens produce toxins that may result not only in enteric disease but also in severe damage in susceptible organs. An example is haemolytic uraemic syndrome, caused by damage to the kidneys from Shiga-like toxins of some Escherichia coli strains. Complications may also arise by immune-mediated reactions: The complications from gastroenteritis normally require medical care, and frequently result in hospitalization.

There may be a substantial risk of mortality in relation to sequelae, and not all patients may recover fully, but may suffer from residual symptoms, which may last a lifetime. Therefore, despite the low probability of complications, the public health burden may be significant. Also, there is a direct risk of mortality related to acute disease, in particular in the elderly, neonates and severely immunocompromised.

Each of these concepts will be discussed below in relation to the different stages of the infection and disease process. Ideally, the dose-response models should represent the following series of conditional events: In reality, however, the necessary data and concepts are not yet available for this approach. Therefore models are also discussed that directly quantify the probability of illness or mortality given exposure.

A threshold exists if there is no effect below some exposure level, but above that level the effect is certain to occur. Attempts to define the numerical value of such thresholds in test populations have typically been unsuccessful, although the concept is widely referred to in the literature as the "minimal infectious dose". An alternative hypothesis is that, due to the potential for microorganisms to multiply within the host, infection may result from the survival of a single, viable, infectious pathogenic organism "single-hit concept".

This implies that, no matter how low the dose, there is always, at least in a mathematical sense, and possibly very small, a non-zero probability of infection and illness. Obviously, this probability increases with the dose. Note that the existence or absence of a threshold, at both the individual and population levels, cannot be demonstrated experimentally. Experimental data are always subject to an observational threshold the experimental detection limit: Therefore, the question of whether a minimal infectious dose truly exists or merely results from the limitations of the data tends to be academic.

A practical solution is to fit dose-response models that have no threshold no mathematical discontinuitybut are flexible enough to allow for strong curvature at low doses so as to mimic a threshold-like dose-response. The probability of illness given infection depends on the degree of host damage that results in the development of clinical symptoms. For such mechanisms, it seems to be reasonable to assume that the pathogens that have developed in vivo must exceed a certain minimum number.

A non-linear relation may be enforced because the interaction between pathogens may depend on their numbers in vivo, and high numbers are required to switch on virulence genes e.

This concept, however, is distinct from a threshold for administered dose, because of the possibility, however small, that a single ingested organism may survive the multiple barriers in the gut to become established and reproduce. In contrast, the hypotheses of maximum and of partial synergism postulate that inoculated pathogens cooperate so that the value of p increases as the size of the dose increases Meynell and Stocker, Several experimental studies have attempted to test these hypotheses and the results have generally been consistent with the hypothesis of independent action for a review, see Rubin, Quorum sensing is a new area of research that is clearly of importance in relation to the virulence of some bacteria.

It means that some phenotypic characteristics such as specific virulence genes are not expressed constitutively, but are rather cell-density dependent, using a variety of small molecules for cell-to-cell signalling, and are only expressed once a bacterial population has reached a certain density De Kievit and Iglewski, While the biology of quorum sensing and response is still being explored, the nature of the effect is clear, it may be that some virulence factors are only expressed once the bacterial population reaches a certain size.

The role of quorum sensing in the early stages of the infectious process has not been investigated in detail, and no conclusion can be drawn about the significance of quorum sensing in relation to the hypothesis of independent action. In particular, the role of interspecies and intraspecies communication is an important aspect.

  • Dose–response relationship

Different models may, however, lead to different interpretations of the same data, and so a rational basis for model selection is needed. Different criteria may be applied when selecting mathematical models. For any model to be acceptable, it should satisfy the statistical criteria for goodness of fit.