Dose-response relationships variability

To study the effect of PGDN on cerebral blood flow, Godin et al. (1995) injected male Sprague-Dawley rats (through a jugular vein cannula) with PGDN at 0.1 to 30 mg/ kg and measured cerebral blood flow with a fiberoptic laser-Doppler flow probe in contact with the brain. Following a small initial drop in cerebral perfusion that lasted 1 min, blood flow rapidly increased and reached a maximum 2 min after injection. The increase in perfusion was correlated with dose, but due to the small number of animals and individual variability, a clear dose-response relationship was not obtained. 

The criterion employed for a positive response in this assay is a reproducible statistically significant increase in mutation frequency (weighted mean for duplicate treated cultures) over the concurrent vehicle control value (weighted mean for four independent control cultures). Ideally, the response should show evidence of a dose-response relationship. When a small isolated significant increase in mutation frequency is observed in only one of the two duplicate experiments, then a third test should be carried out. If the third test shows no significant effects, the initial increase is likely to be a chance result. In cases where an apparent treatment-related increase is thought to be a result of unusually low variability or a low control frequency, comparison with the laboratory historical control frequency may be justified. 

Phase III studies represent the confirmatory phase of drug development, which takes several years and usually involves several thousand patients at multiple trial centers. Large patient numbers are required in these trials to provide convincing documentation of clinical efficacy and safety, a more complete adverse event profile and covariates and estimates of variability in dose response relationship due to individual differences in pharmacokinetics and pharmacodynamics. They are aimed at definitively determining a drug s effectiveness and side-effect profile. Most of these studies are double-blind and placebo-controlled, sometimes with the option of open-label long-term extensions. 

Mathematical modelling of the dose-response relationship is an alternative approach to quantify the estimated response within the experimental range. This approach can be used to determine the BMD or benchmark concentration (BMC) for inhalation exposure, which can be used in place of the LOAEL or NOAEL (Crump, 1984). The BMD (used here for either BMD or BMC) is defined as the lower confidence limit on a dose that produces a particular level of response (e.g., 1%, 5%, 10%) and has several advantages over the LOAEL or NOAEL (Kimmel Gaylor, 1988 Kimmel, 1990 USEPA, 1995 IPCS, 1999). For example, (1) the BMD approach uses all of the data in fitting a model instead of only data indicating the LOAEL or NOAEL (2) by fitting all of the data, the BMD approach takes into account the slope of the dose-response curve (3) the BMD takes into account variability in the data and (4) the BMD is not limited to one experimental dose. Calculation and use of the BMD approach are described in a US EPA... 

Dose-Response Assessment for Chemicals That Cause Deterministic Effects. For hazardous chemicals that cause deterministic effects and exhibit a threshold in the dose-response relationship, the purpose of the dose-response assessment is to identify the dose of a substance below which it is not likely that there will be an adverse response in humans. Establishing dose-response relationships for chemicals that cause deterministic effects has proved to be complex because (1) multiple responses are possible, (2) the dose-response assessment is usually based on data from animal studies, (3) thousands of such chemicals exist, and (4) the availability and quality of data are highly variable. As a consequence, the scientific community has needed to devise and adhere to a number of methods to quantify the most important (low or safe dose) part of the dose-response relationship. 

The use of Monte Carlo and other stochastic analytical methods to characterize the distribution of exposure and dose-response relationships is increasing (IPCS, 2001a). The Monte Carlo method uses random numbers and probability in a computer simulation to predict the outcome of exposure. These methods can be important tools in risk characterization to assess the relative contribution of uncertainty and variability to a risk estimate. 

Pharmacokinetics (PK) can be a major source of variability in the dose response relationship. It manifests itself in interindividual differences in the plasma concentration-time profile of a drug. Factors which lead to variability in the ADME parameters are therefore of importance in understanding overall variability in PK. 

In addition to the effect of biological variability in group response for a given exposure dose, the magnitude of the dose for any given individual also determines the severity of the toxic injury. In general, the considerations for dose—response relationship with respect to both the proportion of a population responding and the severity of the response are similar for local and systemic effects. However, if metabolic activation is a factor in toxicity, then a saturation level may be reached. 

Standards that are derived using SSDs for the soil ecosystem can in some cases be validated in the held. The overview by Posthuma et al. (2002) reported on some validation studies in which it was shown that the HC5 was lower than the no-effect concentration of studied ecosystems (i.e., in mesocosm or held conditions). An array of further studies has been published since that time. However, held studies are often difhcult to interpret in terms of dose-response relationships. This difficulty in interpreting held data is sometimes due to soil heterogeneity and a highly variable soil ecosystem. Nevertheless, held soils are relevant test systems and represent a more realistic environment. Although causality may be difhcult to assess, the use of pragmatic methods, derived from an expert judgment process, can improve the overall accuracy of standards. 

ORNL noted that plasma-ChE values in male rats provided the least variable indicator of the lowest-observed-adverse-effect level (LOAEL) and NOAEL for GA and that there was evidence (based on mean plasma-ChE values) of a dose-response relationship. Therefore, ORNL used that data to determine the LOAEL and NOAEL for ChE inhibition by GA. ORNL considered 56.25 g/kg per day to be the LOAEL because of the significant reduction in plasma-ChE concentrations observed in male rats at this dose (relative to controls and baseline values). Because of the lack of consistent change in plasma- and RBC-AChE values (relative to controls and baseline values), ORNL considered the low dose of 28.13 A[Pg.43]

The critical study (Bucci and Parker 1992) involved a relevant route of exposure (oral) for determining an RfD. Rats were administered GB by oral gavage, a route of administration that exaggerates the exposure that would normally occur from methods resulting in a slower rate of delivery (e.g., in feed or water). However, the study was subchronic in duration (13 weeks) rather than chronic (104 weeks), and ChE measurements varied and did not show a consistent dose-response relationship across ChE types and genders. Thus, the subcommittee believes that the study was too short in duration and that the results were too variable to form an ideal basis for determining a LOAEL. In addition, the methods used to measure ChE were not ideal (see Appendix G). However, in the absence of other well-conducted studies, the subcommittee agrees with ORNL that the study by Bucci and Parker (1992) is the most appropriate of the available studies for derivation of the RfD for GB. 

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