Critical effect sizes in toxicological risk assessment: a comprehensive and critical evaluation

Guidance on the use of the benchmark dose approach in risk assessment

The Scientific Committee (SC) reconfirms that the benchmark dose (BMD) approach is a scientifically more advanced method compared to the no-observed-adverse-effect-level (NOAEL) approach for deriving a Reference Point (RP). The major change compared to the previous Guidance (EFSA SC, 2017) concerns the Section 2.5, in which a change from the frequentist to the Bayesian paradigm is recommended. In the former, uncertainty about the unknown parameters is measured by confidence and significance levels, interpreted and calibrated under hypothetical repetition, while probability distributions are attached to the unknown parameters in the Bayesian approach, and the notion of probability is extended to reflect uncertainty of knowledge. In addition, the Bayesian approach can mimic a learning process and reflects the accumulation of knowledge over time. Model averaging is again recommended as the preferred method for estimating the BMD and calculating its credible interval. The set of default models to be used for BMD analysis has been reviewed and amended so that there is now a single set of models for quantal and continuous data. The flow chart guiding the reader step-by-step when performing a BMD analysis has also been updated, and a chapter comparing the frequentist to the Bayesian paradigm inserted. Also, when using Bayesian BMD modelling, the lower bound (BMDL) is to be considered as potential RP, and the upper bound (BMDU) is needed for establishing the BMDU/BMDL ratio reflecting the uncertainty in the BMD estimate. This updated guidance does not call for a general re-evaluation of previous assessments where the NOAEL approach or the BMD approach as described in the 2009 or 2017 Guidance was used, in particular when the exposure is clearly lower (e.g. more than one order of magnitude) than the health-based guidance value. Finally, the SC firmly reiterates to reconsider test guidelines given the wide application of the BMD approach.

Using historical control data in bioassays for regulatory toxicology

Historical control data (HCD) consist of pooled control group responses from bioassays. These data must be collected and are often used or reported in regulatory toxicology studies for multiple purposes: as quality assurance for the test system, to help identify toxicological effects and their effect-size relevance and to address the statistical multiple comparison problem. The current manuscript reviews the various classical and potential new approaches for using HCD. Issues in current practice are identified and recommendations for improved use and discussion are provided. Furthermore, stakeholders are invited to discuss whether it is necessary to consider uncertainty when using HCD formally and statistically in toxicological discussions and whether binary inclusion/exclusion criteria for HCD should be revised to a tiered information contribution to assessments. Overall, the critical value of HCD in toxicological bioassays is highlighted when used in a weight-of-evidence assessment.

Use of benchmark dose models in risk assessment for occupational handlers of eight pesticides used in pome fruit production

The benchmark dose has been frequently recommended for the creation of points of departure for regulatory dose limits, but many regulations, including pesticide risk assessment and registration in the United States, continues to rely on NOAEL methods as the OECD toxicological standard methods recommend. This study used data from studies in support of pesticide registration for eight different compounds to build dose-response models and calculate benchmark doses and confidence limits. The results were compared to the NOAEL of the same study. A probabilistic estimate of dose was compared with all points of departure to demonstrate differences in the protective ability of each different selected limit. While neither the BMD/BMDL nor the NOAEL was consistently more protective, the advantage of using the BMD in quantifying the uncertainty of the point of departure is highlighted, and the feasibility of using current OECD-guideline studies for derivation of a BMD is demonstrated in these cases.

Proposing new approaches for the risk characterisation of single chemicals and chemical mixtures: The source related Hazard Quotient (HQS) and Hazard Index (HIS) and the adversity specific Hazard Index (HIA)

A hazard quotient (HQ) for a single chemical and the hazard index (HI) for a mixture of chemicals were first described as approaches for risk characterisation by the EPA. HQ is defined as the ratio of exposure to an appropriate reference dose such as the ADI. HI is the sum of the HQs of the chemicals in a mixture. HQ and HI have been used to characterise risk after various exposure scenarios. However, both approaches have a significant limitation in the way they are used. The accurate use of HQ or HI requires estimation of aggregate exposure, that is, exposure to a given chemical(s) from all possible relevant sources. In many studies, risk is assessed assuming exposure from a specific source such as, consumption of water or a specific food item, in which chemical(s) concentration(s) have been measured. In this case the classic HQ/HI approach can result in significant underestimation of risk. For this purpose, we developed an alternative approach, named as Source Related HQ (HQs) where HQS is the ratio of the exposure from the specific source of interest to the respected reference values. According to our approach the HQS, before being compared to the reference dose, should be adjusted by a correction factor, in order to simulate aggregated exposure. A correction factor can be calculated based on the permitted exposure contribution from the specific source to the permitted aggregated exposure. Another important limitation specific to the HI approach is the use of chemical specific ADIs that do not correspond to the same critical effect. In this study, we present an analysis based on the individual critical effects, in order to derive the critical effect and an adversity specific Hazard Index (HIA) and risk characterisation for the whole mixture.

Uncertainty and variability in human exposure limits - a chemical-specific approach for ciprofloxacin and methotrexate

Human exposure limits (HELs) for chemicals with a toxicological threshold are traditionally derived using default assessment factors that account for variations in exposure duration, species sensitivity and individual sensitivity. The present paper elaborates a probabilistic approach for human hazard characterization and the derivation of HELs. It extends the framework for evaluating and expressing uncertainty in hazard characterization recently proposed by WHO-IPCS, i.e. by the incorporation of chemical-specific data on human variability in toxicokinetics. The incorporation of human variability in toxicodynamics was based on the variation between adverse outcome pathways (AOPs). Furthermore, sources of interindividual variability and uncertainty are propagated separately throughout the derivation process. The outcome is a two-dimensional human dose distribution that quantifies the population fraction exceeding a pre-selected critical effect level with an estimate of the associated uncertainty. This enables policy makers to set separate standards for the fraction of the population to be protected and the confidence level of the assessment. The main sources of uncertainty in the human dose distribution can be identified in order to plan new research for reducing uncertainty. Additionally, the approach enables quantification of the relative risk for specific subpopulations. The approach is demonstrated for two pharmaceuticals, i.e. the antibiotic ciprofloxacin and the antineoplastic methotrexate. For both substances, the probabilistic HEL is mainly influenced by uncertainty originating from: (1) the point of departure (PoD), (2) extrapolation from sub-acute to chronic toxicity and (3) interspecies extrapolation. However, when assessing the tails of the two-dimensional human dose distributions, i.e. the section relevant for the derivation of human exposure limits, interindividual variability in toxicodynamics also becomes important.

Low-level lead exposure and elevations in blood pressure during pregnancy

BACKGROUND

Lead exposure is associated with elevated blood pressure during pregnancy; however, the magnitude of this relationship at low exposure levels is unclear.

OBJECTIVES

Our goal was to determine the association between low-level lead exposure and blood pressure during late pregnancy.

METHODS

We collected admission and maximum (based on systolic) blood pressures during labor and delivery among 285 women in Baltimore, Maryland. We measured umbilical cord blood lead using inductively coupled plasma mass spectrometry. Multivariable models were adjusted for age, race, median household income, parity, smoking during pregnancy, prepregnancy body mass index, and anemia. These models were used to calculate benchmark dose values.

RESULTS

Geometric mean cord blood lead was 0.66 μg/dL (95% confidence interval, 0.61-0.70). Comparing blood pressure measurements between those in the highest and those in the lowest quartile of lead exposure, we observed a 6.87-mmHg (1.51-12.21 mmHg) increase in admission systolic blood pressure and a 4.40-mmHg (0.21-8.59 mmHg) increase in admission diastolic blood pressure after adjustment for confounders. Corresponding values for maximum blood pressure increase were 7.72 (1.83-13.60) and 8.33 (1.14-15.53) mmHg. Benchmark dose lower limit values for a 1-SD increase in blood pressure were < 2 μg/dL blood lead for all blood pressure end points.

CONCLUSIONS

A significant association between low-level lead exposures and elevations in maternal blood pressure during labor and delivery can be observed at umbilical blood lead levels < 2 μg/dL.

Derivation of the minimal magnitude of the Critical Effect Size for continuous toxicological parameters from within-animal variation in control group data

Assuming that temporal fluctuations in physiological parameters (e.g. haematology, biochemistry) in individual healthy non-exposed animals are non-adverse, the minimal magnitude of the Critical Effect Size (CES) for a number of continuous parameters of toxicity studies was derived. A total of 36 studies (19 pharmaceutical preclinical studies in dogs and 17 chemical risk assessment studies in rats) were analysed to determine within-animal variation in their control groups. Minimal CES-values were derived for each group of studies, differentiating where necessary between strains and sexes, using the 2.5 percentile (lower limit) and/or 97.5 percentile (upper limit) of the distribution of the within-animal variation around the mean of each parameter. We concluded that minimal CES-values for continuous clinical chemistry and haematology parameters should be established separately per species, strain, sex and study duration investigated. Grouping of minimal CES-values, leading to more or less "general" values, seems possible for those parameters that are subject to tight homeostatic control and consequently show little within-animal variation. Nearly a quarter of the proposed CES-values is 5%, nearly a quarter range from 6% to 10%, a quarter is 15% or 20%, and nearly 30% of the proposed values is 20% of the mean of the control animals.

Deriving a data-based interspecies assessment factor using the NOAEL and the benchmark dose approach

In deriving human health-based exposure limits from animal data, differences in sensitivity to a compound between animals and humans must be taken into account. These interspecies differences can be caused by differences in toxicokinetics and or toxicodynamics. Apart from that, species differ in body size, and this is usually accounted for by scaling doses to body weight (i.e., expressed as mg/kg body weight1.0/day). Adefault assessmentfactor (AF) of 10 is commonly applied to this dose metric to account for potential toxicokinetic and toxicodynamic differences. However, both proportional body weight (BW)scalingand the defaultAFas often applied are not directly based on empirical findings. Attempts have been made to derive data-based assessment factors and allometric scaling powers using various toxicological values such as no-observedadverse-effect-levels (NOAELs). In thisstudy both the NOAEL approach and the benchmark dose (BMD) approach are applied to deriveNOAEL ratios and BMD ratios from mouse and rat studies and, based on that information, toestimate an allometric scaling power and an interspecies AF. To account for interspecies differences in body size, our results confirm earlier findings that allometric body weight scaling with a power of around 0.7 is appropriate. The factor needed to rescale the dose in terms of mg/kgBWto the allometric dose scale ranges from around 1.7 (for dogs) to 10(for mice), similar to other findings. The additional factor required for taking into account interspecies toxicokinetic and toxicodynamic differences, when based on the 95th percentile of the relevant ratio distribution, would be 3.1 for a lower Confidence limit of theBMD (BMDL), and 8.3 for a NOAEL (to be applied to the allometrically scaled dose). These results indicate that the generally used defaultAFof 10 may not cover potential interspecies differences, in particular when applied to results from smaller test species. Therefore, using the default AF of 10 could lead to human exposure limits that are insufficiently protective. Further, our results show that a data-based AF that would be needed for interspecies extrapolation is smaller when the point of departure is aBMDLrather than a NOAEL. In the context of a probabilistic hazard characterization, our results indicate that the (geometric) SD of the interspecies AF distribution should be around 2.0 when the BMDL (or BMD uncertainty distribution) is used, and around 3.4 when the NOAEL is used as a point of departure for further risk assessment.

Within-animal variation as an indication of the minimal magnitude of the critical effect size for continuous toxicological parameters applicable in the benchmark dose approach

In this study, the within-animal variation in routinely studied continuous toxicological parameters was estimated from temporal fluctuations in individual healthy nonexposed animals. Assuming that these fluctuations are nonadverse, this within-animal variation may be indicative of the minimal magnitude of the critical effect size (CES). The CES is defined as the breaking point between adverse and nonadverse changes in a continuous toxicological parameter, at the level of the individual organism. The total variation in the data from individual nonexposed animals was divided in variation parts due to known factors (differences in sex, animal, and day) and a residual variation, by means of analysis of variance. Using the residual variation and the estimated analytical measurement error of a toxicological parameter, the within-animal variation can be estimated. The data showed within-animal variations ranging between 0.6% and 34% for different clinical chemistry and hematological parameters in 90-day rat studies. This indicates that different (minimal) CES values may be applicable for different parameters.

A probabilistic effect assessment model for hazardous substances at the workplace

A major problem in risk assessment is the quantification of uncertainties. A probabilistic model was developed to consider uncertainties in the effect assessment of hazardous substances at the workplace. Distributions for extrapolation factors (time extrapolation, inter- and intraspecies extrapolation) were determined on the basis of appropriate empirical data. Together with the distribution for the benchmark dose obtained from substance-specific dose-response modelling for the exemplary substances 2,4,4-trimethylpentene (TMP) and aniline, they represent the input distributions for probabilistic modelling. These distributions were combined by Monte Carlo simulation. The resulting target distribution describes the probability that an aspired protection level for workers is achieved at a certain dose and the uncertainty associated with the assessment. In the case of aniline, substance-specific data on differences in susceptibility (between species; among humans due to genetic polymorphisms of N-acetyltransferase) were integrated in the model. Medians of the obtained target distributions of the basic models for TMP and aniline, but not of the specific aniline model are similar to deterministically derived reference values. Differences of more than one order of magnitude between the medians and the 5th percentile of the target distributions indicate substantial uncertainty associated with the effect assessment of these substances. The probabilistic effect assessment model proves to be a practical tool to integrate quantitative information on uncertainty and variability in hazard characterisation.

Identification of a Critical Dose Level for Risk Assessment: Developments in Benchmark Dose Analysis of Continuous Endpoints

The benchmark dose (BMD) method has been recommended to replace the no-observed-adverse-effect-level (NOAEL) approach in health risk assessment of chemical substances. In the present article, developments in BMD analysis from continuous experimental data are proposed. The suggested approach defines the BMD as the dose at which the slope of the S-shaped dose-response relationship changes the most in the low-dose region. This dose resides in a region where the sensitivity to chemical exposure may start to change noticeably. It is shown that the response (defined as a percent change relative to the magnitude, or size, of response) corresponding to the dose where the slope changes the most depends on the geometrical shape of the dose-response curve; the response becomes lower as the curve becomes more asymmetrical and threshold-like in the low-dose region. Given a symmetrical case, described by the Hill function, the response associated with the critical dose level becomes 21% (defined as a percent change relative to the magnitude, or size, of response). According to a limiting case of asymmetry and threshold-like characteristics, reflected by a Gompertz curve, the response corresponding to the dose of interest becomes as low as 7.3% (defined as a percent change relative to the magnitude, or size, of response). Use of a response in the range of 5-10% when estimating the BMD conservatively accounts for uncertainties associated with the proposed strategy, and may be appropriate in a risk assessment point of view. The present investigation also indicated that a BMD defined according to the suggested procedure may be estimated more precisely relative to BMDs defined under other approaches for continuous data.

A comparison of ratio distributions based on the NOAEL and the benchmark approach for subchronic-to-chronic extrapolation

One approach to derive a data-based assessment factor (AF) for subchronic-to-chronic extrapolation is to determine ratios between the NOAEL(subchronic) and NOAEL(chronic) for the same compounds. Instead of using ratios of NOAELs, the distribution can also be estimated by ratios of subchronic and chronic Benchmark Doses (or Critical Effect Doses, CEDs, for continuous data). In this study 314 dose-response datasets on body weights and liver weights of mice and rats were selected providing dose-response information after both subchronic and chronic exposure. NOAEL ratios could be derived in only 68 of these datasets, while CED ratios could be derived in 189 datasets. When only the (53) datasets suitable for both approaches were evaluated the variation of the CED ratio distribution (GSD [geometric standard deviation]: 2.9) was smaller than the one of the NOAEL ratio distribution (GSD: 3.3). After correcting for the estimation error of the individual CED ratios the GSD of the CED distribution decreased to 2.3. The geometric means (GMs) of the NOAEL and CED distributions were similar (1.2 and 1.6, respectively). Comparing the NOAEL distribution based on all 68 datasets suitable for deriving NOAEL ratios with the CED distribution based on the 189 ratios suitable for deriving CED ratios resulted in similar GMs (1.5 and 1.7, respectively), but the GSDs differed considerably (5.3 and 2.3 respectively). It is concluded that usage of the CED approach results in less wide distributions. Furthermore, a larger fraction of available datasets is useful to inform the ratio distribution. This results in more accurate, and less conservative distributions of AFs in general compared to the distributions based on NOAEL ratios that have been proposed so far.