A review of Health Canada's progress on human biomonitoring-based risk assessments and the path forward.

Since the launch of the Chemicals Management Plan (CMP) in 2006, Health Canada has initiated screening-level risk assessments (RAs) of approximately 4300 priority substances under the Canadian Environmental Protection Act, 1999 (CEPA). With the availability of nationally representative human biomonitoring (HBM) data, over 300 of these substances were assessed using HBM-based RA approaches. Qualitative and quantitative HBM-based RA approaches for the regulatory risk assessment of the general population of Canada were developed to increase the efficiency of screening the potential health risk of CMP priority substances. To support HBM-based RAs, several biomonitoring equivalents (BE) were derived to interpret HBM data. For some CMP substances, Health Canada conducted cumulative risk assessments of chemical mixtures using HBM data as measures of exposure. In 2023, CEPA was amended to include the assessment of populations who may be disproportionately impacted (vulnerable populations) and the cumulative effects of multiple chemicals. Going forward, Health Canada is exploring modern approaches in HBM-based RAs, including biomarkers of effect and non-traditional biomarkers (e.g., hair, nails) to address CEPA amendments. This manuscript will discuss Health Canada's progress in HBM-based RAs, and the possible path forward in using HBM data to strengthen human health risk assessments.

Simplified single-shot supercontinuum spectral interferometry.

We have experimentally demonstrated a simplified method for performing single-shot supercontinuum spectral interferometry (SSSI) that does not require pre-characterization of the probe pulse. The method, originally proposed by D. T. Vu, D. Jang, and K. Y. Kim, uses a genetic algorithm (GA) and as few as two time-delayed pump-probe shots to retrieve the pump-induced phase shift on the probe [Opt. Express26, 20572 (2018)]. We show that the GA is able to successfully retrieve the transient modulations on the probe, and that the error in the retrieved modulation decreases dramatically with the number of shots used. In addition, we propose and demonstrate a practical method that allows SSSI to be done with a single pump-probe shot (again, without the need for pre-characterization of the probe). This simplified method can prove to be immensely useful when performing SSSI with a low-repetition-rate laser source.

Derivation of biomonitoring equivalents (BE values) for zinc.

Zinc is an essential nutrient in which deficiency or excess exposure can result in adverse health effects. Several organizations have established exposure guidance values to protect against deficiency and toxicity. Population-level biomonitoring data for zinc in whole blood, serum and urine are available from the Canadian Health Measures Survey (CHMS) and the US National Health and Nutrition Examination Survey (NHANES). This paper derived Biomonitoring Equivalent values (BEs) for zinc. BEs are tools based upon exposure guidance values to interpret biomonitoring data in the context of potential health risks. A regression between intake and serum/plasma concentration was derived to generate BEs for serum/plasma and whole blood. BEs for urine were derived using mass balance approach with a urine excretion fraction of 0.04. The BE values for deficiency ranged from 860 to 866, 6017-6059 and 159-206 µg/L for serum/plasma, whole blood and urine, respectively. BEs to protect against toxicity for serum/plasma, whole blood, and urine were in the range of 895-1281, 6265-8969 and 439-3489 µg/L, respectively. When interpreting biomonitoring data in a health-risk context, urinary zinc may be a more reliable biomarker of exposure than blood due to homeostasis in blood.

Statistical power in the analyses of brain weight measures in pesticide neurotoxicity testing and the relationship between brain and body weight.

Few investigations have examined statistical power in studies of pesticide exposure effects on rat brain weight. Using data from developmental neurotoxicity studies conducted by four different laboratories, we evaluated statistical power to detect changes in rat brain weight and examined the relationship between brain and body weight in several age groups. All power calculations assumed an alpha value of 0.05, equal variance between groups, and a sample size of 10/group. Statistical power often varied substantially both within and between laboratories, and for a 5% change in brain weight average between-laboratory differences in power were as large as 34%. Power estimates for individual studies often spanned a range of more than 50% within a given laboratory. The likelihood of detecting a "significant" change smaller than 5% was generally low for all laboratories. Brain weight increased linearly with body weight within age groups but the strength of this relationship decreased with age. Therefore, confounding by body weight may be more likely in brain weight analyses for younger animals. Our findings suggest that caution is required when weighing the importance of statistical "significance" in studies of pesticide exposure effects on brain weight as the power to detect these differences may be low.

Determining normal variability in a developmental neurotoxicity test: a report from the ILSI Research Foundation/Risk Science Institute expert working group on neurodevelopmental endpoints.

With the implementation of the Food Quality Protection Act in 1996, more detailed evaluations of possible health effects of pesticides on developing organisms have been required. As a result, considerable developmental neurotoxicity (DNT) data have been generated on a variety of endpoints, including developmental changes in motor activity, auditory startle habituation, and various learning and memory parameters. One issue in interpreting these data is the level of variability for the measures used in these studies: excessive variability can obscure treatment-related effects, or conversely, small but statistically significant changes could be viewed as treatment related, when they might in fact be within the normal range. To aid laboratories in designing useful DNT studies for regulatory consideration, an operational framework for evaluating observed variability in study data has been developed. Elements of the framework suggest how an investigator might approach characterization of variability in the dataset; identification of appropriate datasets for comparison; evaluation of similarities and differences in variability between these datasets, and of possible sources of the variability, including those related to test conduct and test design. A case study using auditory startle habituation data is then presented, employing the elements of this proposed approach.