Risk assessment of chemicals and pharmaceuticals in the pediatric population: A workshop report

Pohl, Hana R.; Engelen, J.G.M. van; Wilson, J. Deanna; Sips, A. J. A. M.

doi:10.1016/j.yrtph.2005.01.005

Cited by 12 publications

(5 citation statements)

References 55 publications

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“…Indeed, compared to adults, children have differences in metal exposure patterns and have greater vulnerability to metal exposures due to crawling, hand-mouth behaviors, and diet. 56 In addition, environmental metal exposures may be higher than adults due to higher surface-to-volume ratios, higher consumption rates of food and water, higher respiration rates, and different toxicokinetics. 56 Although high levels of many individual metal exposures, such as lead, produce observable toxicity early in life, including on gestational age, lower doses of multiple metals may be detrimental.…”

Section: Discussionmentioning

confidence: 99%

“…56 In addition, environmental metal exposures may be higher than adults due to higher surface-to-volume ratios, higher consumption rates of food and water, higher respiration rates, and different toxicokinetics. 56 Although high levels of many individual metal exposures, such as lead, produce observable toxicity early in life, including on gestational age, lower doses of multiple metals may be detrimental. 57 During critical developmental periods, including gestation, low levels of numerous metal exposures might induce only subtle alterations in endocrine signaling or gene expression but could lead to permanent changes that program health outcomes throughout the life course.…”

Section: Discussionmentioning

confidence: 99%

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Cross-cohort mixture analysis: a data integration approach with applications on gestational age and DNA-methylation-derived gestational age acceleration metrics

Colicino

Ascari

Saddiki

et al. 2023

Preprint

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Background: Data integration of multiple epidemiologic studies can provide enhanced exposure contrast and statistical power to examine associations between environmental exposure mixtures and health outcomes. Extant studies have combined population studies and identified an overall mixture-outcome association, without accounting for differences across studies. Objective: To extend the novel Bayesian Weighted Quantile Sum (BWQS) regression to a hierarchical framework to analyze mixtures across multiple cohorts of different sample sizes. Methods: We implemented a hierarchical BWQS (HBWQS) approach that (i) aggregates the sample size of multiple cohorts to calculate an overall mixture index, thereby identifying the most harmful exposure(s) across cohorts; and (ii) provides cohort-specific associations between the overall mixture index and the outcome. We showed results from six simulated scenarios including four mixture components in five and ten populations, and two real case examples on the association between prenatal metal mixture exposure comprising arsenic, cadmium and lead and both gestational age and gestational age acceleration metrics. Results: Results from simulated scenarios showed good empirical coverage and little bias for all parameters estimated with HBWQS. The Watanabe-Akaike information criterion (WAIC) for the HBWQS regression showed a better average performance across scenarios than the BWQS regression. HBWQS results incorporating cohorts within the national Environmental Influences on Child Health Outcomes (ECHO) program from three different sites (Boston, New York City (NYC), and Virginia) showed that the environmental mixture composed of low levels of arsenic, cadmium, and lead was negatively associated with gestational age in NYC. Conclusions: This novel statistical approach facilitates the combination of multiple cohorts and accounts for individual cohort differences in mixture analyses. Findings from this approach can be used to develop regulations, policies, and interventions regarding multiple co-occurring environmental exposures and it will maximize the use of extant publicly available data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Cross-cohort mixture analysis: a data integration approach with applications on gestational age and DNA-methylation-derived gestational age acceleration metrics

Colicino

Ascari

Saddiki

et al. 2023

Preprint

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“…Even if several weaknesses have been identified, the use of PBPK models has been recommended for paediatric risk assessment following an Expert Panel Workshop held in 2003 [59]. Several recent publications of PBPK model applications illustrated the value of this modelling approach to help in the first time dosing in children as well as the study design (blood sampling times, number of patients).…”

Section: Discussionmentioning

confidence: 99%

Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modelling

Bouzom¹,

Walther

2008

Fundamemntal Clinical Pharma

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Nowadays, 50-90% of drugs used in children have never been actually studied in this population. Consequently, either our children are often exposed to the risk of adverse drug events or to lack of efficacy, or they are unable to benefit from a number of therapeutic advances offered to adults, as no clinical study has been properly performed in children. Actually the main methods used to calculate the dose for a child are based on allometric methods taking into account different categories of age, the body weight and/or the body surface area. Unfortunately, these calculation methods consider the children as small adults, which is not the case. Physiologically based pharmacokinetics is one way to integrate the physiological changes occurring in the childhood and to anticipate their impact on the pharmacokinetic processes: absorption, distribution, metabolism and excretion/elimination. From different examples, the application of this modelling approach is discussed as a possible and valuable method to minimize the ethical and technical difficulties of conducting research in children.

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“…Because of the biological and genetic heterogeneity in human populations, the occurrence of broad differences in disease susceptibility/sensitivity among individuals is not surprising. Despite the emergence of many papers on biomonitoring of individuals exposed to genotoxicants, few molecular epidemiological data exist for neonates, infants, and children, making it difficult to define the role of genetics in determining possible differential susceptibility in this population (Pohl et al, 2005).…”

Section: Genetics and Susceptibilitymentioning

confidence: 99%

Approaches for Assessing Risks to Sensitive Populations: Lessons Learned from Evaluating Risks in the Pediatric Population

Hines

Sargent

Autrup

et al. 2009

Toxicological Sciences

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Assessing the risk profiles of potentially sensitive populations requires a "tool chest" of methodological approaches to adequately characterize and evaluate these populations. At present, there is an extensive body of literature on methodologies that apply to the evaluation of the pediatric population. The Health and Environmental Sciences Institute Subcommittee on Risk Assessment of Sensitive Populations evaluated key references in the area of pediatric risk to identify a spectrum of methodological approaches. These approaches are considered in this article for their potential to be extrapolated for the identification and assessment of other sensitive populations. Recommendations as to future research needs and/or alternate methodological considerations are also made.

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Risk assessment of chemicals and pharmaceuticals in the pediatric population: A workshop report

Cited by 12 publications

References 55 publications

Cross-cohort mixture analysis: a data integration approach with applications on gestational age and DNA-methylation-derived gestational age acceleration metrics

Cross-cohort mixture analysis: a data integration approach with applications on gestational age and DNA-methylation-derived gestational age acceleration metrics

Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modelling

Approaches for Assessing Risks to Sensitive Populations: Lessons Learned from Evaluating Risks in the Pediatric Population

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