Current modeling practice may lead to falsely high benchmark dose estimates

Benchmark dose (BMD) modeling is increasingly used as the preferred approach to define the point-of-departure for health risk assessment of chemicals. As data are inherently variable, there is always a risk to select a model that defines a lower confidence bound of the BMD (BMDL) that, contrary to expected, exceeds the true BMD. The aim of this study was to investigate how often and under what circumstances such anomalies occur under current modeling practice. Continuous data were generated from a realistic dose–effect curve by Monte Carlo simulations using four dose groups and a set of five different dose placement scenarios, group sizes between 5 and 50 animals and coefficients of variations of 5–15%. The BMD calculations were conducted using nested exponential models, as most BMD software use nested approaches. “Non-protective” BMDLs (higher than true BMD) were frequently observed, in some scenarios reaching 80%. The phenomenon was mainly related to the selection of the non-sigmoidal exponential model (Effect = a · e^b·dose). In conclusion, non-sigmoid models should be used with caution as it may underestimate the risk, illustrating that awareness of the model selection process and sound identification of the point-of-departure is vital for health risk assessment.     

氟砷致骨代谢损伤生物暴露限值基准剂量法分析

目的应用基准剂量法探讨燃煤氟砷污染致暴露人群骨代谢损伤的生物暴露限值,为预防氟砷暴露对人体健康的损害提供骨损害方面的参考依据。方法应用BMDS Version 2.1.2软件计算氟砷暴露人群尿氟、尿砷的基准剂量(BMD)及其可信限下限(BMDL)。结果氟、砷混合暴露引起骨代谢损伤的尿氟BMD及BMDL分别为1.96mg/gCr、1.32 mg/gCr;尿砷BMD及BMDL分别为120.11μg/gCr、94.83μg/gCr。结论建议氟、砷混合暴露引起暴露人群骨代谢损伤尿氟和尿砷的生物暴露限值分别为1.32 mg/gCr和94.83μg/gCr。     

Derivation of an occupational exposure limit for diacetyl using dose‐response data from a chronic animal inhalation exposure study

Occupational exposure limits (OELs) have been previously proposed for diacetyl; however, most of these values are based on worker cohort studies that are known to have several limitations and confounders. In this analysis, an 8 hour time‐weighted average (TWA) OEL for diacetyl was derived based on data from a chronic, 2 year animal inhalation study recently released by the US National Toxicology Program. In that study, complete histopathology was conducted on male and female mice and rats exposed to 0, 12.5, 25 or 50 ppm diacetyl. Several responses in the lower respiratory tract of rats (the more sensitive species) were chosen as the critical endpoints of interest. Benchmark concentration (BMC) modeling of these endpoints was used to estimate BMC values associated with a 10% extra risk (BMC₁₀) and the associated 95% lower confidence bound (BMCL₁₀), which were subsequently converted to human equivalent concentrations (HECs) using a computational fluid dynamics‐physiologically based pharmacokinetic (CFD‐PBPK) model to account for interspecies dosimetry differences. A composite uncertainty factor of 8.0 was applied to the human equivalent concentration values to yield 8 hour TWA OEL values with a range of 0.16‐0.70 ppm. The recommended 8 hour TWA OEL for diacetyl vapor of 0.2 ppm, based on minimal severity of bronchiolar epithelial hyperplasia in the rat, is practical and health‐protective.     

Semiparametric Bayesian joint modeling of a binary and continuous outcome with applications in toxicological risk assessment

Many dose–response studies collect data on correlated outcomes. For example, in developmental toxicity studies, uterine weight and presence of malformed pups are measured on the same dam. Joint modeling can result in more efficient inferences than independent models for each outcome. Most methods for joint modeling assume standard parametric response distributions. However, in toxicity studies, it is possible that response distributions vary in location and shape with dose, which may not be easily captured by standard models. To address this issue, we propose a semiparametric Bayesian joint model for a binary and continuous response. In our model, a kernel stick‐breaking process prior is assigned to the distribution of a random effect shared across outcomes, which allows flexible changes in distribution shape with dose shared across outcomes. The model also includes outcome‐specific fixed effects to allow different location effects. In simulation studies, we found that the proposed model provides accurate estimates of toxicological risk when the data do not satisfy assumptions of standard parametric models. We apply our method to data from a developmental toxicity study of ethylene glycol diethyl ether. Copyright © 2013 John Wiley & Sons, Ltd.     

VariBench: A Benchmark Database for Variations

Several computational methods have been developed for predicting the effects of rapidly expanding variation data. Comparison of the performance of tools has been very difficult as the methods have been trained and tested with different datasets. Until now, unbiased and representative benchmark datasets have been missing. We have developed a benchmark database suite, VariBench, to overcome this problem. VariBench contains datasets of experimentally verified high‐quality variation data carefully chosen from literature and relevant databases. It provides the mapping of variation position to different levels (protein, RNA and DNA sequences, protein three‐dimensional structure), along with identifier mapping to relevant databases. VariBench contains the first benchmark datasets for variation effect analysis, a field which is of high importance and where many developments are currently going on. VariBench datasets can be used, for example, to test performance of prediction tools as well as to train novel machine learning‐based tools. New datasets will be included and the community is encouraged to submit high‐quality datasets to the service. VariBench is freely available at http://structure.bmc.lu.se/VariBench .     

基准收益率和内部收益率在医疗设备经济评估中的作用

简介医疗设备经济评估中的两个重要指标.     

Dose-response modeling of continuous endpoints.

A family of (nested) dose-response models is introduced herein that can be used for describing the change in any continuous endpoint as a function of dose. A member from this family of models may be selected using the likelihood ratio test as a criterion, to prevent overparameterization. The proposed methodology provides for a formal approach of model selection, and a transparent way of assessing the benchmark dose. Apart from a number of natural constraints, the model expressions follow from an obvious way of quantifying differences in sensitivity between populations. As a consequence, dose-response data that relate to both sexes can be efficiently analyzed by incorporating the data from both sexes in the same analysis, even if the sexes are not equally sensitive to the compound studied. The idea of differences in sensitivity is closely related to the assessment factors used in risk assessment. Thus, the models are directly applicable to estimating such factors, if data concerning populations to be compared are available. Such information is valuable for further validation or adjustment of default assessment factors, as well as for informing distributional assessment factors in a probabilistic risk assessment. The various applications of the proposed methodology are illustrated by real data sets.