Continuous Model Averaging for Benchmark Dose Analysis: Averaging Over Distributional Forms

Hepatotoxicity of silver nanoparticles: Benchmark concentration modeling of an in vitro transcriptomics study in human iPSC-derived hepatocytes

Despite two decades of research on silver nanoparticle (AgNP) toxicity, a safe threshold for exposure has not yet been established, albeit being critically needed for risk assessment and regulatory decision-making. Traditionally, a point-of-departure (PoD) value is derived from dose response of apical endpoints in animal studies using either the no-observed-adverse-effect level (NOAEL) approach, or benchmark dose (BMD) modeling. To develop new approach methodologies (NAMs) to inform human risk assessment of AgNPs, we conducted a concentration response modeling of the transcriptomic changes in hepatocytes derived from human induced pluripotent stem cells (iPSCs) after being exposed to a wide range concentration (0.01-25 μg/ml) of AgNPs for 24 h. A plausible transcriptomic PoD of 0.21 μg/ml was derived for a pathway related to the mode-of-action (MOA) of AgNPs, and a more conservative PoD of 0.10 μg/ml for a gene ontology (GO) term not apparently associated with the MOA of AgNPs. A reference dose (RfD) could be calculated from either of the PoDs as a safe threshold for AgNP exposure. The current study illustrates the usefulness of in vitro transcriptomic concentration response study using human cells as a NAM for toxicity study of chemicals that lack adequate toxicity data to inform human risk assessment.

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.