In vitro methods offer opportunities to provide mechanistic insight into bioactivity as well as human-relevant toxicological assessments compared to animal testing. One of the challenges for this task is putting in vitro bioactivity data in an in vivo exposure context, for which in vitro to in vivo extrapolation (IVIVE) translates in vitro bioactivity to clinically relevant exposure metrics using reverse dosimetry. This study applies an IVIVE approach to the toxicity assessment of ingredients and their mixtures in e-cigarette (EC) aerosols as a case study. Reported in vitro cytotoxicity data of EC aerosols, as well as in vitro high-throughput screening (HTS) data for individual ingredients in EC liquids (e-liquids) are used. Open-source physiologically based pharmacokinetic (PBPK) models are used to calculate the plasma concentrations of individual ingredients, followed by reverse dosimetry to estimate the human equivalent administered doses (EADs) needed to obtain these plasma concentrations for the total e-liquids. Three approaches (single actor approach, additive effect approach, and outcome-oriented ingredient integration approach) are used to predict EADs of e-liquids considering differential contributions to the bioactivity from the ingredients (humectant carriers [propylene glycol and glycerol], flavors, benzoic acid, and nicotine). The results identified critical factors for the EAD estimation, including the ingredients of the mixture considered to be bioactive, in vitro assay selection, and the data integration approach for mixtures. Further, we introduced the outcome-oriented ingredient integration approach to consider e-liquid ingredients that may lead to a common toxicity outcome (e.g., cytotoxicity), facilitating a quantitative evaluation of in vitro toxicity data in support of human risk assessment.

During typical late-postnatal CNS development, net reductions in dendritic spine densities are associated with activity-dependent learning. Prior results showed agonist exposure in young animals increased spine densities in a subset of song regions while adult exposures did not, suggesting endocannabinoid signaling regulates dendritic spine dynamics important to vocal development. Here we addressed this question using the CB1 receptor-selective antagonist SR141716A (SR) to disrupt endocannabinoid signaling both during and after vocal learning. We hypothesized antagonist exposure during vocal development, but not adulthood, would alter spine densities. Following 25 days of exposure and a 25 day maturation period, 3D reconstructions of Golgi-Cox stained neurons were used to measure spine densities. We found antagonist treatments during both age periods increased densities within Area X (basal ganglia) and following adult treatments within HVC (premotor cortical-like). Results suggest both inappropriate cannabinoid receptor stimulation and inhibition are capable of similar disregulatory effects during establishment of circuits important to vocal learning, with antagonism extending these effects through adulthood. Given clinical evidence of depressant effects of SR, we tested the ability of the antidepressant monoamine oxidase inhibitor (MAOI) phenelzine to mitigate SR-induced spine density increases. This was confirmed implicating interaction between monoamine and endocannabinoid systems. Finally, we evaluated acute effects of these drugs to alter ability of novel song exposure to increase spine densities in auditory NCM and other regions, finding when combined, SR and phenelzine increased densities within Area X. These results contribute to understanding relevance of dendritic spine dynamics in neuronal development, drug abuse, and depression.