A regression analysis using simple descriptors for multiple dermal datasets: Going from individual membranes to the full skin.

In silico methods to estimate and/or quantify skin absorption of chemicals as a function of chemistry are needed to realistically predict pharmacological, occupational, and environmental exposures. The Potts-Guy equation is a well-established approach, using multi-linear regression analysis describing skin permeability (Kp) in terms of the octanol/water partition coefficient (logP) and molecular weight (MW). In this work, we obtained regression equations for different human datasets relevant to environmental and cosmetic chemicals. Since the Potts-Guy equation was published in 1992, we explored recent datasets that include different skin layers, such as dermatomed (including dermis to a defined thickness) and full skin. Our work was consistent with others who have observed that fits to the Potts-Guy equation are stronger for experiments focused on the epidermis. Permeability estimates for dermatomed skin and full skin resulted in low regression coefficients when compared to epidermis datasets. An updated regression equation uses a combination of fitted permeability values obtained with a published 2D compartmental model previously evaluated. The resulting regression equation was: logKp = -2.55 + 0.65logP - 0.0085MW, R2 = 0.91 (applicability domain for all datasets: MW ranges from 18 to >584 g/mol and -4 to >5 for logP). This approach demonstrates the advantage of combining mechanistic with structural activity relationships in a single modeling approach. This combination approach results in an improved regression fit when compared to permeability estimates obtained using the Potts-Guy approach alone. The analysis presented in this work assumes a one-compartment skin absorption route; future modeling work will consider adding multiple compartments.

Understanding and Modeling the Liquid Uptake in Porous Compacted Powder Preparations.

Porous solid materials commonly undergo coating processes during their manufacture, where liquids are put in contact with solids for different purposes. The study of liquid penetration in porous substrates is a process of high relevance in activities in several industries. In particular, powder detergents are subject to coating with surfactants that will boost their performance, although this may affect the flowability and even cause caking of the particulate material, which can be detrimental to consumer acceptance. Here we present a methodology to make compacted preparations of powders relevant to detergent making and evaluate the internal structure of such porous substrates by means of X-ray microcomputed tomography. Liquid penetration in the preparation and the total mass uptake of fluid were monitored by a gravimetric technique based on a modified Wilhelmy plate method consisting of consecutive cycles. Taking into account the geometry of the system, two models were proposed to describe the liquid uptake based on the process being driven by mass (model 1) or pressure (model 2) gradients. A comparison between both from statistical and physical points of view led to the conclusion that the latter was more appropriate for describing the process and retrieving values of the permeability of the solid between 0.03 × 10-12 and 0.95 × 10-12 m2. Finally, with the parameters retrieved from model 2, the force balance observed throughout the experiment was simulated satisfactorily.

Effect of chyme viscosity and nutrient feedback mechanism on gastric emptying.

A comprehensive mathematical model of the digestive processes in humans could allow for better design of functional foods which may play a role in stemming the prevalence of food related diseases around the world. This work presents a mathematical model for a nutrient based feedback mechanism controlling gastric emptying, which has been identified in vivo by numerous researchers. The model also takes into account the viscosity of nutrient meals upon gastric secretions and emptying. The results show that modelling the nutrient feedback mechanism as an on/off system, with an initial emptying rate dependent upon the secretion rate (which is a function of the gastric chyme viscosity) provides a good fit to the trends of emptying rate for liquid meals of low and high nutrient content with varying viscosity.

Predictive performance of next generation human physiologically based kinetic (PBK) models based on in vitro and in silico input data.

The goal of the present study was to assess the predictive performance of a generic human physiologically based kinetic (PBK) model based on in vitro and in silico input data and the effect of using different input approaches for chemical parameterization on those predictions. For this purpose, a dataset was created of 38,772 Cmax predictions for 44 compounds by applying different combinations of in vitro and in silico approaches for chemical parameterization, and these predicted Cmax values were compared to reported in vivo data. Best results were achieved when the hepatic clearance was parameterized based on in vitro (i.e., hepatocytes or liver S9) measured intrinsic clearance values, the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients, and the method of Lobell and Sivarajah for calculating the fraction unbound in plasma. With these parameters, the median Cmax values of 34 out of the 44 compounds were predicted within 5-fold of the observed Cmax, and the Cmax values of 19 compounds were predicted within 2-fold. The median Cmax values of 10 compounds were more than 5-fold overestimated. Underestimations (> 5-fold) did not occur. A comparison of the current generic PBK model structure with chemical-specific PBK models available in literature was made to identify possible kinetic processes not included in the generic PBK model that might explain the overestimations. Overall, the results provide crucial insights into the predictive performance of PBK models based on in vitro and in silico input and the influence of different input approaches on the model predictions.

Next generation risk assessment of human exposure to anti-androgens using newly defined comparator compound values.

Next Generation Risk Assessment (NGRA) can use the so-called Dietary Comparator Ratio (DCR) to evaluate the safety of a defined exposure to a compound of interest. The DCR compares the Exposure Activity Ratio (EAR) for the compound of interest, to the EAR of an established safe level of human exposure to a comparator compound with the same putative mode of action. A DCR ≤ 1 indicates the exposure evaluated is safe. The present study aimed at defining adequate and safe comparator compound exposures for evaluation of anti-androgenic effects, using 3,3-diindolylmethane (DIM), from cruciferous vegetables, and the anti-androgenic drug bicalutamide (BIC). EAR values for these comparator compounds were defined using the AR-CALUX assay. The adequacy of the new comparator EAR values was evaluated using PBK modelling and by comparing the generated DCRs of a series of test compound exposures to actual knowledge on their safety regarding in vivo anti-androgenicity. Results obtained supported the use of AR-CALUX-based comparator EARs for DCR-based NGRA for putative anti-androgenic compounds. This further validates the DCR approach as an animal free in silico/in vitro 3R compliant method in NGRA.

Application of physiologically based kinetic (PBK) modelling in the next generation risk assessment of dermally applied consumer products.

Next Generation Risk Assessment (NGRA) is a procedure that integrates new approach methodologies (NAMs) to assure safety of a product without generating data from animal testing. One of the major challenges in the application of NGRA to consumer products is how to extrapolate from the in vitro points of departure (PoDs) to the human exposure level associated with product use. To bridge the gap, physiologically based kinetic (PBK) modelling is routinely used to predict systemic exposure (Cmax or AUC) from external exposures. A novel framework was developed for assessing the exposure of new ingredients in dermally applied products based on the construction of PBK models describing consumer habits and practices, formulation type, and ADME (absorption, distribution, metabolism and excretion) properties exclusively obtained from NAMs. This framework aims to quantify and reduce the uncertainty in predictions and is closely related to the risk assessment process (i.e., is the margin of safety sufficient to cover the uncertainties in the extrapolation between the in vitro and in vivo toxicodynamics and toxicokinetics?). Coumarin, caffeine, and sulforaphane in four product types (kitchen cleaner liquid, face cream, shampoo, and body lotion) were selected to exemplify how this framework could be used in practise. Our work shows initial levels of the framework provide a conservative estimate of Cmax in most cases which can be refined using sensitivity analysis to inform the choice of follow-up in vitro experiments. These case studies show the framework can increase confidence in use of PBK predictions for safety assessment.

A Next-Generation Risk Assessment Case Study for Coumarin in Cosmetic Products.

Next-Generation Risk Assessment is defined as an exposure-led, hypothesis-driven risk assessment approach that integrates new approach methodologies (NAMs) to assure safety without the use of animal testing. These principles were applied to a hypothetical safety assessment of 0.1% coumarin in face cream and body lotion. For the purpose of evaluating the use of NAMs, existing animal and human data on coumarin were excluded. Internal concentrations (plasma Cmax) were estimated using a physiologically based kinetic model for dermally applied coumarin. Systemic toxicity was assessed using a battery of in vitro NAMs to identify points of departure (PoDs) for a variety of biological effects such as receptor-mediated and immunomodulatory effects (Eurofins SafetyScreen44 and BioMap Diversity 8 Panel, respectively), and general bioactivity (ToxCast data, an in vitro cell stress panel and high-throughput transcriptomics). In addition, in silico alerts for genotoxicity were followed up with the ToxTracker tool. The PoDs from the in vitro assays were plotted against the calculated in vivo exposure to calculate a margin of safety with associated uncertainty. The predicted Cmax values for face cream and body lotion were lower than all PoDs with margin of safety higher than 100. Furthermore, coumarin was not genotoxic, did not bind to any of the 44 receptors tested and did not show any immunomodulatory effects at consumer-relevant exposures. In conclusion, this case study demonstrated the value of integrating exposure science, computational modeling and in vitro bioactivity data, to reach a safety decision without animal data.