Organic compounds percutaneous penetration in vivo in man: Relationship to mathematical predictive model

Machine learning for skin permeability prediction: random forest and XG boost regression

Background: Machine learning algorithms that can quickly and easily estimate skin permeability (Kp) are increasingly being used in drug delivery research. The linear free energy relationship (LFER) developed by Abraham is a practical technique for predicting Kp. The permeability coefficients and Abraham solute descriptor values for 175 organic compounds have been documented in the scientific literature.Purpose: The purpose of this project was to use a publicly available dataset to make skin permeability predictions using the random forest and XBoost regression techniques.Methods: We employed Pandas-based methods in JupyterLab to predict permeability coefficient (Kp) from solute descriptors (excess molar refraction [E], combined dipolarity/polarizability [S], overall solute hydrogen bond acidity and basicity [A and B], and the McGowan's characteristic molecular volume [V]).Results: The random forest and XG Boost regression models established statistically significant association between the descriptors and the skin permeability coefficient.

Determination of potential dermal exposure rates of phosphorus flame retardants via the direct contact with a car seat using artificial skin

Dermal exposure to phosphorus flame retardants (PFRs) has received much attention as a major alternative exposure route in recent years. However, the information regarding dermal exposure via direct contact with a product is limited. In addition, in the commonly used dermal permeability test, the target substance is dissolved in a solvent, which is unrealistic. In this study, a dermal permeability test of PFRs in three car seats was performed using artificial skin. The PFR concentrations in the car seats are 0.12 wt% tris(2-chloroethyl) phosphate (TCEP), 0.030-0.25 wt% tris(2-chloroisopropyl) phosphate (TCPP), 0.15 wt% triphenyl phosphate (TPhP), 0.89 wt% cresyl diphenyl phosphate (CsDPhP), 0.074 wt% tricresyl phosphate (TCsP), and 0.46-4.7 wt% diethylene glycol bis [di (2-chloroisopropyl) phosphate (DEG-BDCIPP). The mean skin permeation rates for a contact time of 24 h are 14 (TCEP), 5.4-160 (TCPP), 0.67 (CsDPhP), 0.38 (TPhP), and 3.3-58 ng cm-2 h-1 (DEG-BDCIPP). The concentrations of TCsP in receptor liquid were lower than the limit of quantification at the contact time of 24 h. The skin permeation rates were significantly affected by the type of car seat (e.g., fabric or non-fabric). The potential dermal TCPP exposure rate for an adult via direct contact with the car seat during the average daily contact time (1.3 h), which was the highest value assessed in this study, was estimated to be 16,000 ng kg-1 day-1, which is higher than that related to inhalation and dust ingestion reported as significant exposure route of PFRs in previous studies. These facts reveal that dermal exposure associated with direct contact with the product might be an important exposure pathway for PFRs.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Efficacy of soap and water based skin decontamination using in vivo animal models: a systematic review

Water-only or soap and water solutions are considered a gold standard for skin decontamination. However, there is lack of conclusive data regarding their efficacy. The aim of this study was to summarize in vivo animal model data on skin decontamination using water-only, and/or soap and water. Covidence, Embase, MEDLINE, PubMed, Web of Science, and Google Scholar were searched to identify relevant articles using water-only or soap and water decontamination methods in in vivo animals. Data extraction was completed from studies, representing three animal models, and 11 contaminants. Results demonstrated water-only decontamination solutions led to complete decontamination in 3.1% (n = 16/524) protocols, incomplete decontamination in 90.6% (n = 475/524) of protocols, and mortality in 6.3% (n = 33/524) of protocols. Soap and water decontamination solutions resulted in complete decontamination in 6.9% (n = 8/116) protocols, incomplete decontamination in 92.2% (n = 107/116) of protocols, and mortality in 6.9% (n = 8/116) of protocols. Although water only, or soap and water is considered a gold standard for skin decontamination, most papers investigated found that water only, and soap and water provided incomplete decontamination. Due to the insufficient data, and limitations that hinder the applicability of available data, evidence indicates that more contemporary studies investigating skin decontamination are needed, and compared to other model species, including humans, when practical.

Ability of mathematical models to predict human in vivo percutaneous penetration of steroids

Human skin is a common route for topical steroids to enter the body. To aid with risk management of therapeutic steroid usage, the US Environmental Protection Agency estimates percutaneous penetration using mathematical models. However, it is unclear how accurate are mathematical models in estimating percutaneous penetration/absorption of steroids. In this study, accuracy of predicted flux (penetration/absorption) by the main mathematical model used by the EPA, the Potts and Guy model based on in vitro data is compared to actual human in vivo data from our laboratory of percutaneous absorption of topical steroids. We focused on steroids due to the availability of steroid in vivo human data in our laboratory. For most steroids the flux was underestimated by a factor 10-60. However, within the group itself, there was an association between the Potts and Guy model and experimental human in vivo data (Pearson Correlation = 0.8925, p = 0.000041). Additionally, some physiochemical parameters used in the Potts and Guy equation, namely log Kp (Pearson Correlation = 0.7307, p = 0.0046) and molecular weight (Pearson correlation = -0.6807, p = 0.0105) correlated significantly with in vivo flux. Current mathematical models used in estimating percutaneous penetration/absorption did not accurately predict in vivo flux of steroids. Why? Proposed limitations to mathematical models currently used include: not accounting for volatility, lipid solubility, hydrogen bond effects, drug metabolism, as well as protein binding. Further research is needed in order to increase the predictive nature of such models for in vivo flux.

A Data-Mining Approach for the Quantitative Assessment of Physicochemical Properties of Molecular Compounds in the Skin Flux

This paper aimed to provide an insight into the mechanism of transdermal penetration of drug molecules with respect to their physicochemical properties, such as solubility (S), the presence of enantiomer (ET) and logarithm of octanol-water partition coefficient (log P), molecular weight (MW), and melting point (MP). Propionic acid derivatives were evaluated for their flux through full-thickness skin excised from hairless mice upon being delivered from silicone-based pressure-sensitive adhesive (PSA) matrices in the presence or absence of various enhancers. The skin fluxes of model compounds were calculated based on the data obtained using the method engaged with the diffusion cell system. The statistical design of experiments (DoE) based on the factorial approach was used to find variables that have a significant impact on the outcomes. For the prediction of skin flux, a quantitative equation was derived using the data-mining approach on the relationship between skin permeation of model compounds (~125 mg/ml) and involved physicochemical variables. The most influential variables for the skin flux of propionic acid derivatives were the melting point (0.97) followed by the presence of enantiomer (0.95), molecular mass (0.93), log P values (0.86), and aqueous solubility (0.80). It was concluded that the skin flux of molecular compounds can be predicted based on the relationship between their physicochemical properties and the interaction with cofactors including additives and enhancers in the vehicles.