Percutaneous penetration of drugs applied in transdermal delivery systems: an in vivo based approach for evaluating computer generated penetration models

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.

Ultradeformable vesicles: concepts and applications relating to the delivery of skin cosmetics

Skin aging is a phenomenon resulting in reduced self-confidence, thus becoming a major factor in social determinants of health. The use of active cosmetic ingredients can help prevent skin aging. Transfersomes are well known to be capable of deeply penetrating the dermis. This scoping review provides an insight into transfersomes and their prospective use in anti-aging cosmetics. Numerous reports exist highlighting the successful skin delivery of therapeutic agents such as high-molecular-weight, poorly water soluble and poorly permeable active ingredients by means of transfersomes. Moreover, in vitro and in vivo studies have indicated that transfersomes increase the deposition, penetration and efficacy of active ingredients. However, the use of transfersomes in the delivery of active cosmetic ingredients is limited. Considering their similar physicochemical properties, transfersomes should possess considerable potential as a delivery system for anti-aging cosmetics.

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.

Multivariate Analytical Approaches to Identify Key Molecular Properties of Vehicles, Permeants and Membranes That Affect Permeation through Membranes

There has been considerable recent interest in employing computer models to investigate the relationship between the structure of a molecule and its dermal penetration. Molecular permeation across the epidermis has previously been demonstrated to be determined by a number of physicochemical properties, for example, the lipophilicity, molecular weight and hydrogen bonding ability of the permeant. However little attention has been paid to modeling the combined effects of permeant properties in tandem with the properties of vehicles used to deliver those permeants or to whether data obtained using synthetic membranes can be correlated with those obtained using human epidermis. This work uses Principal Components Analysis (PCA) to demonstrate that, for studies of the diffusion of three model permeants (caffeine, methyl paraben and butyl paraben) through synthetic membranes, it is the properties of the oily vehicle in which they are applied that dominated the rates of permeation and flux. Simple robust and predictive descriptor-based quantitative structure-permeability relationship (QSPR) models have been developed to support these findings by utilizing physicochemical descriptors of the oily vehicles to quantify the differences in flux and permeation of the model compounds. Interestingly, PCA showed that, for the flux of co-applied model permeants through human epidermis, the permeation of the model permeants was better described by a balance between the physicochemical properties of the vehicle and the permeant rather than being dominated solely by the vehicle properties as in the case of synthetic model membranes. The important influence of permeant solubility in the vehicle along with the solvent uptake on overall permeant diffusion into the membrane was substantiated. These results confirm that care must be taken in interpreting permeation data when synthetic membranes are employed as surrogates for human epidermis; they also demonstrate the importance of considering not only the permeant properties but also those of both vehicle and membrane when arriving at any conclusions relating to permeation data.

Predicting Transdermal Fentanyl Delivery Using Mechanistic Simulations for Tailored Therapy

Transdermal drug delivery is a key technology for administering drugs. However, most devices are “one-size-fits-all”, even though drug diffusion through the skin varies significantly from person-to-person. For next-generation devices, personalization for optimal drug release would benefit from an augmented insight into the drug release and percutaneous uptake kinetics. Our objective was to quantify the changes in transdermal fentanyl uptake with regards to the patient’s age and the anatomical location where the patch was placed. We also explored to which extent the drug flux from the patch could be altered by miniaturizing the contact surface area of the patch reservoir with the skin. To this end, we used validated mechanistic modeling of fentanyl diffusion, storage, and partitioning in the epidermis to quantify drug release from the patch and the uptake within the skin. A superior spatiotemporal resolution compared to experimental methods enabled in-silico identification of peak concentrations and fluxes, and the amount of stored drug and bioavailability. The patients’ drug uptake showed a 36% difference between different anatomical locations after 72 h, but there was a strong interpatient variability. With aging, the drug uptake from the transdermal patch became slower and less potent. A 70-year-old patient received 26% less drug over the 72-h application period, compared to an 18-year-old patient. Additionally, a novel concept of using micron-sized drug reservoirs was explored in silico. These reservoirs induced a much higher local flux (µg cm^-2 h^-1) than conventional patches. Up to a 200-fold increase in the drug flux was obtained from these small reservoirs. This effect was mainly caused by transverse diffusion in the stratum corneum, which is not relevant for much larger conventional patches. These micron-sized drug reservoirs open new ways to individualize reservoir design and thus transdermal therapy. Such computer-aided engineering tools also have great potential for in-silico design and precise control of drug delivery systems. Here, the validated mechanistic models can serve as a key building block for developing digital twins for transdermal drug delivery systems.

The application of label-free imaging technologies in transdermal research for deeper mechanism revealing

The penetration behavior of topical substances in the skin not only relates to the transdermal delivery efficiency but also involves the safety and therapeutic effect of topical products, such as sunscreen and hair growth products. Researchers have tried to illustrate the transdermal process with diversified theories and technologies. Directly observing the distribution of topical substances on skin by characteristic imaging is the most convincing approach. Unfortunately, fluorescence labeling imaging, which is commonly used in biochemical research, is limited for transdermal research for most topical substances with a molecular mass less than 500 Da. Label-free imaging technologies possess the advantages of not requiring any macromolecular dyes, no tissue destruction and an extensive substance detection capability, which has enabled rapid development of such technologies in recent years and their introduction to biological tissue analysis, such as skin samples. Through the specific identification of topical substances and endogenous tissue components, label-free imaging technologies can provide abundant tissue distribution information, enrich theoretical and practical guidance for transdermal drug delivery systems. In this review, we expound the mechanisms and applications of the most popular label-free imaging technologies in transdermal research at present, compare their advantages and disadvantages, and forecast development prospects.

Development of a Self-Emulsifying Drug Delivery System for Optimized Topical Delivery of Clofazimine

A quality-by-design and characterization approach was followed to ensure development of self-emulsifying drug delivery systems (SEDDSs) destined for topical delivery of the highly lipophilic clofazimine. Solubility and water-titration experiments identified spontaneous emulsification capacity of different excipient combinations and clofazimine. After identifying self-emulsification regions, check-point formulations were selected within the self-emulsification region by considering characteristics required to achieve optimized topical drug delivery. Check-point formulations, able to withstand phase separation after 24 h at an ambient temperature, were subjected to characterization studies. Experiments involved droplet size evaluation; size distribution; zeta-potential; self-emulsification time and efficacy; viscosity and pH measurement; cloud point assessment; and thermodynamic stability studies. SEDDSs with favorable properties, i.e., topical drug delivery, were subjected to dermal diffusion studies. Successful in vitro topical clofazimine delivery was observed. Olive oil facilitated the highest topical delivery of clofazimine probably due to increased oleic acid levels that enhanced stratum corneum lipid disruption, followed by improved dermal clofazimine delivery. Finally, isothermal microcalometric experiments studied the compatibility of excipients. Potential interactions were depicted between argan oil and clofazimine as well as between Span®60 and argan-, macadamia- and olive oil, respectively. However, despite some mundane incompatibilities, successful development of topical SEDDSs achieved enhanced topical clofazimine delivery.

Twenty Clinically Pertinent Factors/Observations for Percutaneous Absorption in Humans

At least 20 clinically relevant factors affect percutaneous absorption of drugs and chemicals: relevant physico-chemical properties, vehicle/formulation, drug exposure conditions (dose, duration, surface area, exposure frequency), skin appendages (hair follicles, glands) as sub-anatomical pathways, skin application sites (regional variation in penetration), population variability (premature, infants, and aged), skin surface conditions (hydration, temperature, pH), skin health and integrity (trauma, skin diseases), substantivity and binding to different skin components, systemic distribution and systemic toxicity, stratum corneum exfoliation, washing-off and washing-in, rubbing/massaging, transfer to others (human to human and hard surface to human), volatility, metabolic biotransformation/cutaneous metabolism, photochemical transformation and photosensitivity, excretion pharmacokinetics, lateral spread, and chemical method of determining percutaneous absorption.

The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability

OBJECTIVES

The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (T_exp ) and choice of diffusion cell on model quality and performance.

METHODS

Data were collated from the literature. Static and flow-through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of T_exp was assessed by comparing a range of datasets where T_exp either remained constant or was varied from 22 to 45 °C.

KEY FINDINGS

Using data from flow-through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow-through cell experiments reduces overall model quality. Consideration of T_exp improves model quality when the dataset used exhibits a wide range of experimental temperatures.

CONCLUSIONS

This study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz-type, experiments should be used to construct the model and T_exp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.