In Silico Modelling of Transdermal and Systemic Kinetics of Topically Applied Solutes: Model Development and Initial Validation for Transdermal Nicotine

Systematic review and QSPR analysis of chemical penetration through the nail to inform onychomycosis candidate selection

Recalcitrant nail plate infections can be life-long problems because localizing antifungal agents into infected tissues is problematic. In this systematic review, guided by the SPIDER method, we extracted chemical nail permeation data for 38 compounds from 16 articles, and analyzed the data using quantitative structure-property relationships (QSPRs). Our analysis demonstrated that low-molecular weight was essential for effective nail penetration, with <120 g/mol being preferred. Interestingly, chemical polarity had little effect on nail penetration; therefore, small polar molecules, which effectively penetrate the nail, but not the skin, should be set as the most desirable target chemical property in new post-screen onychomycosis candidate selections.

A Comparative Evaluation of Desoximetasone Cream and Ointment Formulations Using Experiments and In Silico Modeling

The administration of therapeutic drugs through dermal routes, such as creams and ointments, has emerged as an increasingly popular alternative to traditional delivery methods, such as tablets and injections. In the context of drug development, it is crucial to identify the optimal doses and delivery routes that ensure successful outcomes. Physiologically based pharmacokinetic (PBPK) models have been proposed to simulate drug delivery and optimize drug formulations, but the calibration of these models is challenging due to the multitude of variables involved and limited experimental data. One significant research gap that this article addresses is the need for more efficient and accurate methods for calibrating PBPK models for dermal drug delivery. This manuscript presents a novel approach and an integrated dermal drug delivery model to address this gap that leverages virtual in vitro release (IVRT) and permeation (IVPT) testing data to optimize mechanistic models. The proposed approach was demonstrated through a study involving Desoximetasone cream and ointment formulations, where the release kinetics and permeation profiles of Desoximetasone were determined experimentally, and a computational model was created to simulate the results. The experimental studies showed that, even though the cumulative permeation of Desoximetasone at the end of the permeation study was comparable, there was a significant difference seen in the lag time in the permeation of Desoximetasone between the cream and ointment. Additionally, there was a significant difference seen in the amount of Desoximetasone permeated through human cadaver skin at early time points when the cream and ointment were compared. The computational model was optimized and validated, suggesting that this approach has the potential to bridge the existing research gap by improving the accuracy and efficiency of drug development processes. The model results show a good fit between the experimental data and model predictions. During the model optimization process, it became evident that there was variability in both the permeability and the partition coefficient within the stratum corneum. This variability had a significant and noteworthy influence on the overall performance of the model, especially when it came to its capacity to differentiate between cream and ointment formulations. Leveraging virtual models significantly aids the comprehension of drug release and permeation, mitigating the demanding data requirements. The use of virtual IVRT and IVPT data can accelerate the calibration of PBPK models, streamline the selection of the appropriate doses, and optimize drug delivery. Moreover, this novel approach could potentially reduce the time and resources involved in drug development, thus making it more cost-effective and efficient.

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.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Transdermal permeation of curcumin promoted by choline geranate ionic liquid: Potential for the treatment of skin diseases

The transdermal permeation of curcumin aided by choline and geranic acid ionic liquid (CAGE-IL) was addressed as a potential treatment for skin diseases. An in-depth analysis of the effect of CAGE-IL concentration in the enhancement of transdermal permeation of curcumin was performed, and the results were modelled via nonlinear regression analysis. The results obtained showed that a low percentage of CAGE-IL (viz. 2.0%, w/w) was effective in disrupting the skin structure in a transient fashion, facilitating the passage of curcumin dissolved in it.

Computational pharmaceutics - A new paradigm of drug delivery

In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.

An Integrated Biophysical Model for Predicting the Clinical Pharmacokinetics of Transdermally Delivered Compounds

The delivery of therapeutic drugs through the skin is a promising alternative to oral or parenteral delivery routes because dermal drug delivery systems (D³S) offer unique advantages such as controlled drug release over sustained periods and a significant reduction in first-pass effects, thus reducing the required dosing frequency and level of patient noncompliance. Furthermore, D³S find applications in multiple therapeutic areas, including drug repurposing. This article presents an integrated biophysical model of dermal absorption for simulating the permeation and absorption of compounds delivered transdermally. The biophysical model is physiologically/biologically inspired and combines a holistic model of healthy skin with whole-body physiology-based pharmacokinetics through dermis microcirculation. The model also includes the effects of chemical penetration enhancers and hair follicles on transdermal transport. The model-predicted permeation and pharmacokinetics of select compounds were validated using in vivo data reported in the literature. We conjecture that the integrated model can be used to gather insights into the permeation and systemic absorption of transdermal formulations (including cosmetic products) released from novel depots and optimize delivery systems. Furthermore, the model can be adapted to diseased skin with parametrization and structural adjustments specific to skin diseases.

Scalable in silico Simulation of Transdermal Drug Permeability: Application of BIOiSIM Platform

INTRODUCTION

Transdermal drug delivery is gaining popularity as an alternative to traditional routes of administration. It can increase patient compliance because of its painless and noninvasive nature, aid compounds in bypassing presystemic metabolic effects, and reduce the likelihood of adverse effects through decreased systemic exposure. In silico physiological modeling is critical to predicting dermal exposure for a therapeutic and assessing the impact of different formulations on transdermal disposition.

METHODS

The present study aimed at developing a physiologically based transdermal platform, "BIOiSIM", that could be globally applied to a wide variety of compounds to predict their transdermal disposition. The platform integrates a 16-compartment model of compound pharmacokinetics and was used to simulate and predict drug exposure of three chemically and biologically distinct drug-like compounds. Machine learning optimization was composed of two components: exhaustive search algorithm (coarse-tuning) and descent (fine-tuning) integrated with the platform used to quantitatively determine parameters influencing pharmacokinetics (eg permeability, kperm) of test compounds.

RESULTS

The model successfully predicted drug exposure (AUC, Cmax and Tmax) following transdermal application of morphine, buprenorphine and nicotine in human subjects, mostly with less than two-fold absolute average fold error (AAFE). The model was further able to successfully characterize the relationship between observed systemic exposure and intended pharmacological effect. The predicted systemic concentration of morphine and plasma levels of endogenous pain biomarkers were used to estimate the effectiveness of a given therapeutic regimen.

CONCLUSION

BIOiSIM marks a novel approach to in silico prediction that will enable leveraging of machine learning technology in the pharmaceutical space. The approach to model development outlined results in scalable, accurate models and enables the generation of large parameter/coefficient datasets from in vivo clinical data that can be used in future work to train quantitative structure activity relationship (QSAR) models for predicting likelihood of compound utility as a transdermally administered therapeutic.

Measurement of the penetration of 56 cosmetic relevant chemicals into and through human skin using a standardized protocol

OECD test guideline 428 compliant protocol using human skin was used to test the penetration of 56 cosmetic‐relevant chemicals. The penetration of finite doses (10 μL/cm²) of chemicals was measured over 24 hours. The dermal delivery (DD) (amount in the epidermis, dermis and receptor fluid [RF]) ranged between 0.03 ± 0.02 and 72.61 ± 8.89 μg/cm². The DD of seven chemicals was comparable with in vivo values. The DD was mainly accounted for by the amount in the RF, although there were some exceptions, particularly of low DD chemicals. While there was some variability due to cell outliers and donor variation, the overall reproducibility was very good. As six chemicals had to be applied in 100% ethanol due to low aqueous solubility, we compared the penetration of four chemicals with similar physicochemical properties applied in ethanol and phosphate‐buffered saline. Of these, the DD of hydrocortisone was the same in both solvents, while the DD of propylparaben, geraniol and benzophenone was lower in ethanol. Some chemicals displayed an infinite dose kinetic profile; whereas, the cumulative absorption of others into the RF reflected the finite dosing profile, possibly due to chemical volatility, total absorption, chemical precipitation through vehicle evaporation or protein binding (or a combination of these). These investigations provide a substantial and consistent set of skin penetration data that can help improve the understanding of skin penetration, as well as improve the prediction capacity of in silico skin penetration models.

The penetration of 56 chemicals was tested in human skin using a standard protocol. Dermal delivery correlated with the amount in the receptor fluid (RF). The impact of solvent on penetration was evaluated. Despite finite doses being applied, different profiles of cumulative absorption kinetics into the RF were observed. These data may help understand skin penetration and improve the prediction capacity of in silico skin penetration models.

Challenges in working towards an internal threshold of toxicological concern (iTTC) for use in the safety assessment of cosmetics: Discussions from the Cosmetics Europe iTTC Working Group workshop

The Threshold of Toxicological Concern (TTC) is an important risk assessment tool which establishes acceptable low-level exposure values to be applied to chemicals with limited toxicological data. One of the logical next steps in the continued evolution of TTC is to develop this concept further so that it is representative of internal exposures (TTC based on plasma concentration). An internal TTC (iTTC) would provide threshold values that could be utilized in exposure-based safety assessments. As part of a Cosmetics Europe (CosEu) research program, CosEu has initiated a project that is working towards the development of iTTCs that can be used for the human safety assessment. Knowing that the development of an iTTC is an ambitious and broad-spanning topic, CosEu organized a Working Group comprised a balance of multiple stakeholders (cosmetics and chemical industries, the EPA and JRC and academia) with relevant experience and expertise and workshop to critically evaluate the requirements to establish an iTTC. Outcomes from the workshop included an evaluation on the current state of the science for iTTC, the overall iTTC strategy, selection of chemical databases, capture and curation of chemical information, ADME and repeat dose data, expected challenges, as well as next steps and ongoing work.

Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.

In Depth Analysis of Pressure-Sensitive Adhesive Patch-Assisted Delivery of Memantine and Donepezil Using Physiologically Based Pharmacokinetic Modeling and in Vitro/in Vivo Correlations

The objective of this work was to evaluate the feasibility of transdermal delivery of two widely prescribed dementia drugs for the Alzheimer's disease. In this regard, the drug in adhesive patches of memantine (ME) co-loaded with donepezil (DO) was prepared using an ethylene vinyl acetate polymer and characterized for drug content, the crystallinity of drugs in the polymer matrix, and in vitro permeation. To understand the different physical and chemical processes underlying the percutaneous absorption, it is required to employ a comprehensive model that accounts for the anatomy and physiology of the skin. A transdermal physiologically based pharmacokinetic (TPBPK) model was developed and was integrated in a compartmental pharmacokinetic model to predict the plasma drug concentrations in rats. The model predictions showed a good fit with the experimental data, as evaluated by the prediction error calculated for both drugs. It was evident from the simulations that the drug diffusivity and partition coefficient in the polymer matrix are the critical parameters that affect the drug release from the vehicle and subsequently influence the in vivo pharmacokinetic profile. Moreover, a correlation function was built between the in vitro permeation data and in vivo absorption for both ME and DO. A good point-to-point in vitro/in vivo correlation (IVIVC, Level A correlation) was achieved by predicting the plasma concentrations with convolution for the entire study duration. The results of our study suggested that the implementation of mechanistic modeling along with IVIVC can be a valuable tool to evaluate the relative effects of formulation variables on the bioavailability from transdermal delivery systems.

A Measurement and Modeling Study of Hair Partition of Neutral, Cationic, and Anionic Chemicals

Various neutral, cationic, and anionic chemicals contained in hair care products can be absorbed into hair fiber to modulate physicochemical properties such as color, strength, style, and volume. For environmental safety, there is also an interest in understanding hair absorption to wide chemical pollutants. There have been very limited studies on the absorption properties of chemicals into hair. Here, an experimental and modeling study has been carried out for the hair-water partition of a range of neutral, cationic, and anionic chemicals at different pH. The data showed that hair-water partition not only depends on the hydrophobicity of the chemical but also the pH. The partition of cationic chemicals to hair increased with pH, and this is due to their electrostatic interaction with hair increased from repulsion to attraction. For anionic chemicals, their hair-water partition coefficients decreased with increasing pH due to their electrostatic interaction with hair decreased from attraction to repulsion. Increase in pH did not change the partition of neutral chemicals significantly. Based on the new physicochemical insight of the pH effect on hair-water partition, a new quantitative structure property relationship model has been proposed, taking into account of both the hydrophobic interaction and electrostatic interaction of chemical with hair fiber.

Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study

Purpose

The development of a new two-dimensional (2D) model to predict follicular permeation, with integration into a recently reported multi-scale model of transdermal permeation is presented.

Methods

The follicular pathway is modelled by diffusion in sebum. The mass transfer and partition properties of solutes in lipid, corneocytes, viable dermis, dermis and systemic circulation are calculated as reported previously [Pharm Res 33 (2016) 1602]. The mass transfer and partition properties in sebum are collected from existing literature. None of the model input parameters was fit to the clinical data with which the model prediction is compared.

Results

The integrated model has been applied to predict the published clinical data of transdermal permeation of caffeine. The relative importance of the follicular pathway is analysed. Good agreement of the model prediction with the clinical data has been obtained. The simulation confirms that for caffeine the follicular route is important; the maximum bioavailable concentration of caffeine in systemic circulation with open hair follicles is predicted to be 20% higher than that when hair follicles are blocked.

Conclusions

The follicular pathway contributes to not only short time fast penetration, but also the overall systemic bioavailability. With such in silico model, useful information can be obtained for caffeine disposition and localised delivery in lipid, corneocytes, viable dermis, dermis and the hair follicle. Such detailed information is difficult to obtain experimentally.