Mechanistic and empirical modeling of skin permeation of drugs

The role of artificial intelligence and data science in nanoparticles development: a review

Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This review discusses the current data science methodologies related to polymeric drug-loaded nanoparticle production from an innovative multidisciplinary perspective while considering the strictest data science practices. Several methodological and data interpretation flaws were identified by analyzing the few qualified ML studies. Most issues lie in following appropriate analysis steps, such as cross-validation, balancing data, or testing alternative models. Thus, better-planned studies following the recommended data science analysis steps along with adequate numbers of experiments would change the current landscape, allowing the exploration of the full potential of ML.

Development, validation and application of a gas chromatography method for the determination of dillapiole from Piper aduncum essential oil in skin permeation samples

The essential oil extracted from the leaves of Piper aduncum has antifungal, insecticidal and antibacterial activity. Studies with its main compound, dillapiole (DIL) revealed antibacterial and anti-inflammatory potential. Despite all this bioactivity, there is no updated report on the development and validation of analytical and bioanalytical methodology to quantify DIL in skin samples. A selective, precise, accurate and adequate method for the determination of DIL in solutions, porcine ear skin samples and receptor fluid was developed and validated by headspace extraction-gas chromatography with flame ionization detection (HS-GC-FID). HS-GC-FID was applied to determine DIL in Franz cell permeation and retention studies using porcine ear skin samples. In the HS-GC-FID method, matrix-related interferences were not observed at the peak of the DIL retention time. The results showed a high recovery (>97%) after the extraction procedure, allowing the quantification of DIL in complex matrices. In vitro permeation/retention for DIL showed cumulative amounts permeated in the order: receptor fluid (21.98 ± 1.19 μg/cm² ) > epidermis (15.40 ± 1.20 μg/cm² ) > dermis (9.52 ± 1.13 μg/cm² ). HS-GC-FID was successfully validated and the results point to DIL transdermal permeation and to the potential to develop pharmaceutical formulations for skin delivery to treat inflammation or infections.

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

Yeast cell microcarriers for delivery of a model bioactive compound in skin

This study aimed at developing a cell-based encapsulation carrier for topical delivery of bioactives to the skin. The overall objectives were to evaluate affinity of the yeast-cell based carrier to bind to the skin surface following topical application and to quantify controlled release of curcumin as a model bioactive in ex-vivo skin models using a combination of imaging, modeling and analytical measurements. Both porcine skin tissue and clinically obtained human skin biopsies were studied. The results demonstrated that upon incubation with the ex-vivo skin tissues, the cell carriers rapidly bound to the skin surface following topical delivery and provided sustained release of encapsulated curcumin. The microcarrier binding and penetration of curcumin in the dermal compartment also showed to increase with incubation time. The average flux of curcumin in human skin biopsies J_p was 0.89 ± 0.02 μg/cm²/h. These results illustrated the potential of a novel cell-based carrier for high affinity binding to skin surface, efficient encapsulation of a model bioactive and controlled release from the cell carrier to the skin with enhanced permeation to the dermis section. Overall, this study demonstrated a new class of cost-effective carriers for improving delivery of bioactives to the skin and potentially other epithelial tissues.

Physicochemical properties determining drug detection in skin

Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the receiver operating characteristic curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations (SHAP) model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2561 US Food and Drug Administration (FDA)‐approved drugs, we predict that therapeutic drug classes, such as nervous system drugs, are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.

Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials

The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood–brain distribution (log BB), fraction unbounded in the brain (f_u,brain), water-skin permeation (log K_p), binding to human plasma proteins (log K_a,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing.

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.

Role of pharmacokinetic consideration for the development of drug delivery systems: A historical overview

Drug delivery system is defined as a system or technology to achieve optimum therapeutic effects of drugs through precise control of their movements in the body. In order to optimize function of drug delivery systems aiming at targeting, their whole-body distribution profiles should be systematically evaluated and analyzed, where pharmacokinetic analysis based on the clearance concepts plays important role. Organ perfusion experiments combined with statistical moment analysis further supply detailed information on drug disposition at organ and cellular levels. Based on general relationship between physicochemical properties and distribution profile, macromolecular prodrugs or polymer conjugates of proteins are rationally designed and further introduction of ligand structure brings cell-specific delivery for them. These approaches are also applicable for particulate carriers such as liposomes and offer various opportunities for biological drugs such as nucleic acid drugs for their delivery. Mechanistic approach for dermal absorption analysis based on physiological skin model offers another opportunity in rational design of drug delivery. Potential of drug delivery technology in future medicines such as cell therapy and nanomaterial platform application is further discussed in relation to pharmacokinetic consideration.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

A Theoretical Study on Inhibition of Melanoma with Controlled and Targeted Delivery of siRNA via Skin Using SPACE-EGF

Melanoma is a potentially lethal skin cancer with high mortality rate. Recently, the peptide-mediated transdermal delivery of small interference RNA (siRNA) emerges as a promising strategy to treat melanoma by inducing the apoptosis of tumor cells, but the related theoretical model describing the delivery of siRNA under the effect of SPACE-EGF, the growth inhibition of melanoma and the dynamic expanding of the bump on the skin due to the growth of melanoma has not been reported yet. In this article, a theoretical model is developed to describe the percutaneous siRNA delivery mediated by SPACE-EGF to melanoma and the growth inhibition of melanoma. The results present the spatial-temporal distribution of siRNA and the growth of melanoma under the inhibition of siRNA, which shows a good consistency with the experimental results. In addition, this model represents the uplift process of tumors on the skin surface. The model presented here is a useful tool to understand the whole process of the SPACE-EGF-mediated delivery of the siRNA to melanoma through skin, to predict the therapeutic effect, and to optimize the therapeutic strategy, providing valuable references for the treatment of melanoma.

Antioxidant Effect of Nanoemulsions Containing Extract of Achyrocline satureioides (Lam) D.C.-Asteraceae

Ethanolic extracts of Achyrocline satureioides have pronounced antioxidant activity mainly due to the presence of the flavonoid quercetin. However, direct topical application of the extract is not possible due to the presence of high amounts of ethanol. In this sense, nanoemulsions arise as an alternative for topical formulation associating molecules with limited aqueous solubility. This article describes the development of topical nanoemulsions containing either A. satureioides extract or one of its most abundant flavonoid, quercetin. Nanoemulsions composed of octyldodecanol, egg lecithin, water and extract (NEE), or quercetin (NEQ) were prepared by spontaneous emulsification. This process led to monodisperse nanoemulsions presenting a mean droplet size of approximately 200-300 nm, negative zeta potential, and high association efficiency. A study of quercetin skin retention using porcine skin which was performed using a Franz diffusion cell revealed a higher accumulation of quercetin in skin for NEE when compared to NEQ. Finally, the antioxidant activity of formulations was measured by thiobarbituric acid-reactive species and the APPH model. A lower lipoperoxidation for the extract in respect to quercetin solution was observed. However, no difference between NEQ and NEE lipoperoxidation could be seen. The protection against lipoperoxidation by the formulations was also measured in the skin, where lower formation of reactive species was observed after treatment with NEE. In conclusion, this study shows the formulation effect on the physicochemical properties of nanoemulsions as well as on the skin retention and antioxidant activity of quercetin.

Effect of borneol on the transdermal permeation of drugs with differing lipophilicity and molecular organization of stratum corneum lipids

The aim of the present paper was to investigate the promoting activity of borneol on the transdermal permeation of drugs with differing lipophilicity, and probe its alterations in molecular organization of stratum corneum (SC) lipids. The toxicity of borneol was evaluated in epidermal keratinocyte HaCaT and dermal fibroblast CCC-HSF-1 cell cultures and compared to known enhancers, and its irritant profile was also assessed by transepidermal water loss (TEWL) evaluation. The promoting effect of borneol on the transdermal permeation of five model drugs, namely 5-fluorouracil, antipyrine, aspirin, salicylic acid and ibuprofen, which were selected based on their lipophilicity denoted by logp value, were performed using in vitro skin permeation studies. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) was employed to monitor the borneol-induced alteration in molecular organization of SC lipids. The enhancer borneol displayed lower cytotoxicity or irritation in comparison to the well-established and standard enhancer Azone. Borneol could effectively promote the transdermal permeation of five model drugs, and its enhancement ratios were found to be parabolic curve with the logp values of drugs, which exhibited the optimum permeation activity for relatively hydrophilic drugs (an estimated logp value of -0.5 ∼0.5). The molecular mechanism studies suggested that borneol could perturb the structure of SC lipid alkyl chains, and extract part of SC lipids, resulting in the alteration in the skin permeability barrier.

The application of anethole, menthone, and eugenol in transdermal penetration of valsartan: Enhancement and mechanistic investigation

CONTEXT

The main barrier for transdermal delivery is the obstacle property of the stratum corneum. Many types of chemical penetration enhancers have been used to breach the skin barrier; among the penetration enhancers, terpenes are found as the most highly advanced, safe, and proven category.

OBJECTIVE

In the present investigation, the terpenes anethole, menthone, and eugenol were used to enhance the permeation of valsartan through rat skin in vitro and their enhancement mechanism was investigated.

MATERIALS AND METHODS

Skin permeation studies of valsartan across rat skin in the absence and the presence of terpenes at 1% w/v, 3% w/v, and 5% w/v in vehicle were carried out using the transdermal diffusion cell sampling system across rat skin and samples were withdrawn from the receptor compartment at 1, 2, 3, 4, 6, 8, 10, 12, and 24 h and analysed for drug content by the HPLC method. The mechanism of skin permeation enhancement of valsartan by terpenes treatment was evaluated by Fourier transform infrared spectroscopy (FTIR) analysis and differential scanning calorimetry (DSC).

RESULTS

All the investigated terpenes provided a significant (p < 0.01) enhancement in the valsartan flux at a concentration of 1%, and less so at 3% and 5%. The effectiveness of terpenes at 1% concentration was in the following order: anethole > menthone > eugenol with 4.4-, 4.0-, and 3.0-fold enhancement ratio over control, respectively. DSC study showed that the treatment of stratum corneum with anethole shifted endotherm down to lower melting point while FTIR studies revealed that anethole produced maximum decrease in peak height and area than other two terpenes.

CONCLUSION

The investigated terpenes can be successfully used as potential enhancers for the enhancement of skin permeation of lipophilic drug.

Mathematical models for dermal drug absorption

INTRODUCTION

Mathematical models of dermal transport offer the advantages of being much faster and less expensive than in vitro or in vivo studies. The number of methods used to create such models has been increasing rapidly, probably due to the steady rise in computational power. Although each of the various approaches has its own virtues and limitations, it may be difficult to decide which approach is best suited to address a given problem.

AREAS COVERED

Here we outline the basic ideas, drawbacks and advantages of compartmental and quantitative structure-activity relationship models, as well as of analytical and numerical approaches for solving the diffusion equation. Examples of special applications of the different approaches are given.

EXPERT OPINION

Although some models are sophisticated and might be used in future to predict transport through damaged or diseased skin, the comparatively low availability of suitable and accurate experimental data limits extensive usage of these models and their predictive accuracy. Due to the lack of experimental data, the possibility of validating mathematical models is limited.

Essential oil from Zanthoxylum bungeanum Maxim. and its main components used as transdermal penetration enhancers: a comparative study

Our previous studies had confirmed that the essential oil from Zanthoxylum bungeanum Maxim. (Z. bungeanum oil) could effectively enhance the percutaneous permeation of drug molecules as a natural transdermal penetration enhancer. The aim of the present study is to investigate and compare the skin penetration enhancement effect of Z. bungeanum oil and its main components on traditional Chinese medicine (TCM) active components. Toxicities of Z. bungeanum oil and three selected terpene compounds (terpinen-4-ol, 1,8-cineole, and limonene) in epidermal keratinocytes (HaCaT) and dermal fibroblast (CCC-ESF-1) cell lines were measured using an MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Five model drugs in TCM external preparations, namely osthole (OT), tetramethylpyrazine (TMP), ferulic acid (FA), puerarin (PR), and geniposide (GP), which were selected based on their lipophilicity denoted by logKo/w, were tested using in vitro permeation studies in which vertical Franz diffusion cells and rat abdominal skin were employed. The secondary structure changes of skin stratum corneum (SC) and drug thermodynamic activities were investigated to understand their mechanisms of action using Fourier transform infrared (FTIR) spectroscopy and saturation solubility studies, respectively. It was found that Z. bungeanum oil showed lower toxicities in both HaCaT cells and CCC-ESF-1 cells compared with three terpene compounds used alone. The enhancement permeation capacities by all tested agents were in the following increasing order: terpinen-4-ol≈1,8-cineole Collapse

Key Words

• Essential oil
• Fourier transform infrared (FTIR) spectroscopy
• HaCaT
• Limonene
• Penetration enhancer
• Zanthoxylum bungeanum Maxim.

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MESH Headings

• Administration, Cutaneous
• Animals
• In Vitro Techniques
• Male
• Oils, Volatile/chemistry
• Oils, Volatile/pharmacology
• Plant Oils/chemistry
• Plant Oils/pharmacology
• Rats
• Rats, Sprague-Dawley
• Skin Absorption/drug effects
• Skin Absorption/physiology
• Terpenes/analysis
• Terpenes/chemistry
• Zanthoxylum/chemistry

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Grants Collapse

Affiliation(s)

Yi Lan
Hui Li
Yan-yan Chen
Ye-wen Zhang
Na Liu
Qing Zhang
Qing Wu †E-mail:

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Skin permeation of organic gunshot residue: implications for sampling and analysis

Traditional gunshot residue (GSR) analysis is based on detection of particulates formed from metals found in the primer. Recent concerns regarding the interpretation of GSR evidence has led to interest in alternatives such as the organic constituents (organic gunshot residue, OGSR) found in propellants. Previous work has shown OGSR to be detectable on hands for several hours after a firing event, and given the lipophilic nature of these compounds, it was expected that losses due to secondary transfer (an issue with GSR particulates) would be negligible. However, other loss mechanisms have been identified, specifically skin permeation and evaporation. This paper describes experimental and modeling studies used to elucidate characteristics of skin permeation of 5 compounds present in OGSR. Pharmaceutical methods were adapted to characterize skin permeation using a skin surrogate and Franz diffusion cells. The amount of compounds deposited on skin after an authentic firing event (1 and 2 shots) was experimentally determined and applied for the permeation experiments. A fully validated selected ion monitoring GC/MS method was developed for quantitative analysis, and easily accessible online tools were employed for modeling. Results showed that OGSR residues should be detectable on skin for many hours after a firing event of as few as one or two shots, with detection capability being a function of the efficacy of sampling and sample preparation and the instrumental method employed. The permeation rates of the OGSR compounds were sufficiently different to suggest the potential to develop methods to approximate time-since-deposition.

Cross-validation of predictive models for deoxynivalenol in wheat at harvest

To date, several models that predict deoxynivalenol (DON) in wheat at harvest are available. This study aimed to evaluate the performance of two of such models, including a mechanistic model developed in Italy and an empirical model developed in the Netherlands. To this end, field data collected in the periods 2002-2004 and 2009-2011 in Italy, and in the period 2001-2010 in the Netherlands were used. These historical data covered farm observations at 1,306 wheat fields, of which 155 in the Netherlands and 1,151 in Italy. A subset of 10% of the Italian data, derived by random sampling from the total Italian dataset, was used to validate both the Italian and the Dutch model. Additionally, the Italian mechanistic model was validated using the total Dutch dataset. Before validating the Dutch model, it was recalibrated using the remaining 90% of the Italian data. Results showed that predictions of both modelling approaches (mechanistic and empirical) for independent wheat fields were in accordance. Applying a threshold for DON concentration of 1,250 ?g/kg, the mechanistic DON model predicted 90% of the samples correctly. Results for cross-validation of the mechanistic DON model and the recalibrated empirical DON model showed that 93% of the samples were correctly predicted. In general, no more than 6% of underestimates were observed.

Linear and nonlinear quantitative structure-property relationship modelling of skin permeability

In this work, quantitative structure-property relationship (QSPR) models were developed to estimate skin permeability based on theoretically derived molecular descriptors and a diverse set of experimental data. The newly developed method combining modified particle swarm optimization (MPSO) and multiple linear regression (MLR) was used to select important descriptors and develop the linear model using a training set of 225 compounds. The adaptive neuro-fuzzy inference system (ANFIS) was used as an efficient nonlinear method to correlate the selected descriptors with experimental skin permeability data (log Kp). The linear and nonlinear models were assessed by internal and external validation. The obtained models with three descriptors show good predictive ability for the test set, with coefficients of determination for the MPSO-MLR and ANFIS models equal to 0.874 and 0.890, respectively. The QSPR study suggests that hydrophobicity (encoded as log P) is the most important factor in transdermal penetration.

Effect of phonophoresis on skin permeation of commercial anti-inflammatory gels: sodium diclofenac and ketoprofen

This study evaluated the use of ultrasound in combination with the commercial anti-inflammatory drugs ketoprofen and sodium diclofenac, according to the parameters used in physiotherapy. Ketoprofen and sodium diclofenac were used in the Franz diffusion cell model adapted to an ultrasound transducer in three conditions: no ultrasound, one application of ultrasound and two applications of ultrasound. High-performance liquid chromatography was used to quantify the total amount of drug permeating skin per unit area, as well as flux and latency. The results showed that for ketoprofen, the amount of drug permeating skin and flux increased with two ultrasound applications. Permeation of sodium diclofenac decreased in the presence of ultrasound. Ultrasound parameters and drug properties must be considered in the use of phonophoresis.

Design and performance of a spreadsheet-based model for estimating bioavailability of chemicals from dermal exposure

A comprehensive transient model of chemical penetration through the stratum corneum, viable epidermis and dermis formulated in terms of an Excel™ spreadsheet and associated add-in is presented. The model is a one-dimensional homogenization of underlying microscopic transport models for stratum corneum and dermis; viable epidermis is treated as unperfused dermis. The model's salient features are a detailed structural description of the skin layers, a combination of first-principles based transport equations and empirical partition and diffusion coefficients, and the capability of simulating a variety of exposure scenarios. Model predictions are compared with representative in vitro skin permeation data obtained from the literature using as summary parameters total absorption (Q(abs)), maximum flux (J(max)) and skin permeability coefficient (k(p)). The results of this evaluation demonstrate the current state-of-the-art in prediction of transient skin absorption and highlight areas in which further elaborations are needed to obtain satisfactory predictions.

An evaluation of the potential of linear and nonlinear skin permeation models for the prediction of experimentally measured percutaneous drug absorption

Objectives

The developments in combinatorial chemistry have led to a rapid increase in drug design and discovery and, ultimately, the production of many potential molecules that require evaluation. Hence, there has been much interest in the use of mathematical models to predict dermal absorption. Therefore, the aim of this study was to test the performance of both linear and nonlinear models to predict the skin permeation of a series of 11 compounds.

Methods

The modelling in this study was carried out by the application of both quantitative structure permeability relationships and Gaussian process-based machine learning methods to predict the flux and permeability coefficient of the 11 compounds. The actual permeation of these compounds across human skin was measured using Franz cells and a standard protocol with high performance liquid chromatography analysis. Statistical comparison between the predicted and experimentally-derived values was performed using mean squared error and the Pearson sample correlation coefficient.

Key findings

The findings of this study would suggest that the models failed to accurately predict permeation and in some cases were not within two- or three-orders of magnitude of the experimentally-derived values. However, with this set of compounds the models were able to effectively rank the permeants.

Conclusions

Although not suitable for accurately predicting permeation the models may be suitable for determining a rank order of permeation, which may help to select candidate molecules for in-vitro screening. However, it is important to note that such predictions need to take into account actual relative drug candidate potencies.

Magnetophoresis in combination with chemical enhancers for transdermal drug delivery

PURPOSE

The objective of the present work was to investigate the effect of combination of a novel physical permeation enhancement technique, magnetophoresis with chemical permeation enhancers on the transdermal delivery of drugs.

METHODS

The in vitro drug transport studies were carried out across the freshly excised abdominal skin of Sprague-Dawley rats using transdermal patch systems (magnetophoretic and non-magnetophoretic) of lidocaine hydrochloride (LH). LH gel prepared using hydroxypropyl methylcellulose (HPMC) was spread over the magnets as a thin layer. To investigate the effect of chemical permeation enhancers, menthol, dimethyl sulfoxide, sodium lauryl sulfate and urea (5% w/v) were incorporated in the gels prior to loading on the patch system.

RESULTS

The flux of lidocaine from magnetophoretic patch was ~3-fold higher (3.07 ± 0.43 µg/cm(2)/h) than that of the control (non-magnetophoretic patch) (0.94 ± 0.13 µg/cm(2)/h). Incorporation of chemical permeation enhancers in the gel enhanced the magnetophoretic delivery flux by ~4 to 7-fold.

CONCLUSIONS

The enhancement factor due to combination of chemical permeation enhancer was additive and not synergistic. Mechanistic studies indicated that magnetophoresis mediated drug delivery enhancement was via appendageal pathway.

DDSolver: an add-in program for modeling and comparison of drug dissolution profiles

In recent years, several mathematical models have been developed for analysis of drug dissolution data, and many different mathematical approaches have been proposed to assess the similarity between two drug dissolution profiles. However, until now, no computer program has been reported for simplifying the calculations involved in the modeling and comparison of dissolution profiles. The purposes of this article are: (1) to describe the development of a software program, called DDSolver, for facilitating the assessment of similarity between drug dissolution data; (2) to establish a model library for fitting dissolution data using a nonlinear optimization method; and (3) to provide a brief review of available approaches for comparing drug dissolution profiles. DDSolver is a freely available program which is capable of performing most existing techniques for comparing drug release data, including exploratory data analysis, univariate ANOVA, ratio test procedures, the difference factor f (1), the similarity factor f (2), the Rescigno indices, the 90% confidence interval (CI) of difference method, the multivariate statistical distance method, the model-dependent method, the bootstrap f (2) method, and Chow and Ki's time series method. Sample runs of the program demonstrated that the results were satisfactory, and DDSolver could be served as a useful tool for dissolution data analysis.

A theoretical model for transdermal drug delivery from emulsions and its dependence upon formulation

This article presents a theoretical model of transdermal drug delivery from an emulsion-type vehicle that addresses the vehicle heterogeneity and incorporates the prediction of drug transport parameters as function of the vehicle composition. The basic mass transfer model considers interfacial and diffusion resistances within the emulsion and partition/diffusion phenomena across two skin compartments in series. Drug transport parameters are predicted as follows: partition coefficients are derived from regular solutions theory, drug diffusivity in the continuous phase is computed from a free volume theory with segmental motion, and permeability of the surfactant layer around droplets is estimated based on a free surface area model. These relationships are incorporated within the basic mass transfer model, so that the overall model is able to predict temporal profiles of drug release from the vehicle and of drug concentration in plasma, as a function of vehicle composition. In this way, the proposed model provides a sound physicochemical basis to support the development of new formulations and the planning of experiments. A simulated case study regarding a nitroglycerin ointment is presented in detail, illustrating how thermodynamic and kinetic factors inherent to the emulsion vehicle can modulate drug release and subsequent systemic absorption.

In vitro percutaneous permeation and skin accumulation of finasteride using vesicular ethosomal carriers

In order to develop a novel transdermal drug delivery system that facilitates the skin permeation of finasteride encapsulated in novel lipid-based vesicular carriers (ethosomes)finasteride ethosomes were constructed and the morphological characteristics were studied by transmission electron microscopy. The particle size, zeta potential and the entrapment capacity of ethosome were also determined. In contrast to liposomes ethosomes were of more condensed vesicular structure and they were found to be oppositely charged. Ethosomes were found to be more efficient delivery carriers with high encapsulation capacities. In vitro percutaneous permeation experiments demonstrated that the permeation of finasteride through human cadaver skin was significantly increased when ethosomes were used. The finasteride transdermal fluxes from ethosomes containing formulation (1.34 +/- 0.11 microg/cm(2)/h) were 7.4, 3.2 and 2.6 times higher than that of finasteride from aqueous solution, conventional liposomes and hydroethanolic solution respectively (P < 0.01).Furthermore, ethosomes produced a significant (P < 0.01) finasteride accumulation in the skin, especially in deeper layers, for instance in dermis it reached to 18.2 +/- 1.8 microg/cm(2). In contrast, the accumulation of finasteride in the dermis was only 2.8 +/- 1.3 microg/cm(2) with liposome formulation. The study demonstrated that ethosomes are promising vesicular carriers for enhancing percutaneous absorption of finasteride.

Percutaneous permeation enhancement by terpenes: mechanistic view

A popular approach for improving transdermal drug delivery involves the use of penetration enhancers (sorption promoters or accelerants) which penetrate into skin to reversibly reduce the barrier resistance. The potential mechanisms of action of penetration enhancers include disruption of intercellular lipid and/or keratin domains and tight junctions. This results in enhanced drug partitioning into tissue, altered thermodynamic activity/solubility of drug etc. Synthetic chemicals (solvents, azones, pyrrolidones, surfactants etc.) generally used for this purpose are rapidly losing their value in transdermal patches due to reports of their absorption into the systemic circulation and subsequent possible toxic effect upon long term application. Terpenes are included in the list of Generally Recognized As Safe (GRAS) substances and have low irritancy potential. Their mechanism of percutaneous permeation enhancement involves increasing the solubility of drugs in skin lipids, disruption of lipid/protein organization and/or extraction of skin micro constituents that are responsible for maintenance of barrier status. Hence, they appear to offer great promise for use in transdermal formulations. This article is aimed at reviewing the mechanisms responsible for percutaneous permeation enhancement activity of terpenes, which shall foster their rational use in transdermal formulations.

An evaluation of mathematical models for predicting skin permeability

A number of mathematical models have been proposed for predicting skin permeability, mostly empirical and very few are deterministic. Early empirical models use simple lipophilicity parameters. The recent trend is to use more complicated molecular structure descriptors. There has been much debate on which models best predict skin permeability. This article evaluates various mathematical models using a comprehensive experimental dataset of skin permeability for 124 chemical compounds compiled from various sources. Of the seven models compared, the deterministic model of Mitragotri gives the best prediction. The simple quantitative structure permeability relationships (QSPR) model of Potts and Guy gives the second best prediction. The two models have many features in common. Both assume the lipid matrix as the pathway of transdermal permeation. Both use octanol-water partition coefficient and molecular size. Even the mathematical formulae are similar. All other empirical QSPR models that use more complicated molecular structure descriptors fail to provide satisfactory prediction. The molecular structure descriptors in the more complicated QSPR models are empirically related to skin permeation. The mechanism on how these descriptors affect transdermal permeation is not clear. Mathematically it is an ill-defined approach to use many colinearly related parameters rather than fewer independent parameters in multi-linear regression.

Formulation development of transdermal dosage forms: Quantitative structure-activity relationship model for predicting activities of terpenes that enhance drug penetration through human skin

Terpenes and terpenoids have been used as enhancers in transdermal formulations for facilitating penetration of drugs into human skin. Knowledge of the correlation between the human skin penetration effect (HSPE) and the physicochemical properties of these enhancers is important for facilitating the discovery and development of more enhancers. In this work, the HSPE of 49 terpenes and terpenoids were compared by the in vitro permeability coefficients of haloperidol (HP) through excised human skin. A first-order multiple linear regression (MLR) model was constructed to link the permeability coefficient of the drug to the lipophilicity, molecular weight, boiling point, the terpene type and the functional group of each enhancer. The Quantitative Structure-Activity Relationship (QSAR) model was derived from our data generated by using standardized experimental protocols, which include: HP in propylene glycol (PG) of 3 mg/ml as the donor solution containing 5% (w/v) of the respective terpene, the same composition and volume of receptor solution, similar human skin samples, in the same set of automated flow-through diffusion cells. The model provided a simple method to predict the enhancing effects of terpenes for drugs with physicochemical properties similar to HP. Our study suggested that an ideal terpene enhancer should possess at least one or combinations of the following properties: hydrophobic, in liquid form at room temperature, with an ester or aldehyde but not acid functional group, and is neither a triterpene nor tetraterpene. Possible mechanisms revealed by the QSAR model were discussed.

A Fully Computational Model for Predicting Percutaneous Drug Absorption

The prediction of transdermal absorption for arbitrary penetrant structures has several important applications in the pharmaceutical industry. We propose a new data-driven, predictive model for skin permeability coefficients k(p) based on an ensemble model using k-nearest-neighbor models and ridge regression. The model was trained and validated with a newly assembled data set containing experimental data and structures for 110 compounds. On the basis of three purely computational descriptors (molecular weight, calculated octanol/water partition coefficient, and solvation free energy), we have developed a model allowing for the reliable, purely computational prediction of skin permeability coefficients. The model is both accurate and robust, as we showed in an extensive validation (correlation coefficient for leave-one-out cross validation: Q = 0.948, mean standard error: 0.2 for log k(p)).

Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks

The purpose of this work was to develop artificial neural networks (ANN) models to predict in vitro release kinetics of doxorubicin (Dox) delivered by sulfopropyl dextran ion-exchange microspheres. Four ANN models for responses at different time points were developed to describe the release profiles of Dox. Model selection was performed using the Akaike information criterion (AIC). Sixteen data sets were used to train the ANN models and two data sets for the validation. Good correlations were obtained between the observed and predicted release profiles for the two randomly selected validation data sets. The difference factor (f1) and similarity factor (f2) between the ANN predicted and the observed release profiles indicated good performance of the ANN models. The established models were then applied to predict release kinetics of Dox from the microspheres of various initial loadings in media of different ionic strengths and NaCl/CaCl2 ratios. The results suggested that ANN offered a flexible and effective approach to predicting the kinetics of Dox release from the ion-exchange microspheres.