Gary P. Moss

Design, synthesis and characterization of captopril prodrugs for enhanced percutaneous absorption

Most drugs are designed primarily for oral administration, but the activity and stability profiles desirable for this route often make them unsuitable for transdermal delivery. We were therefore interested in designing analogues of captopril, a model drug with poor percutaneous penetration, for which the sustained steady‐state blood plasma level associated with transdermal delivery (and which is unattainable orally) would be particularly beneficial. Quantitative structure—permeability relationships (QSPRs) predicted that ester and thiol prodrug derivatives of captopril would have lower maximal transdermal flux (Jm) than the parent drug, since the increases in permeability coefficient (kp) of prodrugs would be outweighed by the reductions in aqueous solubility. Therefore, the aim of this study was to synthesize a series of prodrugs of captopril and to determine if a QSPR model could be used to design therapeutically viable prodrugs. Molecules with the highest predicted kp values were synthesized and characterized, and Jm measured in Franz diffusion cells from saturated aqueous donor across porcine skin (fresh and frozen). In‐vitro metabolism was also measured. Captopril and the prodrugs crossed the skin relatively freely, with Jm being highest for ethyl to butyl esters. Substantial first‐order metabolism of the prodrugs was observed, suggesting that their enhanced percutaneous absorption was complemented by their metabolic performance. The results suggested that QSPR models provided excellent enhancements in drug delivery. This was not seen at higher lipophilicities, suggesting that issues of solubility need to be considered in conjunction with any such use of a QSPR model.

The application and limitations of mathematical modelling in the prediction of permeability across mammalian skin and polydimethylsiloxane membranes

Objectives  Predicting the rate of percutaneous absorption of a drug is an important issue with the increasing use of the skin as a means of moderating and controlling drug delivery. One key feature of this problem domain is that human skin permeability (as Kp) has been shown to be inherently non‐linear when mathematically related to the physicochemical parameters of penetrants. As such, the aims of this study were to apply and evaluate Gaussian process (GP) regression methods to datasets for membranes other than human skin, and to explore how the nature of the dataset may influence its analysis.

Effect of occlusion on the percutaneous penetration of linoleic acid and glycerol

The effect of occlusion on the in vitro percutaneous absorption of linoleic acid was investigated. A greater skin concentration of linoleic acid from an ethanolic vehicle was observed in non-occluded experiments compared with occluded experiments (P<0.05). Such changes were not observed as consistently when ethanol was replaced with a less volatile organic solvent (cyclomethicone). These observations were attributed to the increase in the concentration gradient due to the unimpeded evaporation of volatile solvents, which provided a greater driving force and enhanced non-occluded delivery in these systems, compared with occluded systems. Conversely, the percutaneous absorption of a polar material (glycerol) from an aqueous solution did not yield any such differences. While more conclusive comparisons between volatile and non-volatile solvents and penetrants would be required to substantiate fully these comparisons, it is apparent that non-occlusion of volatile solvents may enhance percutaneous absorption. The physicochemical properties of the penetrant, for example its natural state at skin temperature (i.e. solid or liquid) may further determine the degree of enhanced percutaneous absorption compared with occluded environments.

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

Objectives The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models.

The application of Gaussian processes in the prediction of percutaneous absorption

OBJECTIVES The aim was to assess mathematically the nature of a skin permeability dataset and to determine the utility of Gaussian processes in developing a predictive model for skin permeability, comparing it with existing methods for deriving predictive models. METHODS Principal component analysis was carried out in order to determine the nature of the dataset. MatLab software was used to assess the performance of Gaussian process, single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs) using a range of statistical measures. KEY FINDINGS Principal component analysis showed that the dataset is inherently non-linear. The Gaussian process model yielded a predictive model that provides a significantly more accurate estimate of skin absorption than previous models, particularly QSPRs (which were consistently worse than Gaussian process or SLN models), and does so across a wider range of molecular properties. Gaussian process models appear particularly capable of providing excellent predictions where previous studies have shown QSPRs to fail, such as where penetrants have high log P and high molecular weight. CONCLUSIONS A non-linear approach was more appropriate than QSPRs or SLNs for the analysis of the dataset employed herein, as the prediction and confidence values in the prediction given by the Gaussian process are better than with other methods examined. Gaussian process provides a novel way of analysing skin absorption data that is substantially more accurate, statistically robust and reflective of our empirical understanding of skin absorption than the QSPR methods so far applied to skin absorption.

An investigation into solvent-membrane interactions when assessing drug release from organic vehicles using regenerated cellulose membranes.

The influence of organic solvents on artificial membranes when assessing drug release from topical formulations is, generally, poorly characterised yet current guidelines require no characterisation of the membrane before, during or after an experiment. Therefore, the aim of this study was to determine the effect of solvent‐membrane interactions when using in‐vitro Franz cell methods for the assessment of corticosteroid release and to assess compliance or otherwise with Higuchis equation. The rate of beclometasone dipropionate monohydrate (BDP) and betamethasone 17‐valerate (BMV) release across a regenerated cellulose membrane (RCM), from both saturated solutions and commercial formulations, was determined. Increasing the ratio of organic solvent, compared with aqueous phase, in the donor fluid (DF) resulted in up to a 416‐fold increase in steady‐state flux. Further, alterations in the receiver fluid (RF) composition caused, in some cases, 337‐fold increases in flux. Analysis indicated that the RCM remained chemically unchanged, that its pore size remained constant and that no drug partitioned into the membrane, regardless of the DF or RF employed. However, it was observed that the organic solvents had a thinning effect on the RCM, resulting in enhanced flux, which was potentially due to the variation in the diffusional path length. Such findings raise issues of the veracity of data produced from any membrane release study involving a comparison of formulations with differing solvent content.

Discriminant analysis as a tool to identify compounds with potential as transdermal enhancers

Structure‐activity relationships were sought for 73 enhancers of hydrocortisone permeation from propylene glycol across hairless mouse skin. Enhancers had chain lengths (CC) from 0 to 16 carbon atoms, 1 to 8 H‐bonding atoms (HB), molecular weight 60 to 450, log P (calculated) −1.7 to 9.7 and log S (calculated) −7.8 to 0.7. These predictive properties were chosen because of their ready availability. Enhancement ratio (ER) was defined as hydrocortisone transferred after 24 h relative to control. Values for the ER ranged from 0.2 to 25.3. Multiple regression analysis failed to predict activity; ER values for the ‘good’ enhancers (ER>10) were underestimated. Simple guidelines suggested that high ER was associated with CC>12 and HB 2–5. This was refined by multivariate analysis to identify significant predictors. Discriminant analysis using CC, HB, and molecular weight correctly assigned 11 of the 12 ‘good’ enhancers (92%). The incorrectly assigned compound was a known, idiosyncratic Br compound. Seventeen of the 61 ‘poor’ enhancers (28%) were incorrectly assigned but four could be considered marginal (ER>8). The success of this simple approach in identifying potent enhancers suggested its potential in predicting novel enhancer activity.

Distribution and Visualisation of Chlorhexidine Within the Skin Using ToF-SIMS: A Potential Platform for the Design of More Efficacious Skin Antiseptic Formulations

ABSTRACTPurposeIn order to increase the efficacy of a topically applied antimicrobial compound the permeation profile, localisation and mechanism of action within the skin must first be investigated.MethodsTime-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to visualise the distribution of a conventional antimicrobial compound, chlorhexidine digluconate, within porcine skin without the need for laborious preparation, radio-labels or fluorescent tags.ResultsHigh mass resolution and high spatial resolution mass spectra and chemical images were achieved when analysing chlorhexidine digluconate treated cryo-sectioned porcine skin sections by ToF-SIMS. The distribution of chlorhexidine digluconate was mapped throughout the skin sections and our studies indicate that the compound appears to be localised within the stratum corneum. In parallel, tape strips taken from chlorhexidine digluconate treated porcine skin were analysed by ToF-SIMS to support the distribution profile obtained from the skin sections.ConclusionsToF-SIMS can act as a powerful complementary technique to map the distribution of topically applied compounds within the skin.

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.

The application of stochastic machine learning methods in the prediction of skin penetration

Abstract: Improving predictions of skin permeability is a significant problem for which mathematical solutions have been sought for around twenty years. However, the current approaches are limited by the nature of the models chosen and the nature of the dataset. This is an important problem, particularly with the increased use of transdermal and topical drug delivery systems. In this work, we apply K-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the skin permeability coefficient of penetrants. A considerable improvement, both statistically and in terms of the accuracy of predictions, over the current quantitative structure-permeability relationships (QSPRs) was found. Gaussian processes provided the most accurate predictions, when compared to experimentally generated results. It was also shown that using five molecular descriptors - molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups - can produce better predictions than when using only lipophilicity and the molecular weight, which is an approach commonly found with QSPRs. The Gaussian process regression with five compound features was shown to give the best performance in this work. Therefore, Gaussian processes would appear to provide a viable alternative to the development of predictive models for skin absorption and underpin more realistically mechanistic understandings of the physical process of the percutaneous absorption of exogenous chemicals.