Structure-permeability Relationships Research Articles

Identification of nonabsorbable inhibitors of the scavenger receptor-BI (SR-BI) for tissue-specific administration

The identification of a low-permeability scavenger receptor BI (SR-BI) inhibitor starting from the ITX-5061 template is described. Structure–activity and structure–permeability relationships were assessed for analogs leading to the identification of compound 8 as a potent and nonabsorbable SR-BI inhibitor.

Ceramides with a pentadecasphingosine chain and short acyls have strong permeabilization effects on skin and model lipid membranes

The composition and organization of stratum corneum lipids play an essential role in skin barrier function. Ceramides represent essential components of this lipid matrix; however, the importance of the individual structural features in ceramides is not fully understood. To probe the structure–permeability relationships in ceramides, we prepared analogs of N-lignoceroylsphingosine with shortened sphingosine (15 and 12 carbons) and acyl chains (2, 4 and 6 carbons) and studied their behavior in skin and in model lipid membranes. Ceramide analogs with pentadecasphingosine (15C) chains were more barrier-perturbing than 12C- and 18C-sphingosine ceramides; the greatest effects were found with 4 to 6C acyls (up to 15 times higher skin permeability compared to an untreated control and up to 79 times higher permeability of model stratum corneum lipid membranes compared to native very long-chain ceramides). Infrared spectroscopy using deuterated lipids and X-ray powder diffraction showed surprisingly similar behavior of the short ceramide membranes in terms of lipid chain order and packing, phase transitions and domain formation. The high- and low-permeability membranes differed in their amide I band shape and lamellar organization. These skin and membrane permeabilization properties of some short ceramides may be explored, for example, for the rational design of permeation enhancers for transdermal drug delivery.

Quantitative structure–permeability relationships at various pH values for acidic and basic drugs and drug-like compounds

Absorption in gastrointestinal tract compartments varies and is largely influenced by pH. Therefore, considering pH in studies and analyses of membrane permeability provides an opportunity to gain a better understanding of the behaviour of compounds and to obtain good permeability estimates for prediction purposes. This study concentrates on relationships between the chemical structure and membrane permeability of acidic and basic drugs and drug-like compounds. The membrane permeability of 36 acidic and 61 basic compounds was measured using the parallel artificial membrane permeability assay (PAMPA) at pH 3, 5, 7.4 and 9. Descriptive and/or predictive single-parameter quantitative structure-permeability relationships were derived for all pH values. For acidic compounds, membrane permeability is mainly influenced by hydrogen bond donor properties, as revealed by models with r2 > 0.8 for pH 3 and pH 5. For basic compounds, the best (r2 > 0.7) structure–permeability relationships are obtained with the octanol–water distribution coefficient for pH 7.4 and pH 9, indicating the importance of partition properties. In addition to the validation set, the prediction quality of the developed models was tested with folic acid and astemizole, showing good matches between experimental and calculated membrane permeabilities at key pHs. Selected QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.166).

Recent developments in skin mimic systems to predict transdermal permeation.

In recent years there has been a drive to create experimental techniques that can facilitate the accurate and precise prediction of transdermal permeation without the use of in vivo studies. This review considers why permeation data is essential, provides a brief summary as to how skin acts as a natural barrier to permeation and discusses why in vivo studies are undesirable. This is followed by an in-depth discussion on the extensive range of alternative methods that have been developed in recent years. All of the major 'skin mimic systems' are considered including: in vitro models using synthetic membranes, mathematical models including quantitative structure-permeability relationships (QSPRs), human skin equivalents and chromatographic based methods. All of these model based systems are ideally trying to achieve the same end-point, namely a reliable in vitro-in vivo correlation, i.e. matching non-in vivo obtained data with that from human clinical trials. It is only by achieving this aim, that any new method of obtaining permeation data can be acknowledged as a potential replacement for animal studies, for the determination of transdermal permeation. In this review, the relevance and potential applicability of the various models systems will also be discussed.

Probing the Physicochemical Boundaries of Cell Permeability and Oral Bioavailability in Lipophilic Macrocycles Inspired by Natural Products.

Cyclic peptide natural products contain a variety of conserved, nonproteinogenic structural elements such as d-amino acids and amide N-methylation. In addition, many cyclic peptides incorporate γ-amino acids and other elements derived from polyketide synthases. We hypothesized that the position and orientation of these extended backbone elements impact the ADME properties of these hybrid molecules, especially their ability to cross cell membranes and avoid metabolic degradation. Here we report the synthesis of cyclic hexapeptide diastereomers containing γ-amino acids (e.g., statines) and systematically investigate their structure-permeability relationships. These compounds were much more water-soluble and, in many cases, were both more membrane permeable and more stable to liver microsomes than a similar non-statine-containing derivative. Permeability correlated well with the extent of intramolecular hydrogen bonding observed in the solution structures determined in the low-dielectric solvent CDCl3, and one compound showed an oral bioavailability of 21% in rat. Thus, the incorporation of γ-amino acids offers a route to increase backbone diversity and improve ADME properties in cyclic peptide scaffolds.

Cell-permeable cyclic peptides from synthetic libraries inspired by natural products.

Drug design efforts are turning to a new generation of therapeutic targets, such as protein-protein interactions (PPIs), that had previously been considered "undruggable" by typical small molecules. There is an emerging view that accessing these targets will require molecules that are larger and more complex than typical small molecule drugs. Here, we present a methodology for the discovery of geometrically diverse, membrane permeable cyclic peptide scaffolds based on the synthesis and permeability screening of a combinatorial library, followed by deconvolution of membrane-permeable scaffolds to identify cyclic peptides with good to excellent passive cell permeabilities. We use a combination of experimental and computational approaches to investigate structure-permeability relationships in one of these scaffolds, and uncover structural and conformational factors that govern passive membrane diffusion in a related set of cyclic peptide diastereomers. Further, we investigate the dependency of permeability on side-chain identity of one of these scaffolds through single-point diversifications to show the adaptability of these scaffolds toward development of permeability-biased libraries suitable for bioactivity screens. Overall, our results demonstrate that many novel, cell permeable scaffolds exist beyond those found in extant natural products, and that such scaffolds can be rapidly identified using a combination of synthesis and deconvolution which can, in principle, be applied to any type of macrocyclic template.

Two- and three-dimensional quantitative structure–permeability relationship of flavonoids in Caco-2 cells using stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and pharmacophore (GALAHAD)-based comparative molecular similarity index analysis (COMSIA)

Limited oral bioavailability has hindered the widespread use of flavonoids as bioactive substances. Several studies have been performed to evaluate the transport characteristics of flavonoids in the intestines using cell models, such as Caco-2 cells, but information regarding the key structural features of flavonoids that influence intestinal uptake is still limited to date. In this study, quantitative structure–permeability relationship (QSPR) models were developed to study the permeability of 36 flavonoids through Caco-2 cells using both 2D and 3D approaches. For the 2D model, stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) resulted in good internal (R SMLR 2 = 0.8, R PLSR 2 = 0.93) and external (R SMLR 2 = 0.93, R PLSR 2 = 0.90) predictability using a set of 409 molecular descriptors. The high cross-validated (leave-one-out) R 2 values (Q 2) for both 2D models (Q SMLR 2 = 0.77, Q PLSR 2 = 0.67) suggest that the models are robust and predictive. A pharmacophore (GALAHAD)-based COMSIA analysis was used to generate the 3D QSPR model, which yielded a predictive and robust model (R training 2 = 0.96, R test 2 = 0.95, Q 2 = 0.625) composed of hydrogen bond acceptor and donor fields. According to the contour plots, the locations of hydrogen bond acceptors and donors play a crucial role in determining Caco-2 permeability of flavonoids. The models provide deeper insight into the QSPR of flavonoids on intestinal absorption using Caco-2 cell models and could be useful for the high-throughput screening of flavonoids and/or flavonoid-like drugs with health-promoting activities.

Prediction of formulation effects on dermal absorption of topically applied ectoparasiticides dosed in vitro on canine and porcine skin using a mixture‐adjusted quantitative structure permeability relationship

Topical application of ectoparasiticides for flea and tick control is a major focus for product development in animal health. The objective of this work was to develop a quantitative structure permeability relationship (QSPeR) model sensitive to formulation effects for predicting absorption and skin deposition of five topically applied drugs administered in six vehicle combinations to porcine and canine skin in vitro. Saturated solutions (20μL) of (14) C-labeled demiditraz, fipronil, permethrin, imidacloprid, or sisapronil were administered in single or binary (50:50 v/v) combinations of water, ethanol, and transcutol (6 formulations, n=4-5 replicates per treatment) nonoccluded to 0.64cm(2) disks of dermatomed pig or dog skin mounted in flow-through diffusion cells. Perfusate flux over 24h and skin deposition at termination were determined. Permeability (logKp), absorption, and penetration endpoints were modeled using a four-term Abrahams and Martin (hydrogen-bond donor acidity and basicity, dipolarity/polarizability, and excess molar refractivity) linear free energy QSPeR equation with a mixture factor added to compensate for formulation ingredient interactions. Goodness of fit was judged by r(2) , cross-validation coefficient, coefficients (q(2) s), and Williams Plot to visualize the applicability domain. Formulation composition was the primary determinant of permeation. Compounds generally penetrated dog skin better than porcine skin. The vast majority of permeated penetrant was deposited within the dosed skin relative to transdermal flux, an attribute for ectoparasiticides. The best QSPeR logKp model for pig skin permeation (r(2) = 0.86, q(2) s = 0.85) included log octanol/water partition coefficient as the mixture factor, while for dogs (r(2) = 0.91, q(2) s = 0.90), it was log water solubility. These studies clearly showed that the permeation of topical ectoparasiticides could be well predicted using QSPeR models that account for both the physical-chemical properties of the penetrant and formulation components.

Structure and Permeability of Ion-channels by Integrated AFM and Waveguide TIRF Microscopy

Membrane ion channels regulate key cellular functions and their activity is dependent on their 3D structure. Atomic force microscopy (AFM) images 3D structure of membrane channels placed on a solid substrate. Solid substrate prevents molecular transport through ion channels thus hindering any direct structure-function relationship analysis. Here we designed a ~70 nm nanopore to suspend a membrane, allowing fluidic access to both sides. We used these nanopores with AFM and total internal reflection fluorescence microscopy (TIRFM) for high resolution imaging and molecular transport measurement. Significantly, membranes over the nanopore were stable for repeated AFM imaging. We studied structure-activity relationship of gap junction hemichannels reconstituted in lipid bilayers. Individual hemichannels in the membrane overlying the nanopore were resolved and transport of hemichannel-permeant LY dye was visualized when the hemichannel was opened by lowering calcium in the medium. This integrated technique will allow direct structure-permeability relationship of many ion channels and receptors.

Assessing vehicle effects on skin absorption using artificial membrane assays

A vast number of variations in drug/vehicle combinations may come into contact with skin. Evaluating the effect of potential drug, vehicle and skin interactions for all possible combinations is a daunting task. A practical solution is a rapid screening technique amenable to high throughput approaches (e.g. 96-well plates). In this study, three artificial membranes (isopropyl myristate (IPM), certramides and Strat-M™) were evaluated for their ability to predict the skin permeability of caffeine, cortisone, diclofenac sodium, mannitol, salicylic acid and testosterone applied in propylene glycol, water and ethanol as unsaturated and saturated concentrations. Resultant absorption data was compared to porcine skin diffusion cell data. The correlations (r2) between membrane and diffusion cell data from saturated and unsaturated concentrations were 0.38, 0.47 and 0.56 for the Strat-M™, certramide and IPM membranes, respectively. This relationship improved when only saturated concentrations were evaluated (r2=0.60, 0.63 and 0.66 for the Strat-M™, certramide and IPM membranes, respectively). A correlation between membrane retention and the amount remaining in skin had r2 values of 0.73 (Strat-M™), 0.67 (certramides), and 0.67 (IPM). Quantitative structure-permeability relationship models for each membrane identified different physicochemical factors influencing the absorption process. Although further investigations exploring complex topical formulations are required, these results suggest potential use as an initial screening approach to assist in narrowing the selection of formulations to be evaluated with a more biologically intact model, thereby assisting in the development of new topical formulations.

Modeling the effect of experimental variables on the in vitro permeation of six model compounds across porcine skin

A majority of quantitative structure-permeability relationships (QSPeRs) predict the permeability coefficient (k(p)) of compounds topically applied as infinite, saturated doses from water vehicles. Alternate delivery vehicles and other experimental variables are rarely incorporated in such models. This research presents the development and statistical validation of QSPeR models that incorporate the effects of penetrant, vehicle, and experimental conditions such as dose volume (finite/infinite), and saturation level (saturated/unsaturated). A composite parameter, a mixture factor (MF), was also included to account for the physicochemical properties of the compound/vehicle mixture components. The resultant models effectively described skin flux and absorption, identifying the summation of hydrogen bond acidity and basicity, excess molar refractivity, dose volume, saturation level, and vehicle as the most prominent factors influencing flux values. The main factors influencing absorption values were the summation of hydrogen bond basicity, dipolarity/polarizability, the McGowan characteristic volume, dose volume, saturation level, and vehicle. The same MF (inverse of the melting point) was considered suitable to describe both flux and absorption. For endpoints involving skin deposition, log propylene glycol solubility was a more suitable MF. Such models show potential for use in drug delivery and toxicology research, specifically in assessing percutaneous absorption data collected under different experimental conditions.

Back to the future: can physical models of passive membrane permeability help reduce drug candidate attrition and move us beyond QSPR?

It is widely recognized that adsorption, distribution, metabolism, excretion, and toxicology liabilities kill the majority of drug candidates that progress to clinical trials. The development of computational models to predict small molecule membrane permeability is therefore of considerable scientific and public health interest. Empirical qualitative structure permeability relationship models of permeability have been a mainstay in industrial applications, but lack a deep understanding of the underlying biologic physics. Others and we have shown that implicit solvent models to predict passive permeability for small molecules exhibit mediocre predictive performance when validated across experimental test sets. Given the vast increase in computer power, more efficient parallelization schemes, and extension of current atomistic simulation codes to general use graphical processing units, the development and application of physical models based on all-atom simulations may now be feasible. Preliminary results from rigorous free energy calculations using all-atom simulations indicate that performance relative to implicit solvent models may be improved, but many outstanding questions remain. Here, we review the current state-of-the-art physical models for passive membrane permeability prediction and present a prospective look at promising new directions for all-atom approaches.

In Silico Modeling of UDP-Glucuronosyltransferase 1A10 Substrates Using the Volsurf Approach

UDP-glucuronosyltransferase 1A10 (UGT1A10) catalyzes glucuronidation of a wide range of chemicals including many drugs. Here, we report the first in silico model quantifying the substrate selectivity and binding affinity (as K(m)) for UGT1A10. The training set for model construction comprises 32 structurally diverse compounds, which are known substrates for UGT1A10. The model was derived by applying the standard VolSurf method involving calculation of VolSurf descriptors and partial least square (PLS) analyses. The yielded PLS model with two components shows statistical significance in both fitting and internal predicting (r(2) = 0.827, q(2) = 0.774). The model predictability was further validated against a test set of 11 external compounds. The activity values for all test substrates were predicted within 1 log unit. Moreover, the model reveals an overlay of chemical features influencing the enzyme-substrate binding. Those include the size and shape, capacity factors, hydrophilic regions, hydrophobic regions, and polarizability. In conclusion, the VolSurf approach is successfully utilized to establish a predictive model for UGT1A10. The derived model should be an efficient tool for high-throughput prediction of UGT1A10 metabolism.

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 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. 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. 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.

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

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 K(p)) 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. Permeability data for absorption across rodent and pig skin, and artificial membranes (polydimethylsiloxane, PDMS, i.e. Silastic) membranes was collected from the literature. Two quantitative structure-permeability relationship (QSPR) models were used to compare with the GP models. Further performance metrics were computed in terms of all predictions, and a range of covariance functions were examined: the squared exponential (SE), neural network (NNone) and rational quadratic (QR) covariance functions, along with two simple cases of Matern covariance function (Matern3 and Matern5) where the polynomial order is set to 1 and 2, respectively. As measures of performance, the correlation coefficient (CORR), negative log estimated predictive density (NLL, or negative log loss) and mean squared error (MSE) were employed. The results demonstrated that GP models with different covariance functions outperform QSPR models for human, pig and rodent datasets. For the artificial membranes, GPs perform better in one instance, and give similar results in other experiments (where different covariance parameters produce similar results). In some cases, the GP predictions for some of the artificial membrane dataset are poorly correlated, suggesting that the physicochemical parameters employed in this study might not be appropriate for developing models that represent this membrane. While the results of this study indicate that permeation across rodent (mouse and rat) and pig skin is, in a statistical sense, similar, and that the artificial membranes are poor replacements of human or animal skin, the overriding issue raised in this study is the nature of the dataset and how it can influence the results, and subsequent interpretation, of any model produced for particular membranes. The size of the datasets, in both absolute and comparative senses, appears to influence model quality. Ideally, to generate viable cross-comparisons the datasets for different mammalian membranes should, wherever possible, exhibit as much commonality as possible.

An investigation of the mechanism of flux across polydimethylsiloxane membranes by use of quantitative structure-permeability relationships.

Quantitative structure-permeability relationships (QSPRs) based on readily calculated parameters have been developed to study penetration across a polydimethylsiloxane membrane. Maximum steady-state flux values for 256 compounds through a polydimethylsiloxane membrane were taken from previous studies. Forty-three physicochemical parameters were calculated for each compound and their significance to flux determined. Removal of fourteen outliers enabled derivation of a significant three-parameter QSPR based on the number of hydrogen-bond acceptor and donor groups and sixth-order path molecular connectivity. Models based on parameters important for penetration across human skin (log P and molecular weight) were comparatively poor. This model suggests that the mechanism of flux across a polydimethylsiloxane membrane is based mainly on hydrogen-bonding effects; as such it occurs via a mechanism of action different from that of penetration of the skin in man.

Mathematical models of skin permeability: An overview

Mathematical models of skin permeability play an important role in various fields including prediction of transdermal drug delivery and assessment of dermal exposure to industrial chemicals. Extensive research has been performed over the last several decades to yield predictions of skin permeability to various molecules. These efforts include the development of empirical approaches such as quantitative structure–permeability relationships and porous pathway theories as well as the establishment of rigorous structure-based models. In addition to establishing the necessary mathematical framework to describe these models, efforts have also been dedicated to determining the key parameters that are required to use these models. This article provides an overview of various modeling approaches with respect to their advantages, limitations and future prospects.

The membrane transport of flavonoids from Crossostephium chinense across the Caco‐2 monolayer

The permeation and efflux of six polysubstituted flavonoids isolated from Crossostephium chinense, a Chinese traditional and herbal drug for the treatment of diabetes, were investigated using the Caco-2 cell monolayer. The six flavonoids (selagin, apometzgerin, tricetin-3',4',5'-trimethylether, quercetagetin-3,6,7-trimethylether, hispidulin and quercetagetin) sharing a similar parent skeleton structure with varied substituents in the heterocyclic ring B were selected for the study. Quercetagetin exhibited a low bi-directional permeability comparable to that of atenolol, suggesting a paracellular diffusion mechanism. The remaining compounds exhibited time- and concentration- dependent permeation with apparent permeability coefficient (P(app)) values in the range 10⁻⁶-10⁻⁵ cm/s, suggesting transcellular diffusion pathways. Selagin exhibited significantly larger basolateral to apical P(app) than that of the reverse direction, suggesting the existence of efflux mechanisms. ATP-depletion and probenecid pretreatment led to a significant inhibition of the efflux of selagin, whereas verapamil had no effect on the basolateral to apical transport of selagin, suggesting that multidrug resistance proteins (MRPs) and not P-glycoprotein play a role in the intestinal efflux of selagin. Based on present and previous results, the structure-permeation relationship and the role of MRPs in mediating the efflux of flavonoids are discussed. Experimental results in this study provide useful information for pharmacological applications of the flavonoids from C. chinense.

Predicting Skin Permeability from Complex Chemical Mixtures: Dependency of Quantitative Structure Permeation Relationships on Biology of Skin Model Used

Dermal absorption of topically applied chemicals usually occurs from complex chemical mixtures; yet, most attempts to quantitate dermal permeability use data collected from single chemical exposure in aqueous solutions. The focus of this research was to develop quantitative structure permeation relationships (QSPR) for predicting chemical absorption from mixtures through skin using two levels of in vitro porcine skin biological systems. A total of 16 diverse chemicals were applied in 384 treatment mixture combinations in flow-through diffusion cells and 20 chemicals in 119 treatment combinations in isolated perfused porcine skin. Penetrating chemical flux into perfusate from diffusion cells was analyzed to estimate a normalized dermal absorptive flux, operationally an apparent permeability coefficient, and total perfusate area under the curve from perfused skin studies. These data were then fit to a modified dermal QSPR model of Abraham and Martin including a sixth term to account for mixture interactions based on physical chemical properties of the mixture components. Goodness of fit was assessed using correlation coefficients (r²), internal and external validation metrics (q²L00, q²L25%, q²EXT), and applicable chemical domain determinations. The best QSPR equations selected for each experimental biological system had r² values of 0.69-0.73, improving fits over the base equation without the mixture effects. Different mixture factors were needed for each model system. Significantly, the model of Abraham and Martin could also be reduced to four terms in each system; however, different terms could be deleted for each of the two biological systems. These findings suggest that a QSPR model for estimating percutaneous absorption as a function of chemical mixture composition is possible and that the nature of the QSPR model selected is dependent upon the biological level of the in vitro test system used, both findings having significant implications when dermal absorption data are used for in vivo risk assessments.

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.