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 model systems will also be discussed

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

Predicting skin permeability of neutral species and ionic species

The skin forms an extremely efficient barrier between internal organs and the external environ-ment, but also provides an attractive administration route. Skin permeability (as log Kp) is a critical parameter for estimating transdermal delivery of chemicals in contact with the skin in pharma-ceutics and cosmetics. Given the fact that measurement of log Kp is quite time-consuming and laborious, various mathematical models based on understanding of the fundamental mechanisms underlying skin permeation have been proposed to estimate the otherwise unavailable log Kp. However, there has been no model up to now for prediction of log Kp of ionic species. In order to solve this problem, we proposed and investigated two potential solutions in this study: one is the Potts-Guy model on the basis of partition parameters in liposome-water systems and molecular volume (MV), and the other is the extended linear free-energy relationship (LFER), which can be used to predict biological membrane permeability of ionic species. In this study, the compounds with a broad structural diversity were selected and their reten-tion factors were measured in liposome electrokinetic chromatography (LEKC), where cerasomes composed mainly of the stratum corneum (SC) lipids and liposomes (POPC80/PS20) were used as the pseudo-stationary phases, respectively. These two negatively charged membrane systems and a neutral immobilized artificial membrane (IAM) system from literature as a surrogate for neutral liposome-water partition were compared with various organic solvent-water partitioning systems using LFERs. It was observed that liposomes display a greatly different chemical environment from those of organic solvents, and no organic solvent can thus provide a general model for lipo-somes in partition processes. What is more, the correlation between the skin-water partition and organic solvent/liposome-water partitions was also investigated. The results show that cerasome exhibits a better chemical similarity with the skin as compared to phospholipid liposomes and all organic solvents. Further, the cerasome-water partition correlates better to skin permeation than other liposome-water partitions and microsomal binding. This is probably due to the unique struc-tures of ceramides that occur in SC and consequently in cerasomes. The log Kp values of nine acid anions and nine base cations were measured in this study. The data were used to construct a LFER equation for skin permeation of neutral species and ionic spe-cies, together with experimental log Kp for both species in literature. The resulting equation, with a R2 value of 0.861 and a SD value of 0.462 log units, can be used to predict log Kp for neutral species and ionic species, as well as partly ionized solutes. The predicted values for the passive permeation of the sodium ion and the tetraethylammonium ion are in good accord with the exper-imental values. It was found that neutral acids and bases are much more permeable than their ion-ized forms, and that the ratio depends on the actual structure. Using the cerasome-water partition as a substitute for the skin-water partition, the effect of ionization of solutes on skin permeation was separated to those on partition and diffusion processes. The poor permeability of ionic species is largely due to slow diffusion through the SC, especially for base cations. In addition, the Potts-Guy model based on the retention factors obtained in cerasome electro-kinetic chromatography (EKC) and MV was discussed. It was found that such a model cannot be applied to predict log Kp for ionic species because MV fails to account for their diffusion through the SC, even empirically. In conclusion, LFER is a very useful tool for predicting skin permeation, not only for neutral species but also for ionic species, whereas the Potts-Guy model may be useful for neutral species but is not applicable for ionic species.Die Haut bildet eine äußerst effiziente Barriere zwischen dem Körperinneren und der äußeren Umwelt und stellt aber auch einen attraktiven Applikationsweg dar. Die Hautpermeabilität (darge-stellt als log Kp) ist dabei auf dem Gebiet der Pharmazie und Kosmetik ein entscheidender Para-meter, um das transdermale Hautpenetrations- bzw. Hautpermeationsverhalten eines Stoffes nach dermaler Applikation abzuschätzen. Direkte Messungen des log Kp sind zeitaufwendig und müh-sam. In der vorliegenden Arbeit wurden verschiedene, auf dem Verständnis grundlegender Me-chanismen der Hautpermeation basierende, mathematische Modelle vorgeschlagen, um nicht ver-fügbare log Kp-Werte zu generieren. Bisher gab es jedoch kein Modell, um den log Kp von ioni-sierten Molekülen vorherzusagen. Um dieses Problem zu lösen, haben wir in dieser Arbeit zwei Lösungsmöglichkeiten für die Vorhersage von log Kp-Werten geladener Substanzen vorgeschlagen und im Weiteren diskutiert: Zum einen das Potts-Guy-Modell auf der Basis von Verteilungs-parametern in Liposom/Wasser-Systemen und dem Molekülvolumen (MV), zum anderen die er-weiterte lineare freie Energie-Beziehung (linear free energy relationship; LFER), die verwendet werden kann, um die Permeabilität geladener Stoffe durch biologische Membranen vorherzusagen. In dieser Arbeit wurden Verbindungen mit einer breiten strukturellen Vielfalt ausgewählt und ihre Retentionsfaktoren mit Hilfe von Liposom-Elektrokinetik-Chromatographie (liposome electrokinetic chromatography; LEKC) gemessen, wobei Cerasomen, hauptsächlich bestehend aus Lipiden des Stratum corneums (SC), bzw. Liposomen (POPC80/PS20) als pseudostationäre Phase dienten. Diese beiden negativ geladenen Membransysteme sowie ein System aus neutralen künst-lichen immobilisierten Membranen (immobilized artificial membranes; IAM) stellvertretend für eine neutrale Liposomen/Wasser-Verteilung wurden mit verschiedenen Verteilungssystemen zwi-schen organischen Lösungsmitteln und Wasser unter Anwendung der LFER verglichen. Es wurde beobachtet, dass Liposomen eine sich von organischen Lösungsmitteln stark unterscheidende chemische Umgebung besitzen. Daher stellen organische Lösungsmittel kein allgemein gültiges Modell für Liposomen in Bezug auf Verteilungsprozesse dar. Der Zusammenhang zwischen der Haut/Wasser-Verteilung und der organischen Lösungsmittel/Liposom-Wasser-Verteilung wurde ebenfalls untersucht. Die Ergebnisse zeigen, dass Cerasomen im Vergleich zu herkömmlichen Liposomen aus Phospholipiden sowie allen organischen Lösungsmitteln eine höhere chemische Ähnlichkeit mit der Haut aufweisen. Darüber hinaus konnte für die Cerasomen/Wasser-Verteilung eine bessere Korrelation mit der Hautpermeation nachgewiesen werden, als für die Liposomen-Wasser-Verteilung oder die mikrosomale Bindung. Grund hierfür ist wahrscheinlich die einzigar-tige Struktur der Ceramide, die im Stratum corneum wie auch hier in den Cerasomen vorhanden ist. Die log Kp-Werte von neun sauren, anionischen Molekülen und neun basischen, kationischen Molekülen wurden in dieser Arbeit gemessen. Aus den generierten Daten wurde in Verbindung mit log Kp Werten aus der Literatur eine LFER-Gleichung für die Hautpermeation von neutralen und ionischen Molekülen entwickelt. Die resultierende Gleichung mit einem R2 von 0,861 und einer Standardabweichung von 0,462 log Einheiten kann sowohl zur Vorhersage des log Kp-Wertes für neutrale und ionische Moleküle, als auch für Vorhersage des log Kp-Wertes teilweise ionisierter Substanzen verwendet werden. Die abgeschätzten Werte für die passive Permeation des Natrium-Ions und des Tetraethylammonium-Ions stimmen mit den experimentellen Werten gut überein. Es wurde erkannt, dass neutrale Säuren und Basen viel stärker permeieren als die dazu-gehörigen ionisierten Formen und dass das Verhältnis von der jeweiligen Struktur abhängt. Unter Verwendung der Cerasome/Wasser-Verteilung als Ersatz für die Haut-Wasser-Verteilung wurde der Effekt der Ionisierung der gelösten Substanzen auf die Hautpermeation von den Effekten auf die Verteilung und Diffusionsprozesse getrennt. Die schlechte Permeation ionischer Moleküle beruht im Wesentlichen auf der langsamen Diffusion durch das SC, vor allem für basische Kationen. Darüber hinaus wurde das Potts-Guy Modell basierend auf den Retentionsfaktoren, welche mittels Cerasome Electrokinetic Chromatography (EKC) bestimmt wurden, und MV diskutiert. Es wurde herausgefunden, dass ein solches Modell nicht für die Schätzung von log Kp-Werten ioni-scher Moleküle angewendet werden kann, da MV daran scheitert, die Diffusion durch das SC zu erfassen - auch empirisch. Zusammenfassend lässt sich sagen, dass LFER ein nützliches Hilfsmittel ist, um die Haut-permeation nicht nur für neutrale, sondern auch für ionische Moleküle vorauszusagen, wohingegen das Potts-Guy Modell gegebenenfalls für neutrale, aber nicht für ionische Moleküle anwendbar ist

Data mining methods for the prediction of intestinal absorption using QSAR

Oral administration is the most common route for administration of drugs. With the growing cost of drug discovery, the development of Quantitative Structure-Activity Relationships (QSAR) as computational methods to predict oral absorption is highly desirable for cost effective reasons. The aim of this research was to develop QSAR models that are highly accurate and interpretable for the prediction of oral absorption. In this investigation the problems addressed were datasets with unbalanced class distributions, feature selection and the effects of solubility and permeability towards oral absorption prediction. Firstly, oral absorption models were obtained by overcoming the problem of unbalanced class distributions in datasets using two techniques, under-sampling of compounds belonging to the majority class and the use of different misclassification costs for different types of misclassifications. Using these methods, models with higher accuracy were produced using regression and linear/non-linear classification techniques. Secondly, the use of several pre-processing feature selection methods in tandem with decision tree classification analysis – including misclassification costs – were found to produce models with better interpretability and higher predictive accuracy. These methods were successful to select the most important molecular descriptors and to overcome the problem of unbalanced classes. Thirdly, the roles of solubility and permeability in oral absorption were also investigated. This involved expansion of oral absorption datasets and collection of in vitro and aqueous solubility data. This work found that the inclusion of predicted and experimental solubility in permeability models can improve model accuracy. However, the impact of solubility on oral absorption prediction was not as influential as expected. Finally, predictive models of permeability and solubility were built to predict a provisional Biopharmaceutic Classification System (BCS) class using two multi-label classification techniques, binary relevance and classifier chain. The classifier chain method was shown to have higher predictive accuracy by using predicted solubility as a molecular descriptor for permeability models, and hence better final provisional BCS prediction. Overall, this research has resulted in predictive and interpretable models that could be useful in a drug discovery context

The Critical Role of Mechanism-Based Models for Understanding and Predicting Liposomal Drug Loading, Binding and Release Kinetics

Liposomal delivery systems hold considerable promise for improvement of cancer therapy provided that critical formulation design criteria can be met. The main objective of the current project was to enable quality by design in the formulation of liposomal delivery systems by developing comprehensive, mechanism-based mathematical models of drug loading, binding and release kinetics that take into account not only the therapeutic requirement but the physicochemical properties of the drug, the bilayer membrane, and the intraliposomal microenvironment. Membrane binding of the drug affects both drug loading and release from liposomes. The influence of bilayer composition and phase structure on the partitioning behavior of a model non-polar drug, dexamethasone, and its water soluble prodrug, dexamethasone phosphate, was evaluated. Consequently, a quantitative dependence of the partition coefficient on the free surface area of the bilayer, a property related to acyl chain ordering, was noted. The efficacy of liposomal formulations is critically dependent on the drug release rates from liposomes. However, various formulation efforts to design optimal release rates are futile without a validated characterization method. The pitfalls of the commonly used dynamic dialysis method for determination of apparent release kinetics from nanoparticles were highlighted along with the experimental and mathematical approaches to overcome them. The value of using mechanism-based models to obtain the actual rate constant for nanoparticle release was demonstrated. A novel method to improve liposomal loading of poorly soluble ionizable drugs using supersaturated drug solutions was developed using the model drug AR-67 (7-t-butyldimethylsilyl-10-hydroxycamptothecin), a poorly soluble camptothecin analogue. Enhanced loading with a drug to lipid ratio of 0.17 was achieved and the rate and extent of loading was explained by a mathematical model that took into account the chemical equilibria inside and outside the vesicles and the transport kinetics of various permeable species across the lipid bilayer and the dialysis membrane. Tunable liposomal release kinetics would be highly desirable to meet the varying therapeutic requirements. A large range of liposome release half-lives from 1 hr to 892 hr were obtained by modulation of intraliposomal pH and lipid composition using dexamethasone phosphate as a model ionizable drug. The mathematical models developed were successful in accounting for the change in apparent permeability with change in intraliposomal pH and bilayer free surface area. This work demonstrates the critical role of mechanism-based models in design of liposomal formulations

Organic solvent nanofiltration (OSN) modelling - from pure solvents to highly rejected solutes

Imperial Users onl

A multiscale biophysical platform for charting design-specific interactions of nanoparticles with model cellular membranes

In this thesis, we outline the development of a multiscale physics-based platform for exploring and ultimately predicting the design-specific interaction of ~1-10 nm particles with model cellular membranes. Nanoparticles (NP) are ever-present in foods and beverages, cosmetics, packaging, cooking products, fertilizers, pesticides, and novel pharmaceuticals, and pose significant challenges related to their increased consumer, occupational, and environmental exposure and their unique bioactivity relative to small molecules and large colloids. Thanks to rapidly advancing fabrication and characterization techniques, NPs are highly tunable in physicochemical properties such as size, surface chemistry, shape, elasticity, roughness, and crystallinity. Currently, however, the influence of these NP design parameters is highly underdeveloped, and difficult to reproducibly demonstrate in in vivo, in vitro, and even model experiments. Specifically, NP interactions with and passive transport across cellular membranes play a significant role in pharmacological and consumer product performance (biodistribution) as well as adverse outcome pathways in toxicology. We thus focus on the fundamental problem of design-specific interactions between NPs and cellular membranes, modeled to a first approximation as lipid bilayers.To provide accurate, efficient, and robust predictions for a range of NP designs, we construct a first-of-its kind, multiscale physics-based platform linking detailed molecular dynamics (MD) simulations, continuum mechanical theory, and multi-compartment modeling. Using this platform, we examine the two most influential design parameters--size and surface chemistry--and through two main case studies: (1) the membrane permeability of sub-nanometer particles and (2) the thermodynamic stability of larger-scale, ~1-10 nm particle-membrane interactions. Within (1), we first simulate the NP-membrane interactions and transport in full detail to test the validity of Overton's Rule, a longstanding structure-property relationship, and the inhomogeneous solubility-diffusion (ISD) model, a microscopic mechanistic continuum model for transport. We show that Overton's Rule is overly simplified for describing transport across a fluctuating lipid bilayer membrane, yet that ISD model holds for small enough particles. Within this range of particles where the solubility-diffusion mechanism holds, we directly link the impact of particle chemistry in the MD simulations to transient (time-dependent) transport outcomes in the macroscopic multi-compartmental models. This allows us to both compare with and evaluate models used in experimental permeability assays and close the orders of magnitude gap between simulation-predicted and experimentally-calculated permeabilities. We also leverage our platform to construct improved structure-property relationships for the steady-state membrane permeability and structure-kinetic relationships, accounting for a wider range of particle chemistries and highlighting the imperative of time in dictating the design rules for membrane permeation.Within case study (2), we probe larger NP-membrane interactions that implicate macroscopic membrane deformations and restructuring. Using the molecular simulations, we map out in particle size and chemistry space the putative NP-membrane interaction configurations, some of which resemble and agree with small-scale solubility-diffusion theory or large-scale membrane elastic theory (e.g. for lipid bilayer or monolayer wrapping of the NP) in stability limits and free energies and some of which require closer examination in the simulation to explain the thermodynamics. We also discover an entirely novel mechanism of interaction for ~4 nm, rough crystalline hydrophobic particles that we call "asymmetric leaflet hopping," wherein the particle preferentially inserts in one bilayer leaflet, forming a pre-pore in the membrane and inducing large-scale membrane curvature, and flips to the other leaflet over extended time scales.We conclude with preliminary phenomenologies to outline the phase behavior of ~1-10 nm particles of varying chemistry, as well as other areas where our platform shows great promise. By accounting for a vast range of NP designs, natural lipid diversity (lipidomics), and variable compartmental size, boundary layer, and transient conditions, this platform has the potential to more intuitively and effectively inform systems-level physiologically-based pharmacokinetic models for NP biodistribution predictions, as well as structure-activity relationships for direct predictions of product efficacy and toxicity. The end result of this multiscale platform is that we can directly link a NP's microscopic physicochemical properties to its macroscopic outcomes in a dynamic biodistribution setting

ADME Profiling in Drug Discovery and a New Path Paved on Silica

The drug discovery and development pipeline have more and more relied on in vitro testing and in silico predictions to reduce investments and optimize lead compounds. A comprehensive set of in vitro assays is available to determine key parameters of absorption, distribution, metabolism, and excretion, for example, lipophilicity, solubility, and plasma stability. Such test systems aid the evaluation of the pharmacological properties of a compound and serve as surrogates before entering in vivo testing and clinical trials. Nowadays, computer-aided techniques are employed not just in the discovery of new lead compounds but embedded as part of the entire drug development process where the ADME profiling and big data analyses add a new layer of complexity to those systems. Herein, we give a short overview of the history of the drug development pipeline presenting state-of-the-art ADME in vitro assays as established in academia and industry. We will further introduce the underlying good practices and give an example of the compound development pipeline. In the next step, recent advances at in silico techniques will be highlighted with special emphasis on how pharmacogenomics and in silico PK profiling can enhance drug monitoring and individualization of drug therapy