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

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Stepwise Protocols for Preparation and Use of Porcine Ear Skin for in Vitro Skin Permeation Studies Using Franz Diffusion Cells

The skin, the largest organ of the body, is an attractive route of topical and systemic drug administration. During the development of topical formulations, in vitro skin permeation studies using biological membranes mounted in Franz diffusion cells are a useful tool to assess the permeation of substances through the skin, and are recommended by the Organization for Economic Cooperation and Development (OECD). Among the types of biological membranes used in such studies, porcine ear skin has been identified as the most promising, due to its similarities to human skin and its greater accessibility as compared to human skin. To standardize techniques for the preparation and use of porcine ear skin as biological membrane, here we present systematic procedures for the selection of porcine ears, their cleaning, the removal of skin from cartilage, its transformation into membranes, and its use for the in vitro assessment of the permeation of drugs from topical formulations. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Obtaining porcine ear membranes Basic Protocol 2: Preparation of membranes from porcine ear skin and use of membranes for in vitro skin permeation studies.

The Influence of Oily Vehicle Composition and Vehicle-Membrane Interactions on the Diffusion of Model Permeants across Barrier Membranes

In many instances, one or more components of a pharmaceutical or cosmetic formulation is an oil. The aims of this study were two-fold. First, to examine the potential of preferential uptake of one oily vehicle component over another into a model barrier membrane (silicone) from blended vehicles (comprising two from the common excipients isohexadecane (IHD), hexadecane (HD), isopropyl myristate (IPM), oleic acid (OA) and liquid paraffin). Second, to study the effect of membrane-vehicle interactions on the diffusion of model permeants (caffeine (CF), methyl paraben (MP) and butyl paraben (BP)) from blended vehicles. Selective sorption and partition of some oils (especially IHD and IPM) at the expense of other oils (such as OA) was demonstrated to take place. For example, the membrane composition of IHD was enriched compared to a donor solution of IHD-OA: 41%, 63% and 82% IHD, compared to donor solution composition of 25%, 50% and 75% IHD, respectively. Pre-soaking the membrane in IHD, HD or LP, rather than phosphate buffer, enhanced the flux of MP through the membrane by 2.6, 1.7 and 1.3 times, respectively. The preferential sorption of individual oil components from mixtures altered the barrier properties of silicone membrane, and enhanced the permeation of CF, MP and BP, which are typically co-formulated in topical products.

Physicochemical characteristics and in vitro permeation of loratadine solid lipid nanoparticles for transdermal delivery

Aim: To prepare loratadine-loaded solid lipid nanoparticles (SLNs) using a modified two-step ultrasound-assisted phase inversion temperature (PIT) process. Results/methodology: Loratadine was dissolved in beeswax and Tween 80 was dissolved in water. The two phases were mixed together to prepare a water-in-oil emulsion preconcentrate (w/o) at a PIT of 85°C, followed by gradual water addition at 25°C to trigger nanoparticles formation (o/w). Kinetic stability was investigated. No change in the size was observed within 6 months. Fourier-transform infrared spectroscopy demonstrated stability of the emulsions via molecular structure of water at the interface of the o/w nanoemulsions. SLNs enhanced the in vitro skin permeation of loratadine. Conclusion: Stable SLNs were successfully prepared by ultrasound-assisted PIT.

Electrophilic Vinylation of Thiols under Mild and Transition Metal-Free Conditions

The iodine(III) reagents vinylbenziodoxolones (VBX) were employed to vinylate a series of aliphatic and aromatic thiols, providing E-alkenyl sulfides with complete chemo- and regioselectivity, as well as excellent stereoselectivity. The methodology displays high functional group tolerance and proceeds under mild and transition metal-free conditions without the need for excess substrate or reagents. Mercaptothiazoles could be vinylated under modified conditions, resulting in opposite stereoselectivity compared to previous reactions with vinyliodonium salts. Novel VBX reagents with substituted benziodoxolone cores were prepared, and improved reactivity was discovered with a dimethyl-substituted core.

Oxidative Formation of Disulfide Bonds by a Chemiluminescent 1,2-Dioxetane under Mild Conditions

The oxidation of alkyl thiols to disulfides has been achieved under mild conditions using a chemiluminescent 1,2-dioxetane as a stoichiometric oxidant. Besides the mild and biocompatible reaction conditions, this approach offers the possibility to monitor the presence of thiols through oxidation and chemiluminescence of the remaining dioxetane.

Development of a Gaussian Process - feature selection model to characterise (poly)dimethylsiloxane (Silastic® ) membrane permeation

OBJECTIVES

The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation.

METHODS

A total of 2942 descriptors were calculated for a data set of 77 chemicals. Data were processed to remove redundancy, single values, imbalanced and highly correlated data, yielding 1363 relevant descriptors. For four independent test sets, feature selection methods were applied and modelled via a variety of Machine Learning methods.

KEY FINDINGS

Two sets of molecular descriptors which can provide improved predictions, compared to existing models, have been identified. Best permeation predictions were found with Gaussian Process methods. The molecular descriptors describe lipophilicity, partial charge and hydrogen bonding as key determinants of PDMS permeation.

CONCLUSIONS

This study highlights important considerations in the development of relevant models and in the construction and use of the data sets used in such studies, particularly that highly correlated descriptors should be removed from data sets. Predictive models are improved by the methodology adopted in this study, notably the systematic evaluation of descriptors, rather than simply using any and all available descriptors, often based empirically on in vitro experiments. Such findings also have clear relevance to a number of other fields.

The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability

OBJECTIVES

The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (T_exp ) and choice of diffusion cell on model quality and performance.

METHODS

Data were collated from the literature. Static and flow-through diffusion cell data were separated, and a series of GPR experiments was conducted. The effect of T_exp was assessed by comparing a range of datasets where T_exp either remained constant or was varied from 22 to 45 °C.

KEY FINDINGS

Using data from flow-through diffusion cells results in poor model performance. Data from static diffusion cells resulted in significantly greater performance. Inclusion of data from flow-through cell experiments reduces overall model quality. Consideration of T_exp improves model quality when the dataset used exhibits a wide range of experimental temperatures.

CONCLUSIONS

This study highlights the problem of collating literature data into datasets from which models are constructed without consideration of the nature of those data. In order to optimise model quality data from only static, Franz-type, experiments should be used to construct the model and T_exp should either be incorporated as a descriptor in the model if data are collated from a range of studies conducted at different temperatures.

Organotitanium Nucleophiles in Asymmetric Cross-Coupling Reaction: Stereoconvergent Synthesis of Chiral α-CF3 Thioethers

Asymmetric Ni-catalyzed cross-coupling reactions have become a very attractive tool for the stereoselective construction of valuable organic chiral materials. While various nucleophiles are used in such transformation, organotitanium(IV) has not been used before. Herein we demonstrate, for the first time, that organotitanium species can serve as efficient coupling partners in asymmetric cross-couplings, which have proven to be beneficial, compared to the commonly used organomagnesium and organozinc counterparts. This principle is exemplified by the first asymmetric catalytic synthesis of CF₃-substituted thioethers via a Ni-catalyzed stereoconvergent cross-coupling reaction. Thioether moieties and their derivatives are common motifs in many biologically active compounds, and their enantioenriched fluorinated analogs should be of great interest in the search for novel drugs and agrichemicals.

Novel Antihypertensive Prodrug from Grape Seed Proanthocyanidin Extract via Acid-Mediated Depolymerization in the Presence of Captopril: Synthesis, Process Optimization, and Metabolism in Rats

Grape seed extract contains a high content of proanthocyanidins that can be depolymerized into C-4-substituted (epi)catechin derivatives in the presence of nucleophiles. However, the biological and medicinal values of depolymerization products have been rarely investigated. Recently, we developed a novel depolymerization product (-)-epicatechin-4β- S-captopril methyl ester (ECC) derived from the reaction of grape seed proanthocyanidin extract with captopril in the presence of acidified methanol. A central composite design was employed to select the most appropriate depolymerization temperature and time to obtain the target product ECC with a high yield. A total of 16 metabolites of ECC in rat urine, feces, and plasma were identified using liquid chromatography quadrupole time-of-flight tandem mass spectrometry. The in vivo results suggested that ECC could release captopril methyl ester and epicatechin, followed by the generation of further metabolites captopril and epicatechin sulfate conjugates. Therefore, ECC may be used as a potential prodrug with synergistic or additive hypotensive effects.

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression

OBJECTIVES

The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters.

METHODS

Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality.

KEY FINDINGS

The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure-permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly.

CONCLUSIONS

The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets.

A prodrug approach to enhance azelaic acid percutaneous availability

Azelaic acid is a dicarboxylic acid compound used in treatment of acne vulgaris. However, high concentration (ca 20%) is needed to guarantee the drug availability in the skin. The latter increases the incidence of side effects such as local irritation. The prodrug strategy to enhance azelaic acid diffusion through skin was not reported before. Thus, a lipophilic prodrug of azelaic acid (diethyl azelate [DEA]) was synthesized and investigated to improve percutaneous availability of azelaic acid, with a subsequent full physical, chemical, and biological characterization. Expectedly, DEA exhibited a significant increase in diffusion compared to azelaic acid through silicone membrane. In contrast, the diffusion results through human stratum corneum (SC) displayed weaker permeation for DEA with expected retention in the SC. Therefore, a desorption study of DEA from SC was conducted to examine the reservoir behavior in SC. Results showed an evidence of sustained release behavior of DEA from SC. Consequently, enhancement of keratolytic effect is expected due to azelaic acid produced from enzymatic conversion of DEA released from SC.

Prodrugs for transdermal drug delivery - trends and challenges

Prodrugs continue to attract significant interest in the transdermal drug delivery field. These moieties can confer favorable physicochemical properties on transdermal drug delivery candidates. Alkyl chain lengthening, pegylation are some of the strategies used for prodrug synthesis. It is usually important to optimize partition coefficient, water and oil solubilities of drugs. In this review, progress made in the field of prodrugs for percutaneous penetration is highlighted and the challenges discussed.

Prodrug strategies for enhancing the percutaneous absorption of drugs

The transdermal application of drugs has attracted increasing interest over the last decade or so, due to the advantages it offers, compared to other delivery methods. The development of an efficient means of transdermal delivery can increase drug concentrations, while reducing their systemic distribution, thereby avoiding certain limitations of oral administration. The efficient barrier function of the skin, however, limits the use of most drugs as transdermal agents. This limitation has led to the development of various strategies to enhance drug-skin permeation, including the use of penetration enhancers. This method unfortunately has certain proven disadvantages, such as the increased absorption of unwanted components, besides the drug, which may induce skin damage and irritancy. The prodrug approach to increase the skin’s permeability to drugs represents a very promising alternative to penetration enhancers. The concept involves the chemical modification of a drug into a bioreversible entity that changes both its pharmaceutical and pharmacokinetic characteristics to enhance its delivery through the skin. In this review; we report on the in vitro attempts and successes over the last decade by using the prodrug strategy for the percutaneous delivery of pharmacological molecules.

Flux through silicone and human skin fitted to a series/parallel model

Background: Recent reports of the good correlation between maximum flux through human skin in vitro from water, JMHAQ, and maximum flux through silicone from water, JMPAQ, demand that the mechanism of maximum flux across these two apparently quite different membranes be compared to understand the bases of the correlation. Results/discussion: A n = 70 log JMPAQ database and a matched n = 55 log JMHAQ database of molecules were found to fit well to a series/parallel model where three parallel solubility dependent pathways existed: a lipid pathway, an aqueous pathway, and a series pathway of alternating lipid and aqueous phases. Conclusion: The results of this analysis surprisingly suggest that the architecture of the two membranes present similar solubility based pathways through which drugs diffuse.

A surrogate for topical delivery in human skin: silicone membranes

We have identified, for any surrogate membrane and human skin in vitro, the maximum flux through the membrane (output) should be measured if a correlation between the two is to be obtained. We also identified from an analysis of the passive permeation process that molecular weight, lipid and aqueous solubilities (which are easily measured) constitute the physicochemical properties of the active (input), upon which prediction of flux through the surrogate membrane and through skin in vitro should be based. Besides providing the bases for predicting flux, changes in these physicochemical properties can be easily implemented by those wishing to optimize new cosmetics or topical products. Maximum flux values through silicone membrane (n = 70) and through human skin in vitro (n = 52) have been collected and a good correlation between the flux through human skin in vitro and flux through silicone membrane (for the same molecules) was found.

The Contributions of the Celtic Masters and Their Associates

This article summarizes the work of 4 researchers in the field of percutaneous absorption - Keith Brain, Mark Cronin, Dermot McCafferty and John Pugh. It summarizes their main achievements in this field and reviews their major contributions to the broader subject area.

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.

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 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.

METHODS

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.

KEY FINDINGS

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.

CONCLUSIONS

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.

Metabolism of captopril carboxyl ester derivatives for percutaneous absorption

Objectives

To determine the metabolism of captopril n-carboxyl derivatives and how this may impact on their use as transdermal prodrugs. The pharmacological activity of the ester derivatives was also characterised in order to compare the angiotensin converting enzyme inhibitory potency of the derivatives compared with the parent drug, captopril.

Methods

The metabolism rates of the ester derivatives were determined in vitro (using porcine liver esterase and porcine ear skin) and in silico (using molecular modelling to investigate the potential to predict metabolism).

Key findings

Relatively slow pseudo first-order metabolism of the prodrugs was observed, with the ethyl ester displaying the highest rate of metabolism. A strong relationship was established between in-vitro methods, while in-silico methods support the use of in-vitro methods and highlight the potential of in-silico techniques to predict metabolism. All the prodrugs behaved as angiotensin converting enzyme inhibitors, with the methyl ester displaying optimum inhibition.

Conclusions

In-vitro porcine liver esterase metabolism rates inform in-vitro skin rates well, and in-silico interaction energies relate well to both. Thus, in-silico methods may be developed that include interaction energies to predict metabolism rates.

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 nonlinear. 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.

Modeling of flux through silicone membranes from water

Do the Roberts-Sloan (RS) or modified Kasting-Smith-Cooper (KSC) equations that provide good fit to data for maximum flux, from water through mouse or human skin also provide a good fit to data for maximum fluxes through silicone membranes (polydimethylsiloxane, PDMS). The maximum fluxes through silicone membranes from water (J(MPAQ)), molecular weights (MW), solubilities in isopropyl myristate (S(IPM)) and water (S(AQ)) of 31 prodrugs and one parent drug have been fitted to the RS equation, which includes a parameter for dependence on S(AQ), and the KSC equation, which does not, to determine which equation gave the better fit. In addition, the J(MPAQ), MW, S(AQ) and solubilities in octanol (S(OCT)) of 26 diverse molecules from other laboratories were collected and fitted to the RS and KSC equations to determine if the choice of lipid parameter (S(IPM) or S(OCT)) had an effect on which equation gave the better fit. RS gave the better fit to the present prodrug database where: logJ(MPAQ)=-2.454+0.716 logS(IPM)+0.284 logS(AQ)+0.00208 MW, r(2)=0.77. RS also gave the better fit to the database from other laboratories where: logJ(MPAQ)=-2.046+0.667 logS(OCT)+0.333 logS(AQ)-0.00374 MW, r(2)=0.878 after four obvious outliers were removed to give n=22. Thus, data for J(MPAQ) can be fitted to the RS equation, which also provides the best fit to maximum flux from water through mouse or human skin and includes a dependence on S(AQ).

Framework for use of toxicity screening tools in context-based decision-making

One of the principal applications of toxicology data is to inform risk assessments and support risk management decisions that are protective of human health. Ideally, a risk assessor would have available all of the relevant information on (a) the toxicity profile of the agent of interest; (b) its interactions with living systems; and (c) the known or projected exposure scenarios: to whom, how much, by which route(s), and how often. In practice, however, complete information is seldom available. Nonetheless, decisions still must be made. Screening-level assays and tools can provide support for many aspects of the risk assessment process, as long as the limitations of the tools are understood and to the extent that the added uncertainty the tools introduce into the process can be characterized and managed. Use of these tools for decision-making may be an end in itself for risk assessment and decision-making or a preliminary step to more extensive data collection and evaluation before assessments are undertaken or completed and risk management decisions made. This paper describes a framework for the application of screening tools for human health decision-making, although with some modest modification, it could be made applicable to environmental settings as well. The framework consists of problem formulation, development of a screening strategy based on an assessment of critical data needs, and a data analysis phase that employs weight-of-evidence criteria and uncertainty analyses, and leads to context-based decisions. Criteria for determining the appropriate screening tool(s) have been identified. The choice and use of the tool(s) will depend on the question and the level of uncertainty that may be appropriate for the context in which the decision is being made. The framework is iterative, in that users may refine the question(s) as they proceed. Several case studies illustrate how the framework may be used effectively to address specific questions for any endpoint of toxicity.