Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

Artificial intelligence for skin permeability prediction: deep learning

BACKGROUND AND OBJECTIVE

Researchers have put in significant laboratory time and effort in measuring the permeability coefficient (Kp) of xenobiotics. To develop alternative approaches to this labour-intensive procedure, predictive models have been employed by scientists to describe the transport of xenobiotics across the skin. Most quantitative structure-permeability relationship (QSPR) models are derived statistically from experimental data. Recently, artificial intelligence-based computational drug delivery has attracted tremendous interest. Deep learning is an umbrella term for machine-learning algorithms consisting of deep neural networks (DNNs). Distinct network architectures, like convolutional neural networks (CNNs), feedforward neural networks (FNNs), and recurrent neural networks (RNNs), can be employed for prediction.

METHODS

In this project, we used a convolutional neural network, feedforward neural network, and recurrent neural network to predict skin permeability coefficients from a publicly available database reported by Cheruvu et al. The dataset contains 476 records of 145 chemicals, xenobiotics, and pharmaceuticals, administered on the human epidermis in vitro from aqueous solutions of constant concentration either saturated in infinite dose quantities or diluted. All the computations were conducted with Python under Anaconda and Jupyterlab environment after importing the required Python, Keras, and Tensorflow modules.

RESULTS

We used a convolutional neural network, feedforward neural network, and recurrent neural network to predict log kp.

CONCLUSION

This research work shows that deep learning networks can be successfully used to digitally screen and predict the skin permeability of xenobiotics.

Electrical Impedance Spectroscopy Quantifies Skin Barrier Function in Organotypic In Vitro Epidermis Models

Three-dimensional human epidermal equivalents (HEEs) are a state-of-the-art organotypic culture model in preclinical investigative dermatology and regulatory toxicology. In this study, we investigated the utility of electrical impedance spectroscopy (EIS) for noninvasive measurement of HEE epidermal barrier function. Our setup comprised a custom-made lid fit with 12 electrode pairs aligned on the standard 24-transwell cell culture system. Serial EIS measurements for 7 consecutive days did not impact epidermal morphology, and readouts showed comparable trends with HEEs measured only once. We determined 2 frequency ranges in the resulting impedance spectra: a lower frequency range termed EISdiff correlated with keratinocyte terminal differentiation independent of epidermal thickness and a higher frequency range termed EISSC correlated with stratum corneum thickness. HEEs generated from CRISPR/Cas9-engineered keratinocytes that lack key differentiation genes FLG, TFAP2A, AHR, or CLDN1 confirmed that keratinocyte terminal differentiation is the major parameter defining EISdiff. Exposure to proinflammatory psoriasis- or atopic dermatitis-associated cytokine cocktails lowered the expression of keratinocyte differentiation markers and reduced EISdiff. This cytokine-associated decrease in EISdiff was normalized after stimulation with therapeutic molecules. In conclusion, EIS provides a noninvasive system to consecutively and quantitatively assess HEE barrier function and to sensitively and objectively measure barrier development, defects, and repair.

Electrical Impedance Spectroscopy Quantifies Skin Barrier Function in Organotypic In Vitro Epidermis Models

3 D human epidermal equivalents (HEEs) are a state-of-the-art organotypic culture model in pre-clinical investigative dermatology and regulatory toxicology. Here, we investigated the utility of electrical impedance spectroscopy (EIS) for non-invasive measurement of HEE epidermal barrier function. Our setup comprised a custom-made lid fit with 12 electrode pairs aligned on the standard 24-transwell cell culture system. Serial EIS measurements for seven consecutive days did not impact epidermal morphology and readouts showed comparable trends to HEEs measured only once. We determined two frequency ranges in the resulting impedance spectra: a lower frequency range termed EISdiff correlated with keratinocyte terminal differentiation independent of epidermal thickness and a higher frequency range termed EISSC correlated with stratum corneum thickness. HEEs generated from CRISPR/Cas9 engineered keratinocytes that lack key differentiation genes FLG, TFAP2A, AHR or CLDN1 confirmed that keratinocyte terminal differentiation is the major parameter defining EISdiff. Exposure to pro-inflammatory psoriasis- or atopic dermatitis-associated cytokine cocktails lowered the expression of keratinocyte differentiation markers and reduced EISdiff. This cytokine-associated decrease in EISdiff was normalized after stimulation with therapeutic molecules. In conclusion, EIS provides a non-invasive system to consecutively and quantitatively assess HEE barrier function and to sensitively and objectively measure barrier development, defects and repair.

A compartment model to predict in vitro finite dose absorption of chemicals by human skin

Dermal uptake is an important and complex exposure route for a wide range of chemicals. Dermal exposure can occur due to occupational settings, pharmaceutical applications, environmental contamination, or consumer product use. The large range of both chemicals and scenarios of interest makes it difficult to perform generalizable experiments, creating a need for a generic model to simulate various scenarios. In this study, a model consisting of a series of four well-mixed compartments, representing the source solution (vehicle), stratum corneum, viable tissue, and receptor fluid, was developed for predicting dermal absorption. The model considers experimental conditions including small applied doses as well as evaporation of the vehicle and chemical. To evaluate the model assumptions, we compare model predictions for a set of 26 chemicals to finite dose in-vitro experiments from a single laboratory using steady-state permeability coefficient and equilibrium partition coefficient data derived from in-vitro experiments of infinite dose exposures to these same chemicals from a different laboratory. We find that the model accurately predicts, to within an order of magnitude, total absorption after 24 h for 19 of these chemicals. In combination with key information on experimental conditions, the model is generalizable and can advance efficient assessment of dermal exposure for chemical risk assessment.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

Molecular docking and physicochemical studies of 1,3-benzodioxole tagged Dacarbazine derivatives as an anticancer agent

Cancer, the biggest cause of death globally, remains a tough illness despite enormous advances in therapy. In the present study, 1,3-benzodioxole-tagged dacarbazine derivates were investigated as microtubule inhibitors in order to control cancer as microtubules are involved in cell proliferation. The tubulin protein was analyzed and its structure was validated by various protein validation tools. The binding potential of 1,3-benzodioxole-based dacarbazine-tagged derivatives with tubulin was checked using molecular docking software HEX 8.0 CUDA and AutoDock Vina. Swiss ADME online Web server and pkCSM are used for studying pharmacokinetic and pharmacological studies of compounds. The docking analysis ADME studies displayed that Compounds 1 and 2 bind effectively with the tubulin protein and showed potential properties to use as a potent anticancer drug.

Fragment contribution models for predicting skin permeability using HuskinDB

Mathematical models to predict skin permeation tend to be based on animal derived experimental data as well as knowing physicochemical properties of the compound under investigation, such as molecular volume, polarity and lipophilicity. This paper presents a strikingly contrasting model to predict permeability, formed entirely from simple chemical fragment (functional group) data and a recently released, freely accessible human (i.e. non-animal) skin permeation database, known as the 'Human Skin Database - HuskinDB'. Data from within the database allowed development of several fragment-based models, each including a calculable effect for all of the most commonly encountered functional groups present in compounds within the database. The developed models can be applied to predict human skin permeability (logKp) for any compound containing one or more of the functional groups analysed from the dataset with no need to know any other physicochemical properties, solely the type and number of each functional group within the chemical structure itself. This approach simplifies mathematical prediction of permeability for compounds with similar properties to those used in this study.

New lipophilic organic nitrates: candidates for chronic skin disease therapy

Organic nitrates are widely used, but their chronic efficacy is blunted due to the development of tolerance. The properties of new tolerance free organic nitrates were studied. Their lipophilicity profile and passive diffusion across polydimethylsiloxane membrane and pig ear-skin, and their efficacy in tissue regeneration using HaCaT keratinocytes were evaluated. The permeation results show that these nitrates have a suitable profile for NO topical administration on the skin. Furthermore, the derivatives with higher NO release exerted a pro-healing effect on HaCaT cells. This new class of organic nitrates might be a promising strategy for the chronic treatment of skin pathologies.

Preliminary study to identify CXCR4 inhibitors as potential therapeutic agents for Alzheimer's and Parkinson's diseases

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.

Formulation and Skin Permeation of Active-Loaded Lipid Nanoparticles: Evaluation and Screening by Synergizing Molecular Dynamics Simulations and Experiments

Encapsulation into nanoparticles (NPs) is a potential method to deliver pharmaceutical/cosmetic actives deep into the skin. However, understanding the NP formulations and underlying mechanism of active delivery to skin has scarcely been studied. We report a simulation platform that screens, evaluates, formulates, and provides atomic-resolution interpretation of NP-based formulations, and reveals the active permeation mechanism from NPs to skin. First, three actives, namely, ferulic acid (FA), clotrimazole (CZE), and tretinoin (TTN), and five lipid excipients' (Compritol, Precirol, Geleol, Gelot, Gelucire) combinations were screened by MD simulations for the best pairs. For each suggested pair, the actual active and lipid compositions for the synthesis of stable NP formulations were then obtained by experiments. MD simulations demonstrate that in NP formulations, FA and CZE actives are present at the surface of the NPs, whereas TTN actives are present at both the surface and interior of the NP core. The NP shapes obtained by simulation perfectly match with experiments. For each NP, separate MD simulations illustrate that active-loaded NPs approach the skin surface quickly, and then actives translocate from NP surface to skin surface followed by penetration of NPs through skin. The driving force for the translocation which initiates during the penetration process, is the stronger active-skin interaction compared to active-NP interaction. Permeation free energy indicates spontaneous transfer of actives from solution phase to the surface of the skin bilayer. The free energy barriers are increased in the order of FA < TTN < CZE. Significantly lower diffusions of actives are obtained in the main barrier region compared to bulk, and the average diffusion coefficients of actives are in the same order of magnitude (∼10^-6 cm²/s). The estimated permeability coefficients (log P) of actives are mainly governed by free energy barriers. The study would facilitate the development of novel lipid-based NP formulations for personal-care/pharmaceutical applications.

Skin permeability prediction with MD simulation sampling spatial and alchemical reaction coordinates

A molecular-level understanding of skin permeation may rationalize and streamline product development, and improve quality and control, of transdermal and topical drug delivery systems. It may also facilitate toxicity and safety assessment of cosmetics and skin care products. Here, we present new molecular dynamics simulation approaches that make it possible to efficiently sample the free energy and local diffusion coefficient across the skin's barrier structure to predict skin permeability and the effects of chemical penetration enhancers. In particular, we introduce a new approach to use two-dimensional reaction coordinates in the accelerated weight histogram method, where we combine sampling along spatial coordinates with an alchemical perturbation virtual coordinate. We present predicted properties for 20 permeants, and demonstrate how our approach improves correlation with ex vivo/in vitro skin permeation data. For the compounds included in this study, the obtained log KPexp-calc mean square difference was 0.9 cm2 h-2.

Predicting skin permeability using HuskinDB

A freely accessible database has recently been released that provides measurements available in the literature on human skin permeation data, known as the ‘Human Skin Database – HuskinDB’. Although this database is extremely useful for sourcing permeation data to help with toxicity and efficacy determination, it cannot be beneficial when wishing to consider unlisted, or novel compounds. This study undertakes analysis of the data from within HuskinDB to create a model that predicts permeation for any compound (within the range of properties used to create the model). Using permeability coefficient (K_p) data from within this resource, several models were established for K_p values for compounds of interest by varying the experimental parameters chosen and using standard physicochemical data. Multiple regression analysis facilitated creation of one particularly successful model to predict K_p through human skin based only on three chemical properties. The model transforms the dataset from simply a resource of information to a more beneficial model that can be used to replace permeation testing for a wide range of compounds.

In Silico Prediction of Skin Permeability Using a Two-QSAR Approach

Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.

Interactions of selected cardiovascular active natural compounds with CXCR4 and CXCR7 receptors: a molecular docking, molecular dynamics, and pharmacokinetic/toxicity prediction study

BACKGROUND

The chemokine CXCL12 and its two receptors (CXCR4 and CXCR7) are involved in inflammation and hematopoietic cell trafficking. This study was designed to investigate molecular docking interactions of four popular cardiovascular-active natural compounds; curcumin, resveratrol, quercetin, and eucalyptol; with these receptors and to predict their drug-like properties. We hypothesize that these compounds can modify CXCL12/CXCR4/CXCR7 pathway offering benefits for coronary artery disease patients.

METHODS

Docking analyses were carried and characterized by Molecular Environment (MOE) software. Protein Data Bank ( http://www.rcsb.org/ ) has been retrieved from protein structure generation and crystal structures of CXCR4 and CXCR7 receptors (PDB code = 3ODU and 6K3F). The active sites of these receptors were evaluated and extracted from full protein and molecular docking protocol was done for compounds against them. The presented parameters included docking scores, ligand binding efficiency, and hydrogen bonding. The pharmacokinetic/toxic properties (ADME/T) were calculated using SwissADME, ProTox-II, and Pred-hERG softwares to predict drug-like properties of the compounds. The thermochemical and molecular orbital analysis, and molecular dynamics simulations were also done.

RESULTS

All compounds showed efficient interactions with the CXCR4 and CXCR7 receptors. The docking scores toward proteins 3ODU of CXCR4 and 6K3F of CXCR7 were - 7.71 and - 7.17 for curcumin, - 5.97 and - 6.03 for quercetin, - 5.68 and - 5.49 for trans-resveratrol, and - 4.88 and - 4.70 for (1 s,4 s)-eucalyptol respectively indicating that all compounds, except quercetin, have more interactions with CXCR4 than with CXCR7. The structurally and functionally important residues in the interactive sites of docked CXCR4-complex and CXCR7-complex were identified. The ADME analysis showed that the compounds have drug-like properties. Only (1 s,4 s)-Eucalyptol has potential weak cardiotoxicity. The results of thermochemical and molecular orbital analysis and molecular dynamics simulation validated outcomes of molecular docking study.

CONCLUSIONS

Curcumin showed the top binding interaction against active sites of CXCR4 and CXCR7 receptors, with the best safety profile, followed by quercetin, resveratrol, and eucalyptol. All compounds demonstrated drug-like properties. Eucalyptol has promising potential because it can be used by inhalation or skin massage. To our knowledge, this is the first attempt to find binding interactions of these natural agents with CXCR4 and CXCR7 receptors and to predict their druggability.

Does skin permeation kinetics influence efficacy of topical dermal drug delivery system?: Assessment, prediction, utilization, and integration of chitosan biomacromolecule for augmenting topical dermal drug delivery in skin

Skin permeation is an integral part of penetration of topical therapeutics. Zero order in addition to Higuchi permeation kinetic is usually preferred in topical drug delivery cargo. Penetration of therapeutic entities through epidermal barrier is a major challenge for scientific fraternity. Furthermore, penetration of therapeutic entities determines the transportation and ultimately therapeutic efficacy of topical dermal dosage forms. Apart from experimentation models, mathematical equations, in silico docking, molecular dynamics (MDs), and artificial neural network (Neural) techniques are being used to assess free energies and prediction of electrostatic attractions in order to predict the permeation phenomena of therapeutic entities. Therefore, in the present review, we have summarized the significance of kinetic equations, in silico docking, MDs, and ANN in assessing and predicting the penetration behavior of topical therapeutics through dermal dosage form. In addition, the role of chitosan biomacromolecule in modulating permeation of topical therapeutics in skin has also been illustrated using computational techniques.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Topical drug delivery: History, percutaneous absorption, and product development

Topical products, widely used to manage skin conditions, have evolved from simple potions to sophisticated delivery systems. Their development has been facilitated by advances in percutaneous absorption and product design based on an increasingly mechanistic understanding of drug-product-skin interactions, associated experiments, and a quality-by-design framework. Topical drug delivery involves drug transport from a product on the skin to a local target site and then clearance by diffusion, metabolism, and the dermal circulation to the rest of the body and deeper tissues. Insights have been provided by Quantitative Structure Permeability Relationships (QSPR), molecular dynamics simulations, and dermal Physiologically Based PharmacoKinetics (PBPK). Currently, generic product equivalents of reference-listed products dominate the topical delivery market. There is an increasing regulatory interest in understanding topical product delivery behavior under 'in use' conditions and predicting in vivo response for population variations in skin barrier function and response using in silico and in vitro findings.

Molecular dynamics simulations to elucidate translocation and permeation of active from lipid nanoparticle to skin: complemented by experiments

One of the most realistic approaches for delivering actives (pharmaceuticals/cosmetics) deep into skin layers is encapsulation into nanoparticles (NPs). Nonetheless, molecular-level mechanisms related to active delivery from NPs to the skin have scarcely been studied despite the large number of synthesis and characterization studies. We herein report the underlying mechanism of active translocation and permeation through the outermost layer of skin, the stratum corneum (SC), via molecular dynamics (MD) simulations complemented by experimental studies. A SC molecular model is constructed using current state-of-the-art methodology via incorporating the three most abundant skin lipids: ceramides, free fatty acids, and cholesterol. As a potent antioxidant, ferulic acid (FA) is used as the model active, and it is loaded into Gelucire 50/13 NP. MD simulations elucidate that, first, FA-loaded NP approaches the skin surface quickly, followed by slight penetration and adsorption onto the upper skin surface; FA then translocates from the NP surface to the skin surface due to stronger NP-skin interactions compared to the FA-NP interactions; then, once FA is released onto the skin surface, it slowly permeates deep into the skin bilayer. Both the free energy and resistance to permeation not only indicate the spontaneous transfer of FA from the bulk to the skin surface, but they also reveal that the main barrier against permeation exists in the middle of the lipid hydrophobic tails. Significantly lower diffusion of FA is obtained in the main barrier region compared to the bulk. The estimated permeability coefficient (log P) values are found to be higher than the experimental values. Importantly, the permeation process evaluated via MD simulations perfectly matches with experiments. The study suggests a molecular simulation platform that provides various crucial insights relating to active delivery from loaded NP to skin, and it could facilitate the design and development of novel NP-based formulations for transdermal delivery and the topical application of drugs/cosmetics.

Tuning the transdermal transport by application of external continuous electric field: a coarse-grained molecular dynamics study

The control of skin permeability to specific substances (e.g., medications, vitamins, and nutrients) through stratum corneum is a challenge. Iontophoresis is an option in spite of the lack of a detailed understanding of the underlying molecular mechanism. In the present work, the simulations concerning application of an external continuous electric field to stratum corneum, in a range of low intensity (0-24 mV nm^-1), were carried out using the coarse-grained molecular dynamics approach. Using a set of random seed replicas of the starting configuration, we observed that in the range of electric field intensity of 22-23 mV nm^-1, water-rich lipid vesicles were formed in 20% of cases. Pores appeared in the remaining 80%. We argue that lipids undergo fast re-orientations under electric field inducing mechanical instability, which originates the pores. We presented a simple electrostatic model to interpret the results where the mismatch between electrical permittivities of the membrane and external media and the gradient of the local electric field in the membrane surface, govern the time scales and electric fields for vesicle formation. Our results indicate that just 10% difference between electrical permittivities of the membrane and external media decreases 1/6 the minimal time required for vesicle formation. The minimal electric field required decreases 10 times. The control and tunning of formation of biologically compatible vesicles, capable of transporting substances under low-intensity electric fields, has a promising application in fields such as drug therapy and dermo-cosmetics allowing the use of hydrophilic substances in dermal applications.

In Silico Simulation of the Systemic Drug Exposure Following the Topical Application of Opioid Analgesics in Patients with Cutaneous Lesions

The use of opioid analgesics in treating severe pain is frequently associated with putative adverse effects in humans. Topical agents that are shown to have high efficacy with a favorable safety profile in clinical settings are great alternatives for pain management of multimodal analgesia. However, the risk of side effects induced by transdermal absorption and systemic exposure is of great concern as they are challenging to predict. The present study aimed to use "BIOiSIM" an artificial intelligence-integrated biosimulation platform to predict the transdermal disposition of opioid analgesics. The model successfully predicted their exposure following the topical application of central opioid agonist buprenorphine and peripheral agonist oxycodone in healthy human subjects with simulation of intra-skin exposure in subjects with burns and pressure wounds. The predicted plasma levels of analgesics were used to evaluate the safety of the therapeutic pain control in patients with the dermal structural impairments caused by acute (burns) or chronic cutaneous lesions (pressure wounds) with topical opioid analgesics.

Challenges and opportunities for small volumes delivery into the skin

Each individual's skin has its own features, such as strength, elasticity, or permeability to drugs, which limits the effectiveness of one-size-fits-all approaches typically found in medical treatments. Therefore, understanding the transport mechanisms of substances across the skin is instrumental for the development of novel minimal invasive transdermal therapies. However, the large difference between transport timescales and length scales of disparate molecules needed for medical therapies makes it difficult to address fundamental questions. Thus, this lack of fundamental knowledge has limited the efficacy of bioengineering equipment and medical treatments. In this article, we provide an overview of the most important microfluidics-related transport phenomena through the skin and versatile tools to study them. Moreover, we provide a summary of challenges and opportunities faced by advanced transdermal delivery methods, such as needle-free jet injectors, microneedles, and tattooing, which could pave the way to the implementation of better therapies and new methods.

Comprehensive two-dimensional liquid chromatography as a biomimetic screening platform for pharmacokinetic profiling of compound libraries in early drug development

A comprehensive two-dimensional liquid chromatography-based biomimetic platform (LCxLC) has been developed and validated for drug diffusion studies. Human serum albumin (HSA) and immobilized artificial membrane (IAM) were thereby used in the first (¹D) and second (²D) separation dimension, respectively. While the former was meant to emulate the blood, the latter was instead intended to mimic the intestinal mucosa epithelium. Therefore, the experimental conditions, i.e. pH, temperature and buffer composition, were modulated to reflect faithfully in vivo conditions. 30 compounds, whose effective intestinal permeability (P_eff) assayed in situ on humans by a validated technique was known from the literature, were used as model drugs. A good and orthogonal separation was achieved for the whole dataset, although for a better distribution of the most polar compounds in the elution window a segmented gradient elution program had to be employed. Interestingly, the passively uptaken compounds having the most favourable P_eff populated a specific area of the 2D plots, implying that the affinity for HSA and IAM has to lie in specific ranges in order for a compound to be satisfactorily absorbed from the intestinal lumen. Although these results should be regarded as preliminary, this work paves an entirely new and unprecedented way to profile pharmaceutically relevant compounds for their in vivo absorption and distribution potential.

QSAR models for the acute toxicity of 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos

In recent decades, the 1,2,4-triazole fungicides are widely used for crop diseases control, and their toxicity to wild lives and pollution to ecosystem have attracted more and more attention. However, how to quickly and efficiently evaluate the toxicity of these compounds to environmental organisms is still a challenge. In silico method, such like Quantitative Structure-Activity Relationship (QSAR), provides a good alternative to evaluate the environmental toxicity of a large number of chemicals. At the present study, the acute toxicity of 23 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos was firstly tested, and the LC₅₀ (median lethal concentration) values were used as the bio-activity endpoint to conduct QSAR modelling for these triazoles. After the comparative study of several QSAR models, the 2D-QSAR model was finally constructed using the stepwise multiple linear regression algorithm combining with two physicochemical parameters (logD and μ), an electronic parameter (Q_N1) and a topological parameter (X^vPC₄). The optimal model could be mathematically described as following: pLC₅₀ = -7.24-0.30X^vPC₄ + 0.76logD - 26.15Q_N1 - 0.08μ. The internal validation by leave-one-out (LOO) cross-validation showed that the R²_adj (adjusted noncross-validation squared correlation coefficient), Q² (cross-validation correlation coefficient) and RMSD (root-mean-square error) was 0.88, 0.84 and 0.17, respectively. The external validation indicated the model had a robust predictability with the q² (predictive squared correlation coefficient) of 0.90 when eliminated tricyclazole. The present study provided a potential tool for predicting the acute toxicity of new 1,2,4-triazole fungicides which contained an independent triazole ring group in their molecules to zebrafish embryos, and also provided a reference for the development of more environmentally-friendly 1,2,4-triazole pesticides in the future.