Quantitative Structure-permeability Relationships Research Articles

Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays Using Cell Monolayers.

The ability to predict the rate of permeation of new compounds across biological membranes is of high importance for their success as drugs, as it determines their efficacy, pharmacokinetics, and safety profile. In vitro permeability assays using Caco-2 monolayers are commonly employed to assess permeability across the intestinal epithelium, with an extensive number of apparent permeability coefficient (Papp) values available in the literature and a significant fraction collected in databases. The compilation of these Papp values for large datasets allows for the application of artificial intelligence tools for establishing quantitative structure-permeability relationships (QSPRs) to predict the permeability of new compounds from their structural properties. One of the main challenges that hinders the development of accurate predictions is the existence of multiple Papp values for the same compound, mostly caused by differences in the experimental protocols employed. This review addresses the magnitude of the variability within and between laboratories to interpret its impact on QSPR modelling, systematically and quantitatively assessing the most common sources of variability. This review emphasizes the importance of compiling consistent Papp data and suggests strategies that may be used to obtain such data, contributing to the establishment of robust QSPRs with enhanced predictive power.

Predicting Human Dermal Drug Concentrations Using PBPK Modeling and Simulation: Clobetasol Propionate Case Study

Quantitative in silico tools may be leveraged to mechanistically predict the dermato-pharmacokinetics of compounds delivered from topical and transdermal formulations by integrating systems of rate equations that describe permeation through the formulation and layers of skin and pilo-sebaceous unit, and exchange with systemic circulation via local blood flow. Delivery of clobetasol-17 propionate (CP) from DermovateTM cream was simulated using the Transdermal Compartmental Absorption & Transit (TCATTM) Model in GastroPlus®. The cream was treated as an oil-in-water emulsion, with model input parameters estimated from publicly available information and quantitative structure-permeation relationships. From the ranges of values available for model input parameters, a set of parameters was selected by comparing model outputs to CP dermis concentration-time profiles measured by dermal open-flow microperfusion (Bodenlenz et al. Pharm Res. 33(9):2229–38, 2016). Predictions of unbound dermis CP concentrations were reasonably accurate with respect to time and skin depth. Parameter sensitivity analyses revealed considerable dependence of dermis CP concentration profiles on drug solubility in the emulsion, relatively less dependence on dispersed phase volume fraction and CP effective diffusivity in the continuous phase of the emulsion, and negligible dependence on dispersed phase droplet size. Effects of evaporative water loss from the cream and corticosteroid-induced vasoconstriction were also assessed. This work illustrates the applicability of computational modeling to predict sensitivity of dermato-pharmacokinetics to changes in thermodynamic and transport properties of a compound in a topical formulation, particularly in relation to rate-limiting steps in skin permeation. Where these properties can be related to formulation composition and processing, such a computational approach may support the design of topically applied formulations.

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.

DIGITAL IMAGES ANALYSIS AND QUANTITATIVE STRUCTURE-PERMEABILITY RELATIONSHIPS

In this work, multiple linear regression was used to obtain mathematical models with which it is possible to predict the permeability of isotropic porous media. With this aim, a database containing the binary files of the digital images of a wide variety of structures was built. These files allowed: 1) the extraction of statistical and morphological descriptors of the solid and void phases that were used as independent variables, and 2) the calculation of permeability (dependent variable) by using the lattice Boltzmann method (LBM). The selection of the descriptors that constitute the models was carried out according to the stepwise method with backward elimination. In order to fulfill the linearity assumption, it was necessary to transform some of the descriptors by taking their natural logarithm. After removing the influential values, the regressions were analyzed by using different statistics and hypotheses testing. One of the models was able to explain the 93.3% of the variability of permeability as a function of the porous structure.

Evaluation of selected quantitative structure permeability relationship (QSPR) based mathematical models for the prediction of skin permeability of Camellia sinensis (tea) compounds

Background: Skin permeability coefficient (logKp) is a major determinant for topical drugs. In vitro and ex vivo determination of logKp is expensive, time and labour intensive, and difficult to apply to large databases. QSPR models derived from statistical correlation between descriptors of the compounds with the in vitro or ex vivo permeation data, are therefore used extensively. Vast number of QSPR equations makes the selection of a particular equation to screen a database, difficult choice. Objective: This study has evaluated common descriptor based equations for selection of the best suitable equation for screening phytochemical library of Camellia sinensis. Methods: Seven QSPR based models were used to estimate and compare the logKp of tea compounds. The best method was selected with respect to the gold standard logKp. Result: The model of Potts and Guy showed close proximation with the gold standard logKp and had highest association along with least RMSE value and least deviation. According to this method, approximately, 37.38%, 35.35% and 27.27% of the tea compounds were found to have high, good and poor logKp respectively. Conclusion: Potts and Guy equation can be effectively used to screen the phytochemical library of Camellia sinensis. This study has potential application in the field of topical medicine and cosmetics.

Corneal Permeability and Uptake of Twenty-Five Drugs: Species Comparison and Quantitative Structure-Permeability Relationships.

The purpose of this study was to determine corneal permeability and uptake in rabbit, porcine, and bovine corneas for twenty-five drugs using an N-in-1 (cassette) approach and relate these parameters to drug physicochemical properties and tissue thickness through quantitative structure permeability relationships (QSPRs). A twenty-five-drug cassette containing β-blockers, NSAIDs, and corticosteroids in solution at a micro-dose was exposed to the epithelial side of rabbit, porcine, or bovine corneas mounted in a diffusion chamber, and the corneal drug permeability and tissue uptake were monitored using an LC-MS/MS method. Data obtained were used to construct and evaluate over 46,000 quantitative structure-permeability (QSPR) models using multiple linear regression, and the best-fit models were cross-validated by Y-randomization. Drug permeability was generally higher in rabbit cornea and comparable between bovine and porcine corneas. Permeability differences between species could be explained in part by differences in corneal thickness. Corneal uptake between species correlated with a slope close to 1, indicating generally similar drug uptake per unit weight of tissue. A high correlation was observed between bovine, porcine, and rabbit corneas for permeability and between bovine and porcine corneas for uptake (R2 ≥ 0.94). MLR models indicated that drug characteristics such as lipophilicity (LogD), heteroatom ratio (HR), nitrogen ratio (NR), hydrogen bond acceptors (HBA), rotatable bonds (RB), index of refraction (IR), and tissue thickness (TT) are of great influence on drug permeability and uptake. When data for all species along with thickness as a parameter was used in MLR, the best fit equation for permeability was Log (% transport/cm2·s) = 0.441 LogD - 8.29 IR + 8.357 NR - 0.279 HBA - 3.833 TT + 10.432 (R2 = 0.826), and the best-fit equation for uptake was Log (%/g) = 0.387 LogD + 4.442 HR + 0.105 RB - 0.303 HBA - 2.235 TT + 1.422 (R2 = 0.750). Thus, it is feasible to explain corneal drug delivery in three species using a single equation.

In vitro and in silico computational methods for assessing vaginal permeability

Objective Vaginal administration is an important alternative to the oral route for both topical and systemic use. Therefore, the development of reliable in silico methods for the study of drugs permeability is becoming popular in order to avoid time-consuming and costly experiments. Methods In the current study, Franz cells and appropriate HPLC or ESI-Q/MS analytical methods were used to experimentally measure the apparent permeability coefficient (P app) of 108 compounds (drugs and non-drugs). P app values were then correlate with 75 molecular descriptors (physicochemical, structural, and pharmacokinetic) by developing two Quantitative Structure Permeability Relationship (QSPR) models, a Partial Least Square (PLS) and a Support Vector Machine (SVM). Both were validated by internal, external and cross-validation. Results Based on the calculated statistical parameters (PLS model A: R 2 = 0.673 and Q 2 = 0.594, PLS model B: R 2 = 0.902 and Q 2 = 0.631, SVM: R 2 = 0.708 and Q 2 = 0.758). SVM presents higher predictability while PLS adequately interprets the theory of permeability. Conclusions The most important parameters for vaginal permeability were found to be the relative PSA, logP, logD, water solubility and fraction unbound (FU). Respectively, the combination of both models could be a useful tool for understanding and predicting the vaginal permeability of drug candidates.

Mechanistic Insights into Passive Membrane Permeability of Drug-like Molecules from a Weighted Ensemble of Trajectories.

Passive permeability of a drug-like molecule is a critical property assayed early in a drug discovery campaign that informs a medicinal chemist how well a compound can traverse biological membranes, such as gastrointestinal epithelial or restrictive organ barriers, so it can perform a specific therapeutic function. However, the challenge that remains is the development of a method, experimental or computational, which can both determine the permeation rate and provide mechanistic insights into the transport process to help with the rational design of any given molecule. Typically, one of the following three methods are used to measure the membrane permeability: (1) experimental permeation assays acting on either artificial or natural membranes; (2) quantitative structure–permeability relationship models that rely on experimental values of permeability or related pharmacokinetic properties of a range of molecules to infer those for new molecules; and (3) estimation of permeability from the Smoluchowski equation, where free energy and diffusion profiles along the membrane normal are taken as input from large-scale molecular dynamics simulations. While all these methods provide estimates of permeation coefficients, they provide very little information for guiding rational drug design. In this study, we employ a highly parallelizable weighted ensemble (WE) path sampling strategy, empowered by cloud computing techniques, to generate unbiased permeation pathways and permeability coefficients for a set of drug-like molecules across a neat 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine membrane bilayer. Our WE method predicts permeability coefficients that compare well to experimental values from an MDCK-LE cell line and PAMPA assays for a set of drug-like amines of varying size, shape, and flexibility. Our method also yields a series of continuous permeation pathways weighted and ranked by their associated probabilities. Taken together, the ensemble of reactive permeation pathways, along with the estimate of the permeability coefficient, provides a clearer picture of the microscopic underpinnings of small-molecule membrane permeation.

Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (Pe) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R2pred=-0.10; PLS- R2pred=0.64, r2m=0.69, r/2m=0.44; SVM- R2pred=0.57, r2m=0.72, r/2m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H0p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.

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.

Multivariate Analytical Approaches to Identify Key Molecular Properties of Vehicles, Permeants and Membranes That Affect Permeation through Membranes.

There has been considerable recent interest in employing computer models to investigate the relationship between the structure of a molecule and its dermal penetration. Molecular permeation across the epidermis has previously been demonstrated to be determined by a number of physicochemical properties, for example, the lipophilicity, molecular weight and hydrogen bonding ability of the permeant. However little attention has been paid to modeling the combined effects of permeant properties in tandem with the properties of vehicles used to deliver those permeants or to whether data obtained using synthetic membranes can be correlated with those obtained using human epidermis. This work uses Principal Components Analysis (PCA) to demonstrate that, for studies of the diffusion of three model permeants (caffeine, methyl paraben and butyl paraben) through synthetic membranes, it is the properties of the oily vehicle in which they are applied that dominated the rates of permeation and flux. Simple robust and predictive descriptor-based quantitative structure–permeability relationship (QSPR) models have been developed to support these findings by utilizing physicochemical descriptors of the oily vehicles to quantify the differences in flux and permeation of the model compounds. Interestingly, PCA showed that, for the flux of co-applied model permeants through human epidermis, the permeation of the model permeants was better described by a balance between the physicochemical properties of the vehicle and the permeant rather than being dominated solely by the vehicle properties as in the case of synthetic model membranes. The important influence of permeant solubility in the vehicle along with the solvent uptake on overall permeant diffusion into the membrane was substantiated. These results confirm that care must be taken in interpreting permeation data when synthetic membranes are employed as surrogates for human epidermis; they also demonstrate the importance of considering not only the permeant properties but also those of both vehicle and membrane when arriving at any conclusions relating to permeation data.

Integrating theoretical and experimental permeability estimations for provisional biopharmaceutical classification: Application to the WHO essential medicines.

The accuracy of the provisional estimation of the Biopharmaceutics Classification System (BCS) is heavily influenced by the permeability measurement. In this study, several theoretical and experimental models currently employed for BCS permeability classification have been analysed. The experimental models included the in situ rat intestinal perfusion, the ex vivo rat intestinal tissue in an Ussing chamber, the MDCK and Caco-2 cell monolayers, and the parallel artificial membrane (PAMPA). The theoretical models included the octanol-water partition coefficient and the QSPeR (Quantitative Structure-Permeability Relationship) model recently developed. For model validation, a dataset of 43 compounds has been recompiled and analysed for the suitability for BCS permeability classification in comparison with the use of human intestinal absorption and oral bioavailability values. The application of the final model, based on a majority voting system showed a 95.3% accuracy for predicting human permeability. Finally, the present approach was applied to the 186 orally administered drugs in immediate-release dosage forms of the WHO Model List of Essential Medicines. The percentages of the drugs that were provisionally classified as BCS Class I and Class III was 62.4%, suggesting that in vivo bioequivalence (BE) may potentially be assured with a less expensive and more easily implemented in vitro dissolution test, ensuring the efficiency and quality of pharmaceutical products. The results of the current study improve the accuracy of provisional BCS classification by combining different permeability models.

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

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

Study of Structure and Permeability Relationship of Flavonoids in Caco-2 Cells.

Flavonoids exhibit a broad range of biological activities. However, poor absorption of some flavonoids is a major limitation for use of flavonoids as nutraceuticals. To investigate the structure requirements for flavonoids intestinal absorption, transepithelial transport and cellular accumulation (CA) of 30 flavonoids were determined using the Caco-2 cell monolayer. The bilateral permeation of five types of flavonoids followed the order: flavanones ≥ isoflavones > flavones ≥ chalcones > flavonols. The concentration of flavonoids accumulated in cells did not correlate with cell penetration since the correlation coefficient between the apparent permeability coefficient (Papp) and their corresponding CA was poor (R2 < 0.3). Most flavonoids exhibited a ratio of 0.8–1.5 for Papp A to B/Papp B to A, suggesting passive diffusion pathways. However, luteolin, morin and taxifolin may involve the efflux mechanisms. The quantitative structure-permeability relationship (QSPR) study demonstrated that the intestinal absorption of flavonoids can be related to atomic charges on carbon 3′ (QC3′), molecule surface area (SlogP_V3), balance between the center of mass and position of hydrophobic region (vsurf_ID1) and solvation energy of flavonoids (E_sol). These results provide useful information for initially screening of flavonoids with high intestinal absorption.

Quantitative structure-skin permeability relationships.

This paper reviews in silico models currently available for the prediction of skin permeability. A comprehensive discussion on the developed methods is presented, focusing on quantitative structure-permeability relationships. In addition, the mechanistic models and comparative studies that analyse different models are discussed. Limitations and strengths of the different approaches are highlighted together with the emergent issues and perspectives.

Use of biopartitioning micellar chromatography and RP-HPLC for the determination of blood–brain barrier penetration of α-adrenergic/imidazoline receptor ligands, and QSPR analysis

For this study, 31 compounds, including 16 imidazoline/α-adrenergic receptor (IRs/α-ARs) ligands and 15 central nervous system (CNS) drugs, were characterized in terms of the retention factors (k) obtained using biopartitioning micellar and classical reversed phase chromatography (log kBMC and log kwRP, respectively). Based on the retention factor (log kwRP) and slope of the linear curve (S) the isocratic parameter (φ0) was calculated. Obtained retention factors were correlated with experimental log BB values for the group of examined compounds. High correlations were obtained between logarithm of biopartitioning micellar chromatography (BMC) retention factor and effective permeability (r(log kBMC/log BB): 0.77), while for RP-HPLC system the correlations were lower (r(log kwRP/log BB): 0.58; r(S/log BB): –0.50; r(φ0/Pe): 0.61). Based on the log kBMC retention data and calculated molecular parameters of the examined compounds, quantitative structure–permeability relationship (QSPR) models were developed using partial least squares, stepwise multiple linear regression, support vector machine and artificial neural network methodologies. A high degree of structural diversity of the analysed IRs/α-ARs ligands and CNS drugs provides wide applicability domain of the QSPR models for estimation of blood–brain barrier penetration of the related compounds.

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

Human intestinal absorption is a key property for orally administered drugs and is dependent on pH. This study focuses on neutral and amphoteric compounds and their membrane permeabilities across the range of pH values found in the human intestine. The membrane permeability values for 15 neutral and 60 amphoteric compounds at pH 3, 5, 7.4 and 9 were measured using the parallel artificial membrane permeability assay (PAMPA). For each data series the quantitative structure–permeability relationships were developed and analysed. The results show that the membrane permeability of neutral compounds is attributed to a single structural characteristic, the hydrogen bond donor ability. Amphoteric compounds are more complex because of their chemical constitution, and therefore require three-parameter models to describe and predict membrane permeability. Analysis of the models for amphoteric compounds reveals that membrane permeability depends on multiple structural characteristics: the partition coefficient, hydrogen bond properties and the shape of the molecules. In addition to conventional validation strategies, two external compounds (isradipine and omeprazole) were tested and revealed very good agreement of pH profiles between experimental and predicted membrane permeability for all of the developed models. Selected QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.184).

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.

Prediction of blood–brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis

Imidazoline receptor ligands are a numerous family of biologically active compounds known to produce central hypotensive effect by interaction with both α2-adrenoreceptors (α2-AR) and imidazoline receptors (IRs). Recent hypotheses connect those ligands with several neurological disorders. Therefore some IRs ligands are examined as novel centrally acting antihypertensives and drug candidates for treatment of various neurological diseases. Effective Blood–Brain Barrier (BBB) permeability (Pe) of 18 IRs/α-ARs ligands and 22 Central Nervous System (CNS) drugs was experimentally determined using Parallel Artificial Membrane Permeability Assay (PAMPA) and studied by the Quantitative-Structure-Permeability Relationship (QSPR) methodology. The dominant molecules/cations species of compounds have been calculated at pH=7.4. The analyzed ligands were optimized using Density Functional Theory (B3LYP/6-31G(d,p)) included in ChemBio3D Ultra 13.0 program and molecule descriptors for optimized compounds were calculated using ChemBio3D Ultra 13.0, Dragon 6.0 and ADMET predictor 6.5 software. Effective permeability of compounds was used as dependent variable (Y), while calculated molecular parametres were used as independent variables (X) in the QSPR study. SIMCA P+ 12.0 was used for Partial Least Square (PLS) analysis, while the stepwise Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) modeling were performed using STASTICA Neural Networks 4.0. Predictive potential of the formed models was confirmed by Leave-One-Out Cross- and external-validation and the most reliable models were selected. The descriptors that are important for model building are identified as well as their influence on BBB permeability. Results of the QSPR studies could be used as time and cost efficient screening tools for evaluation of BBB permeation of novel α-adrenergic/imidazoline receptor ligands, as promising drug candidates for treatment of hypertension or neurological diseases.