Potential of biopartitioning micellar chromatography as an in vitro technique for predicting drug penetration across the blood-brain barrier

Estimation of passive gastrointestinal absorption of new dual DNA gyrase and topoisomerase IV inhibitors using PAMPA and biopartitioning micellar chromatography and quantitative structure-retention relationship analysis

DNA gyrase and topoisomerase IV play significant role in maintaining the correct structure of DNA during replication and they have been identified as validated targets in antibacterial drug discovery. Inadequate pharmacokinetic properties are responsible for many failures during drug discovery and their estimation in the early phase of this process maximizes the chance of getting useful drug candidates. Passive gastrointestinal absorption of a selected group of thirteen dual DNA gyrase and topoisomerase IV inhibitors was estimated using two in vitro tests - parallel artificial membrane permeability assay (PAMPA) and biopartitioning micellar chromatography (BMC). Due to good correlation between obtained results, passive gastrointestinal absorption of remaining ten compounds was estimated using only BMC. With this experimental setup, it was possible to identify compounds with high values of retention factors (k) and highest expected passive gastrointestinal absorption, and compounds with low values of k for which low passive gastrointestinal absorption is predicted. Quantitative structure-retention relationship (QSRR) modelling was performed by creating multiple linear regression (MLR), partial least squares (PLS) and support vector machines (SVM) models. Descriptors with the highest influence on retention factor were identified and their interpretation can be used for the design of new compounds with improved passive gastrointestinal absorption.

Molecular and Pharmacokinetic Aspects of the Acetylcholinesterase-Inhibitory Potential of the Oleanane-Type Triterpenes and Their Glycosides

The acetylcholinesterase-inhibitory potential of the oleanane-type triterpenes and their glycosides from thebark of Terminalia arjuna (Combreatceae), i.e.,arjunic acid, arjunolic acid, arjungenin, arjunglucoside I, sericic acid and arjunetin, is presented. The studies are based on in silico pharmacokinetic and biomimetic studies, acetylcholinesterase (AChE)-inhibitory activity tests and molecular-docking research. Based on the calculated pharmacokinetic parameters, arjunetin and arjunglucoside I are indicated as able to cross the blood-brain barrier. The compounds of interest exhibit a marked acetylcholinesterase inhibitory potential, which was tested in the TLC bioautography test. The longest time to reach brain equilibrium is observed for both the arjunic and arjunolic acids and the shortest one for arjunetin. All of the compounds exhibit a high and relatively similar magnitude of binding energies, varying from ca. -15 to -13 kcal/mol. The superposition of the most favorable positions of all ligands interacting with AChE is analyzed. The correlation between the experimentally determined IC50 values and the steric parameters of the molecules is investigated. The inhibition of the enzyme by the analyzed compounds shows their potential to be used as cognition-enhancing agents. For the most potent compound (arjunglucoside I; ARG), the kinetics of AChE inhibition were tested. The Michaelis-Menten constant (Km) for the hydrolysis of the acetylthiocholine iodide substrate was calculated to be 0.011 mM.

Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds

The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.

Quantitative Retention (Structure)–Activity Relationships in Predicting the Pharmaceutical and Toxic Properties of Potential Pesticides

The micellar liquid chromatography technique and quantitative retention (structure)–activity relationships method were used to predict properties of carbamic and phenoxyacetic acids derivatives, newly synthesized in our laboratory and considered as potential pesticides. Important properties of the test substances characterizing their potential significance as pesticides as well as threats to humans were considered: the volume of distribution, the unbonded fractions, the blood–brain distribution, the rate of skin and cell permeation, the dermal absorption, the binding to human serum albumin, partitioning between water and plants’ cuticles, and the lethal dose. Pharmacokinetic and toxicity parameters were predicted as functions of the solutes’ lipophilicities and the number of hydrogen bond donors, the number of hydrogen bond acceptors, and the number of rotatable bonds. The equations that were derived were evaluated statistically and cross-validated. Important features of the molecular structure influencing the properties of the tested substances were indicated. The QSAR models that were developed had high predictive ability and high reliability in modeling the properties of the molecules that were tested. The investigations highlighted the applicability of combined chromatographic technique and QS(R)ARs in modeling the important properties of potential pesticides and reducing unethical animal testing.

Computer Assisted Models for Blood Brain Barrier Permeation of 1, 5-Benzodiazepines

AIM

To generate and validate predictive models for blood-brain permeation (BBB) of CNS molecules using the QSPR approach.

BACKGROUND

Prediction of molecules crossing BBB remains a challenge in drug delivery. Predictive models are designed for the evaluation of a set of preclinical drugs which may serve as alternatives for determining BBB permeation by experimentation.

OBJECTIVE

The objective of the present study was to generate QSPR models for the permeation of CNS molecules across BBB and its validation using existing in-house leads.

METHODS

The present study envisaged the determination of the set of molecular descriptors which are considered significant correlative factors for BBB permeation property. Quantitative Structure- Property Relationship (QSPR) approach was followed to describe the correlation between identified descriptors for 45 molecules and highest, moderate and least BBB permeation data. The molecular descriptors were selected based on drug-likeness, hydrophilicity, hydrophobicity, polar surface area, etc. of molecules that served the highest correlation with BBB permeation. The experimental data in terms of log BB were collected from available literature, subjected to 2D-QSPR model generation using a regression analysis method like Multiple Linear Regression (MLR).

RESULTS

The best QSPR model was Model 3, which exhibited regression coefficient as R²= 0.89, F = 36; Q²= 0.7805 and properties such as polar surface area, hydrophobic hydrophilic distance, electronegativity, etc., which were considered key parameters in the determination of the BBB permeability. The developed QSPR models were validated with in-house 1,5-benzodiazepines molecules and correlation studies were conducted between experimental and predicted BBB permeability.

CONCLUSION

The QSPR model 3 showed predictive results that were in good agreements with experimental results for blood-brain permeation. Thus, this model was found to be satisfactory in achieving a good correlation between selected descriptors and BBB permeation for benzodiazepines and tricyclic compounds.

Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials

The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood–brain distribution (log BB), fraction unbounded in the brain (f_u,brain), water-skin permeation (log K_p), binding to human plasma proteins (log K_a,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing.

Molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT-IR, FT-Raman), drug likeness and molecular docking of the novel anti COVID-2 molecule (2E)-N-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide (Dimer) - quantum chemical approach

Prospective antiviral molecule (2E)-N-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide has been probed using Fourier transform infrared (FTIR), FT-Raman and quantum chemical computations. The geometry equilibrium and natural bond orbital analysis have been carried out with density functional theory employing Becke, 3-parameter, Lee-Yang-Parr method with the 6-311G++(d,p) basis set. The vibrational assignments pertaining to different modes of vibrations have been augmented by normal coordinate analysis, force constant and potential energy distributions. Drug likeness and oral activity have been carried out based on Lipinski's rule of five. The inhibiting potency of 2(2E)-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide has been investigated by docking simulation against SARS-CoV-2 protein. The optimized geometry shows a planar structure between the chromone and the side chain. Differences in the geometries due to the substitution of the electronegative atom and intermolecular contacts due to the chromone and hydrazinecarbothioamide were analyzed. NBO analysis confirms the presence of two strong stable hydrogen bonded NH⋯O intermolecular interactions and two weak hydrogen bonded CH⋯O interactions. The red shift in NH stretching frequency exposed from IR substantiates the formation of NH⋯O intermolecular hydrogen bond and the blue shift in CH stretching frequency substantiates the formation of CH⋯O intermolecular hydrogen bond. Drug likeness, absorption, distribution, metabolism, excretion and toxicity property gives an idea about the pharmacokinetic properties of the title molecule. The binding energy of the nonbonding interaction with Histidine 41 and Cysteine 145, present a clear view that 2(2E)-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide can irreversibly interact with SARS-CoV-2 protease.

Biomimetic Chromatographic Studies Combined with the Computational Approach to Investigate the Ability of Triterpenoid Saponins of Plant Origin to Cross the Blood-Brain Barrier

Biomimetic (non-cell based in vitro) and computational (in silico) studies are commonly used as screening tests in laboratory practice in the first stages of an experiment on biologically active compounds (potential drugs) and constitute an important step in the research on the drug design process. The main aim of this study was to evaluate the ability of triterpenoid saponins of plant origin to cross the blood-brain barrier (BBB) using both computational methods, including QSAR methodology, and biomimetic chromatographic methods, i.e., High Performance Liquid Chromatography (HPLC) with Immobilized Artificial Membrane (IAM) and cholesterol (CHOL) stationary phases, as well as Bio-partitioning Micellar Chromatography (BMC). The tested compounds were as follows: arjunic acid (Terminalia arjuna), akebia saponin D (Akebia quinata), bacoside A (Bacopa monnieri) and platycodin D (Platycodon grandiflorum). The pharmacokinetic BBB parameters calculated in silico show that three of the four substances, i.e., arjunic acid, akebia saponin D, and bacoside A exhibit similar values of brain/plasma equilibration rate expressed as logPSFubrain (the average logPSFubrain: -5.03), whereas the logPSFubrain value for platycodin D is -9.0. Platycodin D also shows the highest value of the unbound fraction in the brain obtained using the examined compounds (0.98). In these studies, it was found out for the first time that the logarithm of the analyte-micelle association constant (logKMA) calculated based on Foley's equation can describe the passage of substances through the BBB. The most similar logBB values were obtained for hydrophilic platycodin D, applying both biomimetic and computational methods. All of the obtained logBB values and physicochemical parameters of the molecule indicate that platycodin D does not cross the BBB (the average logBB: -1.681), even though the in silico estimated value of the fraction unbound in plasma is relatively high (0.52). As far as it is known, this is the first paper that shows the applicability of biomimetic chromatographic methods in predicting the penetration of triterpenoid saponins through the BBB.

Quantum chemical insight into molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT-IR, FT-Raman), drug likeness and molecular docking of the novel anti COVID-19 molecule 2-[(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluorophenyl)acetamide - dimer

Novel antiviral active molecule 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro- phenyl)acetamide has been synthesised and characterized by FT-IR and FT-Raman spectra. The equilibrium geometry, natural bond orbital calculations and vibrational assignments have been carried out using density functional B3LYP method with the 6-311G++(d,p) basis set. The complete vibrational assignments for all the vibrational modes have been supported by normal coordinate analysis, force constants and potential energy distributions. A detailed analysis of the intermolecular interactions has been performed based on the Hirshfeld surfaces. Drug likeness has been carried out based on Lipinski's rule and the absorption, distribution, metabolism, excretion and toxicity of the title molecule has been calculated. Antiviral potency of 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro-phenyl) acetamide has been investigated by docking against SARS-CoV-2 protein. The optimized geometry shows near-planarity between the phenyl ring and the pyrimidine ring. Differences in the geometries due to the substitution of the most electronegative fluorine atom and intermolecular contacts due to amino pyrimidine were analyzed. NBO analysis reveals the formation of two strong stable hydrogen bonded N-H···N intermolecular interactions and weak intramolecular interactions C-H···O and N-H···O. The Hirshfeld surfaces and consequently the 2D-fingerprint confirm the nature of intermolecular interactions and their quantitative contributions towards the crystal packing. The red shift in N-H stretching frequency exposed from IR substantiate the formation of N-H···N intermolecular hydrogen bond. Drug likeness and absorption, distribution, metabolism, excretion and toxicity properties analysis gives an idea about the pharmacokinetic properties of the title molecule. The binding energy -8.7 kcal/mol of the nonbonding interaction present a clear view that 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro- phenyl) acetamide can irreversibly interact with SARS-CoV-2 protease.

Formulation and evaluation of rutin-loaded solid lipid nanoparticles for the treatment of brain tumor

The primary requirement for curing cancer is the delivery of essential drug load at the cancer microenvironment with therapeutic efficacy. Considering this, the present study aims to formulate "Rutin"-encapsulated solid lipid nanoparticles (SLNs) for effective brain delivery across the blood-brain barrier (BBB). Rutin-loaded SLNs were fabricated by oil-in-water microemulsion technique and were characterized for their physicochemical properties. The in vivo biodistribution study of rutin-loaded SLNs was studied using Rattus norvegicus rats. Subsequently, in silico molecular docking and dynamic calculations were performed to examine the binding affinity as well as stability of rutin at the active site of target protein "epidermal growth factor receptor (EGFR)." Formulated rutin-loaded SLNs were predominantly spherical in shape with an average particle diameter of 100 nm. Additionally, the biocompatibility and stability have been proved in vitro. The presence and biodistribution of rutin in vivo after 54 h of injection were observed as 15.23 ± 0.32% in the brain, 8.68 ± 0.63% in the heart, 4.78 ± 0.28% in the kidney, 5.04 ± 0.37% in the liver, 0.92 ± 0.04% in the lung, and 11.52 ± 0.65% in the spleen, respectively. Molecular docking results revealed the higher binding energy of - 150.973 kJ/mol of rutin with EGFR. Molecular dynamic simulation studies demonstrated that rutin with EGFR receptor complex was highly stable at 30 ns. The observed results exemplified that the formulated rutin-loaded SLNs were stable in circulation for a period up to 5 days. Thus, rutin-encapsulated SLN formulations can be used as a promising vector to target tumors across BBB. Graphical abstract.

Application of separation methods for in vitro prediction of blood-brain barrier permeability-The state of the art

Despite many efforts, drug discovery pipeline is still a highly inefficient process. Nowadays, when combinatorial chemistry enables to synthesize hundreds of new drugs candidates, methods for rapid assessment of biopharmaceutical parameters of new compounds are highly desired. Over one-third of drugs candidates is rejected because of unsatisfactory pharmacokinetic properties. In the drug discovery process, the blood-brain barrier (BBB) permeability plays a critical role for central nervous system active drugs candidates as well as non-central nervous system active drugs. For this reason, knowledge on the BBB permeability of compounds is essential in the development of new medicines. The review was focused on the application of different separation methods for BBB permeability assessment. Both chromatographic and electrophoretic methods were described. In the article, the advantages and limitations of well-established chromatographic methods like immobilized artificial membrane chromatography or micellar liquid chromatography, and less common techniques were discussed. Special attention was devoted to methods were microemulsion is used as mobile or pseudostationary phases.

Prediction of human intestinal absorption using micellar liquid chromatography with an aminopropyl stationary phase

The extent of human intestinal absorption (HIA) for a drug is considered to be an important pharmacokinetic parameter which must be determined for orally administered drugs. Traditional experimental methods relied upon animal testing and are renowned for being time consuming and expensive as well as being ethically unfavourable. As a result, the development of alternative methods to evaluate a drug's pharmacokinetics is crucial. Micellar liquid chromatography is considered to be one of these methods that can replace the use of animals in the prediction of HIA. In this study, the combination of an aminopropyl column with the biosurfactant sodium deoxycholate bile salt was used in the experimental determination of micelle-water partition coefficients (log P_mw ) for a group of compounds. Multiple linear regression was then used for the prediction of HIA using the experimentally determined log P_mw along with other molecular descriptors, leading to the construction of a model equation of R²  = 85% and a prediction power represented by R² _Pred. = 72%. The use of micellar liquid chromatography with an aminopropyl column in combination with sodium deoxycholate was found to be a good method for the prediction of human intestinal absorption, providing data for a far wider range of compounds compared with previous studies.

Multivariate assessment of azo dyes' biological activity parameters

Lipophilicity as key molecular descriptor of potential biological activity for selected derivatives of azo dyes was determined mathematically, by using relevant software packages and by reversed-phase thin-layer chromatography (RPTLC) on C18 and cyano modified carriers in mixtures of water/n-propanol and water/acetone. The obtained chromatographic parameters, R_M⁰ and m, of the examined azo dyes were correlated with the standard measure of lipophilicity, log P, important pharmacokinetic predictors and selected toxicity parameters applying linear regression analysis. Thereby, good correlations for each applied system were obtained (average correlation coefficient, r, 0.944, 0.885 and 0.919). Also, the correlations between the studied parameters of azo dyes were examined applying two multivariate methods (Cluster Analysis and Principal Component Analysis). It was shown that the polarity of the substituent, and to a lesser extent its electronic effects has the greatest influence on the studied parameters of the azo dyes derivatives. Multivariate methods pointed out the similarity of the chromatographic retention constant, R_M⁰, with the parameters of lipophilicity, unlike the chromatographic parameter m, which exhibits better agreement with the toxicity parameters.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

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 k_BMC and log k_wRP, respectively). Based on the retention factor (log k_wRP) 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 k_BMC/log BB): 0.77), while for RP-HPLC system the correlations were lower (r(log k_wRP/log BB): 0.58; r(S/log BB): -0.50; r(φ₀/P_e): 0.61). Based on the log k_BMC 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.

Application of biopartitioning micellar chromatography and QSRR modeling for prediction of gastrointestinal absorption and design of novel β-hydroxy-β-arylalkanoic acids

Gastrointestinal absorption of thirteen novel β-hydroxy-β-arylalkanoic acids (HAA) with anti-inflammatory activity was predicted by use of biopartitioning micellar chromatography and compared to ibuprofen. All tested HAA have lower retention factors (k) and lower expected gastrointestinal absorption than ibuprofen, whereas derivatives with the highest values of k are 1C, 2APTF and 2C. Quantitative structure-retention relationship (QSRR) analysis was performed in order to identify molecular descriptors with the highest influence on k and ANN(k) model was selected as optimal. Descriptors which form this model (nBM, P_VSA_LogP_8 and Eta_L) indicate that replacement of phenyl ring with a saturated or partially unsaturated one, as well as presence of halogens and nitro group should positively affect k values. On the basis of these conclusions, six novel HAA were designed and selected QSRR model was used for the prediction of their k values.

Tuning the predictive capacity of the PAMPA-BBB model

Due to its robustness and versatility, several variations of the blood-brain barrier specific parallel artificial membrane permeability assay (PAMPA-BBB) have been reported in the central nervous system (CNS) drug discovery practice. In this study, the impact of the main assay parameters on the predictive power of PAMPA-BBB was thoroughly investigated with 27, passively BBB-transported drug molecules with in vivo logBB data. The single and combined effects of the following variables were systematically studied and optimized: incubation time and temperature (4 vs. 18h, RT vs. 37°C), type of the read-out (UV-reader vs. HPLC), solvent composition (n-dodecane/n-hexane), lipid concentration (0-10w/v % PBLE), cholesterol content (0-1.66w/v %), and thickness of the lipid membrane, and the DMSO cosolvent content (5-20v/v %), respectively. Based on our results, solvent-driven and lipid-driven mechanisms of diffusion were identified in different assay conditions. Moreover, the analysis of membrane retention (MR%; the mole fraction of solute "lost" to the membrane) data obtained at various membrane compositions (volume of solvent and concentration of phospholipids) revealed the compound-specific nature of this parameter. The optimized conditions for the PAMPA-BBB were the following: 4h incubation at 37°C, detection by HPLC-DAD, iso-pH conditions (pH=7.4) with 5v/v % DMSO content in buffer solutions, and PBLE (10w/v %; without cholesterol) as membrane dissolved in the mixture of n-hexane:n-dodecane 3:1.

Evaluation of sphingomyelin, cholester, and phosphatidylcholine-based immobilized artificial membrane liquid chromatography to predict drug penetration across the blood-brain barrier

Over the past decades, several in vitro methods have been tested for their ability to predict drug penetration across the blood-brain barrier. So far, in high-performance liquid chromatography, most attention has been paid to micellar liquid chromatography and immobilized artificial membrane (IAM) LC. IAMLC has been described as a viable approach, since the stationary phase emulates the lipid environment of a cell membrane. However, research in IAMLC has almost exclusively been limited to phosphatidylcholine (PC)-based stationary phases, even though PC is only one of the lipids present in cell membranes. In this article, sphingomyelin and cholester stationary phases have been tested for the first time towards their ability to predict drug penetration across the blood-brain barrier. Upon comparison with the PC stationary phase, the sphingomyelin- and cholester-based columns depict similar predictive performance. Combining data from the different stationary phases did not lead to improvements of the models.

in vitro and in silico determination of oral, jejunum and Caco-2 human absorption of fatty acids and polyphenols. Micellar liquid chromatography

In this investigation chosen saturated, mono- and polyunsaturated fatty acids as well as polyphenols have been analyzed. The main aim of this study was to determine oral, jejunum and Caco-2 human absorption of chosen fatty acids and polyphenols using in vitro and in silico methods. For in vitro determination of human drug absorption, the usefulness of Micellar Liquid Chromatography (MLC) with mobile phases containing different surfactants (including Brij35-Biopartitioning Micellar Chromatography (BMC)) has been confirmed. On the basis of Foley's equation, 1/k vs. CM correlations for the tested compounds have been done. Satisfactory linearity of the relationships was found over the whole eluents composition range studied with R(2) approximately 0.99 in each case. Moreover, the analyte-micelle association constants (Kma) from Foley's equation have been compared for different micellar environments, containing Brij35, SDS and CTAB as a main component of micellar mobile phases. Completely new models describing human oral as well as Caco-2 and jejunum absorption have been constructed and compared with the cited models. These models are based on the Abraham descriptors and lipophilicity parameters as well as steric descriptors. Furthermore, many different correlations between physicochemical parameters and human intestinal absorption have been done, e.g. the correlation between human jejunum permeability estimated in silico and received using LSER parameters was excellent (R(2) nearly 0.99). Chromatographic parameters have been collated with steric, electronic and physicochemical ones using QRAR (Quantitative Retention - Activity Relationships) and QSAR (Quantitative Structure - Activity Relationships) models. Moreover, retention BMC data have been compared with lipophilicity parameter logPo/w (n-octanol-water partition coefficient). The influence of lipophilicity on oral absorption (%) has been checked. The correlation between predicted oral absorption (%) and logPo/w has been done. Obtained R(2) was 0.82. On the basis of chromatographic, lipophilicity, steric and different physicochemical parameters, the principal components analysis (PCA) has been done.

Reversed-phase liquid chromatography with octadecylsilyl, immobilized artificial membrane and cholesterol columns in correlation studies with in silico biological descriptors of newly synthesized antiproliferative and analgesic active compounds

Reversed-phase liquid chromatography (RPLC) with different stationary phases, i.e., octadecylsilyl, immobilized artificial membrane and immobilized cholesterol, was used to study lipophilicity of 56 newly-designed 7,8-dihydroimidazo[2,1-c][1,2,4]triazin-4(6H)-ones and 2,6,7,8-tetrahydroimidazo[2,1-c][1,2,4]triazine-3,4-diones with potential anti-proliferative, anti-metastatic and analgesic activities. Extrapolated retention parameters that correspond to pure buffer as the mobile phase, i.e., logkw values are used as chromatographic lipophilicities. The lipophilic properties of compounds also are characterized by computed logP values and basic pharmacokinetic descriptors calculated in silico with the use of ACD/Percepta software according to Abraham's linear solvation energy relationship. Chromatographic and partitioning parameters are compared with biological descriptors using principal component analysis (PCA), and similarities and dissimilarities between variables and compounds are described. Highly significant, predictive relationships between biological descriptors and chromatographic parameters are obtained. Reversed parabolic relationships, which have very good statistical quality between various biological descriptors, i.e., logKsc, logKp, logBB, and logKhsa, and the logkw values, indicate the advantages of a cholesterol column in comparison with immobilized artificial membrane and octadecylsilyl stationary phase.

The use of biopartitioning micellar chromatography and immobilized artificial membrane column for in silico and in vitro determination of blood-brain barrier penetration of phenols

Biopartitioning Micellar Chromatography (BMC) is a mode of micellar liquid chromatography that uses C18 stationary phases and micellar mobile phases of Brij35 under adequate experimental conditions and can be useful to mimic human drug absorption, blood-brain barrier distribution or partitioning processes in biological systems. BMC system can be useful in constructing good predictive models because the characteristics of the BMC system are similar to biological barriers and extracellular fluids. Immobilized Artificial Membrane (IAM) chromatography uses stationary phase which consists of a monolayer of phosphatidylcholine covalently immobilized on an inert silica support. IAM columns are thought to mimic very closely a membrane bilayer and are used in a HPLC system with a physiological buffer as eluent. In this paper the usefulness of BMC and IAM system for in silico and in vitro determination of blood-brain barrier (BBB) penetration of phenols has been demonstrated. The most important pharmacokinetic parameters of brain have been obtained for the determination of BBB penetration, i.e. BBB permeability - surface area product (PS), usually given as a logPS, brain/plasma equilibration rate (log(PS×fu,brain)) and fraction unbound in plasma (Fu). Moreover, the relationships between retention of eighteen phenols and different parameters of molecular size, lipophilicity and BBB penetration were studied. Extrapolated to pure water values of the logarithms of retention factors (logkw) have been compared with the corresponding octanol-water partition coefficient (logPo-w) values of the solutes. In addition, different physicochemical parameters from Foley's equation for BMC system have been collated with the chromatographic data. The Linear Solvation Energy Relationship (LSER) using Abraham model for the describing of phenols penetration across BBB has been used. Four equations were developed as a multiple linear regression using retention data from IAM and BMC system (QRAR models) and molecular volume parameter (Vm) and Abraham descriptors to correlate the logBB values. Moreover, in order to establish the relationships between different variables, the principal components analysis (PCA) has been done. The results of PCA were obtained using chromatographic data from IAM and BMC systems as well as from the structures of tested phenols. The four parameters: logkwIAM(exp), logkwBMC(exp), analyte-micelle association constant (Kma) and logPo-w have been checked.

Biopartitioning micellar chromatography with sodium dodecyl sulfate as a pseudo α(1)-acid glycoprotein to the prediction of protein-drug binding

A simple and fast method is of urgent need to measure protein-drug binding affinity in order to meet the rapid development of new drugs. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography (MLC) using micellar mobile phases in adequate experimental conditions, can be useful as an in vitro system in mimicking the drug-protein interactions. In this study, sodium dodecyl sulfate-micellar liquid chromatography (SDS-MLC) was used for the prediction of protein-drug binding based on the similar property of SDS micelles to α(1)-acid glycoprotein (AGP). The relationships between the BMC retention data of a heterogeneous set of 14 basic and neutral drugs and their plasma protein binding parameter were studied and the predictive ability of models was evaluated. Modeling of logk(BMC) of these compounds was established by multiple linear regression (MLR) and second-order polynomial models obtained in two different concentrations (0.07 and 0.09M) of SDS. The developed MLR models were characterized by both the descriptive and predictive ability (R(2)=0.882, R(CV)(2)=0.832 and R(2)=0.840, R(CV)(2)=0.765 for 0.07 and 0.09M SDS, respectively). The p values <0.01 also indicated that the relationships between the protein-drug binding and the logk(BMC) values were statistically significant at the 99% confidence level. The standard error of estimation showed the standard deviation of the regression to be 11.89 and 13.87 for 0.07 and 0.09M, respectively. The application of the developed model to a prediction set demonstrated that the model was also reliable with good predictive accuracy. The external and internal validation results showed that the predicted values were in good agreement with the experimental value.

A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system

Drugs acting on the central nervous system (CNS) have to cross the blood-brain barrier (BBB) in order to perform their pharmacological actions. Passive BBB diffusion can be partially expressed by the blood/brain partition coefficient (logBB). As the experimental evaluation of logBB is time and cost consuming, theoretical methods such as quantitative structure-property relationships (QSPR) can be useful to predict logBB values. In this study, a 2D-QSPR approach was applied to a set of 28 drugs acting on the CNS, using the logBB property as biological data. The best QSPR model [n = 21, r = 0.94 (r² = 0.88), s = 0.28, and Q² = 0.82] presented three molecular descriptors: calculated n-octanol/water partition coefficient (ClogP), polar surface area (PSA), and polarizability (α). Six out of the seven compounds from the test set were well predicted, which corresponds to good external predictability (85.7%). These findings can be helpful to guide future approaches regarding those molecular descriptors which must be considered for estimating the logBB property, and also for predicting the BBB crossing ability for molecules structurally related to the investigated set.

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings.

Permeability profile estimation of flavonoids and other phenolic compounds by biopartitioning micellar capillary chromatography

This paper points out the usefulness of biopartitioning micellar chromatography (BMC) using capillary columns as a high-throughput primary screening tool providing key information about the oral absorption, skin permeability, and brain-blood distribution coefficients of 15 polyphenols (6 flavones, 2 flavonols, a flavanone, 2 flavan-3-ols, 3 phenolic acids, and a phloroglucinol) in a simple and economical way. For the compounds studied, except vicenin-2, rutin, chlorogenic acid, p-hydroxycinnamic acid, and 4-hydroxybenzoic acid, maximal oral absorption (>90%) can be expected, if there are not solubility problems or metabolic processes. On the other hand, the most retained compounds in BMC, that is, 5-hydroxyflavone, flavone, and flavanone, show the highest brain-blood distribution coefficients and skin permeability coefficients.