Biopartitioning Micellar Chromatography Research Articles

In Vitro Evaluation of Pharmacokinetic Properties of Selected Dual COX-2 and 5-LOX Inhibitors

Evaluation of pharmacokinetic properties is a significant step at the early stages of drug development. In this study, an in vitro evaluation of the pharmacokinetic properties of five newly synthesized compounds was performed. These compounds belong to N-hydroxyurea and hydroxamic acid derivatives and analogs of NSAIDs indomethacin, flurbiprofen, diclofenac, ibuprofen, and naproxen (compounds 1, 2, 3, 11, and 12, respectively) with dual COX-2 and 5-LOX inhibitory activity. Two in vitro methods (biopartitioning micellar chromatography (BMC) and PAMPA) were used to evaluate passive gastrointestinal absorption, while high-performance affinity chromatography (HPAC) and differential pulse voltammetry (DPV) were used to evaluate binding to human serum albumin (HSA). The introduction of N-hydroxyurea and hydroxamic acid groups into the structure of NSAIDs decreases both expected passive gastrointestinal absorption (BMC k values were from 3.02 to 9.50, while for NSAIDs were from 5.29 to 13.36; PAMPA –logPe values were between 3.81 and 4.76, while for NSAIDs were ≤3.46) and HSA binding (HPAC logk values were from 2.03 to 9.54, while for NSAIDs were ≥11.03; DPV peak potential shifts were between 7 and 34, while for NSAIDs were ≥54). Structural modifications of all tested compounds that increase lipophilicity could be considered to enhance their passive gastrointestinal absorption. Considering lower expected HSA binding and higher lipophilicity of tested compounds compared to corresponding NSAIDs, it can be expected that the volume of distribution of compounds 1, 2, 3, 11, and 12 will be higher. Reduced HSA binding may also decrease interactions with other drugs in comparison to corresponding NSAIDs. All tested compounds showed significant microsomal instability (25.07–58.44% decrease in concentration) in comparison to indomethacin (14.47%) and diclofenac (20.99%).

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.

The use of polyoxyethylene (20) cetyl ether in assessing the hydrophobicity of compounds of biomedical importance and in the process of drug release from microemulsions

Abstract The creation and study of artificial membranes based on microemulsions is an important direction due to the similarity of the structure of both direct and reverse microemulsions with cell membranes. A microemulsion mobile phase prepared with a non-ionic surfactant in combination with a C18 type stationary phase creates a similar image of the cell membrane in a chromatographic column. In addition, the use of microemulsion systems to transport drugs with low bioavailability into the body can increase their bioavailability. The chromatographic behaviour of model substances of biomedical importance was investigated using micellar mobile phases containing polyoxyethylene (20) cetyl ether in biopartitioning micellar chromatography (BMC) in the concentration range of 1–5 %. Cholic acid was introduced into the polyoxyethylene (20) cetyl ether micellar mobile phase to approximate the structure of the cell membrane. The hydrophobicity of the model compounds was evaluated. Hydrophobicity indices in the micellar mobile phase with and without addition of cholic acid were compared. The release profile of promethazine hydrochloride from microemulsion systems with monomeric and polymeric surfactants was investigated. The kinetic properties of the release of promethazine hydrochloride from microemulsion systems were calculated. It was found that a microemulsion of polyoxyethylene (20) cetyl ether mixed with polyoxyethylene (4) lauryl ether reduced the release of promethazine hydrochloride in weight percent. The release of promethazine hydrochloride from microemulsions does not obey Fick’s diffusion but follows a non-Fick’s transport mechanism, as evidenced by the high values of the diffusion exponent (n > 0.5).

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.

Biopartitioning micellar chromatography under different conditions: Insight into the retention mechanism and the potential to model biological processes

In the present study, 88 structurally- diverse drugs were investigated by biopartitioning micellar chromatography (BMC) using Brij-35 as surfactant under different chromatographic conditions. It was found that temperature and presence of NaCl have only a minor effect in BMC retention. Correlation of BMC retention factors with octanol-water partitioning required the inclusion of fractions of ionized species as additional parameters, showing that there is a weaker effect of ionization in BMC environment. Compared to Immobilized Artificial Membrane (IAM) Chromatography, BMC retention factors cover a relatively narrow span, two-fold smaller than retention factors on IAM stationary phases as a result of the presence of micelles facilitating elution of lipophilic compounds and the absence of secondary attractive electrostatic interactions in the BMC environment. Similarities/dissimilarities between BMC, octanol-water partitioning and IAM Chromatography were investigated by Linear Free Energy Relationships (LSER). BMC retention factors were used to construct relationships with cell permeability,% Human Oral Absorption (%HOA) and Plasma Protein Binding (%PPB). Linear BMC models were obtained with Caco-2 cell lines and Parallel Artificial Membrane Permeability Assay (PAMPA). For %HOA, a hyperbolic model was established upon incorporation of topological polar surface area (tPSA) as additional parameter. A sigmoidal model was constructed for %PPB and a linear one for the corresponding thermodynamic binding constant logK. In both cases inclusion of the fraction of anionic species with a positive sign was required reflecting the preference of human albumin for acidic drugs.

Biopartitioning micellar electrokinetic chromatography – Concept study of cationic analytes

In the early stages of drug discovery, beyond the biological activity screening, the physicochemical properties including lipophilicity, ionization, solubility as well as metabolic profiling and protein-binding affinity should be determined for each drug candidate. The aim of the presented investigation was the transfer of biopartitioning micellar chromatography (BMC) proposed by Medina-Hernández and co-workers to micellar electrokinetic chromatography (MEKC). Therefore, biopartitioning micellar electrokinetic chromatography (BMEKC) was demonstrated as an alternative tool for estimation of biological properties of drug candidates. To simplify the data processing of developed approach and increase the assay throughput, the application of apparent retention factor (kapp) was proposed. Based on introduced kapp, simple molecular descriptors, protein binding constant and biological properties like blood-brain barrier (BBB) permeability, quantitative retention–activity relationships (QRARs) models were established. These results indicated usefulness of BMEKC method. In comparison to BMC, the proposed BMEKC method offers significant reduction of reagents consumption which meet green analytical chemistry principles.

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.

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

ABSTRACTBee 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.

A novel two-dimensional liquid-chromatography method for online prediction of the toxicity of transformation products of benzophenones after water chlorination.

Benzophenone-type UV filters (BPs) are ubiquitous in the environment. Transformation products (TPs) of BPs with suspected toxicity are likely to be produced during disinfection of water by chlorination. To quickly predict the toxicity of TPs, in this study, a novel two-dimensional liquid-chromatography (2D-LC) method was established in which the objective of the first dimension was to separate the multiple components of the BPs sample after chlorination, using a reversed-phase liquid-chromatography mode. A biochromatographic system, i.e. bio-partitioning micellar chromatography with the polyoxyethylene (23) lauryl ether aqueous solution as the mobile phase, served as the second dimension to predict the toxicity of the fraction from the first dimension on the basis of the quantitative retention-activity relationships (QRARs) model. Six BPs, namely 2,4-dihydroxybenzophenone, oxybenzone, 4-hydroxybenzophenone, 2-hydroxy-4-methoxybenzophenone-5-sulfonic acid, 2,2'-dihydroxy-4,4'-dimethoxybenzophenone and 2,2'-dihydroxy-4-methoxybenzophenone, were the target analytes subjected to chlorination. The products of these BPs after chlorination were directly injected to the 2D-LC system for analysis. The results indicated that most TPs may be less toxic than their parent chemicals, but some may be more toxic, and that intestinal toxicity of TPs may be more obvious than blood toxicity. The proposed method is time-saving, high-throughput, and reliable, and has great potential for predicting toxicity or bioactivity of unknown and/or known components in a complex sample. Graphical Abstract The scheme for the 2D-LC online prediction of toxicity of the transformation products of benzophenone-type UV filters after chlorination.

Human serum albumin-mimetic chromatography based hexadecyltrimethylammonium bromide as a novel direct probe for protein binding of acidic drugs

Human serum albumin (HSA) is the most important drug carrier in humans mainly binding acidic drugs. Negatively charged compounds bind more strongly to HSA than it would be expected from their lipophilicity alone. With the development of new acidic drugs, there is a high need for rapid and simple protein binding screening technologies. Biopartitioning micellar chromatography (BMC) is a mode of micellar liquid chromatography, which can be used as an in vitro system to model the biopartitioning process of drugs when there are no active processes. In this study, a new kind of BMC using hexadecyltrimethylammonium bromide (CTAB) as micellar mobile phases was used for the prediction of protein binding of acidic drugs based on the similar property of CTAB micelles to HSA. The use of BMC is simple, reproducible and can provide key information about the pharmacological behavior of drugs such as protein binding properties of new compounds during the drug discovery process. The relationships between the MLC retention data of a heterogeneous set of 17 acidic and neutral drugs and their plasma protein binding parameter were studied and second-order polynomial models obtained in two different concentrations (0.07 and 0.09M) of CTAB. However, the developed models are only being able to distinguish between strongly and weakly binding drugs. Also, the developed models were characterized by both the descriptive and predictive ability (R2=0.885, RCV2=0.838 and R2=0.898, RCV2=0.859 for 0.07 and 0.09M CTAB, respectively). The application of the developed model to a prediction set demonstrated that the model was also reliable with good predictive accuracy.

A novel two-dimensional liquid chromatographic system for the online toxicity prediction of pharmaceuticals and related substances

In this study, a novel two-dimensional liquid chromatographic (2D-LC) system was developed for simultaneous separation and toxicity prediction of pharmaceutical and its related substances. A conventional ODS column was used on the 1st-D to separate the sample; while, bio-partitioning micellar chromatography served as the 2nd-D to predict toxicity of the components. The established system was tested for the toxicity of ibuprofen and its impurities with known toxicity. With only one injection, ibuprofen and its impurities were separated on the 1st-D; and LC50 values of individual impurity were obtained based on the quantitative retention–activity relationships, which agreed well with the reported data. Furthermore, LC50 values of photolysis transformation products (TPs) of carprofen, ketoprofen and diclofenac acid (as unknown compounds) were screened in this 2D-LC system, which could be an indicator of the toxicity of these TPs and was meaningful for the environmental monitoring and drinking water treatment. The established 2D-LC system was cost-effective, time-saving and reliable, and was promising for fast online screening of toxicity of known and unknown analytes in the complex sample in a single step. It may find applications in environment, pharmaceutical and food, etc.

Green mixed micellar liquid chromatography as a toxicity screening method of psychotropic drugs

The knowledge of drug toxicity is necessary for risk assessment and ranking of drugs according to their hazard potential. Also, it is essential to determine potential toxicity problems in the early stage of drug discovery scheme to minimize expensive drug failures due to toxicity being found in the late development process. In this study, the capability of biopartitioning micellar chromatography (BMC) using the pure polyoxyethylene (23) lauryl ether (Brij-35) solution and the mixed micellar system of Brij-35/sodium dodecyl sulfate (Brij-35/SDS, 85:15 molar ratio) as a green mobile phase, respectively, was studied to predict the acute toxicity (lethal dose, LD50) of psychotropic drugs. The relationships between the BMC retention data of 13 basic psychotropic drugs and their LD50 parameter were studied in two different mobile phases and the predictive ability of models was evaluated. A better statistical model was obtained using retention data in Brij-35/SDS, 85:15 (molar ratio), mobile phase (R 2 = 0.936, F = 73.715, SE = 277.33, $$R_{\text{CV}}^{2} = 0.913$$ ). The superiority of mixed micellar mobile phase in BMC is due to the fact that this kind of phase can simulate the resting membrane potential, and the intermolecular interaction among the Brij-35 molecules gradually weakens as the ratio of Brij-35/SDS decreases. The application of the developed model to a prediction set demonstrated that the model was also reliable with good predictive accuracy.

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 R2 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 (R2 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 R2 was 0.82.On the basis of chromatographic, lipophilicity, steric and different physicochemical parameters, the principal components analysis (PCA) has been done.

Biopartitioning micellar chromatography as a predictive tool for skin and corneal permeability of newly synthesized 17β-carboxamide steroids

In this paper, human skin and corneal permeability of twenty-two newly synthesized 17β-carboxamide steroids was predicted using biopartitioning micellar chromatography (BMC). These compounds are potential soft glucocorticoids with local anti-inflammatory activity when applied to the skin or eye. BMC systems are used to simulate physicochemical properties of human skin (BMC-skin) and cornea (BMC-cornea). Micellar mobile phase, consisted of 0.04M solution of polyoxyethylene (23) lauryl ether (Brij 35), was prepared at different pH values – 5.50 (BMC-skin) and 7.50 (BMC-cornea). Retention factors (k), obtained by use of BMC, were calculated for all newly synthesized 17β-carboxamide steroids as well as for parent glucocorticoids (hydrocortisone, prednisolone, methylprednisolone, dexamethasone and betamethasone). Good correlation was obtained between BMC-skin retention factors and permeability coefficients calculated by use of the artificial membrane that simulates stratum corneum of the human skin. Quantitative structure–retention relationship (QSRR) study was performed in order to explain retention factors of these compounds in the tested BMC systems. ANN-QSRR(k), PLS-QSRR(k) and MLR-QSRR(k) models, created by use of BMC-skin retention data, were compared and optimal model (PLS-QSRR(k)) was selected. Molecular descriptors of the selected model indicate that lipophilicity and number of short CC fragments of tested compounds have the strongest influence on the retention in the BMC-skin system and presumably on their in vivo permeability through human skin. The same model can be applied to the BMC-cornea system and the same conclusion can be drawn for corneal permeability. This model could be used as a predictive tool for the synthesis of novel 17β-carboxamide steroids with desirable permeability through human skin or cornea, depending on their potential pharmacological application.

Prediction and Screening of Biologically Active Compounds in Honeysuckle (Lonicera japonica) by Biopartitioning Micellar Chromatography

Prediction and Screening of Biologically Active Compounds in Honeysuckle (Lonicera japonica) by Biopartitioning Micellar Chromatography

Fast profiling ecotoxicity and skin permeability of benzophenone ultraviolet filters using biopartitioning micellar chromatography based on penetrable silica spheres

Penetrable silica possesses hierarchical pores, mesopores and penetrable macropores, offering fast mass transfer, satisfactory mechanical strength as well as low column pressure. In the present study, penetrable octadecyl-bonded silica (ODS) was for the first time used as biopartitioning micellar chromatography (BMC) stationary phase to profile ecotoxicity and skin permeability of benzophenone UV-filters. Mobile phase (MP) pH and concentration of polyoxyethylene(23)lauryl ether in the MP were systematically studied. Quantitative retention–activity relationships (QRARs) model was established to correlate retention factors (k) on BMC with bioconcentration factor (BCF) and transdermal rate (TR) of UV-filters. Coefficient of determination (r2) of the QRARs model between log BCF and logk were 0.9398–0.9753, while r2 between TR and logk were 0.7569–0.8434, which demonstrated satisfactory predictive ability of the methodology. It was a powerful tool for fast screening by combining penetrable ODS with BMC, and avoiding column blockage often occurring in BMC.

SDS-Based Biomembrane Mimetic Chromatography for Prediction of Human Drug Transport as an in Vitro Technique

The main oral drug absorption barriers are fluid cell membranes, and generally drugs are absorbed by a passive diffusion mechanism. On the other hand, the blood–brain barrier (BBB) is considered to be the main barrier to drug transport into the central nervous system (CNS). The BBB restricts the passive diffusion of many drugs from blood to brain. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases in adequate experimental conditions, can be useful as an in vitro system in mimicking the drug partitioning process into biological systems. In this study, relationships between the BMC retention data of a heterogeneous set of 12 drugs and their pharmacokinetics parameters (human oral drug absorption and BBB penetration ability) are studied and the predictive ability of the models is evaluated. Modeling of log kBMC of these compounds was established by multiple linear regression in two different concentrations (0.07 and 0.09 M) of sodium dodecyl sulfate (SDS). The results showed a fair correlation between human oral drug absorption and BMC retention data in 0.09 M SDS (R2 = 0.864) and a good correlation between the blood–brain distribution coefficient and BMC retention data in 0.07 M of SDS (R2 = 0.887). Application of the developed models to a prediction set demonstrated that the model is also reliable with good predictive accuracy. The external and internal validation results showed that the predicted values are in good agreement with the experimental value.

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 Determination of Nucleosides and Bases in Bailing Capsule

Biopartitioning Micellar Chromatography Determination of Nucleosides and Bases in Bailing Capsule

Biopartitioning micellar chromatography with sodium dodecyl sulfate as a pseudo α1-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 α1-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 logkBMC 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 (R2=0.882, RCV2=0.832 and R2=0.840, RCV2=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 logkBMC 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.