Log BB Research Articles

CXCR3-Flavonoid Interaction: A Novel Therapeutic Approach in Cancer Immunotherapy

Objective: To investigate the interactions between select flavonoids (Luteolin, Quercetin, Apigenin, Kaempferol, and Amorphine) and the CXCR3 receptor, evaluating their potential as novel therapeutic agents in cancer immunotherapy. Methods: Molecular docking simulations were employed to analyze flavonoid-CXCR3 receptor interactions. Comprehensive in silico ADMET analyses were conducted to assess pharmacokinetic properties and toxicity profiles of the compounds. Results: Flavonoids exhibited high-affinity binding to the CXCR3 receptor, with binding affinities ranging from -8.7 to -13.0 kcal/mol. Amorphine demonstrated the highest binding affinity (-13.0 kcal/mol), indicating superior inhibition potential. Luteolin showed optimal ADME characteristics, including favorable oral bioavailability (62%) and blood-brain barrier permeability (log BB -1.911). Molecular docking analyses identified critical amino acid residues (TYR205, TYR308, TRP109, PHE131, and ASN132) in flavonoid-CXCR3 interactions. In silico toxicity predictions suggested low risk profiles for all examined flavonoids. Conclusion: This study provides evidence for the potential of flavonoids as CXCR3 receptor antagonists in cancer immunotherapy. The elucidated molecular interactions and favorable ADMET profiles warrant further investigation of these compounds. Future research should focus on optimization of flavonoid-based CXCR3 inhibitors, preclinical and clinical evaluations, and assessment of their immunomodulatory effects within the tumor microenvironment. These findings contribute to the development of novel, flavonoid-derived therapeutic strategies in cancer treatment.

Brain targeted lactoferrin coated lipid nanocapsules for the combined effects of apocynin and lavender essential oil in PTZ induced seizures.

Apocynin (APO) is a plant derived antioxidant exerting specific NADPH oxidase inhibitory action substantiating its neuroprotective effects in various CNS disorders, including epilepsy. Due to rapid elimination and poor bioavailability, treatment with APO is challenging. Correspondingly, novel APO-loaded lipid nanocapsules (APO-LNC) were formulated and coated with lactoferrin (LF-APO-LNC) to improve br ain targetability and prolong residence time. Lavender oil (LAV) was incorporated into LNC as a bioactive ingredient to act synergistically with APO in alleviating pentylenetetrazol (PTZ)-induced seizures. The optimized LF-APO-LAV/LNC showed a particle size 59.7 ± 4.5 nm with narrow distribution and 6.07 ± 1.6mV zeta potential) with high entrapment efficiency 92 ± 2.4% and sustained release (35% in 72 h). Following subcutaneous administration, LF-APO-LAV/LNC brought about ⁓twofold increase in plasma AUC and MRT compared to APO. A Log BB value of 0.2 ± 0.14 at 90 min reflects increased brain accumulation. In a PTZ-induced seizures rat model, LF-APO-LAV/LNC showed a Modified Racine score of 0.67 ± 0.47 with a significant increase in seizures latency and decrease in duration. Moreover, oxidant/antioxidant capacity and inflammatory markers levels in brain tissue were significantly improved. Histopathological and immunohistochemical assessment of brain tissue sections further supported these findings. The results suggest APO/LAV combination in LF-coated LNC as a promising approach to counteract seizures.

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology.

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.

The relative role of polar and nonpolar factors of the structure of substance on the process of their penetration through the blood–brain barrier

The analysis of the relative role of polar and non-polar factors of the molecular structure on the penetration of substances through the blood–brain barrier (BBB) was carried out. Such analysis will allow a preliminary approximate assessment of the ability of substances to cross the blood–brain barrier. Using previously developed computer expert systems on the basis of QSAR/QSPR models in the framework of simplex approach, the water solubility logSw (polar factor model), the lipophilicity logP (nonpolar factor model), and the characteristics of penetration through the blood–brain barrier (logBB, logPS, logP0PAMPA–BBB) were calculated for a set of 614 different organic compounds. It was determined whether substances belong to classes BBB+ or BBB– and whether these substances are substrates of P-glycoprotein (P-gl) or not (no-P-gl). Analysis of the distribution of the points of the investigated molecules in the logP vs. logSw coordinates revealed that lipophilicity and water solubility for the compounds with high penetrating ability should be approximately in following ranges: 3logP6; and –5logSw–1. For the data, which are presented in different scales, continuous scale and classification scale, a half-sign correlation coefficient Rss was calculated. Rss equals 0.93, that confirms the consistency of the results of the prognosis of Log BB parameters and classification BBB+/BBB–. The general trends in the classification of the investigated compounds according to the BBB+/BBB– and P-gl/no-P-gl classes were quantitatively estimated. The sign coefficient of association, which equals –0.35, emphasizes the antibatic nature of the relationship between the data of the two classifications.

Chromatographic Data in Statistical Analysis of BBB Permeability Indices.

Blood-brain barrier (BBB) permeability is an essential phenomena when considering the treatment of neurological disorders as well as in the case of central nervous system (CNS) adverse effects caused by peripherally acting drugs. The presented work contains statistical analyses and the correlation assessment of the analyzed group of active pharmaceutical ingredients (APIs) with their BBB-permeability data collected from the literature (such as computational log BB; Kp,uu,brain, and CNS+/- groups). A number of regression models were constructed in order to observe the connections between the APIs' physicochemical properties in combination with their retention data from the chromatographic experiments (TLC and HPLC) and the indices of bioavailability in the CNS. Conducted analyses confirm that descriptors significant in BBB permeability modeling are hydrogen bond acceptors and donors, physiological charge, or energy of the lowest unoccupied molecular orbital. These molecular descriptors were the basis, along with the chromatographic data from the TLC in log BB regression analyses. Normal-phase TLC data showed a significant contribution to the creation of the log BB regression model using the multiple linear regression method. The model using them showed a good predictive value at the level of R2 = 0.87. Models for Kp,uu,brain resulted in lower statistics: R2 = 0.56 for the group of 23 APIs with the participation of k IAM.

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.

Development of QSAR models to predict blood-brain barrier permeability.

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

Development of novel 2-aminoalkyl-6-(2-hydroxyphenyl)pyridazin-3(2H)-one derivatives as balanced multifunctional agents against Alzheimer's disease

Based on multitarget-directed ligands approach, through two rounds of screening, a series of 2-aminoalkyl-6-(2-hydroxyphenyl)pyridazin-3(2H)-one derivatives were designed, synthesized and evaluated as innovative multifunctional agents against Alzheimer's disease. In vitro biological assays indicated that most of the hybrids were endowed with great AChE inhibitory activity, excellent antioxidant activity and moderate Aβ1-42 aggregation inhibition. Taken both efficacy and balance into account, 12a was identified as the optimal multifunctional ligand with significant inhibition of AChE (EeAChE, IC50 = 0.20 μM; HuAChE, IC50 = 37.02 nM) and anti-Aβ activity (IC50 = 1.92 μM for self-induced Aβ1-42 aggregation; IC50 = 1.80 μM for disaggregation of Aβ1-42 fibrils; IC50 = 2.18 μM for Cu2+-induced Aβ1-42 aggregation; IC50 = 1.17 μM for disaggregation of Cu2+-induced Aβ1-42 fibrils; 81.7% for HuAChE-induced Aβ1-40 aggregation). Moreover, it was equipped with the potential to serve as antioxidant (3.03 Trolox equivalents), metals chelator and anti-neuroinflammation agent for synergetic treatment. Finally, in vivo study demonstrated that 12a, with suitable BBB permeability (log BB = −0.61), could efficaciously ameliorate cognitive dysfunction on scopolamine-treated mice by regulating cholinergic system and oxidative stress simultaneously. Altogether, these results highlight the potential of 12a as an innovative balanced multifunctional candidate for Alzheimer's disease treatment.

CΔlog kw IAM: can we afford estimation of small molecules' blood-brain barrier passage based upon in silico phospholipophilicity?

56 compounds, whose log BB values were known from the scientific literature, were considered and their phospholipophilicity values were calculated in silico. These values, along with either experimentally determined or calculated lipophilicity values, were used to extract cΔ/Δ’log kwIAM parameters. cΔ/Δ’log kwIAM values were found inversely related to data of blood-brain barrier passage, especially in the < -0.20 log BB range and on the IAM.PC.DD2 phase (r2 = 0.79). In multiple linear regression, satisfactory statistic models (r2 (n-1) = 0.76), based on cΔ/Δ’log kwIAM.MG along with other in silico calculated descriptors, were achieved. This method brings the potential to be applied, along with other methodologies, to filter out solutes whose BBB permeation is foreseen to be substandard, thus allowing pharmaceutical companies/research institutes to focus on candidates that are more likely to concentrate in the brain.

Bempedoic acid: A nonstatin drug for the management of hypercholesterolemia.

Bempedoic acid: A nonstatin drug for the management of hypercholesterolemia.

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB−) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and https://doi.org/10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB− compounds.

Pharmacokinetics, Mechanism, and Docking Study of Antioxidant Aryl‐Bisthiourea Derivatives for Alzheimer's Disease

AbstractNovel aryl‐bisthiourea derivatives (3 a–3 j) were synthesized by a two‐step route for the study of free‐radical scavenging property and acetylcholinesterase enzyme (AChE) inhibition study. The effect of the electronic environment on the aryl structure of ArBTU on the AChE inhibition was studied. Lineweaver‐Burk plot was used for kinetic study of inhibition of AChE by using ArBTU derivatives which exhibited very good bioactivity of inhibition against acetylcholinesterase enzyme. Central Nervous System (CNS) permeability and the Blood‐Brain Barrier values of all ArBTU derivatives (3 a–3 j) were also similar to the reference value (&gt;−2 logPS&lt; −3 and &gt;−0.3 log BB&lt; −1), respectively. The molecular docking study of derivative 3 g presented a very good interactions with tested protein. The mechanism of AChE inhibition was found from the kinetic studies of novel ArBTU derivatives. A pharmacokinetic study of AChE inhibition was also evaluated. ArBTU derivatives (3 a–3 j) exhibited the best lead‐like potential. Molecular docking study described the binding ability of the highly effective derivative 3 g with the acetylcholinesterase enzyme.

Evaluation of drug-likeness and ADME properties of sunscreens and preservatives using reversed-phase thin layer chromatographic retention data and calculated descriptors

RP-18 TLC chromatography was used to evaluate the pharmacokinetic properties (volume of distribution, VD; plasma protein binding, %PPB; the ability to cross the blood-brain barrier expressed as log PS and log BB) of several cosmetic raw materials – sunscreen and preservatives. The majority of these compounds are intended for topical use on skin and their drug-likeness and the ability to cross biological barriers are undesired. The retention parameters RM0, S, PC1 and RM75 % obtained for mobile phases containing six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physicochemical properties (PSA, MW, VM) of studied compounds.The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds’ pharmacokinetic properties VD, %PPB and log PS. RM75 % and the novel parameters derived from it (RM75 %/MW and RM75 %/VM) can be considered time- and cost-effective alternatives to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some pharmacokinetic properties investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

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 (fu,brain), water-skin permeation (log Kp), binding to human plasma proteins (log Ka,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.

One Pot Synthesis and Pharmacological Evaluation of Aryl Substituted Imidazoles as Potential Atypical Antipsychotics

Background: Second generation or “atypical” antipsychotics demonstrate an improved therapeutic profile over conventional neuroleptics. These are effective in both positive and negative symptoms of the disease and have a lower propensity to induce adverse symptoms. Objective: The main objective of the research was in silico design and synthesis of potential atypical antipsychotics with combined antiserotonergic / antidopaminergic effect. Methods: A one pot synthesis of aryl substituted imidazole derivatives was carried out in green solvent PEG-400 and the prepared compounds were evaluated for atypical antipsychotic activity in animal models for dopaminergic and serotonergic antagonism. The compounds were designed based on their 3D similarity studies to standard drugs and in silico (docking studies) with respect to 5-HT2A and D2 receptors. Results: Results from the docking studies with respect to 5-HT2A and D2 receptors suggested a potential atypical antipsychotic profile for the test compounds. Theoretical ADME profiling of the compounds based on selected physicochemical parameters suggested an excellent compliance with Lipinski’s rules. The potential of these compounds to penetrate the blood brain barrier (log BB) was computed through an online software program and the values obtained for the compounds suggested a good potential for brain permeation. Reversal of apomorphine induced mesh climbing behaviour coupled with inactivity in the stereotypy assay indicates antidopaminergic effect and a potential atypical profile for the test compounds 1-5. Further, the activity of compounds in DOI assay indicated a 5-HT2 antagonistic profile (5-HT2 antagonism). Conclusion: Compound 5 emerged as important lead compound showing combined antidopaminergic and antiserotonergic (5-HT2A) activity with a potential atypical antipsychotic profile.

Into the first biomimetic sphingomyelin stationary phase: Suitability in drugs’ biopharmaceutic profiling and block relevance analysis of selectivity

State of the artSphingomyelin (SPH) is a type of sphingolipid found in animal nerve tissues, especially in the membranous myelin sheath that surrounds some nerve cell axons. Because of its characteristics, SPH stationary phase represents an ideal tool to mimic the interactions taking place between active pharmaceutical ingredients and neurons. MethodThe IAM.SPH stationary phase (0.821 mg) was suspended in methanol (7.0 mL) and the resulting slurry packed (600 bar) in an HPLC column (10 cm x 2.1 mm). The column was operated at 300 μL min−1 at 25°C using a mobile phase consisting of 60/25/15 (v/v/v) Dulbecco's phosphate buffer saline pH 7.4/methanol/acetonitrile. The elution was achieved isocratically and monitored by UV detection at 220 nm. The investigated dataset consisted of 88 compounds (36 neutrals, 26 bases and 26 acids). The block relevance (BR) analysis was accomplished starting by calculating 82 descriptors using the software VS+ and submitting the data matrices to Matlab. Multiple linear regression and related descriptors were obtained with Vega ZZ 64. Results and discussionThe method developed allowed to achieve a solid and reproducible SPH affinity scale for the assayed compounds. Computational studies produced statistically significant models for the prediction and mechanism elucidation of the retentive behavior of pharmaceutically relevant compounds on the SPH stationary phase. ConclusionsFor ionizable compounds, the IAM.SPH exhibited an original selectivity when compared to the commercially available IAM.PC. Moreover, apart from its suitability to surrogate log BB, IAM.SPH was also found relate significantly with the drugs’ fraction unbound in plasma, a crucial parameter in pharmacokinetics.

Assessment of blood–brain barrier permeability using micellar electrokinetic chromatography and P_VSA-like descriptors

Nowadays screening methods for assessment of biological properties of drug candidates are highly desired. Among naturally occurring membranes, one of the most important is the blood–brain barrier (BBB). BBB plays crucial role for central nervous system active drug candidates, since each molecule targeting a receptor in the brain must pass through the BBB first. This work assesses the possibility to apply micellar electrokinetic chromatography (MEKC) with cetrimonium bromide (CTAB) as surfactant in order to predict logBB. A model set of 45 marketed drugs, with known logBB values, varies in terms of chemical structures and pharmacological activities was used in this study. The established models were additionally supported by P_VSA-like descriptors molecular descriptors calculated by Dragon software. Two regression methods, multiple linear regression and support vector machine were evaluated and compared in terms of effectives of prediction of logBB. Both models showed similar prediction power, evidenced by similar values of root-mean-squared error of cross-validation 0.310 and 0.314 respectively. Proposed models met the Tropsha et al. criteria R2 > 0.6 and Q2 > 0.5 These results indicate that obtained model can be useful to predict BBB permeability of drug candidates and are attractive alternatives of time-consuming and demanding direct methods for log BB measurement.

Novel application of capillary electrophoresis with a liposome coated capillary for prediction of blood-brain barrier permeability

Profiling blood-brain barrier permeability of bioactive molecule is an important issue in early drug development, being a part of the optimization process of a compound's physicochemical properties, and hence pharmacokinetic profile. The study aimed to develop and optimize a new in vitro method for assessment of the compound's brain penetration. The tool is proposed as an alternative to the PAMPA-BBB (Parallel Artificial Membrane Permeability Assay for Blood-Brain Barrier) and based on a capillary electrochromatography (CEC) technique. It utilizes liposomes as structural substitutes of biological membranes, which are used as a capillary inner wall coating material. Following optimization of analysis conditions, migration times for a set of 25 reference drugs (mainly non-ionized in pH 7.4) were examined in a liposome coated capillary. On that basis, the retention factor (log k) was determined for each reference drug. Obtained log k values and experimentally received reference permeability parameters: log BB (in vivo data) and log Pe (PAMPA-BBB data) were compared with one another. Correlation coefficients were calculated, giving comparable results for CEC log k/log BB and analogical PAMPA-BBB log Pe/log BB analyses. Approximate ranges of log k for the central nervous system (CNS) permeable (CNS(+)) and non-permeable (CNS(−)) drugs were established.

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

To generate and validate predictive models for blood-brain permeation (BBB) of CNS molecules using the QSPR approach. 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. 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. 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). 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. 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 the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases.

The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = Cblood/Cbrain) values (R2 = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log kw), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R2 > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important molecules.