Predicting drug penetration across the blood–brain barrier: comparison of micellar liquid chromatography and immobilized artificial membrane liquid chromatography

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood−brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.

Recent Advances in the Prediction of Pharmacokinetics Properties in Drug Design Studies: A Review

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design.

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 = C_blood/C_brain) 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 k_w), 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.

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.

Screening therapeutics according to their uptake across the blood-brain barrier: A high throughput method based on immobilized artificial membrane liquid chromatography-diode-array-detection coupled to electrospray-time-of-flight mass spectrometry

The Blood-Brain Barrier (BBB) plays an essential role in protecting the brain tissues against possible injurious substances. In the present work, 79 neutral, basic, acidic and amphoteric structurally unrelated analytes were considered and their chromatographic retention coefficients on immobilized artificial membrane (IAM) stationary phase were determined employing a mass spectrometry (MS)-compatible buffer based on ammonium acetate. Their BBB passage predictive strength was evaluated and the statistical models based on IAM indexes and in silico physico-chemical descriptors showed solid statistics (r² (n - 1) = 0.78). The predictive strength of the indexes achieved by the MS-compatible method was comparable to that achieved by employing the more "biomimetic" Dulbecco's phosphate buffered saline, even if some differences in the elution order were observed. The method was transferred to the MS, employing a diode-array-detection coupled to an electrospray ionization source and a time-of-flight analyzer. This setup allowed the simultaneous analysis of up to eight analytes, yielding a remarkable acceleration of the analysis time.

Lipophilicity and biomimetic properties to support drug discovery

INTRODUCTION

Lipophilicity, expressed as the octanol-water partition coefficient, constitutes the most important property in drug action, influencing both pharmacokinetic and pharmacodynamics processes as well as drug toxicity. On the other hand, biomimetic properties defined as the retention outcome on HPLC columns containing a biological relevant agent, provide a considerable advance for rapid experimental - based estimation of ADME properties in early drug discovery stages. Areas covered: This review highlights the paramount importance of lipophilicity in almost all aspects of drug action and safety. It outlines problems brought about by high lipophilicity and provides an overview of the drug-like metrics which incorporate lower limits or ranges of logP. The fundamental factors governing lipophilicity are compared to those involved in phospholipophilicity, assessed by Immobilized Artificial Membrane Chromatography (IAM). Finally, the contribution of biomimetic properties to assess plasma protein binding is evaluated. Expert opinion: Lipophilicity and biomimetic properties have important distinct and overlapping roles in supporting the drug discovery process. Lipophilicity is unique in early drug design for library screening and for the identification of the most promising compounds to start with, while biomimetic properties are useful for the experimentally-based evaluation of ADME properties for the synthesized novel compounds, supporting the prioritization of drug candidates and guiding further synthesis.

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R² and Q², hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS.

LogP, a yesterday's value?

INTRODUCTION

There is an increasing demand for high throughput methods at early stages of preclinical radioligand development, in order to predict pharmacokinetic properties (e.g., biodistribution) and blood brain barrier (BBB) penetration. One of the most important physicochemical properties is the lipophilicity, measured by means of shake-flask (logP) or HPLC methods. Yet, a plethora of experimental methods are described in the literature for the determination of logP values. These varying methods often lead to different results for one identical compound, which complicates any comparison or prediction for subsequent preclinical studies. However, a standardized and internationally applied and accepted database with logP values for a reliable comparison of the lipophilic character of radiotracers is still missing.

METHOD

Lipophilicity measurements were performed with 121 molecules using a high throughput HPLC method and ClogP values were calculated using ChemBioDraw®. Furthermore, logP measurements for six representative radiotracers were performed with the conventional shake-flask method and the results were statistically compared to the ClogP and HPLC logP results. Different logP thresholds, suggesting optimal BBB penetration according to literature, were selected and put into relation with the acquired HPLC logP and ClogP values of cerebral tracers.

RESULTS

The results of the tested compounds ranged from -2.1 to 5.4 with the applied HPLC method. The acquired database comprises ClogP values of the whole set of compounds ranging from -4.11 to 6.12. LogP data from different methods were not comparable. The correlation of the obtained logP data to thresholds suggesting an optimal brain uptake resulted in a high number of false positive classifications.

CONCLUSION

The logP determination for prediction of BBB penetration is obsolete. The extensive database, including clinical relevant radiotracers, can be used as comparative set of values for preclinical studies, and serves as a basis for further critical discussions concerning the eligibility of logP.

Skin-mimetic chromatography for prediction of human percutaneous absorption of biologically active compounds occurring in medicinal plant extracts

The main aim of this study was to predict quantitatively human percutaneous absorption of chosen compounds commonly occurring in plants which can be used as medicinal extracts in the drug and beauty industries. The most important human percutaneous descriptors, i.e. logK_p (logarithm of the water/skin partition coefficient) and logJ_max (logarithm of the maximum flux of solutes penetrating the skin), of fatty acids and polyphenols were determined using both in vitro and in silico methods. For in vitro determination of human percutaneous absorption, micellar liquid chromatography based on hexadecyltrimethylammonium bromide, sodium dodecyl sulfate and polyoxyethylene (23) lauryl ether (Brij35) was used. Human percutaneous absorption was characterized by entirely new QSAR/QRAR models based on retention, lipophilic, steric and electronic data as well as on the linear free energy relationship parameters. Many different correlations between human skin absorption and different physicochemical parameters were performed, e.g. the in silico estimated logK_p value was correlated with the retention parameter logk_w (logarithm of the retention factor extrapolated to pure water) from the systems imitating a cutaneous environment (R²  = 0.92). Moreover, the influence of lipophilicity on percutaneous absorption was examined. The obtained correlation was excellent (R²  = 0.95).

Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification

Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines.

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.

A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R(2) = 0.94) and logPS (R(2) = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.