Ionization-Specific Prediction of Blood–Brain Permeability

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.

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.

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.

DeepBBBP: High accuracy Blood-Brain-Barrier Permeability Prediction with a Mixed Deep Learning Model

Blood-brain-barrier permeability (BBBP) is an important property that is used to establish the drug-likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not supposed to cross the barrier, as doing so would lead to toxicity. BBBP can be measured in vivo, in vitro or in silico. With the advent and subsequent rise of in silico methods for virtual drug screening, quite a bit of work has been done to predict this feature using statistical machine learning (ML) and deep learning (DL) based methods. In this work a mixed DL-based model, consisting of a Multi-layer Perceptron (MLP) and Convolutional Neural Network layers, has been paired with Mol2vec. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. These succinct vector representations are utilized as inputs to the mixed DL model that is used for BBBP predictions. Several well-known benchmarks incorporating BBBP data have been used for supervised training and prediction by our mixed DL model which demonstrates superior results when compared to existing ML and DL techniques used for predicting BBBP.

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM

MOTIVATION

Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery.

RESULTS

A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models.

AVAILABILITYAND IMPLEMENTATION

The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

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, V_D; 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 R_M⁰, S, PC₁ and R_M^{75 %} 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, M_W, V_M) of studied compounds. The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds' pharmacokinetic properties V_D, %PPB and log PS. R_M^{75 %} and the novel parameters derived from it (R_M^{75 %}/M_W and R_M^{75 %}/V_M) 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.

The AQUA-MER databases and aqueous speciation server: A web resource for multiscale modeling of mercury speciation

To assess the chemical reactivity, toxicity, and mobility of pollutants in the environment, knowledge of their species distributions is critical. Because their direct measurement is often infeasible, speciation modeling is widely adopted. Mercury (Hg) is a representative pollutant for which study of its speciation benefits from modeling. However, Hg speciation modeling is often hindered by a lack of reliable thermodynamic constants. Although computational chemistry (e.g., density functional theory [DFT]) can generate these constants, methods for directly coupling DFT and speciation modeling are not available. Here, we combine computational chemistry and continuum-scale modeling with curated online databases to ameliorate the problem of unreliable inputs to Hg speciation modeling. Our AQUA-MER databases and web server (https://aquamer.ornl.gov) provides direct speciation results by combining web-based interfaces to a speciation calculator, databases of thermodynamic constants, and a computational chemistry toolkit to estimate missing constants. Although Hg is presented as a concrete use case, AQUA-MER can also be readily applied to other elements. © 2019 Wiley Periodicals, Inc.

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.

RP-18 HPLC Analysis of Drugs’ Ability to Cross the Blood-Brain Barrier

One hundred ten compounds of diverse structures (actives and excipients used in pharmaceutical preparations) were studied by RP-18 HPLC with acetonitrile-pH 7.4 phosphate buffer 1 : 1 (v/v) as the mobile phase. The relationships between the BBB permeation coefficients and the chromatographic parameters log k and (log k)/PSA were compared to those between the blood-brain barrier (BBB) permeation parameters and the RP-18 TLC descriptors Rf and Rf/PSA known from our earlier studies. It was found that the correlations between the BBB permeability and the HPLC data are slightly worse than those achieved for the thin-layer chromatographic data. MLR analysis based upon the physicochemical data confirmed the value of the molecular descriptors, related to the CNS bioavailability. These variables, combined with the HPLC data, made it possible to generate computational models, explaining 70–96% of the total variance of the CNS bioavailability. Contrary to TLC Rf, the advantage of the modification of HPLC log k with PSA (polar surface area) has not been confirmed and the results obtained with log k are superior to those obtained after a novel (log k)/PSA parameter has been introduced. Establishing a firm threshold limit of (log k)/PSA, log k, or even k and k/PSA to distinguish between the CNS+ and CNS− compounds was impossible. On the other hand, discriminant function analyses involving log k and (log k)/PSA as discriminating variables separated the CNS+ and CNS− compounds with the success rate ca. 90%. On the basis of these results, it was concluded that the RP-18 HPLC analytical models are entirely successful in studies and predictions of the BBB permeability.

Physicochemical QSAR Analysis of Passive Permeability Across Caco-2 Monolayers

Caco-2 cell line is frequently used as a simplified in vitro model of intestinal absorption. In this study, a database of 1366 Caco-2 permeability coefficients (P_e) for 768 diverse drugs and drug-like compounds was compiled from public sources. The collected data represent permeation rates measured at varying experimental conditions (pH from 4.0 to 8.0, and stirring rates from 0 to >1000 rpm) that presumably account for passive diffusion across mucosal epithelium. These data were subjected to multistep nonlinear regression analysis using a minimal set of physicochemical descriptors (octanol-water log D, pKa, hydrogen bonding potential, and molecular size). The model was constructed in a mechanistic manner incorporating the following components: (i) a hydrodynamic equation of size- and charge-specific along with nonspecific diffusion across the paracellular pathway; (ii) transcellular diffusion represented by thermodynamic membrane/water partitioning ratio; (iii) stirring-dependent limit of maximum achievable permeability due to the presence of unstirred water layer. The obtained model demonstrates good accuracy of log P_e predictions with a residual mean square error <0.5 log units for all training and validation sets. Given its robust performance and straightforward interpretation in terms of simple physicochemical properties, the proposed model may serve as a valuable tool to guide drug discovery efforts toward readily absorbable compounds.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Activity cliff for 7-substituted pyrrolo-pyrimidine inhibitors of HCK explained in terms of predicted basicity of the amine nitrogen

We previously reported the structure-based design of a highly potent hematopoietic cell kinase (HCK) inhibitor, a pyrrolo-pyrimidine compound designated RK-20449, for treatment of recurrent leukemia. Herein we report the synthesis and structure-activity relationships of some amino acid derivatives of 7-substituted pyrrolo-pyrimidine. Although these derivatives had the same predicted binding conformation as RK-20449, their IC₅₀ values were 100-1000 times larger than that of the parent compound. We assumed that the basicity of the amine nitrogen, which formed an ionic bond with Asp348 of HCK, markedly affected inhibitory activity against HCK. The pKa values of the nitrogen were predicted by means of an ab initio quantum mechanical method, and complexes of the derivatives with HCK were analyzed by X-ray crystallography. We observed a significant correlation between the predicted pKa and IC₅₀ values, and the crystal structures of the less potent derivatives showed various types of defects around the ionic bond.

Synthesis, Biological Activity and Preliminary in Silico ADMET Screening of Polyamine Conjugates with Bicyclic Systems

Polyamine conjugates with bicyclic terminal groups including quinazoline, naphthalene, quinoline, coumarine and indole have been obtained and their cytotoxic activity against PC-3, DU-145 and MCF-7 cell lines was evaluated in vitro. Their antiproliferative potential differed markedly and depended on both their chemical structure and the type of cancer cell line. Noncovalent DNA-binding properties of the most active compounds have been examined using ds-DNA thermal melting studies and topo I activity assay. The promising biological activity, DNA intercalative binding mode and favorable drug-like properties of bis(naphthalene-2-carboxamides) make them a good lead for further development of potential anticancer drugs.

Quantitative prediction of histamine H1 receptor occupancy by the sedative and non-sedative antagonists in the human central nervous system based on systemic exposure and preclinical data

Significant histamine H1 receptor occupation in the central nervous system (CNS) is associated with sedation. Here we examined the time profiles of the H1 receptor occupancy (RO) in the CNS using sedative (diphenhydramine and ketotifen) and non-sedative (bepotastine and olopatadine) antagonists at their therapeutic doses by integrating in vitro and animal data. A pharmacokinetic model was constructed to associate plasma concentrations and receptor binding in the brain. Dissociation and association rate constants with the H1 receptor and plasma and brain unbound fractions were determined in vitro. Passive and active clearances across the blood-brain barrier (BBB) were estimated based on physicochemical properties and microdialysis studies in mice and monkeys. The estimated RO values were comparable with the reported values determined at time to maximum concentration (T_max) of plasma by positron-emission tomography in humans. The simulation suggested that the predicted maximum ROs by bepotastine and olopatadine were greater than the reported values. Sensitivity analysis showed that active transport across BBB had a significant impact on the RO duration of the H1 antagonists examined. The present study demonstrated that modeling and simulation permits a reasonable RO estimation in the human CNS. Our findings will facilitate the development of CNS-acting drugs.

Perillyl Alcohol and Its Drug-Conjugated Derivatives as Potential Novel Methods of Treating Brain Metastases

Metastasis to the central nervous system remains difficult to treat, and such patients are faced with a dismal prognosis. The blood-brain barrier (BBB), despite being partially compromised within malignant lesions in the brain, still retains much of its barrier function and prevents most chemotherapeutic agents from effectively reaching the tumor cells. Here, we review some of the recent developments aimed at overcoming this obstacle in order to more effectively deliver chemotherapeutic agents to the intracranial tumor site. These advances include intranasal delivery to achieve direct nose-to-brain transport of anticancer agents and covalent modification of existing drugs to support enhanced penetration of the BBB. In both of these areas, use of the natural product perillyl alcohol, a monoterpene with anticancer properties, contributed to promising new results, which will be discussed here.

Aqueous acidities of primary benzenesulfonamides: Quantum chemical predictions based on density functional theory and SMD

Aqueous pK(a) of selected primary benzenesulfonamides are predicted in a systematic manner using density functional theory methods and the SMD solvent model together with direct and proton exchange thermodynamic cycles. Some test calculations were also performed using high-level composite CBS-QB3 approach. The direct scheme generally does not yield a satisfactory agreement between calculated and measured acidities due to a severe overestimation of the Gibbs free energy changes of the gas-phase deprotonation reaction by the used exchange-correlation functionals. The relative pK(a) values calculated using proton exchange method compare to experimental data very well in both qualitative and quantitative terms, with a mean absolute error of about 0.4 pK(a) units. To achieve this accuracy, we find it mandatory to perform geometry optimization of the neutral and anionic species in the gas and solution phases separately, because different conformations are stabilized in these two cases. We have attempted to evaluate the effect of the conformer-averaged free energies in the pK(a) predictions, and the general conclusion is that this procedure is highly too costly as compared with the very small improvement we have gained.

Rabbit as an animal model for intravitreal pharmacokinetics: Clinical predictability and quality of the published data

Intravitreal administration is the method of choice in drug delivery to the retina and/or choroid. Rabbit is the most commonly used animal species in intravitreal pharmacokinetics, but it has been criticized as being a poor model of human eye. The critique is based on some anatomical differences, properties of the vitreous humor, and observed differences in drug concentrations in the anterior chamber after intravitreal injections. We have systematically analyzed all published information on intravitreal pharmacokinetics in the rabbit and human eye. The analysis revealed major problems in the design of the pharmacokinetic studies. In this review we provide advice for study design. Overall, the pharmacokinetic parameters (clearance, volume of distribution, half-life) in the human and rabbit eye have good correlation and comparable absolute values. Therefore, reliable rabbit-to-man translation of intravitreal pharmacokinetics should be feasible. The relevant anatomical and physiological parameters in rabbit and man show only small differences. Furthermore, the claimed discrepancy between drug concentrations in the human and rabbit aqueous humor is not supported by the data analysis. Based on the available and properly conducted pharmacokinetic studies, the differences in the vitreous structure in rabbits and human patients do not lead to significant pharmacokinetic differences. This review is the first step towards inter-species translation of intravitreal pharmacokinetics. More information is still needed to dissect the roles of drug delivery systems, disease states, age and ocular manipulation on the intravitreal pharmacokinetics in rabbit and man. Anyway, the published data and the derived pharmacokinetic parameters indicate that the rabbit is a useful animal model in intravitreal pharmacokinetics.

Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set

In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analysis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit classification and correlation functions. The best LDA-based model showed overall accuracies over 85 % and 83 % for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69 % of the variance in the experimental log BB was developed. A brief and general interpretation of proposed models allowed the estimation on how 'near' our computational approach is to the factors that determine the passage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion regarding to these compounds is provided. Finally, our results were compared with methodologies previously reported in the literature showing comparable to better results. The results could represent useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.

Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability

Nowadays most of the CNS acting therapeutic molecules are failing in clinical trials due to efflux transporters at the blood brain barrier (BBB) which imparts resistance and poor ADMET properties of these molecules. CNS acting drug molecules interact with the BBB prior to their target site, so there is a need to develop predictive models for BBB permeability which can be used in the initial phases of drug discovery process. Most of the drug molecules are transported to the brain via passive diffusion which is explored extensively; on the other hand, the role of active efflux transporters in BBB permeability is unclear. Our aim is to develop predictive models for BBB permeability that include active efflux transporters. An in silico model has been developed to assess the role of BCRP on BBB permeation. Eight descriptors were selected, which also include BCRP substrate probabilities used for model development and show a relationship between BCRP and logBB. From our analysis, it was found that 11 molecules satisfied all criteria required for BBB permeation but have low logBB values. These 11 molecules are predicted as BCRP substrates from the model developed, suggesting that the molecules are effluxed by the BCRP transporter. This predictive ability was further validated by docking of these 11 molecules into BCRP protein. This study provides a new mechanistic insight into correlation of low logBB values and efflux mechanism of BCRP in BBB.

Challenges of using in vitro data for modeling P-glycoprotein efflux in the blood-brain barrier

The efficacy of central nervous system (CNS) drugs may be limited by their poor ability to cross the bloodbrain barrier (BBB). Transporters, such as p-glycoprotein, may affect the distribution of many drugs into the CNS in conjunction with the restricted paracellular pathway of the BBB. It is therefore important to gain information on unbound drug concentrations in the brain in drug development to ensure sufficient drug exposure from plasma at the target site in the CNS. In vitro methods are routinely used in drug development to study passive permeability and p-glycoprotein efflux of new drugs. This review discusses the challenges in the use of in vitro data as input parameters in physiologically based pharmacokinetic (PBPK) models of CNS drug disposition of p-glycoprotein substrates. Experience with quinidine demonstrates the variability in in vitro parameters of passive permeability and active pglycoprotein efflux. Further work is needed to generate parameter values that are independent of the model and assay. This is a prerequisite for reliable predictions of drug concentrations in the brain in vivo.

Deuterium content of water increases depression susceptibility: the potential role of a serotonin-related mechanism

Environmental factors can significantly affect disease prevalence, including neuropsychiatric disorders such as depression. The ratio of deuterium to protium in water shows substantial geographical variation, which could affect disease susceptibility. Thus the link between deuterium content of water and depression was investigated, both epidemiologically, and in a mouse model of chronic mild stress. We performed a correlation analysis between deuterium content of tap water and rates of depression in regions of the USA. Next, we used a 10-day chronic stress paradigm to test whether 2-week deuterium-depleted water treatment (91 ppm) affects depressive-like behavior and hippocampal SERT. The effect of deuterium-depletion on sleep electrophysiology was also evaluated in naïve mice. There was a geographic correlation between a content of deuterium and the prevalence of depression across the USA. In the chronic stress model, depressive-like features were reduced in mice fed with deuterium-depleted water, and SERT expression was decreased in mice treated with deuterium-treated water compared with regular water. Five days of predator stress also suppressed proliferation in the dentate gyrus; this effect was attenuated in mice fed with deuterium-depleted water. Finally, in naïve mice, deuterium-depleted water treatment increased EEG indices of wakefulness, and decreased duration of REM sleep, phenomena that have been shown to result from the administration of selective serotonin reuptake inhibitors (SSRI). Our data suggest that the deuterium content of water may influence the incidence of affective disorder-related pathophysiology and major depression, which might be mediated by the serotoninergic mechanisms.

Prediction of drug disposition on the basis of its chemical structure

The chemical structure of any drug determines its pharmacokinetics and pharmacodynamics. Detailed understanding of relationships between the drug chemical structure and individual disposition pathways (i.e., distribution and elimination) is required for efficient use of existing drugs and effective development of new drugs. Different approaches have been developed for this purpose, ranging from statistics-based quantitative structure-property (or structure-pharmacokinetic) relationships (QSPR) analysis to physiologically based pharmacokinetic (PBPK) models. This review critically analyzes currently available approaches for analysis and prediction of drug disposition on the basis of chemical structure. Models that can be used to predict different aspects of disposition are presented, including: (a) value of the individual pharmacokinetic parameter (e.g., clearance or volume of distribution), (b) efficiency of the specific disposition pathway (e.g., biliary drug excretion or cytochrome P450 3A4 metabolism), (c) accumulation in a specific organ or tissue (e.g., permeability of the placenta or accumulation in the brain), and (d) the whole-body disposition in the individual patients. Examples of presented pharmacological agents include "classical" low-molecular-weight compounds, biopharmaceuticals, and drugs encapsulated in specialized drug-delivery systems. The clinical efficiency of agents from all these groups can be suboptimal, because of inefficient permeability of the drug to the site of action and/or excessive accumulation in other organs and tissues. Therefore, robust and reliable approaches for chemical structure-based prediction of drug disposition are required to overcome these limitations. PBPK models are increasingly being used for prediction of drug disposition. These models can reflect the complex interplay of factors that determine drug disposition in a mechanistically correct fashion and can be combined with other approaches, for example QSPR-based prediction of drug permeability and metabolism, pharmacogenomic data and tools, pharmacokinetic-pharmacodynamic modeling approaches, etc. Moreover, the PBPK models enable detailed analysis of clinically relevant scenarios, for example the effect of the specific conditions on the time course of the analyzed drug in the individual organs and tissues, including the site of action. It is expected that further development of such combined approaches will increase their precision, enhance the effectiveness of drugs, and lead to individualized drug therapy for different patient populations (geriatric, pediatric, specific diseases, etc.).

Methodologies to assess drug permeation through the blood-brain barrier for pharmaceutical research

The drug discovery process for drugs that target the central nervous system suffers from a very high rate of failure due to the presence of the blood-brain barrier, which limits the entry of xenobiotics into the brain. To minimise drug failure at different stages of the drug development process, new methodologies have been developed to understand the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of drug candidates at early stages of drug development. Additionally, understanding the permeation of drug candidates is also important, particularly for drugs that target the central nervous system. During the first stages of the drug discovery process, in vitro methods that allow for the determination of permeability using high-throughput screening methods are advantageous. For example, performing the parallel artificial membrane permeability assay followed by cell-based models with interesting hits is a useful technique for identifying potential drugs. In silico models also provide interesting information but must be confirmed by in vitro models. Finally, in vivo models, such as in situ brain perfusion, should be studied to reduce a large number of drug candidates to a few lead compounds. This article reviews the different methodologies used in the drug discovery and drug development processes to determine the permeation of drug candidates through the blood-brain barrier.

New tacrine analogs as acetylcholinesterase inhibitors - theoretical study with chemometric analysis

Computer simulations constitute the basis of the design and discovery of new drugs. This approach is not only significant with regards to finding new structures, but also for selecting the molecules with the highest probability of being useful in the diagnostic process and treatment of numerous diseases. In our work, we used computational software to analyze 32 new acetylcholinesterase (AChE) inhibitors and formulate ADMET predictions. To understand the influence of the structure of our derivatives on binding mode, we docked all structures to the active site of AChE and assigned some pharmacophoric features. Finally, we undertook a chemometric analysis of all the compounds on the basis of FT-IR, which gave us the possibility of performing a fast categorization of the analyzed compounds and design compounds with similar structures.

Improving the prediction of drug disposition in the brain

INTRODUCTION

Ability to cross the blood-brain barrier is one of the key ADME characteristics of all drug candidates regardless of their target location in the body. While good brain penetration is essential for CNS drugs, it may lead to serious side effects in case of peripherally-targeted molecules. Despite a high demand of computational methods for estimating brain transport early in drug discovery, achieving good prediction accuracy still remains a challenging task.

AREAS COVERED

This article reviews various measures employed to quantify brain delivery and recent advances in QSAR approaches for predicting these properties from the compound's structure. Additionally, the authors discuss the classification models attempting to distinguish between permeable and impermeable chemicals.

EXPERT OPINION

Recent research in the field of brain penetration modeling showed an increasing understanding of the processes involved in drug disposition, although most models of brain/plasma partitioning still rely on purely statistical considerations. Preferably, new models should incorporate mechanistic knowledge since it is the prerequisite for guiding drug design efforts in the desired direction. To increase the efficiency of computational tools, a broader view is necessary, involving rate and extent of brain penetration, as well as plasma and brain tissue binding strength, instead of relying on any single property.

Computational prediction of blood-brain barrier permeability using decision tree induction

Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component.

Prediction of pharmacokinetic parameters

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.

Identification of novel functional inhibitors of acid sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans.

Biorelevant pK(a) (37 °C) predicted from the 2D structure of the molecule and its pK(a) at 25 °C

Values of the ionization constants at 37 °C, which are scarcely reported, are more meaningful for interpreting mechanisms of cellular transport by ionizable molecules and in mechanistic dissolution studies, which are often performed at the biorelevant temperature. An equation was developed where the pK(a) values of drug-like molecules determined at 25 °C can be simply converted to values at 37 °C, without additional measurement. The differences between the values, ΔpK(a)=pK(a)³⁷-pK(a)²⁵, were linearly fitted to a function of pK(a)²⁵ and the standard entropy of ionization, ΔS°, where the latter term was approximated by the five Abraham linear free energy solvation descriptors using multiple linear regression. The Abraham descriptors (H-bond donor and acceptor strengths, dipolar solute-solvent interactions potential, the pi- and n-electrons dispersion force, and molar volume) were determined from the 2-dimensional structure of the molecules. A total of 143 mostly drug-like molecules (207 pK(a) values at 25 °C and at 37 °C) were chosen for the study. The pK(a) values of many were determined here for the first time. Included were 34 weak acids, 85 weak bases, and 24 amphoteric compounds (6 ordinary ampholytes, 18 zwitterions).

SAR/QSAR methods in public health practice

Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

Physicochemical selectivity of the BBB microenvironment governing passive diffusion--matching with a porcine brain lipid extract artificial membrane permeability model

PURPOSE

To mimic the physicochemical selectivity of the blood-brain barrier (BBB) and to predict its passive permeability using a PAMPA model based on porcine brain lipid extract (PBLE 10%w/v in alkane).

METHODS

Three PAMPA (BD pre-coated and PBLE with 2 different lipid volumes) models were tested with 108 drugs. Abraham solvation descriptors were used to interpret the in vitro-in vivo correlation with 282 in situ brain perfusion measurements, spanning over 5 orders of magnitude. An in combo PAMPA model was developed from combining measured PAMPA permeability with one H-bond descriptor.

RESULTS

The in combo PAMPA predicted 93% of the variance of 197 largely efflux-inhibited in situ permeability training set. The model was cross-validated by the "leave-many-out" procedure, with q(2) = 0.92 ± 0.03. The PAMPA models indicated the presence of paramembrane water channels. Only the PBLE-based PAMPA-BBB model with sufficient lipid to fill all the internal pore space of the filter showed a wide dynamic range window, selectivity coefficient near 1, and was suitable for predicting BBB permeability.

CONCLUSION

BBB permeability can be predicted by in combo PAMPA. Its speed and substantially lower cost, compared to in vivo measurements, make it an attractive first-pass screening method for BBB passive permeability.

Passive diffusion of acetylcholinesterase oxime reactivators through the blood-brain barrier: influence of molecular structure

In this in vitro study, high-performance liquid chromatography (HPLC) was used to determinate the penetration of 30 acetylcholinesterase (AChE) reactivators through the blood-brain barrier (BBB). According to our method, monoquaternary AChE reactivators were found to be able to penetrate the BBB. In addition to molecular structure, molecular weight appears to be an important factor for passive transport of oximes through the BBB. For bisquaternary reactivators, the connecting linker plays a key role in the ability to penetrate into the central nervous system (CNS): simple, short linkers tend to facilitate permeation. The location of groups on the pyridine ring also influences passive transport into the brain; the optimum position of the oxime group was found to be position four (para) and substitution of the oxime group on the pyridine ring by carbamoyl or amidoxime group markedly decreased penetration of AChE reactivators into the CNS.

Ionization-specific analysis of human intestinal absorption

This study presents a mechanistic QSAR analysis of human intestinal absorption of drugs and drug-like compounds using a data set of 567 %HIA values. Experimental data represent passive diffusion across intestinal membranes, and are considered to be reasonably free of carrier-mediated transport or other unwanted effects. A nonlinear model was developed relating %HIA to physicochemical properties of drugs (lipophilicity, ionization, hydrogen bonding, and molecular size). The model describes ion-specific intestinal permeability of drugs by both transcellular and paracellular routes, and also accounts for unstirred water layer effects. The obtained model was validated on two external data sets consisting of in vivo human jejunal permeability coefficients (P(eff)) and absorption rate constants (K(a)). Validation results demonstrate good predictive power of the model (RMSE = 0.35-0.45 log units for log K(a) and log P(eff)). High prediction accuracy together with clear physicochemical interpretation (log P, pK(a)) makes this model particularly suitable for use in property-based drug design.

P-glycoprotein deficient mouse in situ blood-brain barrier permeability and its prediction using an in combo PAMPA model

The purpose of the study was to assess the permeability of mouse blood-brain barrier (BBB) to a diverse set of compounds in the absence of P-glycoprotein (Pgp) mediated efflux, to predict it using an in combo PAMPA model, and to explore its role in brain penetration classification (BPC). The initial brain uptake (K(in)) of 19 compounds in both wild-type and Pgp mutant [mdr1a(-/-)] CF-1 mice was determined by the in situ brain perfusion technique. PAMPA measurements were performed, and the values were used to develop an in combo model, including Abraham descriptors. Published rodent K(in) values were used to enhance the dataset and validate the model. The model predicted 92% of the variance of the training set permeability. In all, 182 K(in) values were considered in this study, spanning four log orders of magnitude and where Pgp decreased brain uptake by as much as 14-fold. The calculated permeability-surface area (PS) values along with literature reported brain tissue binding were used to group molecules in terms of their brain penetration classification. The in situ BBB permeability can be predicted by the in combo PAMPA model to a satisfactory degree, and can be used as a lower-cost, high throughput first-pass screening method for BBB passive permeability.