Insights for Predicting Blood-Brain Barrier Penetration of CNS Targeted Molecules Using QSPR Approaches

Identification of potential inhibitors against Escherichia coli Mur D enzyme to combat rising drug resistance: an in-silico approach

Indiscriminate use of anti-microbial agents has resulted in the inception, frequency, and spread of antibiotic resistance among targeted bacterial pathogens and the commensal flora. Mur enzymes, playing a crucial role in cell-wall synthesis, are one of the most appropriate targets for developing novel inhibitors against antibiotic-resistant bacterial pathogens. In the present study, in-silico high-throughput virtual (HTVS) and Standard-Precision (SP) screening was carried out with 0.3 million compounds from several small-molecule libraries against the E. coli Mur D enzyme (PDB ID 2UUP). The docked complexes were further subjected to extra-precision (XP) docking calculations, and highest Glide-score compound was further subjected to molecular simulation studies. The top six virtual hits (S1-S6) displayed a glide score (G-score) within the range of -9.013 to -7.126 kcal/mol and compound S1 was found to have the highest stable interactions with the Mur D enzyme (2UUP) of E. coli. The stability of compound S1 with the Mur D (2UUP) complex was validated by a 100-ns molecular dynamics simulation. Binding free energy calculation by the MM-GBSA strategy of the S1-2UUP (Mur D) complex established van der Waals, hydrogen bonding, lipophilic, and Coulomb energy terms as significant favorable contributors for ligand binding. The final lead molecules were subjected to ADMET predictions to study their pharmacokinetic properties and displayed promising results, except for certain modifications required to improve QPlogHERG values. So, the compounds screened against the Mur D enzyme can be further studied as preparatory points for in-vivo studies to develop potential drugs. HIGHLIGHTSE.coli is a common cause of urinary tract infections.E.coli MurD enzyme is a suitable target for drug development.Novel inhibitors against E.coli MurD enzyme were identified.Molecular dynamics studies identified in-silico potential of identified compound.ADMET predictions and Lipinski's rule of five studies showed promising results.Communicated by Ramaswamy H. Sarma.

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.

Computational Approach to Drug Penetration across the Blood-Brain and Blood-Milk Barrier Using Chromatographic Descriptors

Drug penetration through biological barriers is an important aspect of pharmacokinetics. Although the structure of the blood-brain and blood-milk barriers is different, a connection can be found in the literature between drugs entering the central nervous system (CNS) and breast milk. This study was created to reveal such a relationship with the use of statistical modelling. The basic physicochemical properties of 37 active pharmaceutical compounds (APIs) and their chromatographic retention data (TLC and HPLC) were incorporated into calculations as molecular descriptors (MDs). Chromatography was performed in a thin layer format (TLC), where the plates were impregnated with bovine serum albumin to mimic plasma protein binding. Two columns were used in high performance liquid chromatography (HPLC): one with immobilized human serum albumin (HSA), and the other containing an immobilized artificial membrane (IAM). Statistical methods including multiple linear regression (MLR), cluster analysis (CA) and random forest regression (RF) were performed with satisfactory results: the MLR model explains 83% of the independent variable variability related to CNS bioavailability; while the RF model explains up to 87%. In both cases, the parameter related to breast milk penetration was included in the created models. A significant share of reversed-phase TLC retention values was also noticed in the RF model.

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.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods

The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sensitivity (SEN). To improve performance, ensemble models were built to predict the BBB permeability of compounds. In this study, in silico ensemble-learning models were developed using 3 machine-learning algorithms and 9 molecular fingerprints from 1757 chemicals (integrated from 2 published data sets) to predict BBB permeability. The best prediction performance of the base classifier models was achieved by a prediction model based on an random forest (RF) and a MACCS molecular fingerprint with an ACC of 0.910, an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.957, a SEN of 0.927, and a specificity of 0.867 in 5-fold cross-validation. The prediction performance of the ensemble models is better than that of most of the base classifiers. The final ensemble model has also demonstrated good accuracy for an external validation and can be used for the early screening of CNS drugs.

Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

Organotypic Models to Study Human Glioblastoma: Studying the Beast in Its Ecosystem

Glioblastoma is a very aggressive primary brain tumor in adults, with very low survival rates and no curative treatments. The high failure rate of drug development for this cancer is linked to the high-cost, time-consuming, and inefficient models used to study the disease. Advances in stem cell and in vitro cultures technologies are promising, however, and here we present the advantages and limitations of available organotypic culture models and discuss their possible applications for studying glioblastoma.

The Brain Exposure Efficiency (BEE) Score

The blood-brain barrier (BBB), composed of microvascular tight junctions and glial cell sheathing, selectively controls drug permeation into the central nervous system (CNS) by either passive diffusion or active transport. Computational techniques capable of predicting molecular brain penetration are important to neurological drug design. A novel prediction algorithm, termed the Brain Exposure Efficiency Score (BEE), is presented. BEE addresses the need to incorporate the role of trans-BBB influx and efflux active transporters by considering key brain penetrance parameters, namely, steady state unbound brain to plasma ratio of drug (K_p,uu) and dose normalized unbound concentration of drug in brain (C_u,b). BEE was devised using quantitative structure-activity relationships (QSARs) and molecular modeling studies on known transporter proteins and their ligands. The developed algorithms are provided as a user-friendly open source calculator to assist in optimizing a brain penetrance strategy during the early phases of small molecule molecular therapeutic design.

Tuning Properties for Blood-Brain Barrier Permeation: A Statistics-Based Analysis

In the effort to define a set of rules useful in tuning the properties for a successful blood-brain barrier (BBB) permeation, we statistically analyzed a set of 328 compounds and correlated their experimental in vivo logBB with a series of computed descriptors. Contingency tables were constructed, observed and expected distributions were calculated, and chi-square (χ²) distributions were evaluated. This allowed to point out a significant dependence of certain physicochemical properties in influencing the BBB permeation. Of over 15 computed descriptors, 9 resulted to be particularly important showing highly significant χ² distribution: polar surface area (χ² = 66.79; p = 1.08 × 10^-13), nitrogen and oxygen count (χ² = 51.17; p = 2.06 × 10^-10), logP (χ² = 47.38; p = 1.27 × 10^-9), nitrogen count (χ² = 38.29; p = 9.77 × 10^-8), logD (χ² = 36.80; p = 36.80), oxygen count (χ² = 35.83; p = 3.13 × 10^-7), ionization state (χ² = 33.02, p = 3.19 × 10^-7), hydrogen bond acceptors (χ² = 30.80; p = 3.36 × 10^-6), and hydrogen bond donors (χ² = 29.29; p = 6.81 × 10^-6). Other parameters describing the mass and size of the molecules (molecular weight: 11.18; p = 2.46 × 10^-2) resulted in being not significant since the population within the observed and expected distribution was similar. Depending on the combination of the significant descriptors, we set a three cases probabilistic scenario (BBB⁺, BBB^-, BBB⁺/BBB^-) that would prospectively be used to tune properties for BBB permeation.

Functionalised bicyclic tetramates derived from cysteine as antibacterial agents

Routes to bicyclic tetramates derived from cysteine permitting ready incorporation of functionality at two different points around the periphery of a heterocyclic skeleton are reported. This has enabled the identification of systems active against Gram-positive bacteria, some of which show gyrase and RNA polymerase inhibitory activity. In particular, tetramates substituted with glycosyl side chains, chosen to impart polarity and aqueous solubility, show high antibacterial activity coupled with modest gyrase/polymerase activity in two cases. An analysis of physicochemical properties indicates that the antibacterially active tetramates generally occupy physicochemical space with MW of 300-600, clog D7.4 of -2.5 to 4 and rel. PSA of 11-22%. This work demonstrates that biologically active 3D libraries are readily available by manipulation of a tetramate skeleton.

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.

Integrating in vitro and in silico approaches to evaluate the "dual functionality" of palmatine chloride in inhibiting and disassembling Tau-derived VQIVYK peptide fibrils

BACKGROUND

Alzheimer's disease (AD) is the most common neurodegenerative disorder which is characterized by the deposits of intra-cellular tau protein and extra-cellular amyloid-β (Aβ) peptides in the human brain. Understanding the mechanism of protein aggregation and finding compounds that are capable of inhibiting its aggregation is considered to be highly important for disease therapy.

METHODS

We used an in vitro High-Throughput Screening for the identification of potent inhibitors of tau aggregation using a proxy model; a highly aggregation-prone hexapeptide fragment ³⁰⁶VQIVYK³¹¹ derived from tau. Using ThS fluorescence assay we screened a library of 2401 FDA approved, bio-active and natural compounds in attempt to find molecules which can efficiently modulate tau aggregation.

RESULTS

Among the screened compounds, palmatine chloride (PC) alkaloid was able to dramatically reduce the aggregation propensity of PHF6 at sub-molar concentrations. PC was also able to disassemble preformed aggregates of PHF6 and reduce the amyloid content in a dose-dependent manner. Insights obtained from MD simulation showed that PC interacted with the key residues of PHF6 responsible for β-sheet formation, which could likely be the mechanism of inhibition and disassembly. Furthermore, PC could effectively inhibit the aggregation of full-length tau and disassemble preformed aggregates.

CONCLUSIONS

We found that PC possesses "dual functionality" towards PHF6 and full-length tau, i.e. inhibit their aggregation and disassemble pre-formed fibrils.

GENERAL SIGNIFICANCE

The "dual functionality" of PC is valuable as a disease modifying strategy for AD, and other tauopathies, by inhibiting their progress and reducing the effect of fibrils already present in the brain.

Prediction of brain:blood unbound concentration ratios in CNS drug discovery employing in silico and in vitro model systems

Recent years have seen a paradigm shift away from optimizing the brain:blood concentration ratio toward the more relevant brain:blood unbound concentration ratio (K_p,uu,br) in CNS drug discovery. Here, we review the recent developments in the in silico and in vitro model systems to predict the K_p,uu,br of discovery compounds with special emphasis on the in-vitro-in-vivo correlation. We also discuss clinical 'translation' of rodent K_p,uu,br and highlight the future directions for improvement in brain penetration prediction. Important in this regard are in silico K_p,uu,br models built on larger datasets of high quality, calibration and deeper understanding of experimental in vitro transporter systems, and better understanding of blood-brain barrier transporters and their in vivo relevance aside from P-gp and BCRP.

In vitro models and systems for evaluating the dynamics of drug delivery to the healthy and diseased brain

The blood-brain barrier (BBB) plays a crucial role in maintaining brain homeostasis and transport of drugs to the brain. The conventional animal and Transwell BBB models along with emerging microfluidic-based BBB-on-chip systems have provided fundamental functionalities of the BBB and facilitated the testing of drug delivery to the brain tissue. However, developing biomimetic and predictive BBB models capable of reasonably mimicking essential characteristics of the BBB functions is still a challenge. In addition, detailed analysis of the dynamics of drug delivery to the healthy or diseased brain requires not only biomimetic BBB tissue models but also new systems capable of monitoring the BBB microenvironment and dynamics of barrier function and delivery mechanisms. This review provides a comprehensive overview of recent advances in microengineering of BBB models with different functional complexity and mimicking capability of healthy and diseased states. It also discusses new technologies that can make the next generation of biomimetic human BBBs containing integrated biosensors for real-time monitoring the tissue microenvironment and barrier function and correlating it with the dynamics of drug delivery. Such integrated system addresses important brain drug delivery questions related to the treatment of brain diseases. We further discuss how the combination of in vitro BBB systems, computational models and nanotechnology supports for characterization of the dynamics of drug delivery to the brain.

The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability

Drugs acting on central nervous system (CNS) may take longer duration to reach the market as these compounds have a higher attrition rate in clinical trials due to the complexity of the brain, side effects, and poor blood-brain barrier (BBB) permeability compared to non-CNS-acting compounds. The roles of active efflux transporters with BBB are still unclear. The aim of the present work was to develop a predictive model for BBB permeability that includes the MRP-1 transporter, which is considered as an active efflux transporter. A support vector machine model was developed for the classification of MRP-1 substrates and non-substrates, which was validated with an external data set and Y-randomization method. An artificial neural network model has been developed to evaluate the role of MRP-1 on BBB permeation. A total of nine descriptors were selected, which included molecular weight, topological polar surface area, ClogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, P-gp, BCRP, and MRP-1 substrate probabilities for model development. We identified 5 molecules that fulfilled all criteria required for passive permeation of BBB, but they all have a low logBB value, which suggested that the molecules were effluxed by the MRP-1 transporter.

BioPPSy: An Open-Source Platform for QSAR/QSPR Analysis

The reliability of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models is often difficult to assess due to the problems of accessing the tools and data used to build the models. We present here BioPPSy, which aims to fill this gap by providing an easy-to-use open-source software platform. We demonstrate the program capabilities by calculating three key properties used in drug discovery, aqueous solubility, Caco-2 cell permeability and blood-brain barrier permeability. A comparison is made with a number of previously reported methods, taken from the literature, for each property. The software, including source code, current models and databases, is available from https://sourceforge.net/projects/bioppsy/.

QSAR Model of Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain: Incorporating P-glycoprotein Efflux as a Variable

We report development and prospective validation of a QSAR model of the unbound brain-to-plasma partition coefficient, K_p,uu,brain, based on the in-house data set of ∼1000 compounds. We discuss effects of experimental variability, explore the applicability of both regression and classification approaches, and evaluate a novel, model-within-a-model approach of including P-glycoprotein efflux prediction as an additional variable. When tested on an independent test set of 91 internal compounds, incorporation of P-glycoprotein efflux information significantly improves the model performance resulting in an R² of 0.53, RMSE of 0.57, Spearman's Rho correlation coefficient of 0.73, and qualitative prediction accuracy of 0.8 (kappa = 0.6). In addition to improving the performance, one of the key advantages of this approach is the larger chemical space coverage provided indirectly through incorporation of the in vitro, higher throughput data set that is 4 times larger than the in vivo data set.

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.

Alzheimer's disease drug development based on Computer-Aided Drug Design

Alzheimer's disease (AD) is a common neurodegenerative disorder characterized by the excessive deposition of amyloids in the brain. The pathological features mainly include the extracellular amyloid plaques and intracellular neurofibrillary tangles, which are the production of amyloid precursor protein (APP) processed by the α-, β- and γ-secretases. Based on the amyloid cascade hypotheses of AD, a large number of amyloid-β agents and secretase inhibitors against AD have been recently developed by using computational methods. This review article describes pathophysiology of AD and the structure of the Aβ plaques, β- and γ-secretases, and discusses the recent advances in the development of the amyloid agents for AD therapy and diagnosis by using Computer-Aided Drug Design approach.

Molecular determinants of blood-brain barrier permeation

The blood-brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution.

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.

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.

Image-guided synthesis reveals potent blood-brain barrier permeable histone deacetylase inhibitors

Recent studies have revealed that several histone deacetylase (HDAC) inhibitors, which are used to study/treat brain diseases, show low blood-brain barrier (BBB) penetration. In addition to low HDAC potency and selectivity observed, poor brain penetrance may account for the high doses needed to achieve therapeutic efficacy. Here we report the development and evaluation of highly potent and blood-brain barrier permeable HDAC inhibitors for CNS applications based on an image-guided approach involving the parallel synthesis and radiolabeling of a series of compounds based on the benzamide HDAC inhibitor, MS-275 as a template. BBB penetration was optimized by rapid carbon-11 labeling and PET imaging in the baboon model and using the imaging derived data on BBB penetration from each compound to feed back into the design process. A total of 17 compounds were evaluated, revealing molecules with both high binding affinity and BBB permeability. A key element conferring BBB penetration in this benzamide series was a basic benzylic amine. These derivatives exhibited 1-100 nM inhibitory activity against recombinant human HDAC1 and HDAC2. Three of the carbon-11 labeled aminomethyl benzamide derivatives showed high BBB penetration (∼0.015%ID/cc) and regional binding heterogeneity in the brain (high in thalamus and cerebellum). Taken together this approach has afforded a strategy and a predictive model for developing highly potent and BBB permeable HDAC inhibitors for CNS applications and for the discovery of novel candidate molecules for small molecule probes and drugs.

Novel anti-glioblastoma agents and therapeutic combinations identified from a collection of FDA approved drugs

Background

Glioblastoma (GBM) is a therapeutic challenge, associated with high mortality. More effective GBM therapeutic options are urgently needed. Hence, we screened a large multi-class drug panel comprising the NIH clinical collection (NCC) that includes 446 FDA-approved drugs, with the goal of identifying new GBM therapeutics for rapid entry into clinical trials for GBM.

Methods

Screens using human GBM cell lines revealed 22 drugs with potent anti-GBM activity, including serotonergic blockers, cholesterol-lowering agents (statins), antineoplastics, anti-infective, anti-inflammatories, and hormonal modulators. We tested the 8 most potent drugs using patient-derived GBM cancer stem cell-like lines. Notably, the statins were active in vitro; they inhibited GBM cell proliferation and induced cellular autophagy. Moreover, the statins enhanced, by 40-70 fold, the pro-apoptotic activity of irinotecan, a topoisomerase 1 inhibitor currently used to treat a variety of cancers including GBM. Our data suggest that the mechanism of action of statins was prevention of multi-drug resistance protein MDR-1 glycosylation. This drug combination was synergistic in inhibiting tumor growth in vivo. Compared to animals treated with high dose irinotecan, the drug combination showed significantly less toxicity.

Results

Our data identifies a novel combination from among FDA-approved drugs. In addition, this combination is safer and well tolerated compared to single agent irinotecan.

Conclusions

Our study newly identifies several FDA-approved compounds that may potentially be useful in GBM treatment. Our findings provide the basis for the rational combination of statins and topoisomerase inhibitors in GBM.

Computer modeling of blood brain barrier permeability of physiologically active compounds

At present work discusses the current level of computer modeling the relationship structure of organic compounds and drugs and their ability to penetrate the BBB. All descriptors that influence to this permeability within classification and regression QSAR models are generalized and analyzed. The crucial role of H-bond in processes both passive, and active transport across BBB is observed. It is concluded that further research should be focused on interpretation the spatial structure of a full-size P-glycoprotein molecule with high resolution and the creation of QSAR models describing the quantitative relationship between structure and active transport of substances across BBB.

[COMMODE] a large-scale database of molecular descriptors using compounds from PubChem

Background

Molecular descriptors have been extensively used in the field of structure-oriented drug design and structural chemistry. They have been applied in QSPR and QSAR models to predict ADME-Tox properties, which specify essential features for drugs. Molecular descriptors capture chemical and structural information, but investigating their interpretation and meaning remains very challenging.

Results

This paper introduces a large-scale database of molecular descriptors called COMMODE containing more than 25 million compounds originated from PubChem. About 2500 DRAGON-descriptors have been calculated for all compounds and integrated into this database, which is accessible through a web interface at http://commode.i-med.ac.at.

Determination of the Potential of Drug Candidates to Cause Severe Skin Disorders Using Computational Modeling

Efficient and accurate prediction for drugs' potential to cause rare and severe adverse drug reactions (ADRs) is needed to facilitate the evaluation of risk-benefit ratio of drug candidates during drug development. Severe skin disorders like the Stevens Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), which are life-threatening dermatological conditions, are such ADRs that have not received sufficient attention so far. In this study, a total of 1127 marketed drugs were screened for their potential to cause SJS/TEN, of which 255 were found to cause SJS/TEN and 239 were unlikely to cause SJS/TEN. One-class classification method was used to develop multiple prediction models. An applicability domain was determined to define the applicability of the model. Ensemble method was used to develop ensemble models to improve prediction ability. The final ensemble model achieved a sensitivity and specificity of 81 % and 67.4 %, respectively, when estimated using the external 5-fold cross validation method, and a sensitivity of 66.7 % when assessed using an external positive set. The results suggest the methods used in this study are potentially useful for facilitating the prediction of rare and severe ADRs.

Predicting efflux ratios and blood-brain barrier penetration from chemical structure: combining passive permeability with active efflux by P-glycoprotein

In order to reach their pharmacologic targets, successful central nervous system (CNS) drug candidates have to cross a complex protective barrier separating brain from the blood. Being able to predict a priori which molecules can successfully penetrate this barrier could be of significant value in CNS drug discovery. Herein we report a new computational approach that combines two mechanism-based models, for passive permeation and for active efflux by P-glycoprotein, to provide insight into the multiparameter optimization problem of designing small molecules able to access the CNS. Our results indicate that this approach is capable of distinguishing compounds with high/low efflux ratios as well as CNS+/CNS- compounds and provides advantage over estimating P-glycoprotein efflux or passive permeability alone when trying to predict these emergent properties. We also demonstrate that this method could be useful for rank-ordering chemically similar compounds and that it can provide detailed mechanistic insight into the relationship between chemical structure and efflux ratios and/or CNS penetration, offering guidance as to how compounds could be modified to improve their access into the brain.

The significance of acid/base properties in drug discovery

While drug discovery scientists take heed of various guidelines concerning drug-like character, the influence of acid/base properties often remains under-scrutinised. Ionisation constants (pK(a) values) are fundamental to the variability of the biopharmaceutical characteristics of drugs and to underlying parameters such as logD and solubility. pK(a) values affect physicochemical properties such as aqueous solubility, which in turn influences drug formulation approaches. More importantly, absorption, distribution, metabolism, excretion and toxicity (ADMET) are profoundly affected by the charge state of compounds under varying pH conditions. Consideration of pK(a) values in conjunction with other molecular properties is of great significance and has the potential to be used to further improve the efficiency of drug discovery. Given the recent low annual output of new drugs from pharmaceutical companies, this review will provide a timely reminder of an important molecular property that influences clinical success.

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.

Translational CNS medicines research

The major imperative of the pharmaceutical industry is to effectively translate insights gained from basic research into new medicines. This task is toughest for CNS disorders. Compared with non-CNS drugs, CNS drugs take longer to get to market and their attrition rate is greater. This is principally because of the complexity of the human brain (the cause of many brain disorders remains unknown), the liability of CNS drugs to cause CNS side effects (which limits their use) and the requirement of CNS medicines to cross the blood-CNS barrier (BCNSB) (which restricts their ability to interact with their CNS target). In this review we consider the factors that are important in translating neuroscience research into CNS medicines.

Early phase drug discovery: cheminformatics and computational techniques in identifying lead series

Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.

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.

Discovery of isoxazole analogues of sazetidine-A as selective α4β2-nicotinic acetylcholine receptor partial agonists for the treatment of depression

Depression, a common neurological condition, is one of the leading causes of disability and suicide worldwide. Standard treatment, targeting monoamine transporters selective for the neurotransmitters serotonin and noradrenaline, is not able to help many patients that are poor responders. This study advances the development of sazetidine-A analogues that interact with α4β2 nicotinic acetylcholine receptors (nAChRs) as partial agonists and that possess favorable antidepressant profiles. The resulting compounds that are highly selective for the α4β2 subtype of nAChR over α3β4-nAChRs are partial agonists at the α4β2 subtype and have excellent antidepressant behavioral profiles as measured by the mouse forced swim test. Preliminary absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies for one promising ligand revealed an excellent plasma protein binding (PPB) profile, low CYP450-related metabolism, and low cardiovascular toxicity, suggesting it is a promising lead as well as a drug candidate to be advanced through the drug discovery pipeline.

Predictivity approach for quantitative structure-property models. Application for blood-brain barrier permeation of diverse drug-like compounds

The goal of the present research was to present a predictivity statistical approach applied on structure-based prediction models. The approach was applied to the domain of blood-brain barrier (BBB) permeation of diverse drug-like compounds. For this purpose, 15 statistical parameters and associated 95% confidence intervals computed on a 2 × 2 contingency table were defined as measures of predictivity for binary quantitative structure-property models. The predictivity approach was applied on a set of compounds comprised of 437 diverse molecules, 122 with measured BBB permeability and 315 classified as active or inactive. A training set of 81 compounds (~2/3 of 122 compounds assigned randomly) was used to identify the model and a test set of 41 compounds was used as the internal validation set. The molecular descriptor family on vertices cutting was the computation tool used to generate and calculate structural descriptors for all compounds. The identified model was assessed using the predictivity approach and compared to one model previously reported. The best-identified classification model proved to have an accuracy of 69% in the training set (95%CI [58.53–78.37]) and of 73% in the test set (95%CI [58.32–84.77]). The predictive accuracy obtained on the external set proved to be of 73% (95%CI [67.58–77.39]). The classification model proved to have better abilities in the classification of inactive compounds (specificity of ~74% [59.20–85.15]) compared to abilities in the classification of active compounds (sensitivity of ~64% [48.47–77.70]) in the training and external sets. The overall accuracy of the previously reported model seems not to be statistically significantly better compared to the identified model (~81% [71.45–87.80] in the training set, ~93% [78.12–98.17] in the test set and ~79% [70.19–86.58] in the external set). In conclusion, our predictivity approach allowed us to characterize the model obtained on the investigated set of compounds as well as compare it with a previously reported model. According to the obtained results, the reported model should be chosen if a correct classification of inactive compounds is desired and the previously reported model should be chosen if a correct classification of active compounds is most wanted.