Prediction Of Blood-brain Barrier Permeability Research Articles

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy.

The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.

The Application of in silico Methods for Prediction of Blood-Brain Barrier Permeability of Small Molecule PET Tracers.

Designing positron emission tomography (PET) tracers for targets in the central nervous system (CNS) is challenging. Besides showing high affinity and high selectivity for their intended target, these tracers have to be able to cross the blood-brain barrier (BBB). Since only a small fraction of small molecules is estimated to be able to cross the BBB, tools that can predict permeability at an early stage during the development are of great importance. One such tool is in silico models for predicting BBB-permeability. Thus far, such models have been built based on CNS drugs, with one exception. Herein, we sought to discuss and analyze if in silico predictions that have been built based on CNS drugs can be applied for CNS PET tracers as well, or if dedicated models are needed for the latter. Depending on what is taken into account in the prediction, i.e., passive diffusion or also active influx/efflux, there may be a need for a model build on CNS PET tracers. Following a brief introduction, an overview of a few selected in silico BBB-permeability predictions is provided along with a short historical background to the topic. In addition, a combination of previously reported CNS PET tracer datasets were assessed in a couple of selected models and guidelines for predicting BBB-permeability. The selected models were either predicting only passive diffusion or also the influence of ADME (absorption, distribution, metabolism and excretion) parameters. To conclude, we discuss the potential need of a prediction model dedicated for CNS PET tracers and present the key issues in respect to setting up a such a model.

Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (Pe) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R2pred=-0.10; PLS- R2pred=0.64, r2m=0.69, r/2m=0.44; SVM- R2pred=0.57, r2m=0.72, r/2m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H0p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.

Abstract 293: Second-generation tricyclic pyrimido-pyrrolo-oxazine mTOR inhibitors suitable for the treatment of CNS disorders

Abstract Mechanistic target of rapamycin (mTOR) is activated downstream of phosphoinositide 3-kinase (PI3K), and is dysregulated in cancer and neurological disorders.[1,2] Tuberous Sclerosis Complex (TSC) is a genetic disease caused by TSC gene inactivation, where TSC loss of function leads to the hyperactivation of mTOR signaling. TSC is characterized by hyperplastic lesions in multiple organs, and in ~90% of patients it affects the brain causing epilepsy. Rapalogs have recently been explored to alleviate epileptic seizures in TSC[3], however a limited clinical success has been reported. Crossing the blood-brain barrier (BBB) opens up a new challenge in the development of highly selective mTOR inhibitors (TORKi) that could be applied in the treatment of CNS disorders.[4-7] Very recently, we have disclosed a conformational restriction strategy to explore a novel chemical space for TORKi. A first-generation highly selective tricyclic compounds had been developed, but they displayed a limited brain penetration.[8] Herein, we discovered the first pyrimido-pyrrolo-oxazine derivative with predicted BBB permeability using an CRISPR-Cas9 engineered MDCK in vitro assay containing only the human homolog of the P-gp protein[9]. An extensive exploration of the heteroaromatic ring engaging the binding affinity region in mTOR kinase was performed, and chemical moieties, including 3-CF3-substituted pyridine, thiazole and 1,3,4-thiadiazole, have been identified as building blocks providing exquisite mTOR selectivity. Computational modelling studies revealed that the 3-trifluoromethyl group is well accommodated in mTOR, while it induces a steric clash with Lys802 and Asp933 in PI3Kα, highlighting the possibility to further exploit this heteroaromatic ring in the development of highly selective mTOR inhibitors. In MDCK assays, compound 11 showed an excellent passive permeability and was not a human P-gp substrate. Our results pave the way for the application of second-generation tricyclic derivatives in the treatment of neurological disorders and epilepsy. [1] Rageot D. et al. J Med Chem. 2018, 61 (22), 10084-10105 [2] Tarantelli C. et al. Cancers (Basel) 2019, 11 (6), 775. [3] Krueger DA. et al. Neurology 2016, 87, 2408-2415. [4] Singer E. et al. Neuropharmacology 2020, 162, 107812. [5] Brandt C. et al. Neuropharmacology 2018, 140, 107-120. [6] Theilmann W. et al. Neuropharmacology 2020, 180, 108297 [7] Borsari C. et al. J Med Chem. 2020 [ahead of print] doi: 10.1021/acs.jmedchem.0c00620. [8] Borsari C. et al. J Med Chem. 2019, 62 (18), 8609-8630. [9] Karlgren M et al. Drug Metab Dispos. 2018, 46 (11), 1776-1786. Citation Format: Chiara Borsari, Erhan Keles, Andrea Treyer, Martina De Pascale, Paul Hebeisen, Matthias Hamburger, Matthias P. Wymann. Second-generation tricyclic pyrimido-pyrrolo-oxazine mTOR inhibitors suitable for the treatment of CNS disorders [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 293.

A deep learning approach to predict blood-brain barrier permeability.

The blood–brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson’s, Alzheimer’s, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood–brain barrier. However, predicting compounds with “low” permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood–brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

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.

Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method

Background and ObjectiveThe purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors. MethodsUsing a database composed of 300 compounds, 52 structure descriptors obtained based on the universal quasichemical functional group activity coefficients (UNIFAC) group contribution method and the selected 8 molecular property descriptors were used as the network inputs, whereas logBB values of compounds constituted its output. ResultsThe correlation coefficient R of the constructed prediction model, the relative error (RE) and the root mean square error (RMSE) was 0.956, 0.857, and 0.171, respectively. These indicators reflected the feasibility, robustness and accuracy of the prediction model. Compared with the previously published results, a significant improvement in the predictions of the proposed ANN model was observed. ConclusionsANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

Prediction of Blood-Brain Barrier Permeability of Compounds by Fusing Resampling Strategies and eXtreme Gradient Boosting

Computer-aided drug design is an efficient method to analyze the development of disease-related drugs. However, developed as binding targets, medicines perform well in cell models and animal models but fail in human models. One main reason for this failure is that the human body has natural barriers, such as the blood-brain barrier, to block exogenous macromolecules. Thus, efficient and accurate predictions of drug molecules that can effectively pass the blood-brain barrier is necessary in developing drug treatments for brain tissue diseases. In this study, 7658 molecular structure features were extracted from 2354 drug molecule SMILE strings using computational methods. By integrating three feature selection algorithms of machine learning, 33 chemical structure features with significantly discriminant performance were screened out and used to construct multiple discriminant models. After a comprehensive comparison, the XGBoost model was selected as the final prediction model. After data preprocessing and parameter optimization, the model achieved 95% accuracy on the training set. To verify the model's stability, we introduced an external data set, which reached 96% accuracy of the model. This study applies new resampling methods and machine learning algorithms, and adjusts the application of resampling methods to obtain new chemical features to construct machine learning predictors. The features may contribute to the significant drug development that integrates biological analysis and machine learning algorithms.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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

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

A Recurrent Neural Network model to predict blood–brain barrier permeability

The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as “chemoinformatics,” which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood–brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS). In this research, we attempt to solve the problem of BBB permeability by predicting compounds penetration to the CNS. To accomplish this goal: (i) First, an overview is provided to the field of chemoinformatics, its definition, applications, and challenges, (ii) Second, a broad view is taken to investigate previous machine-learning and deep-learning computational models to solve BBB permeability. Based on the analysis of previous models, three main challenges that collectively affect the classifier performance are identified, which we define as “the triple constraints”; subsequently, we map each constraint to a proposed solution, (iii) Finally, we conclude this endeavor by proposing a deep learning based Recurrent Neural Network model, to predict BBB permeability (RNN-BBB model). Our model outperformed other studies from the literature by scoring an overall accuracy of 96.53%, and a specificity score of 98.08%. The obtained results confirm that addressing the triple constraints substantially improves the classification model capability specifically when predicting compounds with low penetration.

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

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

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

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

Human brain microvascular endothelial cell pairs model tissue-level blood-brain barrier function.

The blood-brain barrier plays a critical role in delivering oxygen and nutrients to the brain while preventing the transport of neurotoxins. Predicting the ability of potential therapeutics and neurotoxicants to modulate brain barrier function remains a challenge due to limited spatial resolution and geometric constraints offered by existing in vitro models. Using soft lithography to control the shape of microvascular tissues, we predicted blood-brain barrier permeability states based on structural changes in human brain endothelial cells. We quantified morphological differences in nuclear, junction, and cytoskeletal proteins that influence, or indicate, barrier permeability. We established a correlation between brain endothelial cell pair structure and permeability by treating cell pairs and tissues with known cytoskeleton-modulating agents, including a Rho activator, a Rho inhibitor, and a cyclic adenosine monophosphate analog. Using this approach, we found that high-permeability cell pairs showed nuclear elongation, loss of junction proteins, and increased actin stress fiber formation, which were indicative of increased contractility. We measured traction forces generated by high- and low-permeability pairs, finding that higher stress at the intercellular junction contributes to barrier leakiness. We further tested the applicability of this platform to predict modulations in brain endothelial permeability by exposing cell pairs to engineered nanomaterials, including gold, silver-silica, and cerium oxide nanoparticles, thereby uncovering new insights into the mechanism of nanoparticle-mediated barrier disruption. Overall, we confirm the utility of this platform to assess the multiscale impact of pharmacological agents or environmental toxicants on blood-brain barrier integrity.

Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update.

Blood Brain Barrier (BBB) is the collection of vessels of blood with special properties of permeability that allow a limited range of drug and compounds to pass through it. The BBB plays a vital role in maintaining balance between intracellular and extracellular environment for brain. Brain Capillary Endothelial Cells (BECs) act as vehicle for transport and the transport mechanisms across BBB involve active and passive diffusion of compounds. Efficient prediction models of BBB permeability can be vital at the preliminary stages of drug development. There have been persistent efforts in identifying the prediction of BBB permeability of compounds employing multiple machine learning methods in an attempt to minimize the attrition rate of drug candidates taking up preclinical and clinical trials. However, there is an urgent need to review the progress of such machine learning derived prediction models in the prediction of BBB permeability. In the current article, we have analyzed the recently developed prediction model for BBB permeability using machine learning.

Prediction of Drug Permeability Using In Vitro Blood-Brain Barrier Models with Human Induced Pluripotent Stem Cell-Derived Brain Microvascular Endothelial Cells.

The strong barrier function of the blood–brain barrier (BBB) protects the central nervous system (CNS) from xenobiotic substances, while the expression of selective transporters controls the transportation of nutrients between the blood and brain. As a result, the delivery of drugs to the CNS and prediction of the ability of specific drugs to penetrate the BBB can be difficult. Although in vivo pharmacokinetic analysis using rodents is a commonly used method for predicting human BBB permeability, novel in vitro BBB models, such as Transwell models, have been developed recently. Induced pluripotent stem cells (iPSCs) have the potential to differentiate into various types of cells, and protocols for the differentiation of iPSCs to generate brain microvascular endothelial cells (BMECs) have been reported. The use of iPSCs makes it easy to scale-up iPSC-derived BMECs (iBMECs) and enables production of BBB disease models by using iPSCs from multiple donors with disease, which are advantageous properties compared with models that utilize primary BMECs (pBMECs). There has been little research on the value of iBMECs for predicting BBB permeability. This study focused on the similarity of iBMECs to pBMECs and investigated the ability of iPSC-BBB models (monoculture and coculture) to predict in vivo human BBB permeability using iBMECs. iBMECs express BMEC markers (e.g., VE-cadherin and claudin-5) and influx/efflux transporters (e.g., Glut-1, SLC7A5, CD220, P-gp, ABCG2, and MRP-1) and exhibit high barrier function (transendothelial electrical resistance, >1000 Ω × cm2) as well as similar transporter expression profiles to pBMECs. We determined that the efflux activity using P-glycoprotein (P-gp) transporter is not sufficient in iBMECs, while in drug permeability tests, iPSC-derived BBB models showed a higher correlation with in vivo human BBB permeability compared with a rat BBB model and the Caco-2 model. In a comparison between monoculture and coculture models, the coculture BBB model showed higher efflux activity for compounds with low CNS permeability (e.g., verapamil and thioridazine). In conclusion, iPSC-BBB models make it possible to predict BBB permeability, and employing coculturing can improve iPSC-BBB function.

Predicting Blood–Brain Partitioning of Small Molecules Using a Novel Minimalistic Descriptor-Based Approach via the 3D-RISM-KH Molecular Solvation Theory

Compartmentalization of drug molecules between plasma and brain is important for desired activities. The difficulty in obtaining the blood–brain permeability of drug (like) substances experimentally resulted in the development of several theoretical quantitative structure–activity relationships toward predicting the capability of a given substrate to pass across a tight junction, known as the blood–brain barrier, both qualitatively and quantitatively. Here, we report a novel method based on the molecular solvation theory for predicting blood–brain barrier permeability coefficients of molecules of diverse structures using a minimum set of descriptors derived from solvation energetics. Our finding points to the importance of solvation free energy based descriptors in modeling blood–brain barrier permeability quantitatively.

Ligand and Structure-based Modeling of Passive Diffusion through the Blood-Brain Barrier.

Blood-brain barrier transport is an important process to be considered in drug candidates. The blood-brain barrier protects the brain from toxicological agents and, therefore, also establishes a restrictive mechanism for the delivery of drugs into the brain. Although there are different and complex mechanisms implicated in drug transport, in this review we focused on the prediction of passive diffusion through the blood-brain barrier. We elaborated on ligand-based and structure-based models that have been described to predict the blood-brain barrier permeability. Multiple 2D and 3D QSPR/QSAR models and integrative approaches have been published to establish quantitative and qualitative relationships with the blood-brain barrier permeability. We explained different types of descriptors that correlate with passive diffusion along with data analysis methods. Moreover, we discussed the applicability of other types of molecular structure-based simulations, such as molecular dynamics, and their implications in the prediction of passive diffusion. Challenges and limitations of experimental measurements of permeability and in silico predictive methods were also described. Improvements in the prediction of blood-brain barrier permeability from different types of in silico models are crucial to optimize the process of Central Nervous System drug discovery and development.

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.

2-Substituted-thio-N-(4-substituted-thiazol/1H-imidazol-2-yl)acetamides as BACE1 inhibitors: Synthesis, biological evaluation and docking studies

In this work, a series of 2-substituted-thio-N-(4-substituted-thiazol/1H-imidazol-2-yl)acetamide derivatives were developed as β-secretase (BACE-1) inhibitors. Supported by docking study, a small library of derivatives were designed, synthesized and biologically evaluated in vitro. In addition, the selected compounds were tested with affinity (KD) towards BACE-1, blood brain barrier (BBB) permeability and cytotoxicity. The studies revealed that the most potent analog 41 (IC50 = 4.6 μM) with high predicted BBB permeability and low cellular cytotoxicity, could serve as a good lead structure for further optimization.