Prediction Of Blood-brain Barrier Permeability Research Articles

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R2 = 0.902), IB-IBK (R2 = 0.82), IB-Kstar (R2 = 0.834), IB-MLP (R2 = 0.843), and DRF-AWV (R2 = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability. The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.

Neurons enhance blood–brain barrier function via upregulating claudin-5 and VE-cadherin expression due to glial cell line-derived neurotrophic factor secretion

Blood–brain barrier (BBB) prevents neurotoxins from entering central nervous system. We aimed to establish and characterize an in vitro triple co-culture BBB model consisting of brain endothelial cells hCMEC/D3, astrocytoma U251 cells, and neuroblastoma SH-SY5Y cells. Co-culture of SH-SY5Y and U251 cells markedly enhanced claudin-5 and VE-cadherin expression in hCMEC/D3 cells, accompanied by increased transendothelial electrical resistance and decreased permeability. Conditioned medium (CM) from SH-SY5Y cells (S-CM), U251 cells (U-CM), and co-culture of SH-SY5Y and U251 cells (US-CM) also promoted claudin-5 and VE-cadherin expression. Glial cell line-derived neurotrophic factor (GDNF) levels in S-CM and US-CM were significantly higher than CMs from hCMEC/D3 and U-CM. Both GDNF and US-CM upregulated claudin-5 and VE-cadherin expression, which were attenuated by anti-GDNF antibody and GDNF signaling inhibitors. GDNF increased claudin-5 expression via the PI3K/AKT/FOXO1 and MAPK/ERK pathways. Meanwhile, GDNF promoted VE-cadherin expression by activating PI3K/AKT/ETS1 and MAPK/ERK/ETS1 signaling. The roles of GDNF in BBB integrity were validated using brain-specific Gdnf silencing mice. The developed triple co-culture BBB model was successfully applied to predict BBB permeability. In conclusion, neurons enhance BBB integrity by upregulating claudin-5 and VE-cadherin expression through GDNF secretion and established triple co-culture BBB model may be used to predict drugs’ BBB permeability.

Neurons enhance blood-brain barrier function via upregulating claudin-5 and VE-cadherin expression due to glial cell line-derived neurotrophic factor secretion.

Blood-brain barrier (BBB) prevents neurotoxins from entering central nervous system. We aimed to establish and characterize an in vitro triple co-culture BBB model consisting of brain endothelial cells hCMEC/D3, astrocytoma U251 cells, and neuroblastoma SH-SY5Y cells. Co-culture of SH-SY5Y and U251 cells markedly enhanced claudin-5 and VE-cadherin expression in hCMEC/D3 cells, accompanied by increased transendothelial electrical resistance and decreased permeability. Conditioned medium (CM) from SH-SY5Y cells (S-CM), U251 cells (U-CM), and co-culture of SH-SY5Y and U251 cells (US-CM) also promoted claudin-5 and VE-cadherin expression. Glial cell line-derived neurotrophic factor (GDNF) levels in S-CM and US-CM were significantly higher than CMs from hCMEC/D3 and U-CM. Both GDNF and US-CM upregulated claudin-5 and VE-cadherin expression, which were attenuated by anti-GDNF antibody and GDNF signaling inhibitors. GDNF increased claudin-5 expression via the PI3K/AKT/FOXO1 and MAPK/ERK pathways. Meanwhile, GDNF promoted VE-cadherin expression by activating PI3K/AKT/ETS1 and MAPK/ERK/ETS1 signaling. The roles of GDNF in BBB integrity were validated using brain-specific Gdnf silencing mice. The developed triple co-culture BBB model was successfully applied to predict BBB permeability. In conclusion, neurons enhance BBB integrity by upregulating claudin-5 and VE-cadherin expression through GDNF secretion and established triple co-culture BBB model may be used to predict drugs' BBB permeability.

BBBper: A Machine Learning-based Online Tool for Blood-Brain Barrier (BBB) Permeability Prediction.

Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB). BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance. Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool. A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results. BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools. The BBBper web tool is freely available at http://bbbper.mdu.ac.in.

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E−05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

Prediction of blood-brain barrier permeability using machine learning approaches based on various molecular representation.

The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor-intensive, cost-ineffective, and time-consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease.

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Concurrent Optimizations of Efficacy and Blood-Brain Barrier Permeability in New Macrocyclic LRRK2 Inhibitors for Potential Parkinson's Disease Therapeutics.

The elevated activity of leucine-rich repeat kinase 2 (LRRK2) is implicated in the pathogenesis of Parkinson's disease (PD). The quest for effective LRRK2 inhibitors has been impeded by the formidable challenge of crossing the blood-brain barrier (BBB). We leveraged structure-based de novo design and developed robust three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict BBB permeability, enhancing the likelihood of the inhibitor's brain accessibility. Our strategy involved the synthesis of macrocyclic molecules by linking the two terminal nitrogen atoms of HG-10-102-01 with an alkyl chain ranging from 2 to 4 units, laying the groundwork for innovative LRRK2 inhibitor designs. Through meticulous computational and synthetic optimization of both biochemical efficacy and BBB permeability, 9 out of 14 synthesized candidates demonstrated potent low-nanomolar inhibition and significant BBB penetration. Further assessments of in vitro and in vivo effectiveness, coupled with pharmacological profiling, highlighted 8 as the promising new lead compound for PD therapeutics.

Machine Learning Exploration of the Relationship Between Drugs and the Blood-Brain Barrier: Guiding Molecular Modification.

This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB). We established qualitative and quantitative databases of the ADME properties of drugs and derived characteristic features of compounds with efficient BBB penetration. Using these insights, we developed four machine learning models to predict a drug's BBB permeability by assessing ADME properties and molecular topology. We then validated the models using the B3DB database. For acyclovir and ceftriaxone, we modified the Hydrogen Bond Donors and Acceptors, and evaluated the BBB permeability using the predictive model. The machine learning models performed well in predicting BBB permeability on both internal and external validation sets. Reducing the number of Hydrogen Bond Donors and Acceptors generally improves BBB permeability. Modification only enhanced BBB penetration in the case of acyclovir and not ceftriaxone. The machine learning models developed can accurately predict BBB permeability, and many drug molecules are likely to have increased BBB penetration if the number of Hydrogen Bond Donors and Acceptors are reduced. These findings suggest that molecular modifications can enhance the efficacy of CNS drugs and provide practical strategies for drug design and development. This is particularly relevant for improving drug penetration of the BBB.

Leveraging Machine Learning and Quantum Mechanics for Blood Brain Barrier Permeability Prediction of Drug Molecules (S27.010)

Leveraging Machine Learning and Quantum Mechanics for Blood Brain Barrier Permeability Prediction of Drug Molecules (S27.010)

ANN multi-layer perceptron for prediction of blood–brain barrier permeable compounds for central nervous system therapeutics

Endothelial cells produce a semipermeable barrier known as the blood–brain barrier (BBB) to keep undesired chemicals out of the central nervous system (CNS). However, this barrier also restricts the exploration of potential new medications due to insufficient exposure. To address this challenge, machine learning (ML) algorithms can be useful to predict the BBB permeability of chemical compounds. Support vector machines, continuous neural networks, and deep learning approaches have been used to identify compounds that can penetrate the BBB. However, predicting BBB permeability based solely on chemical structure can be difficult. In the current research, we developed an ML model using a large dataset to predict BBB permeability, which could be used for early-stage drug screening of potential CNS medications. Our artificial neural network ANN algorithm exhibited an accuracy of 0.94, specificity of 0.83, sensitivity of 0.97, AUC of 0.96, and MCC of 0.83. These metrics suggest that our model has a high accuracy rate in predicting BBB permeability and therefore has the potential to advance drug discovery efforts in the CNS. This study’s outcomes demonstrate the potential for ML models to predict BBB permeability accurately, aiding in the identification of new CNS therapeutic options. Communicated by Ramaswamy H. Sarma

NeuroClick: software for mimicking click reaction to generate drug-like molecules permeating the blood-brain barrier.

Background: Traditional methods for chemical library generation in virtual screening often impose limitations on the accessible chemical space or produce synthetically irrelevant structures. Incorporating common chemical reactions into generative algorithms could offer significant benefits. Materials & methods: In this study, we developed NeuroClick, a graphical user interface software designed to perform in silico azide-alkyne cycloaddition, a widely utilized synthetic approach in modern medicinal chemistry. Results & conclusion: NeuroClick facilitates the generation and filtering of large combinatorial libraries at a remarkable rate of 10,000 molecules per minute. Moreover, the generated products can be filtered to identify subsets of pharmaceutically relevant compounds based on Lipinski's rule of five and blood-brain barrier permeability prediction. We demonstrate the utility of NeuroClick by generating and filtering several thousand molecules for dopamine D3 receptor ligand screening.

Astrocyte Involvement in Blood-Brain Barrier Function: A Critical Update Highlighting Novel, Complex, Neurovascular Interactions.

Neurological disorders have been linked to a defective blood-brain barrier (BBB), with dysfunctions triggered by stage-specific disease mechanisms, some of these being generated through interactions in the neurovascular unit (NVU). Advanced knowledge of molecular and signaling mechanisms in the NVU and the emergence of improved experimental models allow BBB permeability prediction and the development of new brain-targeted therapies. As NVU constituents, astrocytes are the most numerous glial cells, characterized by a heterogeneity that occurs as a result of developmental and context-based gene expression profiles and the differential expression of non-coding ribonucleic acids (RNAs). Due to their heterogeneity and dynamic responses to different signals, astrocytes may have a beneficial or detrimental role in the BBB's barrier function, with deep effects on the pathophysiology of (and on the progression of) central nervous system diseases. The implication of astrocytic-derived extracellular vesicles in pathological mechanisms, due to their ability to pass the BBB, must also be considered. The molecular mechanisms of astrocytes' interaction with endothelial cells at the BBB level are considered promising therapeutic targets in different neurological conditions. Nevertheless, a personalized and well-founded approach must be addressed, due to the temporal and spatial heterogeneity of reactive astrogliosis states during disease.

Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates.

Efficiently circumventing the blood-brain barrier (BBB) poses a major hurdle in the development of drugs that target the central nervous system. Although there are several methods to determine BBB permeability of small molecules, the Parallel Artificial Membrane Permeability Assay (PAMPA) is one of the most common assays in drug discovery due to its robust and high-throughput nature. Drug discovery is a long and costly venture, thus, any advances to streamline this process are beneficial. In this study, ∼2,000 compounds from over 60 NCATS projects were screened in the PAMPA-BBB assay to develop a quantitative structure-activity relationship model to predict BBB permeability of small molecules. After analyzing both state-of-the-art and latest machine learning methods, we found that random forest based on RDKit descriptors as additional features provided the best training balanced accuracy (0.70 ± 0.015) and a message-passing variant of graph convolutional neural network that uses RDKit descriptors provided the highest balanced accuracy (0.72) on a prospective validation set. Finally, we correlated in vitro PAMPA-BBB data with in vivo brain permeation data in rodents to observe a categorical correlation of 77%, suggesting that models developed using data from PAMPA-BBB can forecast in vivo brain permeability. Given that majority of prior research has relied on in vitro or in vivo data for assessing BBB permeability, our model, developed using the largest PAMPA-BBB dataset to date, offers an orthogonal means to estimate BBB permeability of small molecules. We deposited a subset of our data into PubChem bioassay database (AID: 1845228) and deployed the best performing model on the NCATS Open Data ADME portal (https://opendata.ncats.nih.gov/adme/). These initiatives were undertaken with the aim of providing valuable resources for the drug discovery community.

DDEL-15. EXPLORING THE USE OF MACHINE LEARNING TO PREDICT BLOOD BRAIN BARRIER PERMEABILITY OF DRUG MOLECULES

Abstract The blood-brain barrier (BBB) is a selective boundary of the central nervous system (CNS) that plays a critical role in protecting the brain microenvironment by allowing passage of certain molecules. Clinical experiments accurately determine which chemotherapy drugs effectively cross the BBB to reach the tumor site but are also time consuming and labor intensive. Machine learning offers the ability to rapidly screen large datasets of drug molecules and assess their potential for CNS therapeutic effect. The goal of this study is to compare the performance across machine learning, deep learning, and transfer learning methods in predicting BBB permeability on public drug datasets. The dataset used for training and validation was composed of 7,807 compounds compiled from 50 published resources. The machine and deep learning methods used in this study included support vector machines (SVMs), deep neural networks (DNNs), and graph convolutional neural networks (GCNNs). For transfer learning, we first trained DNN models to a single quantum chemical property before appending neural network layers and retraining to the new task of BBB permeability. The prediction accuracies on the validation set for SVM, DNN, GCNN, transfer learning of polarizability, and transfer learning of dipole moment were 82.33%, 83.09%, 87.14%, 76.89%, and 70.23%, respectively. Overall, the results indicate that GCNN was the best performing model. This highlights GCNNs ability to learn the molecular features most relevant to the predictive task in the first stage of the algorithm. Future work entails expanding the set of input features to include key chemical and structural information such as the presence or absence of certain functional groups as well as training more transfer learning models to other quantum chemical properties. This study further motivates the predictive capability of machine learning methods in identifying drug compounds with potential CNS-activity.

Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning Algorithms

In the drug development for the Central Nervous System (CNS), the discovery of the compounds that can pass through the brain across the Blood-Brain Barrier (BBB) is the most challenging assessment. Almost 98% of small molecules are unable to permeate BBB, reducing the pharmacokinetics of the drugs in the CNS by affecting its absorption, distribution, metabolism, and excretion (ADME) mechanisms. Since the CNS is often inaccessible to many complex procedures and performing in-vitro permeability studies for thousands of compounds can be laborious, attempts were made to predict the permeation of compounds through BBB by implementing the Machine Learning (ML) approach. In this work, using the KNIME Analytics platform, 4 predictive models were developed with 4 ML algorithms followed by a ten-fold cross-validation approach to predict the external validation set. Among 4 ML algorithms, Extreme Gradient Boosting (XGBoost) overperformed in BBB permeability prediction and was chosen as the prediction model for deployment. Data pre-processing and feature selection enhanced the prediction of the model. Overall, the model achieved 86.7% and 88.5% of accuracy and 0.843 and 0.927 AUC, respectively in the training set and external validation set, proving that the model with high stability in prediction.

Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds

It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability

The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, however, the reliability of these models is questionable due to the smaller and class-imbalance dataset which subsequently leads to a very high false positive rate. In this study, machine learning and deep learning-based predictive models were built using XGboost, Random Forest, Extra-tree classifiers and deep neural network. A dataset of 8153 compounds comprising both the BBB permeable and BBB non-permeable was curated and subjected to calculations of molecular descriptors and fingerprints for generating the features for machine learning and deep learning models. Three balancing techniques were then applied to the dataset to address the class-imbalance issue. A comprehensive comparison among the models showed that the deep neural network model generated on the balanced MACCS fingerprint dataset outperformed with an accuracy of 97.8% and a ROC-AUC score of 0.98 among all the models. Additionally, a dynamic consensus model was prepared with the machine learning models and validated with a benchmark dataset for predicting BBB permeability with higher confidence scores.

Clinical side-effects based drug repositioning for anti-epileptic activity

Several generations of anti-epileptic drugs (AEDs) are available but have several associated side effects apart from a limited success rate. Drug repositioning strategies have gained importance in the last two decades owing to lower failure rates and economic burden. Drugs with similar side effect profiles may share a common mechanism of action and thus can be linked to other disease treatments. The present study was carried out to identify the newly approved drug candidate(s) as AEDs using clinical side-effects drug repositioning strategy. The clinical side effect similarity of drugs available in the SIDER v4.1 database was estimated against common side effects of 5 major marketed AEDs, using the ‘dplyr’ package library in the R. Further drugs were filtered based on Blood Brain Barrier permeability prediction and FDA-approval status. Molecular docking studies were performed for selected 26 hits (drugs) against previously identified epilepsy target receptors: Voltage-gated sodium channel α2 (Nav1.2), GABA receptor α1-β1 (GABAr α1-β1), and Voltage-gated calcium channel α-1 G (Cav3.1). Only 2 drugs (Ziprasidone and Paroxetine) showed better binding affinities against studied epilepsy receptors Nav1.2, GABAr α1-β1, and Cav3.1, than their corresponding standard AEDs, i.e. Carbamazepine, Clonazepam, and Pregabalin, respectively. Ziprasidone reportedly showed seizure-like symptoms in ∼3% of patients and was hence omitted from further study. The MDS study of docked complexes of Paroxetine with selected epilepsy target receptors showed stable RMSD values and better interaction energies. The study reveals Paroxetine as a potential candidate to be repurposed for 1st line epileptic seizure medication. Communicated by Ramaswamy H. Sarma

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.