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Dhavale RP, Choudhari PB, Bhatia MS. Computer Assisted Models for Blood Brain Barrier Permeation of 1, 5-Benzodiazepines. Curr Comput Aided Drug Des 2021; 17:187-200. [PMID: 32003700 DOI: 10.2174/1573409916666200131114018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 12/08/2019] [Accepted: 12/26/2019] [Indexed: 11/22/2022]
Abstract
AIM To generate and validate predictive models for blood-brain permeation (BBB) of CNS molecules using the QSPR approach. BACKGROUND Prediction of molecules crossing BBB remains a challenge in drug delivery. Predictive models are designed for the evaluation of a set of preclinical drugs which may serve as alternatives for determining BBB permeation by experimentation. OBJECTIVE The objective of the present study was to generate QSPR models for the permeation of CNS molecules across BBB and its validation using existing in-house leads. METHODS The present study envisaged the determination of the set of molecular descriptors which are considered significant correlative factors for BBB permeation property. Quantitative Structure- Property Relationship (QSPR) approach was followed to describe the correlation between identified descriptors for 45 molecules and highest, moderate and least BBB permeation data. The molecular descriptors were selected based on drug-likeness, hydrophilicity, hydrophobicity, polar surface area, etc. of molecules that served the highest correlation with BBB permeation. The experimental data in terms of log BB were collected from available literature, subjected to 2D-QSPR model generation using a regression analysis method like Multiple Linear Regression (MLR). RESULTS The best QSPR model was Model 3, which exhibited regression coefficient as R2= 0.89, F = 36; Q2= 0.7805 and properties such as polar surface area, hydrophobic hydrophilic distance, electronegativity, etc., which were considered key parameters in the determination of the BBB permeability. The developed QSPR models were validated with in-house 1,5-benzodiazepines molecules and correlation studies were conducted between experimental and predicted BBB permeability. CONCLUSION The QSPR model 3 showed predictive results that were in good agreements with experimental results for blood-brain permeation. Thus, this model was found to be satisfactory in achieving a good correlation between selected descriptors and BBB permeation for benzodiazepines and tricyclic compounds.
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Affiliation(s)
- Rakesh P Dhavale
- Department of Pharmaceutics, Bharati Vidyapeeth College of Pharmacy, Kolhapur, Maharashtra, India
| | - Prafulla B Choudhari
- Department of Pharmaceutical Chemistry, Bharati Vidyapeeth College of Pharmacy, Kolhapur, Maharashtra, India
| | - Manish S Bhatia
- Department of Pharmaceutical Chemistry, Bharati Vidyapeeth College of Pharmacy, Kolhapur, Maharashtra, India
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A classification model for blood brain barrier penetration. J Mol Graph Model 2020; 96:107516. [DOI: 10.1016/j.jmgm.2019.107516] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 12/19/2019] [Accepted: 12/19/2019] [Indexed: 12/19/2022]
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3
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Morales-Bayuelo A. Analyzing the substitution effect on the CoMFA results within the framework of density functional theory (DFT). J Mol Model 2016; 22:164. [PMID: 27329189 DOI: 10.1007/s00894-016-3036-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 06/09/2016] [Indexed: 10/21/2022]
Abstract
Though QSAR was originally developed in the context of physical organic chemistry, it has been applied very extensively to chemicals (drugs) which act on biological systems, in this idea one of the most important QSAR methods is the 3D QSAR model. However, due to the complexity of understanding the results it is necessary to postulate new methodologies to highlight their physical-chemical meaning. In this sense, this work postulates new insights to understand the CoMFA results using molecular quantum similarity and chemical reactivity descriptors within the framework of density functional theory. To obtain these insights a simple theoretical scheme involving quantum similarity (overlap, coulomb operators, their euclidean distances) and chemical reactivity descriptors such as chemical potential (μ), hardness (ɳ), softness (S), electrophilicity (ω), and the Fukui functions, was used to understand the substitution effect. In this sense, this methodology can be applied to analyze the biological activity and the stabilization process in the non-covalent interactions on a particular molecular set taking a reference compound.
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Affiliation(s)
- Alejandro Morales-Bayuelo
- Grupo de Química Cuántica y Teórica de la Universidad de Cartagena, Facultad de Ciencias, Programa de Química, Cartagena de Indias, Colombia. .,FONDECYT Postdoctoral Project N0 3150035, Universidad de Talca, Talca, Chile.
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Naef R. A Generally Applicable Computer Algorithm Based on the Group Additivity Method for the Calculation of Seven Molecular Descriptors: Heat of Combustion, LogPO/W, LogS, Refractivity, Polarizability, Toxicity and LogBB of Organic Compounds; Scope and Limits of Applicability. Molecules 2015; 20:18279-351. [PMID: 26457702 PMCID: PMC6332030 DOI: 10.3390/molecules201018279] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 09/25/2015] [Accepted: 09/29/2015] [Indexed: 11/21/2022] Open
Abstract
A generally applicable computer algorithm for the calculation of the seven molecular descriptors heat of combustion, logPoctanol/water, logS (water solubility), molar refractivity, molecular polarizability, aqueous toxicity (protozoan growth inhibition) and logBB (log (cblood/cbrain)) is presented. The method, an extendable form of the group-additivity method, is based on the complete break-down of the molecules into their constituting atoms and their immediate neighbourhood. The contribution of the resulting atom groups to the descriptor values is calculated using the Gauss-Seidel fitting method, based on experimental data gathered from literature. The plausibility of the method was tested for each descriptor by means of a k-fold cross-validation procedure demonstrating good to excellent predictive power for the former six descriptors and low reliability of logBB predictions. The goodness of fit (Q2) and the standard deviation of the 10-fold cross-validation calculation was >0.9999 and 25.2 kJ/mol, respectively, (based on N = 1965 test compounds) for the heat of combustion, 0.9451 and 0.51 (N = 2640) for logP, 0.8838 and 0.74 (N = 1419) for logS, 0.9987 and 0.74 (N = 4045) for the molar refractivity, 0.9897 and 0.77 (N = 308) for the molecular polarizability, 0.8404 and 0.42 (N = 810) for the toxicity and 0.4709 and 0.53 (N = 383) for logBB. The latter descriptor revealing a very low Q2 for the test molecules (R2 was 0.7068 and standard deviation 0.38 for N = 413 training molecules) is included as an example to show the limits of the group-additivity method. An eighth molecular descriptor, the heat of formation, was indirectly calculated from the heat of combustion data and correlated with published experimental heat of formation data with a correlation coefficient R2 of 0.9974 (N = 2031).
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Affiliation(s)
- Rudolf Naef
- Department of Chemistry, University of Basel, Basel 4003, Switzerland.
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A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction. BIOMED RESEARCH INTERNATIONAL 2015; 2015:292683. [PMID: 26504797 PMCID: PMC4609370 DOI: 10.1155/2015/292683] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Revised: 05/07/2015] [Accepted: 05/19/2015] [Indexed: 02/07/2023]
Abstract
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.
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Raevsky O, Solodova S, Lagunin A, Poroikov V. Computer modeling of blood brain barrier permeability of physiologically active compounds. ACTA ACUST UNITED AC 2014; 60:161-81. [DOI: 10.18097/pbmc20146002161] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
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.
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Affiliation(s)
- O.A. Raevsky
- Institute of Physiologically Active Compounds, Russian Academy of Science
| | - S.L. Solodova
- Institute of Physiologically Active Compounds, Russian Academy of Science
| | - A.A. Lagunin
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences
| | - V.V. Poroikov
- Orekhovich Institute of Biomedical Chemistry of Russian Academy of Medical Sciences
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7
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Raevsky OA, Solodova SL, Lagunin AA, Poroikov VV. Computer modeling of blood brain barrier permeability for physiologically active compounds. BIOCHEMISTRY MOSCOW-SUPPLEMENT SERIES B-BIOMEDICAL CHEMISTRY 2013. [DOI: 10.1134/s199075081302008x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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SDS-Based Biomembrane Mimetic Chromatography for Prediction of Human Drug Transport as an in Vitro Technique. Chromatographia 2013. [DOI: 10.1007/s10337-013-2480-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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9
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Sano Y, Kashiwamura Y, Abe M, Dieu LH, Huwyler J, Shimizu F, Haruki H, Maeda T, Saito K, Tasaki A, Kanda T. Stable human brain microvascular endothelial cell line retaining its barrier-specific nature independent of the passage number. ACTA ACUST UNITED AC 2012. [DOI: 10.1111/cen3.12001] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yasuteru Sano
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Yoko Kashiwamura
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Masaaki Abe
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Le-Ha Dieu
- Department of Pharmaceutical Sciences; University of Basel; Basel; Switzerland
| | - Jörg Huwyler
- Department of Pharmaceutical Sciences; University of Basel; Basel; Switzerland
| | - Fumitaka Shimizu
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Hiroyo Haruki
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Toshihiko Maeda
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Kazuyuki Saito
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Ayako Tasaki
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
| | - Takashi Kanda
- Department of Neurology and Clinical Neuroscience; Yamaguchi University Graduate School of Medicine; Yamaguchi; Japan
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Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: The nitroindazolinone chemotype. Eur J Med Chem 2011; 46:5736-53. [DOI: 10.1016/j.ejmech.2011.07.053] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2011] [Revised: 07/28/2011] [Accepted: 07/29/2011] [Indexed: 11/15/2022]
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Lanevskij K, Dapkunas J, Juska L, Japertas P, Didziapetris R. QSAR Analysis of Blood–Brain Distribution: The Influence of Plasma and Brain Tissue Binding. J Pharm Sci 2011; 100:2147-60. [DOI: 10.1002/jps.22442] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2010] [Revised: 11/11/2010] [Accepted: 11/16/2010] [Indexed: 11/07/2022]
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Wager TT, Villalobos A, Verhoest PR, Hou X, Shaffer CL. Strategies to optimize the brain availability of central nervous system drug candidates. Expert Opin Drug Discov 2011; 6:371-81. [DOI: 10.1517/17460441.2011.564158] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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13
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Shayanfar A, Soltani S, Jouyban A. Prediction of Blood-Brain Distribution: Effect of Ionization. Biol Pharm Bull 2011; 34:266-71. [DOI: 10.1248/bpb.34.266] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Ali Shayanfar
- Biotechnology Research Center, Tabriz University of Medical Sciences
| | - Somaieh Soltani
- Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences
| | - Abolghasem Jouyban
- Drug Applied Research Center, Tabriz University of Medical Sciences
- Faculty of Pharmacy, Tabriz University of Medical Sciences
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Sá MMD, Pasqualoto KFM, Rangel-Yagui CDO. A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system. BRAZ J PHARM SCI 2010. [DOI: 10.1590/s1984-82502010000400016] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Drugs acting on the central nervous system (CNS) have to cross the blood-brain barrier (BBB) in order to perform their pharmacological actions. Passive BBB diffusion can be partially expressed by the blood/brain partition coefficient (logBB). As the experimental evaluation of logBB is time and cost consuming, theoretical methods such as quantitative structure-property relationships (QSPR) can be useful to predict logBB values. In this study, a 2D-QSPR approach was applied to a set of 28 drugs acting on the CNS, using the logBB property as biological data. The best QSPR model [n = 21, r = 0.94 (r² = 0.88), s = 0.28, and Q² = 0.82] presented three molecular descriptors: calculated n-octanol/water partition coefficient (ClogP), polar surface area (PSA), and polarizability (α). Six out of the seven compounds from the test set were well predicted, which corresponds to good external predictability (85.7%). These findings can be helpful to guide future approaches regarding those molecular descriptors which must be considered for estimating the logBB property, and also for predicting the BBB crossing ability for molecules structurally related to the investigated set.
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Zhang YH, Xia ZN, Qin LT, Liu SS. Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors. J Mol Graph Model 2010; 29:214-20. [PMID: 20637670 DOI: 10.1016/j.jmgm.2010.06.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2009] [Revised: 06/14/2010] [Accepted: 06/17/2010] [Indexed: 11/25/2022]
Abstract
The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability.
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Affiliation(s)
- Yong-Hong Zhang
- College of Bioengineering, Chongqing University, Chongqing 400030, People's Republic of China
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New predictive models for blood-brain barrier permeability of drug-like molecules. Pharm Res 2008; 25:1836-45. [PMID: 18415049 DOI: 10.1007/s11095-008-9584-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2008] [Accepted: 03/27/2008] [Indexed: 01/16/2023]
Abstract
PURPOSE The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood-brain barrier (BBB) modeling. MATERIALS AND METHODS The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models. RESULTS The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80-83% and leave-20%-out testing prediction accuracy between 80-82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs. CONCLUSIONS Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood-brain barrier permeation in drug discovery.
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