QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

Zhang, Liying; Zhu, Hao; Oprea, Tudor I.; Gelbukh, Alexander; Tropsha, Alexander

doi:10.1007/s11095-008-9609-0

Cited by 168 publications

(135 citation statements)

References 67 publications

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“…QSAR modeling has also been helpful in predicting drug distribution/penetration into specific biological compartments such as the blood-brain barrier (BBB); for instance, a highly predictive model of penetration (R 2 = 0.80 in an external validation set of 10 compounds) as well as specific contributors to penetration, such as van der Waals surface area and active transport, was reported recently. 11 A similarly predictive model for FGT penetration would help inform the development process and assist in the search for ideal PrEP candidates.…”

mentioning

confidence: 99%

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

Thompson

Sedykh

Nicol

et al. 2014

AIDS Research and Human Retroviruses

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The exposure of oral antiretroviral (ARV) drugs in the female genital tract (FGT) is variable and almost unpredictable. Identifying an efficient method to find compounds with high tissue penetration would streamline the development of regimens for both HIV preexposure prophylaxis and viral reservoir targeting. Here we describe the cheminformatics investigation of diverse drugs with known FGT penetration using cluster analysis and quantitative structure-activity relationships (QSAR) modeling. A literature search over the 1950-2012 period identified 58 compounds (including 21 ARVs and representing 13 drug classes) associated with their actual concentration data for cervical or vaginal tissue, or cervicovaginal fluid. Cluster analysis revealed significant trends in the penetrative ability for certain chemotypes. QSAR models to predict genital tract concentrations normalized to blood plasma concentrations were developed with two machine learning techniques utilizing drugs' molecular descriptors and pharmacokinetic parameters as inputs. The QSAR model with the highest predictive accuracy had R 2 test = 0.47. High volume of distribution, high MRP1 substrate probability, and low MRP4 substrate probability were associated with FGT concentrations ‡ 1.5-fold plasma concentrations. However, due to the limited FGT data available, prediction performances of all models were low. Despite this limitation, we were able to support our findings by correctly predicting the penetration class of rilpivirine and dolutegravir. With more data to enrich the models, we believe these methods could potentially enhance the current approach of clinical testing.

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confidence: 99%

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

Thompson

Sedykh

Nicol

et al. 2014

AIDS Research and Human Retroviruses

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“…Nevertheless, the model presented in this study is comparable or better than other published 2D-QSAR BBB models and show similar descriptors used by other authors. (Norinder et al, 2002;Katritzky et al, 2006;van Damme et al, 2008;Iyer et al, 2002;Konovalov et al, 2007;Subramanian et al, 2003;Zhao et al, 2007;Zhang et al, 2008;Narayanan et al, 2005;Young et al, 1988;Abraham, 2004;Goodwin et al, 2005). These findings can help future decisions about which groups are favorable or otherwise for CNS entry by BBB permeation, based on the physicochemical properties evaluated here.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, computerassisted drug design methodologies (CADD), such as quantitative structure-property and/or structure-activity relationship studies (QSPR and QSAR, respectively), may help to estimate biological data, reducing synthetic steps, predicting pharmacokinetic and pharmacodynamic profiles, constituting an important tool in the design and development of new drugs and novel leads (Katritzky et al, 2006). A literature review reported several studies, carried out on a number of different chemical structures, and in which 2D and 3D QSPR models have been proposed to predict logBB values (Katritzky et al, 2006;Van Damme et al, 2008;Iyer et al, 2002;Konovalov et al, 2007;Subramanian et al, 2003;Zhao et al, 2007;Zhang et al, 2008;Narayanan et al, 2005). Some of these models show a correlation between logBB and some physicochemical parameters, such as molecular refractivity (MR), molecular volume (V), acid ionization constant (K a and pK a ), thermodynamic parameters (solvation energy, etc.…”

Section: Introductionmentioning

confidence: 99%

A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system

Sá

Pasqualoto

Rangel-Yagui

2010

Braz. J. Pharm. Sci.

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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.Uniterms: Two-dimensional quantitative structure-property relationships (2D-QSPR). Calculated n-octanol/water partition coefficient (ClogP). Blood-brain barrier. Benzodiazepines.Fármacos que atuam no sistema nervoso central (SNC) devem atravessar a barreira hematoencefálica (BHE) para exercerem suas ações farmacológicas. A difusão passiva através da BHE pode ser parcialmente expressa pelo coeficiente de partição entre os compartimentos encefálico e sanguíneo (logBB, brain/blood partition coefficient). Considerando-se que a avaliação experimental de logBB é dispendiosa e demorada, métodos teóricos como estudos das relações entre estrutura química e propriedade (QSPR, Quantitative Structure-Property Relationships) podem ser utilizados na previsão dos valores de logBB. Neste estudo, uma abordagem de QSPR-2D foi aplicada a um conjunto de 28 moléculas com ação central, usando logBB como propriedade biológica. O melhor modelo de QSPR [n = 21, r = 0,94 (r² = 0,88), s = 0,28 e Q² = 0,82] apresentou três descritores moleculares: o coeficiente calculado de partição n-octanol/água (ClogP), área de superfície polar (PSA) e polarizabilidade (α). Seis dos sete compostos do conjunto de avaliação foram bem previstos pelo modelo, o que corresponde a um bom poder de previsão externa (85,7%). Os resultados obtidos podem auxiliar de forma relevante em estudos futuros, orientando quais descritores moleculares devem ser considerados para estimar logBB e prever a passagem através da BHE de moléculas estruturalmente relacionadas às do conjunto investigado. Unitermos:Relações quantitativas bidimensionais entre estrutura química e propriedade (2D-QSPR). Coeficiente calculado de partição n-octanol/água (ClogP). Barreira hematoencefálica. Benzodiazepínicos.

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“…To avoid such unjustified exploration of activity predictions, we used the concept of applicability domain. 41 …”

Section: Güner-henry Scoring Methodsmentioning

confidence: 99%

Pharmacophore generation, atom-based 3D-QSAR, docking, and virtual screening studies of p38-α mitogen activated protein kinase inhibitors: pyridopyridazin-6-ones (part 2)

Bhansali¹,

Kulkarni²

2013

RRMC

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p38-α mitogen-activated protein kinase (MAPK) is a serine/threonine kinase activated by environmental stimuli, like stress, and by various proinflammatory cytokines, such as tumor necrosis factor-α and interleukin-1β. Excessive production of tumor necrosis factor-α and interleukin-1β may lead to various diseases, such as rheumatoid arthritis, psoriasis, and inflammatory bowel disease. Hence, inhibition of p38-α MAPK could be a novel approach for the development of new anti-inflammatory agents. In this study, a combination of pharmacophore generation, an atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and virtual screening was performed for a series of pyridopyridazin-6-ones exhibiting p38-α MAPK inhibition activity. A five-point pharmacophore (AAAHR), ie, three hydrogen bond acceptors (AAA), one hydrophobic (H) group, and one aromatic ring (R) was obtained. A statistically significant 3D-QSAR model was obtained using this pharmacophore hypothesis with a good correlation coefficient (R 2 =0.91) and a high Fisher ratio (F=90.3) for the training set of 47 compounds. The predictive power of the model generated was found to be significant, and was confirmed by the high value of the cross-validated correlation coefficient (q 2 =0.80) and Pearson's R (0.90) for the test set of 16 compounds. Further, the docking study revealed the binding orientations of the active ligand on the amino acid residues Valine 30 (Val30), Glycine 31 (Gly31), Lysine 53 (Lys53), Leucine 75 (Leu75), Aspartic acid 88 (Asp88), Methionine 109 (Met109) of p38-α MAPK at the active site. The results of this ligand-based pharmacophore hypothesis and atom-based 3D-QSAR provide detailed structural insights and highlight the important binding features between pyridopyridazin-6-ones and p38-α MAPK. These findings may provide useful guidelines for rational design of compounds with better p38-α MAPK activity.

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QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

Abstract: Models developed in these studies can be used to estimate the BBB permeability of drug candidates at early stages of drug development.

Cited by 168 publications

References 67 publications

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system

Pharmacophore generation, atom-based 3D-QSAR, docking, and virtual screening studies of p38-α mitogen activated protein kinase inhibitors: pyridopyridazin-6-ones (part 2)

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QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

Abstract: Models developed in these studies can be used to estimate the BBB permeability of drug candidates at early stages of drug development.

Cited by 168 publications

References 67 publications

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

Short Communication: Cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract

A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system

Pharmacophore generation, atom-based 3D-QSAR, docking, and virtual screening studies of p38-&alpha; mitogen activated protein kinase inhibitors: pyridopyridazin-6-ones (part 2)

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Pharmacophore generation, atom-based 3D-QSAR, docking, and virtual screening studies of p38-α mitogen activated protein kinase inhibitors: pyridopyridazin-6-ones (part 2)