In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

Ramamurthi, Narayanan; Gunturi, Sitarama B.

doi:10.1016/j.bmc.2005.01.061

Cited by 68 publications

(64 citation statements)

References 34 publications

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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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Earlier, we reported [37] predictive models for BBB permeation using a systematic variable selection method, namely, "Variable Selection and Model Building Using Prediction" (VSMP) that provides the best solution from a given set of input variables by exploring all possible variable combinations; however, it suffers from the limitation of being computationally intensive, particularly when the number of variables to be selected is high (typically greater than 3). As a solution to this problem, we have reported the application of an ant colony optimization method for the generation of models to predict the binding affinity of a drug to human serum albumin [38].…”

Section: Introductionmentioning

confidence: 99%

In Silico ADME Modeling 3: Computational Models to Predict Human Intestinal Absorption Using Sphere Exclusion and kNN QSAR Methods

Gunturi

Ramamurthi

2007

QSAR Comb. Sci.

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Modeling of human intestinal absorption (HIA) data of 175 diverse drugs and 336 calculated descriptors is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. With this aim, we employed two automated procedures, (a) Sphere Exclusion Algorithm (SEA) to select members of the training and test sets based on structural dissimilarity and (b) k-Nearest Neighbors (kNN) method along with Genetic Algorithms (kNN-QSAR-GA) to select significant and independent descriptors. This methodology helped us to derive optimal Quantitative Structure -Property Relationship (QSPR) models based on three and four descriptors. The best three descriptor model is based on Delta Chi Index of order 3 (Cluster), Hydrogen type E-State index ShsOH, AlogP99 (q ext statistics with reported models using other approaches, it is shown that: (a) the models have high stability and are robust and (b) for the first time in HIA modeling, the combination of an automated training set selection (SEA) followed by variable selection (kNN-QSAR_GA) is shown to be a promising methodology to build multiple stable models that are useful in consensus prediction. From the analysis of the physical meaning of the selected descriptors, it is inferred that the HIA of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (1) lipophilicity, (2) hydrogen bonding capacity, (3) size, and (4) shape and further, the role of new calculated descriptors on the HIA profile of small organic compounds is uncovered. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritization of candidates is required.

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“…These in silico methods build so-called quantitative structure-activity relationships (QSAR) between biological activity values and a series of theoretical descriptors describing chemical and physical characteristics of the molecules. Different authors have built QSAR models for BBB passage using different sets of descriptors and different modelling techniques, like multiple linear regression (MLR) [1][2][3][4], principal component regression (PCR) [5,6], partial least squares (PLS) [5,7] and artificial neural networks (ANN) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of boosted regression trees (BRTs) and two‐step BRT procedures to model and predict blood‐brain barrier passage

Deconinck

Zhang

Coomans

et al. 2007

Journal of Chemometrics

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Two new approaches, boosted regression trees (BRTs) and two-step BRT, were evaluated for modelling and predicting the blood-brain barrier (BBB) passage of drugs. Classification and regression trees (CART) were used as a base learner in BRT. In two-step BRT, a linear model (stepwise multiple linear regression (MLR) or partial least squares (PLS)) was built first, then BRT was applied to model the residuals of the linear model and both models were added. Both approaches were compared with the CART, MLR and PLS models. It was observed that BRT could improve the descriptive and predictive abilities compared to a single CART and that the stepwise MLR-BRT results in slightly improved descriptive and predictive properties compared to the MLR model. The combination of PLS and BRT did not result in an improvement, compared to the individual PLS model. The best models were obtained with stepwise MLR-BRT and PLS. It was shown that the combination of linear models with BRT is an approach that has potential and can be considered for future QSAR modelling.

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In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

Cited by 68 publications

References 34 publications

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

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

In Silico ADME Modeling 3: Computational Models to Predict Human Intestinal Absorption Using Sphere Exclusion and kNN QSAR Methods

Evaluation of boosted regression trees (BRTs) and two‐step BRT procedures to model and predict blood‐brain barrier passage

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