In Silico Prediction of Membrane Permeability from Calculated Molecular Parameters

Refsgaard, Hanne H. F.; Jensen, Berith F.; Brockhoff, Per B.; Padkjær, Søren Berg; Guldbrandt, Mette; Christensen, Michael

doi:10.1021/jm049661n

Cited by 169 publications

(119 citation statements)

References 33 publications

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“…102 They analyzed 712 compounds (380 nonpermeable, 332 permeable) to develop a 2-class classifi cation model. The permeability data were binned into 2 classes based on apparent permeability: those below 4 × 10 − 6 cm/s were classifi ed as low permeability, and those with 4 × 10 − 6 cm/s or higher were classifi ed as high permeability.…”

Section: E34mentioning

confidence: 99%

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Recent progress in the computational prediction of aqueous solubility and absorption

Johnson

Zheng

2006

AAPS J

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The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classifi cation system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We fi nd that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefl y discuss aspects of emerging scientifi c understanding that may lead to breakthroughs in the computational modeling of these properties.

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Section: E34mentioning

confidence: 99%

“…103 Local models may solve this problem by building compound class-specifi c models using, again, selfconsistent data. It is exciting to see such work as Refsgaard et al ' s publication, 102 which was based on a large set of >700 compounds with data from the same laboratory.…”

Section: E34mentioning

confidence: 99%

Recent progress in the computational prediction of aqueous solubility and absorption

Johnson

Zheng

2006

AAPS J

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“…It was chosen to develop a classification model as these data were more appropriate for classification. The model presented in this work was built using the k-NN algorithm based on Mahalanobis distance, which has previous been found very useful for classification of ADME (Absorption, Distribution, Metabolism, and Excretion) properties [21,40]. An advantage of using the k-NN approach, apart from its predictive power, is the simplicity and transparency of the method.…”

Section: Discussionmentioning

confidence: 99%

“…Overlapping fragments were considered as one union fragment. For both decomposition schemes, each H-depleted molecular graph was transformed into fragments and represented in a canonical line notation similar to SYBYL Line Notation (SLN) [20], and an example for Mizolastine, an antihistamine is given by Refsgaard et al [21]. All algorithms were implemented as Cheshire scripts [22].…”

Section: Structural Fragmentsmentioning

confidence: 99%

In Silico Classification of Solubility using Binary k‐Nearest Neighbor and Physicochemical Descriptors

et al. 2007

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Solubility is a key property that highly affects both the absorption and screening efficiency of drug candidates. Here, we present an in silico k-nearest neighbor model applying ten physicochemical descriptors which classify solubility based on a diverse set of chemical drug candidates. Data used for modeling the solubility consisted of turbidimetric solubility values at pH 7.4 for 518 drug candidates. Data were divided into a training set and a test set of 389 and 129 compounds, respectively, which additionally were binned into two classes with respect to the solubility: insoluble class: solubility 0.02 mg/mL and soluble class: solubility > 0.02 mg/mL. Furthermore, a structural fragment analysis of soluble versus insoluble compounds was performed, and structural fragments and functional groups for which we found statistical difference in frequency between the two solubility classes were presented. Of the ten descriptors used for modeling, clog D was found to be the descriptor that separated the two solubility classes most efficiently. When the test set was classified, 84% were predicted to the right class. Validated with 12 soluble marketed drugs, the model predicted 11 of the 12 compounds to the right class. We found that the solubility model could be used to flag molecules with low solubility in an early stage of discovery projects.

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“…Moreover, similarity analysis can be used to select the most diverse subset (diversity selection) from a given set of compounds [Nikolova and Jaworska, 2003]. The structural diversity of a dataset can be evaluated, e.g., by cluster analysis using structural keys as descriptors and a high number of singletons with a given level of Tanimoto similarity suggests that the dataset is diverse [Gao et al, 2002;Refsgaard et al, 2005].…”

Section: Datamentioning

confidence: 99%

In silico prediction of cytochrome P450 inhibitors

Refsgaard

Jensen

Christensen

et al. 2006

Drug Development Research

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Patients often receive several medications at the same time, and if the drugs involved compete for the same enzymes to be metabolized, it can lead to undesired effects with the risk of fatal results. Therefore, early knowledge about the cytochrome P450 (CYP) interaction potential of a drug candidate is central and in silico tools can provide such information even on virtual structures. Most of the in silico CYP information in the literature is on substrates and is based on molecular and protein modeling. However, in early screening information of CYP substrates is rarely available and sometimes only a single concentration is used in screening assays. Recently, in silico CYP modeling applying statistical tools has appeared in the literature and the aim of this review is to give an overview of published in silico prediction studies of CYP inhibition for four of the clinically most important isotypes, namely: CYP1A2, CYP2C9, CYP2D6, and CYP3A4. Furthermore, in the review, we discuss inhibition data, different descriptors and statistical methods applied for in silico prediction of CYP inhibition, and we point to promising approaches in the development of accurate in silico prediction tools of CYP inhibitors. Drug Dev. Res. 67:417-429, 2006.

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In Silico Prediction of Membrane Permeability from Calculated Molecular Parameters

Cited by 169 publications

References 33 publications

Recent progress in the computational prediction of aqueous solubility and absorption

Recent progress in the computational prediction of aqueous solubility and absorption

In Silico Classification of Solubility using Binary k‐Nearest Neighbor and Physicochemical Descriptors

In silico prediction of cytochrome P450 inhibitors

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