A simple model for the prediction of blood–brain partitioning

Fehér, Miklós; Sourial, Elizabeth; Schmidt, J.

doi:10.1016/s0378-5173(00)00422-1

Cited by 98 publications

(63 citation statements)

References 15 publications

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“…Inspection of these results shows that that the linear model performs reasonably well, and only one compound C13 was strongly underestimated and may be considered as an outlier. Most compounds in test set 2 are also included in the test set 2 used by Feher et al 12 In Feher's work, compounds C1, C2, C8, and C14 were not estimated correctly. But in the current work, for all these four compounds, eq 21 gave good predictions.…”

Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

Hou

2003

J. Chem. Inf. Comput. Sci.

112

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In this paper, QSPR models were developed for in vivo blood-brain partitioning data (logBB) of a large data set consisting of 115 diverse organic compounds. The best model is based on three descriptors: n-octanol/ water partition coefficient calculated using the SLOGP approach, logP; high-charged polar surface areas based on the Gasteiger partial charges, HCPSA, and the excessive molecular weight larger than 360, MW 360 . The model bears good statistical significance, n ) 78, r ) 0.88, q ) 0.86, s ) 0.36, F ) 81.5. The actual prediction potential of the model was validated through two external validation sets of 37 diverse compounds. The predicted results demonstrate that the model bears better prediction potential than many other models and can be used for logBB estimations for drug and drug-like molecules. Comparison of several logP calculation approaches suggests that logP calculated by SLOGP can be used as a significant descriptor for the prediction of molecular transport properties because SLOGP gives the most similar results with CLOGP. The QSPR model indicates that larger polar surface areas have a more negative contribution to logBB, but the absolute partial charges on the atoms surrounded by the polar surfaces should be larger than 0.10|e|. Meanwhile, tight junction membranes limit the size of hydrophilic molecules that can cross the membrane with a molecular weight of approximately 360, because when a molecule's weight is larger than 360 it shows a negative contribution to logBB. The computations of molecular surface, partial charges, logP, and logBB have been accomplished using a program called Drug-BB. Moreover, to improve the efficiency of the computations of logP, we made an extensive reparametrization of SLOGP, and the newly developed SLOGP model is only based on simple atomic addition. Further, we developed a set of parameters to calculate the topological polar surface area (TPSA), thus the high-charged topological polar surface area (HCTPSA) could be estimated from the 2D connection information of a molecule. Adopting the new strategies, the estimations of logP, HCTPSA, and logBB are only based on the topological structure of a molecule and therefore, can be used for fast screening of virtual libraries having millions of molecules.

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Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

Hou

2003

J. Chem. Inf. Comput. Sci.

112

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“…The BBB dataset (Feher et al, 2000) consists of 109 structures having a maximum molecule size of 33 and an average size of 16 atoms after removing hydrogens. The target is to predict the logBB value, which describes up to which degree a drug can cross the bloodbrain-barrier.…”

Section: Datasetsmentioning

confidence: 99%

Optimal assignment kernels for attributed molecular graphs

Fröhlich

Wegner

Sieker

et al. 2005

Proceedings of the 22nd International Conference on Machine Learning - ICML '05

123

108

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We propose a new kernel function for attributed molecular graphs, which is based on the idea of computing an optimal assignment from the atoms of one molecule to those of another one, including information on neighborhood, membership to a certain structural element and other characteristics for each atom. As a byproduct this leads to a new class of kernel functions. We demonstrate how the necessary computations can be carried out efficiently. Compared to marginalized graph kernels our method in some cases leads to a significant reduction of the prediction error. Further improvement can be gained, if expert knowledge is combined with our method. We also investigate a reduced graph representation of molecules by collapsing certain structural elements, like e.g. rings, into a single node of the molecular graph.

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“…Further, the prediction of brain penetration for non-CNS drugs is important during drug development, in order to avoid unwanted neurotoxicity. Human pharmacokinetic data from the brain is highly restricted; thus, PBPK modeling is of significant value as it can be used to predict the target site concentrations in inter-species and inter-disease situations [60][61][62]. However, until recently, there has been relatively few published IVIVE-PBPK models to predict BBB permeability, mostly due to limitations in the current in vitro systems used to represent the complex and transporter-rich structure of the in vivo BBB, and due to difficulty in obtaining the appropriate in vitro-in vivo scaling factors [63].…”

Section: Ivive-pbpk Model For the Central Nervous Systemmentioning

confidence: 99%

Utilizing in vitro transporter data in IVIVE-PBPK: an overview

Mallick

2017

ADMET DMPK

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<p class="ADMETabstracttext">In vitro-in vivo extrapolation (IVIVE) integrated in physiologically-based pharmacokinetic (PBPK) models have been increasingly used during drug discovery and development processes to predict human pharmacokinetic (PK) parameters. Drug transporters can influence drug pharmacokinetics and are key aspects contributing to the development of a successful drug. This review provides a snapshot of challenges or shortcomings of in vitro and in vivo techniques for understanding the contribution of drug transporters to a drug’s pharmacokinetics. The paper also describes the potential of IVIVE-PBPK models as prospective approaches to predict the role of drug transporters in drug discovery and development.</p>

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A simple model for the prediction of blood–brain partitioning

Cited by 98 publications

References 15 publications

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

Optimal assignment kernels for attributed molecular graphs

Utilizing in vitro transporter data in IVIVE-PBPK: an overview

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