In silico prediction of blood–brain barrier permeation

Clark, David E.

doi:10.1016/s1359-6446(03)02827-7

Cited by 430 publications

(261 citation statements)

References 30 publications

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“…Analyses of the corresponding plots for other Pipeline Pilot descriptors shows that molecules are more likely to be 5HT3 antagonists if they are: quite small (with 10-30 atoms and with molecular weights less than 450); rigid (containing 3-8 rings and with less than 6 rotatable bonds); and not very lipophilic, with few H-bond acceptors and a low logP value. 5HT3 antagonists are CNS drugs that must be able to pass through the blood-brain barrier (BBB) to exhibit activity, and the analysis of the scores above mirrors closely the physicochemical properties that are known to be necessary for a molecule to pass through the BBB [46].…”

Section: Resultsmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

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Abstract. Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of a naive Bayesian classifier when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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Section: Resultsmentioning

confidence: 99%

Evaluation of machine-learning methods for ligand-based virtual screening

Chen

Harrison

Papadatos

et al. 2007

J Comput Aided Mol Des

113

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“…The low molecular weight (<450) and the polar surface areas of the compounds can also affect penetration across the BBB. 29) At present, there is insufficient knowledge regarding the bioavailability and permeation of methoxsalen into the CNS, but we expect that methoxsalen has the ability to penetrate the BBB, based upon its chemical properties. First, it has only four oxygen atoms in total (molecular formula C 12 H 8 O 4 ), and its molecular weight (216.18) is relatively low.…”

Section: Discussionmentioning

confidence: 99%

Effects of Methoxsalen fromPoncirus trifoliataon Acetylcholinesterase and Trimethyltin-Induced Learning and Memory Impairment

Kim¹,

Choi²,

Bae³

et al. 2011

Bioscience, Biotechnology, and Biochemistry

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“…Other studies have correlated brain uptake with lipophobicity, hydrogen donors/acceptors, and the number of rotatable bonds. 90,91 In silico prediction methods offer the prospect of screening libraries of actual or virtual compounds on the basis of BBB permeation. A number of such models are available.…”

Section: In Silico Prediction Of Bbb Permeationmentioning

confidence: 99%

Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery

et al. 2005

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Summary:The worldwide market for therapies for CNS disorders is worth more than $50 billion and is set to grow substantially in the years ahead. This is because: 1) the incidence of many CNS disorders (e.g., Alzheimer's disease, stroke, and Parkinson's disease) increase exponentially after age 65 and 2) the number of people in the world over 65 is about to increase sharply because of a marked rise in fertility after World War II. However, CNS research and development are associated with significant challenges: it takes longer to get a CNS drug to market (12-16 years) compared with a non-CNS drug (10 -12 years) and there is a higher attrition rate for CNS drug candidates than for non-CNS drug candidates. This is attributable to a variety of factors, including the complexity of the brain, the liability of CNS drugs to cause CNS side effects, and the requirement of CNS drugs to cross the blood-brain barrier (BBB). This review focuses on BBB penetration, along with pharmacokinetics and drug metabolism, in the process of the discovery and development of safe and effective medicines for CNS disorders.

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In silico prediction of blood–brain barrier permeation

Cited by 430 publications

References 30 publications

Evaluation of machine-learning methods for ligand-based virtual screening

Evaluation of machine-learning methods for ligand-based virtual screening

Effects of Methoxsalen fromPoncirus trifoliataon Acetylcholinesterase and Trimethyltin-Induced Learning and Memory Impairment

Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery

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