↓ Skip to main content

Prediction of blood–brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis

Overview of attention for article published in European Journal of Pharmaceutical Sciences, December 2014
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
54 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Prediction of blood–brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis
Published in
European Journal of Pharmaceutical Sciences, December 2014
DOI 10.1016/j.ejps.2014.12.014
Pubmed ID
Authors

Jelica Vucicevic, Katarina Nikolic, Vladimir Dobričić, Danica Agbaba

Abstract

Imidazoline receptor ligands are a numerous family of biologically active compounds known to produce central hypotensive effect by interaction with both α2-adrenoreceptors (α2-AR) and imidazoline receptors (IRs). Recent hypotheses connect those ligands with several neurological disorders. Therefore some IRs ligands are examined as novel centrally acting antihypertensives and drug candidates for treatment of various neurological diseases. Effective Blood-Brain Barrier (BBB) permeability (Pe) of 18 IRs/α-ARs ligands and 22 Central Nervous System (CNS) drugs was experimentally determined using Parallel Artificial Membrane Permeability Assay (PAMPA) and studied by the Quantitative-Structure-Permeability Relationship (QSPR) methodology. The dominant molecules/cations species of compounds have been calculated at pH=7.4. The analyzed ligands were optimized using Density Functional Theory (B3LYP/6-31G(d,p)) included in ChemBio3D Ultra 13.0 program and molecule descriptors for optimized compounds were calculated using ChemBio3D Ultra 13.0, Dragon 6.0 and ADMET predictor 6.5 software. Effective permeability of compounds was used as dependent variable (Y), while calculated molecular parametres were used as independent variables (X) in the QSPR study. SIMCA P+ 12.0 was used for Partial Least Square (PLS) analysis, while the stepwise Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) modeling were performed using STASTICA Neural Networks 4.0. Predictive potential of the formed models was confirmed by Leave-One-Out Cross- and external-validation and the most reliable models were selected. The descriptors that are important for model building are identified as well as their influence on BBB permeability. Results of the QSPR studies could be used as time and cost efficient screening tools for evaluation of BBB permeation of novel α-adrenergic/imidazoline receptor ligands, as promising drug candidates for treatment of hypertension or neurological diseases.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Romania 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 22%
Student > Bachelor 8 15%
Student > Master 8 15%
Researcher 5 9%
Student > Doctoral Student 3 6%
Other 6 11%
Unknown 12 22%
Readers by discipline Count As %
Chemistry 7 13%
Biochemistry, Genetics and Molecular Biology 7 13%
Medicine and Dentistry 6 11%
Pharmacology, Toxicology and Pharmaceutical Science 5 9%
Engineering 4 7%
Other 12 22%
Unknown 13 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 December 2014.
All research outputs
#17,285,036
of 25,371,288 outputs
Outputs from European Journal of Pharmaceutical Sciences
#2,041
of 2,950 outputs
Outputs of similar age
#220,404
of 359,906 outputs
Outputs of similar age from European Journal of Pharmaceutical Sciences
#11
of 16 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,950 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 359,906 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.