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Attention Score in Context
| Title |
Combined application of mixture experimental design and artificial neural networks in the solid dispersion development
|
|---|---|
| Published in |
Drug Development & Industrial Pharmacy, June 2015
|
| DOI | 10.3109/03639045.2015.1054831 |
| Pubmed ID | |
| Authors |
Djordje P. Medarević, Peter Kleinebudde, Jelena Djuriš, Zorica Djurić, Svetlana Ibrić |
| Abstract |
This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged. |
Twitter Demographics
The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 40 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 15% |
| Student > Ph. D. Student | 6 | 15% |
| Student > Bachelor | 4 | 10% |
| Lecturer | 3 | 8% |
| Professor | 2 | 5% |
| Other | 6 | 15% |
| Unknown | 13 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 14 | 35% |
| Medicine and Dentistry | 5 | 13% |
| Chemistry | 2 | 5% |
| Engineering | 2 | 5% |
| Materials Science | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 15 | 38% |
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 21 June 2015.
All research outputs
#17,763,547
of 22,813,792 outputs
Outputs from Drug Development & Industrial Pharmacy
#1,311
of 1,507 outputs
Outputs of similar age
#179,840
of 266,822 outputs
Outputs of similar age from Drug Development & Industrial Pharmacy
#15
of 21 outputs
Altmetric has tracked 22,813,792 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,507 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 11th percentile – i.e., 11% 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 266,822 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.