Automated Author ProfileBrandl, Martin
Brandl, Martin
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.5 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Introduction: With the increasing number of poorly water-soluble compounds in drug discovery pipelines, supersaturating drug delivery systems (SDDS) have attracted increased attention as an effective bioavailability enhancing approach. However, a systematic and quantitative synopsis of the knowledge about performance of SDDS is currently lacking. Such analysis of the recent achievements is to provide insights for formulation scientists dealing with poorly soluble compounds. Areas covered: A systematic search of two evidence-based International databases, Medline and Embase, from 2010 to Dec 2015, has been performed. By conducting meta-analysis, box-plots, and correlation plots of the relevant data retrieved from literature, the current review addresses three quantitative questions: (1) how promising are SDDS for bioavailability enhancement? (2) which types of SDDS perform best? and (3) what are the most promising drug candidates? Four widely reported types of SDDS were compared: amorphous solid dispersions, nano-drug systems, supersaturable lipid-based formulations, and silica-based systems. Expert opinion: While SDDS formulations appear to be a promising candidate-enabling technique for drug development, the prediction of their in vivo performance by in vitro testing remains challenging. A transition from a trial-and-error development approach towards an approach guided by mechanistic insight, as well as the development of more efficient predictive tools for performance ranking is urgently needed.
Authors
- Fong, Sophia Yui Kau ;
- Bauer-Brandl, Annette ;
- Brandl, Martin
Introduction: With the increasing number of poorly water-soluble compounds in drug discovery pipelines, supersaturating drug delivery systems (SDDS) have attracted increased attention as an effective bioavailability enhancing approach. However, a systematic and quantitative synopsis of the knowledge about performance of SDDS is currently lacking. Such analysis of the recent achievements is to provide insights for formulation scientists dealing with poorly soluble compounds. Areas covered: A systematic search of two evidence-based International databases, Medline and Embase, from 2010 to Dec 2015, has been performed. By conducting meta-analysis, box-plots, and correlation plots of the relevant data retrieved from literature, the current review addresses three quantitative questions: (1) how promising are SDDS for bioavailability enhancement? (2) which types of SDDS perform best? and (3) what are the most promising drug candidates? Four widely reported types of SDDS were compared: amorphous solid dispersions, nano-drug systems, supersaturable lipid-based formulations, and silica-based systems. Expert opinion: While SDDS formulations appear to be a promising candidate-enabling technique for drug development, the prediction of their in vivo performance by in vitro testing remains challenging. A transition from a trial-and-error development approach towards an approach guided by mechanistic insight, as well as the development of more efficient predictive tools for performance ranking is urgently needed.
Authors
- Fong, Sophia Yui Kau ;
- Bauer-Brandl, Annette ;
- Brandl, Martin