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Changing R

Overview of attention for article published in Journal of Translational Medicine, April 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#50 of 4,712)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
20 news outlets
blogs
3 blogs
policy
4 policy sources
twitter
21 X users
googleplus
1 Google+ user
video
1 YouTube creator

Citations

dimensions_citation
268 Dimensions

Readers on

mendeley
845 Mendeley
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Title
Changing R&D models in research-based pharmaceutical companies
Published in
Journal of Translational Medicine, April 2016
DOI 10.1186/s12967-016-0838-4
Pubmed ID
Authors

Alexander Schuhmacher, Oliver Gassmann, Markus Hinder

Abstract

New drugs serving unmet medical needs are one of the key value drivers of research-based pharmaceutical companies. The efficiency of research and development (R&D), defined as the successful approval and launch of new medicines (output) in the rate of the monetary investments required for R&D (input), has declined since decades. We aimed to identify, analyze and describe the factors that impact the R&D efficiency. Based on publicly available information, we reviewed the R&D models of major research-based pharmaceutical companies and analyzed the key challenges and success factors of a sustainable R&D output. We calculated that the R&D efficiencies of major research-based pharmaceutical companies were in the range of USD 3.2-32.3 billion (2006-2014). As these numbers challenge the model of an innovation-driven pharmaceutical industry, we analyzed the concepts that companies are following to increase their R&D efficiencies: (A) Activities to reduce portfolio and project risk, (B) activities to reduce R&D costs, and (C) activities to increase the innovation potential. While category A comprises measures such as portfolio management and licensing, measures grouped in category B are outsourcing and risk-sharing in late-stage development. Companies made diverse steps to increase their innovation potential and open innovation, exemplified by open source, innovation centers, or crowdsourcing, plays a key role in doing so. In conclusion, research-based pharmaceutical companies need to be aware of the key factors, which impact the rate of innovation, R&D cost and probability of success. Depending on their company strategy and their R&D set-up they can opt for one of the following open innovators: knowledge creator, knowledge integrator or knowledge leverager.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 X users 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 845 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 <1%
Belgium 1 <1%
Switzerland 1 <1%
Unknown 842 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 160 19%
Student > Ph. D. Student 115 14%
Researcher 112 13%
Student > Bachelor 100 12%
Other 43 5%
Other 116 14%
Unknown 199 24%
Readers by discipline Count As %
Business, Management and Accounting 108 13%
Biochemistry, Genetics and Molecular Biology 93 11%
Pharmacology, Toxicology and Pharmaceutical Science 85 10%
Agricultural and Biological Sciences 65 8%
Medicine and Dentistry 44 5%
Other 219 26%
Unknown 231 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 203. 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 March 2024.
All research outputs
#197,335
of 26,017,215 outputs
Outputs from Journal of Translational Medicine
#50
of 4,712 outputs
Outputs of similar age
#3,548
of 316,408 outputs
Outputs of similar age from Journal of Translational Medicine
#2
of 99 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,712 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done particularly well, scoring higher than 98% of its peers.
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 316,408 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.