ArticlePloS one2017
Modeling timelines for translational science in cancer; the impact of technological maturation.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Toward a new AI winter? How diffusion of technological innovation on networks leads to chaotic boom-bust cycles.Frontiers in artificial intelligence · 2025Article
- A Bibliometric Review of Publications on Oxidative Stress and Chemobrain: 1990-2019.Antioxidants (Basel, Switzerland) · 2020Review
- The life-cycles of skin replacement technologies.PloS one · 2020Article
- Predicting translational progress in biomedical research.PLoS biology · 2019Article
- Contribution of NIH funding to new drug approvals 2010-2016.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- As Technologies for Nucleotide Therapeutics Mature, Products Emerge.Molecular therapy. Nucleic acids · 2017Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work examines translational science in cancer based on theories of innovation that posit a relationship between the maturation of technologies and their capacity to generate successful products. We examined the growth of technologies associated with 138 anticancer drugs using an analytical model that identifies the point of initiation of exponential growth and the point at which growth slows as the technology becomes established. Approval of targeted and biological products corresponded with technological maturation, with first approval averaging 14 years after the established point and 44 years after initiation of associated technologies. The lag in cancer drug approvals after the increases in cancer funding and dramatic scientific advances of the 1970s thus reflects predictable timelines of technology maturation. Analytical models of technological maturation may be used for technological forecasting to guide more efficient translation of scientific discoveries into cures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.