ReviewFrontiers in pharmacology2026
Gaps and paths forward in cancer pharmacology and translational research.
Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As drug development costs continue to rise, there is a need to reframe how drug efficacy is evaluated in preclinical models to reduce the rate of false positives. The "valley of death" refers to the gap between bench research and clinical translation. In particular, oncology chemotherapies have the highest rate of drug failure compared to other drug classes. While there has been progress in overall cancer survival, some cancers and patient populations still have a poor prognosis. To bridge the gaps of drug failure and aid underserved patient populations, drug translation cannot be viewed as a purely linear process, but one in which continual refinement is used to create more efficacious drug candidates. Additionally, pharmacokinetic-pharmacodynamic modeling could prove instrumental in better understanding drug efficacy in cellular models and in evaluating clinical translation potential with greater accuracy. The path forward in clinical pharmacology is to view drug development and efficacy as a dynamic process rather than a purely linear fashion. This review discusses traditional pharmacodynamic and pharmacokinetic evaluation methods, as well as pharmacokinetic-pharmacodynamic models of tumor growth inhibition.
Indexed as
Identifiers
What OpenQuestion holds
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.