ReviewJournal of the National Cancer Institute2013
From bench to bedside: lessons learned in translating preclinical studies in cancer drug development.
Review in Journal of the National Cancer Institute, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Neuroprotection in neurodegenerations of the brain and eye: Lessons from the past and directions for the future.Frontiers in neurology · 2022Review
- Sources of inter-individual variability leading to significant changes in anti-PD-1 and anti-PD-L1 efficacy identified in mouse tumor models using a QSP framework.Frontiers in pharmacology · 2022Article
- Prospective pharmacological methodology for establishing and evaluating anti-cancer drug resistant cell lines.BMC cancer · 2021Article
- Establishment of patient-derived organotypic tumor spheroid models for tumor microenvironment modeling.Cancer medicine · 2021Article
- Visualizing cancer extravasation: from mechanistic studies to drug development.Cancer metastasis reviews · 2021Review
- Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.Nature cancer · 2021Article
- EUS-FNA Biopsies to Guide Precision Medicine in Pancreatic Cancer: Results of a Pilot Study to IdentifyFrontiers in oncology · 2021Article
- Patient-derived cancer modeling for precision medicine in colorectal cancer: beyond the cancer cell line.Cancer biology & therapy · 2020Review
- A Microfluidic Perfusion Platform for In Vitro Analysis of Drug Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships.The AAPS journal · 2020Article
- Preclinical Metrics Correlate With Drug Activity in Phase II Trials of Targeted Therapies for Non-Small Cell Lung Cancer.Frontiers in oncology · 2020Article
- Virtual clinical trials identify effective combination therapies in ovarian cancer.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The development of targeted agents in oncology has rapidly expanded over the past 2 decades and has led to clinically significant improvements in the treatment of numerous cancers. Unfortunately, not all success at the bench in preclinical experiments has translated to success at the bedside. As preclinical studies shift toward defining proof of mechanism, patient selection, and rational drug combinations, it is critical to understand the lessons learned from prior translational studies to gain an understanding of prior drug development successes and failures. By learning from prior drug development, future translational studies will provide more clinically relevant data, and the underlying hope is that the clinical success rate will improve and the treatment of patients with ineffective targeted therapy will be limited.
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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.