ArticlePLoS biology2017
How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient.
Article in PLoS biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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
66 citing papers in PubMed, 147 citations in OpenAlex.
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Optimal control theory as a method for designing multidrug adaptive therapy regimens.NPJ systems biology and applications · 2026Article
- Impacts of competition and phenotypic plasticity on the viability of adaptive therapy.Mathematical biosciences · 2025Article
- Deriving Optimal Treatment Timing for Adaptive Therapy: Matching the Model to the Tumor Dynamics.Bulletin of mathematical biology · 2025Article
- Article
- Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment.bioRxiv : the preprint server for biology · 2024Article
- A protracted war against cancer drug resistance.Cancer cell international · 2024Review
- To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy.Cell systems · 2024Article
- Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.Cancer research · 2024Article
- Modeling spatial evolution of multi-drug resistance under drug environmental gradients.PLoS computational biology · 2024Article
- Steering and controlling evolution - from bioengineering to fighting pathogens.Nature reviews. Genetics · 2023Review
- Modeling spatial evolution of multi-drug resistance under drug environmental gradients.bioRxiv : the preprint server for biology · 2023Article
- Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics.Cancer research · 2023Article
- A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.eLife · 2023Article
- The future of evolutionary medicine: sparking innovation in biomedicine and public health.Frontiers in science · 2023Article
- Hygiene may attenuate selection for antibiotic resistance by changing microbial community structure.Evolution, medicine, and public health · 2023Article
- Spatial structure impacts adaptive therapy by shaping intra-tumoral competition.Communications medicine · 2022Article
- Design principles of collateral sensitivity-based dosing strategies.Nature communications · 2021Article
- Review
- Drug-induced resistance evolution necessitates less aggressive treatment.PLoS computational biology · 2021Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan. Standard practice is to treat aggressively, effectively eliminating drug-sensitive target cells as quickly as possible. This prevents sensitive cells from acquiring resistance de novo but also eliminates populations that can competitively suppress resistant populations. Here we analyse that evolutionary trade-off and consider recent suggestions that treatment regimens aimed at containing rather than eliminating tumours or infections might more effectively delay the emergence of resistance. Our general mathematical analysis shows that there are situations in which regimens aimed at containment will outperform standard practice even if there is no fitness cost of resistance, and, in those cases, the time to treatment failure can be more than doubled. But, there are also situations in which containment will make a bad prognosis worse. Our analysis identifies thresholds that define these situations and thus can guide treatment decisions. The analysis also suggests a variety of interventions that could be used in conjunction with cytotoxic drugs to inhibit the emergence of resistance. Fundamental principles determine, across a wide range of disease settings, the circumstances under which standard practice best delays resistance emergence-and when it can be bettered.
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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.