ArticleeLife2021
Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 42 citations in OpenAlex.
- Review
- Collateral sensitivity-harnessing microbial vulnerabilities as a solution to antimicrobial resistance.Molecular biology reports · 2026Review
- Forecasting Multitrait Resistance Evolution under Antibiotic Stress.Molecular biology and evolution · 2026Article
- Linezolid and levofloxacin: an uncommon pairing that suppresses evolution of resistance inFEMS microbes · 2026Article
- Computational framework for streamlining the success of sequential antibiotic therapy.npj antimicrobials and resistance · 2025Article
- Environment-by-Environment Interactions Differ Across Genetic Backgrounds.Molecular biology and evolution · 2025Article
- A Model of Intratumor and Interpatient Heterogeneity Explains Clinical Trials of Curative Combination Therapy for Lymphoma.Blood cancer discovery · 2025Article
- Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments.PLoS biology · 2025Article
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.eLife · 2024Article
- Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism.Molecular cancer therapeutics · 2024Article
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.bioRxiv : the preprint server for biology · 2024Article
- Article
- Modeling spatial evolution of multi-drug resistance under drug environmental gradients.PLoS computational biology · 2024Article
- Synergistic Drug Combinations Promote the Development of Resistance in Acute Myeloid Leukemia.Blood cancer discovery · 2024Article
- The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component.Evolutionary applications · 2023Article
- Modeling spatial evolution of multi-drug resistance under drug environmental gradients.bioRxiv : the preprint server for biology · 2023Article
- Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications.bioRxiv : the preprint server for biology · 2023Article
- Pervasive genotype-by-environment interactions shape the fitness effects of antibiotic resistance mutations.Proceedings. Biological sciences · 2023Article
- Mutators can drive the evolution of multi-resistance to antibiotics.PLoS genetics · 2023Article
- Sequential antibiotic therapy in the laboratory and in the patient.Journal of the Royal Society, Interface · 2023Article
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 2 institutions in 2 countries.
Funding
Abstract
Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.
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.