ArticleCell reports2020
Multi-scale Predictions of Drug Resistance Epidemiology Identify Design Principles for Rational Drug Design.
Article in Cell reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Dual functional genomics reveals a broad and convergent landscape of asciminib resistance in BCR::ABL1.Genome medicine · 2026Article
- Mutation-biased adaptation is consequential even in large bacterial populations.Nature communications · 2026Article
- Extended sequence context shapes mutational bias inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Predicting evolutionary outcomes through the probability of accessing sequence variants.Science advances · 2023Article
- Shifts in mutation spectra enhance access to beneficial mutations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Mutation bias and the predictability of evolution.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Preparation of photothermal responsive, antibacterial hydrogel by using PVA-Alg and silver nanofibers as building blocks.Frontiers in bioengineering and biotechnology · 2023Article
- Challenges and Opportunities Modeling the Dynamic Tumor Matrisome.BME frontiers · 2023Review
- Agent-Based Models Help Interpret Patterns of Clinical Drug Resistance by Contextualizing Competition Between Distinct Drug Failure Modes.Cellular and molecular bioengineering · 2022Article
- Mutation bias shapes the spectrum of adaptive substitutions.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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
Rationally designing drugs that last longer in the face of biological evolution is a critical objective of drug discovery. However, this goal is thwarted by the diversity and stochasticity of evolutionary trajectories that drive uncertainty in the clinic. Although biophysical models can qualitatively predict whether a mutation causes resistance, they cannot quantitatively predict the relative abundance of resistance mutations in patient populations. We present stochastic, first-principle models that are parameterized on a large in vitro dataset and that accurately predict the epidemiological abundance of resistance mutations across multiple leukemia clinical trials. The ability to forecast resistance variants requires an understanding of their underlying mutation biases. Beyond leukemia, a meta-analysis across prostate cancer, breast cancer, and gastrointestinal stromal tumors suggests that resistance evolution in the adjuvant setting is influenced by mutational bias. Our analysis establishes a principle for rational drug design: when evolution favors the most probable mutant, so should drug design.
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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.