ArticleNature communications2018
Modeling the impact of drug interactions on therapeutic selectivity.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
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- Cuminaldehyde and Tobramycin Forestall the Biofilm Threats of Staphylococcus aureus: A Combinatorial Strategy to Evade the Biofilm Challenges.Applied biochemistry and biotechnology · 2024Article
- Structural basis of efficacy-driven ligand selectivity at GPCRs.Nature chemical biology · 2023Article
- Multimodal perturbation analyses of cyclin-dependent kinases reveal a network of synthetic lethalities associated with cell-cycle regulation and transcriptional regulation.Scientific reports · 2023Article
- Application of Microfluidics in Detection of Circulating Tumor Cells.Frontiers in bioengineering and biotechnology · 2022Review
- Data vignettes for the application of response surface models in drug combination analysis.Data in brief · 2021Article
- Using response surface models to analyze drug combinations.Drug discovery today · 2021Review
- Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater.mSystems · 2021Article
- Anandamide alters the membrane properties, halts the cell division and prevents drug efflux in multidrug resistant Staphylococcus aureus.Scientific reports · 2021Article
- Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening inFrontiers in fungal biology · 2021Article
- A G-Quadruplex-Binding Small Molecule and the HDAC Inhibitor SAHA (Vorinostat) Act Synergistically in Gemcitabine-Sensitive and Resistant Pancreatic Cancer Cells.Molecules (Basel, Switzerland) · 2020Article
- Optimized low-dose combinatorial drug treatment boosts selectivity and efficacy of colorectal carcinoma treatment.Molecular oncology · 2020Article
- Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention.Advanced therapeutics · 2020Article
- Guided screen for synergistic three-drug combinations.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
Abstract
Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity-enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors.
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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.