ArticleNPJ systems biology and applications2020
Stratification and prediction of drug synergy based on target functional similarity.
Article in NPJ systems biology and applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 58 citations in OpenAlex.
- Synergistic antiviral effects of structure-guided peptides and a mutagenic base analog on SARS-CoV-2 replication.Antimicrobial agents and chemotherapy · 2026Article
- Inclusion of JNK-independent drugs within multiagent chemotherapy improves response in relapsed high-risk neuroblastoma.Science advances · 2025Article
- Environment-by-Environment Interactions Differ Across Genetic Backgrounds.Molecular biology and evolution · 2025Article
- Personalizing chemotherapy drug selection using a novel transcriptomic chemogram.PLoS computational biology · 2025Article
- Harnessing p53 for targeted cancer therapy: new advances and future directions.Transcription · 2025Review
- A guide for active learning in synergistic drug discovery.Scientific reports · 2025Article
- MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.Biomolecules · 2024Article
- Integrated ML-Based Strategy Identifies Drug Repurposing for Idiopathic Pulmonary Fibrosis.ACS omega · 2024Article
- Anticancer drugs: How to select small molecule combinations?Trends in pharmacological sciences · 2024Review
- SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.Scientific reports · 2023Article
- CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.Briefings in bioinformatics · 2023Article
- A combination of metformin and epigallocatechin gallate potentiates glioma chemotherapyFrontiers in pharmacology · 2023Article
- SynAI: an AI-driven cancer drugs synergism prediction platform.Bioinformatics advances · 2023Article
- Application of perturbation gene expression profiles in drug discovery-From mechanism of action to quantitative modelling.Frontiers in systems biology · 2023Review
- OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.NAR genomics and bioinformatics · 2022Article
- Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry.iScience · 2022Article
- Craft of Co-encapsulation in Nanomedicine: A Struggle To Achieve Synergy through Reciprocity.ACS pharmacology & translational science · 2022Review
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Discovery of Natural Products With Antifungal Potential Through Combinatorial Synergy.Frontiers in microbiology · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combinations can expand therapeutic options and address cancer's resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to prioritise drug combinations in high-throughput screens and to stratify synergistic responses. At the core of our approach is the observation that the likelihood of synergy increases when targeting proteins with either strong functional similarity or dissimilarity. We estimate the similarity applying a multitask machine learning approach to basal gene expression and response to single drugs. We tested 7 protein target pairs (representing 29 combinations) and predicted their synergies in 33 breast cancer cell lines. In addition, we experimentally validated predicted synergy of the BRAF/insulin receptor combination (Dabrafenib/BMS-754807) in 48 colorectal cancer cell lines. We anticipate that our approaches can be used for prioritization of drug combinations in large scale screenings, and to maximize the efficacy of drugs already known to induce synergy, ultimately enabling patient stratification.
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.