ArticlePLoS computational biology2019
Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers.
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122 citing papers in PubMed, 193 citations in OpenAlex.
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- Rethinking the role of synergy calculations in the next century of drug combination discovery.Med (New York, N.Y.) · 2026Review
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- Absence of synergistic effects by CDK12/13 inhibition in combination with cisplatin or olaparib in ovarian cancer cells.Scientific reports · 2026Article
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- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- High-throughput drug screening identifies EGFR/MAPK pathway targeting sensitivities in organoid models of ovarian carcinosarcoma.Journal of experimental & clinical cancer research : CR · 2026Article
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- Phenotypic Response Surfaces-Guided Optimization (PRS-OPT) of Propolis-Metformin-Regorafenib Combination Therapy for MASLD-Associated Hepatocellular Carcinoma.Oncology research · 2026Article
- MicroRNA-873 Suppresses Viability and Invasion of Colorectal Cancer Through KRAS/MAPK Signaling and Sensitizes Tumor Spheroids to 5-Fluorouracil in a 3D Microwell Model.IEEE open journal of engineering in medicine and biology · 2026Article
- Uncovering the mechanisms of synergistic drug combinations in non-small cell lung cancer through metagene-based classification.PloS one · 2026Article
62 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain.
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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.