ArticleCPT: pharmacometrics & systems pharmacology2023
Guiding model-driven combination dose selection using multi-objective synergy optimization.
Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Biomimetic liposomes synergize with high-intensity focused ultrasound to induce ferroptosis in tumors.Acta pharmaceutica Sinica. B · 2026Article
- NPCDR 2.0: the activity and structure landscape of natural product-based drug combination.Nucleic acids research · 2026Article
- Research progress of artificial intelligence in high-throughput drug screening.Frontiers in pharmacology · 2026Review
- Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers.Endocrine oncology (Bristol, England) · 2025Review
- Mitigating non-genetic resistance to checkpoint inhibition based on multiple states of immune exhaustion.NPJ systems biology and applications · 2024Article
- Role of pharmacometrics and systems pharmacology in facilitating efficient dose optimization in oncology.CPT: pharmacometrics & systems pharmacology · 2023Article
- Guiding model-driven combination dose selection using multi-objective synergy optimization.CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the growing appreciation that the future of cancer treatment lies in combination therapies, finding the right drugs to combine and the optimal way to combine them remains a nontrivial task. Herein, we introduce the Multi-Objective Optimization of Combination Synergy - Dose Selection (MOOCS-DS) method for using drug synergy as a tool for guiding dose selection for a combination of preselected compounds. This method decouples synergy of potency (SoP) and synergy of efficacy (SoE) and identifies Pareto optimal solutions in a multi-objective synergy space. Using a toy combination therapy model, we explore properties of the MOOCS-DS algorithm, including how optimal dose selection can be influenced by the metric used to define SoP and SoE. We also demonstrate the potential of our approach to guide dose and schedule selection using a model fit to preclinical data of the combination of the PD-1 checkpoint inhibitor pembrolizumab and the anti-angiogenic drug bevacizumab on two lung cancer cell lines. The identification of optimally synergistic combination doses has the potential to inform preclinical experimental design and improve the success rates of combination therapies. Jel classificationDose Finding in Oncology.
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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.