ArticleIET systems biology
Double Q-Learning for Intelligent Multi-Drug Scheduling in Cancer Chemotherapy Optimisation.
Article in IET systems biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemotherapy scheduling poses a challenging control problem due to the need to suppress tumour growth whilst maintaining systemic toxicity within clinically acceptable limits. This study develops a double Q-learning-based controller for optimising daily dosing of a three-drug regimen consisting of cisplatin, docetaxel and irinotecan. A pharmacokinetics-pharmacodynamics (PK/PD) tumour model with eight resistance states is used as the simulation environment. The controller aims to minimise tumour burden whilst enforcing strict toxicity constraints aligned with clinical dosing guidelines. Simulation results show that double Q-learning substantially outperforms classical Q-learning, achieving near-complete tumour suppression within the simulation framework, corresponding to a residual tumour fraction on the order of
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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.