ArticlePloS one2010
Optimizing combination therapies with existing and future CML drugs.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- A computational dynamic model of combination treatment for type II inhibitors with asciminib.Protein science : a publication of the Protein Society · 2025Article
- Computational Identification of BCR-ABL Oncogenic Signaling as a Candidate Target of Withaferin A and Withanone.Biomolecules · 2022Article
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- Bioactivity inFrontiers in plant science · 2017Article
- Optimized Treatment Schedules for Chronic Myeloid Leukemia.PLoS computational biology · 2016Article
- Leveraging Hypoxia-Activated Prodrugs to Prevent Drug Resistance in Solid Tumors.PLoS computational biology · 2016Article
- Synergy Maps: exploring compound combinations using network-based visualization.Journal of cheminformatics · 2015Article
- Tyrosine kinase inhibition: a therapeutic target for the management of chronic-phase chronic myeloid leukemia.Expert review of anticancer therapy · 2013Review
- Cancer: calculated treatment.Nature · 2013Article
- Article
- Review
- Moving on up: Second-Line Agents as Initial Treatment for Newly-Diagnosed Patients with Chronic Phase CML.Clinical Medicine Insights. Oncology · 2011Article
- Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2].
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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.