Personalized oncology with artificial intelligence: The case of temozolomide.
Purpose: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability.Methods: Our optimizations are based on a Pharmacokinetics/Pharmacodynamics (PK/PD) model with population variability for temozolo...
| Publicado en: | Artificial Intelligence in Medicine Vol. 99 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139057753&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139057753 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Aug2019 vid: 99 pid: 1004 pub: Elsevier B.V. artinfo: ui: 139057753 139057753 NLM31606107 10.1016/j.artmed.2019.07.001 NLM31606107 139057753 ppct: 1 formats: tig: atl: Personalized oncology with artificial intelligence: The case of temozolomide. aug: au: Houy, Nicolas Le Grand, François affil: University of Lyon, Lyon, F-69007, France sug: subj: Artificial Intelligence Brain Neoplasms Drug Therapy Body Weights and Measures Maximum Tolerated Dose Probability Models, Biological Computer Simulation Algorithms Dose-Response Relationship, Drug ab: Purpose: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability.Methods: Our optimizations are based on a Pharmacokinetics/Pharmacodynamics (PK/PD) model with population variability for temozolomide, inspired by Faivre et al. [10] and Panetta et al. [25,26]. The patient pharmacokinetic parameters can only be partially observed at admission and are progressively learned by Bayesian inference during treatment. For every patient, we seek to minimize tumor size while avoiding severe toxicity, i.e. maintaining an acceptable toxicity level. The optimization algorithm we rely on borrows from the field of artificial intelligence.Results: Optimal personalized protocols (OPP) achieve a sizable decrease in tumor size at the population level but also patient-wise. The tumor size is on average 67.2 g lighter than with the standard maximum-tolerated dose protocol (MTD) after 336 days (12 MTD cycles). The corresponding 90% confidence interval for average tumor size reduction amounts to 58.6-82.7 g. When treated with OPP, less patients experience severe toxicity in comparison to MTD.Major Findings: We quantify in-silico the benefits offered by personalized oncology in the case of temozolomide administration. To do so, we compute optimal personalized protocols for a population of heterogeneous patients using artificial intelligence techniques. At each treatment day, the protocol is updated by taking into account the feedback obtained from patient's reaction to the drug administration. Personalized protocols greatly differ from each other, and from the standard MTD protocol. Benefits of personalization are very sizable: tumor sizes are much smaller on average and also patient-wise, while severe toxicity is made less frequent. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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