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Review

An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling

1
Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
2
Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology and Tumor Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin Humboldt—Universität zu Berlin and Berlin Institute of Health, 10117 Berlin, Germany
3
Department of Human Medicine, Institute for Systems Medicine and Bioinformatics, MSH Medical School Hamburg—University of Applied Sciences and Medical University, 20457 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Cancers 2020, 12(11), 3103; https://doi.org/10.3390/cancers12113103
Received: 1 September 2020 / Revised: 15 October 2020 / Accepted: 20 October 2020 / Published: 23 October 2020
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
Personalized cancer treatments show decreased side-effects and improved treatment success. One aspect of individualized treatment is the timing of medicine intake, which may be optimized based on the biological diurnal rhythm of the patient. The personal biological time can be assessed by a variety of tools not yet commonly included in diagnostics. We review these tools with a focus on their applicability in a clinical context. Using biological samples from the patient, most tools predict individual time using machine learning methodologies, often supported by rhythmicity analysis and mathematical core-clock models. We compare different approaches and discuss possible promising future directions.
Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient’s internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient’s internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity. View Full-Text
Keywords: chronotherapy in cancer; core-clock ODE models; circadian time prediction; machine learning; harmonic regression; computational methods for rhythmicity analysis; circadian network chronotherapy in cancer; core-clock ODE models; circadian time prediction; machine learning; harmonic regression; computational methods for rhythmicity analysis; circadian network
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MDPI and ACS Style

Hesse, J.; Malhan, D.; Yalҫin, M.; Aboumanify, O.; Basti, A.; Relógio, A. An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling. Cancers 2020, 12, 3103. https://doi.org/10.3390/cancers12113103

AMA Style

Hesse J, Malhan D, Yalҫin M, Aboumanify O, Basti A, Relógio A. An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling. Cancers. 2020; 12(11):3103. https://doi.org/10.3390/cancers12113103

Chicago/Turabian Style

Hesse, Janina, Deeksha Malhan, Müge Yalҫin, Ouda Aboumanify, Alireza Basti, and Angela Relógio. 2020. "An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling" Cancers 12, no. 11: 3103. https://doi.org/10.3390/cancers12113103

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