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

SIMPLE SUMMARY: 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. ABSTRACT: 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.