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

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.

Long-term continuous positive airway pressure treatment ameliorates biological clock disruptions in obstructive sleep apnea

“…While machine learning is commonly used to assess the biological clock [88], our study in particular highlights the potential of machine learning approaches based on clock genes expression for OSA diagnosis and treatment response monitoring. A deeper understanding of how OSA and OSA treatment affects the biological clock may thus lead to a better OSA clinical management and might help to counteract the progression of OSA associated comorbidities.…”

A mathematical model of the circadian clock and drug pharmacology to optimize irinotecan administration timing in colorectal cancer

“…Chronotherapy might be more efficient when adapted to the internal time of the patient. Yet, the definition of a single internal time is challenging since the circadian timing system involves multiple inter-connected central and peripheral oscillating processes [18] . We suggest to base chronotherapy individualization on the patient's circadian profiles of selected genes including core-clock genes and genes involved in drug pharmacology.…”

“…We suggest to base chronotherapy individualization on the patient's circadian profiles of selected genes including core-clock genes and genes involved in drug pharmacology. Several patient-friendly methods for measuring clock gene expression using saliva or blood sampling have been recently validated in the clinics [18] . Such patient datasets, combined with mathematical modeling and machine learning, may allow to predict the times of least toxicity to healthy tissues, and optimal antitumor efficacy for an individual patient [18] .…”

“…Several patient-friendly methods for measuring clock gene expression using saliva or blood sampling have been recently validated in the clinics [18] . Such patient datasets, combined with mathematical modeling and machine learning, may allow to predict the times of least toxicity to healthy tissues, and optimal antitumor efficacy for an individual patient [18] . In particular, computational models representing the chronopharmacology of a specific drug can help to predict therapy time windows of decreased toxicity and optimal efficacy [8] , [18] .…”

“…Such patient datasets, combined with mathematical modeling and machine learning, may allow to predict the times of least toxicity to healthy tissues, and optimal antitumor efficacy for an individual patient [18] . In particular, computational models representing the chronopharmacology of a specific drug can help to predict therapy time windows of decreased toxicity and optimal efficacy [8] , [18] . Such models can also be optimized for a given patient and used to generate personalized treatment timing indications.…”