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

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

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

International audienceScheduling anticancer drug administration over 24 h may critically impact treatment success in a patient-specific manner. Here, we address personalization of treatment timing using a novel mathematical model of irinotecan cellular pharmacokinetics and-dynamics linked to a representation of the core clock and predict treatment toxicity in a colorectal cancer (CRC) cellular model. The mathematical model is fitted to three different scenarios: mouse liver, where the drug metabolism mainly occurs, and two human colorectal cancer cell lines representing an in vitro experimental system for human colorectal cancer progression. Our model successfully recapitulates quantitative circadian datasets of mRNA and protein expression together with timing-dependent irinotecan cytotoxicity data. The model also discriminates time-dependent toxicity between the different cells, suggesting that treatment can be optimized according to their cellular clock. Our results show that the time-dependent degradation of the protein mediating irinotecan activation, as well as an oscillation in the death rate may play an important role in the circadian variations of drug toxicity. In the future, this model can be used to support personalized treatment scheduling by predicting optimal drug timing based on the patient's gene expression profile

Diurnal variations in the expression of core-clock genes correlate with resting muscle properties and predict fluctuations in exercise performance across the day

Objectives In this study, we investigated daily fluctuations in molecular (gene expression) and physiological (biomechanical muscle properties) features in human peripheral cells and their correlation with exercise performance. Methods 21 healthy participants (13 men and 8 women) took part in three test series: for the molecular analysis, 15 participants provided hair, blood or saliva time-course sampling for the rhythmicity analysis of core-clock gene expression via RT-PCR. For the exercise tests, 16 participants conducted strength and endurance exercises at different times of the day (9h, 12h, 15h and 18h). Myotonometry was carried out using a digital palpation device (MyotonPRO), five muscles were measured in 11 participants. A computational analysis was performed to relate core-clock gene expression, resting muscle tone and exercise performance. Results Core-clock genes show daily fluctuations in expression in all biological samples tested for all participants. Exercise performance peaks in the late afternoon (15-18 hours for both men and women) and shows variations in performance, depending on the type of exercise (eg, strength vs endurance). Muscle tone varies across the day and higher muscle tone correlates with better performance. Molecular daily profiles correlate with daily variation in exercise performance. Conclusion Training programmes can profit from these findings to increase efficiency and fine-tune timing of training sessions based on the individual molecular data. Our results can benefit both professional athletes, where a fraction of seconds may allow for a gold medal, and rehabilitation in clinical settings to increase therapy efficacy and reduce recovery times