Cancer dynamics and therapy
Lévi et al. (2010; PMID: 20055686) reviewed how anticancer drug efficacy and toxicity varies depending on dosage and timing. Anticancer agents can damage host tissues and their inherent physiological molecular clocks. If such drugs are administered at their most toxic circadian time, which can differ between individuals and genders, disruption of the patient’s clock and physiological rhythms accelerates clinical cancer processes, thereby contributing to morbidity. On the other hand, improved drug efficacy is seen when treatment is given in alignment with physiological rhythms and near the respective times of best tolerability. The molecular basis involves the circadian control of metabolism, detoxification, pharmacokinetics and cell cycle, as well as molecular targets that change over a 24h period. These results clearly demonstrate that mathematical and systems biology approaches can further optimize cancer therapy through the integration of circadian clocks from cells to whole organisms. Moreover, systems biology-based cancer chronotherapy has generated personalized cancer chronotherapy concepts, whose implementation involves mathematical modeling tools both for the design of drug delivery algorithms (Ballesta et al. 2011; PMID: 21931543) and the extraction of proper information from human circadian biomarkers (Scully et al. 2011; PMID: 21544221).
Michor et al. (2005; PMID: 15988530) showed that a model based on the known biology of hematopoietic differentiation can explain the kinetics of the molecular response to imatinib (Glivec). Successful Glivec therapy led to a biphasic exponential decline of leukaemic cells and the model predicted that the drug is a potent inhibitor of the production of differentiated leukaemic cells, but does not deplete leukaemic stem cells. These data, for the first time, provided quantitative insights into the in vivo kinetics of a human cancer. Especially for cancer it becomes imperative that more energy be directed towards developing a quantitative approach for assessing therapeutic success and failure (Abbot and Michor, 2006; PMID: 17031409).
Faratian et al. (2009; PMID: 19638581) used dynamical modelling of growth factor signalling in cell lines to make predictions about which breast cancer patients with oncogenic Her2 amplification were likely to fail to respond to therapy with Herceptin. The model predictions were subsequently validated by studying cancer tissue from a series of patients treated with the drug where outcome was known. Incorporation of this data back into the computational model was then successfully used to predict combinatorial regimens that have now been validated in vitro (Lebedeva et al; 2012 PMID: 22085636).