Identifying mechanisms of chronotolerance and chronoefficacy for the anticancer drugs 5-fluorouracil and oxaliplatin by computational modeling

Altinok, Atilla; Lévi, Françis; Goldbeter, Albert

doi:10.1016/j.ejps.2008.10.024

Cited by 69 publications

(61 citation statements)

References 40 publications

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“…Each of these differences can enhance the differential cytotoxicity of 5-FU towards the two cell populations when a single circadian pattern of 5-FU peaking at 4.00 h is considered. All these effects are additive, so that the differential cytotoxicity can be maximized when they are combined [18,19]. The automaton model thus helps us to uncover plausible mechanisms for the observation that a given circadian pattern of anti-cancer drug delivery can be, at the same time, least cytotoxic to healthy cells and highly toxic for tumour cells.…”

Section: Discussionmentioning

confidence: 99%

“…Together with their variability V, the mean durations of the various phases represent key parameters of the automaton model that can readily be modified. We have previously shown that the response to circadian administration of anti-cancer drugs markedly depends on the total duration of the cell cycle and the mean durations of its phases [19]. The distribution of the total cell duration is centred around t c ¼t G1 þt S þ t G2 þ t M .…”

Section: Dynamics Of a Single Cellmentioning

confidence: 99%

“…However, when the distribution of cells between the various cell cycle phases becomes the predominant issue, biochemical details do not play such a major role, and the automaton model provides a useful, versatile tool for describing the dynamics of a population of proliferating cells. Thus, we previously used the cell cycle automaton model to study the response of a population of proliferating cells to the circadian administration of an anti-cancer drug that acts on a particular phase of the cell cycle [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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An automaton model for the cell cycle

et al. 2010

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We consider an automaton model that progresses spontaneously through the four successive phases of the cell cycle: G1, S (DNA replication), G2 and M (mitosis). Each phase is characterized by a mean duration t and a variability V. As soon as the prescribed duration of a given phase has passed, the transition to the next phase of the cell cycle occurs. The time at which the transition takes place varies in a random manner according to a distribution of durations of the cell cycle phases. Upon completion of the M phase, the cell divides into two cells, which immediately enter a new cycle in G1. The duration of each phase is reinitialized for the two newborn cells. At each time step in any phase of the cycle, the cell has a certain probability to be marked for exiting the cycle and dying at the nearest G1/S or G2/M transition. To allow for homeostasis, which corresponds to maintenance of the total cell number, we assume that cell death counterbalances cell replication at mitosis. In studying the dynamics of this automaton model, we examine the effect of factors such as the mean durations of the cell cycle phases and their variability, the type of distribution of the durations, the number of cells, the regulation of the cell population size and the independence of steady-state proportions of cells in each phase with respect to initial conditions. We apply the stochastic automaton model for the cell cycle to the progressive desynchronization of cell populations and to their entrainment by the circadian clock. A simple deterministic model leads to the same steady-state proportions of cells in the four phases of the cell cycle.

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Section: Discussionmentioning

confidence: 99%

Section: Dynamics Of a Single Cellmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An automaton model for the cell cycle

et al. 2010

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“…On another side, the use of a cellular automaton model [9] including variability on the phase transition function between G 2 and M , stiff or with overlapping between phases, to represent synchrony between cells with respect to cell cycle phase timing, has shown that the response of cell populations to chronotherapeutic schemes for anticancer drugs may be radically different according to this synchronisation status.…”

Section: Tissue Organisation: Intercellular Communications and Outer mentioning

confidence: 99%

“…Techniques to simultaneously identify other transitions are being actively searched-for and will likely be available in short time. Other non-cell-destructive techniques based on luminescence, hybridizing genes with luciferase in cultured cells or by producing transgenic mice [276], rather than fluorescence could be relevant in principle in the same context but do not seem to have been used for the investigation of cell cycle determinants so far.On another side, the use of a cellular automaton model [9] including variability on the phase transition function between G 2 and M , stiff or with overlapping between phases, to represent synchrony between cells with respect to cell cycle phase timing, has shown that the response of cell populations to chronotherapeutic schemes for anticancer drugs may be radically different according to this synchronisation status.In a continuous PDE setting, linear equations endowed with an advection term representing transport of cell populations according to physiological variables (age or cyclin concentration) with a speed of progression in a cell cycle phase may account for such softness or stiffness in phase transitions. This question has been a matter of research for quite a long time already, both from the mathematical and from the experimental point of view, see [21,48,261].…”

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confidence: 99%

Modelling Physiological and Pharmacological Control on Cell Proliferation to Optimise Cancer Treatments

Clairambault

2009

Math. Model. Nat. Phenom.

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Abstract. This review aims at presenting a synoptic, if not exhaustive, point of view on some of the problems encountered by biologists and physicians who deal with natural cell proliferation and disruptions of its physiological control in cancer disease. It also aims at suggesting how mathematicians are naturally challenged by these questions and how they might help, not only biologists to deal theoretically with biological complexity, but also physicians to optimise therapeutics, on which last point the focus will be set here. To this purpose, mathematical modelling should represent proliferating cell population dynamics with natural built-in control targets (which implies modelling the cell division cycle), together with the distribution of drugs in the organism and their molecular actions on different targets at the cell level on proliferation, i.e., molecular pharmacokinetics-pharmacodynamics of antiproliferative drugs. This should make possible optimal control of drug delivery with constraints to be determined according to the main pharmacological issues encountered in the clinic: unwanted toxic side-effects, occurrence of drug resistance. Mathematical modelling should also take into account physiological determinants of cell and tissue proliferation, such as intervention of the immune system, circadian control on cell cycle checkpoint proteins, and activity of intracellular drug processing enzymes together with individual variations in the activities of these proteins (genetic polymorphism). Taking these points into account will add to the rich scenery of normal or disrupted cell and tissue regulations, and their corrections by drugs, a natural environmental, whole body physiological, frame. It is necessary indeed to consider such a framework if one wants to eventually be actually helpful to clinicians who routinely treat by combinations of drugs living Humans with their complex whole body regulations, often dependent on genotypic variations, and not isolated cells or tissues.Key words: cell proliferation, population dynamics, physiologically structured partial differential equations, pharmacological control, pharmacokinetics-pharmacodynamics, physiological modelling, cancer treatments, therapeutic optimisation, individualised medicine AMS subject classification: 92-02, 92B05 Biological common knowledge on cancerThis section shortly presents the general scenery of cancer evolution and features that must be included in models designed to describe and control it, bearing in mind a more descriptive (physicist's) than a therapeutic (physician's) point of view, only to reverse this perspective in the following sections, in which will be stressed the interest of modelling from the point of view of control, at least if one wants to design models for therapeutic applications. This biological section will be only a short sketch, if one considers that on this subject many books have already been written, e.g. [24,78,96,97,223]. Cancer: a challenging public health problemCardiovascular diseases and cancer are by fa...

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Chronotoxicology*Dedicated to the memory of Dr Charles A. Walker who pioneered chronotoxicology

Soliman

Mazzio

2009

General, Applied and Systems Toxicology

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The study of kinetics, dynamics, toxicological responses and side effects of drugs, poisons or toxic substances relative to temporal rhythms occurring in living organisms is referred to as ‘chronotoxicology’. Temporal rhythms are guided by solar time cycles (circadian = 24 hours) based upon photoperiodic stimuli derived from sunlight and further influenced by local external time cues. The ‘main biological clock’ by which rhythms are established is the suprachiasmatic nucleus (SCN) housed proximal to the optic chiasm, which receives transmitted blue light (460–480 nm) through non‐visual photosensitive retinal ganglion cells containing melanopsin in the retinohypothalamic tract. The SCN's basic ‘free‐running’ circadian rhythm then orchestrates dynamic rhythms in millions of peripheral oscillators located within the body's cells, tissues and organs that are not responsive to light, but respond through neural and humoral SCN outputs. Together these orchestrate the precisely timed events to sustain biological life by regulating physiological function of the cardiovascular, nervous, digestive, immune, reproductive, endocrine and nervous systems. Cyclical fluctuations in absorption, distribution, metabolism and excretion regulate toxic substance detoxification, drug toxicity and effectiveness. Many of these processes are further regulated at the genetic level by clock genes which establish basic transcriptional‐translation feedback oscillation loops under the control of Per 1–3, Cry 1,2, Rev‐erbα, Rev‐erbβ, Rorα, Rorβ, cAMP response element‐binding protein (CREB) and the circadian locomotor output cycles kaput/brain and muscle ARNT‐like protein 1 (CLOCK/BMAL1) heterodimers.

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Identifying mechanisms of chronotolerance and chronoefficacy for the anticancer drugs 5-fluorouracil and oxaliplatin by computational modeling

Cited by 69 publications

References 40 publications

An automaton model for the cell cycle

An automaton model for the cell cycle

Modelling Physiological and Pharmacological Control on Cell Proliferation to Optimise Cancer Treatments

Chronotoxicology*Dedicated to the memory of Dr Charles A. Walker who pioneered chronotoxicology

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