Effect of the dosing interval on myelotoxicity and survival in mice treated by cytarabine

Agur, Zvia; Arnon, Ruth; Schechter, Bilha

doi:10.1016/0959-8049(92)90463-c

Cited by 39 publications

(41 citation statements)

References 7 publications

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“…This method is more elaborate and more realistic than the relatively simple one of maximizing efficacy-toxicity ratio, suggested in Agur (1986) and Agur et al (1992).…”

Section: Discussionmentioning

confidence: 99%

Optimizing Chemotherapy Scheduling Using Local Search Heuristics

Agur

Hassin

Levy

2006

Operations Research

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We develop a method for computing efficient patient-specific drug protocols. Using this method, we identify two general categories of anticancer drug protocols, depending on the temporal cycle parameters of the host and cancer cells: a onetime intensive treatment, or a series of nonintensive treatments. Our method is based on a theoretical and experimental work showing that treatment efficacy can be improved by determining the dosing frequency on the drug-susceptible target and host cell-cycle parameters. Simulating the patient's pharmaco-dynamics in a simple model for cell population growth, we calculate the number of drug susceptible cells at every moment of therapy. Local search heuristics are then used to conduct a search for the desired solution, as defined by our criteria. These criteria include the patient's state at the end of a predetermined time period, the number of cancer and host cells at the end of treatment, and the time to the patient's cure. The process suggested here does not depend on the exact biological assumptions of the model, thus enabling its use in a more complex description of the system. We test three solution methods. Simulated annealing is compared to threshold acceptance and old bachelor acceptance, which are less known variants to this method. The conclusions concerning the three approximation methods are that good results can be achieved by choosing the proper parameters for each of the methods, but the computational effort required for achieving good results is much greater in simulated annealing than in the other methods. Also, a large number of iterations does not guarantee better solution quality, and resources would be better used in several short searches with different parameter values than in one long search.

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“…This method is more elaborate and more realistic than the relatively simple one of maximizing efficacy-toxicity ratio, suggested in Agur (1986) and Agur et al (1992).…”

Section: Discussionmentioning

confidence: 99%

Optimizing Chemotherapy Scheduling Using Local Search Heuristics

Agur

Hassin

Levy

2006

Operations Research

Self Cite

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“…If ~( a ) is periodic and if l l t is rational, then ~( ( s + a + i1)mod t) is periodic with period T = p t = ql where p, q are co-prime integers. In this case a necessap and suficient condition for the complete elimination of a cell population is given by (9).…”

Section: K(t)x(t + A) the Function ~( A )mentioning

confidence: 99%

Resonance and Anti‐Resonance in the Design of Chemotherapeutic Protocols

Agur

1998

Computational and Mathematical Methods in Medicine

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Analytical and computational results suggest that one can control the growth of cell populations by exposing them to certain dosing frequencies of cell-cycle phase-specific cytotoxic agents. Thus, it has been shown theoretically that a resonance effect, manifesting itself in maximal population sizes, can be created, if the period of the drug pulse and that of the population are commensurable. Based on this theory a method (denoted The ZMethod) is suggested for improving the efficacy of cancer therapy. The underlying idea of the Z-method is to improve treatment efficacy by selecting treatment periods that create resonance for the limiting normal cell population, and by avoiding resonance for the cancer cells. These theoretical results are supported by in 11ivo murine experiments. mggesting that intermittent delivery of cell-cycle phase-specific drugs at intervals equivalent to the mean cell-cycle time, might minimize harmful toxicity without con~promising therapeutic effects on target cells. A new implementation of the theory, to be denoted the (intiresonance effect, is suggested in the present work. In essence, anti-resonance is a practical method of preventing resonance in systems where cancer cell kill needs to be maximized and toxicity to normal cells is marginal. The idea here is to reduce the effective number of cell lines whose period will resonate with the treatment period, by creating a stochastic treatment protocol. An algorithm has been developed for computing the efficacy of specific treatment protocols. The algorithm is independent of the assumptions of the Z-Method. It supports this method by showing theoretically that one can increase treatment success by generating a resonancekanti-resonance relationship between the frequency of drug administration and those of the involved cell populations.

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“…48) provide some experimental evidence for this in cell culture, although no error bars are reported and the difference between maximum and minimum survival is small. Agur et al 50 and Ubezio et al 51 provide indirect evidence for the possibility of the resonance effect in mice inoculated with lymphoma cells. No clinical trials have been designed to test the resonance effect hypothesis.…”

Section: Resonance Effect Modelsmentioning

confidence: 99%

Cell Cycle Phase-Specific CHemotherapy: Computation Methods for Guiding Treatment

Gardner¹

2002

Cell Cycle

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Computational models of cancer chemotherapy enhance the understanding of in vitro, in vivo, and clinical trial data and have the potential to contribute to the design of rational treatment regimens. In particular, mechanistic, predictive models are superior to statistical, phenomenological descriptions of data. Mechanistic models based on functional data from tumor biopsies will enable the response to treatment to be predicted for a specific patient, in contrast to statistical models in which the probability of response for a given patient may differ substantially from the population average. This review summarizes mathematical models developed to improve the design of treatment regimens using cell-cycle phase-specific chemotherapy. It starts with simple models of dose response, then moves to more complex models of scheduling cell-cycle phase-specific drugs, and finally discusses mechanistic models that incorporate both genetic drug resistance and cell cycle-mediated drug resistance. This last class of models will be most useful in designing treatment regimens tailored for individual patients.

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Effect of the dosing interval on myelotoxicity and survival in mice treated by cytarabine

Cited by 39 publications

References 7 publications

Optimizing Chemotherapy Scheduling Using Local Search Heuristics

Optimizing Chemotherapy Scheduling Using Local Search Heuristics

Resonance and Anti‐Resonance in the Design of Chemotherapeutic Protocols

Cell Cycle Phase-Specific CHemotherapy: Computation Methods for Guiding Treatment

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