Optimal control analysis in the chemotherapy of IgG multiple myeloma

Swan, George W.; Vincent, Thomas L.

doi:10.1007/bf02462912

Cited by 90 publications

(39 citation statements)

References 11 publications

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“…Some studies used differential equation models formulated as deterministic optimal control problems (13)(14)(15)(16), whereas others used stochastic birth-death process models (17)(18)(19)(20)(21), to examine treatment regimens for tumors comprising a sensitive population along with a few resistant subpopulations. However, these studies, many of which dealt only with generic scenarios, focused primarily on drug scheduling, investigating the effects of frequency and dose intensity of drugs on resistance potential.…”

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confidence: 99%

Intratumor heterogeneity alters most effective drugs in designed combinations

Zhao

Hemann

Lauffenburger

2014

Proc. Natl. Acad. Sci. U.S.A.

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The substantial spatial and temporal heterogeneity observed in patient tumors poses considerable challenges for the design of effective drug combinations with predictable outcomes. Currently, the implications of tissue heterogeneity and sampling bias during diagnosis are unclear for selection and subsequent performance of potential combination therapies. Here, we apply a multiobjective computational optimization approach integrated with empirical information on efficacy and toxicity for individual drugs with respect to a spectrum of genetic perturbations, enabling derivation of optimal drug combinations for heterogeneous tumors comprising distributions of subpopulations possessing these perturbations. Analysis across probabilistic samplings from the spectrum of various possible distributions reveals that the most beneficial (considering both efficacy and toxicity) set of drugs changes as the complexity of genetic heterogeneity increases. Importantly, a significant likelihood arises that a drug selected as the most beneficial single agent with respect to the predominant subpopulation in fact does not reside within the most broadly useful drug combinations for heterogeneous tumors. The underlying explanation appears to be that heterogeneity essentially homogenizes the benefit of drug combinations, reducing the special advantage of a particular drug on a specific subpopulation. Thus, this study underscores the importance of considering heterogeneity in choosing drug combinations and offers a principled approach toward designing the most likely beneficial set, even if the subpopulation distribution is not precisely known.systems biology | cancer | combination therapy G enetic intratumor heterogeneity has long been appreciated as present in cancer patients (1). Recent sequencing studies further revealed the extent of this tumor diversity, arising from highly complex clonal evolutionary processes (2). This phenomenon has been observed in many solid (3-5) and hematopoietic cancers (6-8). Moreover, treatments also may have dramatic effects on tumor composition-with a preexisting subclone at diagnosis often becoming dominant at relapse (9-12). To meet this challenge, rational drug combination treatments must be designed so as to account for intratumor heterogeneity to better predict reoccurrence.The history of theoretical studies aimed at drug optimization in cancer therapy is long. Some studies used differential equation models formulated as deterministic optimal control problems (13-16), whereas others used stochastic birth-death process models (17-21), to examine treatment regimens for tumors comprising a sensitive population along with a few resistant subpopulations. However, these studies, many of which dealt only with generic scenarios, focused primarily on drug scheduling, investigating the effects of frequency and dose intensity of drugs on resistance potential. Practical applications of such results have been limited, although some of these strategies recently were combined with experimental validation to examin...

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mentioning

confidence: 99%

Intratumor heterogeneity alters most effective drugs in designed combinations

Zhao

Hemann

Lauffenburger

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…However, literature surveys indicate that fewer clinical studies have compared CI versus bolus application of CNS drugs than of CS drugs. Swan and Vincent (1977) and Swan (1990) used optimal control theory to model tumor reduction for CNS drugs. Although they did not incorporate resistance evolution, they also concluded that CI is superior to discrete dosage regimens.…”

Section: Discussionmentioning

confidence: 99%

Scheduling Chemotherapy: Catch 22 between Cell Kill and Resistance Evolution

Gardner

1999

Computational and Mathematical Methods in Medicine

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Dose response curves show that prolonged drug exposure at a low concentration may kill more cells than short exposures at higher drug concentrations, particularly for cell cycle phase specific drugs. Applying drugs at low concentrations for prolonged periods, however, allows cells with partial resistance to evolve higher levels of resistance through stepwise processes such as gene amplification. Models are developed for cell cycle specific (CS) and cell cycle nonspecific (CNS) drugs to identify the schedule of drug application that balances this tradeoff.The models predict that a CS drug may be applied most effectively by splitting the cumulative dose into many (>40) fractions applied by long-term chemotherapy, while CNS drugs may be better applied in fewer than 10 fractions applied over a shorter term. The model suggests that administering each fraction by continuous infusion may be more effective than giving the drug as a bolus, whether the drug is CS or CNS. In addition, tumors with a low growth fraction or slow rate of cell division are predicted to be controlled more easily with CNS drugs, while those with a high proliferative fraction or fast cell division rate may respond better to CS drugs.

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“…34,42,45,[61][62][63][64] This occurs since continuous infusion: a. exposes more cells to the drug when they are in the sensitive phase of the cell cycle, thus overcoming kinetic resistance, and b. prevents tumor re-growth between bolus treatments.…”

Section: Other Models Of Drug Scheduling and Drug Resistancementioning

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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Optimal control analysis in the chemotherapy of IgG multiple myeloma

Cited by 90 publications

References 11 publications

Intratumor heterogeneity alters most effective drugs in designed combinations

Intratumor heterogeneity alters most effective drugs in designed combinations

Scheduling Chemotherapy: Catch 22 between Cell Kill and Resistance Evolution

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

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