Modelling chemotherapy resistance in palliation and failed cure

Monro, Helen C.; Gaffney, Eamonn A.

doi:10.1016/j.jtbi.2008.12.006

Cited by 49 publications

(98 citation statements)

References 33 publications

(50 reference statements)

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“…The authors found that in general, cytotoxic drugs alone are insufficient to eradicate the tumor, since there are two main barriers to achieving a complete remission of the tumor: evolving populations tend to produce resistant clones which are able to drive the system back to an equilibrium abundance, and drug-induced alterations of the tumor microenvironment change the selection pressure and select for tumor cells with larger evolvability. In 2009, Monro and Gaffney modeled the dynamics of chemotherapy resistance in a model of palliative treatment, when tumor eradication is not feasible [95]. In such cases, the goal of treatment is to prolong survival and improve the quality of life.…”

Section: Previous Workmentioning

confidence: 99%

Evolution of acquired resistance to anti-cancer therapy

Foo

Michor

2014

Journal of Theoretical Biology

232

178

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Acquired drug resistance is a major limitation for the successful treatment of cancer. Resistance can emerge due to a variety of reasons including host environmental factors as well as genetic or epigenetic alterations in the cancer cells. Evolutionary theory has contributed to the understanding of the dynamics of resistance mutations in a cancer cell population, the risk of resistance pre-existing before the initiation of therapy, the composition of drug cocktails necessary to prevent the emergence of resistance, and optimum drug administration schedules for patient populations at risk of evolving acquired resistance. Here we review recent advances towards elucidating the evolutionary dynamics of acquired drug resistance and outline how evolutionary thinking can contribute to outstanding questions in the field.

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Section: Previous Workmentioning

confidence: 99%

Evolution of acquired resistance to anti-cancer therapy

Foo

Michor

2014

Journal of Theoretical Biology

232

178

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“…In those cases, the goal of chemotherapy is to extend survival and improve the quality of life. On this subject, Monro and Gaffney (Monro and Gaffney, 2009) asked whether an intermediate level of chemotherapy would restrict tumor growth and increase the time of survival in a palliative setting. Their populations model based on ODEs predicted that reduced chemotherapy protocols could lead to longer survival times due to competition between resistant and sensitive tumor cells (Fig.…”

Section: Mathematical Modeling Of Mdrmentioning

confidence: 99%

The dynamics of drug resistance: A mathematical perspective

Lavi

Gottesman

Levy

2012

Drug Resistance Updates

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Resistance to chemotherapy is a key impediment to successful cancer treatment that has been intensively studied for the last three decades. Several central mechanisms have been identified as contributing to the resistance. In the case of multidrug resistance (MDR), the cell becomes resistant to a variety of structurally and mechanistically unrelated drugs in addition to the drug initially administered. Mathematical models of drug resistance have dealt with many of the known aspects of this field, such as pharmacologic sanctuary and location/diffusion resistance, intrinsic resistance that is therapy independent, therapy-dependent cellular alterations including induced resistance (dose-dependent) and acquired resistance (dose-independent). In addition, there are mathematical models that take into account the kinetic/phase resistance, and models that investigate intra-cellular mechanisms based on specific biological functions (such as ABC transporters, apoptosis and repair mechanisms). This review covers aspects of MDR that have been mathematically studied, and explains how, from a methodological perspective, mathematics can be used to study drug resistance. We discuss quantitative approaches of mathematical analysis, and demonstrate how mathematics can be used in combination with other experimental and clinical tools. We emphasize the potential benefits of integrating analytical and mathematical methods into future clinical and experimental studies of drug resistance.

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“…8-week) patient-level changes in tumour burden over time and in response to treatment. TUK models have also been used to inform Phase III clinical trial designs or to inform clinical dosing regimens, as originally considered by Goldie and Coldman [27,28]. Multivariate survival models may then be developed that link baseline patient characteristics and model-derived metrics, such as tumour growth rates, to longer-term overall survival (OS) [2,26].…”

Section: Application Of Models To Predict Tumour Progression and Respmentioning

confidence: 99%

“…3d). TUK models have also been used to inform Phase III clinical trial designs or to inform clinical dosing regimens, as originally considered by Goldie and Coldman [27,28]. For example, an in silico model [29] was used to demonstrate that tumours that become small in response to therapy may become resistant, so would require dense, intensive therapy to prevent remission.…”

Section: Application Of Models To Predict Tumour Progression and Respmentioning

confidence: 99%

Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development

Brown

Gaffney

Wagg

et al. 2018

Clinical and Experimental Immunology

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The application of in silico modelling is beginning to emerge as a key methodology to advance our understanding of mechanisms of disease pathophysiology and related drug action, and in the design of experimental medicine and clinical studies. From this perspective, we will present a non-technical discussion of a small number of recent and historical applications of mathematical, statistical and computational modelling to clinical and experimental immunology. We focus specifically upon mechanistic questions relating to human viral infection, tumour growth and metastasis and T cell activation. These exemplar applications highlight the potential of this approach to impact upon human immunology informed by ever-expanding experimental, clinical and 'omics' data. Despite the capacity of mechanistic modelling to accelerate therapeutic discovery and development and to de-risk clinical trial design, it is not widely utilised across the field. We outline ongoing challenges facing the integration of mechanistic modelling with experimental and clinical immunology, and suggest how these may be overcome. Advances in key technologies, including multiscale modelling, machine learning and the wealth of 'omics' data sets, coupled with advancements in computational capacity, are providing the basis for mechanistic modelling to impact on immunotherapeutic discovery and development during the next decade.

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Modelling chemotherapy resistance in palliation and failed cure

Cited by 49 publications

References 33 publications

Evolution of acquired resistance to anti-cancer therapy

Evolution of acquired resistance to anti-cancer therapy

The dynamics of drug resistance: A mathematical perspective

Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development

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