1,648 research outputs found
Exploiting evolution to treat drug resistance: Combination therapy and the double bind
Although many anti cancer therapies are successful in killing a large percentage of tumour cells when initially administered, the evolutionary dynamics underpinning tumour progression mean that often resistance is an inevitable outcome, allowing for new tumour phenotypes to emerge that are unhindered by the therapy. Research in the field of ecology suggests that an evolutionary double bind could be an effective way to treat tumours. In an evolutionary double bind two therapies are used in combination such that evolving resistance to one leaves individuals more susceptible to the other. In this paper we present a general evolutionary game theory model of a double bind to study the effect that such approach would have in cancer. Furthermore we use this mathematical framework to understand recent experimental results that suggest a synergistic effect between a p53 cancer vaccine and chemotherapy. Our model recapitulates the experimental data and provides an explanation for its effectiveness based on the commensalistic relationship between the tumour phenotypes
Addressing current challenges in cancer immunotherapy with mathematical and computational modeling
The goal of cancer immunotherapy is to boost a patient's immune response to a
tumor. Yet, the design of an effective immunotherapy is complicated by various
factors, including a potentially immunosuppressive tumor microenvironment,
immune-modulating effects of conventional treatments, and therapy-related
toxicities. These complexities can be incorporated into mathematical and
computational models of cancer immunotherapy that can then be used to aid in
rational therapy design. In this review, we survey modeling approaches under
the umbrella of the major challenges facing immunotherapy development, which
encompass tumor classification, optimal treatment scheduling, and combination
therapy design. Although overlapping, each challenge has presented unique
opportunities for modelers to make contributions using analytical and numerical
analysis of model outcomes, as well as optimization algorithms. We discuss
several examples of models that have grown in complexity as more biological
information has become available, showcasing how model development is a dynamic
process interlinked with the rapid advances in tumor-immune biology. We
conclude the review with recommendations for modelers both with respect to
methodology and biological direction that might help keep modelers at the
forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society
Interfac
Chemotherapy drug regimen optimization using deterministic oscillatory search algorithm
Purpose: To schedule chemotherapy drug delivery using Deterministic Oscillatory Search algorithm, keeping the toxicity level within permissible limits and reducing the number of tumor cells within a predefined time period.Methods: A novel metaheuristic algorithm, deterministic oscillatory search, has been used to optimize the Gompertzian model of the drug regimen problem. The model is tested with fixed (fixed interval variable dose, FIVD) and variable (variable interval variable dose, VIVD) interval schemes and the dosage presented for 52 weeks. In the fixed interval, the treatment plan is fixed in such a way that doses are given on the first two days of every seven weeks such as day 7, day 14, etc.Results: On comparing the two schemes, FIVD provided a higher reduction in the number of tumor cells by 98 % compared to 87 % by VIVD after the treatment period. Also, a significant reduction in the number was obtained half way through the regimen. The dose level and toxicity are also reduced in the FIVD scheme. The value of drug concentration is more in FIVD scheme (50) compared to VIVD (41); however, it is well within the acceptable limits of concentration. The results proved the effectiveness of the proposed technique in terms of reduced drug concentration, toxicity, tumor size and drug level within a predetermined time period.Conclusion: Artificial intelligent techniques can be used as a tool to aid oncologists in the effective treatment of cancer through chemotherapy.Keywords: Deterministic Oscillatory Search, Chemotherapy scheduling, Drug schedule, Artificial intelligenc
Directed Intervention Crossover Approaches in Genetic Algorithms with Application to Optimal Control Problems
Genetic Algorithms (GAs) are a search heuristic modeled on the
processes of evolution. They have been used to solve optimisation
problems in a wide variety of fields. When applied to the
optimisation of intervention schedules for optimal control problems,
such as cancer chemotherapy treatment scheduling, GAs have been
shown to require more fitness function evaluations than other search
heuristics to find fit solutions. This thesis presents extensions to
the GA crossover process, termed directed intervention crossover
techniques, that greatly reduce the number of fitness function
evaluations required to find fit solutions, thus increasing the
effectiveness of GAs for problems of this type.
The directed intervention crossover techniques use intervention
scheduling information from parent solutions to direct the offspring
produced in the GA crossover process towards more promising areas of
a search space. By counting the number of interventions present in
parents and adjusting the number of interventions for offspring
schedules around it, this allows for highly fit solutions to be
found in less fitness function evaluations.
The validity of these novel approaches are illustrated through
comparison with conventional GA crossover approaches for
optimisation of intervention schedules of bio-control application in
mushroom farming and cancer chemotherapy treatment. These involve
optimally scheduling the application of a bio-control agent to
combat pests in mushroom farming and optimising the timing and
dosage strength of cancer chemotherapy treatments to maximise their
effectiveness.
This work demonstrates that significant advantages are gained in
terms of both fitness function evaluations required and fitness
scores found using the proposed approaches when compared with
traditional GA crossover approaches for the production of optimal
control schedules
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Targeting Oncogenic BRAF: Past, Present, and Future.
Identifying recurrent somatic genetic alterations of, and dependency on, the kinase BRAF has enabled a "precision medicine" paradigm to diagnose and treat BRAF-driven tumors. Although targeted kinase inhibitors against BRAF are effective in a subset of mutant BRAF tumors, resistance to the therapy inevitably emerges. In this review, we discuss BRAF biology, both in wild-type and mutant settings. We discuss the predominant BRAF mutations and we outline therapeutic strategies to block mutant BRAF and cancer growth. We highlight common mechanistic themes that underpin different classes of resistance mechanisms against BRAF-targeted therapies and discuss tumor heterogeneity and co-occurring molecular alterations as a potential source of therapy resistance. We outline promising therapy approaches to overcome these barriers to the long-term control of BRAF-driven tumors and emphasize how an extensive understanding of these themes can offer more pre-emptive, improved therapeutic strategies
Modelling the response of vascular tumours to chemotherapy: A multiscale approach
An existing multiscale model is extended to study the response of a vascularised tumour to treatment with chemotherapeutic drugs which target proliferating cells. The underlying hybrid cellular automaton model couples tissue-level processes (e.g. blood flow, vascular adaptation, oxygen and drug transport) with cellular and subcellular phenomena (e.g. competition for space, progress through the cell cycle, natural cell death and drug-induced cell kill and the expression of angiogenic factors). New simulations suggest that, in the absence of therapy, vascular adaptation induced by angiogenic factors can stimulate spatio-temporal oscillations in the tumour's composition.\ud
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Numerical simulations are presented and show that, depending on the choice of model parameters, when a drug which kills proliferating cells is continuously infused through the vasculature, three cases may arise: the tumour is eliminated by the drug; the tumour continues to expand into the normal tissue; or, the tumour undergoes spatio-temporal oscillations, with regions of high vascular and tumour cell density alternating with regions of low vascular and tumour cell density. The implications of these results and possible directions for future research are also discussed
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