What is cancer modeling? Labmate online

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In the UK alone, cancer kills more than 166,000 each year. Scientists are continually looking for new ways to detect, diagnose and treat cancer, with the ultimate goal of saving lives and increasing life expectancy. Cancer modeling has become an invaluable tool, using mathematical concepts and tools to predict how the disease will affect a patient. In addition to predicting the development and progression of cancers in individual patients, modeling is used to optimize treatment plans and dosing strategies.

Read on to learn more about cancer modeling, what it involves, and the next steps for the field:

The advent of “in silico” techniques

Over the past decade, “in silico” techniques – which rely on computer technologies and simulation – have become increasingly popular in the industry. field of oncology. By combining traditional “in vitro” and “in vivo” methods with new generation “in silico” techniques, cancer researchers have discovered new knowledge about the disease.

Authors Sophie Bekisza and Liesbet Gerisabc present a range of mathematical models developed especially for clinical applications in a recent article published in the Journal of Computational Science.

“In conclusion, the symbiotic approach, combining in vitro, in vivo and in silico models, is of great interest in the field of oncology”, we read in the report entitled “Cancer modeling: From mechanistic to data-driven approach, and from Fundamental insights to clinical applications ”.

“In basic cancer research, a plethora of mathematical and computer tools are available. They have shown that they can contribute to a better understanding of several aspects related to cancer, to the generation of new hypotheses and predictions, and to guide scientists towards the most (most) impactful experiments.

Improve treatment plans

Mathematical modeling is also used to improve cancer treatment plans and optimize drug administration regimens. In principle, it can be used to analyze millions of different treatment options. This includes the analysis of variables such as drug combinations and dosage regimens.

“Drug administration schedules are key factors in the effectiveness of cancer therapies, and mathematical modeling of population dynamics and treatment responses can be applied to identify better drug administration regimens.” and provide mechanistic information, ”reads a summary of a recent article published in the journal Cell. “To capitalize on the promise of this approach, the cancer field must meet the challenges of moving this type of work to clinics. “

Take giant leaps in cell and gene therapy

Cancer modeling is making big waves in cell and gene therapy, with a partnership between Japanese biotech company Takara Bio and BioNTech Cell & Gene Therapies GmbH expected to revolutionize the field. The agreement will see the proprietary RetroNectin® method used to produce cell and gene therapy products that use T cells from cancer patients to treat tumors.

Learn more about the exciting new partnership by “Supply and license agreement paves the way for personalized cancer treatments.” Drug administration schedules are key factors in the effectiveness of cancer therapies, and mathematical modeling of population dynamics and treatment responses can be applied to identify better drug administration regimens and provide mechanistic information. To capitalize on the promises of this approach, the cancer field must meet the challenges associated with transferring this type of work to clinics. Drug administration schedules are key factors in the effectiveness of cancer therapies, and mathematical modeling of population dynamics and responses to treatment can be applied. identify better drug administration regimes and provide mechanistic information. To capitalize on the promises of this approach, the cancer field must meet the challenges associated with transferring this type of work to clinics. Drug administration schedules are key factors in the effectiveness of cancer therapies, and mathematical modeling of population dynamics and responses to treatment can be applied. identify better drug administration regimes and provide mechanistic information. To capitalize on the promises of this approach, the cancer field must meet the challenges associated with transferring this type of work to clinics. Drug administration schedules are key factors in the effectiveness of cancer therapies, and mathematical modeling of population dynamics and responses to treatment can be applied. identify better drug administration regimes and provide mechanistic information. To capitalize on the promise of this approach, the cancer field must meet the challenges of moving this type of work to clinics.


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