Manuscript Topics
Cancer is one of the largest medical challenges of the 21st century, affecting up to 1 in 3 people over their lifetime. Cancer is caused by mutation, cells going through a cascade of mutations that give these cells abnormal behaviour - this includes an increased mutation rate, escape of the mechanisms that constrain growth and an ability to migrate throughout the body (leading to metastasis). Since these cells are effectively 'self', the immune system also has difficulty in developing an immune response against cancer cells.

Cancer progress as a result of the collective dynamics that emerge from interactions between tumor cells and their microenvironmental interactions often requires complex model considerations. Drug development and regulatory decisions driven by information that is compiled primarily from clinical trials and other supportive experiments, but also through clinical experience in the post-market period.

Three critical milestones along the path to mathematically designed cancer treatment can mentioned as obtaining accurate, rigorous and reproducible predictions of cancer progression.
Mathematical applications in cancer has the potential to transform the way we detect and treat cancer in the clinic, avoiding and mitigating therapeutic resistance and merging mechanistic knowledge-based mathematical models with machine learning. In last years the medicine recognize these methodologies, tumour forecasting, patient-specific adaptive therapies with the use of in silico treatment scenarios, virtual clinical trials, and mathematical modelling and simulation have the potential to accelerate our scientific progress in cancer research.

The disease models describe the relationship between biomarkers and clinical outcomes, disease duration and placebo effects. New study models describe the inclusion / exclusion criteria, patient discontinuity and adherence to new therapies. Other relationships will also be addressed in pharmacometry as being the inclusion of pharmacokinetic models. An extended study in pharmacometry can be an excellent solution for the development of a topic like this. In this Special Issue will be essential develop mathematical models to obtain a systems-level understanding of cancer cell metabolism and drug development. This multidisciplinary “Mathematical Applications in Cancer Research” will accept contributions involving:

a) Novel practices of how to use artificial intelligence and machine learning models in translational and clinical studies;
b) Development and evaluation of methods for efficient and robust model building, as estimation algorithms, sequential procedures and methods for model diagnosis;
c) Specific therapeutic areas or for particular therapeutic/pharmacological principles, involving the time-course of a biomarker or a system of biomarkers normal, disease and/or provoked situations;
d) Models for the purpose of designing studies, deciding upon dosing strategies and other developmental decisions;
e) Dose-concentration-response data from trials to understand therapies with existing drugs with the aim of allowing improved therapy;
f) Drug models to describe the relationship between expose, response for both desired and undesired effects, and individual patient characteristics.

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