Adaptive therapy is an evolution-based therapeutic method that uses minimal effective drug doses or periodic drug holidays to preserve tumor volume. Recent research has found that the success of adaptive therapy is influenced by a variety of parameters, including geographical limitation, the fitness of the resistant cell population, the initial number of resistant cells, and resistance mechanisms. Finding the correct timing of treatment switch points is crucial for excellent adaptive therapy outcomes.
However, it’s unclear how the cost of resistance affects a tumor’s adaptive therapeutic response. Adaptive therapy dose and switch time points are made easier by mathematical models. The globe was abuzz in the early 1970s with sensationalized tales of a miracle bullet that might heal cancer. To investigate interactions between drug-sensitive and drug-resistant cells in a tumor, scientists devised two alternative mathematical models.
The first model implies that drug-sensitive and resistant populations are genetically fixed and compete for limited resources. Sensitive cells stop resistant cells from growing. The second model considers drug-sensitive and drug-resistant cell switching phenotypes. To evaluate the actual delivered dose to the patient, use RayStation’s dose tracking module.
Fraction photographs are acquired daily or on a regular basis for patient placement during therapy. When CT, PET/CT, cone-beam CT, or MR pictures of the patient are collected during therapy, these images can be used to assess the daily given dose.
Overall, the models estimate that adaptive therapy would have a 6-25 month delay in time to progression when compared to continuous therapy, with dosage rates ranging from 6% to 74%. When the initial population of sensitive cells is high enough and the sensitive cells have a substantial competitive effect on resistant cells, the first model predicts 6-20 months of benefit from continued therapy.
When the switching rate from resistant to sensitive cells is high and the development rate of sensitive cells is modest, the second model estimates 20-25 months gained by continued therapy. Adaptive therapy has also been shown to provide dosimetric benefits, particularly in the case of head and neck tumors. It is a personalized radiation treatment approach in which clinicians adjust their strategy in response to changes in the anatomy and physiology of the patient.
Adaptive therapy aims to keep each patient’s tumor volume under control by allowing a sufficient number of sensitive cells to survive treatment breaks or dose adjustment. They can account for variations in tumor shape, size, and location, as well as changes in adjacent organs, by altering treatment based on daily imaging data. Competition is the central mechanism of adaptive therapy, in which any remaining sensitive cells compete with the resistant population, successfully limiting their growth and extending progression-free life dramatically.
Patients’ results could be improved by using an intelligent, accessible adaptive therapy method, which could lead to a long disease-free life expectancy. Additionally, it may pave the way for more opportunities, research, and cancer treatment improvements. In preclinical xenograft model systems, adaptive therapy has successfully suppressed tumors. Tumors with higher cell density and lower levels of pre-existing resistance performed better under adaptive therapy circumstances, according to the researchers.
Identifying predictive characteristics for selecting individuals who would most likely benefit from this adaptive therapy is crucial. Adaptive therapy’s effectiveness will differ by patient, as seen in the prostate cancer trial. Adaptive therapy now requires a long time to implement, which slows clinics’ ability to adopt and use these technologies to treat patients. Adaptive therapy can be slow and inconvenient for patients, as they must lie absolutely still on a treatment couch for roughly 45 minutes.
Cell turnover, the researchers discovered, is a crucial component that influences the cost of resistance and adaptive therapy outcomes by boosting competition between sensitive and resistant cells. To compute the aggregate administered dose over the course of treatment, deform and accumulate doses from distinct fractions in a single geometry. Adaptive therapy now necessitates costly capital equipment and a large number of clinician hours, making it prohibitively expensive for the vast majority of patients.
The ramifications of having spontaneous vs. induced resistance responses to medication have been demonstrated using mathematical modeling. Adaptive therapy works best for tumors with a large number of responsive cells at the start. The percent time on treatment for adaptive therapy varied by the patient (i.e., the cumulative dose rate ranged from 12% to 100% of continuous therapy), with an overall average of 65 percent. Adaptive therapy tools and solutions have only recently become available. They make the process possible, but not practical or efficient.
Adaptive therapy may not be ideal in its current condition, but it marks the next step in a personalized healthcare transformation and shines a bright light on the potential for intelligent medicine to change substantially in the future years. If the tumor load falls below 20% of the initial burden, treatment is postponed. When the tumor’s burden recovers to its original level, the tumor is re-challenged with therapy. Although adaptive therapy is now the path less traveled, it has the potential to provide tailored, successful treatment to a large number of cancer patients, which will make all the difference.
