Model predictive fuzzy control in chemotherapy with Hessian based optimization

Usually, clinical cancer treatment therapies that use chemotherapy are generalized for patients and take into consideration only a few parameters from the patients, for example, body mass, age, and chronic diseases. As one would expect, this information is essential in chemotherapy treatment, but not enough. To improve the efficiency of the drugs we implemented an algorithm based on pharmacokinetic and pharmacodynamic mathematic models. This algorithm contains a fuzzy logic controller that provides an initial value for a minimum search function. Therefore, our research group is working to reuse the available drugs by generating personalized therapy plans for each and every person that suffers from cancer. The development of these types of therapies could result in the reduction of drug doses and achieving the remission of the tumor. On the other hand the more frequent but smaller doses lower the chances of developing drug resistance and causing the reduction of side effects. These personalized treatment plans could be a replacement for traditional chemotherapy protocols in the nearby future.