Periodic Reporting for period 4 - Tamed Cancer (Personalized Cancer Therapy by Model-based Optimal Robust Control Algorithm)
Okres sprawozdawczy: 2021-01-01 do 2021-10-31
Medical treatment of diseases includes drug therapies that are specified by protocols. These protocols define the dosage of the drug. Optimized protocols can lead to reduced drug usage which eventuates cheaper therapies and fewer side effects, and can also lead to reduced recovery time. In the case of diseases where no cure is known, but the disease can be treated, optimized and automated treatments can greatly increase the quality of life of the patient. Automation can reduce human costs, side effects, and the burden caused by treatment.
Our society's current, advanced technology is based on acquiring deeper knowledge about our world through mathematical modeling and using these mathematical models to create automated algorithms that can substitute and outperform human operators in many applications. Our ancestors envisaged that this technology may lead to social catastrophe since machines would take away jobs from humans, however, it turned out that these new technologies greatly improved the quality of life of humans, created many new jobs and fields, and also improved the efficiency of human workflows. Time proved that this technology can not replace the intuitive capabilities of the human mind, but it can efficiently complete human labor by solving highly complex (mostly mathematical) problems that are not adequate for human thinking; moreover, it increased the knowledge level and education system of society as well. Our research focuses on using these techniques in medicine to create optimized therapies, focusing on tumor treatment. Such techniques can be applied either as a medical decision support system that gives recommendations about the doses to the oncologists based on the available data of a specific patient, or can be used in stand-alone devices like an insulin pump to continuously treat patients, thus eliminating the need of frequent visits to doctors or managing self-injections.
Our project contains three objectives that support reaching our long-term goals. Our first objective is to create a tumor growth model that describes the effect of the drug on the growth of the tumor. Creating a reliable tumor growth model is fundamental for the creation of optimal therapies since all the algorithms are based on this model. The model creation is carried out by collecting knowledge from experts and converting this knowledge into mathematical equations, and the results are validated by mice experiments. We carry out experiments only when they are unavoidable and do everything to minimize the suffering of the animals during these experiments according to ethical requirements.
Our second objective is to create optimal treatments that can be implemented in clinical decision support systems that provide a patient-specific recommendation of the doses. The underlying engineering problem is called discrete-time impulsive control, which is a relatively new field in engineering. Thus, this objective greatly contributes to the medical and engineering fields as well.
Our third objective is to create algorithms that can control stand-alone, wearable devices like an insulin pump that can provide continuous, unsupervised treatment of the patient. The engineering background of this problem is more grounded than that of the previous objective, however, there are some engineering problems specific to the treatment problem that are open research areas of control engineering science as well.
Our society's current, advanced technology is based on acquiring deeper knowledge about our world through mathematical modeling and using these mathematical models to create automated algorithms that can substitute and outperform human operators in many applications. Our ancestors envisaged that this technology may lead to social catastrophe since machines would take away jobs from humans, however, it turned out that these new technologies greatly improved the quality of life of humans, created many new jobs and fields, and also improved the efficiency of human workflows. Time proved that this technology can not replace the intuitive capabilities of the human mind, but it can efficiently complete human labor by solving highly complex (mostly mathematical) problems that are not adequate for human thinking; moreover, it increased the knowledge level and education system of society as well. Our research focuses on using these techniques in medicine to create optimized therapies, focusing on tumor treatment. Such techniques can be applied either as a medical decision support system that gives recommendations about the doses to the oncologists based on the available data of a specific patient, or can be used in stand-alone devices like an insulin pump to continuously treat patients, thus eliminating the need of frequent visits to doctors or managing self-injections.
Our project contains three objectives that support reaching our long-term goals. Our first objective is to create a tumor growth model that describes the effect of the drug on the growth of the tumor. Creating a reliable tumor growth model is fundamental for the creation of optimal therapies since all the algorithms are based on this model. The model creation is carried out by collecting knowledge from experts and converting this knowledge into mathematical equations, and the results are validated by mice experiments. We carry out experiments only when they are unavoidable and do everything to minimize the suffering of the animals during these experiments according to ethical requirements.
Our second objective is to create optimal treatments that can be implemented in clinical decision support systems that provide a patient-specific recommendation of the doses. The underlying engineering problem is called discrete-time impulsive control, which is a relatively new field in engineering. Thus, this objective greatly contributes to the medical and engineering fields as well.
Our third objective is to create algorithms that can control stand-alone, wearable devices like an insulin pump that can provide continuous, unsupervised treatment of the patient. The engineering background of this problem is more grounded than that of the previous objective, however, there are some engineering problems specific to the treatment problem that are open research areas of control engineering science as well.
We have developed tumor growth models for various applications. We created models that specify the development of the different regions of a tumor and the development of the blood vessels providing the tumor with nutrients. We have tested these models using the results from animal experiments and they proved to describe the effect of the drug sufficiently. We have also created models based on chemotherapy and evaluated these models using results from mice experiments.
We have created general, minimal models as well that describe the most important phenomena and model the volume of the living and dead tumor cells and the level of the drug, not focusing on specific phenomena like the development of blood vessels. Our general model was tested based on experimental results with the drug inhibiting blood vessel formation (called bevacizumab) and also a chemotherapeutic drug (called Doxorubicin) and showed sufficient modeling capabilities for both therapies.
We have carried out experiments with the drug inhibiting blood vessel formation and the combination of this drug with chemotherapeutic drugs. We have applied the protocols used currently for human treatments (the dosages were converted from human to animal dosage) and compared them with protocols with reduced doses. The protocols with reduced doses showed good results with small tumor volume, however, the original protocol caused serious side effects. We have created algorithms that can generate recommended dosages by using a mathematical model of tumor growth. These algorithms are the first step toward clinical support decision software.
We have developed control algorithms for the continuous control problem that can be used to control a wearable device. Our control algorithms focus on handling the differences among the patients.
We have created general, minimal models as well that describe the most important phenomena and model the volume of the living and dead tumor cells and the level of the drug, not focusing on specific phenomena like the development of blood vessels. Our general model was tested based on experimental results with the drug inhibiting blood vessel formation (called bevacizumab) and also a chemotherapeutic drug (called Doxorubicin) and showed sufficient modeling capabilities for both therapies.
We have carried out experiments with the drug inhibiting blood vessel formation and the combination of this drug with chemotherapeutic drugs. We have applied the protocols used currently for human treatments (the dosages were converted from human to animal dosage) and compared them with protocols with reduced doses. The protocols with reduced doses showed good results with small tumor volume, however, the original protocol caused serious side effects. We have created algorithms that can generate recommended dosages by using a mathematical model of tumor growth. These algorithms are the first step toward clinical support decision software.
We have developed control algorithms for the continuous control problem that can be used to control a wearable device. Our control algorithms focus on handling the differences among the patients.
Our models can describe the growth of tumors and also incorporate the pharmacodynamics of the drug, and our general model can describe the effect of multiple drugs with significantly different mechanisms. These models have been validated using mice experiments
Due to the best of our knowledge, our discrete-time therapy optimization algorithm is currently the only one in the literature tested in experiments. In the continuous control problem, we have developed a method that incorporates the positivity of the input into the modeling process, which is a novel technique in the control literature.
Due to the best of our knowledge, our discrete-time therapy optimization algorithm is currently the only one in the literature tested in experiments. In the continuous control problem, we have developed a method that incorporates the positivity of the input into the modeling process, which is a novel technique in the control literature.