Fuzzy, synergetic and non-linear state feedback control of chemotherapy drug for a cancerous tumor

Simultaneous Multi-Treatment Strategy for Brain Tumor Reduction via Nonlinear Control

Background: Recently proposed brain-tumor treatment strategies prioritize fast reduction of tumor cell population while often neglecting the radiation or chemotherapeutic drug dosage requirements to achieve it. Moreover, these techniques provide chemotherapy based treatment strategies, while ignoring the toxic side effects of the drugs employed by it. Methods: This study updates the recently proposed brain-tumor system dynamics by incorporating radiotherapy along with chemotherapy to simultaneously initiate both therapies for a more comprehensive and effective response against tumor proliferation. Afterwards, based on the upgraded system dynamics, this study proposes a novel multi-input sigmoid-based smooth synergetic nonlinear controller with the aim to reduce the dosage requirements of both therapies while keeping the overall system response robust and efficient. The novelty of this study lies in the combination of radiotherapy and chemotherapy inputs in a way that prioritizes patients health and well-being, while integrating advanced synergetic control technique with a sigmoid function based smoothing agent. Results: The proposed method reduced baseline radiation and chemo drug dosages by 57% and 33% respectively while effectively suppressing tumor growth and proliferation. Similarly, the proposed controller reduced the time required for complete tumor mitigation by 60% while reducing the radiation and chemotherapeutic drug intensity by 93.8% and 21.3% respectively. Conclusions: This study offers significant improvement in tumor treatment methodologies by providing a safer, less riskier brain-tumor treatment strategy that has promising potential to improve survival rates against this menacing health condition so that the affected patients may lead a healthier and better quality of life.

Deep reinforcement learning-based control of chemo-drug dose in cancer treatment

BACKGROUND AND OBJECTIVE

Advancement in the treatment of cancer, as a leading cause of death worldwide, has promoted several research activities in various related fields. The development of effective treatment regimens with optimal drug dose administration using a mathematical modeling framework has received extensive research attention during the last decades. However, most of the control techniques presented for cancer chemotherapy are mainly model-based approaches. The available model-free techniques based on Reinforcement Learning (RL), commonly discretize the problem states and variables, which other than demanding expert supervision, cannot model the real-world conditions accurately. The more recent Deep Reinforcement Learning (DRL) methods, which enable modeling the problem in its original continuous space, are rarely applied in cancer chemotherapy.

METHODS

In this paper, we propose an effective and robust DRL-based, model-free method for the closed-loop control of cancer chemotherapy drug dosing. A nonlinear pharmacological cancer model is used for simulating the patient and capturing the cancer dynamics. In contrast to previous work, the state variables and control action are modeled in their original infinite spaces to avoid expert-guided discretization and provide a more realistic solution. The DRL network is trained to automatically adjust the drug dose based on the monitored states of the patient. The proposed method provides an adaptive control technique to respond to the special conditions and diagnosis measurements of different categories of patients.

RESULTS AND CONCLUSIONS

The performance of the proposed DRL-based controller is evaluated by numerical analysis of different diverse simulated patients. Comparison to the state-of-the-art RL-based method, which uses discretized state and action spaces, shows the superiority of the approach in the process and duration of cancer chemotherapy treatment. In the majority of the studied cases, the proposed model decreases the medication period and the total amount of administrated drug, while increasing the rate of reduction in tumor cells.

Adaptive fuzzy control of drug delivery in cancer treatment using combination of chemotherapy and antiangiogenic therapy

This paper introduces the adaptive fuzzy control scheme as a promising control technique for cancer treatment from a theoretical point of view. A mathematical model describing the dynamics of tumor growth under the drug interventions of chemotherapy and antiangiogenic therapy is considered. The model incorporates the effects of normal cells, cancer cells, and endothelial cells. Then, the control goals in cancer treatment are discussed and the desired trajectory for a typical patient is derived using the optimal control theory. Since the dynamic model of tumor growth is not accurate and also varies from patient to patient, an adaptive fuzzy controller is designed to make the outputs of the dynamic model track the desired trajectory. The proposed control system is model-independent and identifies the dynamic model of tumor growth over time. The performance of the designed controller is assessed by several numerical simulations. Finally, a hardware-in-the-loop simulation is conducted to validate the results.

Stability and bifurcation control analysis of a delayed fractional-order eco-epidemiological system

Considering the factor of artificial intervention in biological control, a delayed fractional eco-epidemiological system with an extended feedback controller is proposed. By using the digestion delay as bifurcation parameter, the stability and Hopf bifurcation are investigated, and the branching conditions are given. The system undergoes Hopf bifurcation, when the parameter τ passes through the critical value. In addition, it can be pointed out that the negative feedback gain and the feedback delay could affect the bifurcation critical value of the system. Therefore, the Hopf bifurcation can also be induced by taking the feedback delay as a bifurcation parameter. Finally, by plotting the solution curve of the system, the significance of the controller to the stability of the eco-epidemiological system is verified.

The current scenario on anticancer activity of artemisinin metal complexes, hybrids, and dimers

Cancer, the most significant cause of morbidity and mortality, has already posed a heavy burden on health care systems globally. In recent years, cancer treatment has made a significant breakthrough, but cancer cells inevitably acquire resistance, and the efficacy of the treatment is greatly reduced as the tumor progresses. To overcome the above issues, novel chemotherapeutics are needed urgently. Artemisinin and its derivatives-sesquiterpene lactone compounds possessing a unique peroxy bridge moiety-exhibit excellent safety and tolerability profiles. Mechanistically, artemisinin derivatives can promote cancer cell apoptosis, induce cell cycle arrest and autophagy, and inhibit cancer cell invasion and migration. Accordingly, artemisinin derivatives demonstrate promising anticancer efficacy both in vitro and in vivo, and even in clinical Phase I/II trials. The purpose of the present review article is to provide an emphasis on the current scenario (January 2017-January 2022) of artemisinin derivatives with potential anticancer activity, inclusive of artemisinin metal complexes, hybrids, and dimers. The structure-activity relationships and mechanisms of action are also discussed to facilitate the further rational design of more effective candidates.