A new fuzzy reinforcement learning method for effective chemotherapy

A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the dosage of drugs for any treatment. Motivated by this issue, in the present research paper a Caputo–Fabrizio fractional-order model of cancer chemotherapy treatment was elaborated and analyzed. A fix-point theorem and an iterative method were implemented to prove the existence and uniqueness of the solutions of the proposed model. Afterward, in order to control cancer through chemotherapy treatment, a fuzzy-reinforcement learning-based control method that uses the State-Action-Reward-State-Action (SARSA) algorithm was proposed. Finally, so as to assess the performance of the proposed control method, the simulations were conducted for young and elderly patients and for ten simulated patients with different parameters. Then, the results of the proposed control method were compared with Watkins’s Q-learning control method for cancer chemotherapy drug dosing. The results of the simulations demonstrate the superiority of the proposed control method in terms of mean squared error, mean variance of the error, and the mean squared of the control action—in other words, in terms of the eradication of tumor cells, keeping normal cells, and the amount of usage of the drug during chemotherapy treatment

A review of mathematical models for the formation of vascular networks

Two major mechanisms are involved in the formation of blood vasculature: vasculogenesis and angiogenesis. The former term describes the formation of a capillary-like network from either a dispersed or a monolayered population of endothelial cells, reproducible also in vitro by specific experimental assays. The latter term describes the sprouting of new vessels from an existing capillary or post-capillary venule. Similar mechanisms are also involved in the formation of the lymphatic system through a process generally called lymphangiogenesis. A number of mathematical approaches have been used to analyse these phenomena. In this article, we review the different types of models, with special emphasis on their ability to reproduce different biological systems and to predict measurable quantities which describe the overall processes. Finally, we highlight the advantages specific to each of the different modelling approaches. The research that led to the present paper was partially supported by a grant of the group GNFM of INdA

Taste-immune associative learning amplifies immunopharmacological effects and attenuates disease progression in a rat glioblastoma model

Mechanistic target of rapamycin (mTOR)-signaling is one key driver of glioblastoma (GBM), facilitating tumor growth by promoting the shift to an anti-inflammatory, pro-cancerogenic microenvironment. Even though mTOR inhibitors such as rapamycin (RAPA) have been shown to interfere with GBM disease progression, frequently chaperoned toxic drug side effects urge the need for developing alternative or supportive treatment strategies. Importantly, previous work document that taste-immune associative learning with RAPA may be utilized to induce learned pharmacological placebo responses in the immune system. Against this background, the current study aimed at investigating the potential efficacy of a taste-immune associative learning protocol with RAPA in a syngeneic GBM rat model. Following repeated pairings of a novel gustatory stimulus with injections of RAPA, learned immune-pharmacological effects could be retrieved in GBM-bearing animals when re-exposed to the gustatory stimulus together with administering 10 % amount of the initial drug dose (0.5 mg/kg). These inhibitory effects on tumor growth were accompanied by an up-regulation of central and peripheral pro-inflammatory markers, suggesting that taste-immune associative learning with RAPA promoted the development of a pro-inflammatory anti-tumor microenvironment that attenuated GBM tumor growth to an almost identical outcome as obtained after 100 % (5 mg/kg) RAPA treatment. Together, our results confirm the applicability of taste-immune associative learning with RAPA in animal disease models where mTOR overactivation is one key driver. This proof-of-concept study may also be taken as a role model for implementing learning protocols as alternative or supportive treatment strategy in clinical settings, allowing the reduction of required drug doses and side effects without losing treatment efficacy

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Simple Summary Metastatic colorectal cancer (mCRC) has high incidence and mortality. Nevertheless, innovative biomarkers have been developed for predicting the response to therapy. We have examined the ability of learning methods to build prognostic and predictive models to predict response to chemotherapy, alone or combined with targeted therapy in mCRC patients, by targeting specific narrative publications. After a literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. We showed that all investigations conducted in this field provided generally promising results in predicting the response to therapy or toxic side-effects, using a meta-analytic approach. We found that radiomics and molecular biomarker signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Our study supports the use of computer science for developing personalized treatment decision processes for mCRC patients. Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set

Recipes for calibration and validation of agent-based models in cancer biomedicine

Computational models and simulations are not just appealing because of their intrinsic characteristics across spatiotemporal scales, scalability, and predictive power, but also because the set of problems in cancer biomedicine that can be addressed computationally exceeds the set of those amenable to analytical solutions. Agent-based models and simulations are especially interesting candidates among computational modelling strategies in cancer research due to their capabilities to replicate realistic local and global interaction dynamics at a convenient and relevant scale. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature to explore strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on validation approached as simulation calibration. We argue that simulation calibration goes beyond parameter optimization by embedding informative priors to generate plausible parameter configurations across multiple dimensions

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models.

Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2 breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2 breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2 breast cancer management, we also review mathematical models pertaining to the dynamics of HER2 breast cancer and immune checkpoint inhibitors

Analysis of matrix metalloproteinases in cancer cell signaling and extracellular behavior

Despite the fact that over the past two decades the total death rate has declined up to twenty percent, cancer remains the second leading cause of death in the United States and accounts for nearly one in every four deaths. It is therefore of paramount importance that new strategies continue to develop in an effort to curb both incidence and treatment of disease. The current research landscape is focused on developing strategies to disrupt molecular signatures of cancer cell types, commonly known as targeted therapy. Of particular importance in the advancement of targeted therapies are matrix metalloproteinases (MMPs), a family of endopeptidases whose primary function lies in cleaving extracellular matrix (ECM) proteins and are frequently dysregulated in cancer. While research regarding MMPs is decades old, their significance in the signal transduction of several oncogenic pathways is yet to be fully explored. In addition, a dearth of quantitative data exists describing the action of MMPs in three dimensional (3D) networks, a configuration that causes cells to express vastly different behaviors compared to traditional two-dimensional (2D) in vitro culture methods. This dissertation aims to further elucidate the intimate relationships between MMPs, the ECM, cancer pathway signaling, and cell migration. First, the behavioral crosstalk between MMPs and the ECM is studied using quantitative methods in 3D matrices. Next, the role of MMPs in both Ras oncogenic and HER2 positive breast cancer is probed via extensive protein expression analysis. Finally, the behavioral aspects of MMPs in 3D are assessed marrying both in vitro data with a computational model to predict migration response. The results reveal that MMPs exhibit a bidirectional relationship with respect to matrix architecture, and the ability to regulate and be regulated by the ECM. In addition, it is concluded that MMPs play a significant role in both active Ras and HER2 upregulated cancer signaling. Finally, the data demonstrates the robustness and accuracy of our methods in manufacturing a model to predict migration in 3D matrices. The work described here promises to further enhance the knowledge of MMPs in cancer and potentially inform future drug development endeavors

Effects of dopaminergic pathways on human neutrophil

The existence of a bidirectional communication between the immune system and the central nervous system was postulated some years ago by different researchers. More recently some evidence supports the notion that immune system can be affected by dopamine (DA). DA is a neurotransmitter of the central nervous system that exerts its effects through the activation of the five dopaminergic receptors (DR). DA can affect some functions of the cells of the immune system and this topic was widely investigated on the cells of adaptive immunity. Therefore, we decided to focus our attention on the different cell populations of the innate immunity and to explore the data present in literature about the evidence of the existence of a dopaminergic regulation of these cells. The first part of the thesis is a description of dopamine and of the dopaminergic system, with reference to interactions with the immune system, in particular the innate immunity. Moreover, in the last part of this first chapter are mentioned some diseases involving the innate immunity in which the role of dopaminergic pathway was postulated and in some case demonstrated. The second chapter is devoted to the characterization from the physiological point of view of the other major actors of the work, neutrophils (PMN). Also in this case, at the end of the chapter there is a section dedicated to the relevance of PMN in diseases in which the immune component is relevant. The third chapter represents the main results of my PhD project, based on the investigation of the role and relevance of the dopaminergic system in human neutrophils. The aim of this PhD research program was in fact, to characterize the presence of DR and if dopaminergic agent can affect some pivotal function of neutrophil in a receptor-dependent manner. Finally, a last chapter resumed the other projects that I have followed during the three year of my PhD course. The two attached files represent the results of some of them, that were conclude and published