Auricular vagus nerve stimulation (aVNS) is a novel neuromodulatory therapy used for treatment of various chronic systemic disorders. Currently, aVNS is non-individualized, disregarding the physiological state of the patient and therefore making it difficult to reach optimum therapeutic outcomes. A closed-loop aVNS system is required to avoid over-stimulation and under-stimulation of patients, leading to personalized and thus improved therapy. This can be achieved by continuous monitoring of individual physiological parameters that serve as a basis for the selection of optimal aVNS settings. In this work we developed a novel aVNS hardware for closed-loop application, which utilizes cardiorespiratory sensing using embedded sensors (and/or external sensors), processes and analyzes the acquired data in real-time, and directly governs settings of aVNS. We show in-lab that aVNS stimulation can be arbitrarily synchronized with respiratory and cardiac phases (as derived from respiration belt, electrocardiography and/or photo plethysmography) while mimicking baroreceptor-related afferent input along the vagus nerve projecting into the brain. Our designed system identified > 90% of all respiratory and cardiac cycles and activated stimulation at the target point with a precision of ± 100 ms despite the intrinsic respiratory and heart rate variability reducing the predictability. The developed system offers a solid basis for future clinical research into closed-loop aVNS in favour of personalized therapy.
Introduction Epidermal growth factor receptor (EGFR) mutations occur in about 40% of Asian and 13-25% of Western patients with lung adenocarcinoma (LUAD) [1,2]. EGFR tyrosine kinase inhibitors (TKIs) have been developed to target tumors with an EGFR driver mutation. However such tumors also harbor additional mutations and genotypic alterations, which contribute to the variability in treatment response. Overall, intratumor heterogeneity is a dynamic source of therapeutic resistance. Here, we assessed the impact of tumor mutational profile on inter-patient treatment response variability by using a mechanistic model of late stage EGFR-mutant LUAD [3]. Methods We developed in the jinko platform a knowledge-based model of late stage EGFR-mutant LUAD, whose parameters each hold a pathophysiology-related meaning. Indeed, causality between disease-related biological phenomena is inherent to the mechanistic knowledge-based model, easing the biological interpretation of the impact of parameter values on model outcomes, which is highly valuable in the context of uncommon populations. To explore the impact of tumor heterogeneity, tumors are implemented with distinct subpopulations of cancer cells, called subclones. Each subclone is defined by a set of mutations and a corresponding proliferating phenotype. A virtual population representative of real world EGFR-mutant LUAD patients was developed to reproduce the variability observed between and within patients’ tumors for the modeled mutations. A physiologically-based pharmacokinetics model of a TKI drug, integrating a mechanistic modeling of the drug’s mechanism of action, is connected to the disease model. Results After simulating a clinical trial on a virtual population in the jinko platform using the developed model, we were able to follow the proliferating phenotype of each cancer subclone over time, as well as the overall evolution of the tumor size of each patient. The model reproduced the emergence of treatment-resistant subclones on EGFR TKI therapy. Modeling tumor size evolution throughout the clinical trial enabled computing the patients’ time to progression as clinical outcome, based on the RECIST 1.1 criteria, and generating corresponding survival curves. Stratification of virtual patients according to their tumor mutations allowed us to pinpoint key mutations that negatively impacted treatment response. Conclusion Modeling and simulation can help understanding how intratumor heterogeneity affects cancer evolution and drives resistance to treatment. Clinical trial simulations using knowledge-based models of disease and treatment provide relevant additional tools to help clinicians in exploring new hypotheses, providing treatment guidance and supporting therapeutic innovation. References [1] Dearden et al., AnnOnc, 2013 [2] Zhao et al., Sci Rep, 2017 [3] Palgen et al., ActaBiotheor., 2022 Citation Format: Perrine Masson, Claire Couty, Arnaud Nativel, Evgueni Jacob, Raphaël Toueg, Michaël Duruisseaux, Adèle L'Hostis, Jean-Louis Palgen, Claudio Monteiro. Simulations of tumor heterogeneity impact on treatment response using a mechanistic model of EGFR-mutant lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 859.
Introduction: 16,4% of lung adenocarcinomas (LUAD) are presenting a mutation in the Epidermal Growth Factor Receptor (EGFR), as reported in the Epidemiological Strategy and Medical Economics database[1], resulting in its constitutive activation and leading to uncontrolled cell proliferation. While some tyrosine kinase inhibitors (TKIs) have been developed to target EGFR mutations, their efficacy is not long-lasting, due to the emergence of resistance mutations[2]. Based on in silico approaches, we investigate and compare the impact of two TKIs (1st and 3rd generation) on tumor size evolution and clinical outcome, depending on the target population. Materials and Methods: We developed in Novadiscovery's jinkō platform a detailed mechanistic disease model of EGFR-mutant LUAD that predicts patients’ disease progression, based on their characteristics. We added on top of this disease model, a mechanistic physiologically-based pharmaco-kinetics model for each TKI drug, integrating their mechanisms of action. Publicly available data were used to calibrate the drug models and assess their credibility.We used the combination of the disease model with the two drug models to simulate clinical trials to compare the impact of both drugs on the course of the disease. Results:Both the 1st and 3rd generation TKI drug models can reproduce the pharmacokinetics in mice and humans. Combination of these models with the EGFR-mutant LUAD disease model is used to predict the tumor evolution in mice and the clinical outcome in humans. Differences in disease progression between treatments are observed according to the patients’ tumor mutational profiles. Discussion and Conclusion: The knowledge based construction of this EGFR mutant LUAD disease and treatment model successfully reproduced publicly available real-world data and will be challenged to reproduce the results from the FLAURA trial for an additional step of validation. The credibility of the model thereby acquired is a first step in the use of the model to compare existing treatments to investigational treatments and further support innovative therapies development. As such, in silico approaches are a complementary and valuable tool to existing in vitro or in animal experiments, alongside with clinical trials performance. References: [1] Chouaid et al, TargOnc, 2021, https://doi.org/10.1007/s11523-021-00848-9. [2] Vyse et al, Signal Transduction and Targeted Therapy, 2019 https://doi.org/10.1038/s41392-019-0038-9. Citation Format: Hippolyte Darré, Bastien Martin, Firas Hammami, Arnaud Nativel, Diane Lefaudeux, Raphaël Toueg, Michaël Duruisseaux, Jean-Louis Palgen, Perrine Masson, Adèle L'Hostis, Nicoletta Ceres, Claudio Monteiro. Comparison of the effect of two EGFR-TKI in patients with EGFR-mutant lung adenocarcinoma using in silico clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 856.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.