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Zhou Q, Ye Z, Xu X, Zhong Y, Luo J, Zhang Z, Chen J, Chen Z, Cai J, Zhang X, Qian J. Drug-induced enzyme activity inhibition and CYP3A4 genetic polymorphism significantly shape the metabolic characteristics of furmonertinib. Toxicology 2024; 507:153903. [PMID: 39098371 DOI: 10.1016/j.tox.2024.153903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 08/01/2024] [Accepted: 08/02/2024] [Indexed: 08/06/2024]
Abstract
This study aimed to elucidate the impact of variations in liver enzyme activity, particularly CYP3A4, on the metabolism of furmonertinib. An in vitro enzyme incubation system was established for furmonertinib using liver microsomes and recombinant CYP3A4 baculosomes, with analytes detected by LC-MS/MS. The pharmacokinetic characteristics of furmonertinib were studied in vivo using Sprague-Dawley rats. It was found that telmisartan significantly inhibited the metabolism of furmonertinib, as demonstrated by a significant increase in the AUC of furmonertinib when co-administered with telmisartan, compared to the furmonertinib-alone group. Mechanistically, it was noncompetitive in rat liver microsomes, while it was mixed competitive and noncompetitive in human liver microsomes and CYP3A4. Considering the genetic polymorphism of CYP3A4, the study further investigated its effect on the kinetics of furmonertinib. The results showed that compared to CYP3A4.1, CYP3A4.29 had significantly increased activity in catalyzing furmonertinib, whereas CYP3A4.7, 9, 10, 12, 13, 14, 18, 23, 33, and 34 showed markedly decreased activity. The inhibitory activity of telmisartan varied in CYP3A4.1 and CYP3A4.18, with IC50 values of 8.56 ± 0.90 μM and 27.48 ± 3.52 μM, respectively. The key loci affecting the inhibitory effect were identified as ARG105, ILE301, ALA370, and LEU373. Collectively, these data would provide a reference for the quantitative application of furmonertinib.
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Affiliation(s)
- Qi Zhou
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhize Ye
- Department of Pharmacy, Shaoxing People's Hospital, Shaoxing, China
| | - Xiaoyu Xu
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yunshan Zhong
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jianchao Luo
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zheyan Zhang
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jing Chen
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhongxi Chen
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jianping Cai
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China; The Ministry of Health (MOH) Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, China.
| | | | - Jianchang Qian
- Institute of Molecular Toxicology and Pharmacology, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Wei Z, Huang X, Sun A, Peng L, Lou Z, Hu Z, Wang H, Xing L, Yu J, Qian J. A model that predicts a real-time tumour surface using intra-treatment skin surface and end-of-expiration and end-of-inhalation planning CT images. Br J Radiol 2024; 97:980-992. [PMID: 38547402 DOI: 10.1093/bjr/tqae067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/06/2023] [Accepted: 03/25/2024] [Indexed: 05/09/2024] Open
Abstract
OBJECTIVES To develop a mapping model between skin surface motion and internal tumour motion and deformation using end-of-exhalation (EOE) and end-of-inhalation (EOI) 3D CT images for tracking lung tumours during respiration. METHODS Before treatment, skin and tumour surfaces were segmented and reconstructed from the EOE and the EOI 3D CT images. A non-rigid registration algorithm was used to register the EOE skin and tumour surfaces to the EOI, resulting in a displacement vector field that was then used to construct a mapping model. During treatment, the EOE skin surface was registered to the real-time, yielding a real-time skin surface displacement vector field. Using the mapping model generated, the input of a real-time skin surface can be used to calculate the real-time tumour surface. The proposed method was validated with and without simulated noise on 4D CT images from 15 patients at Léon Bérard Cancer Center and the 4D-lung dataset. RESULTS The average centre position error, dice similarity coefficient (DSC), 95%-Hausdorff distance and mean distance to agreement of the tumour surfaces were 1.29 mm, 0.924, 2.76 mm, and 1.13 mm without simulated noise, respectively. With simulated noise, these values were 1.33 mm, 0.920, 2.79 mm, and 1.15 mm, respectively. CONCLUSIONS A patient-specific model was proposed and validated that was constructed using only EOE and EOI 3D CT images and real-time skin surface images to predict internal tumour motion and deformation during respiratory motion. ADVANCES IN KNOWLEDGE The proposed method achieves comparable accuracy to state-of-the-art methods with fewer pre-treatment planning CT images, which holds potential for application in precise image-guided radiation therapy.
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Affiliation(s)
- Ziwen Wei
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
- Science Island Branch of the Graduate School, University of Science and Technology of China, Hefei 230026, Anhui, P.R. China
| | - Xiang Huang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
| | - Aiming Sun
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
| | - Leilei Peng
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
| | - Zhixia Lou
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
| | - Zongtao Hu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
| | - Hongzhi Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
| | - Ligang Xing
- Department of Radiation Oncology, School of Medicine, Shandong University, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong, P.R. China
| | - Jinming Yu
- Department of Radiation Oncology, School of Medicine, Shandong University, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong, P.R. China
| | - Junchao Qian
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, P.R. China
- Science Island Branch of the Graduate School, University of Science and Technology of China, Hefei 230026, Anhui, P.R. China
- Department of Radiation Oncology, School of Medicine, Shandong University, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong, P.R. China
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Wu Y, Wang Z, Chu Y, Peng R, Peng H, Yang H, Guo K, Zhang J. Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review. Biomimetics (Basel) 2024; 9:170. [PMID: 38534855 DOI: 10.3390/biomimetics9030170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/29/2024] [Accepted: 03/09/2024] [Indexed: 03/28/2024] Open
Abstract
Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.
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Affiliation(s)
- Yuwen Wu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Zhisen Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Yuyi Chu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Renyuan Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Haoran Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Hongbo Yang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Kai Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Juzhong Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
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Kim BJ, Ahn HY, Song C, Ryu D, Goh TS, Lee JS, Lee C. A novel computer modeling and simulation technique for bronchi motion tracking in human lungs under respiration. Phys Eng Sci Med 2023; 46:1741-1753. [PMID: 37787839 DOI: 10.1007/s13246-023-01336-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 09/10/2023] [Indexed: 10/04/2023]
Abstract
In this work, we proposed a novel computer modeling and simulation technique for motion tracking of lung bronchi (or tumors) under respiration using 9 cases of computed tomography (CT)-based patient-specific finite element (FE) models and Ogden's hyperelastic model. In the fabrication of patient-specific FE models for the respiratory system, various organs such as the mediastinum, diaphragm, and thorax that could affect the lung motions during breathing were considered. To describe the nonlinear material behavior of lung parenchyma, the comparative simulation for biaxial tension-compression of lung parenchyma was carried out using several hyperelastic models in ABAQUS, and then, Ogden's model was adopted as an optimal model. Based on the aforementioned FE models and Ogden's material model, the 9 cases of respiration simulation were carried out from exhalation to inhalation, and the motion of lung bronchi (or tumors) was tracked. In addition, the changes in lung volume, lung cross-sectional area on the axial plane during breathing were calculated. Finally, the simulation results were quantitatively compared to the inhalation/exhalation CT images of 9 subjects to validate the proposed technique. Through the simulation, it was confirmed that the average relative errors of simulation to clinical data regarding to the displacement of 258 landmarks in the lung bronchi branches of total subjects were 1.10%~2.67%. In addition, the average relative errors of those with respect to the lung cross-sectional area changes and the volume changes in the superior-inferior direction were 0.20%~5.00% and 1.29 ~ 9.23%, respectively. Hence, it was considered that the simulation results were coincided well with the clinical data. The novelty of the present study is as follows: (1) The framework from fabrication of the human respiratory system to validation of the bronchi motion tracking is provided step by step. (2) The comparative simulation study for nonlinear material behavior of lung parenchyma was carried out to describe the realistic lung motion. (3) Various organs surrounding the lung parenchyma and restricting its motion were considered in respiration simulation. (4) The simulation results such as landmark displacement, lung cross-sectional area/volume changes were quantitatively compared to the clinical data of 9 subjects.
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Affiliation(s)
- Byeong-Jun Kim
- Department of Biomedical Engineering, Graduate School, and University Research Park, Pusan National University, Busan, 49241, Republic of Korea
| | - Hyo Yeong Ahn
- Department of Thoracic and Cardiovascular Surgery, School of Medicine, Biomedical Research Institute, Pusan National University, Pusan National University Hospital, Busan, 49241, Republic of Korea
| | - Chanhee Song
- Medical Research Institute, Pusan National University, Busan, 49241, Republic of Korea
| | - Dongman Ryu
- Medical Research Institute, Pusan National University, Busan, 49241, Republic of Korea
| | - Tae Sik Goh
- Department of Orthopaedic Surgery, School of Medicine, Biomedical Research Institute, Pusan National University, Pusan National University Hospital, Busan, 49241, Republic of Korea
| | - Jung Sub Lee
- Department of Orthopaedic Surgery, School of Medicine, Biomedical Research Institute, Pusan National University, Pusan National University Hospital, Busan, 49241, Republic of Korea.
| | - Chiseung Lee
- Department of Biomedical Engineering, School of Medicine, Pusan National University, Busan, Republic of Korea.
- Biomedical Research Institute, Pusan National University Hospital, Busan, 49241, Republic of Korea.
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Zhang J, Wang Y, Bai X, Chen M. Extracting lung contour deformation features with deep learning for internal target motion tracking: a preliminary study. Phys Med Biol 2023; 68:195009. [PMID: 37586388 DOI: 10.1088/1361-6560/acf10e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/16/2023] [Indexed: 08/18/2023]
Abstract
Objective. To propose lung contour deformation features (LCDFs) as a surrogate to estimate the thoracic internal target motion, and to report their performance by correlating with the changing body using a cascade ensemble model (CEM). LCDFs, correlated to the respiration driver, are employed without patient-specific motion data sampling and additional training before treatment.Approach. LCDFs are extracted by matching lung contours via an encoder-decoder deep learning model. CEM estimates LCDFs from the currently captured body, and then uses the estimated LCDFs to track internal target motion. The accuracy of the proposed LCDFs and CEM were evaluated using 48 targets' motion data, and compared with other published methods.Main results. LCDFs estimated the internal targets with a localization error of 2.6 ± 1.0 mm (average ± standard deviation). CEM reached a localization error of 4.7 ± 0.9 mm and a real-time performance of 256.9 ± 6.0 ms. With no internal anatomy knowledge, they achieved a small accuracy difference (of 0.34∼1.10 mm for LCDFs and of 0.43∼1.75 mm for CEM at the 95% confidence level) with a patient-specific lung biomechanical model and the deformable image registration models.Significance. The results demonstrated the effectiveness of LCDFs and CEM on tracking target motion. LCDFs and CEM are non-invasive, and require no patient-specific training before treatment. They show potential for broad applications.
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Affiliation(s)
- Jie Zhang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, People's Republic of China
| | - Yajuan Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, People's Republic of China
| | - Xue Bai
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, People's Republic of China
| | - Ming Chen
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, People's Republic of China
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Combining the wavelet transform with a phase-lead compensator to a respiratory motion compensation system with an ultrasound tracking technique in radiation therapy. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Maghsoudi-Ganjeh M, Mariano CA, Sattari S, Arora H, Eskandari M. Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework. Front Bioeng Biotechnol 2021; 9:684778. [PMID: 34765590 PMCID: PMC8576180 DOI: 10.3389/fbioe.2021.684778] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 10/04/2021] [Indexed: 02/02/2023] Open
Abstract
Pulmonary diseases, driven by pollution, industrial farming, vaping, and the infamous COVID-19 pandemic, lead morbidity and mortality rates worldwide. Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hindered by the lung’s complex and hierarchical structure, and the lack of mechanical experiments linking the load-bearing organ-level response to local behaviors. In this study we address these impedances by introducing a novel reduced-order surface model of the lung, combining the response of the intricate bronchial network, parenchymal tissue, and visceral pleura. The inverse finite element analysis (IFEA) framework is developed using 3-D digital image correlation (DIC) from experimentally measured non-contact strains and displacements from an ex-vivo porcine lung specimen for the first time. A custom-designed inflation device is employed to uniquely correlate the multiscale classical pressure-volume bulk breathing measures to local-level deformation topologies and principal expansion directions. Optimal material parameters are found by minimizing the error between experimental and simulation-based lung surface displacement values, using both classes of gradient-based and gradient-free optimization algorithms and by developing an adjoint formulation for efficiency. The heterogeneous and anisotropic characteristics of pulmonary breathing are represented using various hyperelastic continuum formulations to divulge compound material parameters and evaluate the best performing model. While accounting for tissue anisotropy with fibers assumed along medial-lateral direction did not benefit model calibration, allowing for regional material heterogeneity enabled accurate reconstruction of lung deformations when compared to the homogeneous model. The proof-of-concept framework established here can be readily applied to investigate the impact of assorted organ-level ventilation strategies on local pulmonary force and strain distributions, and to further explore how diseased states may alter the load-bearing material behavior of the lung. In the age of a respiratory pandemic, advancing our understanding of lung biomechanics is more pressing than ever before.
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Affiliation(s)
- Mohammad Maghsoudi-Ganjeh
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
| | - Crystal A Mariano
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
| | - Samaneh Sattari
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States
| | - Hari Arora
- Faculty of Science and Engineering, Swansea University, Swansea, United Kingdom
| | - Mona Eskandari
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, United States.,BREATHE Center, School of Medicine, University of California, Riverside, Riverside, CA, United States.,Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
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