Dataset Information

Dissecting the impact of target-binding kinetics of protein binders on tumor localization.

ABSTRACT: Systematic control of in vivo behavior of protein-based therapeutics is considered highly desirable for improving their clinical outcomes. Modulation of biochemical properties including molecular weight, surface charge, and binding affinity has thus been suggested to enhance their therapeutic effects. However, establishing a relationship between the binding affinity and tumor localization remains a debated issue. Here we investigate the influence of the binding affinity of proteins on tumor localization by using four repebodies having different affinities to EGFR. Biochemical analysis and molecular imaging provided direct evidence that optimal affinity with balanced target binding and dissociation can facilitate deep penetration and accumulation of protein binders in tumors by overcoming the binding-site-barrier effect. Our findings suggest that binding kinetics-based protein design can be implicated in the development of fine-tuned protein therapeutics for cancers.

SUBMITTER: Song Y

PROVIDER: S-EPMC7881221 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Dissecting the impact of target-binding kinetics of protein binders on tumor localization.

Song Yunjin Y Jeong Hoibin H Kim Song-Rae SR Ryu Yiseul Y Baek Jonghwi J Kwon Jinhak J Cho Hyeongjun H Kim Kil-Nam KN Lee Joong-Jae JJ

iScience 20210129 2

Systematic control of <i>in vivo</i> behavior of protein-based therapeutics is considered highly desirable for improving their clinical outcomes. Modulation of biochemical properties including molecular weight, surface charge, and binding affinity has thus been suggested to enhance their therapeutic effects. However, establishing a relationship between the binding affinity and tumor localization remains a debated issue. Here we investigate the influence of the binding affinity of proteins on tum ...[more]

PMID: 33615202

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Project description:PURPOSE:Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. Because of the anatomic characteristics and location, on-board target verification for radiation delivery to pancreatic tumors is a challenging task. Our goal was to use a deep neural network to localize the pancreatic tumor target on kV x-ray images acquired using an on-board imager for image guided radiation therapy. METHODS AND MATERIALS:The network is set up in such a way that the input is either a digitally reconstructed radiograph image or a monoscopic x-ray projection image acquired by the on-board imager from a given direction, and the output is the location of the planning target volume in the projection image. To produce a sufficient number of training x-ray images reflecting the vast number of possible clinical scenarios of anatomy distribution, a series of changes were introduced to the planning computed tomography images, including deformation, rotation, and translation, to simulate inter- and intrafractional variations. After model training, the accuracy of the model was evaluated by retrospectively studying patients who underwent pancreatic cancer radiation therapy. Statistical analysis using mean absolute differences (MADs) and Lin's concordance correlation coefficient were used to assess the accuracy of the predicted target positions. RESULTS:MADs between the model-predicted and the actual positions were found to be less than 2.60 mm in anteroposterior, lateral, and oblique directions for both axes in the detector plane. For comparison studies with and without fiducials, MADs are less than 2.49 mm. For all cases, Lin's concordance correlation coefficients between the predicted and actual positions were found to be better than 93%, demonstrating the success of the proposed deep learning for image guided radiation therapy. CONCLUSIONS:We demonstrated that markerless pancreatic tumor target localization is achievable with high accuracy by using a deep learning technique approach.

Dataset Information

Dissecting the impact of target-binding kinetics of protein binders on tumor localization.

Publications

Dissecting the impact of target-binding kinetics of protein binders on tumor localization.

Similar Datasets

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