Selective Intracellular Delivery of Antibodies in Cancer Cells with Nanocarriers Sensing Endo/Lysosomal Enzymatic Activity.
Angewandte Chemie(2024)
Univ Tokyo | Kawasaki Inst Ind Promot
Abstract
The differential enzymatic activity in the endo/lysosomes of particular cells could trigger targeted endosomal escape functions, enabling selective intracellular protein delivery. However, this strategy may be jeopardized due to protein degradation during endosomal trafficking. Herein, using custom made fluorescent probes to assess the endosomal activity of cathepsin B (CTSB) and protein degradation, we found that certain cancer cells with hyperacidified endosomes grant a spatiotemporal window where CTSB activity surpass protein digestion. This inspired the engineering of antibody-loaded polymeric nanocarriers having CTSB-activatable endosomal escape ability. The nanocarriers selectively escaped from the endo/lysosomes in the cells with high endosomal CTSB activity and delivered active antibodies to intracellular targets. This study provides a viable strategy for cell-specific protein delivery using stimuli-responsive nanocarriers with controlled endosomal escape. Endo/lysosomal cathepsin B activity differentiates among cell types and serves as a stimulus to trigger cell-specific endosomal escape. By designing nanocarriers armed with cathepsin B-activatable endosomal escape function, endosomal escape can be controlled to only occur in targeted cancer cells with upregulated endo/lysosomal cathepsin B activity. This innovative approach offers a new means to achieve targeted intracellular protein delivery. image
MoreTranslated text
Key words
Drug delivery,Antibody,Cathepsin,Endosomal escape,Protein therapeutics
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2008
被引用149 | 浏览
2009
被引用335 | 浏览
2014
被引用1313 | 浏览
2015
被引用75 | 浏览
2016
被引用51 | 浏览
2017
被引用50 | 浏览
2018
被引用82 | 浏览
2018
被引用111 | 浏览
2019
被引用11 | 浏览
2019
被引用55 | 浏览
2020
被引用170 | 浏览
2020
被引用9 | 浏览
2021
被引用56 | 浏览
2023
被引用1 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper