Back

Low-density lipoprotein receptor-targeting chimeras for membrane protein degradation and enhanced drug delivery

Zhao, F.; Wu, Y.; Schaefer, K.; Zhang, Y.; Miao, K.; Yao, Z.; Ganjave, S. D.; Kumru, K.; Peters-Clarke, T. M.; Inague, A.; Olzmann, J. A.; Leung, K. K.; Wells, J. A.

2025-06-09 bioengineering
10.1101/2025.06.06.658366 bioRxiv
Show abstract

Antibody-based therapeutics encompass diverse modalities for targeting tumor cells. Among these, antibody-drug conjugates (ADCs) and extracellular targeted protein degradation (eTPD) specifically depend on efficient lysosomal trafficking for activity. However, many tumor antigens exhibit poor internalization, limiting ADC effectiveness. To address this, we developed low-density lipoprotein receptor-targeting chimeras (LIPTACs), leveraging the constitutive endocytic and recycling activity of the LDLR to enhance lysosomal delivery. LIPTACs enable efficient and selective degradation of diverse extracellular membrane proteins. Additionally, by coupling LIPTACs with cytotoxic payloads to generate degrader-drug conjugates, we can achieve superior intracellular delivery and enhanced cytotoxicity compared to conventional ADCs. The dual modality addresses key challenges of inadequate internalization in conventional ADCs and cytotoxic potency for current eTPD strategies. Our findings demonstrate that LDLR-mediated trafficking can enhance eTPD and ADCs, providing a hybrid blueprint for developing next-generation antibody therapeutics with broader utility and improved efficacy in cancer treatment.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 5%
25.4%
Similar papers in this journal
2
Nature Chemical Biology
○ 119 papers in training set
Top 0.1%
11.4%
Similar papers in this journal
3
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.1%
7.5%
Similar papers in this journal
4
Science Advances
● 1243 papers in training set
Top 6%
4.9%
Similar papers in this journal
5
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 11%
4.6%
Similar papers in this journal
50% of probability mass above
6
Nature Metabolism
○ 69 papers in training set
Top 0.7%
3.1%
7
Cell Chemical Biology
○ 94 papers in training set
Top 0.4%
3.1%
8
Nature Biotechnology
○ 172 papers in training set
Top 2%
3.1%
9
ACS Central Science
● 71 papers in training set
Top 0.4%
3.0%
10
Nature Nanotechnology
○ 32 papers in training set
Top 0.3%
2.3%
11
Nature Methods
○ 385 papers in training set
Top 4%
2.0%
12
Science
● 477 papers in training set
Top 6%
1.7%
13
Nature
○ 645 papers in training set
Top 7%
1.7%
14
Nature Chemistry
○ 42 papers in training set
Top 0.5%
1.7%
15
ACS Nano
● 113 papers in training set
Top 1%
1.6%
16
Nature Materials
○ 28 papers in training set
Top 0.3%
1.4%
17
Advanced Science
○ 286 papers in training set
Top 5%
1.4%
18
Cell Reports
○ 1498 papers in training set
Top 21%
1.4%
19
eLife
● 5828 papers in training set
Top 61%
1.1%
20
Nature Cell Biology
○ 118 papers in training set
Top 3%
1.0%
21
Cell
○ 431 papers in training set
Top 9%
1.0%
22
Nature Medicine
○ 125 papers in training set
Top 3%
1.0%
23
Nature Cancer
○ 39 papers in training set
Top 1%
1.0%
24
Science Translational Medicine
● 127 papers in training set
Top 3%
0.9%
25
Neuron
○ 337 papers in training set
Top 5%
0.9%
26
Communications Biology
○ 993 papers in training set
Top 28%
0.9%
27
Nature Neuroscience
○ 252 papers in training set
Top 5%
0.8%
28
Cancer Research
● 130 papers in training set
Top 4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.