All-in-one glycol chitosan nanoparticles for co-delivery of doxorubicin and anti-PD-L1 peptide in cancer immunotherapy

Synergistic immunotherapy of immune checkpoint blockade (ICB) and immunogenic cell death (ICD) has shown remarkable therapeutic efficacy in various cancers. However, patients show low response rates and undesirable outcomes to these combination therapies owing to the recycling mechanism of programmed death-ligand 1 (PD-L1) and the systemic toxicity of ICD-inducing chemotherapeutic drugs. Herein, we propose all-in-one glycol chitosan nanoparticles (CNPs) that can deliver anti-PD-L1 peptide (PP) and doxorubicin (DOX) to targeted tumor tissues for a safe and more effective synergistic immunotherapy. The PP-CNPs, which are prepared by conjugating ᴅ-form PP (NYSKPTDRQYHF) to CNPs, form stable nanoparticles that promote multivalent binding with PD-L1 proteins on the targeted tumor cell surface, resulting in effective lysosomal PD-L1 degradation in contrast with anti-PD-L1 antibody, which induces recycling of endocytosed PD-L1. Consequently, PP-CNPs prevent subcellular PD-L1 recycling and eventually destruct immune escape mechanism in CT26 colon tumor-bearing mice. Moreover, the ICD inducer, DOX is loaded into PP-CNPs (DOX-PP-CNPs) for synergistic ICD and ICB therapy, inducing a large number of damage-associated molecular patterns (DAMPs) in targeted tumor tissues with minimal toxicity in normal tissues. When the DOX-PP-CNPs are intravenously injected into CT26 colon tumor-bearing mice, PP and DOX are efficiently delivered to the tumor tissues via nanoparticle-derived passive and active targeting, which eventually induce both lysosomal PD-L1 degradation and substantial ICD, resulting in a high rate of complete tumor regression (CR: 60%) by a strong antitumor immune response. Collectively, this study demonstrates the superior efficacy of synergistic immunotherapy using all-in-one nanoparticles to deliver PP and DOX to targeted tumor tissues.

A structure translation model for crystal compounds

High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively.During this process,the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory(DFT)calculations.Here,we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains,enabling data-driven structural translations.The model predicts the materials formation energy with a small mean absolute error without DFT relaxations,and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures.The utility of the proposed concept is not restricted to the structural domains,and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties.