Peptide-anchored neutrophil membrane-coated biomimetic nanodrug for targeted treatment of rheumatoid arthritis.

Macrophage polarization determines the production of cytokines that fuel the initiation and evolution of rheumatoid arthritis (RA). Thus, modulation of macrophage polarization might represent a potential therapeutic strategy for RA. However, coordinated modulation of macrophages in the synovium and synovial fluid has not been achieved thus far. Herein, we develop a biomimetic ApoA-I mimetic peptide-modified neutrophil membrane-wrapped F127 polymer (R4F-NM@F127) for targeted drug delivery during RA treatment. Due to the high expression of adhesion molecules and chemokine receptors on neutrophils, the neutrophil membrane coating can endow the nanocarrier with synovitis-targeting ability, with subsequent recruitment to the synovial fluid under the chemotactic effects of IL-8. Moreover, R4F peptide modification further endows the nanocarrier with the ability to target the SR-B1 receptor, which is highly expressed on macrophages in the synovium and synovial fluid. Long-term in vivo imaging shows that R4F-NM@F127 preferentially accumulates in inflamed joints and is engulfed by macrophages. After loading of the anti-inflammatory drug celastrol (Cel), R4F-NM@F127-Cel shows a significant reduction in hepatotoxicity, and effectively inhibits synovial inflammation and alleviates joint damage by reprogramming macrophage polarization. Thus, our results highlight the potential of the coordinated targeted modulation of macrophages as a promising therapeutic option for the treatment of RA.

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine.

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.