Bridging Smart Nanosystems with Clinically Relevant Models and Advanced Imaging for Precision Drug Delivery

Qiaoxia Zhou,Qiongliang Liu, Yan Wang,Jie Chen,Otmar Schmid,Markus Rehberg, Lin Yang

Advanced science (Weinheim, Baden-Wurttemberg, Germany)（2024）

German Ctr Lung Res DZL | Univ Hlth & Rehabil Sci | Cent South Univ

Cited 7|Views13

Abstract

AbstractIntracellular delivery of nano‐drug‐carriers (NDC) to specific cells, diseased regions, or solid tumors has entered the era of precision medicine that requires systematic knowledge of nano‐biological interactions from multidisciplinary perspectives. To this end, this review first provides an overview of membrane‐disruption methods such as electroporation, sonoporation, photoporation, microfluidic delivery, and microinjection with the merits of high‐throughput and enhanced efficiency for in vitro NDC delivery. The impact of NDC characteristics including particle size, shape, charge, hydrophobicity, and elasticity on cellular uptake are elaborated and several types of NDC systems aiming for hierarchical targeting and delivery in vivo are reviewed. Emerging in vitro or ex vivo human/animal‐derived pathophysiological models are further explored and highly recommended for use in NDC studies since they might mimic in vivo delivery features and fill the translational gaps from animals to humans. The exploration of modern microscopy techniques for precise nanoparticle (NP) tracking at the cellular, organ, and organismal levels informs the tailored development of NDCs for in vivo application and clinical translation. Overall, the review integrates the latest insights into smart nanosystem engineering, physiological models, imaging‐based validation tools, all directed towards enhancing the precise and efficient intracellular delivery of NDCs.

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Key words

advanced imaging,intracellular delivery,nano-bio interaction,nano-drug-carries,physiological models

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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

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