Coassembling functionalized glycopolypeptides to prepare pH-responsive self-indicating nanocomplexes to manipulate self-assembly/drug delivery and visualize intracellular drug release

The pH-responsive polymeric micelles (PMs) have been widely used as smart nano drug delivery systems to treat tumors. However, synchronously manipulating these PMs' self-assembly properties, drug release dynamics and tracing their pH-dependent intracellular fate remain challenges. Herein, we have first synthesized hyaluronic acid (HA) based glycopolypeptides modified by tetraphenylethylene (TPE) and a pH-sensitive doxorubicin (DOX) prodrug through Diels-Alder reaction, respectively. Then, the pH-responsive nanocomplexes (NCs) were prepared by coassembling the two obtained glycopolypeptides with different formulations. Controllable size within the range of 60-125 nm and morphologies like spherical, vesicular and oblate micelles can be easily accomplished by using this method; High drug encapsulating and loading efficiency can be easily realized and adjusted within a range of 86-97% and 7-25%, respectively; Acid sensitive drug release dynamics of these NCs are also tunable by using this way. Additionally, the programmed drug release induced by subtle pH variations can be extracellularly self-indicated by detecting the blue AIE changes of the TPE units through fluorescence resonance energy transfer (FRET) effect between DOX and TPE. More importantly, the dynamic pH-triggered DOX release can be easily traced inside the tumor cells by visualizing blue emission changes of the TPE through the FRET effect. In addition, both the size and the shape can affect the endocytic routes of the NCs; The HA coated NCs targeting the tumor cells can effectively inhibit the proliferation of the HeLa cells. This work can provide a new route to acquire the stimuli-responsive self-indicating PMs with the ability to adjust their self-assembly properties and their pH-triggered drug release dynamics, and even to simultaneously visualize the PMs' intracellular fate in a real-time.