Programmable Ce6 Delivery via Cyclopamine Based Tumor Microenvironment Modulating Nano-System for Enhanced Photodynamic Therapy in Breast Cancer
Authors: Yun Lin
Chan Feng
Chunyan Dong
Yonglin Lu
Yongyong Li
Jie Liu
Lv Chen
Shujing Liang
Journal: Frontiers in Chemistry (2019)
Discipline: CHEMISTRY
Keywords: breast cancer; chlorin e6; cyclopamine; drug delivery; photodynamic therapy.
Affiliations: Cancer Center, Shanghai East Hospital, Tongji University, Shanghai, China., The Institute for Biomedical Engineering & Nano Science (iNANO), School of Medicine, Tongji University, Shanghai, China.

Abstract:  Photodynamic therapy (PDT) has shown great promise in breast cancer treatment. However, simplex target ligand modification or stimuli release cannot meet the requirement of effective drug delivery to solid tumor tissue. To overcome continuous bio-barriers existing in the tumor microenvironment, multi-stage response drug delivery was desirable. Herein, we developed a unique tumor microenvironment tailored nanoplatform for chlorin e6 (Ce6) delivery. We chose bovine serum albumin (BSA) as "mother ships" material for effective tumor periphery resident, cyclopamine (CYC) as extracellular matrix (ECM) inhibitor and synergistic anti-tumor agent, and diselenide containing amphiphilic hyaluronic acid-chlorin e6 polymers (HA-SeSe-Ce6) synthesized as "small bombs" for internal tissue destruction. The above three distinct function compositions were integrated into an independent CYC and HA-SeSe-Ce6 co-delivery albumin nano-system (ABN@HA-SeSe-Ce6/CYC). The obtained nano-system presents good biocompatible, long circulation and effective tumor accumulation. After entering tumor microenvironment, CYC gradually releases to disrupt the ECM barrier to open the way for further penetration of HA-SeSe-Ce6. Subsequently, targeted tumor cell internalization and intracellular redox response release of Ce6 would achieve. Moreover, CYC could also make up the deficiency of Ce6 in hypoxia area, owing to its anti-tumor effect. Improved therapeutic efficacy was verified in a breast cancer cell line and tumor-bearing mice model. 

Retracted: No

PubPeer comments:
 My first round of comments was partly addressed by the authors. Not the same can be said about my second round of comments, which were ignored by either authors or editor. As I think that some experiments in this manuscript lack rigor and or explaination, I re-iterate here for other readers that may be tempted by incredibly good results. The presented method achieves perfect score on 10 out of 13 classes (fig 5); is this possible? At this stage, I realise: I am confused about the predictions the use of accuracy as the only comparison metric in some instances. In 2.7 the authors describe  multi-class classifiers , which to me is a model that predicts 1 class out of multiple classes for any input protein. From previous reading (e.g. 2.4), this interpretation seems to be substantiated. Based on results though, I m starting to believe the model might actually predict multiple classes for the same protein. Perfect accuracy (100%!) to me is a high indicator that something is wrong. Maybe accuracy is actually recall in these cases? Incidentally, there is no indication of recall or precision. One strategy to overcome this problem would be to limit the test dataset on single locations: checking section 2.1 I see that this is the case for Yeast, but I don t see any mention of this for Human data. Yet, on the Yeast results (3.6), a comparison a-la Fig. 6 is missing. At this stage the confusion is just too much; I d encourage: 1. detailing how accuracy is calculated in 2.7 2. including precision recall (even better: confusion matrices, they are well understood!) 3. make it extremely clear what the model predicts, how many classes are predicted for any given protein 4. How evaluation is treated in cases where proteins have multiple locations Further reading prompts more questions: - The first sentence of 2.7 makes me believe cross-validation is performed on the trained method; this would defy the use of cross validation? - Is cross validation stratified? Why not? What s the consequence of a cross fold with 0 participants of an underrepresented class (0 to predict = 100% accuracy?) - Is cross validation applied to artificial (SMOTE) samples? - What is the final validation set (post- x-fold), and how is it split from testing)? Why not take something from literature, e.g. DeepLoc? This would avoid having to detail how splits are made, etc. and clarify a lot of the points just mentioned above. Regarding MCC: why does DeepLoc use Gorodkin vs. MCC here (to me, it seems that the MCC described in this manuscript may be Gorodkin)? To what do the authors attribute the significant worsening of MCC accuracy reported in this manuscript w.r.t. the Gorodkin accuracy reported in DeepLoc (accuracy here: 0.66 vs. DeepLoc: 0.72 MCC here: 0.57 vs. DeepLoc Gorodkin: 0.69?) , 
 Hi, Thanks for your comments. We will answer the comments in several weeks. We need to discuss it. BW Yudong Cai , 
    Please see the attaches for our response. , 
 Thank you for the extensive responses! ,