QSAR prediction, synthesis, anticancer evaluation, and mechanistic investigations of novel sophoridine derivatives as topoisomerase I inhibitors.

Sophoridine, which is derived from the Leguminous plant Sophora alopecuroides L., has certain pharmacological activity as a new anticancer drug. Herein, a series of novel N-substituted sophoridine derivatives was designed, synthesized and evaluated with anticancer activity. Through QSAR prediction models, it was discovered that the introduction of a benzene ring as a main pharmacophore and reintroduced into a benzene in para position on the phenyl ring in the novel sophoridine derivatives improved the anticancer activity effectively. In vitro, 28 novel compounds were evaluated for anticancer activity against four human tumor cell lines (A549, CNE-2, HepG-2, and HEC-1-B). In particular, Compound 26 exhibited remarkable inhibitory effects, with an IC50 value of 15.6 µM against HepG-2 cells, surpassing cis-Dichlorodiamineplatinum (II). Molecular docking studies verified that the derivatives exhibit stronger binding affinity with DNA topoisomerase I compared to sophoridine. In addition, 26 demonstrated significant inhibition of DNA Topoisomerase I and could arrest cells in G0/G1 phase. This study provides valuable insights into the design and synthesis of N-substituted sophoridine derivatives with anticancer activity.

Advances in liposomes loaded with photoresponse materials for cancer therapy.

Cancer treatment is presently a significant challenge in the medical domain, wherein the primary modalities of intervention include chemotherapy, radiation therapy and surgery. However, these therapeutic modalities carry side effects. Photothermal therapy (PTT) and photodynamic therapy (PDT) have emerged as promising modalities for the treatment of tumors in recent years. Phototherapy is a therapeutic approach that involves the exposure of materials to specific wavelengths of light, which can subsequently be converted into either heat or Reactive Oxygen Species (ROS) to effectively eradicate cancer cells. Due to the hydrophobicity and lack of targeting of many photoresponsive materials, the use of nano-carriers for their transportation has been extensively explored. Among these nanocarriers, liposomes have been identified as an effective drug delivery system due to their controllability and availability in the biomedical field. By binding photoresponsive materials to liposomes, it is possible to reduce the cytotoxicity of the material and regulate drug release and accumulation at the tumor site. This article provides a comprehensive review of the progress made in cancer therapy using photoresponsive materials loaded onto liposomes. Additionally, the article discusses the potential synergistic treatment through the combination of phototherapy with chemo/immuno/gene therapy using liposomes.

Laser-Ignited Lipid Peroxidation Nanoamplifiers for Strengthening Tumor Photodynamic Therapy Through Aggravating Ferroptotic Propagation and Sustainable High Immunogenicity.

Photodynamic therapy (PDT) is extensively investigated for tumor therapy in the clinic. However, the efficacy of PDT is severely limited by the tissue penetrability of light, short effective half-life and radius of reactive oxygen species (ROS), and the weak immunostimulatory effect. In this study, a glutathione (GSH)-activatable nano-photosensitizer is developed to load with arachidonic acid (AA) and camouflage by erythrocyte membrane, which serves as a laser-ignited lipid peroxidation nanoamplifier (MAR). The photosensitive effect of MAR is recovered accompanied by the degradation in the tumor microenvironment and triggers the peroxidation of AA upon laser excitation. Interestingly, it aggravates the propagation of ferroptosis among cancer cells by driving the continuous lipid peroxidation chain reactions with the participation of the degradation products, ferrous ions (Fe2+), and AA. Consequently, even the deep-seated tumor cells without illumination also undergo ferroptosis owing to the propagation of ferroptotic signal. Moreover, the residual tumor cells undergoing ferroptosis still maintain high immunogenicity after PDT, thus continuously triggering sufficient tumor-associated antigens (TAAs) release to remarkably promote the anti-tumor immune response. Therefore, this study will provide a novel "all-in-one" nano-photosensitizer that not only amplifies the damaging effect and expands the effective range of PDT but also improves the immunostimulatory effect after PDT.

α-Glucosidase inhibitors from the husks of rice Oryza sativa L.

Rice husk is one of the most plentiful agriculture by-products in rice producing areas, which harbors a substantial proportion of biological metabolites, however, it has not been well studied. As an attempt to utilize it as a productive manner, phytochemical investigation on rice husk has performed and led to the isolation of three undescribed (1, 2, and 7), along with twelve known components (3-6, and 8-15). Those chemical structures were elucidated based on massive spectroscopic methods. Among them, compounds 4, 6-8, and 10-13 have been shown to act as α-glucosidase inhibitors. Notably, the most active compounds, 10/11, demonstrated comparable α-glucosidase inhibitory effect (IC50 = 1.83 ± 0.11 µg/mL) to that of 1-deoxynojirimycin (IC50 = 1.02 ± 0.16 µg/mL). For the molecular docking simulation studies, compounds 10/11 showed relative binding interactions with α-glucosidase enzyme (PDB ID: 3A4A) that similar to those reference inhibitors. Additionally, the crude extract of O. sativa demonstrated better α-glucosidase inhibitory effect to that of isolated components, with the IC50 value at 1.25 ± 0.07 µg/mL.

Research Progress in Nanopharmaceuticals with Different Delivery Routes in the Antivirus Field.

Human health is significantly threatened by infectious diseases caused by viral infection. Over the years, there have been numerous virus epidemics worldwide, causing millions of deaths. Traditional antiviral medications have many problems, including poor solubility and antiviral resistance. Additionally, because different drug delivery methods have different biological barriers to overcome, the drug's bioavailability will be significantly affected. Therefore, it is essential that researchers create more effective antiviral drugs. To serve as a guide for the future development of nanosized antiviral drugs with stronger and more precise therapeutic effects, research has been performed on nanotechnology in the field of antiviral therapy. This review summarizes the recent developments in antiviral nanopharmaceuticals with different delivery routes. Research on 7 typical viruses, including COVID-19, has been included in this review. After being loaded into nanoparticles, antiviral drugs can be delivered through several drug modes of delivery, overcoming biological barriers. Moreover, some nanoparticles themselves have the ability to combat infections, so they can be used in conjunction with antiviral medication. The use of nanoparticle medications through various routes of administration can result in their unique benefits. They can be capable of overcoming its limitations as well as retaining the advantages of this method of delivery. This will motivate researchers to conducted a new investigation on nanoparticle medicines from the standpoint of the route of administration in order to increase the practicability of antiviral medications.

A Lightweight AMResNet Architecture with an Attention Mechanism for Diagnosing COVID-19.

AIMS: COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered. We proposed a lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19, named AMResNet. BACKGROUND: COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered. OBJECTIVE: A lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19. METHOD: By introducing the channel attention mechanism and image spatial information attention mechanism, a better level can be achieved without increasing the number of model parameters. RESULT: In the collected data sets, we achieved an average accuracy rate of more than 92%, and the sensitivity and specificity of specific disease categories were also above 90%. CONCLUSION: The convolution neural network framework can be used as a novel method for artificial intelligence to diagnose COVID-19 or other diseases based on medical images.

Preparation and cytotoxicity evaluation of folic acid-modified YF8-OA self-assembled lipid prodrug nanoparticles.

This study aimed to improve the use of YF8, a matrine derivative obtained through chemical transformation of matrine extracted from Sophora alopecuroides. YF8 has demonstrated improved cytotoxicity compared to matrine, but its hydrophobic nature hinders its application. To overcome this, the lipid prodrug YF8-OA was synthesized by linking oleic acid (OA) to YF8 through an ester bond. Although YF8-OA could self-assemble into unique nanostructures in water, it was not sufficiently stable. To enhance the stability of YF8-OA lipid prodrug nanoparticles (LPs), we employed the strategy of PEGylation using DSPE-mPEG2000 or DSPE-mPEG2000 conjugated with folic acid (FA). This resulted in the formation of uniform spherical nanoparticles with greatly improved stability and a maximum drug load capacity upto 58.63%. Cytotoxicity was evaluated in A549, HeLa, and HepG2 cell lines. The results showed that in HeLa cells, the IC50 value of YF8-OA/LPs with FA-modified PEGylation was significantly lower than that of YF8-OA/LPs modified by PEGylation alone. However, no significant enhancement was observed in A549 and HepG2 cells. In conclusion, the lipid prodrug YF8-OA can form nanoparticles in aqueous solution to address its poor water solubility. Modification with FA resulted in further enhanced cytotoxicity, providing a potential avenue for exerting the antitumor activity of matrine analogs.

Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network.

Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied to process cell line features and combine them with drug features. Furthermore, the latent vectors generated during the encoding process are being used to predict drug synergistic scores using a convolutional neural network. By measuring prediction performance using AUC, AUPR, and F1 score, GAECDS superior to other state-of-the-art models. In addition, four pairs of the predicted top 10 drug combinations were found to work well enough for evaluation. The case study shows that the GAECDS approach is useful for identifying potential drug synergy.

A compact review of progress and prospects of deep learning in drug discovery.

BACKGROUND: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. RESULTS: This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.

Research Progress of Nanocarriers for the Treatment of Alzheimer's Disease.

Currently, many therapeutic drugs are difficult to cross the blood-brain barrier (BBB), making it difficult to reach the site of action and thus fail to achieve the desired efficacy. In recent years, researchers and drug designers have increasingly focused on nanotechnology to break through the difficulty of small molecule inhibitors to cross the blood-brain barrier (BBB) and improve the success rate of drug delivery to the central nervous system. Among the common central neurological diseases, such as encephalitis, Parkinson's, Alzheimer's disease, and epilepsy, Alzheimer's disease has attracted much attention from researchers. Alzheimer's disease is a specific neurodegenerative disease, which causes irreversible degeneration of neurons as well as synapses in the brain, resulting in memory and cognitive dysfunction, along with other psychiatric symptoms and behavioral disorders, which seriously affects people's everyday life. Moreover, nanotechnology has excellent potential for application in AD treatment. Studies have shown that nanocarriers can target the delivery of chemotherapeutic drugs, antioxidants, and other therapeutic substances to brain tissue using existing physiological mechanisms, thus effectively alleviating the disease progression of AD. Therefore, various nanoparticles and nanomedicine have been developed and constructed for diagnosing and treating AD in the past decades, such as nanoparticles, bionanoparticles, liposomes, nano-gel, dendrimers, and self-assembled nanoparticles. This study aims to review the applications and results of nanotechnology in the treatment of Alzheimer's disease in recent years and provide some ideas and clues for future research and development of more effective drug delivery systems.

A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials.

Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.

Knockdown of TTC9 inhibits the proliferation, migration and invasion, but induces the apoptosis of lung adenocarcinoma cells.

Lung adenocarcinoma (LUAD) is one of the most commonly diagnosed subtypes of lung cancer, and one of the deadliest cancers. Tetratricopeptide repeat domain 9A (TTC9) is upregulated and has played an oncogenic role in some malignant tumors. However, the expression and role of TTC9 has not yet been elucidated in LUAD. Here, we investigated the expression profiles, biological functions and potential molecular mechanism of the TTC9 gene in LUAD. TTC9 expression was significantly overexpressed in LUAD tissues compared with that in normal lung tissues. TTC9 expression was closely correlated with gender, lymph node metastasis, and survival status in the TCGA-LUAD cohort. Subsequent cellular function assays demonstrated that knockdown of TTC9 promoted PC9 cell apoptosis and inhibited cell proliferation, migration and invasion, leading to cell cycle arrest in G2 phase. Moreover, inhibition of TTC9 suppressed the tumorigenicity of PC9 cells in nude mice. TTC9 might serve as oncogene in LUAD through cancer-related signaling pathways including p38 MAPK pathway. The expression of TTC9 gene might be modulated by DNA copy number variant and DNA methylation. TTC9 was significantly associated with tumor immune infiltration patterns. Accordingly, TTC9 may be a novel therapeutic target for the treatment of LUAD.

Overexpression of ERCC6L correlates with poor prognosis and confers malignant phenotypes of lung adenocarcinoma.

Excision repair cross­complementation group 6 like (ERCC6L) has been reported to be upregulated in a variety of malignant tumors and plays a critical oncogenic role. However, the role and molecular mechanism of ERCC6L in lung adenocarcinoma (LUAD) remain unclear, and were therefore investigated in the present study. Clinical data of patients with LUAD were obtained and bioinformatics analysis was performed to investigate the expression characteristics, prognostic value, and biological function of ERCC6L. In addition, cell function experiments were performed to detect the effect of ERCC6L silencing on the biological behavior of LUAD cells. The results revealed that ERCC6L expression was significantly higher in LUAD tissues vs. normal lung tissues and closely associated with nodal invasion, advanced clinical stage and survival in LUAD. Overexpression of ERCC6L was an independent prognostic biomarker of overall survival, progression­free interval, and disease­specific survival in patients with LUAD. DNA amplification and low methylation levels of ERCC6L suggested regulation at both the genetic and epigenetic levels. The most significant positive genes co­expressed with ERCC6L were mainly enriched in the cell cycle signaling pathway. The major functions of ERCC6L in LUAD cells were positively correlated with the cell cycle, DNA damage, DNA repair, proliferation, invasion and epithelial­mesenchymal transition (EMT). Knockdown of ERCC6L inhibited the proliferative, migratory and invasive abilities of A549 and PC9 cells. It also promoted cell apoptosis, and led to cell cycle arrest in the S phase. ERCC6L may regulate the EMT process through the Wnt/ß­catenin and Wnt/Notch 3 signaling pathways, thus regulating the tumorigenesis and progression of LUAD. The overexpression of ERCC6L may be a biological indicator for the diagnosis and prognosis of LUAD. ERCC6L may be a novel molecular target for the treatment of lung cancer.

Transfer Learning of the ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning.

OBJECTIVE: The aim of the study was to verify the ability of the deep learning model to identify five subtypes and normal images in non-contrast enhancement CT of intracranial hemorrhage. METHODS: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) who underwent intracranial hemorrhage noncontrast enhanced CT were selected, obtaining 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. RESULTS: The overall accuracy of ResNet-18 and DenseNet-121 models was obtained as 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of the DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76, respectively. The AUC values of the two deep learning models were found to be above 0.9. CONCLUSION: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.

A Genetic Variant of miR-34a Contributes to Susceptibility of Ischemic Stroke Among Chinese Population.

miRNAs are small non-coding RNAs modulating gene expression, and variants in miRNA genes are involved in the pathogenesis of ischemic stroke (IS). However, the effect of miR-34a polymorphisms on IS susceptibility has rarely been reported. In the present study, we investigated the association between rs12128240, rs2666433, and rs6577555 of the miR-34a gene and IS susceptibility. Snapshot assay was used to detect miR-34a polymorphisms in 548 IS patients and 560 controls. Relative expression of miR-34a was measured by quantitative real-time PCR. We found that rs2666433 was associated with a significantly increased risk of IS (AA vs. GG: OR = 1.61, 95% CI = 1.05-2.52, P = 0.031; AA vs. GG+GA: OR = 1.58, 95% CI = 1.05-2.45, P = 0.026). For the IS subtypes, rs2666433 was associated with large artery atherosclerosis (AA vs. GG: OR = 2.09, 95% CI = 1.16-3.51, P = 0.007; AA vs. GG+GA: OR = 2.02, 95% CI = 1.15-3.33, P = 0.007; A vs. G: OR = 1.36, 95% CI = 1.07-1.81, P = 0.021). Additionally, the level of miR-34a was significantly up-regulated in IS patients compared to the controls (P < 0.001), and patients with rs2666433 AA genotype had a higher level of miR-34a than those with GG+GA genotypes (P < 0.001). Furthermore, increased level of homocysteine was observed in IS patients compared to the controls (P < 0.001), especially in patients carrying the rs2666433AA genotype compared to those carrying the rs2666433 GG+GA genotypes (P < 0.001). However, no significant association between rs12128240 or rs6577555 and IS was found. Collectively, our study found the association between miR-34a polymorphisms and the risk of IS among the Chinese population. The results may provide an explanation for etiology of IS and a potential biomarker or therapeutic target for IS. HIGHLIGHTS-MiR-34a rs2666433 polymorphism was associated with an increased risk of ischemic stroke.-The level of miR-34a was significantly up-regulated in ischemic stroke patients compared with controls, and patients with rs2666433 AA genotype had a higher level miR-34a than those with GG+GA genotypes.-Furthermore, increased level of homocysteine was showed in IS patients compared to controls, and in patients carrying the rs2666433AA compared to those carrying the rs2666433 GG+GA.

Synergistic Combination of Doxorubicin and Paclitaxel Delivered by Blood Brain Barrier and Glioma Cells Dual Targeting Liposomes for Chemotherapy of Brain Glioma.

Brain glioma has become a great threat to human health in decades. To maximize the therapeutic efficacy of brain glioma as well as minimize the side effects, drugs should be penetrated through the blood brain barrier (BBB) and then targeted to the brain carcinoma cells with effective concentration. A dual-ligand delivery strategy was employed to achieve both of these goals. Herein, both specific targeting ligand transferrin and cell-penetrating peptide TAT were conjugated onto liposomes (TF/TAT-LP) to develop a brain glioma dual-ligand delivery system. Synergistic combination of doxorubicin (DOX) and paclitaxel (PTX), compared with using them separately, could more efficiently suppress tumor aggravation. In vitro studies including cellular uptake and three-dimensional (3D) tumor spheroid penetration assays proved that TF/TAT-LP could target brain endothelial and carcinoma cells with deeply penetration through the endothelial monolayers and target to the core of the tumor spheroids. In vivo imaging proved that the TF/TAT-LP possesses the highest tumor distribution, which was also confirmed by fluorescent images of the brain section. Ultimately, the DOX and PTX-loaded TF/TAT-LP (TF/TAT-PTX/DOX-LP) shows the best anti-glioma effect with improvement of glioma bearing survival time. In conclusion, synergistic combination of doxorubicin and paclitaxel delivered by the TF/TAT-LP could efficiently target to the brain glioma with satisfying treatment efficiency, which may be a promising formulation for glioma therapy.

Targeted delivery of transferrin and TAT co-modified liposomes encapsulating both paclitaxel and doxorubicin for melanoma.

The purpose of this study was to develop an efficient dual-ligand based liposomal drug delivery system with targeting specificity as well as properties that would kill melanoma cells. Liposomes modified with transferrin (Tf) and cell-penetrating peptide TAT was prepared, which encapsulated two kinds of chemotherapy drugs, paclitaxel and doxorubicin (Tf/TAT-PTX/DOX-LP). The Tf ligands specifically bind to the overexpressed Tf receptors on the surface of melanoma cells, while the TAT ligands functioned as a classical cell penetrating peptide, helping dual-ligand liposomes be internalized by melanoma cells. The effect of dual-targeting system and "double-drug" combination therapy were evaluated both in vitro and in vivo. In vitro, cellular uptake, intracellular distribution and tumor spheroids penetration studies demonstrated that the system could not only be selectively and efficiently penetrate melanoma cells. Besides, apoptosis staining assay and cytotoxicity showed effective anti-tumor capability and obvious synergistic effect of combination therapy of PTX and DOX. In vivo imaging and fluorescent images of tumor section further demonstrated that Tf/TAT-PTX/DOX-LP had the highest tumor distribution. The results of these experiments demonstrated that double-drug liposomal drug delivery systems (DDS) had both enhanced targeting efficiency and increased therapeutic efficacy.

Tumor microenvironment sensitive doxorubicin delivery and release to glioma using angiopep-2 decorated gold nanoparticles.

Glioma is still hard to be treated due to their complex microenvironment. In this study, a gold nanoparticle-based delivery system was developed. The system, An-PEG-DOX-AuNPs, was loaded with doxorubicin (DOX) through hydrazone, an acid-responsive linker, and was functionalized with angiopep-2, a specific ligand of low density lipoprotein receptor-related protein-1 (LRP1), which could mediate the system to penetrate blood brain barrier and target to glioma cells. The particle size of An-PEG-DOX-AuNPs was 39.9 nm with a zeta potential of -19.3 mV, while the DOX loading capacity was 9.7%. In vitro, the release of DOX from DOX-AuNPs was pH-dependent. At lower pH values, especially 5.0 and 6.0, release of DOX was much quicker than that at pH 6.8 and 7.4. After coating with PEG, the acid-responsive release of DOX from PEG-DOX-AuNPs was almost the same as that from DOX-AuNPs. Cellular uptake study showed obviously higher intensity of intracellular An-PEG-DOX-AuNPs compared with PEG-DOX-AuNPs. In vivo, An-PEG-DOX-AuNPs could distribute into glioma at a higher intensity than that of PEG-DOX-AuNPs and free DOX. Correspondingly, glioma-bearing mice treated with An-PEG-DOX-AuNPs displayed the longest median survival time, which was 2.89-fold longer than that of saline. In conclusion, An-PEG-DOX-AuNPs could specifically deliver and release DOX in glioma and significantly expand the median survival time of glioma-bearing mice.

Increased delivery of doxorubicin into tumor cells using extracellularly activated TAT functionalized liposomes: in vitro and in vivo study.

The development of highly efficient tumor-targeted delivery systems is crucial for successful tumor treatment. Previously, a novel cell-penetrating peptide TAT and cleavable polyethylene glycol (PEG) co-modified liposome delivery system (C-TAT-Lipo) showed enhanced accumulation in tumor regions. Under the control of cysteine (Cys), the liposomes were activated extracellularly and achieved increased delivery of their cargo into tumor cells efficiently. In this study, we developed an optimal formulation for the encapsulation of Doxorubicin (DOX) by this delivery system for tumor treatment. The in vitro study showed that the C-TAT-Lipo with Cys delivery system not only enhanced the amount of DOX delivered by at least 100% compared to other DOX-containing formulations, but also displayed high cytotoxicity against tumorigenic cell lines. Compared to other groups, the DOX-loaded C-TAT-Lipo formulation in the presence of cysteine enhanced treatment efficacy by lowering the IC50 (1.67 +/- 0.14 microM) and increasing the cancer cell apoptosis percentage (37.10%). Moreover, the in vivo antitumor activity also showed that DOX-loaded C-TAT-Lipo with injection of cysteine achieved the best tumor growth inhibition with a tumor growth rate of only 58.40 +/- 16.33% (% of initial volume/day), which was significant less than that achieved by other DOX formulations.