In silico screening and in vivo experimental validation of 15-PGDH inhibitors from traditional Chinese medicine promoting liver regeneration.

The enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH), which acts as a negative regulator of prostaglandin E2 (PGE2) levels and activity, represents a promising pharmacological target for promoting liver regeneration. In this study, we collected data on 15-PGDH homologous family proteins, their inhibitors, and traditional Chinese medicine (TCM) compounds. Leveraging machine learning and molecular docking techniques, we constructed a prediction model for virtual screening of 15-PGDH inhibitors from TCM compound library and successfully screened genistein as a potential 15-PGDH inhibitor. Through further validation, it was discovered that genistein considerably enhances liver regeneration by inhibiting 15-PGDH, resulting in a significant increase in the PGE2 level. Genistein's effectiveness suggests its potential as a novel therapeutic agent for liver diseases, highlighting this study's contribution to expanding the clinical applications of TCM.

Design and evaluation of α-helix-based peptide inhibitors for blocking PD-1/PD-L1 interaction.

The current research in tumor immunotherapy indicates that blocking the protein-protein interaction (PPI) between PD-1 and its ligand, PD-L1, may be one of the most effective treatments for cancer patients. The α-helix is a common elements of protein secondary structure and is often involved in protein interaction. Thus, α-helix-based peptides could mimic proteins involved in such interactions and are also capable of modulating PPI in vivo. In this study, starting from a potential α-helix-rich protein, we designed a series of α-helix-based peptide candidates to block PD-1/PD-L1 interaction. These candidates were first screened using molecular docking and molecular dynamics simulations, and then their capacities to inhibit PD-1/PD-L1 interactions and to restore antitumor immune activities were investigated using the HTRF assay, SPR assay, cellular co-culture experiments and animal model experiments. Two peptides exhibited the best anti-tumor effects and the strong ability to restore the immunity of tumor-infiltrating T-cells. Further D-amino acid substitution was employed to improve the serum stability of peptide candidate, making the intravenous administration easier while maintaining the therapeutic efficacy. The resultant peptides showed promise as checkpoint inhibitors for application in tumor immunotherapy. These findings suggested that our strategy for developing peptides starting from an α-helical structure could be used in the design of bioactive inhibitors to potential block protein-protein interactions.

Identification of Potential RBPJ-Specific Inhibitors for Blocking Notch Signaling in Breast Cancer Using a Drug Repurposing Strategy.

Notch signaling is a key parameter in regulating cell fate during tissue homeostasis, and an aberrant Notch pathway can result in mammary gland carcinoma and has been associated with poor breast cancer diagnosis. Although inhibiting Notch signaling would be advantageous in the treatment of breast cancer, the currently available Notch inhibitors have a variety of side effects and their clinical trials have been discontinued. Thus, in search of a more effective and safer Notch inhibitor, inhibiting recombinant signal binding protein for immunoglobin kappaJ region (RBPJ) specifically makes sense, as RBPJ forms a transcriptional complex that activates Notch signaling. From our established database of more than 10,527 compounds, a drug repurposing strategy-combined docking study and molecular dynamic simulation were used to identify novel RBPJ-specific inhibitors. The compounds with the best performance were examined using an in vitro cellular assay and an in vivo anticancer investigation. Finally, an FDA-approved antibiotic, fidaxomicin, was identified as a potential RBPJ inhibitor, and its ability to block RBPJ-dependent transcription and thereby inhibit breast cancer growth was experimentally verified. Our study demonstrated that fidaxomicin suppressed Notch signaling and may be repurposed for the treatment of breast cancer.

A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model.

Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model's good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.

Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies.

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.

Development of simultaneous interaction prediction approach (SiPA) for the expansion of interaction network of traditional Chinese medicine.

BACKGROUND: Due to the lack of enough interaction data among compositions, targets and diseases, it is difficult to construct a complete network of Traditional Chinese Medicine (TCM) that comprehensively reflects active compositions and their synergistic network in terms of specific diseases. Therefore, mapping of the full spectrum of interaction between compounds and their targets is of central importance when we use network pharmacology approach to explore the therapeutic potential of the TCM. METHODS: To address this challenge, we developed a large-scale simultaneous interaction prediction approach (SiPA) integrated one interaction network based simple inference model (SIM), focusing on 'logical relevance' between compounds, proteins or diseases, and another compound-target correlation space based interaction prediction model (CTCS-IPM) that was built on the basis of the canonical correlation analysis (CCA) to estimate the position of compounds (or targets) in compound-protein correlated space. Then SiPA was applied to discover reliable multiple interactions for interaction network expansion of a TCM, compound Salvia miltiorrhiza. By means of network analysis, potential active compounds and their related network synergy underlying cardiovascular diseases were evaluated between expanded and original interaction networks. Part of new interactions were validated with existing experimental evidence and molecular docking. RESULTS: As evaluated with known test dataset, the established combination approach was proved to make highly accurate prediction, showing a well prediction performance for the SIM and a high recall rate of 85.2% for the CTCS-IPM. Then 710 pairs of new compound-target interactions, 24 pairs of new compound-cardiovascular disease interactions and 294 pairs of new cardiovascular disease-protein interactions were predicted for compound Salvia miltiorrhiza. Results of network analysis suggested the network expansion could dramatically improve the completeness and effectiveness of the network. Validation results of literature and molecular docking manifested that inferred interactions had good reliability. CONCLUSIONS: We provided a practical and efficient way for large-scale inference of multiple interactions of TCM ingredients, which was not limited by the lack of negative samples, sample size and target 3D structures. SiPA could help researchers more accurately prioritize the effective compounds and more completely explore network synergy of TCM for treating specific diseases, indicating a potential way for effectively identifying candidate compound (or target) in drug discovery.

Use of Gamithromycin as a Chiral Selector in Capillary Electrophoresis.

In this short communication, we report the use of a second-generation macrolide antibiotic, gamithromycin (Gam), as a novel chiral selector for enantioseparation in capillary electrophoresis (CE). A preliminary analysis of the experiment results shows that Gam is especially suitable for the separation of chiral primary amines. Factors influencing enantioseparations were systematically investigated including the composition of the background electrolyte (BGE), concentration of Gam, the type and proportion of organic solvents, applied voltage, etc. In particular, N-Methylformamide (NMF) was successfully used as a non-aqueous solvent for Gam, and shown to be extremely effective for the separation of primaquine (PMQ) and 1-aminoindan (AMI) when used alone or mixed with other commonly used non-aqueous solvents (e.g. methanol). To our knowledge this was also the first application of NMF as a non-aqueous solvent for antibiotic chiral selectors in CE. The best separations were obtained with 100 mM Tris, 125 mM H3BO3 and 80 mM Gam in methanol/NMF (25:75) solvent for PMQ and AMI, or 80-100 mM Gam in methanol for the other model analytes. Among the analytes, the resolution (Rs) of amlodipine (AML) reached up to 15.65, which is to our knowledge the highest value ever reported in CE studies for this compound (except for using molecularly imprinted polymers technique).

Reimaging biological barriers affecting distribution and extravasation of PEG/peptide- modified liposomes in xenograft SMMC7721 tumor.

Liposomes, as one of the most successful nanotherapeutics, have a major impact on many biomedical areas. In this study, we performed laser scanning confocal microscope (LSCM) and immunohistochemistry (IHC) assays to investigate the intra-tumor transport and antitumor mechanism of GE11 peptide-conjugated active targeting liposomes (GE11-TLs) in SMMC7721 xenograft model. According to classification of individual cell types in high resolution images, biodistribution of macrophages, tumor cells, cells with high epidermal growth factor receptor (EGFR) expression and interstitial matrix in tumor microenvironment, in addition, their impacts on intra-tumor penetration of GE11-TLs were estimated. Type I collagen fibers and macrophage flooded in the whole SMMC7721 tumor xenografts. Tumor angiogenesis was of great heterogeneity from the periphery to the center region. However, the receptor-binding site barriers were supposed to be the leading cause of poor penetration of GE11-TLs. We anticipate these images can give a deep reconsideration for rational design of target nanoparticles for overcoming biological barriers to drug delivery.

Investigation of the synergistic effect of chiral ionic liquids as additives in non-aqueous capillary electrophoresis for enantioseparation.

In this work, tetraalkylammonium amino acid ionic liquids (TAA-AAILs) were first applied to non-aqueous capillary electrophoresis (NACE) to establish synergistic systems with a conventional chiral selector, native ß-cyclodextrin (ß-CD). Excellent enantioseparations of some dansyl-amino acid (Dns-AA) samples were achieved. A series of comparison experiments and a molecular docking study were performed to validate the synergistic effect of TAA-AAILs and ß-CD in NACE. Several interesting results were observed compared with previously reported chiral ILs-related aqueous CE studies. In particular, the direct enantioselectivity of TAA-AAILs was observed for the first time by using it as sole chiral selector in NACE. This was an encouraging finding because it was the first direct and convincing evidence that AAILs were able to participate in the enantiorecognition process in the conventional chiral selectors-based synergistic systems. The new TAA-AAILs synergistic NACE system was further optimized in terms of alkyl chain length, TAA-AAILs concentration, ß-CD concentration, electrolyte composition and applied voltage, etc. Best enantioseparations of Dns-AAs were obtained when 100 mM ß-CD and 10 mM tetramethylammonium-l-arginine (TMA-l-Arg) were added in an NMF buffer containing 50 mM Tris and 35 mM CA (apparent pH 7.85) under UV detection. The applied voltage was set at 30 kV. The method was then successfully employed for the determination of enantiomeric impurities of a real AA sample. This work proved that the use of chiral ILs as additives in NACE is a promising approach for enantioseparation.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, the deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis. In this study, a gene expression data based deep learning model was developed to predict DILI in advance by using gene expression data associated with DILI collected from ArrayExpress and then optimized by feature gene selection and parameters optimization. In addition, the previous machine learning algorithm support vector machine (SVM) was also used to construct another prediction model based on the same data sets, comparing the model performance with the optimal DL model. Finally, the evaluation test using 198 randomly selected samples showed that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity, 96.8% specificity, 0.942 matthews correlation coefficient, and 0.989 area under the ROC curve, while the performance of SVM model only reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794 matthews correlation coefficient, and 0.901 area under the ROC curve. Furthermore, external data sets verification and animal experiments were conducted to assess the optimal DL model performance. Finally, the predicted results of the optimal DL model were almost consistent with experiment results. These results indicated that our gene expression data based deep learning model could systematically and accurately predict DILI in advance. It could be a useful tool to provide safety information for drug discovery and clinical rational drug use in early stage and become an important part of drug safety assessment.

Ratiometric co-encapsulation and co-delivery of doxorubicin and paclitaxel by tumor-targeted lipodisks for combination therapy of breast cancer.

Combination therapy is a promising treatment for certain advanced drug-resistant cancers. Although effective inhibition of various tumor cells was reported in vitro, combination treatment requires improvement in vivo due to uncontrolled ratiometric delivery. In this study, a tumor-targeting lipodisk nanoparticle formulation was developed for ratiometric loading and the transportation of two hydrophobic model drugs, doxorubicin (DOX) and paclitaxel (PTX), in one single platform. Furthermore, a slightly acidic pH-sensitive peptide (SAPSP) incorporated into lipodisks effectively enhanced the tumor-targeting and cell internalization. The obtained co-loaded lipodisks were approximately 30 nm with a pH-sensitive property. The ratiometric co-delivery of two drugs via lipodisks was confirmed in both the drug-resistant MCF-7/ADR cell line and its parental MCF-7 cell line in vitro, as well as in a tumor-bearing mouse model in vivo compared with a cocktail solution of free drugs. Co-loaded lipodisks exerted improved cytotoxicity to tumor cells in culture, particularly to drug-resistant tumor cells at synergistic drug ratios. In an in vivo xenograft mouse model, the anti-tumor ability of co-loaded lipodisks was evidenced by the remarkable inhibitory effect on tumor growth of either MCF-7 or MCF-7/ADR tumors, which may be attributed to the increased and ratiometric accumulation of both drugs in the tumor tissues. Therefore, tumor-specific lipodisks were crucial for the combination treatment of DOX and PTX to completely exert a synergistic anti-cancer effect. It is concluded that for co-loaded lipodisks, cytotoxicity data in vitro could be used to predict their inhibitory activity in vivo, potentially enhancing the clinical outcome of synergistic therapy.

Preparation and optimization of poly (lactic acid) nanoparticles loaded with fisetin to improve anti-cancer therapy.

Fisetin is a natural flavonoid with promising antitumor activity, whereas its clinical application is limited by its hydrophobic property. In this study, we aimed to load fisetin into poly(lactic acid) (PLA) nanoparticles to increase fisetin's solubility and therapeutic efficacy. Based on spontaneous emulsification solvent diffusion (SESD) method, the formulation of PLA nanoparticles was optimized by two successive experimental designs. One-factor-at-a-time variation experiments were first applied to investigate the effects of four process variables on three responses, including drug encapsulation efficiency, average particles size and cumulative drug release ratio, followed by determining the possible ranges of these variables. Subsequently, the combinations of four variables at best levels were evaluated using a Taguchi orthogonal array design with regard to the same three responses. Eventually, the nanoparticle prepared by optimized procedure showed a narrow size distribution around 226.85 ±â€¯4.78 nm with a high encapsulation efficiency of 90.35%. The incorporation of fisetin in nanoparticles was subsequently confirmed by FT-IR and DSC spectroscopy. Furthermore, cytotoxicity assay against HCT116 colon cancer cells in vitro and antitumor test in a xenograft 4T1 breast cancer model in vivo demonstrated that the antitumor effect of drug-loaded nanoparticles was superior to that of free drug solution.

Enhanced enantioselectivity of native α-cyclodextrins by the synergy of chiral ionic liquids in capillary electrophoresis.

In the cyclodextrins family, the native α-cyclodextrin has almost been abandoned in capillary electrophoresis chiral separation due to its much weaker enantioselectivity compared with ß-cyclodextrin and their derivatives. In this work, several amino acid chiral ionic liquids were selected to establish synergistic enantioseparation systems with native α-cyclodextrin. Enhanced enantioselectivities were observed in the chiral ionic liquids/α-cyclodextrin synergistic systems compared with single α-cyclodextrin system. A series of comparison experiments were performed to demonstrate the superiority of the synergistic systems. Primary parameters affecting the enantioseparation were systematically optimized, including the type and concentration of chiral ionic liquids, α-cyclodextrin concentration, buffer pH, and applied voltage. Best separations of the model enantiomers were obtained in a 20 mM Tris/H3 PO4 buffer at pH 2.5 containing 3% (m/v) α-cyclodextrin and 30 mM tetramethylammonium-l-arginine. The results show that the α-cyclodextrin is also worth our attention when selecting chiral selectors for capillary electrophoresis enantioseparation of specific racemic compound.

Tumor-specific delivery of doxorubicin through conjugation of pH-responsive peptide for overcoming drug resistance in cancer.

The major obstacles opposed to doxorubicin (Dox) based chemotherapy are the induction of drug resistance, together with non-specific toxicities for healthy cells. In this study, we prepared a peptide-Dox conjugate aimed at offering Dox molecules a tumor-specific functionality and improving the therapeutic effects of Dox against resistant tumor cells. A slightly acidic pH-sensitive peptide (SAPSP) with high selectivity for cancer cells was attached to Dox to obtain SAPSP-Dox prodrug. The structures and properties of this prodrug were characterized, confirming several merits, including desirable pH-sensitive property, good serum stability and favorable release behavior. Cellular uptake studies demonstrated that SAPSP-Dox was preferably accumulated in cancer cells (Dox-sensitive MCF-7 and Dox-resistant MCF-7/ADR), followed by displaying 26-fold less toxic toward noncancerous MCF-10A cells than free Dox do. The conjugated peptides enabled Dox to escape the efflux effect of P-glycoprotein mediated pump via endocytotic pathway, giving rise to remarkable cytotoxicity and apoptotic effect on MCF-7/ADR cells. Moreover, the superior inhibition efficacy of SAPSP-Dox in vivo was more evident in the both drug-sensitive and drug-resistant xenograft tumor animal models, which enabled Dox to primarily accumulated in tumor. The conjugates also demonstrated a longer half-life in plasma and a lower side effect, for example, reduced cardiac toxicity. Evidence of this study suggests that SAPSP-Dox has the potential to be a potent prodrug for treating drug resistant cancers.

Simultaneous delivery of anti-miR21 with doxorubicin prodrug by mimetic lipoprotein nanoparticles for synergistic effect against drug resistance in cancer cells.

The development of drug resistance in cancer cells is one of the major obstacles to achieving effective chemotherapy. We hypothesized that the combination of a doxorubicin (Dox) prodrug and microRNA (miR)21 inhibitor might show synergistic antitumor effects on drug-resistant breast cancer cells. In this study, we aimed to develop new high-density lipoprotein-mimicking nanoparticles (HMNs) for coencapsulation and codelivery of this potential combination. Dox was coupled with a nuclear localization signal (NLS) peptide to construct a prodrug (NLS-Dox), thereby electrostatically condensing miR21 inhibitor (anti-miR21) to form cationic complexes. The HMNs were formulated by shielding these complexes with anionic lipids and Apo AI proteins. We have characterized that the coloaded HMNs had uniformly dispersed distribution, favorable negatively charged surface, and high coencapsulation efficiency. The HMN formulation effectively codelivered NLS-Dox and anti-miR21 into Dox-resistant breast cancer MCF7/ADR cells and wild-type MCF7 cells via a high-density-lipoprotein receptor-mediated pathway, which facilitated the escape of Pgp drug efflux. The coloaded HMNs consisting of NLS-Dox/anti-miR21 demonstrated greater cytotoxicity with enhanced intracellular accumulation in resistant MCF7/ADR cells compared with free Dox solution. The reversal of drug resistance by coloaded HMNs might be attributed to the suppression of miR21 expression and the related antiapoptosis network. Furthermore, the codelivery of anti-miR21 and NLS-Dox by HMNs showed synergistic antiproliferative effects in MCF7/ADR-bearing nude mice, and was more effective in tumor inhibition than other drug formulations. These data suggested that codelivery of anti-miR21 and chemotherapeutic agents by HMNs might be a promising strategy for antitumor therapy, and could restore the drug sensitivity of cancer cells, alter intracellular drug distribution, and ultimately enhance chemotherapeutic effects.

Targeted Biomimetic Nanoparticles for Synergistic Combination Chemotherapy of Paclitaxel and Doxorubicin.

Codelivery of multiple chemotherapeutics has become a versatile strategy in recent cancer treatment, but the antagonistic behavior of combined drugs limited their application. We developed a recombinant high-density lipoprotein (rHDL) nanoparticle for the precise coencapsulation and codelivery of two established drugs and hypothesized that they could act synergistically to improve anticancer efficacy. The coloaded rHDL was formulated by passively incorporating hydrophobic paclitaxel (PTX), and subsequently remotely loading hydrophilic doxorubicin (Dox) into the same nanoparticles. The resultant rHDL system restored targeted delivery function toward cancer cells via scavenger receptor class B (SR-BI), as confirmed by in vitro confocal imaging and flow cytometry. These coloaded rHDL nanoparticles were remarkably effective in increasing the ratiometric accumulation of drugs in cancer cells and enhancing antitumor response at synergistic drug ratios. In particular, they exhibited more efficacious anticancer effects in an in vitro cytotoxicity evaluation and in a xenograft tumor model of hepatoma compared with free drug cocktail solutions. These results confirm that the coloaded rHDL nanoparticles are promising candidates for the synergistic delivery of drugs with diverse physicochemical properties in cancer treatment integrating efficiency and safety considerations.

Three chiral ionic liquids as additives for enantioseparation in capillary electrophoresis and their comparison with conventional modifiers.

The combined use of chiral ionic liquids (ILs) and conventional chiral selectors in CE to establish synergistic system has proven to be a convenient and effective approach for enantioseparation. In this work, three amino acid chiral ILs, tetramethylammonium-l-arginine (TMA-l-Arg), tetramethylammonium-l-hydroxyproline (TMA-l-Hyp) and tetramethylammonium-l-isoleucine (TMA-l-Ile), were first applied in CE enantioseparation to investigate their potential synergistic effect with hydroxypropyl-ß-cyclodextrin (HP-ß-CD). Markedly improved separations were obtained in the chiral ILs/HP-ß-CD synergistic systems compared with single HP-ß-CD system. Parameters, such as the chiral ILs concentration, HP-ß-CD concentration, buffer pH, applied voltage and capillary temperature, were optimized. A systematic comparison of chiral ILs with conventional (commonly used) modifiers was also performed, including the use of achiral ILs, conventional salts and molecular organic solvents. In addition, the chiral configuration of ILs was investigated to demonstrate the existence of synergistic effect between chiral ILs and HP-ß-CD. All these results indicate that chiral ILs, as additives for CE chiral separation, has significant superiority over conventional modifiers in certain cases.

Targeting and liposomal drug delivery to CD40L expressing T cells for treatment of autoimmune diseases.

CD40L is considered as an important target for the treatment of autoimmune diseases. There have been many efforts devoted to the development of antibodies and other molecules to disrupt CD40/CD40L interaction for therapeutic benefits. In this study, we designed a CD40L specific peptide ligand - A25 based on CD40L crystal structure and molecular docking studies. Its binding affinity and specificity to CD40L were confirmed by Surface Plasmon Resonance (SPR) measurements. The peptide A25 was then conjugated on the surface of liposomes and shown to be able to mediate specific liposomal drug delivery to CD40L+ cells. Loaded with the cytostatic drug methotrexate (MTX), the A25 modified liposome could significantly reduce the CD40L+ cell ratios in the experimental autoimmune encephalomyelitis (EAE) mice, resulting in great improvement in clinical scores. Since CD40L+ cells are involved in the pathological development of many auto-immune diseases, A25 conjugated drug targeting systems may be useful for developing therapies that are more efficacies and with less side effects.

Effects of surface displayed targeting ligand GE11 on liposome distribution and extravasation in tumor.

Targeting ligands displayed on liposome surface had been used to mediate specific interactions and drug delivery to target cells. However, they also affect liposome distribution in vivo, as well as the tissue extravasation processes after IV injection. In this study, we incorporated an EGFR targeting peptide GE11 on liposome surfaces in addition to PEG at different densities and evaluated their targeting properties and antitumor effects. We found that the densities of surface ligand and PEG were critical to target cell binding in vitro as well as pharmacokinetic profiles in vivo. The inclusion of GE11-PEG-DSPE and PEG-DSPE at 2% and 4% mol ratios in the liposome formulation mediated a rapid accumulation of liposomes within 1 h after IV injection in the tumor tissues surrounding neovascular structures. This is in addition to the EPR effect that was most prominently described for surface PEG modified liposomes. Therefore, despite the fact that the distribution of liposomes into interior tumor tissues was still limited by diffusion, GE11 targeted doxorubicin loaded liposomes showed significantly better antitumor activity in tumor bearing mice as a result of the fast active-targeting efficiency. We anticipate these understandings can benefit further optimization of targeted drug delivery systems for improving efficacy in vivo.

Contrast imaging and gene delivery through the combined use of novel cationic liposomal microbubbles and ultrasound in rat carotid arteries.

INTRODUCTION: Lipid-coated cationic microbubbles represent a new class of agents with both diagnostic and therapeutic applications. The main goal of this study was to evaluate the efficiency of gene transfer through the combined use of microbubbles and ultrasound in rat carotid arteries. Furthermore, we assessed whether the cationic liposomal microbubbles could allow long-term enhanced imaging, comparing with SonoVue(®). MATERIAL AND METHODS: Normal rat carotid arteries were imaged after intravenous bolus injections of 0.5 ml/kg of two contrast agents (SonoVue(®) and the cationic liposomal microbubbles). Forty Sprague-Dawley rats were divided into 5 groups according to ultrasound parameters and were treated with or without microbubbles. All rats were sacrificed after being transfected for 2 days. The level of protein expression was determined by western blot analysis. RESULTS: The enhancing time of self-made microbubbles was much longer than that of SonoVue(®) in rat carotid arteries (p < 0.05). The results of the western blot analysis revealed that the expression of SR-BI DNA in the carotid artery was highest in the SR-BI + US/CLM group (p < 0.05). CONCLUSIONS: These results suggest that the novel cationic liposomal microbubbles enhance image quality over a longer period than does SonoVue(®). Additionally, the combination of ultrasound and this new type of microbubble can act synergistically to increase SR-BI DNA transfection.