A general prediction model for compound-protein interactions based on deep learning.

Background: The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning.

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.

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 of traditional and 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.

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.

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).

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.

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.

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.

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 β-cyclodextrin (β-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 β-CD in NACE.

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.

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.

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.

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 β-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.

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.

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.

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.

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-β-cyclodextrin (HP-β-CD). Markedly improved separations were obtained in the chiral ILs/HP-β-CD synergistic systems compared with single HP-β-CD system.

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.

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.

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(®).

Hypolipidemic effect of SR‑BI gene delivery by combining cationic liposomal microbubbles and ultrasound in hypercholesterolemic rats.

High-density lipoprotein (HDL) is a key mediator in reverse cholesterol transport and is involved in a mechanism known as 'selective lipid uptake', a process mediated by scavenger receptor B type I (SR‑BI), which is a HDL receptor. The aim of the present study was to investigate the therapeutic effect of the SR‑BI gene when delivered by combining cationic liposomal microbubbles (CLMs) and ultrasound (US) in hypercholesterolemic rats. Hypercholesterolemia was induced by administration of excessive doses of vitamin D3 and cholesterol in rats.

Recombinant high density lipoprotein nanoparticles for target-specific delivery of siRNA.

Purpose: Regulation of gene expression using small interfering RNA (siRNA) is a promising strategy for treatments of numerous diseases. However, the progress towards broad application of siRNA requires the development of safe and effective vectors that target to specific cells. In this study, we developed a novel recombinant high density lipoprotein (rHDL) vector with high siRNA encapsulation efficiency.

Recombinant high-density lipoprotein nanoparticles containing gadolinium-labeled cholesterol for morphologic and functional magnetic resonance imaging of the liver.

Background: Natural high-density lipoproteins (HDL) possess important physiological functions to the transport of cholesterol from the peripheral tissues to the liver for metabolic degradation and excretion in the bile.

Methods And Results: In this work, we took advantage of this pathway and prepared two different gadolinium (Gd)-DTPA-labeled cholesterol-containing recombinant HDL nanoparticles (Gd-chol-HDL) and Gd-(chol)(2)-HDL as liver-specific magnetic resonance imaging (MRI) contrast agents. The reconstituted HDL nanoparticles had structural similarity to native HDL, and could be taken up by HepG2 cells via interaction with HDL receptors in vitro.

In vitro and in vivo study of a nanoliposomal cisplatin as a radiosensitizer.

Objective: To investigate the in vitro and in vivo radiosensitization effect of an institutionally designed nanoliposome encapsulated cisplatin (NLE-CDDP).

Materials And Methods: NLE-CDDP was developed by our institute. In vitro radiosensitization of NLE-CDDP was evaluated by colony forming assay in A549 cells.