Enhanced Pharmacokinetics of Celastrol via Long-Circulating Liposomal Delivery for Intravenous Administration.

Background: Rheumatoid Arthritis (RA) involves prolonged inflammation of the synovium, damaging joints and causing stiffness and deformity. Celastrol (Cel), derived from the Chinese herbal medicine Tripterygium wilfordii Hook F, offers immunosuppressive effects for RA treatment but is limited by poor solubility and bioavailability. Purpose: In this study, long-circulating Cel-loaded liposomes (Cel-LPs) were used to increase the pharmacokinetics of Cel, thereby improving drug delivery and efficacy for the treatment of RA. Methods: Cel-LPs were prepared and administered orally and intravenously to compare the elimination half-life of drugs and bioavailability of Cel. Cel-LPs were prepared using the lipid thin-layer-hydration-extrusion method. Human rheumatoid arthritis synovial (MH7A) cells were used to investigate the compatibility of Cel-LPs. The pharmacokinetic studies were performed on male Sprague-Dawley (SD) rats. Results: The Cel-LPs had an average size of 72.20 ± 27.99 nm, a PDI of 0.267, a zeta potential of -31.60 ± 6.81 mV, 78.77 ± 5.69% drug entrapment efficiency and sustained release (5.83 ± 0.42% drug loading). The cytotoxicity test showed that liposomes had excellent biocompatibility and the fluorescence microscope diagram indicated that liposome entrapment increased intracellular accumulation of Rhodamine B by MH7A cells. Furthermore, the results exhibited that Cel-LPs improved the pharmacokinetics of Cel by increasing the elimination half-life (t1/2) to 11.71 hr, mean residence time (MRT(0-∞)) to 7.98 hr and apparent volume of distribution (Vz/F) to 44.63 L/kg in rats, compared to the Cel solution. Conclusion: In this study, liposomes were demonstrated to be effective in optimizing the delivery of Cel, enabling the formulation of Cel-LPs with prolonged blood circulation and sustained release characteristics. This formulation enhanced the intravenous solubility and bioavailability of Cel, developing a foundation for its clinical application in RA and providing insights on poorly soluble drug management.

CD-Loop: a chromatin loop detection method based on the diffusion model.

Motivation: In recent years, there have been significant advances in various chromatin conformation capture techniques, and annotating the topological structure from Hi-C contact maps has become crucial for studying the three-dimensional structure of chromosomes. However, the structure and function of chromatin loops are highly dynamic and diverse, influenced by multiple factors. Therefore, obtaining the three-dimensional structure of the genome remains a challenging task. Among many chromatin loop prediction methods, it is difficult to fully extract features from the contact map and make accurate predictions at low sequencing depths. Results: In this study, we put forward a deep learning framework based on the diffusion model called CD-Loop for predicting accurate chromatin loops. First, by pre-training the input data, we obtain prior probabilities for predicting the classification of the Hi-C contact map. Then, by combining the denoising process based on the diffusion model and the prior probability obtained by pre-training, candidate loops were predicted from the input Hi-C contact map. Finally, CD-Loop uses a density-based clustering algorithm to cluster the candidate chromatin loops and predict the final chromatin loops. We compared CD-Loop with the currently popular methods, such as Peakachu, Chromosight, and Mustache, and found that in different cell types, species, and sequencing depths, CD-Loop outperforms other methods in loop annotation. We conclude that CD-Loop can accurately predict chromatin loops and reveal cell-type specificity. The code is available at https://github.com/wangyang199897/CD-Loop.

Biomimetic nanoparticles for effective Celastrol delivery to targeted treatment of rheumatoid arthritis through the ROS-NF-κB inflammasome axis.

Previous study has indicated that Celastrol (Cel) has various physiological and pharmacological effects, including antibacterial, antioxidant, pro-apoptotic, anticancer and anti-rheumatoid arthritis (RA) effects. However, low water solubility, low oral bioavailability, narrow treatment window, and high incidence of systemic adverse reactions still limit the further clinical application of Cel. Here, aiming at effectively overcome those shortcomings of Cel to boost its beneficial effects for treating RA, we developed the leukosome (LEUKO) coated biomimetic nanoparticles (NPs) for the targeted delivery of Cel to arthritis injury area in RA. LEUKO were synthesized using membrane proteins purified from activated J774 macrophage. LEUKO and Cel-loaded LEUKO (Cel@LEUKO) were characterized using dynamic light scattering and transmission electron microscopy. Our results demonstrated that Cel@LEUKO can inhibit the inflammatory response of lipopolysaccharide (LPS) induced mouse monocyte macrophage leukemia cells (RAW264.7 cells) and human rheumatoid arthritis synovial fibroblasts (MH7A) cells through the inhibition of reactive oxygen species (ROS)-NF-κB pathway. In addition, research has shown that LEUKO effectively targets and transports Cel to the inflammatory site of RA, increased drug concentration in affected areas, reduced systemic toxicity of Cel, and reduced clinical symptoms, inflammatory infiltration, bone erosion, and serum inflammatory factors in collagen-induced arthritis (CIA) rats.

Comparison of the early clinical efficacy of the SuperPath approach versus the modified Hardinge approach in total hip arthroplasty for femoral neck fractures in elderly patients: a randomized controlled trial.

PURPOSE: To investigate the clinical efficacy and advantages of the SuperPath approach for total hip arthroplasty in the treatment of femoral neck fractures in the elderly population. METHODS: From February 2018 to March 2019, 120 patients were randomly divided into two groups with 60 patients each: the SuperPath group and the conventional group. The results evaluated included the general operation situation, serum markers, blood loss, pain score, hip function and prosthesis location analysis. RESULTS: There was no demographic difference between the two groups. Compared with the conventional group, the SuperPath group had a shorter operation time (78.4 vs. 93.0 min, p = 0.000), a smaller incision length (5.8 vs. 12.5 cm, p = 0.000), less intraoperative blood loss (121.5 vs. 178.8 ml, p = 0.000), a shorter hospitalization time (8.0 vs. 10.8 days, p = 0.000) and less drainage volume (77.8 vs. 141.2 ml, p = 0.000). The creatine kinase level in the SuperPath group was significantly lower than that in the conventional group, while there was no difference in the C-reactive protein level and erythrocyte sedimentation rate level. The visual analog scale score was lower one month postoperatively, and the Harris hip score was higher three months postoperatively in the SuperPath group (p < 0.05). There was no difference in the cup abduction angle or anteversion angle of the two groups. CONCLUSION: We found better clinical efficacy after using the SuperPath approach with less muscle damage, less postoperative pain and better postoperative function than after using the modified Hardinge approach. Trial registration The randomized clinical trial was retrospectively registered at the Chinese Clinical Trial Registry on 31/12/2020 (ChiCTR-2000041583, http://www.chictr.org.cn/showproj.aspx?proj=57008 ).

Deep learning approach for cancer subtype classification using high-dimensional gene expression data.

MOTIVATION: Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results. RESULTS: In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Specifically, DCGN first uses the synthetic minority oversampling technique algorithm to equalize data. The CNN can handle high-dimensional data without stress and extract important local features, and the BiGRU can analyse deep features and retain their important information; the DCGN captures key features by combining both neural networks to overcome the challenges of small sample sizes and sparse, high-dimensional features. In the experiments, we compared the DCGN to seven other cancer subtype classification methods using breast and bladder cancer gene expression datasets. The experimental results show that the DCGN performs better than the other seven methods and can provide more satisfactory classification results.

BreakNet: detecting deletions using long reads and a deep learning approach.

BACKGROUND: Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs. RESULTS: In this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to feature vectors. Third, BreakNet employs a bidirectional long short-term memory (BLSTM) model to analyse the produced set of continuous feature vectors in both the forward and backward directions. Finally, a classification module determines whether a region refers to a deletion. On real long-read sequencing datasets, we demonstrate that BreakNet outperforms Sniffles, SVIM and cuteSV in terms of their F1 scores. The source code for the proposed method is available from GitHub at https://github.com/luojunwei/BreakNet . CONCLUSIONS: Our work shows that deep learning can be combined with long reads to call deletions more effectively than existing methods.

Effects of dacomitinib on the pharmacokinetics of poziotinib in vivo and in vitro.

CONTEXT: Dacomitinib and poziotinib, irreversible ErbB family blockers, are often used for treatment of non-small cell lung cancer (NSCLC) in the clinic. OBJECTIVE: This study investigates the effect of dacomitinib on the pharmacokinetics of poziotinib in rats. MATERIALS AND METHODS: Twelve Sprague-Dawley rats were randomly divided into two groups: the test group (20 mg/kg dacomitinib for 14 consecutive days) and the control group (equal amounts of vehicle). Each group was given an oral dose of 10 mg/kg poziotinib 30 min after administration of dacomitinib or vehicle at the end of the 14 day administration. The concentration of poziotinib in plasma was quantified by UPLC-MS/MS. Both in vitro effects of dacomitinib on poziotinib and the mechanism of the observed inhibition were studied in rat liver microsomes and human liver microsomes. RESULTS: When orally administered, dacomitinib increased the AUC, Tmax and decreased CL of poziotinib (p < 0.05). The IC50 values of M1 in RLM, HLM and CYP3A4 were 11.36, 30.49 and 19.57 µM, respectively. The IC50 values of M2 in RLM, HLM and CYP2D6 were 43.69, 0.34 and 0.11 µM, respectively, and dacomitinib inhibited poziotinib by a mixed way in CYP3A4 and CYP2D6. The results of the in vivo experiments were consistent with those of the in vitro experiments. CONCLUSIONS: This research demonstrates that a drug-drug interaction between poziotinib and dacomitinib possibly exists when readministered with poziotinib; thus, clinicians should pay attention to the resulting changes in pharmacokinetic parameters and accordingly, adjust the dose of poziotinib in clinical settings.

Effects of Voriconazole on the Pharmacokinetics of Vonoprazan in Rats.

PURPOSE: The purpose of this study was to examine the effects of voriconazole on the pharmacokinetics of vonoprazan. METHODS: Fifteen Sprague-Dawley rats were randomly divided into three groups: five rats in each group, including control group, single-dose group (a single dose of 30 mg/kg of voriconazole), and multiple-dose group (multiple doses of 30 mg/(kg•day) per dose of voriconazole). Each group of rats was given an oral dose of 10 mg/kg vonoprazan 30 min after the administration of voriconazole or vehicle. After the oral administration of vonoprazan, 50 µL of blood was collected into 1.5-mL heparinized tubes via the caudal vein. The concentration of vonoprazan in plasma was quantified by ultra-performance liquid chromatography/tandem mass spectrometry. Both in vitro effects of voriconazole on vonoprazan and the mechanism of the observed inhibition were studied in rat liver microsomes. RESULTS: When orally administered, voriconazole increased the area under the plasma concentration-time curve (AUC), prolonged the elimination half-life (t1/2), and decreased the clearance (CL) of vonoprazan; there was no significant difference between the single-dose and multiple-dose groups. Voriconazole inhibited the metabolism of vonoprazan at an IC50 of 2.93 µM and showed mixed inhibition. The results of the in vivo experiments were consistent with those of the in vitro experiments. CONCLUSION: Our findings provide the evidence of drug-drug interactions between voriconazole and vonoprazan that could occur with pre-administration of voriconazole. Thus, clinicians should pay attention to the resulting changes in pharmacokinetic parameters and accordingly, adjust the dose of vonoprazan in clinical settings.

The protective effect of myricitrin in osteoarthritis: An in vitro and in vivo study.

Osteoarthritis (OA) is a long-term, chronic, progressive joint condition caused by a pathology characterized by the deterioration of joint cartilage and proliferation of subchondral bone. Myricitrin (Myr) is a flavonoid compound extracted from myrica rubra with potent anti-inflammatory properties, as demonstrated in various studies. However, the mechanisms by which Myr plays a protective role in OA are not completely understood. In this study, the anti-inflammatory properties and potential mechanisms of Myr on mouse chondrocytes treated with interleukin (IL) -1beta (ß) were explored in vitro and the role of Myr in a mouse model of OA in vivo. The production of pro-inflammatory factors, such as IL-6, tumor necrosis factor alpha (TNF-α), prostaglandin E2 (PGE2) and nitric oxide (NO) were assessed by enzyme linked immunosorbent assay (ELISA) and the Griess reaction. Protein expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), Collagen-II, matrix metalloproteinase(MMP)-13, MMP-3, thrombospondin motifs 5(ADAMTS5), inhibitor ofnuclear factor kappa-B (IκB), p-IκB, p65, p-p65, c-jun-terminal kinase (JNK), p-JNK, extracellular regulated protein kinases (ERK), p-ERK, p38 and p-p38 were quantified using Western blot analysis. In the present study, we found that Myr inhibited IL-1ß-induced production of NO and PGE2, expression of MMP-13, MMP-3 and ADAMTS5 and degradation of collagen-II in mouse chondrocytes. Mechanistically, Myr inhibited the activation of mitogen-activated protein kinase (MAPK) and nuclear factor kappa-B (NF-κB) treated with IL-1ß in mouse chondrocytes. In vivo, Myr decreased OA Research Society International (OARSI) scores in a surgically-induced mouse model of OA. These data suggest that Myr could be developed as a potential therapyfor OA.

Effects of naringenin on the pharmacokinetics of tofacitinib in rats.

Context: Naringenin and tofacitinib are often used together for treatment of rheumatoid arthritis in Chinese clinics.Objective: This experiment investigates the effect of naringenin on the pharmacokinetics of tofacitinib in rats.Materials and methods: Twelve Sprague-Dawley rats were randomly divided into two groups (experimental group and control group). The experimental group was pre-treated with naringenin (150 mg/kg/day) for two weeks before dosing tofacitinib, and equal amounts of CMC-Na solution in the control group. After a single oral administration of 5 mg/kg of tofacitinib, 50 µL blood samples were directly collected into 1.5 mL heparinized tubes via the caudal vein at 0.083, 0.5, 1, 2, 3, 4, 6, 8, 10, 12 and 24 h. The plasma concentration of tofacitinib was quantified by UPLC/MS-MS.Results: Results indicated that naringenin could significantly affect the pharmacokinetics of tofacitinib. The AUC0-24 of tofacitinib was increased from 1222.81 ± 222.07 to 2016.27 ± 481.62 ng/mL/h, and the difference was significant (p < 0.05). Compared with the control group, the Tmax was increased from 0.75 ± 0.29 to 3.00 ± 0.00 h (p < 0.05), and the MRT(0-24) was increased from 4.90 ± 0.51 to 6.57 ± 0.66 h (p < 0.05), but the clearance was obviously decreased from 4.10 ± 0.72 to 2.42 ± 0.70 L/h/kg (p < 0.05) in experimental group. Although the Cmax and t1/2 of tofacitinib were increased, there were no significant differences (p > 0.05).Conclusions: This research demonstrated a drug-drug interaction between naringenin and tofacitinib possibly when preadministered with naringenin; thus, we should pay attention to this possibility in the clinic.