北京若生智能科技有限公司
Drug Database
HG

HGP-0904 + HGP-0608 + HGP-0816 (HCP 1305 / HCP1305 / Amosartan Q)

✓ Approved

Hanmi Pharmaceutical · 小分子 · 小分子

什么是 HGP-0904 + HGP-0608 + HGP-0816？

HGP-0904 + HGP-0608 + HGP-0816 是一种小分子，由Hanmi Pharmaceutical研发。该药已获批，用于治疗相关适应症，给药途径：Unknown。

药物档案

商品名HCP 1305, HCP1305, Amosartan Q
公司Hanmi Pharmaceutical
药物类别小分子
给药途径Unknown
状态Approved
来源： ClinicalTrials.gov, Hanmi Pharmaceutical

治疗适应症

HGP-0904 + HGP-0608 + HGP-0816 针对 2 个适应症，涉及 2 个治疗领域。

治疗领域疾病/病症分期
Metabolism and nutrition disordersHypercholesterolaemia✓ Approved
Vascular disordersHypertension✓ Approved

相关研究文献

PubMedMicroorganisms2026-08-27

Effects of Intestinal Microbiota on Individual Growth and Development of Pontastacus leptodactylus.

Pi Mengjie M, Li Bin B, Wang Haorui H, Zhao Qi Q et al.

Pontastacus leptodactylus is an aquatic resource endemic to Xinjiang, and its growth performance is closely related to the composition and structure of its intestinal microbiota. This study used male P. leptodactylus, divided into a high-growth-performance (HGP) group and a low-growth-performance (LGP) group based on growth rates, to analyze differences in intestinal microbiota using microbial sequencing technology. Results showed that the number of ASVs unique to the HGP group (7840, accounting for 65.97%) was substantially higher than that in the LGP group (3383, 28.46%). ALDEx2 analysis identified six ASVs significantly enriched in the LGP group, assigned to Methylobacterium, Devosia, order Rhizobiales, Renibacterium, Stenotrophomonas, and class Mollicutes, which were near-absent in the HGP group. At the phylum level, TM7 was differentially abundant with higher relative abundance in HGP. At the genus level, ten genera showed significant differences: Pseudomonas, Hydrogenophaga, Mesorhizobium, Reyranella, Mycobacterium, Flavobacterium, Ralstonia, Bradyrhizobium, and Aeromicrobium were enriched in HGP, whereas the opportunistic pathogen Plesiomonas was depleted. Alpha diversity analysis revealed significantly higher Chao1, observed species, Shannon, and Faith's pd indices in the HGP group (p < 0.05). Beta diversity analysis confirmed significant separation in microbial community structure between the two groups (adonis p = 0.018). KEGG pathway analysis showed upward trends in retinol metabolism, shigellosis, geraniol degradation, and xenobiotics metabolism pathways in HGP (p < 0.10), and a significant upregulation of coumarin biosynthesis (p < 0.01). COG analysis revealed eight downregulated functional genes in HGP (p < 0.01), suggesting more complex host-microbiota interactions. This study identifies distinct intestinal microbial signatures associated with growth performance in P. leptodactylus, providing candidate targets for probiotic development and aquaculture management, while causal verification remains the critical next step.

PMID 42655084
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PubMedGels (Basel, Switzerland)2026-08-26

Organic-Inorganic Hybrid Gel Microspheres as a Plugging Agent for Ultra-High Temperature and High-Salinity Water-Based Drilling Fluids.

Sun Yuanwei Y, Sun Jinsheng J, Lv Kaihe K, Huang Xianbin X et al.

With the continuous expansion of ultra-deep and deep well drilling toward complex geological formations, the performance stability of water-based drilling fluids and wellbore stability under ultra-high temperature and high-salinity conditions have become critical challenges. High temperature and salt contamination can induce the degradation or failure of drilling fluid additives, while the development of pores and fractures in complex formations further increases the risk of filtrate invasion. Conventional polymer and inorganic plugging agents often suffer from insufficient thermal stability, poor salt tolerance, or limited adaptability to complex pore structures. In this study, an organic-inorganic hybrid gel microsphere plugging agent (HGP) with a core-shell structure was developed by in situ polymerization of AMPS, styrene (St), and sodium styrene sulfonate (SSS) on KH570-modified nano-SiO2. The hybrid microspheres consisted of a rigid SiO2 core and a flexible polymer shell, providing synergistic thermal stability, mechanical strength, and deformation capability. Structural characterization confirmed the successful formation of the designed organic-inorganic hybrid structure. After aging at 240 °C, HGP maintained stable morphology and dispersion characteristics, while exerting minimal influence on drilling fluid rheological properties. The addition of 3 wt% HGP reduced API fluid loss by approximately 30% and decreased sand bed invasion by approximately 50% after high-temperature aging. Under 35 wt% NaCl and 5 wt% CaCl2 contamination, HGP maintained effective filtration control, reducing fluid loss by more than 50% compared with the base fluid. Furthermore, HGP achieved core plugging efficiencies above 94% and reduced mud cake permeability by over 70%, demonstrating superior plugging performance compared with polymer microspheres NF-1 and SiO2 particles. The enhanced performance was considered to arise from the synergistic effects of stable dispersion, pore-throat bridging, deformation filling, and structural stabilization. This study provides a rigid-flexible hybrid strategy for designing high-performance plugging agents for ultra-high temperature and high-salinity water-based drilling fluids.

PMID 42644979
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PubMedScientific reports2026-08-22

Developing machine learning regression model optimized using Tabu search harmony search algorithm for prediction of mass transfer in membranes.

Abu-Hamdeh Nidal H NH, Milyani Ahmad H AH, Almitani Khalid H KH

We used several advanced machine learning models for analysis of a separation unit in water purification and treatment via pressure-driven membrane. After removing outliers using the Isolation Forest method and normalizing features with a Min-Max scaler, three regression models-Gaussian Process Regression (GPR), Deep Gaussian Process Regression (DGP), and Heteroscedastic Gaussian Process Regression (HGP)-were trained and optimized for correlation of data. Hyperparameters were tuned using a hybrid Tabu Search-Harmony Search (TS-HS) algorithm. Among the models, HGP showed the strongest performance, with an R2 of 0.9913 on the training set and 0.9846 on the test set, along with the lowest RMSE (5.91 train; 7.51 test) and MAE (3.89 train; 6.51 test). Model interpretability was assessed through SHAP analysis, confirming Time and Pressure as the most influential features. These performance values were verified using 5-fold cross-validation and supported by narrow confidence intervals, confirming the statistical reliability of the results. The results demonstrate that heteroscedastic modeling improves prediction accuracy for membrane separation system operating under variable conditions, supporting more reliable performance estimation in pressure-driven membrane applications.

PMID 42629399
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PubMedProteomics2026-08-10

A Deep Learning Model for Prediction of Unknown Gene Functionality: Gene Bio-BERT.

Kudipudi Srinivas S, Durga Chirumamilla Vyshnavi V, Ippili Pavani P

The Human Genome Project (HGP) was a large international research effort that timelined between 1990 and 2003, marking the successful mapping of the entire human genome. Despite the promising performance of deep learning models, especially LLMs like Bio-BERT and ProtBERT on well-annotated datasets. Bio-BERT is a pre-trained language model designed for biomedical corpora, it is not able to independently predict gene functionality. A pre-trained model called ProtBERT uses protein sequences to learn features. It is unable to predict gene functionality directly, without more training data. In order to overcome these challenges, a Gene Bio-BERT based framework is proposed for automated gene function prediction utilizing deep learning methods in biomedical data analysis. This Gene Bio-BERT Framework is divided into 3 modules such as Data collection and Preprocessing, Gene Bio-BERT model training, Feature aggregation layer and prediction of functionality. The initial module focuses on data collection and preprocessing. Data is collected using entrez API from NCBI (National Center for Biotechnology Information) to retrieve human gene data. Preprocessing techniques like Tokenization and feature extraction are then used to handle the data. In the second module, a Gene Bio-BERT transformer encoder with an attention-based feature fusion layer and optimized hyperparameters is used to train the model and learn contextual embeddings. The third module generates results by using aggregated transformer representations to produce functional predictions for unknown genes. In predicting gene function, the proposed Gene Bio-BERT model attains an exceptional accuracy of 94.5% and F1 score of 0.87. Additionally, the model's predictive accuracy remains similar when tested on an unannotated gene which gives similarity score 0.84.

PMID 42573472
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PubMedRSC advances2026-08-06

Enhanced tetracycline removal from complex real wastewater using a sustainable hog plum-ZnO-Ag2O composite.

Egbedina Abisola O AO, Akinseye Morenike O MO, Bayonle Oladoja A OA, Olu-Owolabi Bamidele I BI et al.

The increasing occurrence of pharmaceutical contaminants such as tetracycline (TC) in aquatic environments poses significant environmental and public health concerns due to their persistence and contribution to antibiotic resistance. In this study, a ZnO/Ag2O-functionalized hog plum (Spondias mombin) composite (MHGP) was synthesized and evaluated for TC removal from aqueous systems. To the best of our knowledge, this represents the first report on the simultaneous ZnO and Ag2O nanoparticle modification of Spondias mombin biomass for antibiotic adsorption and its application in a real wastewater matrix. The successful incorporation of the ZnO and Ag2O nanoparticles onto the hog plum biomass (HGP) was confirmed using multiple physicochemical characterization techniques. Batch adsorption studies revealed that optimum TC removal occurred at pH 2, with an adsorbent dosage of 50 mg achieving maximum TC sequestration from a 25 mg L-1 solution within 4 h. The maximum adsorption capacity (Q max) in ultrapure water was 1.96 mg g-1. Isotherm studies showed that the Brouers-Sotolongo model best described the adsorption in ultrapure water, whereas the Sips isotherms provided a superior fit for real wastewater, indicating heterogeneous adsorption in the presence of competing ions. Kinetic modelling showed that the adsorption followed the pseudo-second-order, suggesting that adsorption may be governed by interactions involving surface functional groups and metal active sites. Despite the chemical complexity of the real wastewater matrix, the MHGP composite maintained a TC removal efficiency of 55%. Overall, these results demonstrate the potential of MHGP as a practical and sustainable adsorbent for pharmaceutical wastewater remediation.

PMID 42559476
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PubMedBritish journal of cancer2026-07-29

Spatial transcriptomics differences of histopathological growth patterns in colorectal cancer liver metastases.

Escriva Conde Maria M, Andersson Axel A, Vermeulen Peter P, Nilsson Mats M et al.

Histopathological growth patterns (HGPs) have emerged as prognostic and predictive biomarkers in colorectal liver metastases (CLM). The desmoplastic/encapsulating HGP (EHGP) is associated with improved survival after surgery, whereas the replacement HGP (RHGP) is associated with poorer outcomes. However, HGPs can only be reliably assessed postoperatively, limiting their use as preoperative biomarkers. A deeper molecular understanding of HGPs could inform biomarker development and therapeutic strategies, including approaches to promote EHGP. We applied in situ sequencing (ISS), an image-based spatial transcriptomics method, to map RNA expression in CLM tissue. Two custom gene panels (150 and 175 genes) were analysed in a chemonaïve cohort of resected CLM tissue from 19 patients with EHGP and RHGP. Distinct molecular programmes characterised the two patterns. RHGP was associated with damaged SAA1+ hepatocytes, KRT18+ neoplastic cells, and bifunctional hepatocyte-cholangiocyte cells. EHGP showed interferon-γ signalling, cytotoxic T-cell infiltration, and a fibrotic capsule with zonation-specific features: hepatic stellate cell activation, angiogenesis, and immune recruitment on the liver-facing side, and cancer-associated fibroblasts on the tumour-facing side. These differences imply that growth patterns emerge from tumour-host driven interactions. They reveal new molecular features of the metastatic niche. These insights may inform the search for novel treatment strategies.

PMID 42521772
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