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Respiratory Diseases

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neuraminidase	1

（1）评价该吸入制剂在健康受试者中单次给药的耐受性和药代动力学特征；（2）以药代动力学参数作为主要终点评价指标，比较该吸入制剂与注射制剂在健康受试者体内的药代动力学行为，计算该吸入制剂的绝对生物利用度；（3）比较该吸入制剂与注射制剂在健康受试者鼻咽部及肺泡中的暴露量；评价该吸入制剂在健康受试者中多次给药的耐受性及药代动力学特征。

[Translation]

(1) Evaluate the tolerability and pharmacokinetic characteristics of the inhalation preparation after a single dose in healthy subjects; (2) Compare the pharmacokinetic behavior of the inhalation preparation and the injection preparation in healthy subjects using pharmacokinetic parameters as the primary endpoint evaluation indicators, and calculate the absolute bioavailability of the inhalation preparation; (3) Compare the exposure of the inhalation preparation and the injection preparation in the nasopharynx and alveoli of healthy subjects; Evaluate the tolerability and pharmacokinetic characteristics of the inhalation preparation after multiple doses in healthy subjects.

Start Date24 Jun 2024

Sponsor / Collaborator

Langtian Pharmaceutical Technology (Wuhan) Co., Ltd.

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100 Clinical Results associated with Langtian Pharmaceutical (Hubei) Co., Ltd.

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0 Patents (Medical) associated with Langtian Pharmaceutical (Hubei) Co., Ltd.

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3

Literatures (Medical) associated with Langtian Pharmaceutical (Hubei) Co., Ltd.

01 Apr 2023·Drug development and industrial pharmacy

Optimization of dropping process of Xuesaitong dropping pills based on quality by design concept and machine vision

Article

Author: Hou, Yizhe ; Wang, Xi ; Li, Zheng ; Li, Wenlong ; Zhang, Zhiyong ; Wu, Jiaheng ; Cai, Xiang ; Li, Pian

OBJECTIVEThe drooping process of the Xuesaitong dropping pills (XDPs) was optimized based on quality by design concept. Meanwhile, a machine vision (MV) technology was creatively introduced in this study to predict the critical quality attributes (CQAs) rapidly and accurately.SIGNIFICANCEThis study improves the understanding of dropping process, and has reference value for the guidance of pharmaceutical process research and industrial production.METHODSThe study mainly consisted of three stages: the first stage involved the prediction model to establish and evaluate the CQAs, and the second stage involved assessing the quantitative relationships between critical process parameters (CPPs) and CQAs by the mathematical models that were established through the Box-Behnken experimental design. Finally, a probability-based design space for the dropping process was calculated and verified according to the qualification criteria of each quality attribute.RESULTSThe results show that the prediction accuracy of the random forest (RF) model was high and met the analysis requirements, and the CQAs of dropping pills can meet the standard by running in the design space.CONCLUSIONThe MV technology developed in this study can be applied to the optimization process of the XDPs. In addition, the operation in the design space can not only ensure the quality of XDPs to meet the criteria, but also help to improve the consistency of XDPs.

01 Sep 2021·Spectrochimica Acta Part A: Molecular and Biomolecular SpectroscopyQ2 · CHEMISTRY

A feasibility research on the application of machine vision technology in appearance quality inspection of Xuesaitong dropping pills

Q2 · CHEMISTRY

Article

Author: Li, Shunan ; Li, Zheng ; Cai, Xiang ; Shu, Chengren ; Li, Pian ; Hou, Yizhe ; Miao, Peiqi ; Li, Wenlong

Defect detection is a critical issue for the quality control of dropping pills, which is a special dosage form of traditional Chinese Medicine. Machine vision is a non-destructing testing technology and cost-effective with high accuracy that can be used to predict the detects of both interior and exterior of the sample by employing the camera. In this research, a machine vision system for inspecting quality of the Xuesaitong dropping pills (XDPs) that include non-spherical, abnormal sizes and colors was developed to evaluate the appearance quality of XDPs rapidly and accurately. Firstly, 270 images of XDPs containing qualified and three different types of defects were collected. Subsequently, the processing of the XDPs images were carried out. Finally, Three defecting categories classification models were developed and compared based on contour and color features. The experimental results showed that the Random Forest outperformed all the explored models and the classification accuracy for non-spherical, abnormal sizes and colors reached 98.52%, 100.00% and 100.00%, respectively. In summary, the method established in this research is scientific, reliable, fast and accurate, which has great application potential and can provide technical support for the automatic defect detection of dropping pills.

01 Nov 2020·Journal of Zhejiang University-SCIENCE BQ3 · BIOLOGY

A near-infrared spectroscopy-based end-point determination method for the blending process of Dahuang soda tablets

Q3 · BIOLOGY

Article

Author: Qiu, Ping ; Li, Zheng ; Wu, Si-Jun ; Li, Wen-Long ; Li, Pian

OBJECTIVESThis study is aimed to explore the blending process of Dahuang soda tablets. These are composed of two active pharmaceutical ingredients (APIs, emodin and emodin methyl ether) and four kinds of excipients (sodium bicarbonate, starch, sucrose, and magnesium stearate). Also, the objective is to develop a more robust model to determine the blending end-point.METHODSQualitative and quantitative methods based on near-infrared (NIR) spectroscopy were established to monitor the homogeneity of the powder during the blending process. A calibration set consisting of samples from 15 batches was used to develop two types of calibration models with the partial least squares regression (PLSR) method to explore the influence of density on the model robustness. The principal component analysis-moving block standard deviation (PCA-MBSD) method was used for the end-point determination of the blending with the process spectra.RESULTSThe model with different densities showed better prediction performance and robustness than the model with fixed powder density. In addition, the blending end-points of APIs and excipients were inconsistent because of the differences in the physical properties and chemical contents among the materials of the design batches. For the complex systems of multi-components, using the PCA-MBSD method to determine the blending end-point of each component is difficult. In these conditions, a quantitative method is a more suitable alternative.CONCLUSIONSOur results demonstrated that the effect of density plays an important role in improving the performance of the model, and a robust modeling method has been developed.

100 Deals associated with Langtian Pharmaceutical (Hubei) Co., Ltd.

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100 Translational Medicine associated with Langtian Pharmaceutical (Hubei) Co., Ltd.

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Pipeline

Pipeline Snapshot as of 07 Nov 2024

The statistics for drugs in the Pipeline is the current organization and its subsidiaries are counted as organizations,Early Phase 1 is incorporated into Phase 1, Phase 1/2 is incorporated into phase 2, and phase 2/3 is incorporated into phase 3

Phase 1 Clinical

1

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