Mini Review on the Lyophilization: A Basic Requirement for Formulation Development and Stability Modifier.
Assay and drug development technologies(2025)
Department of Pharmaceutical Quality Assurance | Department of Pharmaceutics | Department of Pharmaceutical Chemistry
Abstract
Freeze-drying is popular for producing pharmaceutical formulations with structurally complicated active components and drug delivery system carriers. It is the process of eliminating water from ice crystals through the sublimation mechanism. Some formulations may require drug-specific excipients such as stabilizers, buffers, and bulking agents to maintain the appearance and assure the long-term stability of the drug product. This approach is utilized for therapeutic compounds that are moisture sensitive, thermolabile, and degrade in the atmosphere. Freezing and primary and secondary drying are critical processes in the lyophilization process because they directly impact the end result. This approach is effective for producing a variety of dosage forms, including oral, inhalation, and parenteral. As a result, lyophilization may be an important method for improving the therapeutic efficacy and delivery of various dosage forms delivered via different routes. Additionally, lyophilization is used in pharmacological research to preserve biological samples, stabilize reference/standards, and increase the solubility and bioavailability of poorly soluble drugs. Thus, lyophilization is critical for maintaining the stability, efficacy, and safety of pharmaceutical products throughout their development and lifecycles. This article includes a broad overview of the lyophilization process, principle, excipients for lyophilized medicine compositions, and new lyophilization technologies as well as their applications in a variety of fields.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest