关键词: 药物-辅料相互作用, 药物释放速率, 机制模型, 分子模拟, 机器学习

Abstract: Pharmaceutical excipients, also known as "inactive ingredients", are other components in pharmaceutical preparations besides active ingredients. Pharmaceutical excipients are an indispensable and important component in pharmaceutical preparations, and they can significantly affect the release performance of pharmaceutical preparations by forming drug excipient interactions, which is crucial for the effectiveness and safety of pharmaceutical preparations. The development of high-end preparations also puts higher requirements on excipients. Therefore, it is necessary to analyze the mechanism by which excipients in high-end formulations affect the quality of drug formulations. Although the addition of excipients can enhance the release and bioavailability of active ingredients in drugs, improve and maintain drug stability, achieve controllable targeted release of drugs, and act as masking and sweeteners to improve drug bioavailability and patient adherence, more and more studies have shown that excipients can produce physiological activity and affect drug pharmacokinetics, causing adverse reactions such as allergies or intolerance. Large amounts of ingested excipients may also inhibit drug release by interacting with drugs. This review briefly describes the impact mechanisms of commonly used excipients on drug release from the perspective of drug excipient interactions, such as polymers and mesoporous silica. At the same time, it summarizes the research progress of excipient controlled drug release mechanisms based on mathematical models, molecular simulations, and machine learning methods based on drug excipient interactions, and proposes the development direction of future pharmaceutical excipient database establishment for high-throughput screening of suitable pharmaceutical excipients. Determine the optimal drug loading and excipient addition, and provide data support and theoretical guidance for selecting appropriate production processes.

Key words: drug-excipient interaction, drug release rate, mechanism model, molecular simulation, machine learning