摘要
药物稳定性研究是确保药品质量与安全性的关键环节，其对货架期预测具有重要意义。本研究旨在通过系统分析影响药物稳定性的主要因素，建立科学的货架期预测模型，为药品研发和生产提供理论支持。研究采用加速和长期稳定性试验相结合的方法，结合化学降解动力学模型，评估了温度、湿度、光照等因素对药物活性成分的影响，并引入多变量回归分析以优化数据处理过程。结果表明，特定条件下药物降解速率呈现显著差异，且所构建的动力学模型能够准确描述药物稳定性变化趋势。此外，本研究创新性地提出了一种基于机器学习算法的货架期预测方法，该方法在传统模型基础上进一步提高了预测精度和适用范围。研究表明，这一综合方法不仅可有效缩短研究周期，还能显著提升预测结果的可靠性，为药品质量控制提供了新思路。总体而言，本研究为药物稳定性评估及货架期预测提供了更为精确和高效的解决方案，对推动药品研发和质量管理具有重要价值。
关键词：药物稳定性；货架期预测；降解动力学模型；机器学习算法；多变量回归分析
Abstract
Drug stability studies are a critical component in ensuring the quality and safety of pharmaceuticals, playing a significant role in shelf-life prediction. This study aims to systematically analyze the primary factors influencing drug stability and establish a scientific shelf-life prediction model to provide theoretical support for drug development and production. By integrating accelerated and long-term stability testing methods with chemical degradation kinetic models, the effects of temperature, humidity, light exposure, and other factors on the active pharmaceutical ingredients were evaluated. Additionally, multivariate regression analysis was introduced to optimize the data processing procedure. The results indicate that drug degradation rates exhibit significant differences under specific conditions, and the constructed kinetic model accurately describes the trends in drug stability changes. Furthermore, this study innovatively proposes a shelf-life prediction method based on machine learning algorithms, which enhances the prediction accuracy and applicability beyond traditional models. The findings demonstrate that this integrated approach not only effectively shortens the research cycle but also significantly improves the reliability of prediction outcomes, offering new insights into drug quality control. Overall, this study provides a more precise and efficient solution for drug stability assessment and shelf-life prediction, contributing substantially to the advancement of pharmaceutical development and quality management.
Keywords：Drug Stability; Shelf Life Prediction; Degradation Kinetics Model; Machine Learning Algorithm; Multivariate Regression Analysis
目 录
摘要 I
Abstract II
一、绪论 1
(一) 药物稳定性研究的背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、药物稳定性的影响因素分析 2
(一) 化学稳定性的影响因素 2
(二) 物理稳定性的影响因素 3
(三) 生物学稳定性的影响因素 3
三、药物稳定性试验设计与实施 4
(一) 稳定性试验的基本原则 4
(二) 加速试验的设计与结果分析 4
(三) 长期试验的设计与数据处理 5
四、药物货架期预测模型构建 6
(一) 货架期预测的理论基础 6
(二) 数学模型的选择与优化 6
(三) 模型验证与误差分析 7
结 论 8
参考文献 9
