Arnold and Marie Schwartz College of Pharmacy, Long Island University, 75 Dekalb Ave L130, Brooklyn, New York, 11201, USA.
Drug Product Technologies, Process Development, One Amgen Center Drive, Amgen, Thousand Oaks, California, 91320, USA.
AAPS PharmSciTech. 2024 Oct 23;25(8):255. doi: 10.1208/s12249-024-02970-z.
The rapid progress in artificial intelligence (AI) has revolutionized problem-solving across various domains. The global challenge of pharmaceutical product recalls imposes the development of effective tools to control and reduce shortage of pharmaceutical products and help avoid such recalls. This study employs AI, specifically machine learning (MI), to analyze critical factors influencing formulation, manufacturing, and formulation complexity which could offer promising avenue for optimizing drug development processes. Utilizing FDAZilla and SafeRX tools, an open database model was constructed, and predictive statistical models were developed using Multivariate Analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) Approach. The study focuses on key descriptors such as delivery route, dosage form, dose, BCS classification, solid-state and physicochemical properties, release type, half-life, and manufacturing complexity. Through statistical analysis, a data simplification process identifies critical descriptors, assigning risk numbers and computing a cumulative risk number to assess product complexity and recall likelihood. Partial Least Square Regression and the LASSO approach established quantitative relationships between key descriptors and cumulative risk numbers. Results have identified key descriptors; BCS Class I, dose number, release profile, and drug half-life influencing product recall risk. The LASSO model further confirms these identified descriptors with 71% accuracy. In conclusion, the study presents a holistic AI and machine learning approach for evaluating and forecasting pharmaceutical product recalls, underscoring the importance of descriptors, formulation complexity, and manufacturing processes in mitigating risks associated with product quality.
人工智能（AI）的快速发展彻底改变了各个领域的问题解决方式。全球范围内的药品召回挑战要求开发有效的工具来控制和减少药品短缺，并帮助避免此类召回。本研究利用人工智能，特别是机器学习（MI），分析影响配方、制造和配方复杂性的关键因素，为优化药物开发过程提供有前途的途径。利用 FDAZilla 和 SafeRX 工具，构建了一个开放数据库模型，并使用多元分析和最小绝对值收缩和选择算子（LASSO）方法开发了预测统计模型。本研究关注关键描述符，如给药途径、剂型、剂量、BCS 分类、固态和物理化学性质、释放类型、半衰期和制造复杂性。通过统计分析，数据简化过程确定了关键描述符，分配风险编号并计算累积风险编号，以评估产品复杂性和召回可能性。偏最小二乘回归和 LASSO 方法建立了关键描述符和累积风险编号之间的定量关系。结果确定了影响产品召回风险的关键描述符；BCS 分类 I、剂量数、释放曲线和药物半衰期。LASSO 模型进一步以 71%的准确率确认了这些已识别的描述符。总之，本研究提出了一种整体的人工智能和机器学习方法来评估和预测药品召回，强调了描述符、配方复杂性和制造过程在降低与产品质量相关风险方面的重要性。
