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<p>This study presents an AI-driven pharmaceutical data analysis tool designed to streamline drug formulation and quality control processes. Built using R’s app framework, the application integrates advanced machine learning techniques, statistical analyses, and interactive visualizations into a user-friendly interface. Users can upload their own datasets, perform comprehensive exploratory data analysis (EDA), and generate actionable insights through predictive modeling and simulated real-time monitoring. The tool demonstrates proof-of-concept alert mechanisms designed to highlight potential anomalies and formulation trends, though these features are currently implemented in a simulated environment. Results from the analysis highlight critical factors influencing tablet <i>hardness</i> and disintegration time, such as excipients like <i>Mannitol</i> and <i>Microcrystalline Cellulose</i>, as well as physical properties like Angle of Repose and Bulk Density. Classification models achieve high accuracy (85.71%) and robust performance metrics, including sensitivity, specificity, and Kappa statistics. Regression models reveal challenges in predicting <i>hardness</i> due to complex interactions between variables. The ROC curve demonstrates excellent discriminatory power, underscoring the reliability of the classification model. This integrated approach empowers formulation scientists to prioritize key variables, address anomalies, and enhance product consistency. By combining AI-driven insights with interactive features, this tool bridges the gap between data science and pharmaceutical research, offering a scalable solution for optimizing drug development and manufacturing processes.</p>

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AI-Driven Pharmaceutical Data Analysis Tool for Drug Formulation Development

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Ahmed Ould Boudia

摘要

This study presents an AI-driven pharmaceutical data analysis tool designed to streamline drug formulation and quality control processes. Built using R’s app framework, the application integrates advanced machine learning techniques, statistical analyses, and interactive visualizations into a user-friendly interface. Users can upload their own datasets, perform comprehensive exploratory data analysis (EDA), and generate actionable insights through predictive modeling and simulated real-time monitoring. The tool demonstrates proof-of-concept alert mechanisms designed to highlight potential anomalies and formulation trends, though these features are currently implemented in a simulated environment. Results from the analysis highlight critical factors influencing tablet hardness and disintegration time, such as excipients like Mannitol and Microcrystalline Cellulose, as well as physical properties like Angle of Repose and Bulk Density. Classification models achieve high accuracy (85.71%) and robust performance metrics, including sensitivity, specificity, and Kappa statistics. Regression models reveal challenges in predicting hardness due to complex interactions between variables. The ROC curve demonstrates excellent discriminatory power, underscoring the reliability of the classification model. This integrated approach empowers formulation scientists to prioritize key variables, address anomalies, and enhance product consistency. By combining AI-driven insights with interactive features, this tool bridges the gap between data science and pharmaceutical research, offering a scalable solution for optimizing drug development and manufacturing processes.