Artificial Intelligence in Oral Solid Dosage Form Development and Optimization

Engineering notes

AI also significantly enhances Quality by Design (QbD) implementation by defining robust design spaces.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Artificial Intelligence (AI) has emerged as a transformative technology in the pharmaceutical industry, shifting oral solid dosage form (OSDF) development from conventional trial-and-error methods to efficient, data-driven strategies. The design of OSDFs involves a complex interplay of multiple variables, such as physicochemical properties, excipient selection, manufacturing parameters, and stability considerations. The integration of AI—primarily driven by machine learning (ML) and deep learning (DL) architectures—enables researchers to rapidly analyze large, multi-dimensional datasets to establish accurate predictive insights throughout the product lifecycle. This review highlights the diverse applications of AI across multiple stages of OSDF development. In pre-formulation and formulation design, AI algorithms predict drug-excipient compatibility, optimize composition, and forecast critical quality attributes (CQAs) like tablet hardness, dissolution kinetics, and shelf life. Furthermore, AI technologies revolutionize manufacturing and process control within the industry 4.0 framework. By integrating Process Analytical Technology (PAT) and computer vision, AI facilitates real-time monitoring and parameter optimization during critical unit operations, including granulation, compression, and coating, thereby dramatically reducing batch defects and material waste. AI also significantly enhances Quality by Design (QbD) implementation by defining robust design spaces. Despite persisting challenges regarding high-quality data availability, model interpretability ("black-box" models), implementation costs, and evolving regulatory frameworks, the future of AI in pharmaceuticals remains highly promising. Emerging paradigms like digital twins, autonomous self-driving laboratories, and personalized medicine are set to further modernize OSDF manufacturing, ensuring the rapid, cost-effective production of safe and high-quality therapeutics.

Links and sources

Comments