A novel paradigm on data and knowledge-driven drug formulation development: Opportunities and challenges of machine learning

Abstract

In the context of Industry 4.0, and Pharma 4.0, the application of machine learning (ML) is gaining growing recognition in the field of drug formulation, where the application of these technologies has the potential to significantly improve the agility, efficiency, flexibility, and quality of production in the pharmaceutical industry. Establishing control strategies that meet product performance requirements and have robust processes allows for precise quality control, enabling pharmaceutical scientists to enhance the safety and effectiveness of drug formulations. Compared to traditional prescription development, big data-based ML formulation development focuses on integrating and mining data and extracting data features to better guide the formulation design. This review starts from the perspective of big data-based ML drug formulation development processes, summarizes recent advancements in utilizing ML tools to address significant challenges, and highlights successful cases in formulation research and development. It provides a comprehensive summary and synthesis of quality control measures and process evaluation methodologies employed in ML-driven drug formulation development and manufacturing, effectively implementing the entire life-cycle of drug formulations. This review is devoted to an in-depth discussion on the Intelligence of drug formulation production and development, which is of great significance in guiding the application of efficient and safe drug formulation.