Apply artificial intelligence and machine learning techniques across drug development and manufacturing Pharmaceutical professionals face mounting pressure to accelerate drug development while maintaining quality and regulatory compliance. Data-Driven Pharmaceutical Processing and Drug Development provides a thorough overview of data-driven methodologies from drug discovery to manufacture. This reference examines how artificial intelligence, machine learning, and big data analytics transform every stage of pharmaceutical production, covering data sources and advanced collection methods, AI and machine learning applications in liquid and solid drug formulation, statistical analysis techniques, and real-world data impacts on development decisions. Case studies demonstrate successful implementation, including COVID-19 vaccine development. Detailed coverage of FDA and EMA regulatory guidelines ensures readers understand compliance requirements for data-driven processes. The book also includes: • Coverage of nanotechnology applications in drug formulation and manufacturing processes, with specific guidance on implementation and quality control • Examination of digital twins, IoT devices, and real-time monitoring systems representing the future of pharmaceutical manufacturing operations • Analysis of computational chemistry and molecular modeling techniques that enhance accuracy in drug design and personalized medicine approaches Pharmaceutical industry professionals, academic researchers, and scientists applying data-driven methodologies in drug development and manufacturing will all benefit from this reference. The book connects advanced technologies with practical implementation strategies, enabling readers to optimize processes, ensure regulatory compliance, and accelerate innovation in pharmaceutical production.
