Data-Driven Pharmaceutical Processing and Drug Development

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.

Md. Faiyazuddin, PhD, is a senior professor at Al-Karim University, India, with seventeen years’ experience in pharmacy education, product development, and research consultation. He has authored more than 145 published papers in Q1 journals, holds 12 patents, and has written 12 books and 48 book chapters.

Abdul Wasy Zia, PhD, is an Assistant Professor at the Institute of Mechanical Process and Energy Engineering at Heriot-Watt University, Edinburgh. A Fellow of the Higher Education Academy and endorsed as a Global Talent in Materials Performance by the Royal Academy of Engineering, he leads the Advanced Materials and Manufacturing Laboratory.