RT Journal Article
SR Electronic
T1 A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management
JF PDA Journal of Pharmaceutical Science and Technology
JO PDA J Pharm Sci Technol
FD Parenteral Drug Association (PDA)
SP 604
OP 612
DO 10.5731/pdajpst.2023.012922
VO 78
IS 5
A1 Batalha, Maria Ana Martins da Cruz Borges
A1 Pais, Daniel Alexandre Marques
A1 Almeida, Rui Alexandre Estrela de
A1 Martinho, Ângela Sofia Gomes
YR 2024
UL http://journal.pda.org/content/78/5/604.abstract
AB The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning (ML) and artificial intelligence (AI) marking significant milestones in this journey. ML, a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging the available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture, storage, and accessibility for manufacturing and process operations. This paper highlights the pivotal role of AI in converting process data into actionable knowledge to support critical functions throughout the whole product life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.