PT  - JOURNAL ARTICLE
AU  - Batalha, Maria Ana Martins da Cruz Borges
AU  - Pais, Daniel Alexandre Marques
AU  - Almeida, Rui Alexandre Estrela de
AU  - Martinho, Ângela Sofia Gomes
TI  - A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management
AID  - 10.5731/pdajpst.2023.012922
DP  - 2024 Sep 01
TA  - PDA Journal of Pharmaceutical Science and Technology
PG  - 604--612
VI  - 78
IP  - 5
4099  - http://journal.pda.org/content/78/5/604.short
4100  - http://journal.pda.org/content/78/5/604.full
SO  - PDA J Pharm Sci Technol2024 Sep 01; 78
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.