Mario Stassen, Catarina S Leitão, Toni Manzano, Francisco Valero, Benjamin Stevens, Matt Schmucki, David Hubmayr, Ferran Mirabent Rubinat, Sandrine Dessoy, Antonio Moreira
{"title":"人工智能在持续过程验证中的应用建议。","authors":"Mario Stassen, Catarina S Leitão, Toni Manzano, Francisco Valero, Benjamin Stevens, Matt Schmucki, David Hubmayr, Ferran Mirabent Rubinat, Sandrine Dessoy, Antonio Moreira","doi":"10.5731/pdajpst.2024.012950","DOIUrl":null,"url":null,"abstract":"<p><p>This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and opportunities associated with integrating AI into CPV, focusing on real-time data analysis and proactive process adjustments. The paper highlights the importance of aligning AI solutions with regulatory standards and offers a set of comprehensive recommendations to bridge the gap between AI's potential and its practical, compliant, and safe application in pharmaceutical manufacturing. Emphasizing transparency, interpretability, and risk management, the research contributes to establishing best practices for AI implementation, ensuring the highest quality pharmaceutical products while meeting regulatory expectations. The conclusions drawn provide valuable insights for navigating the evolving landscape of AI in pharmaceutical manufacturing. This paper serves as a guideline for implementing AI, Machine Learning and Deep Learning models to the pharma industry. Nevertheless, the specific algorithms used in the CPV of the Future are not relevant for our paper (Good Practices), since we have to generalize the process independent of the algorithm.</p>","PeriodicalId":19986,"journal":{"name":"PDA Journal of Pharmaceutical Science and Technology","volume":" ","pages":"68-87"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recommendations for Artificial Intelligence Application in Continued Process Verification: A Journey Toward the Challenges and Benefits of AI in the Biopharmaceutical Industry.\",\"authors\":\"Mario Stassen, Catarina S Leitão, Toni Manzano, Francisco Valero, Benjamin Stevens, Matt Schmucki, David Hubmayr, Ferran Mirabent Rubinat, Sandrine Dessoy, Antonio Moreira\",\"doi\":\"10.5731/pdajpst.2024.012950\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and opportunities associated with integrating AI into CPV, focusing on real-time data analysis and proactive process adjustments. The paper highlights the importance of aligning AI solutions with regulatory standards and offers a set of comprehensive recommendations to bridge the gap between AI's potential and its practical, compliant, and safe application in pharmaceutical manufacturing. Emphasizing transparency, interpretability, and risk management, the research contributes to establishing best practices for AI implementation, ensuring the highest quality pharmaceutical products while meeting regulatory expectations. The conclusions drawn provide valuable insights for navigating the evolving landscape of AI in pharmaceutical manufacturing. This paper serves as a guideline for implementing AI, Machine Learning and Deep Learning models to the pharma industry. Nevertheless, the specific algorithms used in the CPV of the Future are not relevant for our paper (Good Practices), since we have to generalize the process independent of the algorithm.</p>\",\"PeriodicalId\":19986,\"journal\":{\"name\":\"PDA Journal of Pharmaceutical Science and Technology\",\"volume\":\" \",\"pages\":\"68-87\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PDA Journal of Pharmaceutical Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5731/pdajpst.2024.012950\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PDA Journal of Pharmaceutical Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5731/pdajpst.2024.012950","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
Recommendations for Artificial Intelligence Application in Continued Process Verification: A Journey Toward the Challenges and Benefits of AI in the Biopharmaceutical Industry.
This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and opportunities associated with integrating AI into CPV, focusing on real-time data analysis and proactive process adjustments. The paper highlights the importance of aligning AI solutions with regulatory standards and offers a set of comprehensive recommendations to bridge the gap between AI's potential and its practical, compliant, and safe application in pharmaceutical manufacturing. Emphasizing transparency, interpretability, and risk management, the research contributes to establishing best practices for AI implementation, ensuring the highest quality pharmaceutical products while meeting regulatory expectations. The conclusions drawn provide valuable insights for navigating the evolving landscape of AI in pharmaceutical manufacturing. This paper serves as a guideline for implementing AI, Machine Learning and Deep Learning models to the pharma industry. Nevertheless, the specific algorithms used in the CPV of the Future are not relevant for our paper (Good Practices), since we have to generalize the process independent of the algorithm.