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Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.

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Názov: Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.
Autori: Warke S; Centre for Pharmaceutical Nanotechnology, Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Sec-67, S.A.S. Nagar, Punjab, 160062, India., Katari O; Centre for Pharmaceutical Nanotechnology, Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Sec-67, S.A.S. Nagar, Punjab, 160062, India., Jain S; Centre for Pharmaceutical Nanotechnology, Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Sec-67, S.A.S. Nagar, Punjab, 160062, India. sanyogjain@niper.ac.in.
Zdroj: AAPS PharmSciTech [AAPS PharmSciTech] 2025 Nov 26; Vol. 27 (1), pp. 44. Date of Electronic Publication: 2025 Nov 26.
Spôsob vydávania: Journal Article; Review
Jazyk: English
Informácie o časopise: Publisher: Springer Country of Publication: United States NLM ID: 100960111 Publication Model: Electronic Cited Medium: Internet ISSN: 1530-9932 (Electronic) Linking ISSN: 15309932 NLM ISO Abbreviation: AAPS PharmSciTech Subsets: MEDLINE
Imprint Name(s): Publication: New York : Springer
Original Publication: Arlington, VA : American Association of Pharmaceutical Scientists, c2000-
Výrazy zo slovníka MeSH: Artificial Intelligence*/trends , Drug Industry*/methods , Drug Industry*/trends , Drug Development*/methods , Drug Development*/trends , Drug Compounding*/methods, Humans ; Machine Learning ; Chemistry, Pharmaceutical/methods
Abstrakt: Competing Interests: Declarations. Conflict of interest: The authors report no financial interest that might pose a potential, perceived or real conflict of interest.
Since Pfizer developed the mRNA vaccine for COVID-19 by leveraging artificial intelligence (AI) for designing the vaccine, integrating AI and allied domains in the drug development process has escalated at an unimaginable rate. Owing to the complex and time-consuming process of drug development, many firms, including big pharma and medium-scale industries, are constantly looking for ways to reduce the time for providing lifesaving medications to patients in need without compromising the safety and efficacy of the product. Formulation of novel drug products in a pharmaceutical R&D and scaling up the process to a large-scale production involves a huge investment and an eye for detail in the intricacies of the processes. Intervention of AI and machine learning (ML) can solve many problems in this aspect. With the rise of Industry 4.0, the relative shift of industry towards process automation, accelerated development has become vital in all domains. The investments in R&D by the large pharmaceutical companies reached up to $190 bn in 2024, according to a report by IQVIA. There is a noted upsurge in investments in the domains interlinking AI and ML with pharmaceutical research. Pharmaceutical formulation development can excel in the early stages, and the productivity can witness a steady growth if AI and ML tools are utilized. Most of the research in this domain remains in the budding stages, and its adoption in the industry needs further refinement by delineating structured guidance from the experts and regulatory agencies. The current review speaks about the current studies reported in the arena of formulation development and also sheds light on some of the areas where the pharmaceutical product development on a larger scale can benefit from AI and ML.
(© 2025. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.)
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Contributed Indexing: Keywords: algorithms; artificial intelligence; drug delivery systems; machine learning; pharmaceutical product development
Entry Date(s): Date Created: 20251126 Date Completed: 20251127 Latest Revision: 20251126
Update Code: 20251127
DOI: 10.1208/s12249-025-03296-0
PMID: 41299184
Databáza: MEDLINE
Popis
Abstrakt:Competing Interests: Declarations. Conflict of interest: The authors report no financial interest that might pose a potential, perceived or real conflict of interest.<br />Since Pfizer developed the mRNA vaccine for COVID-19 by leveraging artificial intelligence (AI) for designing the vaccine, integrating AI and allied domains in the drug development process has escalated at an unimaginable rate. Owing to the complex and time-consuming process of drug development, many firms, including big pharma and medium-scale industries, are constantly looking for ways to reduce the time for providing lifesaving medications to patients in need without compromising the safety and efficacy of the product. Formulation of novel drug products in a pharmaceutical R&D and scaling up the process to a large-scale production involves a huge investment and an eye for detail in the intricacies of the processes. Intervention of AI and machine learning (ML) can solve many problems in this aspect. With the rise of Industry 4.0, the relative shift of industry towards process automation, accelerated development has become vital in all domains. The investments in R&D by the large pharmaceutical companies reached up to $190 bn in 2024, according to a report by IQVIA. There is a noted upsurge in investments in the domains interlinking AI and ML with pharmaceutical research. Pharmaceutical formulation development can excel in the early stages, and the productivity can witness a steady growth if AI and ML tools are utilized. Most of the research in this domain remains in the budding stages, and its adoption in the industry needs further refinement by delineating structured guidance from the experts and regulatory agencies. The current review speaks about the current studies reported in the arena of formulation development and also sheds light on some of the areas where the pharmaceutical product development on a larger scale can benefit from AI and ML.<br /> (© 2025. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.)
ISSN:1530-9932
DOI:10.1208/s12249-025-03296-0