In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method

Timothy Hornick, Chen Mao, Athanas Koynov, Phillip Yawman, Prajwal Thool, Karthik Salish, Morgan Giles, Karthik Nagapudi and Shawn Zhang ()
Additional contact information
Timothy Hornick: DigiM Solution LLC
Chen Mao: Genentech, Inc., Synthetic Molecule Pharmaceutical Sciences
Athanas Koynov: Merck & Co.
Phillip Yawman: DigiM Solution LLC
Prajwal Thool: Genentech, Inc., Synthetic Molecule Pharmaceutical Sciences
Karthik Salish: Genentech, Inc., Synthetic Molecule Pharmaceutical Sciences
Morgan Giles: Merck & Co.
Karthik Nagapudi: Genentech, Inc., Synthetic Molecule Pharmaceutical Sciences
Shawn Zhang: DigiM Solution LLC

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which can be time-consuming and costly. This manuscript presents a generative artificial intelligence method that creates digital versions of drug products from images of exemplar products. This approach employs an image generator guided by critical quality attributes, such as particle size and drug loading, to create realistic digital product variations that can be analyzed and optimized digitally. This paper shows how this method was validated through two case studies: one for the determination of the amount of material that will create a percolating network in an oral tablet product and another for the optimization of drug distribution in a long-acting HIV inhibitor implant. The results demonstrate that the generative AI method accurately predicts a percolation threshold of 4.2% weight of microcrystalline cellulose and generates implant formulations with controlled drug loading and particle size distributions. Comparisons with real samples reveal that the synthesized structures exhibit comparable particle size distributions and transport properties in release media.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-54011-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54011-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-54011-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().