Presentation
DH2 - Digital Twin Implementation in the Pharmaceutical Industry
DescriptionIntroduction
A significant investment of time and money needs to be made to make it through the many steps of the drug development process. There is high uncertainty and regulation within the pharmaceutical industry when developing new products. Continuous investment is made while the drug makes its way through the development process, and the development process could end at any point due to failure which would render any investment made up until that point a loss.
The recent development of new technologies and processes from Pharma 4.0 has introduced digital twins to the pharmaceutical industry. Digital twins have the potential to greatly improve the drug development process through their modeling capabilities. The objective of this research is to determine where digital twins have been implemented this far and determine any future benefits that have been identified through this research. This is the first review focused on the applications of digital twins within the pharmaceutical manufacturing industry.
Methods
A scoping review was conducted on PubMed.com using a Boolean string of Mesh terms. Six mapping questions were used to extract necessary details, such as frequency per year and their publication sources, as well as any relevant findings. Trends can be found to determine which areas of the drug development process are being impacted the most and how much is changing over time. After multiple screening rounds, a total of 9 publications were found to be relevant for this scoping review.
Results
There was an increase in publications over time from 2020 (N = 1) to 2022 (N = 4), and that 2023 already had 1 publication at the time of the study on March 20, 2023. The publications made advancements in a wide range of areas within the drug development process, with the largest number being drug manufacturing (N=4/9, 44%), then drug target discovery (N=3/9, 33%), then clinical trials (N=2/9, 22%), then finally pre-clinical research (N=1/9, 11%). Additionally, one of the publications created a framework that could be implemented at any phase of the drug development process, so this was classified separately as an impact for the overall drug development process (N=1/9, 11%).
The digital twins were found to optimize different aspects of the drug development process, especially within the drug manufacturing process through simulations. The advancements found within the clinical trials and pre-clinical research will allow drugs to move quicker through the drug development process since the digital twin is able to supplement the human trial data through models and simulations, lowering the number of participants needed. In addition, the framework for digital twin implementation allows for new advancements to be made in the future.
Conclusion
With an increasing frequency over time, the benefit of digital twins within the drug development process is clear. Overall, the digital twins were found to both minimize the investment needed, optimize the manufacturing process, as well as speed up the clinical trails. This can help bring new drugs to market faster and allow them to be sold for less money, since a lower investment will be needed throughout the drug development process. In the end, these differences will benefit the patients that need them allowing to get these drugs faster and cheaper.
A significant investment of time and money needs to be made to make it through the many steps of the drug development process. There is high uncertainty and regulation within the pharmaceutical industry when developing new products. Continuous investment is made while the drug makes its way through the development process, and the development process could end at any point due to failure which would render any investment made up until that point a loss.
The recent development of new technologies and processes from Pharma 4.0 has introduced digital twins to the pharmaceutical industry. Digital twins have the potential to greatly improve the drug development process through their modeling capabilities. The objective of this research is to determine where digital twins have been implemented this far and determine any future benefits that have been identified through this research. This is the first review focused on the applications of digital twins within the pharmaceutical manufacturing industry.
Methods
A scoping review was conducted on PubMed.com using a Boolean string of Mesh terms. Six mapping questions were used to extract necessary details, such as frequency per year and their publication sources, as well as any relevant findings. Trends can be found to determine which areas of the drug development process are being impacted the most and how much is changing over time. After multiple screening rounds, a total of 9 publications were found to be relevant for this scoping review.
Results
There was an increase in publications over time from 2020 (N = 1) to 2022 (N = 4), and that 2023 already had 1 publication at the time of the study on March 20, 2023. The publications made advancements in a wide range of areas within the drug development process, with the largest number being drug manufacturing (N=4/9, 44%), then drug target discovery (N=3/9, 33%), then clinical trials (N=2/9, 22%), then finally pre-clinical research (N=1/9, 11%). Additionally, one of the publications created a framework that could be implemented at any phase of the drug development process, so this was classified separately as an impact for the overall drug development process (N=1/9, 11%).
The digital twins were found to optimize different aspects of the drug development process, especially within the drug manufacturing process through simulations. The advancements found within the clinical trials and pre-clinical research will allow drugs to move quicker through the drug development process since the digital twin is able to supplement the human trial data through models and simulations, lowering the number of participants needed. In addition, the framework for digital twin implementation allows for new advancements to be made in the future.
Conclusion
With an increasing frequency over time, the benefit of digital twins within the drug development process is clear. Overall, the digital twins were found to both minimize the investment needed, optimize the manufacturing process, as well as speed up the clinical trails. This can help bring new drugs to market faster and allow them to be sold for less money, since a lower investment will be needed throughout the drug development process. In the end, these differences will benefit the patients that need them allowing to get these drugs faster and cheaper.
Event Type
Poster Presentation
TimeMonday, March 254:45pm - 6:15pm CDT
LocationSalon C
Digital Health
Simulation and Education
Hospital Environments
Medical and Drug Delivery Devices
Patient Safety Research and Initiatives