Artificial intelligence (AI) is the technology that helps computers make decisions and perform tasks similar to what humans do. In the pharmaceutical industry, AI helps in developing drugs faster, more cost-efficiently, and with higher accuracy.
It can also help identify patients who are most likely to respond to a certain treatment based on their genetic makeup or other factors that may not have been identified yet. In this blog, we explore some of the top use cases of artificial intelligence in pharma. Read on to know more…
Develop better AI tools for drug discovery
Artificial intelligence is being used to identify new targets and design new experiments as well as to predict drug candidates’ performance in humans. AI tools are being used to analyze and predict the efficacy of drugs and drug candidates using big data. This is helping in discovering novel molecules and in developing the computational model to accelerate the drug discovery process.
For example, a team of researchers from the University of California, San Francisco (UCSF) and the Research Institute for Biomedical Sciences in Japan used AI to predict which disease proteins and cell signaling pathways a drug would affect. In their study, they used a computer algorithm to predict which proteins were being affected by the drugs being tested. Their model was able to predict the precise changes in protein expression, which led to the discovery of new targets for drugs.
Automated decision-making support for clinical trials and marketing
There is a constant push to automate decision-making for clinical trial management to reduce the time taken to create and approve marketing authorizations for drugs. These include the decision on clinical trial start, end, and end dates, submission of the marketing authorization application (MAA), and review process.
AI is being used to generate reports that can be used to help in the decision-making process. This will not only help in reducing the time taken by trial management but will also help in streamlining the whole drug development process for each company.
Robust clinical data management systems will be able to store, manage, and analyze data in a way that is accessible to AI tools. This will thus make regulatory review easier by automating the decision-making process. Clinical trial management is also being automated to reduce the human error factor and increase transparency in the process. It will help in creating a digital trail that can be accessed anytime.
Enhanced patient engagement and communication
Artificial intelligence can help in increasing patient engagement in drug development and healthcare processes. Patients could be informed about the drugs in a novel way to create better engagement. It can also be used for creating engaging content for communications with patients. Communication between patients and healthcare providers is essential for achieving the best possible outcomes from treatments.
It can thus be improved with the help of AI. The healthcare system is one of the most regulated industries. AI can help in automating the communication process and in tracking feedback from patients. It could be used to create a detailed chart that maps the response of a drug to the various patient groups. This will help in quickly identifying variations in the drug response and in developing treatment protocols accordingly.
Better predictive analytics for sales, pricing, and contract negotiations
With the use of AI, sales and pricing predictions can be made more accurate. It can help in predicting the demand for drugs, which will in turn help in developing a sales strategy. It can also be used in contract negotiations to derive useful insights from the data.
For example, an AI tool that keeps track of consumer behavior and has access to data from the drug development and healthcare processes can help in predicting the sales potential of drugs. This would help in developing a pricing strategy for selling the drug, which might have a low sales-predictability score.
Help in automating human activities for safety and quality assurance
AI tools are helpful in automating the safety and quality assurance activities of a drug development organization. They can be used to identify defects and inefficiencies in the processes. It can also be used to track compliance with instructions and regulations.
For example, a tool that has access to the drug development process and healthcare data could be used to find deviations from the instructions and regulations. It could be used during the manufacturing process to track defects and inefficiencies in quality assurance and to identify deviations from regulations.
Conclusion
Artificial intelligence is a crucial tool for data-driven decision-making in the pharmaceutical industry. AI can help in accelerating the drug discovery process, automate decision-making support for clinical trials and marketing, enhance patient engagement and communication, better predictive analytics for sales, pricing, and contract negotiations, and much more. These are just a few of the use cases of artificial intelligence in pharma. With all these benefits, it is no wonder that artificial intelligence is in such high demand in healthcare!