According to the United States Food and Drug Administration (FDA), the process for creating a new drug involves five steps:
- Discovery and Development
- Preclinical Research
- Clinical Research
- FDA Review
- FDA Post-Market Safety Monitoring
From 2010 through 2019, the FDA approved an average of 38 new drugs each year. 2018 saw the most, with 59 new drugs approved. The yearly average during that span was 60 percent more than in the previous decade.
Progress is being made with technological advancements, but overall, drug creation can be a tedious process. According to the Congressional Budget Office, only about 12 percent of drugs entering clinical trials are ultimately approved for introduction by the FDA. It can take years for that process to unfold, and there is a lot of failure involved at each step along the way.
Discovery and development
The process of discovery and development involves, to put it simply, the “discovery” of a target within a disease, and then the “development” of compounds that can become drugs to fight the disease. There are certain times, depending on what is happening in the medical world, that defeating specific diseases becomes the goal for finding a new drug.
One escalating problem in the medical world is the emergence and spread of antimicrobial resistance (AR) and rising infections. The U.S. Centers for Disease Control and Prevention calls AR an urgent global public health threat, with more than 2.8 million antimicrobial-resistant infections occurring each year in the United States, 35,000 of which result in deaths. Antibiotic resistance is part of that increase, leading to an increased effort to find new antibiotics that bacteria are less resistant to. The challenge is to find something powerful enough to fight the bacteria that is also different from products that already exist.
Finding new medicine and the role of AI
There are countless possibilities for potential new medicines. Technology has made the painstaking process of sorting through it all if not easier, then more manageable. Artificial intelligence (AI), though, has shown the potential to move the process along even more quickly.
Labs can train the AI to learn which molecules’ chemical structures could possibly fight certain bacteria, allowing for a quicker gathering of potential solutions from large data sets. The data sets can be incredibly large – one of the main sources of bacteria-fighting bacteria is dirt. In a gram of soil there can be tens of thousands of different types of bacteria, the typical kind of problem for which machine learning has been used to provide a solution. The AI also has the ability to differentiate what molecules are different enough from current antibiotics, increasing its chances, if ever so slightly, of resulting in a successful new medicine.
Not unlike how it is used in identifying possible medical problems from a large set of x-rays, for example, AI in this instance can’t replace the work being done by people. There is much more work to be done at each stage of the drug development process. But similar to how it is used to ease the strain on medical professionals who otherwise would have to look at many images, increasing the potential for a mistake, AI can make the early, time-consuming steps in the process easier on humans, allowing them to focus their energies in different ways.
In the same way, technology can help make your organization more efficient. By taking some of the time-consuming work out of your processes, the right analytics solution can provide the data you need to make important decisions about healthcare. With real-time updates from the financial side to data about successful treatments and other operations, analytics can help your organization be a leader in the digital-first future of healthcare.
- How Gathering Compliance Data Can Help Organizations - October 10, 2024
- Beverage Alcohol Trends Heading into 2025 - October 8, 2024
- Rescheduling Cannabis: Impact on Organizations - October 3, 2024
