How Digital Technology is Paving Way for Drug Discovery
A study conducted by the Journal of Health Economics showed that, on average, a drug takes nearly 10 years and $2.6 billion to reach the marketplace.
Today, the chances of a new drug for entering and getting approved in clinical trials are less than 12%. For instance, to make a reliable medication for Alzheimer’s, the total number of unsuccessful trials were a whopping 123, while successful trials were merely 4.
What do these numbers suggest?
They point us in one direction- how can digital technology pave the way for drug discovery to improve the process of the development of a commercially successful drug?
If we look at the current trends of the pharmaceutical industry, we find that the technologies which are making strides in serving drug discovery services are-machine learning and big data. So, let’s find out how these two digital technologies are helping in making the world a healthy place!
#1. Machine Learning
Machine Learning is a field of AI (Artificial Intelligence) that gives a system the ability to learn steadily and improve on its own through experience.
As machine learning enables a system to see more than we can, it can help in modernizing the process of drug discovery. The best example of the application of machine learning in drug discovery can be said to be in the case of the Silicon Valley startup, twoXAR:
They used a machine learning system to point out the potential medications and drugs which could counter Parkinson’s disease. All they did was enter sets of information and data they had on Parkinson’s disease into the machine learning system. Once the system started processing the information, within a few minutes, it presented a drug list. The list had the names of several drugs that were classified as highly efficacious.
Later, it was found that one of the highly efficacious drugs was being developed by Dr. Tim Collier, who is the Director of the Udall Center of Excellence for Parkinson’s Disease Research at Michigan State University. The machine learning system was able to validate all his work within minutes, which was advancing in Collier’s lab for years.
This shows the potential of the application of machine learning in drug discovery. It can give lightning-fast and reliable results.
#2. Big Data
Big Data refers to the process of collecting and processing large sets of data, with the motive of finding functional hidden patterns and useful insights.
To start the development of a new drug, the researchers have to know about what has been published in biomedical journals about the compound they are planning to use. This process can be extremely time-consuming and expensive. This is where big data comes into the picture.
It can scrutinize the terabytes of data and give you quick and valid results. Big data would help you in finding the most relevant results from the most unusual and generic sources ranging from regulatory information to posts and comments on social media.
Apart from helping researchers in the development of new drugs, big data can also help pharmaceutical companies through drug-repurposing. It refers to the process of finding new applications and uses for existing drugs, which would benefit both the patients and pharmaceutical companies.
Today, the demand for effectual drugs is on the rise. With new conditions and ailments making the headlines every day, there is a dire need for accelerating the process of drug discovery. By incorporating machine learning and big data into drug discovery informatics, we’ll be able to find the cure of several diseases whose medication has still undeveloped.