How AI is helping speed drug development
- By making data and insights available everywhere, every time, AI can help accelerate the development of cures, and make the healthcare industry more efficient.
- By using the latest big data analytics technologies, pharma companies can better forecast the success of a drug sooner in the development process
- By improving the intelligence of the machines processing researcher queries, we can match the right data with the appropriate research, reducing the time it takes to come up with a successful drug.
The convergence of healthcare and technology is creating a lot of buzz, especially as the healthcare policy debates are in a gridlock amongst U.S. lawmakers. Fortunately, technology companies and healthcare experts are working together to innovate at breakneck speeds, regardless of policy challenges.
There are many ways in which the healthcare industry is being disrupted, one of the most notable being the development of new treatments and drugs. Through the use of advanced data technologies, pharma companies and healthcare providers are turning to tech innovation to solve problems to meet patient needs, while staying profitable and serving a wider customer base.
Why Drug Development is Difficult
On average, it can take up to 12 years for a drug to be developed and make it to your medicine cabinet. This is due to a number of important factors. One of the lengthiest steps in developing a drug is identifying which of the 10,000+ chemical compounds available will help in the treatment of an illness.
Unfortunately, a wealth of healthcare data is largely unstructured, meaning it is difficult for researchers to search and reference effectively. We frequently encounter researchers turning to search engines like Google to find key data, simply because other sources in the industry are outdated or too difficult to use. For most researchers, this means accessing information takes more time, and needs to undergo scrutiny, which can prevent a drug from advancing through its development.
How AI Can Help
This is one of the many areas sophisticated AI can help speed the development process. Researchers can only peruse so much information regarding the application of a chemical compound to a particular treatment. Intelligent machines, however, can crawl, aggregate, visualize and contextualize troves of unstructured data to help identify possible applications and get them to clinical trials faster.
Disrupting traditional processes & fundamentally transforming how intelligence and analytics are generated and consumed in life sciences is essential for helping companies deliver faster. By making data and insights available everywhere, every time, we can help accelerate the development of cures, and make the healthcare industry more efficient.
Improving the Research Process
Most pharma companies and medical practitioners struggle when it comes to warehousing and searching all the information available to them. What companies need to effectively research is an innovative and intuitive semantic search facility that understands biomedical concepts. For example, such a system would allow them to query for ‘NSCLC’ or ‘crc, ’ and the system automatically understands it as ‘Non Small Cell Lung Cancer’ or ‘Colorectal Cancer.’
If leaders in these industries have tools that cater to their needs specifically, they can access all of the data they need in a fraction of the time. Additionally, insights that could power a new treatment won’t go unnoticed or overlooked. By improving the intelligence of the machines processing researcher queries, we can match the right data with the appropriate research, reducing the time it takes to come up with a successful drug.
Another barrier to the successful development of new treatments is the high attrition rate in the industry. Studies show that only about 9.6% of drugs that start Phase I trials eventually get approved to market. At first glance, this might seem like a good thing. If a drug doesn’t work, or if it is potentially harmful it should never make it to shelves.
However, a better way of looking at it is that less of these drugs should make it to trials in the first place. By using the latest big data analytics technologies, pharma companies can better forecast the success of a drug sooner in the development process. This can speed the development of new cures because if unsuccessful drugs are abandoned sooner, there will be more capacity for researching better ones.
Healthcare and life sciences companies looking to implement any data solution should do their due diligence in finding qualified partners. Through the use of AI, we can speed the development of new treatments, and democratize access to medical data, making it available to every layer of the healthcare industry.
About the author:
Gunjan Bhardwaj is the founder and CEO of Innoplexus, a leader in AI and analytics as a service for life science industries. With a background at Boston Consulting Group and Ernst & Young, he bridges the worlds of AI, consulting, and life science to drive innovation.