May 31, 2022
Data Is Pharma's Next Blockbuster
Anupam Nandwana1 is co-founder and CEO of P360, a leading developer of technology for the life sciences industry.
When it comes to bringing life-saving therapeutics to market, the first significant investment for pharmaceutical companies is within research and development, which includes drug discovery, drug development and clinical research. The general rule of thumb within the industry is that 25% of a pharmaceutical company's annual budget goes to R&D. In 2019 alone, the Congressional Budget Office2 estimated that the industry spent upwards of $83 billion on R&D. However, some estimate that the sector far surpassed those figures in 2021 with the 15 largest pharmaceutical companies alone investing more than $133 billion3.
In addition to excessive costs, pharmaceutical R&D is also an extremely time-consuming proposition. Unless the Food and Drug Administration grants some sort of emergency use authorization, it takes 10 or more years for a new therapeutic to go from discovery to the marketplace. And this is one of the factors contributing to our ever-increasing health care costs. But the pharmaceutical industry is working hard to reverse the trend.
The Digital Transformation Of Pharma
Against the backdrop of increasing costs and regulation, pharmaceutical companies have turned to digital transformation as a potential remedy for their woes. Everything from R&D to how sales reps connect with physicians is being reinvented. And this has pharma opening its coffers in a major way. One report estimates that the industry could spend more than $4.5 billion on digital transformation by 20304. For those of us that develop technologies for the pharmaceutical industry, these are exciting times. Technology initiatives have gone from the basement to the executive suite to shaping the future of the industry. And because of this, technologies like artificial intelligence (AI) and machine learning are having their pharma moment. But in my work, which spans more than two decades of technology development for pharma, I’ve discovered that companies are missing a critical first step: good data management.
You Need A Solid Data Foundation
Data is the foundation that drives other technologies forward. Without the right tools to help manage and process it, the best data in the world can quickly become useless. That’s why step one on the journey to building a digitally transformed organization is a commitment to good data management. This is especially important when it comes to pharmaceutical R&D.
The bedrock of pharmaceutical R&D is data. Companies can buy the most cutting-edge technologies, but those technologies will not be worth the investment without reliable data and proper data management tools and practices in place. And this is important because pharmaceutical companies generate a ton of data. A 2021 study from Tufts CSDD5 suggests that Phase III clinical trials alone generate an average of 3.6 million data points, which, according to the report, is three times the data collected during late-stage trials 10 years ago. The point is that pharmaceutical companies are collecting hundreds of terabytes of data across all phases of R&D each year. But that data comes in from hundreds of various sources, many of which use different data management practices and tools. The result of which is a tangled, decentralized web of unstructured data. However, suppose it were centralized, standardized and managed properly, that data could help save pharmaceutical companies billions of dollars and significantly improve the time it takes to bring life-saving new drugs to market.
Good Data Can Pay Dividends
Beyond its functional uses, data can also be a profit center for pharmaceutical companies, especially as third-party patient data and concepts like open-source clinical trials increase in popularity. According to Visiongain6 (download required), the market for big data analytics in health care could reach more than $101 billion by 2031. But for pharmaceutical companies to make effective use of their proprietary data or third-party data, they will need to get their data house in order. Then they will be able to leverage other innovative technologies to their full potential. Putting data in order will require a heavy investment in time and money for a lot of pharmaceutical companies. But the effort can pay dividends in a short amount of time as the organization can realize the full potential of its vast amounts of data. And the benefits can compound as the data moves through the organization more freely and with fewer quality checks, inputs and administrative steps. This untangling of data can also enable AI-powered tools to run more efficiently and effectively.
Opening The Door For AI
But what is AI anyway? Simply stated, it is a machine’s ability to acquire and apply knowledge. The goal of AI is to stimulate natural intelligence to solve complex problems while making assumptions, testing ideas and learning autonomously. It is the most human-like way of thinking, without the shortcomings of the human brain. Many expect AI-led drug discovery, development and clinical research to be the future of pharma. That is why investments to bring AI’s power to drug discovery have soared recently. According to Stanford University’s 2021 Artificial Intelligence Index7, the money committed to companies and projects focused on AI-powered drug discovery reached $13.8 billion in 2020. That was more than 4.5 times what was invested in 2019. Those investments are expected to pay off big for pharma too. The Information Technology & Innovation Foundation estimates that AI will save pharmaceutical companies almost $54 billion in R&D costs each year.
The Future Is Yet To Come
It is true; pure AI is still on the horizon. But not the distant horizon. And that is exactly the point I wanted to leave you with. Although AI is still in what many consider the early stages of development, breakthroughs like IBM’s Watson and the supercomputer at Oak Ridge National Laboratory8 have displayed some impressive advances. However, despite these advances, commercial applications for AI have yet to hit their stride. That means pharmaceutical companies still have time to prepare for the coming AI tide. And to do that, pharmaceutical companies first need to get their digital foundations in order by investing time and money into practices, processes and tools that enable the optimal use of their most valuable asset: data.
This article is published by Forbes:
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