What Is Big Data Science?
The application of Big Data methods in science refers to the use of large amounts of data for advanced data analytics methods in order to progress interactions and functions in scientific areas. Big Data is used in many other contexts on top of that, such as marketing (e.g. ad targeting), sales, fraud detection (e.g. at banks or credit card companies), and even in intelligence and military contexts.
When it comes to scientific areas, Big Data methods are used in healthcare, climate science, astronomy, and many more areas. The healthcare area is one of the most promising and one of the most important areas of Big Data usage — both from an economic perspective and from an “impact on society” perspective.
Big Data In Biotech And Drug Discovery
The process of developing new drugs has, so far, been very lengthy and extremely costly. Some statistics point to median costs of close to $1 billion to bring a new drug to the market. Other studies see the average development cost of new drugs even higher (in the multi-billion dollar range).
Since these costs have to be recouped as drug‘s patent protection runs out, many newly-developed drugs are very costly, which adds to already high healthcare costs in many countries around the globe. The following graph shows the increasing R&D spending among pharma companies around the world.
Making the drug development progress faster and easier not only helps patients that aren‘t getting their medical needs met, but faster and easier drug development processes could also reduce costs of new drugs and thus overall healthcare costs.
Drug data sets are notoriously large and heterogeneous, which makes them hard to analyze. The usage of Big Data, ideally combined with AI approaches, allows researchers to better find cues in these large data sets when it comes to efficacy, safety, and other important factors in the drug development process.
Big Data methods and AI can be used even earlier in the drug development process as well, like when it comes to finding promising molecules and agents that could be used to treat health issues in the future.
Once drugs are fully developed, have made it through the approval process, and are on the market, Big Data has further advantages in the biotech and pharma field.
Big Data can be used to optimize personalized medication plans, as different patients may react differently to the same drug. Personalizing medication plans can increase the treatment’s efficacy, while also reducing the risk of adverse side effects.
Profiting From The Big Data Megatrend In Biotech and Drug Discovery
With the help of Big Data, the biotech and pharma industries have the potential to find more effective and safer drugs in the future, with the potential for significant healthcare cost savings on top of that. However, the Big Data MegaTrend in the biotech and pharma industry also has implications for investors.
Pfizer (NYSE: PFE), one of the largest pharmaceutical companies in the world, implements Big Data and AI across different stages of the drug development path. It invests in a machine-learning research hub that aims to create better models for drug discovery. Pfizer also partners with other companies to get access to outside expertise.
These partnerships with companies like Gero, Truveta, and Tempus have all been made over the last 18 months, suggesting PFE is increasingly interested in this AI-driven collaborative trend.
Johnson & Johnson (NYSE: JNJ) pharma unit Janssen is also a big proponent of using Big Data and AI in combination when it comes to the discovery of potential agents and when it comes to developing these further and getting them approved.
Other large biotech and pharma companies use Big Data approaches as well, including the likes of AstraZeneca (NYSE: AZN), AbbVie (NASDAQ: ABBV), or Bristol-Myers Squibb (NYSE: BMY).
Even non-pharma companies may benefit from the Big Data trend in the biotech and pharma space. IBM (NYSE: IBM), with its Big Data and AI offering Watson, is one non-pharma company that‘s poised to benefit from increased Big Data and AI usage in the healthcare space, for example.
The Big Data Megatrend In The Biotech Industry
The drug discovery and development process could benefit a lot from increased Big Data and AI usage. This MegaTrend has the potential to result in better medicines for those that require treatment, and it could also lower healthcare costs in the future.
While pure-play AI and Big Data companies obviously can benefit from this trend, there are also many other ways for investors to be a part of this macro theme.
Some of the “boring” big pharma companies are very active when it comes to forging partnerships and investing in better development processes, and non-pureplay IT companies, such as IBM, will benefit from increased market opportunities as well.
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