How Big Data Is Powering Precision Medicine
Data science has earned a prominent place on the front lines of precision medicine – the ability to target treatments to the specific physiological makeup of an individual’s disease. As cloud computing services and open-source big data have accelerated the digital transformation, small, agile research labs all over the world can engage in development of new drug therapies and other innovations. Previously, the necessary open-source databases and high-throughput sequencing technologies were accessible only by large research centers with the necessary processing power. In the evolving big data landscape, startup and emerging biopharma organizations have a unique opportunity to make valuable discoveries in this space.
The drive for real-world data
Through big data, researchers can connect with previously untold volumes of biological data. They can harness the processing power to manage and analyze this information to detect disease markers and otherwise understand how we can develop treatments targeted to the individual patient. Genomic data alone will likely exceed 40 exabytes by 2025 according to 2015 projections published by the Public Library of Science journal Biology. As data volume increases, its accessibility to emerging researchers improves as the cost of big data technologies decreases.
A recent report from Accenture highlights the importance of big data in downstream medicine, specifically oncology. Among surveyed oncologists, 65% said they want to work with pharmaceutical reps who can fluently discuss real-world data, while 51% said they expect they will need to do so in the future.
The application of artificial intelligence in precision medicine relies on massive databases the software can process and analyze to predict future occurrences. With AI, your teams can quickly assess the validity of data and connect with decision support software that can guide the next research phase. You can find links and trends in voluminous data sets that wouldn’t necessarily be evident in smaller studies.
Applications of precision medicine
Among the oncologists Accenture surveyed, the most common applications for precision medicine included matching drug therapies to patients’ gene alterations, gene sequencing, liquid biopsy, and clinical decision support. In one example of the power of big data for personalized care, the Cleveland Clinic Brain Study is reviewing two decades of brain data from 200,000 healthy individuals to look for biomarkers that could potentially aid in prevention and treatment.
AI is also used to create new designs for clinical trials. These programs can identify possible study participants who have a specific gene mutation or meet other granular criteria much faster than a team of researchers could determine this information and gather a group of the necessary size.
A study published in the journal Cancer Treatment and Research Communications illustrates the impact of big data on cancer treatment modalities. The research team used AI to mine National Cancer Institute medical records and find commonalities that may influence treatment outcomes. They determined that taking certain antidepressant medications correlated with longer survival rates among the patients included in the dataset, opening the door for targeted research on those drugs as potential lung cancer therapies.
Other common precision medicine applications of big data include:
- New population-level interventions based on socioeconomic, geographic, and demographic factors that influence health status and disease risk
- Delivery of enhanced care value by providing targeted diagnoses and treatments to the appropriate patients
- Flagging adverse reactions to treatments
- Detection of the underlying cause of illness through data mining
- Human genomics decoding with technologies such as genome-wide association studies and next-generation sequencing software programs
These examples only scratch the surface of the endless research and development possibilities big data unlocks for start-ups in the biopharma sector. Consult with the team at RCH Solutions to explore custom AI applications and other innovations for your lab, including scalable cloud services for growing biotech and pharma research organizations.
Do You Need Support with Your Cloud Strategy?
Cloud services are swiftly becoming standard for those looking to create an IT strategy that is both scalable and elastic. But when it comes time to implement that strategy—particularly for those working in life sciences R&D—there are a number of unique combinations of services to consider.
Here is a checklist of key areas to examine when deciding if you need expert support with your Cloud strategy.
- Understand the Scope of Your Project
Just as critical as knowing what should be in the cloud is knowing what should not be. The act of mapping out the on-premise vs. cloud-based solutions in your strategy will help demonstrate exactly what your needs are and where some help may be beneficial.
- Map Out Your Integration Points
Speaking of on-premise vs. in the Cloud, do you have an integration strategy for getting cloud solutions talking to each other as well as to on-premise solutions?
- Does Your Staff Match Your Needs?
When needs change on the fly, often your staff needs to adjust. However, those adjustments are not always so easily implemented, which can lead to gaps. So when creating your cloud strategy, ensure you have the right team to help understand the capacity, uptime and security requirements unique to a cloud deployment.
Check our free eBook, Cloud Infrastructure Takes Research Computing to New Heights, to help uncover the best cloud approach for your team. Download Now
- Do Your Solutions Meet Your Security Standards?
There are more than enough examples to show the importance of data security. It’s no longer enough however, to understand just your own data security needs. You now must know the risk management and data security policies of providers as well.
- Don’t Forget About Data
Life Sciences is awash with data and that is a good thing. But all this data does have consequences, including within your cloud strategy so ensure your approach can handle all your bandwidth needs.
- Agree on a Timeline
Finally, it is important to know the timeline of your needs and determine whether or not your team can achieve your goals. After all, the right solution is only effective if you have it at the right time. That means it is imperative you have the capacity and resources to meet your time-based goals.
Using RCH Solutions to Implement the Right Solution with Confidence
Leveraging the Cloud to meet the complex needs of scientific research workflows requires a uniquely high level of ingenuity and experience that is not always readily available to every business. Thankfully, our Cloud Managed Service solution can help. Steeped in more than 30 years of experience, it is based on a process to uncover, explore, and help define the strategies and tactics that align with your unique needs and goals.
We support all the Cloud platforms you would expect, such as AWS and others, and enjoy partner-level status with many major Cloud providers. Speak with us today to see how we can help deliver objective advice and support on the solution most suitable for your needs.
Benefits of investing in advanced visualization innovations.
Life science innovators have increasingly realized the value of visualization to drive real insights in data analytics. Exploring the capabilities of these cloud-based tools beyond simple presentation can inspire groundbreaking developments for emerging biotech and pharmaceutical start-ups. As noted in a 2021 article in Frontiers in Bioinformatics, every major development in genomics has come in the wake of a new invention within data computation and statistics. These are six strategic benefits of investing in data visualization as a leader in this innovative area.
Enhanced data processing and comprehension
Cloud-based information analytics provide a powerful tool for visual storytelling that illuminates the impact of your organization’s research and development efforts. For example, your scientists can access, gather, and display media from multiple platforms, databases, and sources through a single dashboard.
Cloud-based data analysis allows deeper interaction, including the ability to revise visualizations to highlight various aspects of the narrative. You can even combine multiple complex graphics to create sophisticated views.
Advanced data tools also accelerate discovery by reducing noisy data volume to highlight relevant patterns and connections. This benefits biopharma researchers who need to correlate market opportunities with possible drug treatments, diseases with causative agents, and chemicals with intended and unintended effects.
Simplified, stress-free sharing and collaboration
Most data visualization software tools come in a so-called container, a plug-and-play platform that includes everything you need to run the program. Since the necessary systems in the container have already been configured to work with one another, your team won’t face the challenges that arise when various components don’t interact as intended. With this structure, researchers who don’t share the same physical space can view and comment on the same 3D data visualization in a real-time virtual environment.
Faster, more effective clinical trials
Data visualization also facilitates greater speed and value among your organization’s clinical trial programs. With these tools, your teams can:
- Monitor key performance indicators at a glance on a customizable data dashboard
- Instantly summarize results in a reader-friendly format
- See a real-time overview of the trial’s progress to date
- Track potential risks for early identification of concerning developments
- Iterate immediately to create new reports as needed to support updated findings
A clear competitive landscape
Adolescent biopharma companies need to understand their market rivals to have a hope of competing in the crowded drug patent landscape. With data visualization, your leaders can clarify product pipelines and intellectual property information across your pharmaceutical or biotech environment. These tools draw indelible lines between different scientists, drug classifications, mergers and acquisitions, and patent activity so you can see exactly where your firm stands and take advantage of gaps in the market.
Space beyond size limits
You can see drug data and other research visualizations in 3D space outside the size of your team’s screens. With such an expansive view, data visualization lets researchers completely immerse themselves in the data from a 360-degree perspective to avoid missing connections that could change the direction of their efforts. As a result, you can have the confidence that comes from clear, transparent data representation. At the same time, you can simplify and reduce the size of large data sets when needed to visualize them in an understandable way.
In a 2017 example reported by Biopharma Trend, Novartis used virtual reality to create a three-dimensional exploration of small molecules and targets for protein. In the 3D VR landscape, the company’s scientists viewed and analyzed interactions between these structures.
Comprehensive knowledge graphs
Many growing companies in pharmaceutical and biotech research rely on global teams at international sites in various time zones. By building knowledge graphs through data visualization, scientists can break down data access silos for integrated analysis, management, and search. This approach helps reduce errors, illuminate understanding gaps, and prevent repeated efforts.
If data visualization has shifted from an afterthought to a concept at the forefront of your biopharma company’s future, consider outsourcing this type of tech to true experts. An experienced team can create the tools you need to innovate in the competitive pharmaceutical and biotech IT space.