Top 4 Computing Resolutions Every Emerging Biopharma Should Adopt in 2023
Robust data infrastructure, cloud, and emerging technologies can enhance drug discovery for 2023.?
“Civilization advances by extending the number of operations we can perform without thinking about them.”?
Of course, British philosopher Alfred North Whitehead was referring to human psychology and the tasks we perform that become somewhat second nature. However, the phrase holds true in the very competitive business of medical device and drug research, discovery, and development, as well.?
Your drug discovery and development process will advance if scientists (and associated business roles, which today also include those in data science, clinical data management, and biostatistics to name just a few) can easily visualize, analyze and monetize data. Period. Sounds quite simple, and perhaps that’s the way these teams would like it to be.??
But in reality, that statement assumes the complex ecosystem of computing infrastructure, technologies, software, and workflows are humming along without issue, and optimized for performance and speed at scale. And that’s not often the case. What’s more common, particularly in the startup and emerging space, is that the environment doesn’t even fully exist and our team is asked to vision and architect something from the ground up.?
In short, science advances quickly if your team has the tools they need, and if those tools all play well together in the sandbox. Only then can R&D teams make better decisions quickly and efficiently.?
Now that it’s January and resolutions are in the air, let’s collectively decide to empower our scientists, and commit to building compute environments that enable innovation without sacrificing standards. After all, small changes add up (my short game is proof of that) and there’s never a better time to renew our intentions than the beginning of a new year.??
And so I offer you, four scientific computing resolutions you should consider for for 2023:
Better manage data (through FAIR data practices)
Ethically sourcing health records and genomics data for drug discovery is a crucial step in every biopharmaceutical company's research and development pipeline. This data-driven drug discovery process involves storing and analyzing billions or sometimes even trillions of records. Proper handling and management of such an enormous data store eventually determine the likelihood of finding successful clinical candidates during drug discovery.?
While most scientists and research executives believe that data-driven drug development solutions will improve drug discovery, managing aggregated data for optimal results remains one of the most challenging tasks. The answer to all data management issues at biopharma companies is FAIR data principles.
FAIR data stands for Findable, Accessible, Interoperable, and Reusable data. To build your data management pipeline using FAIR data principles, you must first replace the existing departmental data silos with democratized data infrastructure. This allows your R&D teams across your company to access data for data-driven computing easily.
The next step could involve automating time-consuming manual processes using advanced tools like AI and machine learning while ensuring that different processes in your data management pipeline can easily communicate.?
Deploying FAIR data practices to build your data infrastructure will streamline your research and development process, giving you a competitive edge over other life sciences organizations in 2023 (and beyond).?
Leverage the cloud to its fullest potential?
We all know research institutes have historically maintained on-premise servers for scientific computing and discovery—and some still do. However, keeping an on-premise computing infrastructure requires expensive hardware and additional IT staff.?
There’s a reason a PwC survey suggested that 60% of pharma executives had either moved to the cloud or were migrating their legacy systems to the cloud. Moderna’s CEO Stéphane Bancel credited cloud computing for successfully producing the COVID-19 vaccine for clinical trials in just 42 days following virus sequencing.?
While switching from legacy IT infrastructure to cloud computing is the first step to reducing IT costs and drug discovery, the main focus of 2023 should be optimizing and leveraging your cloud environment.?
For instance, horizontal scaling with the right infrastructure helps biopharma companies leverage their cloud infrastructure for high-performance computing by allocating resources on an as-needed basis. Similarly, using infrastructure-as-code (platform DevOps) and other AI-enabled drug discovery practices could speed up the process by allowing dynamic scaling of IT resources.
We’ve covered this and more in our recent Cloud Computing ebook, so give that a read if you’re excited about the possibilities of cloud computing.
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Optimize scientific applications and instruments to realize value vs. debt
From life sciences, to research institutes, to biopharma and biotech companies, the world joined forces to develop a COVID-19 vaccine for public use in record time. Nonetheless, the pandemic exposed many weaknesses of the existing healthcare infrastructure and emphasized the need to use emerging technologies for scientific computing and research.?
Our guide to re-accelerating drug discovery in the post-pandemic world highlights that research institutes across the United States are facing budget shortfalls of up to $23 billion, while many life sciences companies have also faced economic strain in recent times. Poorly configured and sometimes even unnecessary bio-IT technologies or workflows will ultimately amplify the economic strain, as they will eventually require additional work—or worse, become an unused relic and therefore skunk cost— minimizing a company’s commuting tech into little more than company debt.?
When your research team and scientists are equipped with well-considered technologies and properly designed workflows, the overall productivity of your organization increases significantly. To avoid the tech-debt trap, it’s better to eliminate the shortcuts and optimize your research and development workflow from inception.?
So, while state-of-the-art equipment like electron microscopes and sophisticated analysis tools are helpful for scientists, they need to make sense for your entire ecosystem. How does your data flow within departments? How does your equipment integrate with your current data management systems? These questions are critical to consider before investing millions of dollars on equipment.
And remember what we discussed just above, about FAIR data principles?? Without proper data accessibility, research teams from different departments will have a hard time accessing siloed data and interdepartmental research results, creating friction in collaboration. Otherwise, lack of data interoperability will be a bottleneck for leveraging real-time data from multiple sources.??
Ensure you have the right strategic partner to support the journey
Let’s be honest—emerging biopharmas specialize in life sciences research initiatives and often do not have the in-house IT expertise to support their dynamic computational requirements. At RCH solutions, we offer science-IT-as-a-service to bridge this IT gap by improving every step of drug discovery. Whether leveraging big data for data-rich analysis or using high-performance computing to complete tasks in hours instead of weeks, we do all the IT heavy lifting so that your research teams can spend more time on science and research tasks rather than fixing IT bottlenecks and breakdowns.?
Before you select a scientific computing partner, you must ensure it meets all your computing requirements, compliance needs, security practices, and automation demands, among other parameters. Choosing a competent and reliable strategic partner for your computational needs will help you keep up with the rapid technological changes in the bio-IT field and produce high-quality research results in record time.?
Bridge the gap between science and IT in 2023
With exabytes of data to analyze and strict research timelines, scientific computing in bio-IT needs a robust IT infrastructure. Your infrastructure should be able to process heavy computational tasks with minimal errors in a time-constrained research environment. RCH solutions provides that and more. Our highly experienced, expert teams can manage your cloud data infrastructure, optimize your research workflow, and deliver AI-based high-quality analytics in a seamless and extensible way. If you need help setting or achieving your 2023 scientific computing resolutions, get in touch with our team today.
Sources:
“An Introduction to Mathematics” by Alfred North Whitehead
Business Consultant at ProPharma Digital Transformation
2 年Resolution 1 can be much more readily achieved if execution on resolution 3 enables structured data capture and generates rich, standardized metadata through implementation of digital lab environment :-). Get it right the first time- data born FAIR!