Transformative change means rethinking the scientific computing workflow.
The need to embrace and enhance data science within the Life Sciences has never been greater. Yet, many Life Sciences organizations performing drug discovery face significant obstacles when transforming their legacy workflows.
Multiple factors contribute to the friction between the way Life Science research has traditionally been run and the way it needs to run moving forward. Companies that overcome these obstacles will be better equipped to capitalize on tomorrow’s research advances.
5 Obstacles to the Cloud-First Data Strategy and How to Address Them
Life Science research organizations are right to dedicate resources towards maximizing research efficiency and improving outcomes. Enabling the full-scale Cloud transformation of a biopharma research lab requires identifying and addressing the following five obstacles.
1. Cultivating a Talent Pool of Data Scientists
Life Science researchers use a highly developed skill set to discover new drugs, analyze clinical trial data, and perform biostatistics on the results. These skills do not always overlap with the demands of next-generation data science infrastructure. Life Science research firms that want to capitalize on emerging data science opportunities will need to cultivate data science talent they can rely on.
Aligning data scientists with therapy areas and enabling them to build a nuanced understanding of drug development is key to long-term success. Biopharmaceutical firms need to embed data scientists in the planning and organization of clinical studies as early as possible and partner them with biostatisticians to build productive long-term relationships.
2. Rethinking Clinical Trials and Collaborations
Life Science firms that begin taking a data science-informed approach to clinical studies in early drug development will have to ask difficult questions about past methodologies:
- Do current trial designs meet the needs of a diverse population?
- Are we including all relevant stakeholders in the process?
- Could decentralized or hybrid trials drive research goals in a more efficient way?
- Could we enhance patient outcomes and experiences using the tools we have available?
- Will manufacturers accept and build the required capabilities quickly enough?
- How can we support a global ecosystem for real-world data that generates higher-quality insights than what was possible in the past?
- How can we use technology to make non-data personnel more capable in a cloud-first environment?
- How can we make them data-enabled?
All of these questions focus on the ability for data science-backed cloud technology to enable new clinical workflows. Optimizing drug discovery requires addressing inefficiencies in clinical trial methodology.
3. Speeding Up the Process of Achieving Data Interoperability
Data silos are among the main challenges that Life Science researchers face with legacy systems. Many Life Science organizations lack a company-wide understanding of the total amount of data and insights they have available. So much data is locked in organizational silos that merely taking stock of existing data assets is not possible.
The process of cleaning and preparing data to fuel AI-powered data science models is difficult and time-consuming. Transforming terabyte-sized databases with millions of people records into curated, AI-ready databases manually is slow, expensive, and prone to human error.
Automated interoperability pipelines can reduce the time spent on this process to a matter of hours. The end result is a clean, accurate database fully ready for AI-powered data science. Researchers can now create longitudinal person records (LPRs) with ease.
4. Building Infrastructure for Training Data Models
Transforming legacy operations into fast, accurate AI-powered ones requires transparent access to many different data sources. Setting up the infrastructure necessary takes time and resources. Additionally, it can introduce complexity when identifying how to manage multiple different data architectures. Data quality itself may be inconsistent between sources.
Building a scalable pipeline for training AI data models requires scalable cloud technology that can work with large training datasets quickly. Without reputable third-party infrastructure in place, the process of training data models can take months.
5. Protecting Trade Secrets and Patient Data
Life Science research often relies on sensitive technologies and proprietary compounds that constitute trade secrets for the company in question. Protecting intellectual property has always been a critical challenge in the biopharmaceutical industry, and today’s cybersecurity landscape only makes it more important.
Clinical trial data, test results, and confidential patient information must be protected in compliance with privacy regulations. Life Science research organizations need to develop centralized policies that control the distribution of sensitive data to internal users and implement automated approval process workflows for granting access to sensitive data.
Endpoint security solutions help ensure sensitive data is only downloadable to approved devices and shared according to protocol. This enables Life Science researchers to share information with partners and supply chain vendors without compromising confidentiality.
A Robust Cloud-First Strategy is Your Key to Life Science Modernization
Deploying emergent technologies in the Life Science industry can lead to optimal research outcomes and better use of company resources. Developing a cloud computing strategy that either supplements or replaces aspects of your legacy system requires input and buy-in from every company stakeholder it impacts. Consult with the expert Life Science research consultants at RCH Solutions to find out how your research team can capitalize on the digital transformation taking place in Life Science.
RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.
Key Takeaways from NVIDIA’s GTC Conference Keynote
I recently attended NVIDIA’s GTC conference. Billed as the “number one AI conference for innovators, technologists, and creatives,” the keynote by NVIDIA’s always dynamic CEO, Jensen Huang, did not disappoint.
Over the course of his lively talk, Huang detailed how NVIDIA’s DGX line, which RCH has been selling and supporting since shortly after the inception of DGX, continues to mature as a full-blown AI enabler.
How? Scale, essentially.
More specifically, though, NVIDIA’s increasing lineup of available software and models will facilitate innovation by removing much of the software infrastructure work and providing frameworks and baselines on which to build.
In other words, one will not be stuck reinventing the wheel when implementing AI (a powerful and somewhat ironic analogy when you consider the impact of both technologies—the wheel and artificial intelligence—on human civilization).
The result, just as RCH promotes in Scientific Compute, is that the workstation, server, and cluster look the same to the users so that scaling is essentially seamless.
While cynics could see what they’re doing as a form of vendor lock, I’m looking at it as prosperity via an ecosystem. Similar to the way I, and millions of other people around the world, are vendor-locked into Apple because we enjoy the “Apple ecosystem”, NVIDIA’s vision will enable the company to transcend its role as simply an emerging technology provider (which to be clear, is no small feat in and of itself) to become a facilitator of a complete AI ecosystem. In such a situation, like Apple, the components are connected or work together seamlessly to create a next-level friction-free experience for the user.
From my perspective, the potential benefit of that outcome—particularly within drug research/early development where the barriers to optimizing AI are high—is enormous.
The Value of an AI Ecosystem in Drug Discovery
The Cliff’s Notes version of how NVIDIA plans to operationalize its vision (and my take on it), is this:
- Application Sharing: NVIDIA touted Omniverse as a collaborative platform — “universal” sharing of applications and 3D.
- Data Centralization: The software-defined data center (BlueField-2 & 3 / DPU) was also quite compelling, though in the world of R&D we live in at RCH, it’s really more about Science and Analytics than Infrastructure. Nonetheless, I think we have to acknowledge the potential here.
- Virtualization: GPU virtualization was also impressive (though like BlueField, this is not new but evolved). In my mind, I wrestle with virtualization for density when it comes to Scientific Compute, but we (collectively) need to put more thought into this.
- Processing: NVIDIA is pushing its own CPU as the final component in the mix, which is an ARM-based processor. ARM is clearly going to be a force moving forward, and Intel x86_64 is aging … but we also have to acknowledge that this will be an evolution and not a flash-cut.
What’s interesting is how this approach could play to enhance in-silico Science.
Our world is Cloud-first. Candidly, I’m a proponent of that for what I see as legitimate reasons (you can read more about that here). But like any business, Public Cloud vendors need to cater to a wide audience to better the chances of commercial success. While this philosophy leads to many beneficial services, it can also be a blocker for specialized/niche needs, like those in drug R&D.
To this end, Edge Computing (for those still catching up, a high-bandwidth and very low latency specialty compute strategy in which co-location centers are topologically close to the Cloud), is a solution.
Edge Computing is a powerful paradigm in Cloud Computing, enabling niche features and cost controls while maintaining a Cloud-first tact. Thus, teams are able to take advantage of the benefits of a Public Cloud for data storage, while augmenting what Public Cloud providers can offer by maintaining compute on the Edge. It’s a model that enables data to move faster than the more traditional scenario; and in NVIDIA’s equation, DGX and possibly BlueField work as the Edge of the Cloud.
More interestingly, though, is how this strategy could help Life Sciences companies dip their toes into the still unexplored waters of Quantum Computing through cuQuantum … Quantum (qubit) simulation on GPU … for early research and discovery.
I can’t yet say how well this works in application, but the idea that we could use a simulator to test Quantum Compute code, as well as train people in this discipline, has the potential to be downright disruptive. Talking to those in the Quantum Compute industry, there are anywhere from 10 – 35 people in the world who can code in this manner (today). I see this simulator as a more cost-effective way to explore technology, and even potentially grow into a development platform for more user-friendly OS-type services for Quantum.
A Solution for Reducing the Pain of Data Movement
In summary, what NVIDIA is proposing may simplify the path to a more synergistic computing paradigm by enabling teams to remain—or become—Cloud-first without sacrificing speed or performance.
Further, while the Public Cloud is fantastic, nothing is perfect. The Edge, enabled by innovations like what NVIDIA is introducing, could become a model that aims to offer the upside of On-prem for the niche while reducing the sometimes-maligned task of data movement.
While only time will tell for sure how well NVIDIA’s tools will solve Scientific Computing challenges such as these, I have a feeling that Jensen and his team—like our most ancient of ancestors who first carved stone into a circle—just may be on to something here.
RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.