Architectural Considerations & Optimization Best Practices

The integration of high-performance computing (HPC) in the Cloud is not just about scaling up computational power; it’s about architecting systems that can efficiently manage and process the vast amounts of data generated in Biotech and Pharma research. For instance, in drug discovery and genomic sequencing, researchers deal with datasets that are not just large but also complex, requiring sophisticated computational approaches.

However, designing an effective HPC Cloud environment comes with challenges. It requires a deep understanding of both the computational requirements of specific workflows and the capabilities of Cloud-based HPC solutions. For example, tasks like ultra-large library docking in drug discovery or complex genomic analyses demand not just high computational power but also specific types of processing cores and optimized memory management.

In addition, the cost-efficiency of Cloud-based HPC is a critical factor. It’s essential to balance the computational needs with the financial implications, ensuring that the resources are utilized optimally without unnecessary expenditure.

Understanding the need for HPC in Bio-IT

In Life Sciences R&D, the computational demands require sophisticated computational capabilities to extract meaningful insights. HPC plays a pivotal role by enabling rapid processing and analysis of extensive datasets. For example, HPC facilitates multi-omics data integration, combining genomics with transcriptomics and metabolomics for a comprehensive understanding of biological processes and disease. It also aids in developing patient-specific simulation models, such as detailed heart or brain models, which are pivotal for personalized medicine.

Furthermore, HPC is instrumental in conducting large-scale epidemiological studies, helping to track disease patterns and health outcomes, which are essential for effective public health interventions. In drug discovery, HPC accelerates not only ultra-large library docking but also chemical informatics and materials science, fostering the development of new compounds and drug delivery systems.

This computational power is essential not only for advancing research but also for responding swiftly in critical situations like pandemics. Additionally, HPC can integrate environmental and social data, enhancing disease outbreak models and public health trends. The advanced machine learning models powered by HPC, such as deep neural networks, are transforming the analytical capabilities of researchers.

HPC’s role in handling complex data also involves accuracy and the ability to manage diverse data types. Biotech and Pharma R&D often deal with heterogeneous data, including structured and unstructured data from various sources. The advanced data visualization and user interface capabilities supported by HPC allow for intricate data patterns to be revealed, providing deeper insights into research data.

HPC is also key in creating collaboration and data-sharing platforms that enhance the collective research efforts of scientists, clinicians, and patients globally. HPC systems are adept at integrating and analyzing these diverse datasets, providing a comprehensive view essential for informed decision-making in research and development.

Architectural Considerations for HPC in the Cloud

In order to construct an HPC environment that is both robust and adaptable, Life Sciences organizations must carefully consider several key architectural components:

• Scalability and flexibility: Central to the design of Cloud-based HPC systems is the ability to scale resources in response to the varying intensity of computational tasks. This flexibility is essential for efficiently managing workloads, whether they involve tasks like complex protein-structure modeling, in-depth patient data analytics, real-time health monitoring systems, or even advanced imaging diagnostics.
• Compute power: The computational heart of HPC is compute power, which must be tailored to the specific needs of Bio-IT tasks. The choice between CPUs, GPUs, or a combination of both should be aligned with the nature of the computational work, such as parallel processing for molecular modeling or intensive data analysis.
• Storage solutions: Given the large and complex nature of datasets in Bio-IT, storage solutions must be robust and agile. They should provide not only ample capacity but also fast access to data, ensuring that storage does not become a bottleneck in high-speed computational processes.
• Network architecture: A strong and efficient network is vital for Cloud-based HPC, facilitating quick and reliable data transfer. This is especially important in collaborative research environments, where data sharing and synchronization across different teams and locations are common.
• Integration with existing infrastructure: Many Bio-IT environments operate within a hybrid model, combining Cloud resources with on-premises systems. The architectural design must ensure a seamless integration of these environments, maintaining consistent efficiency and data integrity across the computational ecosystem.

Optimizing HPC Cloud environments

HPC in the Cloud is as crucial as its initial setup. This optimization involves strategic approaches to common challenges like data transfer bottlenecks and latency issues.

Efficiently managing computational tasks is key. This involves prioritizing workloads based on urgency and complexity and dynamically allocating resources to match these priorities. For instance, urgent drug discovery simulations might take precedence over routine data analyses, requiring a reallocation of computational resources.

But efficiency isn’t just about speed and cost; it’s also about smooth data travel. Optimizing the network to prevent data transfer bottlenecks and reducing latency ensures that data flows freely and swiftly, especially in collaborative projects that span different locations.

In sensitive Bio-IT environments, maintaining high security and compliance standards is another non-negotiable. Regular security audits, adherence to data protection regulations, and implementing robust encryption methods are essential practices. 

Maximizing Bio-IT potential with HPC in the Cloud

A well-architected HPC environment in the Cloud is pivotal for advancing research and development in the Biotech and Pharma industries.

By effectively planning, considering architectural needs, and continuously optimizing the setup, organizations can harness the full potential of HPC. This not only accelerates computational workflows but also ensures these processes are cost-effective and secure.

Ready to optimize your HPC/Cloud environment for maximum efficiency and impact? Discover how RCH can guide you through this transformative journey.

Sources:
https://www.rchsolutions.com/high-performance-computing/
https://www.nature.com/articles/s41586-023-05905-z
https://www.rchsolutions.com/ai-aided-drug-discovery-and-the-future-of-biopharma/
https://www.nature.com/articles/s41596-021-00659-2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318494/
https://pubmed.ncbi.nlm.nih.gov/37702944/
https://link.springer.com/article/10.1007/s42514-021-00081-w
https://www.rchsolutions.com/resource/scaling-your-hpc-environment-in-a-cloud-first-world/ https://www.rchsolutions.com/how-high-performance-computing-will-help-scientists-get-ahead-of-the-next-pandemic/ https://www.scientific-computing.com/analysis-opinion/how-can-hpc-help-pharma-rd
https://www.rchsolutions.com/storage-wars-cloud-vs-on-prem/
https://www.rchsolutions.com/hpc-migration-in-the-cloud/
https://www.mdpi.com/2076-3417/13/12/7082
https://www.rchsolutions.com/resource/hpc-migration-to-the-cloud/

Considerations for Enhancing Your In-house Bio-IT Team

As research becomes increasingly data-driven, the need for a robust IT infrastructure, coupled with a team that can navigate the complexities of bioinformatics, is vital to progress. But what happens when your in-house Bio-IT services team encounters challenges beyond their capacity or expertise?

This is where strategic augmentation comes into play. It’s not just a solution but a catalyst for innovation and growth by addressing skill gaps and fostering collaboration for enhanced research outcomes.

Assessing in-house Bio-IT capabilities

The pace of innovation demands an agile team and diverse expertise. A thorough evaluation of your in-house Bio-IT team’s capabilities is the foundational step in this process. It involves a critical analysis of strengths and weaknesses, identifying both skill gaps and bottlenecks, and understanding the nuances of your team’s ability to handle the unique demands of scientific research.

For startup and emerging Biotech organizations, operational pain points can significantly alter the trajectory of research and impede the desired pace of scientific advancement. A comprehensive blueprint that includes team design, resource allocation, technology infrastructure, and workflows is essential to realize an optimal, scalable, and sustainable Bio-IT vision.

Traditional models of sourcing tactical support often clash with these needs, emphasizing the necessity of a Bio-IT Thought Partner that transcends typical staff augmentation and offers specialized experience and a willingness to challenge assumptions.

Identifying skill gaps and emerging needs

Before sourcing the right resources to support our team, it’s essential to identify where the gaps lie. Start by:

1. Prioritizing needs.  While prioritizing “everything” is often the goal, it’s also the fastest way to get nothing done. Evaluate the overarching goals of your company and team, and decide what skills and efforts represent support mission-critical, versus “nice to have” efforts.
2. Auditing current capabilities: Understand the strengths and weaknesses of your current team. Are they adept at handling large-scale genomic data but struggle with real-time data processing? Recognizing these nuances is the first step.
3. Project forecasting: Consider upcoming projects and their specific IT demands. Will there be a need for advanced machine learning techniques or Cloud-based solutions that your team isn’t familiar with?
4. Continuous training: While it’s essential to identify gaps, it’s equally crucial to invest in continuous training for your in-house team. This ensures that they remain updated with the latest in the field, reducing the skill gap over time.

Evaluating external options

Once you’ve identified the gaps, the next step is to find the right partners to fill them. Here’s how:

1. Specialized expertise: Look for partners who offer specialized expertise that complements your in-house team. For instance, if your team excels in data storage but lacks in data analytics, find a partner who can bridge that gap.
2. Flexibility: The world of Life Sciences is dynamic. Opt for partners who offer flexibility in terms of scaling up or down based on project requirements.
3. Cultural fit: Beyond technical expertise, select an external team that aligns with your company’s culture and values. This supports smoother collaboration and integration. 

Fostering collaboration for optimal research outcomes

Merging in-house and external teams can be challenging. However, with the right strategies, collaboration can lead to unparalleled research outcomes.

1. Open communication: Establish clear communication channels. Regular check-ins, updates, and feedback loops help keep everyone on the same page.
2. Define roles: Clearly define the roles and responsibilities of each team member, both in-house and external. This prevents overlaps and ensures that every aspect of the project is adequately addressed.
3. Create a shared vision: Make sure the entire team, irrespective of their role, understands the end goal. A shared vision fosters unity and drives everyone towards a common objective.
4. Leverage strengths: Recognize and leverage the strengths of each team member. If a member of the external team has a particular expertise, position them in a role that maximizes that strength.

Making the right choice

For IT professionals and decision-makers in Pharma, Biotech, and Life Sciences, the decision to augment the in-house Bio-IT team is not just about filling gaps; it’s about propelling research to new heights, ensuring that the IT infrastructure is not just supportive but also transformative.

When making this decision, consider the long-term implications. While immediate project needs are essential, think about how this augmentation will serve your organization in the years to come. Will it foster innovation? Will it position you as a leader in the field? These are the questions that will guide you toward the right choice.

Life Science research outcomes can change the trajectory of human health, so there’s no room for compromise. Augmenting your in-house Bio-IT team is a commitment to excellence. It’s about recognizing that while your team is formidable, the right partners can make them invincible. Strength comes from recognizing when to seek external expertise.

 Pick the right team to supplement yours. Talk to RCH Solutions today.

Sources:
https://www.rchsolutions.com/harnessing-collaboration/
https://www.rchsolutions.com/press-release-rch-introduces-solution-offering-designed-to-help-biotech-startups/
https://www.rchsolutions.com/what-is-a-bio-it-thought-partner-and-why-do-you-need-one/
 https://www.rchsolutions.com/our-people-are-our-key-point-of-difference/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3652225/
https://www.forbes.com/sites/forbesbusinesscouncil/2023/01/10/three-best-practices-when-outsourcing-in-a-life-science-company/?sh=589b57a55575
https://www.cio.com/article/475353/avoiding-the-catch-22-of-it-outsourcing.html 

Consider the Advantages of Guardrails in the Cloud

Cloud integration has quite deservedly become the go-to digital transformation strategy across industries, particularly for businesses in the pharmaceutical and biotech sectors. By integrating Cloud technology into your IT approach, your organization can access unprecedented flexibility while taking advantage of real-time collaboration tools. What’s more, Cloud solutions deliver sustained value compared to on-premises solutions, which require resources (both time and money) to upgrade and maintain the associated hardware, since companies can easily scale Cloud platforms in tandem with accelerating growth.

At the same time, leaders must carefully balance the flexibility and adaptability of Cloud technology with the need for robust security and access controls. With effective guardrails administered appropriately, emerging biopharma companies can optimize research and development within boundaries that shield valuable data and ensure regulatory compliance. Explore these advantages of adding the right guardrails to your biotech or pharmaceutical organization’s digital landscape to inform your planning process.

Prevent unintended security risks

One of the most appealing aspects of the Cloud is the ability to leverage its incredible ecosystem of knowledge, tools, and solutions within your own platform. Having effective guardrails in place allows your team to quickly install and benefit from these tools, including brand-new improvements and implementations, without inadvertently creating a security risk. 

Researchers can work freely in the digital setting while the guardrail monitors activity and alerts users in the event of a security risk. As a result, the organization can avoid these common issues that lead to data breaches:

• Maintaining open access to completed projects that should have privileges in place
• Disabling firewalls or Secure Shell systems to access remote systems
• Using sensitive data for testing and development purposes
• Collaborating on sensitive data without proper access controls

Honor the shared responsibility model

Biopharma companies tend to appreciate the autonomous, self-service approach of Cloud platforms, as the dynamic infrastructure offers nearly endless experimentation. At the same time, most security issues in the Cloud result from user errors such as misconfiguration. The implementation of guardrails creates a stopgap so that even with the shortest production schedules, researchers won’t accidentally expose the organization to potential threats. Guardrails also help your company comply with your Cloud service provider’s shared responsibility policy, which outlines and defines the security responsibilities of both organizations.

Establish and maintain best practices for data integrity

Adolescent biopharma companies often experience such accelerated growth that they can’t keep up with the need to create and follow organizational best practices for data management. By putting guardrails in place, you also create standardized controls that ensure predictable, consistent operation. Available tools abound, including access and identity management permissions, security groupings, network policies, and automatic enforcement of these standards as they apply to critical Cloud data. 

A solid information security and management strategy becomes even more critical as your company expands. Entrepreneurs who want to prepare for future acquisitions should be ready to show evidence of a culture that prizes data integrity.

According to IBM, the cost of a single Cloud-based security breach in the United States averaged nearly $4 million in 2020. Guardrails provide a solution that truly serves as a golden means, preserving critical Cloud components such as accessibility and collaboration without sacrificing your organization’s valuable intellectual property, creating compliance issues and compromising research objectives.

Part Five in a Five-Part Series for Life Sciences Researchers and IT Professionals  

Because scientific research is increasingly becoming a multi-disciplinary process that requires researcher scientists, data scientists and technical engineers to work together in new ways, engaging an IT partner that has the specialized skills, service model and experience to meet your compute environment needs can be a difference-maker in the success of your research initiatives.  

If you’re unsure what specifically to look for as you evaluate your current partners, you’ve come to the right place!  In this five-part blog series, we’ve provided a range of considerations important to securing a partner that will not only adequately support your research compute environment needs, but also help you leverage innovation to drive greater value out of your research efforts. Those considerations and qualities include: 

1. Unique Life Sciences Specialization and Mastery
2. The Ability to Bridge the Gap Between Science and IT
3. A High Level of Adaptability
4. A Service Model That Fits Research Goals

In this last installment of our 5 part series, we’ll cover one of the most vital considerations when choosing your IT partner: Dedication and Accountability. 

You’re More than A Service Ticket

Working with any vendor requires dedication and accountability from both parties, but especially in the Life Sciences R&D space where project goals and needs can shift quickly and with considerable impact.

Deploying the proper resources necessary to meet your goals requires a partner who is proactive, rather than reactive, and who brings a deep understanding and vested interest in your project outcomes (if you recall, this is a critical reason why a service model based on SLA’s rather than results can be problematic).  

Importantly, when scientific computing providers align themselves with their customers’ research goals, it changes the nature of their relationship. Not only will your team have a reliable resource to help troubleshoot obstacles and push through roadblocks, it will also have a trusted advisor to provide strategic guidance and advice on how to accomplish your goals in the best way for your needs (and ideally, help you avoid issues before they surface). And if there is a challenge that must be overcome? A vested partner will demonstrate a sense of responsibility and urgency to resolve it expeditiously and optimally, rather than simply getting it done or worse—pointing the finger elsewhere.   

It’s this combination of knowledge, experience and commitment that will make a tangible difference in the value of your relationship.

Don’t Compromise

Now you have all 5 considerations, it’s time to put them into practice. Choose a scientific computing partner whose services reflect the specialized IT needs of your scientific initiatives and can deliver robust, consistent results. 

RCH Solutions Is Here to Help You Succeed

RCH has long been a provider of specialized computing services exclusively to the Life Sciences. For more than 30 years, our team has been called upon to help biotechs and pharmas across the globe architect, implement, optimize and support compute environments tasked with driving performance for scientific research teams.  Find out how RCH can help support your research team and enable faster, more efficient scientific discoveries, by getting in touch with our team here.

How to Tell if Your Computing Partner is Actually Adding Value to Your Research Process: Service Model

Part Four in a Five-Part Series for Life Sciences Researchers and IT Professionals  

If 2020 and 2021 proved anything to us, it’s that change is inevitable and often comes when we least expect it. The pandemic shifted the way virtually every company operates. While change can feel unnerving, it is important to make changes that better your work and your company. 

The Life Sciences industry is no different. Whether your company shifted drastically in response to the pandemic or not at all, it’s still important to take a look at your business or team operations to see in what areas you can continue to improve. For teams conducting drug discovery,  development or even pre-clinical workce such area that can often be improved is your external scientific computing support. 

We’ve highlighted several items for teams to take into consideration when evaluating their current partners. So far in our five  part blog series we’ve taken a look at following three considerations:

• #1 – Life Science Specialization and Mastery
• #2 –  Bridging the Gap Between Science and IT
• #3 – A High Level of Adaptability

In this installment, we take a deeper look at Consideration #4: A Service Model that Fits Research Goals. 

Consideration #4: A Service Model that Fits Research Goals

It’s no surprise that every company is likely to have different research goals. A one size fits all approach is not an acceptable strategy. Do you know what sets your current partner apart from their competitors? Do they offer a commodity service, or is there a real and tangible value in what they deliver, and how they deliver it? Your partner’s service model can make an enormous difference in the value you get from their expertise. 

There are two models that life science organizations typically use; computing partners operating under a staff augmentation model or a Managed Service providers model. It is no surprise that these two models work in very different ways and in turn offer very different results for the companies that use them. 

IT staff augmentation may allow your organization to scale its IT team up or down based on current needs. This can help scientific IT teams retain project control and get short-term IT support on an as-needed basis, but it often requires the researchers to obtain, deploy and manage human resources on their own. This can be time consuming and tedious for the organization.  Often, outcomes related to staff augmentation services are guided by rigid, standardized service level agreements that prioritize process over results. Unlike in many other industries, these standards can be limiting in the dynamic world of scientific research and discovery, preventing teams from appropriately adapting their scope as project needs and goals change.  

Managed IT services, on the other hand, offer a more balanced approach between operations and project management. This allows research teams to save time they would otherwise spend managing IT processes, and it enables the delivery of specialized services tailored to your team’s specific needs. And, unlike the staff augmentation model that provides an individual resource to “fill a seat,” a managed services model is based on a team approach.  Often a diverse team of experts with a range of specialization work collaboratively to find a solution to a single issue.  This shifts the focus to prioritize outcomes and enables for a fluid and nimple approach, in a cost and time efficient manner. The end result is better Outcomes for all. 

How Your Computing Partner’s Service Model Influences Research Success

Meeting your research goals requires efficiency and expertise and when comparing the staff augmentation model versus the managed IT model, you can see the clear differences. When choosing the managed IT model you’re going to be offered a level of continuity and efficiency that the staff augmentation model can not compete with. When your organization is pressed for time and resources, having a managed IT model allows you to focus and expedite your work, ultimately accelerating the journey toward  your discovery and development goals.

When you work through evaluating your current partners, be sure to consider whether they operate with a service model that fits your research and development needs and goals. 

And stay tuned for the final installment of this series on how to evaluate your external scientific computing resources, in which we’ll discuss our last but certainly not least important consideration: Dedication and Accountability.

How to Tell if Your Computing Partner is Actually Adding Value to Your Research Process: Adaptability

Part Three in a Five Series for Life Sciences Researchers and IT Professionals  

 If you’re still not sure you’ve sourced the ideal scientific computing partner to help your team realize it’s research compute goals, here’s another quality to evaluate: adaptability. 

By this point, we hope you’ve already read the first two installments in this five part series on how to tell if your partner is adding value (if not, start with #1 – Life Sciences Specialization and Mastery and #2 – The Ability to Bridge the Gap Between Science and IT). Here, we will take a look at the importance of adaptability, and why that quality matters to R&D teams (and their IT counterparts). 

Consideration #3: High Level of Adaptability

In today’s world adaptability is a highly sought after skill. Whether it’s in your personal or professional life, the ability to shift and adjust to change is vital. 

In the context of scientific computing, adaptability is less about survival and more about the ability to see things from different perspectives. In a research environment, though the end goal often remains, the process or needs associated with achieving that goal, can often be fluid.  Being able to or evolve to reach new or shifting goals with precision and performance is a skill not everyone one—or every team—possesses. 

In a research environment, scientists are not always able to predict the results their work will yield, thus needing to work hand in hand with a partner that is able to adjust when necessary. Whether you and your team need a few new resources or a new strategy entirely, a good computing partner will be able to adapt to your needs!

There are even more benefits to having a highly adaptable partner including increased level of performance, smoother transition from one project to the next, and more efficiency in your company’s research. A great scientific computing partner should be able to meet these needs using scalable IT architecture and a flexible service model. If your partner’s service model is too rigid, it may indicate they lack the expertise to readily provide dynamic solutions.

A Better Model for Your Dynamic Needs

Rigid service models may be the norm in many industries, but it does not predict success in the world of life science research. And too often, those partners that fall into the “good enough” category (as we mentioned above) follow these strict SLAs that don’t account for nuance or research environments.  

A partner that is not adaptable will inevitably be incapable of keeping up with the demands of shifting research. Choose a scientific computing partner whose services align with your scientific initiatives and deliver robust, consistent results. Prepare for the next year’s challenges by reaching out to a partner that offers highly specialized scientific computing services to life science research organizations like yours.

As you take all of these points into account, be sure to come back for consideration #4: A Service Model that Fits Research goals. 

Attention R&D IT decision makers: 

If you’re expecting different results in 2022 despite relying on the same IT vendors and rigid support model that didn’t quite get you to your goal last year, it may be time to hit pause on your plan.

At RCH, we’ve spent the past 30+ years paying close attention to what works — and what doesn’t—while providing specialty scientific computing and research IT support exclusively in the Life Sciences. We’ve put together this list of must-do best practices that you, and especially your external IT partner, should move to the center of your strategy to help you to take charge of your R&D IT roadmap. 

And if your partners are not giving you this advice to get your project back track?  Well, it may be time to find a new one.

1. Ground Your Plan in Reality
In the high-stakes and often-demanding environment of R&D IT, the tendency to move toward solutioning before fully exposing and diagnosing the full extent of the issue or opportunity is very common. However, this approach is not only ineffective, it’s also expensive. Only when your strategy and plan is created to account for where you are today — not where you’d like to be today — can you be confident that it will take you where you want to go. Otherwise, you’ll be taking two steps forward, and one (or more) step back the entire time.

2. Start with Good Data
Research IT professionals are often asked to support a wide range of data-related projects. But the reality is, scientists can’t use data to drive good insights, if they can’t find or make sense of the data in the first place. Implementing FAIR data practices should be the centerprise of any scientific computing strategy. Once you see the full scope of your data needs, only then can you deliver on high-value projects, such as analytics or visualization.

3. Make “Fit-For-Purpose” Your Mantra
Research is never a one size fits all process. Though variables may be consistent based on the parameters of your organization and what has worked well in the past, viewing each challenge as unique affords you the opportunity to leverage best-of-breed design patterns and technologies to answer your needs. Therefore, resist the urge to force a solution, even one that has worked well in other instances, into a framework if it’s not the optimal solution, and opt for a more strategic and tailored approach. 

4. Be Clear On the Real Source of Risk
Risk exists in virtually all industries, but in a highly regulated environment, the concept of mitigating risk is ubiquitous, and for good reason.  When the integrity of data or processes drives outcomes that can actually influence life or death, accuracy is not only everything, it’s the only thing. And so the tendency is to go with what you know. But ask yourself this: does your effort to minimize risk stifle innovation? In a business built on boundary-breaking innovation, mistaking static for “safe” can be costly.  Identifying which projects, processes and/or workloads would be better managed by other, more specialized service providers may actually reduce risk by improving project outcomes.   

Reaching Your R&D IT Goals in 2022:  A Final Thought

Never substitute experience. 

Often, the strategy that leads to many effective scientific and technical computing initiatives within an R&D IT framework differs from a traditional enterprise IT model. And that’s ok because, just as often, the goals do as well. That’s why it is so important to leverage the expertise of R&D IT professionals highly specialized and experienced in this niche space.

Experience takes time to develop. It’s not simply knowing what solutions work or don’t, but rather understanding the types of solutions or solution paths that are optimal for a particular goal, because, well—‘been there done that. It’s having the ability to project potential outcomes, in order to influence priorities and workflows. And ultimately, it’s knowing how to find the best design patterns. 

It’s this level of specialization — focused expertise combined with real, hands-on experience — that can make all the difference in your ability to realize your outcome. 

And if you’re still on the fence about that, just take a look at some of these case studies to see how it’s working for others.  

Part Two in a Five-Part Series for Life Sciences Researchers and IT Professionals 

As you continue to evaluate your strategy for 2022 and beyond, it’s important to ensure all facets of your compute environment are optimized— including the partners you hire to support it. 

Sometimes companies settle for working with partners that are just “good enough,” but in today’s competitive environment, that type of thinking can break you.  What you really need to move the needle is a scientific computing partner who understands both Science and IT. 

In part two of this five-part blog series on what you should be measuring your current providers against, we’ll examine how to tell if your external IT partner has the chops to meet the high demands of science, while balancing the needs of IT. If you haven’t read our first post, Evaluation Consideration #1: Life Science Specialization and Mastery, you can jump over there, first.  

Evaluation Consideration #2: Bridging the Gap Between Science and IT 

While there are a vast number of IT partners available, it’s important to find someone that has a deep understanding of the scientific industry and community. It can be invaluable to work with a specialized IT group, considering being an expert in one or the other is not enough.  The computing consultant that works with clients in varying industries may not have the best combination of knowledge and experience to drive the results you’re looking for.  

Your computing partner should have a vast understanding of how your research drives value for your stakeholders. Their ability to leverage opportunities and implement IT infrastructure that meet scientific goals, is vital. Therefore, as stated in consideration #1: Life Science Specialization and Mastery, it’s vital that your IT partner have significant IT experience.  

This is an evaluation metric best captured during strategy meetings with your scientific computing lead. Take a moment to consider the IT infrastructure options that are presented to you. Do they use your existing scientific infrastructure as a foundation? Do they require IT skills that your research team has? 

These are important considerations because you may end up spending far more than necessary on IT infrastructure that goes underutilized. This will make it difficult for your life science research firm to work competitively towards new discoveries. 

The Opportunity Cost of Working with the Wrong Partner is High

Overspending on underutilized IT infrastructure draws valuable IT resources away from critical research initiatives. Missing opportunities to deploy scientific computing solutions in response to scientific needs negatively impacts research outcomes. 

Determining if your scientific computing partner is up to the task requires taking a closer look at the quality of expertise you receive. Utilize your strategy meetings to gain insight into the experience and capabilities of your current partners, and pay close attention to Evaluation Consideration #2: Bridging the Gap Between Science and IT.  Come back next week to read more about our next critical consideration in your computing partnership, having a High Level of Adaptability. 

A Five-Part Series for Life Sciences Researchers and IT Professionals

The New Year is upon us and for most, that’s a time to reaffirm organizational goals and priorities, then develop a roadmap to achieve them. For many enterprise and R&D IT teams, that includes working with external consultants and providers of specialized IT and scientific computing services. 

But much has changed in the last year, and more change is coming in the next 12 months. Choosing the right partner is essential to the success of your research and, in the business where speed and performance are critical to your objectives, you don’t want to be the last to know when your partner isn’t working out quite as well as you had planned (and hoped). 

But what should you look for in a scientific computing partner?  

This blog series will outline five qualities that are essential to consider … and what you should be measuring your current providers against throughout the year to determine if they’re actually adding value to your research and processes.  

Evaluation Consideration #1: Life Science Specialization and Mastery

There are many different types of scientific computing consultants and many different types of organizations that rely on them. Life science researchers regularly perform incredibly demanding research tasks and need computing infrastructure that can support those needs in a flexible, scalable way.

A scientific computing consultant that works with a large number of clients in varied industries may not have the unique combination of knowledge and experience necessary to drive best-in-class results in the life sciences. 

Managing IT infrastructure for a commercial enterprise is very different from managing IT infrastructure for a life science research organization. Your computing partner should be able to provide valuable, highly specialized guidance that caters to research needs – not generic recommendations for technologies or workflows that are “good enough” for anyone to use.

In order to do this, your computing partner must be able to develop a coherent IT strategy for supporting research goals. Critically, partners should also understand what it takes to execute that strategy, and connect you with the resources you need to see it through.

Today’s Researchers Can’t Settle for “Good Enough”

In the past, the process of scientific discovery left a great deal of room for trial and error. In most cases, there was no alternative but to follow the intuition of scientific leaders, who could spend their entire career focused on solving a single scientific problem.

Today’s research organizations operate in a different environment. The wealth of scientific computing resources and the wide availability of emerging technologies like artificial intelligence (AI) and machine learning (ML) enable brand-new possibilities for scientific discovery.

Scientific research is increasingly becoming a multi-disciplinary process that requires researchers and data scientists to work together in new ways. Choosing the right scientific partner can unlock value for research firms and reduce time-to-discovery significantly.

Best-in-class scientific computing partnerships enable researchers to:

• Predict the most promising paths to scientific discovery and focus research on the avenues most likely to lead to positive outcomes.
• Perform scientific computing on scalable, cloud-enabled infrastructure without overpaying for services they don’t use.
• Automate time-consuming research tasks and dedicate more time and resources to high-impact, strategic initiatives.
• Maintain compliance with local and national regulations without having to compromise on research goals to do so.

If your scientific computing partner is one step ahead of the competition, these capabilities will enable your researchers to make new discoveries faster and more efficiently than ever before.

But finding out whether your scientific computing partner is up to the task requires taking a closer look at the quality of expertise you receive. Pay close attention to Evaluation Consideration #1: Life Science Specialization and Mastery and come back next week to read more about our next critical consideration in your computing partnership, the Ability to Bridge the Gap Between Science and IT.

If the events of the past year have taught us anything, it’s that Life Science organizations—like most other businesses—need infrastructure that can adapt to unpredictable and disruptive risks. 

Those that adopt certain actionable strategies today are going to be better-suited to ensure disruption-free activity in the future. Here are five you can implement now to help your team prepare.

1) Make Smart Automation Core to Delivering Value

Cloud-based technology has already begun to fundamentally change the way data storage and scientific computing takes place in the R&D environment. As Cloud capabilities improve over time, and the ability to securely capture and interpret scientific data increases, scientific research companies are going to become more efficient, compliant, and secure than ever before.

2) Leverage Interoperable Data to Drive New Value-Generating Insights 

The rise of automation in this industry will enable a far greater degree of data interoperability and transparency. Scientific organizations will have to drive value with their ability to derive accurate insight from widely available data conveners. Disparities between the way research organizations make use of their tech platforms will always exist, but the rules of the game are likely to change when everyone at the table can see all of the pieces.

3) Let Platform Infrastructure Developers Take Center Stage

It’s clear that the future of biotech and pharmaceutical development will require massive changes to the data infrastructure that researchers use to drive progress and communicate with one another.  Over the next two decades, health solutions may no longer be limited to medical devices and pharmaceuticals. Software applications and data-centric communication algorithms are going to become increasingly important when informing healthcare professionals about what actions to take. The availability of personalized therapies will transform the way research and development approach their core processes.

4) Focus on Highly Personalized Data-Powered Therapies 

Until now, biopharmaceutical companies largely focused their efforts on developing one-size-fits-all therapies to treat chronic illnesses that impact large swaths of the population. However, the post-COVID healthcare world is likely to challenge this strategy in the long term, as more researchers focus on highly personalized therapies developed using highly efficient, data-centric methods. These methods may include using patient genomics to predict drug efficacy and gathering data on patients’ microbiome samples for less understood conditions like Alzheimer’s.

5) Commit to Ongoing Research and Empower Smaller-Volume Therapies

The wide availability of patient data will allow medical researchers to interpret patient data well after a drug hits the market. The raw data now available will enable the researcher to identify an opportunity to work with clinicians on developing new treatment pathways for a particular group of patients. Smaller-volume therapies will require new manufacturing capabilities, as well as cross-disciplinary cooperation empowered by remote collaboration tools – all of which require investment in new Cloud-based data infrastructure.

Future Development Depends on Interoperable Data Accessibility

Taking the actionable steps above will drive up value in the biotech and pharmaceutical space exponentially. The availability of interoperable data is already showing itself to be a key value driver for enabling communication between researchers. As the industry leans on this technology more and more over time, integrating state-of-the-art solutions for collecting, handling, and communicating data will become a necessity for R&D organizations to remain competitive. Adapting to the “new normal” is going to mean empowering researchers to communicate effectively with patients, peers, and institutions remotely, leveraging real-time data in an efficient, organized way.

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. 

Reputable scientific computing consultants don’t say these things.

Today’s Life Science and biopharmaceutical research processes rely heavily on high-performance computing technology. Your scientific computing partner plays a major role in supporting discovery and guaranteeing positive research outcomes.

It should come as no surprise that not just anyone can fulfill such a crucial role. Life Science executives and research teams place a great deal of trust in their scientific computing advisors – it’s vital that you have absolute confidence in their abilities.

But not all scientific computing vendors are equally capable, and it can be difficult to tell whether you’re dealing with a real expert. Pay close attention to the things vendors say and be on the lookout for any of these five indicators that they may not have what it takes to handle your research firm’s IT needs.

5 Things Your Scientific Computing Vendor Should Never Say 

If you feel like your partner could be doing more to optimize research processes and improve outcomes, pay close attention to some of the things they say. Any one of these statements can be cause for concern in a scientific computing partnership:

1. “But you never told us you needed that.”

Scientific computing is a dynamic field, with ongoing research into emerging technologies leading to a constant flow of new discoveries and best practices. Your scientific computing partner can’t assume it’s your job to stay on top of those developments. They must be proactive, offering solutions and advice even when not specifically solicited.

Your focus should be on core life science research – not how the latest high-performance computing hardware may impact that research. A great scientific computing vendor will understand what you need and recommend improvements to your processes of their own initiative.  

2. “It worked for our other clients.” 

It doesn’t matter what “it” is. The important part is that your scientific computing vendor is explicitly comparing you to one of your competitors. This indicates a “one-size-fits-all” mentality that does not predict success in the challenging world of Life Science research.

Every Life Science research firm is unique especially with respect to their processes. Setting up and supporting a compute environment involves assessing and responding to a wide variety of unique challenges. If your computing vendor doesn’t recognize those challenges as unique, they are probably ignoring valuable opportunities to help you improve research outcomes.

3. “Yes, we are specialists in that field too.” 

There is little room for jacks-of-all-trades in the world of scientific computing. Creating, implementing, and maintaining scientific computing frameworks to support research outcomes requires in-depth understanding and expertise. Life Science research has a unique set of requirements that other disciplines do not generally share.

If your Life Science computing vendor also serves research organizations in other disciplines, it may indicate a lack of specialization. It might mean that you don’t really have reliable field-specific expertise on-hand but a more general advisor who may not always know how best to help you achieve research outcomes.

4. “We met the terms of our service-level agreement.”

This should be a good thing, but watch out for scientific computing vendors who use it defensively. Your vendors may be more focused on meeting their own standards and abiding by contractually defined service-level agreements than helping you generate value through scientific research.

Imagine what happens when your project’s standards and objectives come into conflict with your vendor’s revenue model. If your vendor doesn’t have your best interests at heart, falling back to the service-level agreement is a convenient defense mechanism.

5. “We’re the only approved partner your company can use.”

If the primary reason you’re working with a specific vendor is that they are the only approved partner within your company, that’s a problem. It means you have no choice but to use their services, regardless of how good or bad they might be.

Giving anyone monopolistic control over the way your research team works is a risky venture. If you don’t have multiple approved vendors to choose from, you may need to make your case to company stakeholders and enact change.

Don’t Miss Out on Scientific Computing Innovation 

There are many opportunities your Life Science research firm may be positioned to capitalize on, but most of them rely on a productive partnership with your scientific computing vendor. Life Science organizations can no longer accept scientific computing services that are merely “good enough” when the alternative is positive transformational change. 

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. 

Many researchers already know how useful DevOps is in the Life Sciences. 

Relying on specialty or proprietary applications and technologies to power their work, biotech and pharmaceutical companies have benefited from DevOps practices that enable continuous development meant micro-service-based system architectures. This has dramatically expanded the scope and capabilities of these focused applications and platforms, enabling faster, more accurate research and development processes.

But most of these applications and technologies rely on critical infrastructure that is often difficult to deploy when needed. Manually provisioning and configuring IT infrastructure to meet every new technological demand can become a productivity bottleneck for research organizations, while the cost surges in response to increased demand.

As a result, these teams are looking toward Platform DevOps—a model for applying DevOps processes and best practices to infrastructure—to address infrastructural obstacles by enabling researchers to access IT resources in a more efficient and scalable way.

Introducing Platform DevOps: Infrastructure-as-Code

One of the most useful ways to manage IT resources towards Platform DevOps is implementing infrastructure-as-code solutions in the organization. This approach uses DevOps software engineering practices to enable continuous, scalable delivery of compute resources for researchers.

These capabilities are essential, as Life Science research increasingly relies on complex hybrid Cloud systems. IT teams need to manage larger and more granular workloads through their infrastructure and distribute resources more efficiently than ever before.

7 Benefits to Adopting Infrastructure-as-Code

The ability to create and deploy infrastructure with the same agile processes that DevOps teams use to build software has powerful implications for life science research. It enables transformative change to the way biotech and pharmaceutical companies drive value in seven specific ways:

1. Improved Change Control
Deploying an improved change management pipeline using infrastructure-as-code makes it easy to scale and change software configurations whenever needed. Instead of ripping and replacing hardware tools, all it takes to revert to a previous infrastructural configuration is the appropriate file. This vastly reduces the amount of time and effort it takes to catalog, maintain, and manage infrastructure versioning.

2. Workload Drift Detection
Over time, work environments become unique. Their idiosyncrasies make them difficult to reproduce automatically. This is called workload drift, and it can cause deployment issues, security vulnerabilities, and regulatory risks. Infrastructure-as-code solves the problem of workload drift using a mathematical principle called idempotence – the fundamental property of repeatability.

3. Better Separation of Duties
It’s a mistake to think separation of duties is incompatible with the DevOps approach. In fact, DevOps helps IT teams offer greater quality, security, and auditability through separation of duties than traditional approaches. The same is true for infrastructure, where separation of duties helps address errors and mitigate the risk of non-compliance.

4. Optimized Review and Approval Processes
The ability to audit employees’ work is crucial. Regulators need to be able to review the infrastructure used to arrive at scientific conclusions and see how that infrastructure is deployed. Infrastructure-as-code enables stakeholders and regulators to see eye-to-eye on infrastructure.

5. Faster, More Efficient Server and Application Builds
Before Cloud technology became commonplace, deploying a new server could take hours, days or even longer depending upon the organization. Now, it takes mere minutes. However, configuring new servers to reflect the state of existing assets and scaling them to meet demand manually is challenging and expensive. Infrastructure-as-code automates this process, allowing users to instantly deploy or terminate server instances.

6. Guaranteed Compliance
Since the state of your IT infrastructure is defined in code, it is easily readable and reproducible. This means that the process of establishing compliant workflows for new servers and application builds is automatic. There is no need to verify a carbon copy of a fully compliant server because it was directly built with compliant architecture.

7. Tougher Security
Shifting to infrastructure-as-code allows Life Science researchers to embed best-in-class security directly into new servers from the very beginning. There is no period where unsecured servers are available on the network waiting for cybersecurity personnel to secure them. The entire process is saved to the configuration file, making it infinitely repeatable.

Earn Buy-In for Platform DevOps From Your IT Team

Implementing infrastructure-as-code can be a difficult sell for IT team members, who may resist the concept. Finding common ground between IT professionals and researchers is key to enabling the optimal deployment of DevOps best practices for research compute environments.

Clear-cut data and a well-organized implementation plan can help you make the case successfully. Contact RCH Solutions to find out how we helped a top-ten global pharmaceutical company implement the Platform DevOps model into its research compute environment.

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.