Maximizing Efficiency in BioPharma: The Essential Role of Non-Clinical Statistics and Experimental Design
Design of Experiments (DOE) is one of the most essential tools scientists can use to accelerate timelines, optimize costs, maximize insights, and minimize risks when making informed decisions. For example, clinical trials employ a variety of experimental designs to determine whether a new medicine effectively improves patients’ lives and to what extent.
If you are developing therapies with the goal of entering human clinical trials, the expertise of statisticians in the field of Experimental Design is indispensable. Regulatory agencies require a thorough understanding of the study’s structure: the number of patients involved, how outcomes are measured, the statistical power necessary to detect a significant effect, and the methods you plan to use for data analysis and reporting. To meet these demands, BioPharma companies must engage a clinical statistics CRO or build an in-house clinical team that includes statisticians, programmers, operations specialists, and data managers. Although these teams may begin small, as trials progress, organizational needs and staffing often scale quickly.
So, why do agencies invest so much time to ensure these plans are robust? As we know, health authorities have a mandate to ensure that medicines are both safe and effective. The public relies on these agencies to minimize risks, guarantee the quality of medicines, and confirm their efficacy for intended uses.
If this level of statistical rigor is required for clinical trials, why don’t more companies prioritize a similar approach with non-clinical statistics? The current economic climate in BioPharma might provide some insight. In 2024 alone, more than 140 layoff announcements have led to a substantial reduction in the workforce, putting pressure on companies to prioritize short-term savings over long-term gains. With a focus on cost-cutting, roles or functions, like non-clinical statisticians, that may be perceived as optional, are often the first to be scaled back or excluded.
However, consider the benefits of applying non-clinical statistical expertise from the early stages of development.
How can we leverage this expertise from the very beginning of the product lifecycle?
How can we design experimental plans that seamlessly guide us through process development, characterization, analytical validation, tech transfer, and, ultimately, commercialization?
By starting with a clear understanding of our desired outcomes, it’s possible to maximize resource efficiency and avoid costly missteps throughout R&D.
Non-clinical statistics can significantly streamline the development process. With a well-executed preclinical statistical plan, companies can craft an IND package that stands up to regulatory scrutiny, reduce the volume of experiments needed for complete process or method qualification for the BLA, and create a robust narrative that supports product development history, specification setting, and process comparability designs. What do all these benefits have in common? They reflect not an ‘extra’ but a strategic investment in efficiency that can smooth and accelerate medicine development.
Engaging non-clinical statisticians, much like clinical statisticians, is crucial to the success of your BioPharma organization. Leveraging tools such as Design of Experiments not only brings rigor to research and development, but also contributes to substantial savings in time, resources, and inefficiency. In today’s competitive and cost-conscious BioPharma landscape, employing non-clinical statistics is a forward-thinking, yet critical approach, that ensures every development dollar is spent effectively, bringing high-quality treatments to patients sooner.
Learn more about how RCH Solutions can support your non-clinical statistical efforts with the expertise of industry veterans, including seasoned non-clinical statisticians like JoAnn Coleman.
Driving Success from Discovery to Commercialization
Throughout the BioPharma industry, many think statistics are critical only to human clinical trials. However, Non-Clinical Statistics plays a pivotal role in moving assets through discovery, research, and development—all the way to commercialization. Though lesser known, these specialized statisticians are essential to ensuring that every aspect of a drug’s journey from lab bench to market is grounded in rigorous, data-driven decision-making.
The Power of Non-Clinical Statistics
At RCH Solutions, there is a keen awareness that drug development is a complex, high-stakes process. Success rates hover around 7-8%1, and setbacks in the early development or manufacturing stages can result in costly delays. A skilled non-clinical statistician can distinguish between a program that stalls and moves forward confidently. Non-clinical statisticians specialize in addressing challenges that arise long before clinical trials begin. They support diverse teams across Discovery, Research, and Chemistry, Manufacturing, and Controls (CMC), ensuring your program is designed to answer the right questions from the outset.
Early-Stage Impact: Target Identification and Method Development
Designing the suitable experiments in the early stages of drug discovery is critical. Non-clinical statisticians help BioPharma organizations by guiding the setup of studies that provide reliable, actionable data. Whether designing NGS studies to identify targets or working with chemists to optimize analytical methods, non-clinical statisticians help ensure that your data answers the questions that matter.
With proper statistical guidance, teams could save time and resources by quantifying value and avoiding chasing the wrong or inconclusive outcomes. A non-clinical statistician helps to mitigate this risk, maximizing the value of your early-stage research and putting you on the path to success.
Optimizing Manufacturing Processes and Ensuring Quality
Regarding manufacturing, non-clinical statisticians are critical players in developing robust process understanding and product characterization. They collaborate with engineers and chemists to design experiments that optimize processes, minimize variation, and consistently produce high-quality products.
Statistical methods can also be applied to issues like impurity reduction, process transfer to Contract Manufacturing Organizations (CMOs), or method validation—tasks vital to smooth regulatory submission and approval. In this way, Non-Clinical Statistics mitigate risk and keep the drug development pipeline moving forward.
Bridging the Gap Between Science and Regulation
Regulatory submissions can be a significant hurdle in getting a product to market. A well-designed statistical plan can help address concerns from agencies regarding impurities, method validation, or product stability. Non-clinical statisticians, equipped with the ability to model complex scenarios and collaborate with scientific teams, play a critical role in ensuring the readiness of an asset for regulatory approval.
Their expertise enables your team to present data compellingly and scientifically soundly, meeting the rigorous expectations of regulatory bodies. From INDs to BLAs and NDAs, they ensure your program’s foundation is built on solid, data-driven decisions.
Partnering with RCH Solutions: The Non-Clinical Statistics Advantage
At RCH Solutions, we understand Non-Clinical Statistics critical role in BioPharma’s success. Our team of expert statisticians works collaboratively with your R&D and CMC teams to ensure programs are designed for optimal outcomes, not bottlenecks. From target selection to regulatory approval, we deliver data-driven insights that save time and resources, minimizing trial and error. By leveraging our expertise, you can streamline processes, enhance production, and confidently move your drug development program forward—ultimately bringing life-changing medicines to patients faster.
Get in touch with our team of expert statisticians today to learn more about our Non-Clinical Statistics services.
1 Source: Biotechnology Innovation Organization (BIO), Informa, QLS Advisors, Clinical Development Success Rates 2011-2020.
The Life Sciences industry is eager to reap the benefits of artificial intelligence (AI), and for good reason. AI has the potential to revolutionize drug discovery by leveraging vast datasets to identify novel drug targets, predict drug-target interactions, and optimize molecular structures. AI algorithms can screen millions of compounds in a matter of days, a task that would take human Researchers years to accomplish. In clinical trials, AI has the potential to streamline patient recruitment, improve trial design, and enable more targeted therapies by analyzing genomic data and identifying biomarkers for personalized medicine.
The promise of AI to transform patient care is equally compelling. Applications range from early disease detection through medical imaging analysis to personalized treatment recommendations based on a patient’s unique genetic profile.
These are not new revelations, however. For several years now, the term AI—or more specifically, the term “AI-enabled”—has permeated our space for (probably) far longer than deserved.
As a technologist who cut my teeth as a software engineer (and still holds a soft spot for programming), the potential of AI is thrilling to me personally. While I’m as excited about the potential of AI as the next person, I’ve learned that the reality is often more complicated than the hype suggests.
At RCH Solutions, we’ve been helping companies navigate the AI landscape for a while now, and we’ve seen firsthand the challenges and opportunities that come with implementing AI in this highly regulated and complex industry.
Navigating the AI Frontier with Confidence and Care
The barriers to successful AI adoption are significant, from data quality and accessibility issues to the need for specialized talent and infrastructure. Not to mention, the regulatory landscape for AI in Life Sciences is still evolving, with guidelines and standards that lag behind the rapid pace of technological advancements. Ensuring compliance with data privacy and security regulations, such as HIPAA and GDPR, only adds more layers of complexity.
Despite these challenges, AI’s potential benefits in Life Sciences are too impactful to ignore. However, implementation will require careful navigation of the regulatory landscape, investing in robust data management practices, and fostering collaboration between domain experts and data scientists—simply barreling forward with untested methodologies isn’t an option when lives are on the line. It’s crucial to approach AI adoption with a strategic and measured approach, recognizing that it is not a magic bullet but a powerful tool that requires careful implementation and ongoing refinement.
Separating Hype from Hope
First, let’s talk about the good stuff. AI has the potential to revolutionize drug discovery by analyzing vast amounts of data and identifying potential drug targets faster than any human could. It’s like having a team of super-intelligent research assistants working 24/7. Machine learning algorithms can sift through millions of compounds, predict their properties, and narrow down the most promising candidates for further testing. This can save pharmaceutical companies years and billions of dollars in the early stages of drug development.
Additionally, AI can help optimize the design of drug molecules, improving their efficacy and reducing side effects. It’s a game-changer for the industry.
But here’s the thing: AI is only as good as the data you feed it. If your data is a mess, your AI insights will be too. Garbage in, garbage out, as they say. That’s why we ‘always’ tell our clients to focus on data quality and governance first.
Before implementing AI, companies need to ensure that their data is accurate, complete, and properly labeled. They must also establish clear data standards and protocols to ensure consistency across different datasets. This is a foundational step that can’t be overlooked.
AI and Clinical Trials
Another area where AI is making waves is clinical trials. By analyzing electronic health records and other real-world data sources, AI can help identify potential trial participants and predict outcomes more accurately. This can lead to faster, more targeted trials and, ultimately, better patient treatments. For example, AI algorithms can comb through patient data to find individuals who meet specific inclusion criteria for a trial, saving time and resources on recruitment. They can also analyze data from wearable devices and other sensors to monitor patient response to treatment in real-time, enabling quick adjustments to dosing or other parameters.
But again, there are challenges to consider. Privacy and security are top concerns when dealing with sensitive patient data. Companies must implement robust data protection measures and ensure compliance with regulations like HIPAA and GDPR. There’s also the risk of bias creeping into the AI algorithms, which could lead to unfair or even harmful outcomes. For instance, if an AI model is trained on data that is not representative of the broader population, it may make inaccurate or discriminatory predictions for certain groups.
It’s crucial to audit AI systems for bias regularly and ensure they are used ethically and responsibly.
Charting a Course for Realistic Progress
So, what’s the key to successful AI adoption in Life Sciences? It’s all about balance. AI is a powerful tool, but it’s not a magic wand. It needs to be used in conjunction with human expertise and governance. AI can generate novel insights and hypotheses, but it’s up to human experts to validate and interpret the results.
For example, AI might identify a potential new drug target, but it takes a team of experienced scientists to design and conduct experiments to confirm its viability. Similarly, AI can help identify patterns and trends in clinical trial data, but it’s up to human clinicians to make sense of those findings and apply them to patient care.
At RCH Solutions, we’re currently working on a cutting-edge generative AI project with a global pharma company, and the collaboration between the AI and the human experts is crucial. The AI system is trained on vast amounts of scientific literature and experimental data, allowing it to generate novel hypotheses and suggest new avenues for exploration. But human scientists bring their deep domain knowledge and intuition to the table, guiding the AI system and ensuring that its outputs are scientifically valid and relevant. It’s a symbiotic relationship that leverages the strengths of both human and machine intelligence.
Another thing to remember is that as AI becomes more prevalent in Life Sciences, regulators are starting to take notice. The FDA has already released guidelines for AI in medical devices, outlining requirements for transparency, reproducibility, and robustness. We’ll see more regulations coming down the pipeline as AI advances and its healthcare applications become more widespread. Companies must be prepared to adapt and ensure their AI systems are compliant and transparent. This means documenting the data and algorithms used, conducting rigorous validation and testing, and explaining how the AI system arrives at its conclusions.
Shaping the Future of Healthcare Together
At the end of the day, AI has the potential to do a lot of good in the Life Sciences industry. It can accelerate drug discovery, improve clinical trial efficiency, and personalize patient care. But we must approach it with a healthy dose of pragmatism and caution. It’s not about jumping on the AI bandwagon just because everyone else is doing it. It’s about carefully considering the specific use case, the data requirements, the ethical implications, and the regulatory landscape. And most importantly, it’s about ensuring that AI is being used to augment and enhance human expertise, not replace it. AI should be a tool in the toolbox, not replace human judgment and decision-making.
So, if you are considering embarking on an AI project in Life Sciences, my advice is to partner with a team that has been there and done that – a team that understands this industry’s unique challenges and opportunities and a team that can help you navigate the AI frontier with confidence and care.
At RCH Solutions, we’ve worked at the intersection of Life Sciences and AI for years. We’ve seen what works and what doesn’t, and we’ve helped countless companies harness the power of AI to drive innovation and improve patient outcomes. So, if you’re ready to take the plunge, give us a call. We’ll be there every step of the way.
Unleash your full potential with effective scientific computing solutions that add value and align with your business needs.
Finding the right Bio-IT partner to navigate the complex landscape that is science-IT is no easy task. With a multitude of factors to consider (such as expertise, outcomes, scalability, data security, and adherence to industry regulations), evaluating potential partners can be an overwhelming process. That’s where a Bio-IT scorecard approach comes in handy.
By using a structured evaluation approach, organizations can focus on what truly matters—aligning their organizational requirements with the capabilities and expertise of potential Bio-IT partners. Not the other way around. Here’s how using a scorecard can help streamline decision-making and ensure successful collaborations.
1. Bio-IT Requirements Match
Every Biopharma is on a mission, whether it’s to develop and deliver new, life-changing therapeutics, or advance science to drive innovation and change. While they share multiple common needs, such as the ability to process large and complex datasets, the way in which each organization uses IT and technology can vary.
Biopharma companies must assess how well their current or potential Bio-IT partner’s services align with the organization’s unique computing needs, such as data analysis, HPC, cloud migration, or specialized software support. And that’s where a Bio-IT scorecard can be helpful. For example, a company with multiple locations must enable easy, streamlined data sharing between facilities while ensuring security and authorized-only access. A single location may also benefit from user-based privileges, but their needs and processes will vary since users are under the same roof.
Organizations must also evaluate the partner’s proficiency in addressing specific Bio-IT challenges relevant to their operations by asking questions such as:
- Can they provide examples of successfully tackling similar challenges in the past, showcasing their domain knowledge?
- Can they demonstrate proficiency in utilizing relevant technologies, such as high-performance computing, cloud infrastructure, and data security?
- How do they approach complex Bio-IT challenges?
- Can they share any real-world examples of solving challenges related to data integration, interpretation, or regulatory compliance?
Questions like these on your own Bio-IT scorecard can help your organization better understand a potential partner’s proficiency in areas specific to your needs and objectives. And this ultimately helps your team understand if the partner is capable of helping prepare your firm scale by reducing bottlenecks and clearing a path to discovery.
2. Technical Proficiency and Industry Experience
Organizations can rate their shortlist partners on relevant technologies, platforms, and scientific computing to learn more about technical proficiency. It’s equally important to apply proficiency to the unique needs of Biopharmas.
According to a industry survey, respondents agree that IT and digital innovation are needed to solve fundamental challenges that span the entire spectrum of operations, including “dedicated funding (59%), a better digital innovation strategy (49%), and the right talent (47%) to scale digital innovation.” It’s essential that IT partners can connect solutions to these and other business needs to ensure organizations are poised for growth.
It’s also critical to verify the partner’s track record of delivering Bio-IT services to organizations within the Life Sciences industry specifically. And the associated outcomes they’ve achieved for similar organizations. To do this, organizations can obtain references and ask specific questions about technical expertise, such as:
- Whether the company proposed solutions that met core business needs
- Whether the IT technology provided a thorough solution
- Whether the solutions were implemented on time and on budget
- How the company continues to support the IT aspect
Successful IT partners are those who can speak from a place of authority in both science and IT. This means being able to understand the technical aspect as well as applying that technology to the nuances of companies conducting pre-clinical and clinical R&D. While IT companies are highly skilled in the former, very few are specialized enough to also embrace the latter. It’s essential to work with a specialized partner that understands this niche segment – the Life Sciences industry. And creating a Bio-IT scorecard based on your unique needs can help you do that.
3. Research Objectives Alignment
IT companies make it their goal to provide optimal solutions to their clients. However, they must also absorb their clients’ goals as their own to ensure they’re creating and delivering the technology needed to drive breakthroughs and accelerate discovery and time-to-value.
Assess how well the partner’s capabilities and services align with your specific research objectives and requirements by asking:
- Do they have expertise in supporting projects related to your specific research area, such as genomics, drug discovery, or clinical trials?
- Can they demonstrate experience in the specific therapeutic areas or biological processes relevant to our research objectives?
- What IT infrastructure and tools do they have in place to support our data-intensive research?
The more experience in servicing research areas that are similar to yours, the less guesswork involved and the faster they can implement optimal solutions.
4. Scalability and Flexibility
In the rapidly evolving field of Life Sciences, data generation rates are skyrocketing, making scalability and extensibility vital for future growth. Each project may require unique analysis pipelines, tools, and integrations with external software or databases. A Bio-IT partner should be able to customize its solutions based on individual requirements and handle ever-increasing volumes of data efficiently without compromising performance. To help uncover their ability to do that, your team might consider:
- Ask about their approach to adapting to changing requirements, technologies, and business needs. Inquire about their willingness to customize solutions to fit your specific workflows and processes.
- Request recent and similar examples of projects where the Bio-IT partner has successfully implemented scalable solutions.
By choosing a Bio-IT partner that prioritizes flexibility and scalability, organizations can future-proof their research infrastructure from inception. They can easily scale up resources as their data grows exponentially while also adapting to changing scientific objectives seamlessly. This agility allows scientists to focus more on cutting-edge research rather than getting bogged down in technical bottlenecks or outdated systems. The potential for groundbreaking discoveries in healthcare and biotechnology becomes even more attainable.
5. Data Security and Regulatory Compliance
In an industry governed by strict regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation), partnering with a Bio-IT company that is fully compliant with these regulations is essential. Compliance ensures that patient privacy rights are respected, data is handled ethically, and legal implications are avoided.
As part of your due diligence, you should consider the following as it relates to a potential partner’s approach to data security and regulatory compliance:
- Verify their data security measures, encryption protocols, and adherence to industry regulations (e.g., HIPAA, GDPR, 21 CFR Part 11) applicable to the organization’s Bio-IT data.
- Ensure they have undergone relevant audits or certifications to demonstrate compliance.
- Ask about how they stay up-to-date on compliance and regulatory changes and how they communicate their ongoing certifications and adherence to their clients.
6. Collaboration and Communication
A strong partnership relies on open lines of communication, where both parties can share and leverage their subject matter expertise in order to work towards a common goal. Look for partners who have experience working with diverse and cross-functional teams, and have successfully integrated technology into various workflows.
Evaluate the partner’s communication channels, responsiveness, and willingness to collaborate effectively with the organization’s IT team and other important stakeholders. Consider their approach to project management, reporting, and transparent communication, and how it aligns with your internal processes and preferences.
Conclusion
The value of developing and using a Bio-IT scorecard to ensure a strong alignment between the organization’s Bio-IT needs and the right vendor fit cannot be overstated. Using a scorecard model gives you a holistic, systematic, objective way to evaluate potential or current partners to ensure your needs are being met—and expectations hopefully exceeded.
Biotechs and Pharmas can benefit greatly from specialized Bio-IT partners like RCH Solutions. With more than 30 years of exclusive focus servicing the Life Sciences industry, organizations can gain optimal IT solutions that align with business objectives and position outcomes for today’s needs and tomorrow’s challenges. Learn more about what RCH Solutions offers and how we can transform your Bio-IT environment.
Sources:
https://www.genome.gov/genetics-glossary/Bioinformatics
In today’s fast-paced and rapidly evolving world of Life Sciences, successful organizations know innovation is the key to success. But game-changing innovation without effective collaboration is not possible.
Think about it. Bringing together diverse minds, specialized skill sets, and unique perspectives is crucial for making breakthroughs in scientific research, data analysis, and clinical advancements. It’s like the X-factor that can unlock new discoveries, achieve remarkable results, and fast-track time-to-value.
But as always — the $64,000 question remains: But how?
In leading RCH and working with dozens of different teams across the Life Sciences space, I’ve seen what works—some things better than others—within organizations looking to foster a greater sense of collaboration to drive innovation.
Here are my top 5 strategies for your team to consider:
- Break Down Silos for Collective Success:
One of the critical advantages of collaboration in the Life Sciences is the ability to leverage diverse perspectives and expertise. But traditionally, many organizations have functioned within siloed structures, with each department working independently towards their goals. However, this approach often leads to fragmented progress, limited knowledge sharing, and missed opportunities. By embracing cross-functional collaboration, Life Sciences organizations can break down these barriers and foster an environment that encourages the free flow of ideas, expertise, and resources. As the saying goes, “Two heads are better than one,” which is all the more true in the case of collaboration—the potential for breakthrough solutions expands exponentially.
- Leverage the Power of Advisors:
By collaborating with specialized service providers, organizations can leverage their expertise and extend to a broader ecosystem to help streamline processes, implement robust data management strategies, and ensure compliance with regulatory requirements. Such partnerships bring fresh perspectives, complementary expertise and can help drive efficiencies, by leveraging specialized resources, experience and expertise. This ultimately allows Life Sciences companies to focus on their core competencies—research and science.
- Drive Innovation Through Interdisciplinary Teams:
Life Sciences is a multidisciplinary field that requires expertise in biology, research and development, information technology, data analysis, and more. Creating cross-functional teams that bring together individuals with diverse backgrounds can foster creativity and innovation through the pooling of data, the sharing of insights, and the generation of new hypotheses—ultimately leading to faster insights and more meaningful outcomes. When scientists, data analysts, bioinformaticians, software developers, and domain experts collaborate, they can collectively develop novel solutions, generate new insights, and optimize processes—more efficiently.
- Enhance Problem-Solving Capabilities:
Collaboration and the power of strategic partnerships allows Life Sciences organizations to tackle complex problems—and opportunities—from multiple angles. By leveraging the collective intelligence of cross-functional teams, and external specialists, organizations can tap into a wealth of knowledge and experience. This enables them to analyze challenges from different perspectives, identify potential blind spots, and develop comprehensive solutions. The synergy created by collaboration often leads to breakthrough discoveries and more efficient problem-solving.
- Agile Adaptation to Rapid Technological Advances:
As we all know, technology is constantly evolving, and keeping pace with the latest advancements can be a daunting task. Collaborating with the right Bio-IT partner helps Life Sciences organizations remain at the forefront of innovation. By fostering partnerships with R&D IT experts, organizations gain access to cutting-edge tools, methodologies, and insights, enabling them to adopt new technologies swiftly and effectively. The ideal Bio-IT partner also has a deep understanding of the complete life cycle of the Cloud journey, for both enterprise and emerging Biopharmas, and from inception to optimization and beyond, enabling them to provide tailored and specialized support at any stage of their distinct Cloud journey. This ultimately assists them in attaining their individual discovery goals and again, allows scientists the bandwidth to focus on their core competencies—research, science and discovery.
Final Thoughts
In the world of Life Sciences, I truly believe that collaboration, both internally through cross-functional teams, and externally through strategic partnerships, is the key to unlocking transformative breakthroughs. And organizations that aren’t focused on creating and sustaining a collaborative culture or cross-functional strategy? They’ll get left behind.
By harnessing collaboration, organizations can tap into a wealth of knowledge that can drive innovation, enhance problem-solving capabilities, and adapt to rapid technological advances. Embracing collaboration not only accelerates progress but also cultivates a culture of continuous learning and excellence. And the latter is the type of organization that top talent will flock to and thrive within.
As a leading Bio-IT organization, the team at RCH Solutions believes that it is essential to prioritize collaboration, foster meaningful partnerships, and nurture cross-functional teams to shape the future of the Life Sciences industry. Why? Because we’ve seen the accelerative power it brings—driving breakthroughs, accelerating discovery and smashing outcomes—time and time again.
The Importance of Value in an Evolving Business Climate
As signs of Spring start to boom around us, I can’t help but think of the exciting opportunities ahead especially after coming through a gloomy business cycle for the past several quarters.
Those opportunities only become recognized if we are willing to confront the sometimes brutal realities of the current business climate.
And that reality is this: Businesses [across all industries] are looking closer at their budgets and questing spends. They’re asking hard questions and examining new projects and, quite frankly legacy partners, with a heightened level of scrutiny.
While the accompanying uncertainty that looms as a result can certainly keep business leaders awake at night, I can’t help but think, for some very specific reasons—it’s about time.
While cost cutting alone is sometimes a necessary reality, the larger thrust is really about driving more value, rather than simply lowering expenses. After all, work still needs to get done.
Having led a business built on driving value for most of my career, here’s what “value” questions sound like, according to direct conversations I’ve had with many of our customers:
- Are your customers on the business side pleased with the outcomes?
- Can you demonstrate a return to management?
- Has paying more yielded better results, other than convenience?
- Has paying less yielded any results, other than savings?
- What is the return you are getting for the investment you’ve made?
- Are you reaching your goals?
Let me be more specific, without naming names of course. I’m referring to the large professional services and consulting companies that work with many Biotech and large Pharma companies for strategic and then operational services. Ok, let’s call them company A and company D. Then there are also the large multinational outsourcing companies that offer low-cost/low-value staff augmentation. We will call them, well, there are too many to list.
Please tell me the last time you said: “Wow, company A or company D did such a great job! They finished on-time, under budget and actually did what they said they were going to do! Let’s give them more projects (and overspend more money next time)!”
You can sense my sarcasm, of course. But the truth is, many providers in this space are doing Biotech and Pharma companies a disservice in the way they scope, execute, and hold themselves accountable for the outcomes of services that are mission critical to companies in the business of advancing science. In fact, a large pharma customer of ours recently shared, and I quote, “We only use D because A is much worse.”
Take some time to let that sink in.
On the low-cost, outsourced side, we see much of the same. Poor service. Inconsistency and turnover within the support team. Lack of accountability. And the inability (or worse, an unwillingness) to evolve and learn more about business in favor of following a dated and static runbook.
I find myself asking, how much lower can the bar go? And further, why do companies continue with these vendors for any critical scientific computing projects?
The Way Back to Better
I’ve spent a lot of time thinking about why companies continue a relationship with partners that either overcharge or underdeliver (or sadly, both). I’ve asked our customers as well. And what I’ve concluded is that it’s about mitigating risk—or rather, the perception of mitigating risk.
But the question then becomes, what happens if you stay with these providers? Why would you expect the outcome to be different? In fact, I wrote a piece last year on the inherent risk of doing what you’ve always done, and expecting different outcomes. You know what they call that ….
Of course, I have an answer. My answer and solution is based not on what we believe at RCH but what our customers tell us and what they have done.
Our customers are challenged with the market dynamics of having to do more with less—and they’re looking for greater value out of the support engaged to support them.
In fact, several of our large enterprise customers recently cut their spend on the large PS/Consulting companies and transitioned or are in discussions to transition those projects to RCH as their partner of choice. Why? Because the bar has been elevated and these customers, now more than ever, recognize who has the skills, service model and specialization to rise to the occasion.
For those that have already pulled the trigger, we continue to earn their approval and trust through results that speak for themselves.
And for those who haven’t yet made that wise call? Well, we’re here, we’re proven and we’re ready to add value where the others have not, whenever you’re ready.
Because “good” is no longer good enough, see what to look for, and what to avoid, in a specialized Bio-IT partner.
Gone are the days where selecting a strategic Bio-IT partner for your emerging biotech or pharma was a linear or general IT challenge. Where good was good enough because business models were less complex and systems were standardized and simple.
Today, opportunities and discoveries that can lead to significant breakthroughs now emerge faster than ever. And your scientists need sustainable and efficient computing solutions that enable them to focus on science, at the speed and efficiency that’s necessary in today’s world of medical innovation. The value your Biot-IT partner adds can be a missing link to unlocking and accelerating your organization’s discovery and development goals … or the weight that’s holding you back.
Read on to learn 5 important qualities that you should not only expect but demand from your Bio-IT partner. As well as the red flags that may signal you’re working with the wrong one.
Subject Matter Expertise & Life Science Mastery vs. General IT Expertise & Experience
Your organization needs a Bio-IT partner with the ability to bridge the gap between science and IT, or Sci-T as we call it, and this is only possible when their unique specialization in the life sciences is backed by their proven subject matter expertise in the field. This means your partner should be up-to-date on the latest technologies but, more importantly, have demonstrable knowledge about your business’ unique needs in the landscape in which it’s operating. And be able to provide working recommendations and solutions to get you where you want—and need —to be. That is what separates the IT generalists from subject matter and life science experts.
Vendor Agnostic vs. Vendor Follower
Technologies and programs that suit your biotech or pharma’s evolving needs are different from organization to organization. Your firm has a highly unique position and individualized objectives that require solutions that are just as bespoke —and we get that. But unfortunately, many Bio-IT partners still build their recommendation based on existing and mutually beneficial supplier relationships that they prioritize, alongside their margins, even when significantly better solutions might be available. And that’s why seeking a strategic partner that is vendor agnostic is so critical. The right Bio-IT partner will look out for your best interest and focus on solutions that propel you to your desired outcomes most efficiently and effectively, ultimately accelerating your discovery.
Collaborative and Thought Partner vs. Order Taker
Anyone can be an order taker. But your organization doesn’t always know what they want to—or should—order. And that is where a collaborative and strategic partner comes in, and can be the difference maker. Your strategic Bio-IT partner should spark creativity, drive innovation, and ultimately cultivate business success. They’ll dive deep into your organizational needs to intimately understand what will propel you to your desired outcomes, and recommend agnostic industry-leading solutions that will get you there. Most importantly, they work on effectively implementing them to streamline systems and processes to create a foundation for sustainability and scalability, which is where the game-changing transformation occurs for your organization.
Individualized and Inventive vs. One-Size-Fits-All
A strategic Bio-ITpartner needs to understand that success in the life sciences depends on being able to collect, correlate and leverage data to uphold a competitive advantage. But no two organizations are the same, share the same objectives, or have the same considerations and dependencies for a compute environment.
Rather than doing more of the same, your Bio-IT partner should view your organization through your individualized lens and seek fit-for-purpose paths that align to your unique challenges and needs. And because they understand both the business and technology landscapes, they should ask probing questions, and have the right expertise to push beyond the surface, and introduce novel solutions to legacy issues, routinely. The result is a service that helps you accelerate the development of your next scientific breakthrough.
Dynamic and Modern Business Acumen vs. Centralized Business Processes
With the pandemic came new business and work processes and procedures, and employees and offices are no longer centralized like they once were. Or maybe yours never was. Either way, the right Bio-IT partner needs to understand the unique technical requirements and the volume of data and information that is now exchanged between employees, partners, and customers globally, and at once. And solutions need to work the same, if not better, than if teams were sitting alongside each other in a physical office. So, the right strategic partner must have modern business acumen and the dynamic expertise that’s necessary to build and effectively implement solutions that enable teams to work effectively and efficiently from anywhere in the world.
Your Bio-IT Partner Can Make or Break Success
We’ll say it again – good is not good enough. And frankly, just good enough is not up to par, either. It takes a uniquely qualified, seasoned and modern Bio-IT partner that understands that the success—and the failure—of a life science company hinges on its ability to innovate, and that your infrastructure is the foundation upon which that ability, and your ability to scale, sits. They must understand which types of solutions work best for each of your business pain points and opportunities, including those that still might be undiscovered. But most importantly, valuable partners can drive and effectively implement necessary changes that enable and position life science companies to reach and surpass their discovery goals. And that’s what it takes in today’s fast-paced world of medical innovation.
So, if you feel like your Bio-IT partner might be underdelivering in any of our top 5 areas, then it might be time to find one that can—and will—truly help you leverage scientific computing innovation to reach your goals.
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.
References:
https://www.biopharmatrend.com/post/35-novartis-explores-virtual-reality-tools-in-drug-discovery-rd/
https://www.frontiersin.org/articles/10.3389/fbinf.2021.669186/full
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.
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.