Managing and analyzing macromolecular and protein-ligand structural data is a crucial yet challenging task in the complex world of Life Sciences Research. To address this need, RCH Solutions brings extensive expertise in deploying and managing Chemical Computing Group’s (CCG) PSILO platform to streamline the protein structure management processes for Biotech and Pharma companies of all sizes.

Whether for startups, mid-size, or global players, RCH Solutions ensures that customers maximize the efficiency and effectiveness of their structural data management through seamless implementation, support, and ongoing optimization of PSILO. 

What is PSILO? 

PSILO, or Protein Silo, is a sophisticated database system designed by CCG to provide a consolidated repository for proprietary and public macromolecular and protein-ligand structural information. It is tailored to meet the needs of Research organizations by offering a systematic way to register, annotate, track, and disseminate structural data derived from experimental and computational sources. 

Key Features of PSILO 

• Centralized Data Repository: PSILO centralizes structural data from crystallographic, NMR, and computational sources, making it easy for Researchers to ensure timely access to critical information. 

• PSILO Families: Curated collections of protein structures, including critical structural motifs, are automatically updated with new public and proprietary structures, ensuring the latest data is available. 

• Integration with MOE: Seamless integration with CCG’s Molecular Operating Environment (MOE) ensures continuous access to updated data for Research and drug design purposes. 

• Advanced Search and Analysis Tools: PSILO’s bioinformatics and cheminformatics tools enable detailed searches, data analysis, and structure visualization, supported by a federated database architecture. 

• Collaborative Features: Version control, commenting, and deposit validation promote collaboration and continuous improvement in data quality across Research teams. 

Benefits of Using PSILO with RCH Solutions’ Expertise 

As an experienced scientific computing service provider, RCH Solutions specializes in helping Biotech and Pharma companies of all sizes optimize PSILO for maximum impact. 

• Enhanced Data Accessibility: RCH Solutions ensures a smooth implementation of PSILO, centralizing data and simplifying access, reducing Research delays. 

• Improved Data Quality: With RCH’s tailored support, organizations can leverage PSILO’s version control and collaborative tools to maintain the accuracy and reliability of their structural data. 

• Streamlined Research Processes: RCH’s expertise ensures that the integration between PSILO and MOE operates efficiently, enabling faster, more productive Research workflows. 

• Secure Data Management: RCH Solutions adheres to the highest IT best practices to safeguard sensitive protein structure data, ensuring secure data management. 

• Scalable Solutions: Whether managing data for a startup or a global Pharma organization, RCH Solutions helps scale PSILO’s capabilities to meet evolving Research needs. 

General Applications and Use Cases 

• Drug Discovery and Design: Pharmaceutical Researchers can quickly identify drug targets and design molecules using up-to-date structural data managed through PSILO. 

• Biotech Development: Biotech companies streamline the development of innovative solutions by leveraging PSILO’s robust search and analysis tools. 

• Collaborative Research Projects: PSILO’s collaborative features and RCH Solutions’ support allow Research teams across sites to work more cohesively on improving the quality of structural data. 

Conclusion 

RCH Solutions’ expertise with PSILO ensures that Biotech and Pharma companies of all sizes can effectively manage and utilize protein-ligand and macromolecular structural data. By centralizing, organizing, and securing structural information, RCH Solutions enhances the benefits of CCG’s PSILO platform, driving more efficient workflows, fostering collaboration, and advancing scientific Research. Whether a company is focused on drug discovery, innovation, or collaborative Research, RCH Solutions ensures that their PSILO deployment is fine-tuned and right-sized for optimal performance, empowering scientists to focus on the science and their next big breakthrough. 

Let’s chat! For more information about optimizing or leveraging CCG PSILO at your Biotech or Pharma, get in touch with our team at www.rchsolutions.com or marketing@rchsolutions.com.  

Sources: 

Chemical Computing Group (CCG) | Computer-Aided Molecular Design 

Chemical Online 

PSILO® – Structure Database – CCG Video Library 

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.

In Life Sciences, and medical fields in particular, there is a premium on expertise and the role of a specialist. When it comes to scientists, researchers, and doctors, even a single high-performer who brings advanced knowledge in their field often contributes more value than a few average generalists who may only have peripheral knowledge. Despite this premium placed on specialization or top-talent as an industry norm, many life science organizations don’t always follow the same measure when sourcing vendors or partners, particularly those in the IT space. 

And that’s a mis-step. Here’s why.

Why “A” Talent Matters

I’ve seen far too many organizations that had, or still have, the above strategy, and also many that focus on acquiring and retaining top talent. The difference? The former experienced slow adoption which stalled outcomes which often had major impacts to their short and long term objectives. The latter propelled their outcomes out of the gates, circumventing cripping mistakes along the way. For this reason and more, I’m a big believer in attracting and retaining only “A” talent. The best talent and the top performers (Quality) will always outshine and out deliver a bunch of average ones. Most often, those individuals are inherently motivated and engaged, and when put in an environment where their skills are both nurtured and challenged, they thrive.  

Why Expertise Prevails

While low-cost IT service providers with deep rosters may similarly be able to throw a greater number of people at problems, than their smaller, boutique counterparts, often the outcome is simply more people and more problems.  Instead, life science teams should aim to follow their R&D talent acquisition processes and focus on value and what it will take to achieve the best outcomes in this space. Most often, it’s not about quantity of support/advice/execution resources—but about quality.

Why Our Customers Choose RCH

Our customers are like minded and also employ top talent, which is why they value RCH—we consistently service them with the best. While some organizations feel that throwing bodies (Quantity) at a problem is one answer, often one for optics, RCH does not. We never have. Sometimes you can get by with a generalist, however, in our industry, we have found that our customers require and deserve specialists. The outcomes are more successful. The results are what they seek— Seamless transformation.  

In most cases, we are engaged with a customer who has employed the services of a very large professional services or system integration firm. Increasingly, those customers are turning to RCH to deliver on projects typically reserved for those large, expensive, process-laden companies.  The reason is simple. There is much to be said for a focused, agile and proven company.  

Why Many Firms Don’t Restrategize

So why do organizations continue to complain but rely on companies such as these? The answer has become clear—risk aversion. But the outcomes of that reliance are typically just increased costs, missed deadlines or major strategic adjustments later on – or all of the above. But why not choose an alternative strategy from inception? I’m not suggesting turning over all business to a smaller organization. But, how about a few? How about those that require proven focus, expertise and the track record of delivery? I wrote a piece last year on the risk of mistaking “static for safe,” and stifling innovation in the process. The message still holds true. 

We all know that scientific research is well on its way to becoming, if not already, a multi-disciplinary, highly technical process that requires diverse and cross functional teams to work together in new ways. Engaging a quality Scientific Computing partner that matches that expertise with only “A” talent, with the specialized skills, service model and experience to meet research needs can be a difference-maker in the success of a firm’s research initiatives. 

My take? Quality trumps quantity—always in all ways. Choose a scientific computing partner whose services reflect the specialized IT needs of your scientific initiatives and can deliver robust, consistent results. Get in touch with me below to learn more. 

Data science has earned a prominent place on the front lines of precision medicine – the ability to target treatments to the specific physiological makeup of an individual’s disease. As cloud computing services and open-source big data have accelerated the digital transformation, small, agile research labs all over the world can engage in development of new drug therapies and other innovations.

Previously, the necessary open-source databases and high-throughput sequencing technologies were accessible only by large research centers with the necessary processing power. In the evolving big data landscape, startup and emerging biopharma organizations have a unique opportunity to make valuable discoveries in this space. 

The drive for real-world data

Through big data, researchers can connect with previously untold volumes of biological data. They can harness the processing power to manage and analyze this information to detect disease markers and otherwise understand how we can develop treatments targeted to the individual patient. Genomic data alone will likely exceed 40 exabytes by 2025 according to 2015 projections published by the Public Library of Science journal Biology. As data volume increases, its accessibility to emerging researchers improves as the cost of big data technologies decreases. 

A recent report from Accenture highlights the importance of big data in downstream medicine, specifically oncology. Among surveyed oncologists, 65% said they want to work with pharmaceutical reps who can fluently discuss real-world data, while 51% said they expect they will need to do so in the future. 

The application of artificial intelligence in precision medicine relies on massive databases the software can process and analyze to predict future occurrences. With AI, your teams can quickly assess the validity of data and connect with decision support software that can guide the next research phase. You can find links and trends in voluminous data sets that wouldn’t necessarily be evident in smaller studies. 

Applications of precision medicine

Among the oncologists Accenture surveyed, the most common applications for precision medicine included matching drug therapies to patients’ gene alterations, gene sequencing, liquid biopsy, and clinical decision support. In one example of the power of big data for personalized care, the Cleveland Clinic Brain Study is reviewing two decades of brain data from 200,000 healthy individuals to look for biomarkers that could potentially aid in prevention and treatment. 

AI is also used to create new designs for clinical trials. These programs can identify possible study participants who have a specific gene mutation or meet other granular criteria much faster than a team of researchers could determine this information and gather a group of the necessary size. 

A study published in the journal Cancer Treatment and Research Communications illustrates the impact of big data on cancer treatment modalities. The research team used AI to mine National Cancer Institute medical records and find commonalities that may influence treatment outcomes. They determined that taking certain antidepressant medications correlated with longer survival rates among the patients included in the dataset, opening the door for targeted research on those drugs as potential lung cancer therapies. 

Other common precision medicine applications of big data include:

• New population-level interventions based on socioeconomic, geographic, and demographic factors that influence health status and disease risk
• Delivery of enhanced care value by providing targeted diagnoses and treatments to the appropriate patients
• Flagging adverse reactions to treatments
• Detection of the underlying cause of illness through data mining
• Human genomics decoding with technologies such as genome-wide association studies and next-generation sequencing software programs

These examples only scratch the surface of the endless research and development possibilities big data unlocks for start-ups in the biopharma sector. Consult with the team at RCH Solutions to explore custom AI applications and other innovations for your lab, including scalable cloud services for growing biotech and pharma research organizations.

Do You Need Support with Your Cloud Strategy?

Cloud services are swiftly becoming standard for those looking to create an IT strategy that is both scalable and elastic. But when it comes time to implement that strategy—particularly for those working in life sciences R&D—there are a number of unique combinations of services to consider. 

Here is a checklist of key areas to examine when deciding if you need expert support with your Cloud strategy. 

• Understand the Scope of Your Project
  Just as critical as knowing what should be in the cloud is knowing what should not be. The act of mapping out the on-premise vs. cloud-based solutions in your strategy will help demonstrate exactly what your needs are and where some help may be beneficial. 
• Map Out Your Integration Points
  Speaking of on-premise vs. in the Cloud, do you have an integration strategy for getting cloud solutions talking to each other as well as to on-premise solutions? 
• Does Your Staff Match Your Needs?
  When needs change on the fly, often your staff needs to adjust. However, those adjustments are not always so easily implemented, which can lead to gaps. So when creating your cloud strategy, ensure you have the right team to help understand the capacity, uptime and security requirements unique to a cloud deployment.

Check our free eBook, Cloud Infrastructure Takes Research Computing to New Heights, to help uncover the best cloud approach for your team. Download Now

• Do Your Solutions Meet Your Security Standards?
  There are more than enough examples to show the importance of data security.  It’s no longer enough however, to understand just your own data security needs. You now must know the risk management and data security policies of providers as well. 
• Don’t Forget About Data
  Life Sciences is awash with data and that is a good thing. But all this data does have consequences, including within your cloud strategy so ensure your approach can handle all your bandwidth needs. 
• Agree on a Timeline
  Finally, it is important to know the timeline of your needs and determine whether or not your team can achieve your goals. After all, the right solution is only effective if you have it at the right time. That means it is imperative you have the capacity and resources to meet your time-based goals. 

Using RCH Solutions to Implement the Right Solution with Confidence

Leveraging the Cloud to meet the complex needs of scientific research workflows requires a uniquely high level of ingenuity and experience that is not always readily available to every business. Thankfully, our Cloud Managed Service solution can help. Steeped in more than 30 years of experience, it is based on a process to uncover, explore, and help define the strategies and tactics that align with your unique needs and goals. 

We support all the Cloud platforms you would expect, such as AWS and others, and enjoy partner-level status with many major Cloud providers. Speak with us today to see how we can help deliver objective advice and support on the solution most suitable for your needs. 

Benefits of investing in advanced visualization innovations.

Life science innovators have increasingly realized the value of visualization to drive real insights in data analytics. Exploring the capabilities of these cloud-based tools beyond simple presentation can inspire groundbreaking developments for emerging biotech and pharmaceutical start-ups. As noted in a 2021 article in Frontiers in Bioinformatics, every major development in genomics has come in the wake of a  new invention within data computation and statistics. These are six strategic benefits of investing in data visualization as a leader in this innovative area.

Enhanced data processing and comprehension

Cloud-based information analytics provide a powerful tool for visual storytelling that illuminates the impact of your organization’s research and development efforts. For example, your scientists can access, gather, and display media from multiple platforms, databases, and sources through a single dashboard. 

Cloud-based data analysis allows deeper interaction, including the ability to revise visualizations to highlight various aspects of the narrative. You can even combine multiple complex graphics to create sophisticated views. 

Advanced data tools also accelerate discovery by reducing noisy data volume to highlight relevant patterns and connections. This benefits biopharma researchers who need to correlate market opportunities with possible drug treatments, diseases with causative agents, and chemicals with intended and unintended effects. 

Simplified, stress-free sharing and collaboration

Most data visualization software tools come in a so-called container, a plug-and-play platform that includes everything you need to run the program. Since the necessary systems in the container have already been configured to work with one another, your team won’t face the challenges that arise when various components don’t interact as intended. With this structure, researchers who don’t share the same physical space can view and comment on the same 3D data visualization in a real-time virtual environment.

Faster, more effective clinical trials

Data visualization also facilitates greater speed and value among your organization’s clinical trial programs. With these tools, your teams can:

• Monitor key performance indicators at a glance on a customizable data dashboard
• Instantly summarize results in a reader-friendly format
• See a real-time overview of the trial’s progress to date
• Track potential risks for early identification of concerning developments 
• Iterate immediately to create new reports as needed to support updated findings

A clear competitive landscape

Adolescent biopharma companies need to understand their market rivals to have a hope of competing in the crowded drug patent landscape. With data visualization, your leaders can clarify product pipelines and intellectual property information across your pharmaceutical or biotech environment. These tools draw indelible lines between different scientists, drug classifications, mergers and acquisitions, and patent activity so you can see exactly where your firm stands and take advantage of gaps in the market.

Space beyond size limits

You can see drug data and other research visualizations in 3D space outside the size of your team’s screens. With such an expansive view, data visualization lets researchers completely immerse themselves in the data from a 360-degree perspective to avoid missing connections that could change the direction of their efforts. As a result, you can have the confidence that comes from clear, transparent data representation. At the same time, you can simplify and reduce the size of large data sets when needed to visualize them in an understandable way.

In a 2017 example reported by Biopharma Trend, Novartis used virtual reality to create a three-dimensional exploration of small molecules and targets for protein. In the 3D VR landscape, the company’s scientists viewed and analyzed interactions between these structures.

Comprehensive knowledge graphs

Many growing companies in pharmaceutical and biotech research rely on global teams at international sites in various time zones. By building knowledge graphs through data visualization, scientists can break down data access silos for integrated analysis, management, and search. This approach helps reduce errors, illuminate understanding gaps, and prevent repeated efforts.

If data visualization has shifted from an afterthought to a concept at the forefront of your biopharma company’s future, consider outsourcing this type of tech to true experts. An experienced team can create the tools you need to innovate in the competitive pharmaceutical and biotech IT space.

References:

https://www.technologynetworks.com/informatics/articles/data-visualization-innovations-in-life-sciences-and-drug-discovery-296360

https://www.technologynetworks.com/biopharma/articles/data-visualization-in-biopharma-leveraging-ai-vr-and-mr-to-support-drug-discovery-320108

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 

Bio-IT Teams Must Focus on Five Major Areas in Order to Improve Efficiency and Outcomes

Life Science organizations need to collect, maintain, and analyze a large amount of data in order to achieve research outcomes. The need to develop efficient, compliant data management solutions is growing throughout the Life Science industry, but Bio-IT leaders face diverse challenges to optimization.

These challenges are increasingly becoming obstacles to Life Science teams, where data accessibility is crucial for gaining analytic insight. We’ve identified five main areas where data management challenges are holding these teams back from developing life-saving drugs and treatments.

Five Data Management Challenges for Life Science Firms

Many of the popular applications that Life Science organizations use to manage regulated data are not designed specifically for the Life Science industry. This is one of the main reasons why Life Science teams are facing data management and compliance challenges. Many of these challenges stem from the implementation of technologies not well-suited to meet the demands of science.

Here, we’ve identified five areas where improvements in data management can help drive efficiency and reliability.

1. Manual Compliance Processes

Some Life Sciences teams and their Bio-IT partners are dedicated to leveraging software to automate tedious compliance-related tasks. These include creating audit trails, monitoring for personally identifiable information, and classifying large volumes of documents and data in ways that keep pace with the internal speed of science.

However, many Life Sciences firms remain outside of this trend towards compliance automation. Instead, they perform compliance operations manually, which creates friction when collaborating with partners and drags down the team’s ability to meet regulatory scrutiny.

Automation can become a key value-generating asset in the Life Science development process. When properly implemented and subjected to a coherent, purpose-built data governance structure, it improves data accessibility without sacrificing quality, security, or retention.

2. Data Security and Integrity

The Life Science industry needs to be able to protect electronic information from unauthorized access. At the same time, certain data must be available to authorized third parties when needed. Balancing these two crucial demands is an ongoing challenge for Life Science and Bio-IT teams.

When data is scattered across multiple repositories and management has little visibility into the data lifecycle, striking that key balance becomes difficult. Determining who should have access to data and how permission to that data should be assigned takes on new levels of complexity as the organization grows.

Life Science organizations need to implement robust security frameworks that minimize the exposure of sensitive data to unauthorized users. This requires core security services that include continuous user analysis, threat intelligence, and vulnerability assessments, on top of a Master Data Management (MDM) based data infrastructure that enables secure encryption and permissioning of sensitive data, including intellectual properties.

3. Scalable, FAIR Data Principles

Life Science organizations increasingly operate like big data enterprises. They generate large amounts of data from multiple sources and use emerging technologies like artificial intelligence to analyze that data. Where an enterprise may source its data from customers, applications, and third-party systems, Life Science teams get theirs from clinical studies, lab equipment, and drug development experiments.

The challenge that most Life Science organizations face is the management of this data in organizational silos. This impacts the team’s ability to access, analyze, and categorize the data appropriately. It also makes reproducing experimental results much more difficult and time-consuming than it needs to be.

The solution to this challenge involves implementing FAIR data principles in a secure, scalable way. The FAIR data management system relies on four main characteristics:

Findability. In order to be useful, data must be findable. This means it must be indexed according to terms that IT teams, scientists, auditors, and other stakeholders are likely to search for. It may also mean implementing a Master Data Management (MDM) or metadata-based solution for managing high-volume data.

Accessibility. It’s not enough to simply find data. Authorized users must also be able to access it, and easily. When thinking about accessibility—while clearly related to security and compliance, including proper provisioning, permissions, and authentication—ease of access and speed can be a difference-maker, which leads to our next point.

Interoperability. When data is formatted in multiple different ways, it falls on users to navigate complex workarounds to derive value from it. If certain users don’t have the technical skills to immediately use data, they will have to wait for the appropriate expertise from a Bio-IT team member, which will drag down overall productivity.

Reusability. Reproducibility is a serious and growing concern among Life Science professionals. Data reusability plays an important role in ensuring experimental insights can be reproduced by independent teams around the world. This can be achieved through containerization technologies that establish a fixed environment for experimental data.

4. Data Management Solutions

The way your Life Sciences team stores and shares data is an integral component of your organization’s overall productivity and flexibility. Organizational silos create bottlenecks that become obstacles to scientific advancement, while robust, accessible data storage platforms enable on-demand analysis that improves time-to-value for various applications.

The three major categories of storage solutions are Cloud, on-premises, and hybrid systems. Each of these presents a unique set of advantages and disadvantages, which serve specific organizational goals based on existing infrastructure and support. Organizations should approach this decision with their unique structure and goals in mind.

Life Science firms that implement MDM strategy are able to take important steps towards storing their data while improving security and compliance. MDM provides a single reference point for Life Science data, as well as a framework for enacting meaningful cybersecurity policies that prevent unauthorized access while encouraging secure collaboration.

MDM solutions exist as Cloud-based software-as-a-service licenses, on-premises hardware, and hybrid deployments. Biopharma executives and scientists will need to choose an implementation approach that fits within their projected scope and budget for driving transformational data management in the organization.

Without an MDM strategy in place, Bio-IT teams must expend a great deal of time and effort to organize data effectively. This can be done through a data fabric-based approach, but only if the organization is willing to leverage more resources towards developing a robust universal IT framework.

5. Monetization

Many Life Science teams don’t adequately monetize data due to compliance and quality control concerns. This is especially true of Life Science teams that still use paper-based quality management systems, as they cannot easily identify the data that they have – much less the value of the insights and analytics it makes possible.

This becomes an even greater challenge when data is scattered throughout multiple repositories, and Bio-IT teams have little visibility into the data lifecycle. There is no easy method to collect these data for monetization or engage potential partners towards commercializing data in a compliant way.

Life Science organizations can monetize data through a wide range of potential partnerships. Organizations to which you may be able to offer high-quality data include:

• Healthcare providers and their partners
• Academic and research institutes
• Health insurers and payer intermediaries
• Patient engagement and solution providers
• Other pharmaceutical research organizations
• Medical device manufacturers and suppliers

In order to do this, you will have to assess the value of your data and provide an accurate estimate of the volume of data you can provide. As with any commercial good, you will need to demonstrate the value of the data you plan on selling and ensure the transaction falls within the regulatory framework of the jurisdiction you do business in.

Overcome These Challenges Through Digital Transformation

Life Science teams who choose the right vendor for digitizing compliance processes are able to overcome these barriers to implementation. Vendors who specialize in Life Sciences can develop compliance-ready solutions designed to meet the incredibly unique needs of science, making fast, efficient transformation a possibility.

RCH Solutions can help teams like yours capitalize on the data your Life Science team generates and give you the competitive advantage you need to make valuable discoveries. Rely on our help to streamline workflows, secure sensitive data, and improve Life Sciences outcomes.

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 interested in learning how RCH can support your goals, get in touch with us here.