Build Versus Buy: Multi-Omic Platform

Build Versus Buy: Multi-Omic Platform

White Paper

Build Versus Buy: Multi-Omic Platform

Navigating A Complex Landscape In 2024

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Author: Dr Craig Davison, Sonrai Discovery Partnerships Lead at Sonrai Analytics

Updated: 19/04/2024

Dr Craig Davison

Dr Craig Davison

Sonrai Discovery Partnerships

Craig joined Sonrai Analytics as Sonrai Discovery Partnership Lead in July 2023, bringing with him a wealth of expertise in preclinical R&D and translational medicine. In his current role, he aids prospecting clients to decide if Sonrai Discovery is the right solution for their challenges through engaging discussions and bespoke live demonstrations. He is also responsible for producing accurate and engaging marketing material to showcase Sonrai’s wealth of capabilities. Additionally, he works closely with the product team to understand the product roadmap and to shape this based on feedback from prospective clients and developments in the world of precision medicine. Before joining Sonrai, Craig worked as a Scientist jointly within academia/industry at Queen’s University Belfast and CV6 Therapeutics. He earned his PhD in Molecular Biology and has amassed 9 years of experience in precision medicine research.

Chapter Overview

Introduction

Understanding The Landscape

Challenges Faced By Precision Medicine Companies

Solution: Cloud-Based Research Platform

Build Versus Buy

Decision Time: Does The Shoe Fit

Conclusion

Introduction

Big data, multi-omics, cloud computing, machine learning, multimodal, FAIR data, TREs - these are more than just buzzwords found in companies' pitch decks. Each of these represents potential opportunities and challenges for organizations working in precision medicine.

In recent years, the healthcare biotech field has exploded with novel therapy types and diagnostic approaches. From the rise of immunotherapies, DNA/RNA technologies, and microbiome based diagnostics to name a few, alongside the AI revolution, the precision medicine space has never been more exciting. The global precision medicine market is estimated to reach $50.2 billion by 2028, up from an estimated $29.1 billion in 2023 (1). Central to the success of precision medicine is the integration and analysis of vast amounts of multi-omic data, encompassing genomics, proteomics, metabolomics, and beyond. This wealth of information holds the key to unlocking insights into disease mechanisms, treatment efficacy, and patient outcomes.

However, as precision medicine continues to advance, companies operating in this space are confronted with a critical decision: whether to build their own platform for managing and analyzing multi-omic data or to buy an existing solution from a vendor. This decision carries significant implications for the company's strategic direction, resource allocation, and ultimately, its ability to deliver value to patients and healthcare providers.

This white paper aims to explore the nuances of this decision-making process, providing insights into the advantages and challenges of both building and buying platforms for multi-omic data management in precision medicine. By examining industry trends, and key decision factors, we aim to equip stakeholders with the knowledge and guidance necessary to navigate this complex terrain effectively.

Understanding The Landscape

As precision medicine continues to gain traction, it becomes increasingly evident that the integration and analysis of multi-omic data are at the forefront of this transformative approach to healthcare.

The advent of high-throughput sequencing technologies, such as next-generation sequencing (NGS), has revolutionized the field of genomics, enabling the rapid and cost-effective sequencing of entire genomes and transcriptomes. Similarly, advancements in mass spectrometry and other analytical techniques have facilitated the profiling of proteins, metabolites, and other biomolecules at an unprecedented scale. Adding single cell, spatial omics and imaging data into the mix provides incredible potential for disease understanding and life-changing developments in precision medicine.

As a result, precision medicine companies are increasingly leveraging multi-omic data to elucidate disease mechanisms, identify biomarkers, and develop targeted therapies. Integrating data from multiple omics domains offers a more comprehensive understanding of disease complexity and heterogeneity, paving the way for more precise diagnosis and treatment selection. In 2023, the US National Institute of Health (NIH) was awarded $50.3 million for multi-omics research to advance human health (2). This investment underscores the growing significance of multi-omics in precision medicine and its potential to revolutionize clinical research by providing a more holistic view of disease mechanisms and the effectiveness of treatments. In the UK, the 100,000 Genomes Project, managed by Genomics England, is an example of the commitment to advancing precision medicine through genomic research and improving the understanding and treatment of rare diseases, cancer, and infections (3). The project integrated genomic and clinical data to gain insights into precision oncology, with data published in Nature Medicine in 2024 (4).

Challenges Faced By Precision Medicine Companies:

Despite the promises of precision medicine, companies operating in this space encounter numerous challenges in managing and analyzing multi-omic data effectively. These challenges include:

Data Volume and Complexity: The sheer volume and complexity of multi-omic data require scalable computational infrastructure and sophisticated analytical tools capable of handling big data analytics and machine learning algorithms.

Data Privacy and Security: Given the sensitive nature of genomic and health-related data, precision medicine companies must adhere to strict data privacy regulations and implement robust security measures to safeguard patient information.

Data Integration: Integrating heterogeneous data sources from genomics, proteomics, metabolomics, and clinical records presents technical challenges related to data interoperability, standardization, missingness, and harmonization.

Traceable and Reproducible Methodology: With complex analytical methods becoming a necessity with these data types, being able to keep track of (i) data versions, (ii) data processing methods, (iii) methods used to discover novel findings, (iv) who did what and when, all become more important but also more challenging.

Interpretation and Clinical Utility: Translating multi-omic data into actionable insights for clinical decision-making remains a significant bottleneck, requiring multidisciplinary collaboration between bioinformaticians, clinicians, and domain experts.

The main challenge in adopting analytics in the industry is the lack of high-quality data sources and data integration, followed by the lack of talent to adopt analytical technologies (5). These can be overcome by ready-built cloud-based data analytics platforms coupled with expert support of data scientists and bioinformaticians.

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Solution: Cloud-Based Research Platform

Many companies are still utilizing traditional approaches with local data storage and analysis. Some institutions are also investing in high performance computing (HPC). Others are working with a hybrid approach, using the cloud for some data storage and analysis and local devices for others. In 2024 we believe that, for most organizations, the only solution to the above challenges is a cloud-based research platform. Cloud-computing offers some very clear advantages over traditional or HPC based data storage and analysis. According to a report by McKinsey from 2023, the life sciences industry is increasingly adopting cloud technologies, with over 80% of the top 20 global pharma and medtech companies operating in the cloud in 2021 (5). McKinsey found that companies are increasingly leveraging the cloud to shift IT infrastructure management and to fill gaps in their software development and analytics capabilities, buying software as a service rather than building and operating it themselves. 

  1. Scalability: Cloud-based platforms provide virtually limitless scalability, allowing precision medicine companies to scale their computational resources up or down based on fluctuating workloads and data volumes. Unlike on-premises HPC clusters, which are constrained by physical infrastructure limitations, cloud environments can seamlessly accommodate growing data demands without upfront investment in hardware procurement or infrastructure upgrades.
  2. Flexibility and Agility: Cloud-based platforms offer unparalleled flexibility and agility, enabling rapid prototyping, experimentation, and innovation. With a vast ecosystem of managed services, development tools, and third-party integrations, cloud environments empower precision medicine researchers and data scientists to explore novel algorithms, workflows, and analysis pipelines.
  3. Cost Efficiency: Cloud computing operates on a pay-as-you-go pricing model, allowing precision medicine companies to optimize costs by only paying for the resources and services they consume. Preventing unnecessary copies of data can save projects up to 99.9% storage costs (6).
  4. Global Accessibility: Cloud-based platforms provide global accessibility and collaboration capabilities, enabling geographically distributed teams to seamlessly collaborate on multi-omic data analysis projects. With data stored centrally in the cloud, researchers and clinicians can access and share datasets securely from anywhere in the world, facilitating collaborative research initiatives and accelerating scientific discoveries.
  5. Security and Compliance: Leading cloud providers invest heavily in robust security measures and compliance certifications to ensure sensitive healthcare data's confidentiality, integrity, and availability. Cloud-based platforms adhere to industry best practices for data encryption, access control, and audit logging, providing peace of mind to precision medicine companies and ensuring compliance with regulatory requirements such as HIPAA and GDPR.

Cloud and edge computing is one of the top three tech investments of life sciences companies, attributing to 45% of overall tech spend together with AI and ML, and expecting to derive most of their short- to medium-term benefits from them (5).

In conclusion, cloud-based platforms offer a compelling solution to the challenges faced by precision medicine companies in managing and analyzing multi-omic data. However, for the vast majority of use-cases, researchers will require more than a simple cloud-based instance. Navigating data management and analysis in the cloud requires a skill set most researchers simply don’t have. The solution is a platform built in the cloud which streamlines and simplifies data management and analysis. The question therefore is, to build or to buy your multi-omic cloud based platform?

Build Versus Buy

The decision to build or buy a platform for multi-omic data management becomes paramount for precision medicine companies, with implications for resource allocation, time-to-market, and long-term competitiveness. Here we will break down the pros and cons of build versus buy and provide a checklist to aid this decision.

Build: The DIY Approach

Advantages

  • Tailored & Specific: Your platform will be designed to meet your specific needs at the time
  • Control: You will have full control over the features and capabilities
  • Innovation: There is potential to innovate in a way that differentiates you from the competition and you decide the platform roadmap for development

Disadvantages

  • Upfront costs: High upfront costs for the resources required to build the platform with one report demonstrating that 66% of projects cost more than expected (7)
  • Time to ROI: The development process for a complex multi-omic platform is long and difficult to estimate. Reports suggest 33% of projects take longer to deploy than expected (7)
  • Maintain & update: Maintaining a complex platform is an ongoing, challenging and costly process. Ensuring you have the necessary expertise at your disposal to do this and to continue to meeting your organizations changing and scaling needs can prove challenging even for large enterprises
  • Difficulties in finding and retaining necessary talent: Demand for experts in data science, cloud engineering and software engineering is increasing, leading to increased level of competition
  • Ensuring security and safety of data: According to the 2023 IBM Security report, healthcare data breach costs have increased by 53.3% since 2020 (8). Tackling this ongoing and evolving challenge requires specialist technology and expertise, which could be a daunting task to maintain in-house

Buy: Third Party Solution

Advantages

  • Speed: If the third party software meets your requirements 'out of the box' then the speed to implementation and results will be significantly faster compared to self build.  
  • Cost savings: Depending on the vendor, the cost involved should be lower than DIY, especially for initial investment.  Setting up and implementing an in-house system can cost up to a million dollars (9).
  • Dependability: Access to established features and functionalities that are already fully functional and thoroughly tested
  • Ongoing support: Support and updates provided by the vendor will maintain and improve the platform over time

Challenges and considerations

  • Customization: A lack of ability to customize the platform due to third party deployment and ultimate control, unless the vendor is agile and flexible and willing to implement client specific features
  • Integration: Integration with existing systems can be challenging as this will likely require work from the third party vendor and not all vendors support integrations
  • Vendor lock-in and dependency: Once you have integrated your data and your teams into a third party platform there may be concerns that moving to a new platform in the future may be a challenge

According to Deloitte, drug and medical device developers will need to lean on data analysis providers to remain competitive and embrace the changing industry that is focused on reducing the time to market through advances in analytics (10). 

Decision Time: Does The Shoe Fit

We hate to see precision medicine data not fulfill its full potential. With that in mind we have put together a guide of things we think are crucial to consider when planning your multi-omics platform.

Should You Build or Buy?

We've developed this guide to help you make the right decision for your organization. Access the guide to get a tailored recommendation based on your specific needs and budget. You can also opt in to get personalized feedback from our experts.

When Evaluating A Third Party Solution

  • Does their platform handle all our data modalities? 
  • Does their platform handle all our analysis needs? 
  • Does their platform support integrations with our current infrastructure?
  • Is the platform intuitive/user friendly?
  • Does their team have the necessary expertise to fill our gaps?
  • Does their platform meet our necessary security and compliance needs?
  • What is the cost and timeline for deployment?
  • Does the vendor have a quality management system in place?
  • Does the platform have full auditability, traceability and reporting functionality for; 
    • Users
    • Data (including version control)
    • Analysis
    • Code
    • Discoveries (e.g. biomarker management)
  • Does the platform enable our full end-end requirements both now and for the future? 
    • e.g. pre-clinical development moving into clinical trials
  • Does the platform meet all our end-user requirements? 
    • e.g. some users require point and click data management and analysis, some want code-based analysis, etc
  • Are there limits on the number of users and can we add/control users ourselves?
  • Is full training and ongoing support available?
  • Will we have a dedicated project manager?
  • Do we/will we need any customization, is that possible, and if so how is that managed?
  • How does the platform comply with FAIR data standards (Findability, Accessibility, Interoperability, and Reusability)?
  • Is our data and our discoveries still our IP and do we have complete control over the platform (who has access, what data is added, etc)?
  • What will happen if we decide to later end our agreement? (e.g. How will we get data and IP out?)

If you'd like to talk to Craig or another expert from our team, reach out to us

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Conclusion

We hope this guide helps you decide what is right for you. If you decide to build, we wish you every success. If you decide to buy, in 2024 there are a plethora of cloud-based data management and analysis platforms to choose from. Websites from vendors often sound very similar, making it difficult to interrogate capabilities. We always recommend detailed live demonstrations to ensure a platform will meet your requirements and we hope this guide helps you ask the right questions.

 

If you want to see if and how the Sonrai Discovery platform can meet your requirements, we would be more than happy to meet with you.

References

  1. Markets and Markets (2023), Precision Medicine Market by Type (Inhibitors, Monoclonal Antibodies, Cell & Gene Therapy, Antivirals, Antiretroviral), Indication  (Oncology, Rare diseases, Hematology, Infectious), End User (Hospitals & Clinics, Home care) & Region - Global Forecast to 2028. https://www.marketsandmarkets.com/Market-Reports/precision-medicine-market-215185595.html Accessed: 17/04/2024
  2. National Institute of Health (2023),  NIH awards $50.3 million for “multi-omics” research on human health and disease. https://www.nih.gov/news-events/news-releases/nih-awards-503-million-multi-omics-research-human-health-disease Accessed: 17/04/2024
  3. Genomics England, Cancer in the 100,000 Genomes Project. Genomics England. https://www.genomicsengland.co.uk/initiatives/100000-genomes-project/cancer Accessed: 17/04/2024
  4. Sosinsky, A., Ambrose, J., Cross, W. et al. Insights for precision oncology from the integration of genomic and clinical data of 13,880 tumors from the 100,000 Genomes Cancer Programme. Nat Med 30, 279–289 (2024). https://doi.org/10.1038/s41591-023-02682-0 
  5. Top ten observations from 2022 in life sciences digital and analytics. https://www.mckinsey.com/industries/life-sciences/our-insights/top-ten-observations-from-2022-in-life-sciences-digital-and-analytics#/#/ Accessed: 17/04/2024
  6. The All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature 627, 340–346 (2024). https://doi.org/10.1038/s41586-023-06957-x 
  7. McKinsey & Company (2023), Delivering large-scale IT projects on time, on budget, and on value. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/delivering-large-scale-it-projects-on-time-on-budget-and-on-value
  8. IBM (2023), Cost of a Data Breach Report 2023. https://www.ibm.com/reports/data-breach Accessed: 17/04/2024
  9. Fortune Business Insights (2024), Life Sciences Analytics Market. https://www.fortunebusinessinsights.com/life-science-analytics-market-108603 Accessed: 17/04/2024
  10. Deloitte (2020), The future of pharma services. The growing impact of data in outsourced pharma services. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/deloitte-uk-future-of-pharma-services.pdf Accessed: 17/04/2024

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