CASE STUDY

Multi-Omic Drug Discovery

Data types in this case study: RNA-Seq, Proteomics, WES

Case Study Highlights

Our client required multi-omic data integration and analysis to compare expression levels of specific biomarkers across various tissues, disease indications, and disease stages. Their primary challenges were lacking the required domains of expertise, wrestling with data normalization and weighting, and navigating the complexities of AI analysis needed for this type of challenge. Sonrai overcame these challenges through our cloud-based platform and data science team, which integrated and normalized the data and built an AI-based tool for comparing expression levels through a no-code intuitive interface. This tool and the data were made accessible to the team through their instance of the Sonrai Discovery.

6 Months to 0 Weeks

Data Integration Speed Improvement

Sonrai’s automated data normalization, integration, and analysis time for complex datasets like RNA-Seq, proteomics, and WES were drastically cut. This rapid integration facilitated a comprehensive analysis, leading to a 66% improvement in understanding biomarker expressions across different tissues and disease stages.

0 Months

Time-to-Market Acceleration

The streamlined processes and integrated analytics contribute to a faster transition from discovery to clinical trials. Utilising Sonrai Discovery led to a 13-month decrease in time from target discovery to IND acceptance compared to the company’s previous compound.

0% Increase

Biomarker Discovery Efficiency

The biomarker selection process for validation was significantly improved by reducing the validation time from 18 to 12 months through improved data integration and quality, increased throughput, and enhanced accuracy with AI tools. This optimization led to more efficient resource use, higher throughput success, and minimized errors.

The Company

Our Client is a pioneering oncology-focused drug developer committed to advancing cancer therapeutics through innovative research and development. Despite a strong foundation in traditional methodologies, the company faced significant challenges in effectively integrating multi-omic data into its drug development pipeline. The client initially wished to analyze expression levels of specific biomarkers across various tissues, disease indications, and disease stages. This analysis was designed to compare biomarker expressions across healthy tissues and across the different stages of disease progression. The collaboration with Sonrai allowed the client to deepen their understanding of the drug’s mechanisms of action and the effects on patients.

The Challenge

As our client expanded its research into more complex forms of cancer, it faced significant challenges:

  • Data Integration Difficulties: Integrating data from multiple datasets and databases can be complex, especially when dealing with different formats and structures. The drug developer struggled to consolidate this information effectively.
  • Complex Data Analysis: Analyzing and interpreting relationships between biomarkers across various tissues and disease indications requires sophisticated tools. Without specialized analytics capabilities, this task is both time-consuming and technically challenging. 
  • Visualization Limitations: Creating intuitive and informative visualizations from complex biomarker data is not straightforward. Without the visualization tools provided by Sonrai, the drug developer might find it difficult to represent their data in a way that is easily understandable and useful for decision-making.
  • Resource Intensiveness: Processing large datasets to extract meaningful insights can be resource-intensive. The lack of an efficient data platform can lead to increased demands on computational resources and human expertise.
  • Data Management and Organization: Managing and tracking various datasets, especially when dealing with large volumes of data, can be cumbersome. Without Sonrai, organizing and accessing this data efficiently might pose a significant challenge.
  • Error Propensity in Manual Processes: Manual data handling and analysis are prone to errors. Lack of automation increases the risk of inaccuracies in data interpretation and analysis.
  • Scalability Issues: Scaling the analysis to include more datasets or to delve deeper into the data can be challenging without a platform that can efficiently handle large-scale data processing.

Our client faced significant challenges with their existing lab techniques and infrastructure, which were time-consuming and error-prone. Does your organization face similar issues? Sonrai specializes in transforming lab profiles like yours streamlining processes through advanced integration and AI-driven technologies.

Lab Techniques and Methodologies:

  • High-throughput DNA sequencers (e.g., Illumina HiSeq, NovaSeq)
  • RNA sequencing systems
    Mass spectrometers (e.g., LC-MS/MS)
  • CRISPR-Cas9 technologies
  • PET and SPECT scanners
    Cell culture and bioreactor systems
  • Automated HTS systems
  • Mathematical modeling software
  • Structural biology techniques

Lab Infrastructure:

  • Multiple computational platforms and tools
  • Various bioinformatics tools for combining datasets
  • Structured clinical study protocols

     

Data Outputs:

  • High-throughput DNA sequencers, RNA sequencing systems,
  • Mass spectrometers
    Various visualization tools
  • Various data management tools
  • High-throughput sequencing, proteomics platforms

The Difference Sonrai Makes

Every organization is unique, and generic solutions fall short in addressing their distinct challenges. Sonrai is purpose-built to tackle the complexities of precision medicine, offering tailor-made solutions that align with your specific needs.

Discover Predictive Biomarkers 52% Faster

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57% Acceleration in MoA Studies

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14% Reduction in Preclinical Phase

Discover how Sonrai helps pharmaceutical companies overcome challenges in Invivo Drug Efficacy with AI-powered analytics.

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"Leveraging Sonrai Discovery to integrate our multi-omic data significantly accelerated our drug development timeline. Being able to combine our sequencing data with proteomics enabled us to identify new biomarkers faster and more efficiently, as well as compare biomarker data across different tissues."

The Strategy

  • Implementing a Centralized Biomarker Management Tool: Our client employed Sonrai’s biomarker management tool as a central repository for all biomarker-related data. This approach ensures organized access to all biomarker data, facilitating efficient retrieval and analysis.
  • Facilitating Biomarker Discovery: Our client could now leverage the advanced analytics capabilities of the biomarker management tool to discover new biomarkers. Analyze patterns and correlations of biomarker expressions with different disease states and tissues to identify potential therapeutic targets.
  • Streamlining Comparative Analysis Across Indications: The biomarker management tool can efficiently compare biomarker expressions across various disease indications and stages, which is crucial for understanding biomarker roles in disease progression and treatment.
  • Implementing a Centralized Biomarker Management Tool: Our client employed Sonrai’s biomarker management tool as a central repository for all biomarker-related data. This approach ensures organized access to all biomarker data, facilitating efficient retrieval and analysis.
  • Facilitating Biomarker Discovery: Our client could now leverage the advanced analytics capabilities of the biomarker management tool to discover new biomarkers. Analyze patterns and correlations of biomarker expressions with different disease states and tissues to identify potential therapeutic targets.
  • Streamlining Comparative Analysis Across Indications: The biomarker management tool can efficiently compare biomarker expressions across various disease indications and stages, which is crucial for understanding biomarker roles in disease progression and treatment.
  • Integrating Biomarker Data with Clinical and Molecular Data: Ensuring the integration of biomarker data with other molecular and clinical data provided our client with a comprehensive view of disease mechanisms and facilitated a deeper understanding of complex biological interactions.
  • Scaling Data Management for Expansive Studies: By utilizing the biomarker management tool’s scalability to handle increasing volumes of data effectively, the client could maintain flexibility to incorporate new data types or sources as research progresses.
  • Enhancing Data Visualization and Reporting: Incorporate data visualization and reporting features within the biomarker management tool to effectively interpret and communicate complex datasets to various stakeholders, including regulatory bodies.
  • Ensuring Regulatory Compliance in Data Handling: Adapt the biomarker management tool to comply with regulatory standards, which is crucial for drug development.
  • Integrated Cloud Notebooks for Custom Analysis: Researchers can use the integrated cloud notebooks to run custom R or Python scripts, leveraging environments like Sagemaker, JupyterLab, or PySpark. This flexibility allows for tailored analyses of multi-omic data, including complex computational tasks and custom algorithm development, which are vital for discovering new drug targets or pathways.
  • Bioinformatic Pipelines for High-Throughput Data Processing: Using NextFlow bioinformatic pipelines, Sonrai can handle large-scale analyses like bulk RNA sequencing or proteomics. This capability is essential for processing high-throughput omic data, enabling the identification of novel biomarkers or therapeutic targets. The automated report generation feature also aids in summarizing findings, facilitating the interpretation and communication of complex data.

The Weaver Plot lets you analyze all your gene data from multiple studies in a single view. It gives teams a more clear understanding of cancer-related gene expression across the body to better identify targets for treatment.

"Sonrai’s platform transformed our data management and analysis processes. The automation and advanced analytics capabilities saved us time and reduced errors significantly, allowing us to focus more on strategic research initiatives."

Results

AI
66% Improved Understanding of Biomarker Expressions
Comprehensive Integration of 22 Terabytes of Omic Data

Sonrai’s biomarker management tool successfully integrated over 22 terabytes of diverse omic data sets, leading to a comprehensive analysis that improved understanding of biomarker expressions across various tissues and disease stages by 66%. This deeper insight significantly advanced knowledge of their roles in cancer progression.

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Doubling the Rate of Biomarker Discovery
Identifying 27 New Biomarkers in One Year

Our client identified 27 new biomarkers for targeted therapy development within the first year. This achievement doubled the rate of biomarker discovery, enhancing the precision of therapeutic strategies and opening new avenues for personalized medicine in oncology.

Data Integration
70% Faster High-Throughput Data Processing
Reducing Time from Acquisition to Insights

Through Sonrai’s bioinformatic pipelines, the client efficiently processed high-throughput data, reducing the time from data acquisition to actionable insights by 70%. 

security
82% Reduction in Data Handling Errors
Efficient Management of Over 50 Data Sets

Sonrai’s tools revolutionized data management by organizing and efficiently handling large volumes of omic data—over 50 different data sets. This transformation resulted in an 82% reduction in data handling errors and doubled operational efficiency.

AI
40% More Data Sets and 30% Deeper Analyses
Expanding Research Scope with Scalable Infrastructure

The scalable infrastructure of Sonrai allowed the client to seamlessly expand their research scope, incorporating 40% more data sets and conducting 30% deeper analyses without compromising efficiency or accuracy.

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Advancing Three New Targets to Clinical Trials
Establishing a Robust Model for Future Research

This project established a robust and scalable model for multi-omic drug discovery in oncology. The collaboration facilitated the movement of three new oncological targets into clinical trials and significantly enhanced safety profile analyses.

Hollistic Data Overview
Strategic Insights for Two Major Therapeutic Projects
Optimizing Drug Efficacy with Multi-Omic Analysis

The integrated multi-omic analysis provided crucial strategic insights, influencing the development of two major therapeutic projects. It helped identify two key patient subpopulations that respond differently to the new treatments, optimizing drug efficacy and tailoring therapies for improved patient outcomes.

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Regulatory Compliance

Sonrai fully complies with industry standards such as HIPAA and GDPR. Our robust data handling and security protocols ensure that all data is managed with the utmost integrity and confidentiality, minimizing the risk of regulatory penalties and enhancing trust.

What's Next?

"In partnership with Sonrai we are now able to transition from discovery to clinical trials faster than ever before. Our partnership will continue to be crucial as we aim to bring innovative cancer therapies to market swiftly and efficiently."
  • Expand Multi-Omic Data Types: Incorporate additional omic data types such as metabolomics and epigenomics.
  • Leverage Advanced AI and Machine Learning: Apply advanced AI techniques for predictive modelling and personalized medicine.
  • Integrate Real-World Data: Use real-world data to validate and refine research findings and improve treatment outcomes.
  • Optimize Clinical Trials: Design and execute more efficient and targeted clinical trials.
  • Engage in Collaborative Research Projects: Partner with external organizations for large-scale research studies.
  • Facilitating Biomarker Discovery: Our client could now leverage the advanced analytics capabilities of the biomarker management tool to discover new biomarkers. Analyze patterns and correlations of biomarker expressions with different disease states and tissues to identify potential therapeutic targets.

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