CASE STUDY

Multi-Omic Drug Discovery

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 required for this type of challenge. Sonrai overcame all of these challenges through our cloud-based platform and data science team, which integrated and normalised 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 Platform.

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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 a complex task, 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 keeping track of 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 leads to the risk of inaccuracies in data interpretation and analysis could increase.
  • Scalability Issues: Scaling the analysis to include more datasets or to delve deeper into the data can be challenging without a platform that can handle large-scale data processing efficiently.

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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, 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, 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: Utilizing the scalability of the biomarker management tool 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 aid in compliance with regulatory standards, crucial for the drug development process.
  • 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, 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.

Results

Successful Integration and Analysis of Multi-Omic Data: The deployment of Sonrai’s biomarker management tool led to the successful integration of diverse omic data sets. This facilitated a comprehensive analysis of biomarker expressions across different tissues and disease stages, yielding a richer understanding of their roles in cancer progression.

Enhanced Biomarker Discovery: Using Sonrai’s advanced analytics, the drug developer identified new biomarkers for targeted therapy development. This enhanced the precision of their therapeutic strategies and opened avenues for personalized medicine in oncology.

Efficient High-Throughput Data Processing: Through Sonrai’s bioinformatic pipelines, the client efficiently processed high-throughput data such as microRNA sequencing and proteomics. This capability accelerated the discovery process, quickly identifying novel biomarkers and potential therapeutic targets.

Streamlined Data Management and Organization: The project overcame previous challenges in data management, with Sonrai’s tools ensuring organized, efficient, and error-reduced handling of large volumes of omic data.

Scalable and Flexible Research Approach: Sonrai’s scalable infrastructure allowed the client to expand their research scope, incorporating more data sets and conducting deeper analyses without compromising efficiency or accuracy.

Foundational Work for Future Research: The project laid the groundwork for future research endeavors, establishing a robust and scalable model for multi-omic drug discovery in oncology. The collaboration allowed the client to move to the next stage of the discovery process, bringing new targets into clinical trials and analyzing the safety profiles. 

Strategic Insights for Drug Development: The insights gained from the integrated multi-omic analysis provided strategic direction for the drug developer’s ongoing and future projects, particularly in identifying responsive patient subpopulations and optimizing drug efficacy.

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