CASE STUDY

Multi-Modal Drug Discovery

Data types in this case study: RNA-Seq, Digital Pathology, H&E, IHC, Proteomics, Clinical

Case Study Highlights

In this case study we showcase how we accelerated the process of identifying and validating potential therapeutic targets in oncology by leveraging our AI-driven analytics and multi-modal data analysis capabilities. We expedited the drug development process by efficiently analyzing data to pinpoint relevant therapeutic targets but also ensured these targets were rigorously validated and biologically relevant, facilitating quicker translation into clinical applications. Through optimized resource use, enhanced collaboration, and a focus on precision medicine, the client is poised to make strategic advancements in developing targeted therapies for specific patient populations.

The Company

A leading American drug development company focusing on cancer treatment is revolutionizing the process of finding and confirming effective therapeutic targets. Their comprehensive approach combines various types of data, including genetic information, protein studies, medical imaging, and patient health records. This integration plays a crucial role in improving the accuracy and efficiency of identifying potential treatment targets. However, the company’s strong emphasis on target identification means that resources for bioinformatics and data science are somewhat constrained, with most of their staff comprising laboratory scientists.

"Our partnership with Sonrai has been a game-changer in our quest to accelerate our oncology pipeline. Thanks to the integration and analysis of multi-modal data, we were able to identify and validate potential therapeutic targets, and significantly streamline our research operations. Working with Sonrai marks a pivotal advancement in our efforts to bring innovative cancer treatments to patients more swiftly."

The Challenge

Integration and Analysis of Diverse, High-Volume Data: Combining multi-modal data from genomics, proteomics, imaging, and clinical records presents a significant challenge due to varying data formats, quality, and scales. Additionally, these datasets’ sheer volume and complexity demand advanced computational infrastructure and algorithms for efficient processing and analysis while also being mindful of cost-effectiveness.

Siloed Analysis and Expertise: The company’s previous attempts to analyze its multi-modal data resulted in teams that were working in silos due to data being stored separately. This was caused by challenges in the data formats and sizes involved. This made analyzing and obtaining results from the combined dataset impossible.

Identification of Relevant Features: Extracting relevant features from multi-modal data correlating with potential therapeutic targets requires advanced feature selection techniques. Identifying the most crucial data points amidst many variables is a significant challenge. (Linking to biomarker repository)

Biological Complexity and Variability: Understanding the complex biological mechanisms underlying diseases is intricate. Correlating multi-modal data to identify potential targets involves deciphering intricate biological interactions and variations across different patients or disease subtypes. (Will’s AI single cell use case here – biological interpretation LLMs)

Validation Robustness: Ensuring the reliability and reproducibility of identified targets is critical. Validation methods using multi-modal data must be rigorous, requiring comprehensive validation processes, including cross-validation and external validation against independent datasets. (Linking across independent datasets – Matthew C)

Ethical and Regulatory Considerations: Handling sensitive patient data and ensuring compliance with regulations (such as HIPAA, GDPR) adds complexity to data collection, storage, and analysis. Ethical considerations regarding data privacy and consent need to be addressed.

Biological and Clinical Relevance: It is crucial to identify targets with biological relevance and clinical significance. Validated targets should be clearly connected to disease mechanisms and potential therapeutic interventions.

Resource Constraints: Access to skilled personnel with expertise in data science and biology and the availability of computational resources was limited. This company lacked the engineering expertise to handle this large amount of complex data and also the expertise to analyze all the modalities. This constraint hindered the speed and efficiency of target identification and validation processes.

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.

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The Strategy

Why did they choose Sonrai overview

Data Integration and Harmonization: Partnering with Sonrai enables the creation of a unified data ecosystem. Sonrai’s cloud-based platform seamlessly integrated all the multi-modal data involved and linked to additional validation datasets. This enabled linking these data types in one place and storing large amounts of data in a scalable and cost-effective way. This capability is unique to Sonrai and a pivotal reason Sonrai was chosen for this project.

Advanced AI Analytics: Analyzing the complex multi-modal data in this project required advanced AI analytics. Sonrai’s image management and analysis module, alongside code and no-code applications, enabled analysis over this unified multi-modal dataset. This enabled all the necessary experts to access the data and access to AI tools to perform their analysis.

Robust Validation Processes: Sonrai’s platform allows for iterative validation processes, including cross-validation and external validation against independent datasets. This ensures the robustness and reproducibility of identified targets, bolstering confidence in the selection process.

Biological and Clinical Contextualization: Sonrai’s expertise in multi-modal data analysis coupled with domain-specific knowledge can aid in interpreting data in the context of biological mechanisms and clinical relevance. This ensures that identified targets have significance in disease mechanisms and potential therapeutic interventions.

Ethical and Regulatory Compliance: Sonrai’s platform incorporates robust security measures and protocols, ensuring compliance with healthcare regulations (HIPAA, GDPR). This addresses concerns about handling sensitive patient data and ensuring ethical and legal compliance throughout data analysis.

Resource Optimization: Partnering with Sonrai alleviates the burden on in-house computational resources. Sonrai’s cloud-based infrastructure provides scalable computational power, minimizing the need for extensive infrastructure investments and allowing the company to focus resources on research and development.

Interdisciplinary Collaboration: Sonrai fosters collaboration among multidisciplinary teams by providing a centralized platform for data analysis. This promotes seamless communication and collaboration between data scientists, biologists, and clinicians, enhancing the synergy between different expertise areas.

Iterative and Accelerated Pipeline: Sonrai’s AI technology accelerates the target identification process, reducing timelines for potential therapeutic candidates. This expedites the drug development pipeline, enabling quicker translation of discoveries into clinical applications.

Results

The collaborative partnership between Sonrai and our client significantly expedited target identification and validation by integrating and analyzing multi-modal data. Sonrai’s technology enabled efficient data utilization, rapidly selecting potential therapeutic targets and ultimately accelerating the drug development process in oncology.

Accelerated Target Identification: Sonrai’s AI-driven analytics and unified data ecosystem expedited identifying potential therapeutic targets by efficiently analyzing correlations and patterns within multi-modal data. Significantly reducing the time typically required for this phase of drug development.

Improved Target Validation: Leveraging Sonrai’s robust validation processes, our client rigorously validated identified targets. Iterative validation, including cross-validation and external validation against independent datasets, enhanced the reliability and reproducibility of identified targets.

Enhanced Precision and Relevance: Sonrai’s expertise in interpreting multi-modal data within the biological and clinical context ensured that identified targets had strong biological relevance and clinical significance. This guaranteed the selected targets aligned closely with disease mechanisms and potential therapeutic interventions.

Optimized Resource Utilization: Partnering with Sonrai allowed our client to optimize resources. Sonrai’s cloud-based infrastructure provides scalable computational power, reducing the burden on in-house resources and allowing focus on research and development.

Streamlined Collaboration and Communication: Sonrai fosters collaboration among multidisciplinary teams, facilitating seamless communication and collaboration between data scientists, biologists, and clinicians. This synergy enhances the efficiency of the target identification and validation process.

Faster Translation to Clinical Applications: Accelerated target identification and robust validation processes means quicker translation of discoveries into clinical applications. This expedites our client’s pipeline, and can potentially lead to faster development of targeted therapies for specific patient populations.

Enhanced Confidence in Results: Rigorous validation processes, interpretability of results, and compliance with ethical and regulatory standards instill confidence in the reliability and relevance of identified targets. This confidence is essential for decision-making.

Strategic Advancements in Precision Medicine: The results derived from working with Sonrai could position the drug developer at the forefront of precision medicine advancements. Insights gained from multi-modal data analysis may lead to the development of targeted therapies tailored to specific patient groups, contributing to improved patient outcomes.

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