Integration of Omics Data into Clinical Drug Development

Omics data has the promise of bringing precision medicine and targeted drug discovery to life. However, its use in clinical drug development has been quite limited due to challenges that span technical, practical, ethical and regulatory domains. Here are some of the key problems one might encounter: 

• Data Integration and Interoperability: 
  • Omics data often comes from multiple sources and platforms, each with its own format and standards. Integrating these heterogeneous datasets into a cohesive whole can be difficult. 
• Data Quality and Reproducibility: 
  • Ensuring the accuracy, consistency and reproducibility of omics data across laboratories and platforms is challenging. 
• Data Storage and Management: 
  • Omics data, especially from high-throughput technologies, can be extremely large, requiring substantial storage and computational resources. 
• Complexity of Data Analysis: 
  • Analysing large, complex omics datasets requires advanced bioinformatics tools and expertise, which may not be readily available in all clinical trial settings.

As it currently stands, the CDISC model cannot handle the majority of the omics data types and analyses. Work on this subject is ongoing, and there are some domains that address a few omics needs, but they address specific questions. Additionally, the tabular format of SDTM will likely never be able to accommodate some of the data types generated for omics.  

As a response, a standard called BioCompute has been developed in collaboration with the FDA to describe computational workflows. It aims to document and facilitate communication in the use of bioinformatics tools and procedures by integrating existing best practices. BioCompute aims to address points 1, 2 and 4 from the Problem Statement section of this document. It does not aim to replace CDISC, but rather to complement it in the areas where it needs support. As a very new standard, there are some challenges related to its implementation. These include: 

• Low maturity of the standard 

• Lack of widespread adoption 

• Submission process currently in development 

• Specific use case guidelines lacking or missing altogether 

• Limited user experience 

• Validation of the bioinformatics pipelines being used 

We are expecting submissions such as omics data and bioinformatics workflows to become more mainstream. We can certainly see that it’s also the view of the FDA, who organised the BioCompute Workshop and the FDA Omics Days in 2024. By establishing a Working Group at an early stage, we can ensure seamless adoption of the standard as well as provide its maintainers with useful feedback for improving it. We can also establish a platform for exchange of experiences and create an SME area as a go-to knowledge resource.  

The team proposes an initial short-term smaller scope of the WG focused on BioCompute and its implementation. This would ensure a focused short to mid-term delivery. The scope would entail: 

• Establishing best practices when working with BioCompute 
• A guide for creating and validating BioCompute Objects 
• Submission considerations for BioCompute Objects 
• Sharing of BioCompute Objects across the industry. 

Additional long-term work and deliverables related to the general omics data types could include: 

• Standard Operating Procedures (SOPs): Develop SOPs for the collection, processing and analysis of omics data to ensure consistency and compliance with regulatory standards 

• Data Integration Framework: Create a framework for integrating omics data with other clinical and nonclinical data sources to provide a comprehensive view of patient health and treatment outcomes 

• Biomarker Identification: Identify and validate biomarkers for disease diagnosis, prognosis and treatment response using omics data 

• Precision Medicine Strategies: Develop strategies for implementing precision medicine approaches, including using omics data to tailor patient treatments 

• Regulatory Compliance Guidelines: Establish guidelines for ensuring omics data and analyses comply with regulatory requirements, including Good Clinical Practice (GCP), CDISC and BioCompute standards 

• Training Programmes: Develop training programmes for researchers and clinicians on the use of omics data and technologies 

• Collaborative Research Projects: Initiate collaborative research projects to explore the potential of omics data in drug discovery and development 

• Quality Control Protocols: Establish quality control protocols for omics data to ensure the accuracy and reliability of the results