BigPharma, a global pharmaceutical company, aims to accelerate drug discovery and personalized medicine by integrating clinical, target and genomic data from internal and external data sources into a unified Cloud DataLake. This initiative seeks to enhance data FAIRification, harmonisation, ontology integration, accessibility, facilitate advanced analytics, and drive innovation in drug development.
Data fragmentation was a key issue, as clinical, genomic and target data resided in disparate internal systems and external sources such as public databases, academic research, and collaborations. Additionally, data standardization posed a challenge due to variability in formats, terminologies, and metadata, making integration complex. Scalability and performance were also concerns, given the need to manage large-scale genomic datasets with robust storage and computational capabilities.
Impact we deliver
The genomic data lake has significantly improved BigPharma’s ability to harness genomic data for research and development. Researchers now have seamless access to integrated datasets, enabling faster identification of disease biomarkers and drug targets. The AI-driven analytics platform has accelerated hypothesis testing, reducing the time needed for target validation and preclinical trials. Additionally, the scalable cloud infrastructure ensures long-term sustainability, supporting future expansion of genomic datasets and research initiatives.
3
domain integration
20+
data sources
60
improved data ingestion
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Our solutions
Data FAIRification pipeline
An automated data ingestion pipeline to ingest, consolidate, validate, summerize and track error on the clinical, genomic and target fed from multiple sources. Internal data was collected from lab experiments, clinical trials, and proprietary databases, while external data was sourced from public repositories such as Ensembl, GenBank, and TCGA. The Extract, Transform, and Load (ETL) process was used to harmonize data formats and ensure seamless integration into the genomic data lake using FAIR principles (Findable, Accessible, Interoperable and Reusable)
Cloud genomic data lake architecture
To address scalability and performance challenges, BigPharma adopted a cloud-based genomic data lake architecture. Structured and unstructured genomic data were stored in scalable cloud solutions like AWS S3 and Azure Data Lake. High-performance big data frameworks such as Apache Spark and Databricks enabled efficient data processing. Metadata management followed FAIR principles, allowing researchers to easily retrieve and utilize the data.
AI/ML-driven analytics
Advanced AI and machine learning models were deployed to derive meaningful insights from the genomic data lake. These models facilitated target discovery by analyzing genomic interactions and identifying potential drug targets. AI algorithms were also applied for genomic variant analysis to predict disease susceptibility and drug responses. Additionally, Semantic Model, ontologies mapping, and interactive visualisation tools provided researchers with real-time insights and improved hypothesis generation.
Scalability
built tech architecture that
can be on scaled rapidly
Integration
automated data integration
engine for performance
Harmonization
ensures data compliance
with FAIR principles
Our approach
Genomic data integration
Integrated genomic data from internal and external sources for a PoC pipeline, reducing retrieval time by 60% and enhancing efficiency. Built and integrated AI/ML pipelines, leading to robust workflows and the identification of novel drug targets.
Compliance & security
We implemented role-based access control (RBAC) to restrict access based on user roles, enhancing security and accountability. This minimizes risks and unauthorized access while ensuring compliance with GDPR, HIPAA, and SOC 2. Our framework protects data privacy and meets regulatory standards for security and compliance.
Technology stack
Files migration
Ontology integration
ETL
Compute
Data FAIRificaiton
Data security
Cloud infra
BackEnd APIs
Search database
Front end
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