Driving Cost-Effective CRO Collaboration through IoT
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Pharmaceuticals
Services
- Data Science Services
About The Customer
The customers in this case study are two clinical-stage startup companies in the pharmaceutical and biotech industry. These companies often collaborate with Contract Research Organizations (CROs) for absorption, distribution, metabolism, and excretion (ADME) testing of pharmacokinetics (PK) properties of drug candidates. They faced challenges in harmonizing data from different CRO reports, which was critical to scale up their ADME/PK processes. The manual data workflows for their pharmacokinetics and pharmacodynamics (PK/PD) studies were laborious and prone to errors.
The Challenge
Pharmaceutical and biotech organizations frequently collaborate with Contract Research Organizations (CROs) for absorption, distribution, metabolism, and excretion (ADME) testing of pharmacokinetics (PK) properties of drug candidates. However, most CROs use their own data formats for standard assays, which can pose data aggregation challenges to biopharma companies. The ability to harmonize data from different CRO reports is critical to scale up ADME/PK processes. The manual data workflows for pharmacokinetics and pharmacodynamics (PK/PD) studies are laborious. Scientists have to manually check the reports from CROs, which is time-consuming and prone to errors.
The Solution
TetraScience provided a solution by building a cloud-based data processing system to automate Tecan, Titian, Dotmatics, and PerkinElmer Envision workflows with upstream data science applications. This vendor-agnostic integration enabled scientists to access all of their instrument data, information of informatics software, and scientific data visualization in one place, eliminating the need to move and update files from multiple locations. The system also automated CRO data entry, processing, and transfer, saving scientists' time and reducing manual errors. This harmonized data can be used in visualizations of structure activity relationships (SAR), data science, artificial intelligence, and machine learning (AI/ML).
Operational Impact
Quantitative Benefit
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