TetraScience Case Studies Driving Cost-Effective CRO Collaboration through IoT

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	Driving Cost-Effective CRO Collaboration through IoT TetraScience

Driving Cost-Effective CRO Collaboration through IoT

TetraScience

Technology Category	Analytics & Modeling - Machine Learning Application Infrastructure & Middleware - Data Visualization
Applicable Industries	Pharmaceuticals
Services	Data Science Services
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. Read More
About 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. Read More
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). Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Application Infrastructure & Middleware - Data Visualization

Applicable Industries

Pharmaceuticals

Services

Data Science Services

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.

About 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.

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).

Impact #1	The implementation of TetraScience's cloud-based data processing solution led to a more streamlined and efficient workflow for the two clinical-stage startup companies. The automation of data entry, processing, and transfer reduced the time scientists spent on manual tasks and minimized the risk of errors. The harmonization of data from different CRO reports enabled the companies to scale up their ADME/PK processes more effectively. Furthermore, the ability to access all instrument data, informatics software information, and scientific data visualization in one place eliminated the need to move and update files from multiple locations, further enhancing operational efficiency.

Impact #1

The implementation of TetraScience's cloud-based data processing solution led to a more streamlined and efficient workflow for the two clinical-stage startup companies. The automation of data entry, processing, and transfer reduced the time scientists spent on manual tasks and minimized the risk of errors. The harmonization of data from different CRO reports enabled the companies to scale up their ADME/PK processes more effectively. Furthermore, the ability to access all instrument data, informatics software information, and scientific data visualization in one place eliminated the need to move and update files from multiple locations, further enhancing operational efficiency.

Benefit #1	Significant reduction in time spent on manual data workflows
Benefit #2	Decrease in manual errors due to automation of data entry, processing, and transfer
Benefit #3	Cost-effective results due to automated data management

Benefit #1

Significant reduction in time spent on manual data workflows

Benefit #2

Decrease in manual errors due to automation of data entry, processing, and transfer

Benefit #3

Cost-effective results due to automated data management

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Overview

Driving Cost-Effective CRO Collaboration through IoT

Operational Impact

Quantitative Benefit