Solving humanity's grand challenges by accelerating and improving scientific outcomes: TetraScience

TetraScience is the Scientific Data Cloud company with a mission to accelerate scientific discovery and improve and extend human life. While the company was founded in 2014, TetraScience began its Scientific Data Cloud journey in 2019 with the origin of the Tetra Data Platform (TDP). The Tetra Scientific Data Cloud^™ is the only open, cloud-native platform purpose-built for science and provides life sciences companies with the flexibility, scalability, and data-centric capabilities to enable easy access to centralized, harmonized, and actionable scientific data.

TetraScience has built the largest integration network of lab instruments, informatics applications, CRO/CDMOs, analytics, and data science partners, creating seamless interoperability and an innovation feedback loop that will drive the future of life sciences and the delivery of life-saving therapeutics. TetraScience’s team combines deep domain knowledge, the industry’s only open, purpose-built scientific data cloud, and a global community of instrument, informatics, data science, and integration partners to drive the future of life sciences and harness the power of the world's scientific data. Backed by Insight Partners, Alkeon Capital Management, Underscore, and Impetus Ventures, TetraScience currently counts over 100 leading pharmaceutical and biotech companies as customers.

Accelerate scientific outcomes by maximizing the value of your data

Across biopharma research, development and delivery, scientists and data scientists spend over 50% of their time on manual extraction, validation, and transformation of large and complex datasets — shifting focus away from higher-value science, data science, AI/ML, and advanced analyses that are key to bringing new life-saving therapeutics to market. Scientific data that are siloed, fragmented, in hundreds of different proprietary formats, moved with USB drives or handwritten notes, and connected through fragile, custom; point-to-point integrations impose enormous costs and risks. The Tetra Scientific Data Cloud connects the entire laboratory ecosystem and eliminates the manual, time-consuming data management tasks inherent to biopharma. Productized integrations with instruments, informatics applications, and new/legacy software systems across R&D and manufacturing allow for near real-time data capture and increased data integrity. Data are centralized in the cloud and engineered into an open, universally adoptable, vendor-agnostic format. This access to FAIR scientific data in the cloud allows organizations to leverage the potential of AI/ML and advanced analytics to improve outcomes and operational efficiencies, extract deeper insights to fuel new discoveries, and bring treatments to market faster.

Unlock and enhance your data through the industry’s leading platform that engineers raw and primary scientific data to become compliant, harmonized, liquid, and actionable. Accelerate delivery of life-changing therapies by accessing TetraScience’s global community of instrument, informatics, data science, and advanced analytics partners. Biopharma organizations spend countless hours manually collecting and transforming siloed, fragmented data in different proprietary formats. This time spent building large, complex data sets could have instead go towards the higher-value science, data science, and analytics key to bringing life-saving therapeutics to market. The Tetra Scientific Data Cloud connects the entire laboratory and eliminates manual, time-consuming data management. Productized integrations with instruments and informatics applications allow for near real-time data capture, which is centralized in the cloud in an open, vendor-agnostic format. In this solution brief you will learn how the Tetra Scientific Data Cloud enables research, development, and manufacturing organizations to gain insights.

Accelerating High-Throughput Screening Insights With the Tetra Scientific Data Cloud^™

Biopharma organizations use high-throughput screening (HTS) to parallelize experiments to identify higher-quality drug discovery and development leads more quickly. HTS currently relies on manual, error-prone process to collect and transform scientific data into formats capable of being used within informatics applications or analytics, delaying downstream drug development. High-throughput screening (HTS) still dominates the early discovery landscape – small molecules, large molecules, materials, and even cell therapies use this technology to parallelize experiments. While it enables biopharma organizations to more quickly iterate across parallel experiments to identify leads, the efficiency of HTS is gated by reliance on manual, error-prone processes to acquire large, complex data sets, transform them into usable formats, and update ELN and lab inventory management systems. These inefficient processes reduce “hit” or target ID throughput and ultimately delay downstream drug development phases.

The challenges lie largely with the data and, R&D IT and informatics teams play a key role in improving efficiency. Traditionally, HTS campaigns acquire large, complex data sets with multivariate information about a given library or pool of entities. These data, after (manual) review and triage, are loaded into a structure-activity relationship (SAR) database that allows for optimization along a vector (e.g. improved binding or lowered ½ in plasma). So what becomes of all that data? The process of manually searching for sample barcodes, assembling and transforming them into usable formats, and publishing to an electronic laboratory notebook (ELN) or lab information management system (LIMS) is tedious, error prone, and creates inefficient workflows, ultimately delaying screening campaigns.

The solution to these data challenges involves the following: automating end-to-end pipelines, connecting instruments and applications seamlessly via productized integrations, and harmonizing the data so it’s FAIR (findable, accessible, interoperable, and reusable) in the cloud. TetraScience’s High-Throughput Screening Tetra Scientific Application brings cohesion and automation to your high-throughput screening workflows. With this application, IT can enable research teams to screen more entities in less time, identify higher quality lead candidates sooner, and bring critical treatments to market faster.

Untangling Biopharma Data

Biopharma organizations use data to unlock insights and drive innovation. R&D IT teams often consider building a data management solution in house to gather, organize, and make data accessible to laboratory scientists, data scientists, and others across the organization and business. Teams start by connecting a few simple point-to-point integrations between individual lab instruments or informatics applications, but often find complexity increases as they need to incorporate more solutions. Building these solutions requires R&D IT and scientists to dedicate time to researching vendor technologies, developing custom software, and supporting integrations and data processing. Beyond the time and cost, building and maintaining DIY solutions takes life sciences organizations away from the core mission of scientific discovery.

Unanticipated complexities and costs arise when developing custom integrations to acquire data from different vendors, harmonizing and unifying multiple data formats, automating manual processes, and building cloud-native data storage. Scarcity of cloud + science expertise and knowledge of vendor technologies increases development time and costs and shifts the focus of the organization to researching, building, and supporting solutions and away from solving scientific challenges. Maintenance and enhancements — needed to resolve bugs, add new capabilities, and integrate new data — require dedicating valuable software development and support to improving the DIY data management solution. Limited, point-to-point solutions address specific problems but may not have the flexibility and extensibility to solve future challenges. Additional development efforts are needed to integrate new data, update vendor protocols, and unify scientific data access.

Patrick Grady, Chairman & CEO 

“Reduce time to find and access scientific data with a centralized data cloud, extract insights through advanced data exploration and analysis, and gain the adaptability to integrate new data solutions or enhance existing ones.”

“Benefit from dedicated domain experts who monitor and update system capabilities and provide purpose-built integrations that can be adapted to the unique needs of biopharma organizations.”