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Siloed systems. Manual workflows. Lost opportunities.

CMC teams are stuck managing data instead of driving development.

This infographic shows how top biopharmas are transforming fragmented scientific data into a unified, AI-ready foundation—unlocking value across the entire CMC lifecycle:

• Cell line development – 70% shorter clone selection and stability studies
• ‍Downstream development – 7x faster insights
• ‍Method development – Up to 75% fewer out-of-spec events

See how data-driven CMC delivers speed, precision, and impact.

Maximizing ROI from scientific data and workflow solutions hinges on aligning IT, scientific, and data science teams. However, many organizations struggle with limited resources, best practices, and expertise in new technologies.

Tetra Sciborgs, our elite team of experts, address these challenges by bridging gaps between departments, identifying high-impact scientific use cases, and developing strategies to optimize data utilization and AI readiness. This approach boosts the value of Tetra Scientific Data and AI Cloud™ deployments by up to 10x.

See how the Tetra Scientific Data and AI Cloud enables seamless, bidirectional data exchange between your ELN and CDS.

In this demo, we walk through an automated workflow using Revvity Signals Notebook and Waters Empower CDS. But the same approach can be applied to any informatics software (e.g., ELN, LIMS) or chromatography data system (CDS).

Watch how:

• Sample information moves from ELN to CDS with a single click
• Raw chromatography results are automatically uploaded and engineered into open, analytics-ready data
• Results return to the ELN—fully traceable and easy to access

See how the Tetra Scientific Data and AI Cloud streamlines stability studies with end-to-end data automation.

In this short demo, you’ll learn how to:

1. Automatically upload raw files from Karl Fischer titrators and particle size analyzers to the cloud
2. Contextualize and harmonize data into an open, vendor-agnostic JSON format
3. Populate specific rows and columns in Revvity Signals Notebook—no manual steps required

Watch the entire workflow in action, from instrument output to ELN update, in just minutes.

Get a guided tour of the Tetra Data Platform. As part of the Tetra Scientific Data and AI Cloud, it streamlines how you find, manage, and use your scientific data.

In this short video, you’ll learn how to:

• Search, preview, and access scientific files and metadata
• Understand file categories like raw, processed, and IDS
• Work with schemas and artifacts to standardize your data
• Configure agents and pipelines to automate ingestion and processing
• Use the Data and AI Workspace to extract insights and collaborate

Whether you're new to TetraScience or just need a refresher, this demo covers the core features of the platform.

Optimizing bioprocess conditions requires more than good science. It takes data that’s accessible, contextualized, and ready for action.

That’s where the Cell Culture Insights app comes in. It harmonizes readings from instruments, metadata from your LIMS, and context from your ELN into a single, analysis-ready dataset.

See how Cell Culture Insights empowers:

• Analytical & Process Development Teams – Visualize trends across media, conditions, and cell lines, accelerating optimization and boosting yield.
• Quality & Manufacturing Science Teams – Detect process drift early and investigate root causes before product quality is impacted.
• Data Teams – Build models and uncover patterns using complete, FAIR datasets.
• IT Teams – Save time, reduce ad hoc requests, and provide scalable access to high-quality data.

Transform the way you manage, monitor, and optimize cell culture data.

A leading biopharma relied on reversed-phase ion-pair (RP-IP) chromatography to assess mRNA stability. However, manual peak detection slowed throughput and introduced variability into a critical analysis.

To scale and streamline its chromatography workflow, the company partnered with TetraScience. As a result, the team:

• Saved up to 60 minutes of hands-on time per assay by automating data processing
• Reduced time to analysis by up to 24 hours through automated data transfer
• Enriched data with contextual metadata to unlock deeper insights
• Improved model performance through continuous feedback-driven learning

When scientists process data on personal computers, results often get stranded—isolated on local drives and disconnected from the broader data ecosystem.

The Tetra Data Sync Utility closes the loop. It automates the movement of raw and processed data—from instruments to desktops and back to the cloud—preserving traceability, reducing manual work, and protecting data integrity.

In this demo, we illustrate the workflow using a flow cytometry experiment analyzed in FlowJo.

See how the Data Sync Utility:

• Automates downloads and uploads of raw and processed data
• Seamlessly integrates with the Tetra Data Platform to send results to ELNs
• Uses flexible filters to sync only the data you need—by site, timeframe, or file path

Scientific IT is at a crossroads. Siloed systems, manual workflows, and inconsistent data formats are creating a massive utility gap, slowing progress and straining resources.

This infographic reveals how leading biopharma organizations are turning scientific data from a bottleneck into a competitive edge, delivering measurable results:

• 1 PB of data replatformed annually
• Up to 98% reduction in data prep time
• $2.5M saved in data management costs
• 60% fewer steps in scientific workflows

Explore how data replatforming and engineering unlock scalable analytics and AI—while making your data more accessible, reliable, and ready to drive results.

Biopharmas face significant challenges when managing data from contract organizations—from inconsistent formats to high volumes of unstructured files.

In this video, see how the Tetra Scientific Data and AI Cloud simplifies and scales external data workflows with automation, engineering, and human-in-the-loop review.

You’ll learn how:

• CRO data is automatically ingested and harmonized, making it available to electronic lab notebooks and platforms like Snowflake
• Custom apps in the Tetra Data and AI Workspace enable scientists to easily review, analyze, and approve data from external partners
• AI-powered pipelines extract structured data from handwritten PDF files provided by CMOs

Researchers at a top biopharma relied on inefficient manual steps to record and analyze cross-interaction chromatography (CIC) data. The complexity of the process discouraged them from running CIC assays, slowing discovery.

The Tetra Scientific Data and AI Cloud transformed their workflow with automation, data engineering, and seamless LIMS/ELN integration. Scientists can now focus on research instead of data processing.

With TetraScience, they:

• Saved 3 to 4 hours per run by eliminating 384 manual steps
• Increased assay throughput
• Improved data accuracy by reducing errors and inconsistencies
• Enabled future use of data for advanced analytics and AI

In biopharma, chromatography data holds critical insights—but it's often locked in disconnected systems across multiple vendors, projects, and sites.

The Chromatography Insights app in the Tetra Data and AI Workspace changes that. Unify CDS data from all vendors, access contextualized data in tools like Databricks and Snowflake, and leverage an enterprise-scale solution built to handle millions of injections.

See how Chromatography Insights empowers:

• Analytical Teams – Accelerate method development with centralized data and powerful troubleshooting tools like Chromatogram Overlay.
• Quality Teams – Gain unprecedented visibility into product quality, detect issues early, and streamline audits with features like Column Logbook.
• IT Teams – Eliminate CDS integration headaches and reduce manual workload with ready-made dashboards.
• Data Teams – Harness AI-ready datasets for advanced analytics and predictive modeling.

Transform the way you manage chromatography data and unlock powerful insights across your organization.

Next-generation sequencing (NGS) generates massive amounts of data, but fragmented storage, inconsistent metadata, and inefficient workflows hinder discovery. The Tetra Scientific Data and AI Cloud™ eliminates these roadblocks by automating metadata management, centralizing access, and enhancing quality control—so scientists can focus on insights, not data handling.

In this solution brief, you'll learn:

• The biggest challenges in NGS data management
• How TetraScience simplifies and accelerates the process
• Real-world applications across the biopharma value chain

Lead clone selection is a critical yet resource-intensive step in biopharmaceutical development, traditionally taking around 8 months to complete. Technical, operational, and regulatory hurdles slow time to clinic and drive up costs.

The Tetra Lead Clone Selection Assistant transforms this process by combining the Tetra Scientific Data and AI Cloud™ with advanced AI models like NVIDIA’s VISTA-2D and Geneformer. This approach reduces lead clone selection timelines from months to weeks while improving stability and productivity.

In this solution brief, you'll learn:

• The biggest challenges in lead clone selection
• How the Lead Clone Selection Assistant overcomes these obstacles
• The scientific and business outcomes of the solution

Discover how the Tetra File-Log Agent, as part of the Tetra Scientific Data and AI Cloud, simplifies data collection, contextualization, engineering, and retrieval across a wide range of scientific instruments.

In this video, you'll see how the File-Log Agent streamlines data workflows with:‍‍

• ‍Seamless file uploading with progress monitoring‍
• Contextual labeling for enhanced searchability within the Tetra Data Platform (TDP)‍
• Automated file management, eliminating the need for manual archiving and deletion‍
• Bidirectional file transfers between TDP and on-premise systems‍
• Checksum verification to maintain data integrity‍
• Scheduler monitoring to handle system outages

See how the Tetra Scientific Data and AI Cloud transforms quality testing workflows by eliminating manual data entry, reducing errors, and accelerating batch release.

What you'll learn:

• The batch release process and key QC steps
• Automated data flows for purity analysis by HPLC and moisture testing by Karl Fischer titration—seamlessly connecting Signals Notebook, Empower CDS, LabX, and Tiamo
• How TetraScience integrates with Snowflake and Databricks to unlock advanced analytics and AI-driven insights

Experience the power of lab data automation and AI enablement to drive smarter, faster decisions in batch release and quality testing.

Software deployments in biopharma often fail to meet expectations. Why? They don't focus on scientific use cases.

TetraScience takes a science-led approach that supports scientific use cases end to end through lab data automation and Tetra Data Apps. Tetra Sciborgs work with customers to identify and prioritize the most impactful use cases. Altogether, this approach enables faster, more informed decisions across the biopharma value chain, unlocking the full value of scientific data.

Discover the latest updates to the Tetra Data Platform designed to streamline scientific data engineering.

In this video, we showcase:

• Improved pipeline UX: Quickly configure and update pipelines for a more efficient workflow.
• Pipeline versioning: Access a complete version history of your pipelines and roll back to previous versions with a single click.

Sciborgs are transforming biopharma. These exceptional experts live at the intersection of science, data, and AI, driven by an unwavering focus on results. They hold the key to unlocking the full potential of scientific data.
Are you a Sciborg? Explore what sets them apart in our infographic—and see how Tetra Sciborgs go even further to accelerate and improve scientific outcomes.

Learn more about Sciborgs.

Tired of error-prone manual data entry? The Tetra Data Capture App transforms routine tasks like weighing samples into streamlined, error-free processes.

In this video, you’ll see:

• The challenges of traditional weighing workflows
• A step-by-step demo of using the app to capture data from balances
• Seamless integration with ELN/LIMS platforms and the Tetra Scientific Data and AI Cloud

Discover how our vendor-agnostic, user-friendly solution ensures data accuracy, traceability, and compliance—saving you time and reducing errors.

Cell and gene therapies (CGTs) offer new hope for untreatable conditions, but their personalized nature adds immense complexity to quality control (QC). Delays or errors can have life-or-death consequences, requiring streamlined data management, traceability, and scalability.

Discover how TetraScience helps biopharma deliver life-saving therapies faster, safer, and at scale.

See how TetraScience simplifies data capture from simple instruments like balances through an Internet of Things (IoT) approach.

In this demo, you'll learn:

• Common pain points of traditional weighing workflows
• Two IoT-based methods for data capture:
  1. Serial command and response
  2. Print button (serial forwarding)

Life sciences organizations struggle to harness their scientific data—siloed, unstructured, and locked in vendor-proprietary formats. The Tetra Scientific Data and AI Cloud, combined with the Snowflake AI Data Cloud, transforms raw scientific data into analytics-ready and AI-native datasets shared across both platforms.

In this video, explore a real-world use case in cell culture fermentation—just one of many—and see how:

• TetraScience seamlessly integrates with Snowflake
• Harmonized scientific data powers apps, accelerates analysis, and enables rapid, informed decisions
• Scientists and data scientists can easily collaborate to find, share, and explore data

Explore how the Tetra Scientific Data and AI Cloud seamlessly moves quantitative PCR (qPCR) data into the Veeva Vault LIMS platform—just one of many use cases. This automated, end-to-end workflow ensures the efficient and accurate transfer of data from scientific instruments to Vault LIMS.

As scientific data grows in complexity and volume, extracting meaningful insights becomes increasingly difficult. The Tetra Lakehouse architecture simplifies data replatforming and engineering, making it easier for biopharma companies to harness advanced analytics and AI.

In this demo, we explore:

• The key features of a data lakehouse for scientific data
• Data workflows within the Tetra Lakehouse
• Transforming IDS JSON data into Delta tables
• How to access and query Delta tables in the Tetra Data Platform

Laboratory organizations are increasingly asked to improve their productivity while reducing their costs. One way can be optimizing the usage of laboratory equipment, but organizations often lack the access to instrument-related data and the insights to improve their asset utilization.

In this fact sheet, we share an example of potential savings by using this data to gain actionable insights into instrument usage and performance enabled by the Tetra Scientific Data and AI Cloud. This helps to reduce unnecessary costs for maintenance, make strategic decisions on instrument allocation and purchase, and increase lab productivity.

Selecting a lead clone and conducting stability studies are among the most time-consuming and resource-intensive steps in cell line development. However, AI is poised to transform this process.

Introducing the Tetra Lead Clone Selection Assistant, powered by the Tetra Scientific Data and AI Cloud™ and NVIDIA's AI models (VISTA-2D and Geneformer). This Tetra Data App visualizes data from cell line development and helps scientists predict the most promising lead clones using analytics and AI. As a result, it can shorten selection and stability studies from 8 months to just 2.5 months while delivering higher-producing and more stable cell lines.

The Challenge

Chromatography labs in biopharma face delays, reduced productivity, and compliance risks caused by fragmented data spread across chromatography data systems (CDSs) and sites. Analyzing this data is often slow, costly, and retrospective, hampered by fragmented data access.

The Solution

Chromatography Insights is a universal dashboard that centralizes data from all chromatography data systems (CDSs) across an organization—regardless of vendor, project, site, department, and version. It provides near real-time visibility into method, instrument, and column performance. This enables early detection of out-of-spec results, leading to improved process control, faster batch release, and better stability studies. 

By visualizing data from tens of millions of injections—both recent and historical—at an enterprise scale, users and leaders can uncover deeper insights for faster, smarter decisions. This robust foundation paves the way for predictive modeling and AI, enabling organizations to leverage their data for strategic advantage.

What You’ll See in the Demo

• Batch Release Continuity: Track impurity trends across batches run at multiple sites to quickly address performance variations before they lead to out-of-spec events.
• Proactive Tech Transfer Management: Resolve tech transfer issues ahead of audits by comparing method performance across multiple CDSs.‍
• Column Performance Tracking: Monitor and optimize column usage to prevent method failures.

Chromatography Insights is the industry’s first universal chromatography dashboard, compatible with all major CDSs. It consolidates method, column, and instrument performance data into an interactive dashboard within the Tetra Scientific Data and AI Cloud™, providing near real-time insights without the need for additional visualization tools.

Engineered for enterprise scale, Chromatography Insights visualizes tens of millions of recent and historical injections with minutes-level latency. This supports robust process control and proactive monitoring to detect deviations, including out-of-specification (OOS) events, before they occur.

At TetraScience, we know the key to unlocking the power of your scientific data lies in solutions built around scientific use cases. See how our tools and expertise help you identify, design, and prioritize these use cases—turning raw data into actionable insights.

Learn more about our scientific outcomes.

A leading biotechnology company uses AI to predict drug properties from high-throughput screening (HTS) data. Previously, scientists collected and prepared this data through many labor-intensive steps—an unscalable approach.

In partnership with TetraScience, the company completely overhauled its HTS data workflow. The new solution accelerates R&D, delivering immediate and lasting value to scientific, IT, and data teams.

Key outcomes:

• Streamlined operations, freeing up 240 hours per year for scientists
• Improved data accessibility with centralized, contextualized data in the cloud
• Enhanced data integrity by minimizing manual, error-prone tasks
• Future-proofed integrations and pipelines 
• Automatically prepared AI-native data

High-content imaging produces rich datasets, but managing them was a headache for bioengineers at a leading synthetic biology company. They relied on inefficient manual processes to prepare data from Agilent BioTek Gen5 software for analysis, reducing screening throughput and straining resources.

To overcome these hurdles, the company deployed the Tetra Scientific Data and AI Cloud, which automatically collects and engineers Gen5 data into AI-native datasets at scale. 

Key results of the streamlined workflow:

• Increased screening throughput by up to 35%
• Reduced scientists’ hands-on time for plate reader runs by approximately 75%
• Decreased data processing time by over 50%
• Enhanced data completeness, consistency, and quality
• Enabled cross-analysis through significantly improved data accessibility and reliability

Quality testing of drug substances and products is time-consuming and often considered a bottleneck. Decisions for a single batch typically take 40 hours, and non-compliance can be costly.

View this infographic to identify the key areas for improvement through a data-driven approach and discover the tangible outcomes biopharmaceutical companies can realize by leveraging TetraScience.

In large enterprises, QC labs need to test and document the results for tens of thousands of samples per year. When deviations are identified and investigations follow, these efforts increase substantially.

Read this solution brief to learn how TetraScience can reduce the efforts of biopharmaceutical companies in QC data transfer, validation and compliance, report creation, control charts and trending, and deviations and investigations. TetraScience’s data-centric, science-driven approach can help achieve digital QC, accelerating time-to-clinic and time-to-patient, reducing batch inventory, improving revenue realization, and fulfilling patient needs earlier.

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Discover how the Tetra Scientific Data and AI Cloud™ enhances data management and search capabilities with advanced labeling and contextualization.

In this demo, you'll learn how to apply:

• Static labels: Automatically applied to incoming files based on source information (e.g., laboratory number and site).
• Dynamic labels: Extracted from file names, paths, or metadata (e.g., well locations and instrument info).
• Third-party labels: Added from external sources like CSV files or databases (e.g., sample ID).

Explore these features to streamline your data management and make data searching more efficient.

The world’s most important data is biopharma’s scientific data. In this on-demand webinar, we deep dive into how to revolutionize CMC data workflows, scientific use cases, and biopharma and CxO collaboration. Gain insights into optimizing your data strategy to drive CMC efficiency and innovation.

Hear from a panel of CMC biopharma executives as they explore the path to harnessing the power of liquid data and its groundbreaking impact on furthering scientific outcomes.

The biopharmaceutical industry is racing to harness advanced analytics and AI to improve drug discovery, development, manufacturing, and quality control. However, a significant barrier remains: the current state of scientific data. Despite the massive volume of available data—measured in exabytes—much of it is not yet accessible or optimized for advanced analytics and AI/ML applications. Bridging this "data utility gap" is essential for speeding up scientific breakthroughs and delivering life-changing therapies to patients more efficiently and cost-effectively.

Databricks and TetraScience have partnered to overcome these challenges, unlocking the full potential of scientific data. Together, we are revolutionizing the way scientific data is utilized, driving innovation across a growing number of Scientific AI use cases throughout the biopharmaceutical value chain.

Watch the on-demand webinar to explore:

• ‍‍How scientific data must be replatformed and engineered into large-scale, compliant datasets optimized for analytics and AI
• The utilization of AI-native datasets across the pharmaceutical value chain, delivering novel insights and enabling data-driven decision-making

As a practical example, learn how Databricks and TetraScience enable the consolidation and comparison of data from chromatography data systems into a single, interactive dashboard. This empowers teams to assess performance variability, identify root causes, and track trends of this critical analytical technique in real time, leading to faster, more informed decisions across labs.

Discover how leading pharmaceutical companies are transforming bioprocess development by leveraging scientific data and AI. Our infographic highlights key trends and strategies to keep you ahead of the curve.

What’s inside:

• Improve process efficiency: Proven techniques to streamline workflows and shorten development timelines.
• Unlock data’s full potential: Strategies to turn raw data into actionable insights for faster, smarter decisions.
• Enhance product quality: Innovative methods to maximize quality while scaling up production.

One of the biggest challenges in bioprocess development is accurately predicting and identifying critical process parameters. Scientists often struggle to collect and analyze fragmented, siloed data. The resulting lack of actionable intelligence slows development timelines and limits organizations from making real-time, data-driven decisions. 

What's missing is a robust data foundation to fully support bioprocess development and optimization. To help you get started, we’ve compiled this playbook.

Inside, you'll learn:

• Key industry challenges in optimizing bioprocessing
• How predictive tools and dashboards can accelerate and improve scientific outcomes
• Data-driven use cases in bioprocess development and optimization 
• The necessity of scalable data engineering to enable advanced analytics and AI

Biopharma companies must carefully control access to scientific data based on user roles. TetraScience addresses this need with two powerful data governance features—Custom Roles and Data Access Controls. These tools enable administrators to precisely manage access to data and functionalities within the Tetra Scientific Data and AI Cloud.

Watch this demo to see how Data Access Controls and Custom Roles provide secure, tailored access, helping you maintain compliance and protect proprietary information across your organization.

Quality control (QC) scientists at a large biopharmaceutical company grappled with "human middleware" in their data workflows. These manual, labor-intensive processes slowed down batch release testing and compromised data integrity. 

In response, they partnered with TetraScience. The Tetra Scientific Data and AI Cloud™ automates their data workflows end to end through seamless bidirectional integration with LabWare LIMS and other software. The result is faster, more reliable, and fully traceable QC processes.

With TetraScience, the biopharma achieved:

• Faster batch release testing
• Higher productivity for scientists
• Enhanced data integrity
• Analytics-ready data for future chromatography dashboards

As scientific data grows in complexity and volume, deriving insights from it becomes increasingly challenging. TetraScience’s lakehouse architecture offers a transformative solution, seamlessly bridging the gap between data lakes and data warehouses. 

With this architecture, you can:

• Efficiently query and transform scientific data, unlocking advanced analytics and Scientific AI.
• Manage datasets for both data lake and data warehouse use cases with SQL and Delta tables.
• Create datasets optimized for specific scientific use cases.
• Share datasets across platforms like Databricks without incurring additional governance burdens or costs from data duplication.

The Chromatography Insights app in the Tetra Data and AI Workspace makes it easy to track column, method, and system performance across all your chromatography systems.

In this demo, you'll discover:

• The Tetra Data Lakehouse architecture
• 3 illustrative use cases
• Scientific outcomes driven by the Chromatography Insights app

Producing cell and gene therapies is exceptionally complex, and the stakes are incredibly high. For the contract development and manufacturing organizations (CDMOs) enabling these treatments, operational efficiency isn’t just about saving time and money; it can be a matter of life and death.

Recognizing this, an industry-leading CDMO modernized its scientific data strategy for developing and analyzing cell and gene therapies. Initially, its scientists relied on paper-based records and spreadsheets to track and record data. These methods were slow and prone to human error.

The CDMO decided to implement an integrated solution combining LabVantage LIMS with the Tetra Scientific Data and AI Cloud™. The solution led to several key outcomes:

• Higher productivity for scientists
• Improved data integrity
• Future-proof, AI-ready data

Discover how the Tetra Scientific Data and AI Cloud integrates with Bio-Rad's droplet digital polymerase chain reaction (ddPCR) systems and Veeva Vault LIMS to enable a seamless flow of data between them. The platform also replatforms and engineers the ddPCR data, optimizing it for analytics and AI in downstream applications.

Watch the demo to see this workflow in action.

Discover how the Tetra Scientific Data and AI Cloud integrates with Waters Empower Chromatography Data System (CDS) and Veeva Vault LIMS to enable a seamless flow of data between them. The platform also replatforms and engineers the chromatography data, optimizing it for analytics and AI in downstream applications.

Watch the demo to see this workflow in action.

Discover how low-code scripting on the Tetra Data Platform streamlines data processing and boosts productivity. This feature allows scientific data engineers to easily create pipelines using Python directly within the platform.

In this demo, you'll learn how to label files using a Python script.

The biopharmaceutical industry is undergoing a seismic shift in how scientific data is managed and analyzed. Emerging technologies like AI are driving the change, offering the potential to bring better therapies to market faster and at lower costs. To achieve these goals, organizations must fully leverage their scientific data. 

Yet, by and large, these datasets are trapped in silos and proprietary formats. Biopharma leaders need to embrace data-centric strategies and frameworks to unlock the full value of their scientific data. Otherwise, they risk failing to capitalize on Scientific AI and falling behind the competition.

In this white paper, you'll learn:

• The inherent challenges of scientific data
• Why traditional data management is no longer sufficient
• 8 modern strategies to maximize data value

Discover file versioning and lineage within the Tetra Data Platform. The platform allows you to easily monitor each file's entire lifecycle. 

In this demo, you'll learn how to:

• Explore comprehensive file details: Access detailed information about each file, such as metadata and source.
• Visualize the complete file journey: Track every step, including upload, indexing, and triggered pipelines.
• Manage file versions: View, upload, and switch between different versions of files with ease.

Explore the powerful search functionality within the Tetra Data Platform. Learn how to search, filter, and manage your scientific data with ease.

In this demo, you'll learn about:

• Indexed data: Understand what data and metadata are indexed for search within the platform.
• Tetra Data Platform user interface: Use the intuitive UI to search and filter your data.
• Querying in Snowflake: Build and execute queries within Snowflake to extract specific data.‍
• Querying with Elasticsearch: Leverage Elasticsearch and the platform's API for advanced data searches.

Discover how the Tetra Data Platform empowers you with robust data access controls, ensuring secure access to your files.

In this demo, you'll learn about:

• Metadata collection and how it helps to define access groups
• Components of data access controls
• Setting up access groups
• Impact of access groups on search results

Scientists at a biopharmaceutical company faced a data bottleneck in their high-throughput screens. Most could not access the screening data directly and had to rely on computational biologists to manually gather, process, and analyze it. This inefficient process slowed drug discovery.

To resolve the issue, the biopharma adopted the Tetra Scientific Data and AI Cloud™. This scalable solution automatically replatforms and engineers the screening data, making it readily available for visualization through a dashboard. Now, all discovery scientists can easily access high-quality data.

Key outcomes include:

• Rapid data search and aggregation: reduced from 1–2 days to 10 minutes 
• Higher productivity: refocused computational biologists on more valuable work
• Faster decisions in hit selection: decreased from 2 weeks to 1–2 days

Event subscriptions allow you to programmatically notify downstream systems of key events within the Tetra Data Platform (TDP). By subscribing to specific TDP events, you can automatically trigger custom apps and business processes to accomplish follow-on actions.

In this demo, you'll learn about:

• Supported event types
• Examples of use cases
• Creating event subscriptions

Data is at the heart of scientific decisions. The Tetra Scientific Data and AI Cloud places the scientist's user experience at the forefront. With its intuitive search and filter features, scientists can easily access, organize, and analyze data.

This demo (Part 3) introduces a new feature called Projects, which helps you quickly organize and access data associated with specific labs, experiments, instruments, or any other type of project. Projects are organized by user-defined attributes and automatically update with new data when relevant files are uploaded to the platform.

Watch the other videos in the series: 

• Part 1 – Search Page
• Part 2 – Saved Searches

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Data is at the heart of scientific decisions. The Tetra Scientific Data and AI Cloud places the scientist's user experience at the forefront. With its intuitive search and filter features, scientists can easily access, organize, and analyze data.

In this demo (Part 2), you'll discover how to store and share search criteria for reuse.

Watch the other videos in the series: 

• Part 1 – Search Page
• Part 3 – Projects

Get a 5-minute recap of our webinar on how a modern, cloud-based platform and laboratory information management system (LIMS) can transform QC to meet the demands of expedited product releases. 

This webinar discussed how to:

• Move from a legacy LIMS to an advanced, future-proof, and compliant LIMS in the cloud
• Build a foundational data strategy for digital QC, enabling easier compliance and improved quality
• Empower data-driven decision making through dashboards and AI
• Accelerate batch release with improved right-first-time test execution
• Reduce total cost of ownership while increasing decision speed and confidence using a holistic approach to quality 

Watch the entire webinar on demand.

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Development scientists at a leading biopharmaceutical company use quantitative polymerase chain reaction (qPCR) to evaluate the impact of process changes on gene therapy products. These assays are crucial for chemistry, manufacturing, and controls (CMC). However, the team's manual-intensive data workflow significantly limited their throughput and data analysis capabilities.

The biopharma's leadership turned to TetraScience to streamline their qPCR process. The solution employs the Tetra Scientific Data and AI Cloud™ to automatically replatform and engineer the qPCR results into analytics- and AI-ready data.

As a result, the improved workflow:

• Increases assay throughput by up to 6x
• Minimizes manual steps, saving up to 1 hour per assay
• Enables meta-analyses across multiple experiments

This video demonstrates how structured UNICORN data can be analyzed and compared using the FPLC Data Explorer with the Tetra Data and AI Workspace. The Tetra Data and AI Workspace integrates data and data apps in a single environment, so scientists can analyze data intuitively and more efficiently than ever before.

With UNICORN data, scientists can gain valuable insights by visualizing, comparing, and analyzing chromatograms and peak alignments. Secure data storage, automation, and integration allows users to access data globally and significantly reduce non-value-adding activities.

This innovative approach has proven to decrease time to insight from 1 week to 1 day, accelerate basic tasks like overlaying chromatograms from 1 day to 5 minutes, and improve scientific workflow efficiency allowing the reallocation of about 5,000 hours per year of valuable resources.

A leading contract research organization (CRO) performs drug transporter assays for biopharma companies, providing crucial insights into the pharmacokinetics and potential drug-drug interactions of lead compounds. However, its scientists struggled with slow and error-prone data processes, impacting the scalability of these assays.

To address this challenge, the CRO partnered with TetraScience to streamline the entire workflow. The Tetra Scientific Data and AI Cloud™ automatically replatforms and engineers the data from each study, eliminating many inefficient steps.

As a result, the CRO has achieved:

• 50% decrease in manual data entry
• 80% fewer items for QA to review
• 3,000 hours per year reallocated toward increasing throughput

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How drugs bind to blood plasma proteins influences their bioavailability and efficacy in the body. At a top 25 biopharma company, scientists measure this interaction using rapid equilibrium dialysis followed by mass spectrometry (RED-MS). However, they were spending significant time on manual file preparation and statistical calculations, which slowed down their absorption, distribution, metabolism, and excretion (ADME) studies.

Discover how the Tetra Scientific Data and AI Cloud™ streamlined and enhanced their workflows by automatically preparing and processing data for RED-MS assays. The new solution:

• Cut data preparation and processing time from hours to minutes
• Centralized and contextualized data for rapid search and retrieval
• Eliminated manual steps for improved scalability and data integrity

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The flow cytometry core facility at a leading biopharma faced complex data challenges. Scientists struggled with data curation, while facility staff had difficulty monitoring instrument performance. Both groups needed more efficient ways to manage and analyze their data.

Tetra Sciborgs provided a way forward. They helped optimizing flow cytometry workflows with a science-led approach that combines the Tetra Scientific Data and AI Cloud™, deep domain expertise, and a library of scientific use cases and supporting artifacts.

With the help of our Sciborgs, the biopharma was able to:

• Redirect scientists’ time spent on manual data processes to more valuable work
• Collect and engineer data automatically in the cloud for rapid search and analysis
• Visualize and trend instrument calibration data, enabling predictive maintenance
• Recover over $50,000 per year through time savings

Data is at the heart of scientific decisions. The Tetra Scientific Data and AI Cloud places the scientist's user experience at the forefront. With its intuitive search and filter features, scientists can easily access, organize, and analyze data.

In this demo (Part 1), you'll discover how to:

• Navigate the platform’s search page
• Employ hierarchical data views
• Utilize advanced filtering and bulk operations to streamline your workflow

Dive in and discover how our search features can transform your scientific data journey.

Watch the other videos in the series: 

• Part 2 – Saved Searches
• Part 3 – Projects

Learn about the paradigm shift in scientific data management as organizations worldwide adopt modern strategies for analytics and scientific AI. This video introduces the shift from on-premises to cloud-based storage, the transformation from file-based to engineered data, and the move to an event-driven processing model. Learn how TetraScience's automated workspace and endpoint-agnostic data cloud empower multi-company collaboration, enabling scientists to transform data into insights that fuel advanced analytics, machine learning, and AI. Discover the future of data management today.

At a top 10 pharmaceutical company, scientists developing purification methods were bogged down by inefficient data processes. They struggled to consolidate analytical data from UPLC and connect it with upstream processing data from FPLC. This hampered chemistry, manufacturing, and controls (CMC) efforts.

The Tetra Scientific Data and AI Cloud™ transformed the workflow by enabling a seamless exchange of data between chromatography data systems (UNICORN and Empower) and Signals Notebook. Scientists can now compare multiple runs effortlessly, identify anomalies rapidly, and access historical data instantly.

Find out how TetraScience helped this pharma:

• Eliminate 25 to 100 hours per week of manual data transcription
• Reduce data search times by up to 8 hours
• Streamline report preparation, saving 10 to 20 days per year
• Speed up process development by predicting optimal purification conditions to maximize yield and purity
• Reclaim $375,000 per year in scientists’ time

How efficiently cultured cells can produce a therapeutic protein depends largely on the media. However, developing an optimal formulation is a complex challenge. The permutations of media components and their concentrations are immense. Scientists often have to screen dozens to hundreds of formulations in the lab to identify the most effective ones. Now, AI can drastically speed up this process.

The Tetra Media Formulation Assistant uses an advanced in silico model to predict the best formulations for a given cell line. This innovation has the potential to reduce wet lab experiments by up to 88 percent.

Despite its immense value, scientific data in chemistry, manufacturing, and controls (CMC) remains largely untapped. As a result, biopharma companies are struggling to accelerate timelines and reduce costs during late-phase development. A group of industry experts explored the root causes and how best to address them. This white paper summarizes their key findings.

Inside, you'll learn:

• The top 3 business challenges facing CMC and their underlying causes
• Key areas for future development within CMC
• The industry's readiness for electronic regulatory submissions
• Opportunities for using AI in CMC and their potential impact

Scientists in the Chemistry, Manufacturing, and Controls (CMC) division at a top-ten biopharma company specialize in developing methods for purifying biologics. However, they struggled with inefficient data practices. Manually assembling and analyzing heterogeneous sets of chromatography data (from FPLC and UPLC) was far from scalable.

Discover how TetraScience accelerated their development cycle by replatforming and engineering chromatography data. This analytics-ready data powers an in-house application to further streamline and enhance analysis.

The new solution enables the CMC team to:

• Redirect thousands of hours of manual data handling to model building
• Search and retrieve chromatography data faster and easier
• Streamline troubleshooting during scale-up to facilitate tech transfer
• Generate AI-native data for future AI applications

Discover how combining the Tetra Scientific Data and AI Cloud™ with AWS HealthOmics streamlines and enhances the analysis of next-generation sequencing (NGS) data. This solution provides a comprehensive framework for metadata management, facilitating the analysis of properly contextualized datasets.

Droplet digital polymerase chain reaction (ddPCR) is increasingly being used in the development and manufacturing of gene therapies. The technique can measure the quantity and quality of gene therapy vectors as well as evaluate cells treated with the therapy.

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In this solution brief, we explore the applications of ddPCR in gene therapy and the data challenges faced by scientists who routinely use the technique. We also show how the Tetra Scientific Data and AI Cloud™ streamlines and enhances ddPCR workflows, leading to faster and better scientific outcomes.

Biopharma scientists and IT struggle to ensure a seamless flow of data between an electronic lab notebook (ELN) and a chromatography data system (CDS). They often resort to inefficient manual processes. Consequently, only a fraction of the chromatography data and context is captured, leaving the rest siloed. To make matters worse, most CDS data is trapped in proprietary formats, making it inaccessible.

Bidirectional data workflows using the Tetra Benchling Connector and Tetra Chromeleon Agent solve these challenges and bring round-trip lab automation into scientific workflows. See the tools in action in this video.

For more information, read our blog post.

When a large biopharma needed to digitize their batch release workflows, eliminate manual data handling, and prepare their scientific data for downstream use in analytics applications and AI, they chose TetraScience.

The Tetra Scientific Data and AI Cloud™ will enable the seamless transfer of data between Signals Notebook, LabX, and Tiamo through integrations and advanced data engineering while simultaneously preparing data for downstream use in analytics and AI applications.

This global biopharma can expect:

• 40% increase in lab productivity
• 4x reduction in error rate
• Reduction in FTEs required for QC review
• AI-native data for prediction and troubleshooting

For the full story, read our 1-page customer story.

At Charles River Laboratories Hungary (SOLVO), scientists perform drug transporter assays to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of drug candidates. They follow a rigid and inefficient sampling scheme to generate dose-response curves, adding cost and time to projects.

Learn how TetraScience helped SOLVO streamline ADME/Tox screening with artificial intelligence (AI). By replatforming and engineering scientific data, the Tetra Scientific Data and AI Cloud™ supports the development and validation of an in silico model to optimize sampling and improve IC₅₀ calculations.

The in silico model, built in just 5 months, will enable SOLVO to:

• Cut the number of sampling points by half while improving IC₅₀ accuracy
  
• Reduce wet lab experiments by 50%
• Decrease manual data curation by 1.5 FTE

TetraScience employs a comprehensive, 3 pillared approach to regulatory compliance—adhering to essential industry guidance and standards, including:

• Good Manufacturing Practices (GMP)
• Good Laboratory Practices (GLP)
• Good Automated Manufacturing Practices (GAMP 5 2nd edition)
• Food and Drug Administration (FDA) 21 CFR Part 11
• FDA Computer Software Assurance (CSA) Guidance
• EU Annex 11

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Learn how TetraScience can accelerate your journey to quality and compliance by:

• Reducing validation effort by 80%
• Increasing ROI by 2-3x
• Slashing deployment time by two-thirds
• Simplifying change management

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Learn how the Tetra Data Platform collects files from scientific instruments and centralizes them in the cloud. This demo covers the following topics:

• Automated data collection
• Automated house cleaning
• Centralized scientific data cloud
• Automated notifications

Discover how the Tetra Data Platform collects data from applications, including chromatography data systems, ELNs, and LIMSs. This demo highlights:

• Automated data collection
• Leveraging of vendor APIs/SDKs
• Contextualization with metadata
• Advanced search

The biopharmaceutical industry stands at a crossroads. R&D costs are rising. Sales are falling. And the process from discovery to commercialization remains long and complicated. Many biopharma leaders believe artificial intelligence (AI) will reverse these trends, potentially revolutionizing the entire value chain. They’re placing big bets on AI through investments in technology, people, and partnerships.

Explore the latest trends in Scientific AI by the numbers.

Biopharmaceutical organizations that want to speed therapeutics development and reduce costs through Scientific AI must bridge the gap between their scientific raw data and value-generating AI applications.

The Tetra Scientific Data and AI Cloud offers the only path to closing that gap. It is the factory for producing large-scale, liquid, compliant Tetra Data, which is purpose engineered for science and AI. The platform enables organizations to fully harness their data for Scientific AI, accelerating and improving scientific outcomes.

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Discover more in our solution brief.

Explore how the Tetra Data Platform contextualizes data to make it findable and accessible. This demo shows how the platform enables users to enrich primary data with metadata through various methods, such as

• Manual addition
• Agent configuration
• Extraction from file names
• Retrieval from registries or applications
• API

Biopharma leaders today are poised to capitalize on the power of artificial intelligence (AI) to speed delivery of new therapeutics and drive down fast-rising costs. But for many biopharma companies, there is a gap between the vision for AI and the present reality. They struggle to launch AI initiatives or capitalize on previous efforts.

Why? The answer centers on data. The vast volumes of data that biopharma companies generate and collect are not ready for advanced analytics, AI, or machine learning applications. Moving forward will require a revolutionary scientific data paradigm.

In this white paper, you’ll learn:

• The promise of Scientific AI in biopharma
• 3 primary obstacles to Scientific AI
  
• How to close the AI gap

High-throughput screening of genomic edits in mice is a complex, multi-step process. Each genetic modification requires unique reagents for detection by quantitative PCR. Read this customer story to learn how the Tetra Scientific Data and AI Cloud increased productivity through the automation and enhancement of sample records.

Challenge:
A top 20 biopharma company operates its qPCR-based genotyping workflow at 40% capacity due to limitations in sample tracking.

Solution:
The Tetra Scientific Data and AI Cloud enables per-well sample tracking by replatforming and engineering scientific data.

Result:
33% increase in genotyping throughput so far; expected to increase by 150% total soon.

Hear how the Tetra Scientific Data and AI Cloud can help organizations derive more value from their chromatography data. This webinar video shows how organizations can use the Tetra Scientific Data and AI Cloud to help create a system suitability dashboard with TIBCO Spotfire to identify instrument issues and avoid misleading experimental results. It also shows how the Tetra Scientific Data and AI Cloud can simplify the search and analysis of data generated by chromatography data system (CDS) instruments, such as Waters Empower.

Creating scientific data for analytical development

Heterogeneous data formats prohibit advanced analytics and AI

The pharmaceutical industry is experiencing a surge in scientific data due to the growing complexity of new therapeutic modalities and the urgency to bring improved therapeutics to market more efficiently. However, this valuable scientific data is often trapped in on-premise instrument control and acquisition software, compounded by the challenge of dealing with diverse vendor-specific data formats, thus inhibiting the potential of AI.

Is engineered scientific data the force multiplier for AI?

In this webinar, we will explore a use case involving a leading pharmaceutical company that aims to provide its scientists and engineers with analytics and AI-ready data across multiple laboratories, departments, and facilities.

By making this data available, scientists can leverage prior work to inform their actions and improve scientific outcomes. Moreover, they can identify and accelerate the most promising scientific strategies , leading to substantial time and cost savings.

This webinar will address several crucial aspects of maximizing the value of scientific data in the biopharmaceutical industry, including:

• Centralizing and harmonizing scientific data
• Improving collaboration and data sharing
• Leveraging Semantic AI in biopharma
• Managing diverse taxonomies efficiently
• Automating data preparation and engineering

Don’t miss this opportunity to discover how your organization can maximize the value of your data and accelerate scientific advancements through AI.

Get the essentials from an expert discussion on AI featuring AWS and TetraScience.

Artificial intelligence (AI) stands on the brink of revolutionizing the biopharmaceutical industry. It promises to slash time to market, lower costs, mitigate risks, and create novel therapies. However, the main barrier to realizing these benefits is not the development of AI models but rather the quality and accessibility of the scientific data that feeds them.

The recent webinar "Scalable data strategies for scientific AI-enabled breakthroughs" featured insights from industry experts Naveen Kondapalli, Senior Vice President of Product and Engineering at TetraScience, and Himanshu Jain, Healthcare and Life Sciences Strategy Leader at AWS. They delved into the data challenges underlying scientific AI and discussed strategies to overcome them.

In this recap, we summarize four key takeaways from the conversation that will jumpstart your AI journey.

A top 20 global biopharma needed to integrate 350+ scientific instruments into a centralized data environment, but each integration required five minutes of manual configuration. Learn how the company paired Atlassian Jira project management software with the Tetra Scientific Data and AI Cloud™ to automate the configuration of Tetra File-Log Agents and achieve 25x faster instrument onboarding.

Surface plasmon resonance (SPR) enables scientists to measure molecular interactions between a potential drug and its target. Read this customer story to learn how the Tetra Scientific Data and AI Cloud accelerated lead characterization by replatforming and engineering SPR data.

Challenge:
A top 10 biopharma company performs manual QC of SPR data for lead characterization. The process takes up to 80% of scientists’ time, throttling assay throughput.

Solution:
The Tetra Scientific Data Cloud streamlines the entire QC process by automating data ingestion, engineering, and publishing.

Result:
Over 3x higher assay throughput and better quality data.

Ginkgo Bioworks is pioneering new ways to design biology. The company’s innovative cell programming platform enables scientists to edit DNA and produce biological designs that can identify new therapeutics for pharmaceutical companies, enable agriculture companies to protect crops from pests, and more.

Learn how the Tetra Scientific Data and AI Cloud™ enabled Ginkgo to optimize lab operations and allowed software engineers to focus on high-value work while also helping the company prepare data for analytics and AI applications.

Meet Katja and Tal, two of our Sciborgs currently helping customers accelerate scientific outcomes with the Tetra Scientific Data and AI Cloud™. In this video, you'll learn how they improved bioassay development and high-throughput screening for two biopharma customers.

The engagement with Tetra Sciborgs is a key part of our Tetra Catalysts offering. Our Sciborgs are an elite team of scientific business analysts and scientific data architects embedded in your organization who focus on business outcomes and identify high-impact areas to maximize ROI.

For a detailed look at our Sciborgs, read our blog post on how Sciborgs Drive Better Science.

Learn how the release of Empower Agent v5.2 by TetraScience improves agent performance, reliability, and scalability as you ingest data from the Waters Empower Chromatography Data System (CDS) into the Tetra Scientific Data Cloud™. New capabilities provide scientists with significantly faster access to data and reduce the likelihood of processing errors—even as organizations scale the volume of chromatography projects.

This video highlights key features and enhancements, including:

• Horizontal scalability
• Project prioritization
• Maximum number of projects per scan
• Agent connectivity monitoring
• Ingestion of project and user access permission data

Learn how Tetra Data Platform v3.6 makes it easier for scientists to find their data on the TetraScience platform. In particular, this video shows how scientists can more easily query for multiple terms. It also shows how they can select elements within Intermediate Data Schema (IDS) files, search for those terms, and find all IDS and raw files that contain those elements, providing greater visibility into raw data than before.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Learn how Tetra Data Platform v3.6 streamlines the process of creating custom pipelines that transform raw scientific data into AI-ready Tetra Data. This video provides two examples of custom pipelines: one adds labels to data; the other also harmonizes, further enriches, and then pushes data to a third-party application. For both examples, the video shows how newly introduced protocol.yml files help simplify pipeline development.

To learn more about the new features and enhancements in v3.6, read the full release notes.

See how the file journey timeline capabilities in Tetra Data Platform v3.6 enables platform administrators to visualize file activity in various stages of processing. Using health monitoring, file information, and events timeline pages, users can track Tetra Data creation at any moment and trace files to confirm data integrity.

To learn more about the new features and enhancements in v3.6, read the full release notes.

See how the pluggable connector framework provided with Tetra Data Platform v3.6 enables faster integration of scientific instruments and applications with the Tetra Scientific Data Cloud. Teams can take advantage of newly released connectors without having to wait for a platform upgrade. This video shows how a user can install a new connector in the cloud, configure the connector, and then later upgrade the connector to a new version as soon as it’s available.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Explore some of the new features and enhancements in Tetra Data Platform v3.6 designed to benefit scientific data engineers. This video shows how low-code DataWeave scripting allows engineers to build data pipelines with fewer developer resources than building custom protocols. It also demonstrates how bulk data processing makes it easier to reprocess files after changes to pipeline functionality.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Capitalizing on the power of AI for biopharma requires a new approach to data. In this webinar presented by TetraScience and Körber, hear how your company can collect scientific data from all sources across the product life cycle and then transform it into AI-ready data. And you’ll hear how a top 25 global biopharma company is leveraging scientific data in the Tetra Scientific Data and AI Cloud to gain real-time process intelligence to understand sources of variability.  ‍

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You’ll also learn how to:

• Generate new insights by visualizing and analyzing data across multiple teams
• Employ advanced analytics to define a scientifically sound chemistry manufacturing and controls (CMC) strategy
• Find the root causes of process deviations by implementing process design and modeling best practices
• Automatically generate reports to facilitate new drug application (NDA) or biologics license application (BLA) filing, good manufacturing practices (GMP), and continued process verification (CPV)

For a few highlights from the webinar, read this Q&A blog post with industry experts and webinar participants Prof. Dr. Christoph Herwig, senior scientific advisor, Körber Business Area Pharma, and Ken Fountain, vice president, scientific applications, at TetraScience.

Artificial Intelligence (AI) has the potential to transform the biopharmaceutical value chain. However, its utility across the enterprise is hampered not by the science but by the data – its quality, quantity, and accessibility. As the volume and complexity of scientific data surge, and with the arrival of generative AI and large language models (LLMs), it has become even more challenging to reduce the gap between raw scientific data and AI-enabled outcomes that are accurate, reliable, and unbiased.

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In this webinar, we’ll show how a cloud-native platform, purpose-built for science, and powered by AWS, creates large-scale, liquid data sets that can fuel generative AI models. Migrating raw scientific data to the cloud is insufficient for the meaningful utilization of AI. Only by replatforming and engineering data at scale are biopharma organizations able to deploy accurate LLMs that can radically accelerate and improve scientific outcomes. We’ll also share real-world use cases where generative AI has created novel breakthroughs.

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IN THIS WEBINAR YOU’LL LEARN HOW TO:

• Assess the generative AI readiness of your data architecture
• Replatform and engineer scientific data in the cloud using a centralized or federated strategy
• Enable rapid consumption of engineered scientific data while optimizing cloud performance, scale, and cost
• Securely develop and deploy generative AI models to derive scientific insights across multiple use cases such as integrated omics, protein binding, and formulations
• Utilize large datasets with LLMs for predictive analyses in toxicology, and in silico ADMET
• Ensure privacy, security, and transparency through generative AI services

Learn how the Tetra Scientific Data and AI Cloud can help streamline upstream bioprocessing by automating the collection, transformation, and publication of data. As this video explains, the Tetra Scientific Data and AI Cloud can reduce data management burdens and help ensure data integrity while also facilitating file searches and enabling the consumption of data in a variety of endpoints, including analytics and AI tools. The video shows how a real-time TIBCO Spotfire dashboard built on the Tetra Scientific Data and AI Cloud can help scientists visualize and analyze experimental results to generate new insights rapidly.

Discover how biopharma teams can automate data processing and unlock new data-driven insights with the Tetra Scientific Data Cloud. Through a plate reader demo, this video describes how the Tetra Scientific Data Cloud harmonizes and enriches raw instrument data. It can then automatically correlate sample information with scientific results, eliminating the time-consuming, error-prone process of copying and pasting data.

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The video also explains how teams can translate scientific data into insights using visualization tools built on the Tetra Scientific Data Cloud. In the demo, a visualization tool transforms plate reader information into heatmaps and IC50 curves, which can be used to accelerate discovery of unique drug candidates through data science analysis.

Discover how the Tetra Scientific Data and AI Cloud can simplify data management for high-throughput screening (HTS). This demo video shows how the Tetra Scientific Data and AI Cloud eliminates manual data collection and transformation, as well as the time-consuming process of constructing point-to-point integrations. Using the example of liquid handler and plate reader files, the video shows how the Tetra Scientific Data and AI Cloud automatically:

• Collects instrument files
• Adds relevant contextual information
• Harmonizes files into the vendor-agnostic Tetra Data format
• Distributes files to an ELN

Organizations can then easily reuse Tetra Data in additional analytics, data visualization, and AI applications.

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Learn how the Tetra Scientific Data Cloud can help organizations simplify data processing while employing electronic lab notebooks (ELNs), instrument control software, and visualization tools. Using a small-scale fermentation experiment as an example, this video highlights how the Tetra Scientific Data Cloud can help an upstream processing team reduce manual steps as they create an experiment in an ELN, add metadata, trigger a run, and then automatically capture results as harmonized data back in the ELN. Harmonized data can then be used in a visualization dashboard or other downstream analytics or AI applications.

TetraScience is joined by Clustermarket and WattIQ for a webinar discussing how to optimize resources with harmonized scientific data in your lab

Learn how the Tetra Scientific Data and AI Cloud augments the capabilities of the Waters Empower chromatography data system (CDS)—as well as other scientific instruments and applications. This webinar video explains how the Tetra Scientific Data and AI Cloud can:

• Automatically collect data from Empower
• Contextualize data
• Harmonize data into vendor-agnostic, AI-ready Tetra Data
• Verify data quality and enrich data
• Simplify searches for particular files and enable exploration of SQL tables
• Allow users to analyze data with tools such as TIBCO Spotfire
• Publish data to other data science, visualization, and AI applications

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Learn how the Tetra Scientific Data and AI Cloud streamlines data processing workflows, eliminating the tedious manual steps of transferring data from scientific instruments to electronic lab notebooks (ELNs). This demo video shows how teams can automatically transfer raw data from instruments to the Tetra Scientific Data Cloud; transform data into the standardized JSON-based Tetra Data format; and then easily initiate the automatic transfer of harmonized files to IDBS E-WorkBook using an integrated connector from TetraScience.

Learn how the Tetra Scientific Data and AI Cloud uses automation to eliminate error-prone, inefficient manual transfer of information between instruments and informatics systems. Using a titration experiment workflow as an example, this video shows how the Tetra Scientific Data and AI Cloud automates transfer of sample information, weight data, and titration experiment results between Signals Notebook and both LabX and tiamo instrument software.

Discover how organizations can eliminate manual steps during data processing through integration of scientific instruments and applications with the Tetra Scientific Data and AI Cloud. This video shows how scientists can create an experiment in the Benchling electronic lab notebook (ELN), send information to instrument control software, start a run, and then automatically extract experimental data. The Tetra Scientific Data and AI Cloud harmonizes data and passes it back to the ELN. Team members can track data movement in real time within the Tetra Scientific Data and AI Cloud interface.

Too many biopharma organizations spend excessive resources manually transferring data among instruments and applications, or constructing rigid point-to-point data connections. They waste significant time curating and preparing data for advanced analytics and AI. 

Learn how the Tetra Scientific Data Cloud can help you harness the full value of your scientific data and improve scientific outcomes by accelerating the scientific data journey, enabling end-to-end scientific workflows, and capitalizing on the power of AI/ML. 

This video provides a walkthrough of the Tetra Scientific Data Cloud, highlighting how it enables organizations to:

• Automatically collect and centralize data for easy data access
• Contextualize data with rich metadata for immediate data retrieval
• Standardize and harmonize data and metadata for comparison and visualization
• Prepare data for advanced analytics and AI

When Andelyn Biosciences broke ground on two state-of-the-art gene therapy CDMO facilities, they needed the optimal scientific data strategy. They decided to create a “Connected Plant” by leveraging The Tetra Scientific Data and AI Cloud™ across their entire enterprise.

Read this case study to learn how TetraScience and Andelyn successfully launched “The Connected Plant” where the Tetra Scientific Data and AI Cloud:

• Provides the data infrastructure, automation, and engineered data Andelyn requires to become “The Digital CDMO”
• Lays the foundation for AI-enabled workflows
• Powers an easy-to-use search for all assay data
• Consolidated every independent data story across the scientific organization
• Onboarded 100% of Andelyn’s legacy gene therapy data
  

Find out why Andelyn says “TetraScience is the cornerstone of 'The Connected Plant Strategy.'”

Learn how the Tetra Scientific Data and AI Cloud streamlines scientific workflows by automating the bidirectional movement of data between informatics systems and chromatography data systems.

This video shows how the Tetra Data Platform collects data from an electronic lab notebook (Revvity Signals), transforms that data into a standardized format, and writes a new sample set method, which can be used to run an experiment in a chromatography data system (Waters Empower). The platform then collects experiment results and harmonizes the scientific data so it can be easily integrated back into the ELN.

Learn more about Tetra Integrations and see a full list of current and upcoming releases.

In response to increasing demands on biopharmaceutical manufacturing facilities, many companies are looking toward “smart” factory, or Pharma 4.0, solutions. However, most biopharmas lack the data maturity to achieve smart factory capabilities.

The artificial intelligence (AI), advanced analytics, and enterprise interconnectivity that enable Pharma 4.0 principles cannot function without a data infrastructure that provides large-scale, liquid, purpose-engineered, and compliant data.

In this white paper, we review why data maturity is no longer an option for biopharmaceutical manufacturers and how the four data tenets of Pharma 4.0 enable data maturity, which enables manufacturing plant maturity. We then discuss precisely how mature data feeds into numerous smart factory use cases, including:

• Optimized tech transfer
• Collaborative data supersets
• Advanced process controls
• Continuous manufacturing
• Continued process verification
• Digital quality monitoring
• Recursive data exchange
• Real-time release testing
• Agile manufacturing
• Predictive maintenance

If you want to know how TetraScience can help you achieve data maturity, contact our team or request a demo today.

The purpose of this document is to outline the Verification and Validation (V&V) strategy employed by TetraScience to ensure the Tetra Data Platform (TDP) performs as intended against user requirements. By aligning with this strategy, customers can leverage TetraScience deliverables to significantly reduce in-house validation efforts. It should be noted that a customer purchasing this package is still responsible for defining and ensuring the “last mile” validation of their own specific configuration of the platform, as well as defining sufficient standard operating procedures to operate and secure the system.

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Flow cytometry is a powerful tool to screen and optimize protein therapeutics. But the raw data is complex and requires preparation before downstream analysis. Performed manually, this process creates a bottleneck for high-throughput approaches.

Facing this challenge, a leading biopharma company looked to increase the throughput of its antibody screening method by streamlining the scientific data workflow.

Read the case study to learn how TetraScience helped this company achieve:

• 25X faster data preparation, including gating and QC
• Up to 48 hours saved per screen
• Over 3X screening throughput by supporting a multiplexing approach
• Higher-quality data, centralized and searchable in the cloud
  

The outcome was faster, better lead identification without increasing instrument footprint or personnel.

Biopharmaceutical organizations need a robust approach to maintaining data integrity. The value, volume, and variability of their scientific data demand it.

ALCOA++ is a set of principles used in the life sciences and other regulated spaces to ensure data integrity. Learn how the Tetra Scientific Data and AI Cloud™ upholds ALCOA++ throughout the entire scientific data journey, from data collection to analysis.

A few years after TetraScience decided to build an Intermediate Data Schema (IDS) on JSON, Allotrope Foundation developed the Allotrope Simple Model (ASM), which also uses JSON.

If your organization is interested in adopting the ASM, you can use the Tetra Scientific Data and AI Cloud to transform your data into the ASM at scale. To fine-tune this process, TetraScience collaborated with a top 25 pharma company to integrate primary lab data into the cloud, engineer the data into our IDS, and then transform data into the ASM.

Download this fact sheet to learn how and why to transform data into the ASM by first converting files to the TetraScience IDS.

Alexion, AstraZeneca Rare Disease, needed a technology partner to streamline a bioprocessing purification workflow so they could advance their analytics capabilities and transform the lives of patients with rare diseases.

TetraScience successfully developed an automated, FAIR-data powered (findable, accessible, interoperable, reusable), analytics-enabled FPLC workflow that significantly improves time-to-insight and throughput for their downstream laboratory.

Read the case study to find out how TetraScience helped Alexion:

• Decrease time-to-insight from 1 week to 1 day
• Reallocate ~5000 hours per year to higher value work
• Increase FPLC throughput
• Create the lab’s first centralized, searchable, cloud data store
• Eliminate the operating costs associated with retrieving difficult-to-find data

Get a quick recap of our webinar exploring how dispersed scientific data can easily be accessed, enriched, and harmonized for analytics and AI/ML with the Tetra Scientific Data Cloud™.

Featured case studies include:

• High-throughput screening
• Collaboration with contract research organizations (CROs)
• Batch release
• Instrument utilization

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Watch the entire webinar on demand.

AI/ML-powered science is rapidly advancing biopharmaceutical science. However, biopharma companies have a problem. AI/ML is not possible without connected ecosystems, centralized data stores, and harmonized, enriched, analytics-ready data. In other words, biopharmas lack the data maturity AI/ML requires to function. That’s where TetraScience comes in.

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Read in this fact sheet, how TetraScience can help unlock your data and unleash your AI/ML with the Tetra Scientific Data and AI Cloud.

Digital transformation only matters if it delivers results. It takes the right combination of technology and know-how. Tetra Sciborgs have both. Learn how our end-to-end, "Cloud and Sciborg" offering benefits biopharma throughout the entire value chain.

Read the solution brief for a deeper dive.

Learn how Sciborgs identify the most painful processes for scientists and design solutions with the Tetra Scientific Data and AI Cloud™. Watch this webinar to learn how Sciborgs:

• Support you in the identification of suitable use cases to uncover new, high-impact insights
• Help you achieve faster, better implementation of scientific data solutions that enable all stakeholders (scientists, data scientists, data engineers, IT staff)​​​​​​​
• Guide you through replatforming to replatform scientific data to the cloud and leverage it for improved scientific outcomes

While hot air balloons and regulatory compliance may seem worlds apart, this video explores how the preparation, testing, and validation required for ballooning provide a compelling metaphor for ensuring compliance.

Learn precisely how TetraScience uses a structured process for quality and compliance controls. This includes: 

• A quality management system that determines if TetraScience can safely contain, hold, and manage data that is intended for GxP use downstream (ISO 9001)
• A software development lifecycle that ensures our business operates both appropriately and ethically and enables us to build our cloud services in a standardized, repeatable way
• A verification suite for function, unit, and downstream testing that ensures “fit for purpose” 
• End-to-end use case testing where TetraScience runs data through simulations of field usage (GAMP 5

Learn how the TetraScience partnership with PerkinElmer Informatics accelerates scientific insights for biopharmaceutical customers. This partnership connects the full PerkinElmer Informatics Signals Research™ Suite to the Tetra Scientific Data Cloud™, creating a single source for all laboratory information that benefits scientists and IT departments. By focusing on solving customer challenges, this partnership demonstrates how two leading companies work together to maximize scientific data for improved outcomes.

Meet Katja and Tal, two of our Sciborgs currently helping customers accelerate time-to-value with the Tetra Scientific Data and AI Cloud™.

In this video, you will learn about their background, what makes scientific business analysts and data architects unique, and why having Sciborgs work directly with scientists brings so much value to customers.

Tetra Sciborgs, like Tal and Katja, are an elite team of classically trained scientists with technology expertise and industry-leading experience in:

• Speeding time-to-value when deploying the Tetra Scientific Data and AI Cloud
• Identifying new opportunities to improve the use of your scientific data
• Augmenting science by leveraging industry best practices

Watch the video to meet our Sciborgs.

For a detailed look at our Sciborgs, read our blog about how Sciborgs Drive Better Science.

A new research survey of 500 biopharma executives, commissioned by TetraScience and AWS, reveals why life sciences companies need to rethink their data platforms. Replatforming scientific data to an open cloud platform while transforming the data to FAIR (Findable, Accessible, Interoperable, Reusable) principles significantly impacts three key industry drivers:

• Speed: Learning from experiments faster, reducing time to market for final molecules.
• Cost: Improving productivity, increasing operational efficiency, increasing return on investment (ROI).
• Risk: Improving safety, reducing errors, improving compliance with regulatory requirements

‍The speakers will also share insights and updates from the Bio-IT World Conference. Whether you’re just getting started or are developing the next phases of your replatforming strategy, this webinar will provide you with essential information and key insights.
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‍Learning Objectives:‍

• Competitive advantages to developing a scientific data cloud strategy
• Best approaches to getting started with replatformed scientific data in the cloud
• Long-term advantages, including moving into ML, AI, and advanced analytics

Collaboration is commonly the basis for scientific innovation. Collaboration between life science vendors is less common.

The success of the biopharmaceutical industry depends upon leveraging scientific knowledge derived from the best instrumentation and software available, which produce a multitude of disconnected data formats.  Biopharma customers are now demanding that the vendor industry work collaboratively to connect their scientific workflows.

This webinar will provide valuable insights for biopharmaceutical scientists, lab directors, IT professionals, and life science vendors who want to learn:

• How an open network of life-science vendors accelerates scientific outcomes
• Before and after impact on scientific workflows, such as protein purification and cell profiling by flow cytometry
• The cloud and data engineering technologies that enable data liquidity independent of instrument modality
• How life science vendors are replatforming, moving from a closed to open ecosystem model focused on maximizing the value of scientific data

High-throughput screening (HTS) offers the promise of identifying higher-quality leads faster and bringing ground-breaking drugs to the market. Unfortunately, the HTS processes at many companies rely on manual, error-prone processes to collect data sets, add critical metadata, and transform data to be usable by informatics applications and analytics.

You will learn:

• How scientific data challenges reduce the effectiveness of high-throughput screening workflows
• Ways the Tetra Scientific Data and AI Cloud™ automates processes, improves quality, and enables insights
• High-throughput screening scientific data best practices

This webinar features a product demo where you can see first hand how the Tetra Scientific Data and AI Cloud can help your organization with its HTS workflows.

Our speakers explore how science-based organizations are producing more data than ever before, and why digital transformation strategies to leverage this data, like AI and machine learning, require the right infrastructure of connected, searchable, and harmonized data.

The presentation features real-world case studies in high-throughput screening (HTS), contract research organization (CRO) data management, batch release and testing, as well as the digitalization of a contract development and manufacturing organization (CDMO).

Andelyn Biosciences is a biopharmaceutical CDMO, pioneering solutions that turn hope into reality™.

In this short video, Bryan Holmes, Vice President of Digital and Technology Solutions at Andelyn, discusses how despite advances in technology, many biopharmaceutical companies still rely on manual data processing.

See how Andelyn is solving this challenge by streaming data to the Tetra Scientific Data Cloud™. As a result, Andelyn can aggregate data faster, provide better insights, and accelerate development timelines and bring more treatments to more patients.

Biopharma executives need to accelerate discovery, advance better drug candidates, and deliver a more robust portfolio. This one-page guide features real-world case studies from leading biopharmaceutical companies demonstrating how the Tetra Scientific Data and AI Cloud™ has proven capabilities in:

• Solving immediate data problems and accelerating workflows
• Improving use cases across the value chain (HTS, chromatography, CDMO automation, etc.)
• Identifying hidden data problems that TetraScience can solve
• Reducing cost, improving productivity, and accelerating time to scientific insight

Fundamental breakthroughs in life sciences are possible for the biopharmaceutical organizations that fully harness their scientific data. Read our guide and discover how we can help.

Accelerating biotech and biopharma research, development, and manufacturing requires a purpose-engineered platform for ingesting and transforming data, making it Compliant, Harmonized, Liquid, and Actionable.

Co-sponsored by:

We spoke with 500 biopharma executives to discover how industry leaders plan to deliver better therapeutics to patients faster. Their answer was clear. Maximize the value of scientific data with cloud-enabled FAIR data to improve three key industry drivers.

• Speed: Learning from experiments faster, reducing time-to-market for final molecules
• Cost: Improving productivity, increasing operational efficiency, increasing return on investment (ROI)
• Risk: Improving safety, reducing errors, improving compliance with regulatory requirements

Explore our key findings by downloading this infographic or see the full research report.

Biopharma organizations continue to spend time and money managing scientific data scattered across disconnected silos, ad hoc file shares, and legacy solutions. Why not future-proof with one open platform for all scientific data?

The Tetra Scientific Data Cloud™, purpose built for scientific data, enables IT to say goodbye to countless hours spent maintaining dozens or hundreds of instruments and informatics software applications. With open and centralized data in the cloud, scientists are able to search faster and more easily, create new workflows, and gain insights from analytics, visualizations, and AI/ML. Unlock innovation with the Tetra Scientific Data Cloud.

Watch the video to see how the Tetra Scientific Data Cloud simplifies data management and unlocks innovation.

In order to help our customers maximize the value of the Tetra Scientific Data and AI Cloud™, TetraScience created Tetra Sciborg packages. These offerings combine a library of deployment-ready, Scientific AI use cases, supporting artifacts and deliverables, and the engagement of Tetra Sciborgs—experts in science, data, and AI. They provide best practices derived from long-term industry expertise to accelerate and improve scientific discovery, development, and manufacturing. They help customers improve lab productivity through data automation and Scientific AI, derive new insights, and accelerate scientific outcomes.

Read this solution brief to understand how Tetra Sciborgs can minimize the burden on your teams while maximizing the value of your data through Scientific AI as well as the ROI of your deployment.

A leading biopharmaceutical company was searching for a data solution that would help them accelerate biologics discovery and development, improve time-to-insight, and bring therapies to patients faster.

This case study explores how the Tetra Scientific Data and AI Cloud helped them accomplish their goals by:

• Automating CRO data ingestion
• Eliminating time-consuming manual data cleansing
• Accelerating .CZI image processing
• Streamlining HTS workflows
  

Read this case study to learn how TetraScience optimized large scale scientific data.

Findable, Accessible, Interoperable, and Reusable (FAIR) data helps life science organizations to drive innovation. This process requires collecting, transforming, and standardizing scientific data to make it searchable, ready for analytics, and able to flow freely among instruments and applications.

This ebook — Unlock the Full Potential of Your Scientific Data — describes how harmonized scientific data in the cloud can help biopharma companies make the most of their data.

You'll learn:

• The challenges of legacy data
• The steps in the scientific data journey
• The essential properties of Tetra Data

Download your copy today to gain a deeper understanding of how Tetra Data eliminates manual data processes and can help your organization gain insights to accelerate ground-breaking treatments.

Life sciences IT across R&D, Quality, and Manufacturing face several challenges that impede their ability to collect, manage, and distribute data to their teams. These challenges include:

• Scattered, disconnected silos that make accessing and utilizing scientific data difficult
• Maintaining and developing custom, point-to-point instrument and application integrations
• Time and expertise to build in-house data solutions

This guide for scientific IT provides a side-by-side comparison of data access, technology flexibility, and solution scalability before and after TetraScience. You will learn how the Tetra Scientific Data and AI Cloud addresses these scientific data challenges and enables you to achieve better and faster science.

Scientists face several data challenges that impede workflow efficiency and their ability to access and analyze experimental results. These challenges include:

• Scattered scientific data across instruments and applications
• Inaccessible or poor-quality experimental data
• Manual processes to move and prepare scientific data
  

This guide for scientists provides a side-by-side comparison of scientific data before and after TetraScience. You will learn how the Tetra Scientific Data and AI Cloud addresses scientific data challenges and enables you to achieve better and faster science.

Watch this webinar with Andelyn Biosciences, a full-spectrum CDMO, to learn about their critical initiative of a Connected Plant that will enable biopharma to replatform their scientific data and workflows to the cloud. This innovative connected approach represents a generational movement transforming the way therapeutics are developed, validated, and brought to market.

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Following the US FDA’s 21 CFR Part 11 regulation is a critical element for FDA-regulated industries to ensure compliance. In this document we assess the different sections regarding the compliance of the Tetra Data Platform with the requirements for Electronic Records and Electronic Signatures as defined in the guidance document. It helps identify the coverage and responsibility of each of these sections.

TetraScience teamed up with Pharma IQ to survey biopharma industry insiders and ask about their organizations’ strategies for cloud adoption, digital labs, FAIR data, and the use of artificial intelligence and machine learning (AI/ML).

The 2022 State of Digital Lab Transformation in Biopharma Report shows the importance of digitalization in the pharmaceutical industry and makes a strong case for cloud data platform automation. Industry leaders identify this move as critical in order to improve scientific outcomes and accelerate discovery.

Key findings include:

• 87% of survey respondents claim that maintaining point-to-point, DIY integrations is burdensome.
• 74% of biopharma organizations are replatforming their scientific data to the cloud to enable scale, resilience, and faster time to market.
• 49% of bench scientists' time is spent on manual data retrieval, cleansing, and transformation.

Basic data-to-cloud replatforming is accelerating, but life sciences organizations still face obstacles. About half of the respondents of our survey say that “less than half” or “none” of their data can now be considered FAIR.

Accelerate Digital Transformation in Biopharma

A cloud-based scientific data platform can enable life sciences organizations to speed lab digitization, provide FAIR data to accelerate research and scientific outcomes– adapting to their changing business needs. Download our full report and discover why these initiatives of data automation in the cloud are gaining momentum in biopharma.

A groundbreaking research survey of 500 biopharma executives reveals why life sciences companies need to rethink their data platforms. Replatforming scientific data to an open cloud platform while transforming the data to FAIR (Findable, Accessible, Interoperable, Reusable) principles impacts three key industry drivers:

• Speed: Learning from experiments faster, reducing time to market for final molecules
• Cost: Improving productivity, increasing operational efficiency, increasing return on investment (ROI)
• Risk: Improving safety, reducing errors, improving compliance with regulatory requirements

Executives say that replatforming scientific data to an open cloud platform would accelerate time to market by 65%.

Each one-day delay in getting a drug or therapeutic to market costs biopharmas an average of $1.26 million in lost revenue and/or loss of first-mover advantage.

Slowness to market and lost revenue are not the end of the story. Cloud ‘laggards’ spend 3.7X more on inefficient infrastructure compared to cloud leaders.

Download this report to calibrate your scientific data strategy.   Learn how utilizing FAIR data in the cloud is a game changer.

A leading biotech company was facing challenges with their flow cytometry workflows due to inefficient, manual processes that were time consuming, high effort and error prone.

This case study shows how this biotech organization was able to automate, simplify, and improve the quality of their flow cytometry workflows by addressing the pain points that include:

• Manual data manipulation
• High utilization of instruments
• Slow, complex, and risky data preparation processes
  

Read this full case study to learn about how to improve and accelerate flow cytometry workflows with the Tetra Scientific Data and AI 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.

In this solution brief, you will learn how the High-Throughput Screening Tetra Scientific Application brings cohesion to your HTS workflows by:

• Increasing screening throughput through end-to-end automation
• Eliminating error-prone, manual updates to inventory and registration systems
• Harmonizing data across multiple formats to enable analytics and visualizations, such as SAR and in-silico evaluations
• Providing a centralized historical record and scientific context to scientific data and results

Leading biopharma organizations are reimagining research, development, and delivery of life-saving therapeutics through automated data acquisition, wrangling, and transformation.

This white paper — Paving the Way to Automated Data Acquisition and Transformation — describes how this world of data science automation has become a reality.

You will learn:

• The benefits of findable, accessible, interoperable, reusable (FAIR) data in the cloud
• The importance of multi-point productized integrations
• The steps to automate the scientific data lifecycle

Why End-to-end Data Lifecycle Automation Is Critical

With an automated end-to-end data lifecycle, harmonized scientific data flows freely throughout the biopharma data pipeline to accelerate innovation and support quality. Decoupling instrument control from data publishing enables scaled-up data science automation.

We discuss these benefits in detail in our white paper:

• With an automated and remote control interface to interact with instruments, organizations don't need to worry about collecting data with the heterogeneous and fragmented nature of lab data systems.
• With automated data acquisition across different instruments and lab systems, scientists no longer have to manually locate, transfer, and clean the data.
• With data pipelines for automated processing and interactive analysis, the data is automatically prepared, analyzed, and presented to the researcher, driving faster decision-making.

The Tetra Scientific Data and AI Cloud automates the collection and transformation of data in a format that is compliant, harmonized, liquid, and actionable. See what a modern-day end-to-end data pipeline looks like with TetraScience. Read our entire white paper now.

As a process for separating components of a sample, chromatography is critical to modern pharmaceutical and biotech workflows. One challenge is the difficulty in aggregating data across multiple runs or across diverse in-house instrumentation. Manual data reporting and transformation between data formats are time-consuming and prone to errors.

Biopharma organizations are partnering with TetraScience to unlock the full value of chromatography data – and faster, more reliable quality control.

Our case study – Data-Driven Quality Control – describes how TetraScience helped a top 15 global biopharma to improve chromatography data analysis. You'll learn about our automated quality control workflows for:

• System suitability tests (SST)
• Column degradation checks
• Shelf-life for active pharmaceutical ingredients (APIs)

TetraScience ingested and processed high-performance liquid chromatography (HPLC) data into a FAIR format.

Researchers saved an estimated 8 hours/week in manual data gathering, visual comparison and reporting. Our client was also able to use existing runs to predict failures before they occurred while resolving inefficiencies in communication.

Enhance Your Data-driven Quality Control  

Read our case study for an in-depth look at how TetraScience has advanced chromatography data analysis in the cloud. We will review:

1. Cost savings in manual labor and consumables
2. Streamlined reporting between functions 
3. Harmonized data adhering to FAIR principles

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Most Contract Research Organizations (CROs) use their own data formats for standard assays, which can pose data aggregation challenges to biopharma companies. Being able to harmonize data from different CRO reports is critical to scale up absorption, distribution, metabolism, and excretion (ADME) testing of pharmacokinetics (PK) properties of drug candidates.

This case study demonstrates how TetraScience helped two clinical-stage startup companies get the most out of their CRO collaborations for ADME/PK needs.

It covers:

• The challenges of laborious manual data workflows for pharmacokinetics and pharmacodynamics (PK/PD) studies. Scientists have to manually check the reports from CROs.
• The benefits of an optimized process that connects, parses, and harmonizes ADME/PK reports from various CROs.

Harmonized data allow visualizations of structure activity relationships (SAR), data science, artificial intelligence, and machine learning (AI/ML). By automating CRO data entry, processing, and transfer, scientists save time and reduce manual errors. With automated data management, CRO outsourcing can help biopharma companies realize more cost-effective results. Read the full case study to learn more.

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The clinical-stage biopharmaceutical company Prelude Therapeutics (PTx) deployed the Tetra Data Platform to eliminate manual data processing and accelerate scientific discoveries.

This case study describes:

• The challenges of manual collection, transfer, and manipulation of data for the integration of PTx instruments & software
• The benefits of scientific process automation and workflow management with the Tetra Data Platform (TDP)
  

TetraScience built a cloud-based data processing solution to automate Tecan, Titian, Dotmatics, and PerkinElmer Envision workflows with upstream data science applications. Our vendor-agnostic integrations enabled scientists to access all of their instrument data, information of informatics software, and scientific data visualization in one place. No more need to move and update files from multiple locations.

Learn how to reduce manual tasks by automating workflows, enabling scientists to focus on high-value activities like developing new cancer treatments. Read the full case study.