Events

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.

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.

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.

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.

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

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

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

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

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).

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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