Pharmaceutical Case Studies that Illustrate a Data Strategy for Developing Better MedicinesSeeq® to Redefine Your Development Process

Data Rich, Information Poor. As with many industries, pharmaceutical and biopharmaceutical scientists and engineers at the R&D, pilot, and production scales, need a new way to harness the power of the data gathered. These industries need a new way to drive innovation through an enhanced data strategy.

Crucial elements for enabling pharmaceutical process innovation include:

• Understanding the key physical situation or process
• Identifying the right process analytical technology to obtain the required data
• Connecting disparate data sources
• Implementing data analysis and visualization applications that make it easy to analyze and make changes, to improve quality and quantity of medicines

With a streamlined process development vision, including the right data management strategy and analysis applications in place, opportunities abound to dramatically improve the quality of processes and the resulting medicines, including:

• Reduced equipment cycle times and cost-saving predictive maintenance
• Development of game-changing data-driven models relating product quality to key process variables. This includes support for a proactive first principles-based process model development approach, essential for supporting regulatory filings
• Streamlined assessments of new process analytical technologies by rapidly getting insight into your process
• Automation of repeated analysis tasks and reporting for future reference
• Establishment of a truly collaborative problem solving environment

Developing better processes begins with having a broad process development vision, including a well-defined and well-understood physical situation. Without this vision, there is a slim chance that the right data will be available for analysis. In addition to understanding what data is critical, it is equally important to have the appropriate automation of sampling, electronic storage of data, and connectivity between historians and other data repositories (e.g., SQL databases).

Next, choosing the right data management and visualization components is key. How many times have you wanted to be able to investigate an important issue, or to be proactive at designing a new process, only to stop because you don’t have the data you need? Do you often decide that the pain of gathering the data, that you know exists somewhere, doesn’t warrant the time and attention it takes to pull it together?

Pharma and biopharma plants often possess all the data they need to improve operations within their various data repositories, including historians, databases, etc.. However, creating insight from this information can be difficult, expensive, and time-consuming using traditional data analytics tools. Too much time and effort goes into simply getting the data into a format to be evaluated, usually resulting in frustration and a lack of thorough analysis.

With a facile data strategy, the world looks different. Imagine having the ability to easily search and interact with past and present time-series data in a “google-like” fashion and collaborate in real time. Imagine being able to make business critical decisions with more confidence, simply because you have the data in hand.

It is important to ensure that your data management applications provide:

• A strong connection with your data. Historians typically reside near the sensors and equipment. LIMS and other data stores are often spread across your business network. The right software, like Seeq®, makes it easy to connect to everything and view it all in one place.
• Automatic indexing of the sensor names or tags in the historian to make them easy to search and access the related data.
• A comprehensive connection to other data sources for the data points of interest.
• Streamlines future analysis in a way that facilitates collaboration. Work can be stored for reuse or shared with colleagues, either as a way to capture expertise, or in real-time to enable distributed discussions across an organization.

In this webinar, we will focus on specific pharmaceutical case study examples that illustrate how to drive innovation through an enhanced data strategy. Our expert speakers will discuss how you can:

• Uncover important issues related to bioreactor scale-up, including quick comparisons of key process steps across multiple batches.
• Quickly calculate and compare durations of sensitive growth periods (e.g. during a temperature ramp).
• Rapidly deploy filtration flux calculations and rapid evaluate chromatography curves to easily visualize downstream processing data, right alongside other process data.

Join us for a chance to learn about a data strategy that enables you to redefine your business.

Speakers

Lisa J. Graham, Ph.D., P.E., CEO and Founder, Alkemy Innovation Inc.

Dr. Lisa Graham is a registered professional chemical engineer with 20 years technical and business experience focused on Pharmaceuticals, life sciences and specialty chemicals. She has a strong business background developed through COO, SVP, and CEO leadership positions, including development of new business divisions to support company diversification strategies. Technical strengths include: Chemical engineering expertise, applied mathematics/process model development, product development expertise, process analytical technology applications, and guiding application development for enabling streamlined data analysis and visualization of results.

Lisa holds a B.S. degree and Ph.D. degree in Chemical Engineering from Oregon State University. She is the author of 12 scientific papers and 21 professional presentations, with 2 patents pending. She is also actively involved in supporting higher education and specific STEM* education initiatives. (*STEM: Science, Technology, Engineering, Math)

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Brian Crandall, Analytics Engineer, Seeq

Brian Crandall has 16 years experience in process automation, in a variety of roles, each with an emphasis in biotech and Pharma. As a project engineer, he architected batch control strategies for several large-scale commercial production facilities. During his software engineering role, he developed applications to analyze batch programs and historical process data. As a product marketing manager he focused on data integration between the control system and business platforms to improve manufacturing efficiency. His current passion is providing data analytics to accelerate production and empower operators, engineers, and data scientists.

Brian holds a M.S. degree in Software Engineering from the University of Texas and a B.S. degree in Engineering from the Colorado School of Mines. He currently lives in Austin, TX.

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