Pittcon, March 17-21, 2019 | ACD/Labs
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March 17-21, 2019
Pennsylvania Convention Center - West, Philadelphia, PA, USA

Related Materials

Related presentations, posters, and scientific talks from this event have been posted here for your reference. Please click the associated link to download.

Efficient Identification and Management of Degradant Data in Process Development, J. DiMartino, A. Anderson, S. Bhal

Looking forward to Pittcon 2019, A. Anderson
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Oral Session: 380—Quality Assurance, Regulatory, and Data Integrity
Room 121B

8:05 AM: Impurity Control Strategies Using Impurity Mapping with Dynamic Purge Factor Calculations
Andrew Anderson, Graham McGibbon, Sanjivanjit K. Bhal, and Joe DiMartino (ACD/Labs)
View Abstract

Impurity Control Strategies Using Impurity Mapping with Dynamic Purge Factor Calculations

Abstract Number: 380-1

Regulatory authorities expect pharmaceutical development to demonstrate process and product understanding according to Quality by Design (QbD) principles. The overarching goal of this is to ensure that medicines are safe and efficacious. To achieve these goals, control strategies must be developed which comprehensively assess, classify, and report process route development. Ultimately controlling process inputs and materials, their attributes, the design spaces around unit operations, methods, variability, and final product specifications. Impurity mapping lies as the foundation of this. While scientist have developed practices to gather this information, it remains a tedious manual process.

In this presentation we will provide an overview of a new software application developed specifically to address these challenges. LuminataTM offers the ability to construct process maps—allowing for visualization of the impurities at each stage of the route, and visual comparison of molecular composition across unit operations. This enables rapid assessment and decision-making around the effectiveness and efficiency of impurity control measures. The software also stores the context of the experiment and expert interpretations, and offers project teams the capability to perform both risk and comparative assessments. Connection of live analytical data with chemical entities enables easy visual confirmation of the veracity of numerical/textual interpretations and processed results without the need for access to multiple software applications.

Through the identification of process related impurities from well-managed and assembled analytical data, and impurity fate mapping with “real time” quantitative purge factor determination, the software empowers scientists to work with process data and develop control strategies more efficiently.

10:05 AM: Improving CMC Development Through Improved Decision-Support Capabilities
Jordan Stobaugh and Mathew Mulhern (AbbVie), and Andrew Anderson, Sarah Brooks, and Joe DiMartino (ACD/Labs)
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Improving CMC Development Through Improved Decision-Support Capabilities

Abstract Number: 380-6

The pace of CMC development has continued to increase with most programs moving to later stages of development facing acceleration pressures. This necessitates the rapid expansion of teams and a challenge to onboard and transfer knowledge to the additional personnel. Additionally, the number of programs actively under development at a given time has increased, placing an even greater burden on individual contributors. Complicating this process are the workflows and tools in place across the various organizations that comprise the CMC team of an asset. Each area, and frequently each department within an area, will have unique documentation systems and data streams. Decision support is hindered as resolutions to challenges are data-driven and this foundational data is located across a myriad of silos. Further exacerbating the challenge is a minimal integration of systems. Ultimately, ad-hoc tools, typically Microsoft Office-based, are created and re-created both within and across project teams. To solve this challenge software designed for impurity data management and control has been deployed which enables many needed capabilities. Improved visibility to process schemes and tracking of impurities are central to the system. Additionally, analytical data is attached to each entity within a scheme to facilitate review of current and historical data. Over 150 data formats are supported enabling the direct comparison of data, independent of source location. Ultimately, decision-support is improved, detailed understanding of a process is increased, and onboarding is accelerated.

Oral Session: 400—Strategies for High Throughput Analysis
Room 123

10:25 AM: Design to Decision—Enhancing the value of high throughput chemistry in drug development while minimizing the risks and bottlenecks associated with data management
Michael Boruta and Andrew Anderson (ACD/Labs)
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Design to Decision—Enhancing the value of high throughput chemistry in drug development while minimizing the risks and bottlenecks associated with data management

Abstract Number: 400-2

High throughput chemistry experiments have been around for many years and, with their associated robotics, have proven themselves as an effective method for screening many reactions or conditions while minimizing the use of materials. While the efficiency of the actual experiment execution has been improved, the means to transfer and manage information from design of experiment, material collection, stock solution creation, plating, execution of experiments, analytical data collection, data analysis and review, sample registration and submission, and reporting and archiving is only achieved by several different pieces of software. Using multiple pieces of software decreases the overall efficiency and creates risks where information needs to be manually associated or transferred. This presentation will examine some initial problems and results from several implementations of a solution designed to address the overall efficiency of these systems.


Poster Session: Process Analytical

10:00 AM–12:30 PM: Efficient Identification and Management of Degradant Data in Process Development
Poster #: 970-2
Joe DiMartino, Andrew Anderson, Sanjivanjit K. Bhal (ACD/Labs)
View Abstract

Efficient Identification and Management of Degradant Data in Process Development
Joe DiMartino, Andrew Anderson, Sanjivanjit K. Bhal (ACD/Labs)

Poster #: 970-2

During drug development, a large amount of time and effort is spent on chromatographic method development. Forced degradation studies (stress tests) are carried out to build the Stability Indicating Method. Efficient development of this requires a complete understanding of the manufacturing process and open communication between various interrelated departments—from process chemistry through to analytical R&D. Theoretical degradants are used by scientists for targeted analysis in forced degradation studies. Often this vital chemical information is disconnected from observed degradant data making the identification of impurities and degradants challenging and thereby slowing down the process.

In this poster we will present software which manages structural, analytical, and all process related data in a structured and searchable manner to facilitate inter- and intra- departmental communications. LuminataTM intelligently links chemical information with related live analytical data. In addition, it will also capture both observed and theoretical degradants (structures and meta data from predicted third-party software) in the same interface. Empowering scientists to conveniently review and analyze stress test data in a single application and identify degradants more efficiently.


Software Demonstration

12:00 PM: Supporting High Throughput Experimentation From Design to Decide
Demo Zone 1, Booth #3242

ACD/Labs 25th Anniversary Party at 2nd Story Brewing Co.