January 2009

INDUSTRY INSIGHT
Business Resolutions for the Year Ahead
By Robert Di Scipio, Aegis President & CEO

At the recent 2009 JP Morgan Healthcare Conference, presenting executives expressed relief that the life sciences industry is not taking the brunt of the global recession. But they also stressed the need to improve operating efficiencies in areas such as clinical trial management, product and process development and manufacturing. 

ARC analyst, Janice Abel, agrees that, to meet the current economic challenges, “industry needs to improve innovation, introduce new technologies, and ultimately improve efficiencies.” (see full article). We at Aegis expect to see even leaner supply chains, more contract manufacturing activity and a laser-like focus on ROI from new investments as well as leveraging existing technology for greater returns.

For a decade, Aegis has helped drive innovation and improved efficiency with our products, services and domain expertise in both life sciences and complex manufacturing environments.  We’re resolved to work harder to help customers demonstrate the financial benefits from the investment made in our technology – such as reduced headcount allocated to Annual Product Reviews and data retrieval, improved tech transfer, reduced process variability, better quality compliance and enhanced value from existing IT infrastructure investments.

To demonstrate the tangible returns Discoverant can offer, I encourage you to try out the Discoverant ROI Calculator – and see the impact Discoverant can have on your organization’s bottom line.

If Quality by Design (QbD) is part of your company’s strategic plan, be sure to read “Bridging the Gap Between Process Development and Manufacturing,” – a topic Aegis will address at several industry events this year.

AEGIS INTELLIGENCE
Discoverant 3.3 Debuts

Discoverant 3.3, the latest version of Aegis’ process intelligence solution, is now available. The new release provides many improvements to users and their IT colleagues – including enhanced modeling functionality, communication, configuration management and analytical capabilities. These enhancements, coupled with Discoverant’s existing capabilities, will increase an already significant return on users’ investments.

“Manufacturing Intelligence, like Discoverant, goes beyond simple data aggregation and visualization to take users to the next level of value,” said Tom Fiske, Ph.D., senior analyst, ARC Advisory Group. “It not only provides users with greater insight and understanding of complex processes, but it also creates a solid foundation for designing quality into processes and ensuring quality during production.”

Highlights of the 3.3 release include:
Statistical Modeling:

• Multi-Factor ANOVA – Primarily used in process development, this capability allows users to test and experiment with the most critical combinations of process factors. As a result, users can understand potential impacts on the design space and final product quality.
• Non-Parametric Smoothing – A smoothing technique that provides a more accurate analysis of non-normal data.
• Normality Plotting – A visual representation of non-normally distributed data which enables users to determine the appropriate statistical and quality monitor functions to apply.

Communication Management:

• Automated Alerts – Discoverant includes the ability to automatically scan hundreds of control charts instantly and at the time they are created or refreshed. Email alerts are sent when the results meet pre-defines criteria thereby allowing users to benefit from the efficiencies of “review by exception.”

Configuration Management:

• Hierarchy Verification Utility (HVU) – One of the key advantages of Discoverant is its ability to collect, organize and analyze data across disparate systems. The HVU module accelerates implementations of Discoverant by providing an automated capability for entering, managing and verifying test data.

• Paper Record Input ManageR (PRIMR) Enhanced Reconciliation – Rather than reviewing all of the manually-entered data associated with a batch or process, reconcilers can be assigned to individual pages, relating to specific steps in the manufacturing process, which makes the data available more rapidly.

Already a Discoverant User? Your Aegis account representative will be contacting you in the following weeks with more information on how to upgrade to 3.3, including dates/times for web seminars that will showcase new product features.

Interested in Becoming a Discoverant User? Please email info@aegiscorp.com for more information.

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Aegis Adds Excellis to Partner Network
Excellis Consulting Corp., an IT consulting company specializing in business and integration services to FDA regulated industries, will work with Aegis’ life sciences customers to provide the advice and professional services necessary for a successful implementation of Discoverant. Learn more about Aegis’ partnership with Excellis at http://www.aegiscorp.com/newsandevents.asp.

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Industry Analyst Reports Include Aegis

• AMR Research: “From EMI to Operations Intelligence, Part 2: Choosing the Best Mix of Applications for Your Manufacturing Scenario,” by Alison Smith provides a synopsis of the diverse vendor landscape, offering a detailed matrix of capabilities organizations can use to mix and match vendor products in their overall EMI/OI architectures to help companies figure what mix of applications they should plan on assembling to satisfy their intelligence needs. 

“Aegis’s Discoverant product brings the sophistication of an enterprise-class statistical analysis tool to pharmaceutical and biopharmaceutical production environments. Unlike conventional statistical analysis tools that require staged data, Discoverant employs an underlying data model and device connectors to provide users with a central point of contextual access to disparate sources of process and product data.

The product features the ability to cache data for historical comparisons and data analysis, but also gives users the ability to run ad-hoc queries against extractions of data from historians as well as directly from PLCs and a forms-based, manual data entry interface. This capability makes it ideal for the real-time discovery of complex interactions between process variables and product performance.”

• ARC Advisory Group: “Pharmaceutical, Biotech, and Medical Devices Business Challenges and Strategies: Adapt or Die,” by John Blanchard and Steve Clouther, includes Aegis among software suppliers “expanding the functionality of their solutions to meet a rapidly growing manufacturing need.” Contact: info@arcweb.com

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QbD starts in process development where the best opportunity exists to design improved processes based on new process measurements and by taking advantage of previous experiences with similar processes. With the introduction of PAT as a tool for achieving QbD in pharmaceutical and biotechnology processes, the volume of electronic and paper-based data collected during process development and manufacturing has increased dramatically. A critical success factor for achieving the goals of QbD is, thus, to provide on-demand access to all of this data for end users in a collaborative analytics, trending and reporting environment in which the multidisciplinary team can collaborate productively. 

The most important requirements for this system are:

• A simple user-centric interface for direct, on-demand access to all the data from disparate sources
• A practical way to capture paper-based data and make it easily available in electronic form
• The ability to work with continuous (on-line) and discrete data together; sharing data, analysis results and reports across disciplines, scales of operation, and geographically dispersed sites
• Simplifying the preparation and distribution of analysis results
• Automating the generation of periodic reviews and reports of batches and campaigns.

To view a presentation by Justin Neway that speaks to this topic, along with how Discoverant meets the challenges, click here.

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HDPs reference other parameters in the Hierarchy via values entered in the ‘Link ID’ column. HDP formulas are entered as just the formula (no equal sign) with parameter references in double quotes (‘ ” ’) including the parameter name, parameter attribute and link ID. For example, a parameter returning the product of discrete Weight and Purity parameters would be configured as “Weight,0,10201”*”Purity,0,10202” if the Link IDs are 10201 and 10202 respectively. Functions also may be included in the formula, so a parameter returning the left three characters of the Lot Number might be left (“Lot Number,0,10103”,3).

The parameter attribute term in the formula is specified as 0 (zero) for discrete parameters and for the entire series of continuous parameters. The parameter attribute may also specify a summary value for replicate or continuous parameters, so a parameter returning the product of replicate Weight and Purity parameters would be configured as “Weight,mean,10201”*” Purity,mean,10202”.

Link IDs must be unique within a Hierarchy, so it is recommended to use five or six-digit IDs based on the location in the Hierarchy to avoid accidentally repeating IDs or needing to update IDs as development progresses. With longer IDs, each digit can distinguish a level of the Hierarchy (e.g., a 1 in the first digit might indicate the first Universe, a 2 – the second digit, etc. and the second digit may indicate the number of the node under the Universe node, and so on to the parameter level).

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Remember to stop by the CoP at www.discoverant.net to exchange ideas with industry peers. If you’re not currently a Discoverant user but would like to be considered for temporary access into the portal, please complete the registration form on the site.

Please let us know what you like about the Process Intelligence Post. Send your feedback and suggestions to Lisa Fairbanks: lfairbanks@aegiscorp.com.

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©2009 Aegis Analytical Corporation