Good Reads: Half of FDA GMP Warning Letters Tied to Batch Failures

Opportunities abound for the adoption of best practices and better process intelligence in pharma manufacturing. A recent eye-opening analysis in In-Pharma Technologist revealed that in 2011 nearly half of all FDA Good Manufacturing Practices (GMP) warning letters sent contained references to the companies’ “…failure to thoroughly investigate batch failure.” The article also highlighted the warnings companies received for failure to establish or follow written policies and procedures.

Companies that establish best practices such as those found in a QbD framework not only decrease regulatory warnings and costly investigations but also reap many business benefits: faster time to market, lower cost of goods, more efficient tech transfer and scale up and many more. Learn more about how companies have embraced a process intelligence culture in our customer case studies.

-- Bob Di Scipio

Process Intelligence Outlook: Getting Products to Market Faster

If you attended last week’s Interphex Conference you may have noticed that the word “Process” showed up in many session titles. Process Understanding, Process Validation, Process Improvement and Process Intelligence appear to be moving from buzzwords to substantive, resourced initiatives in biopharma manufacturing.

Factors such as patent expirations, downward price pressure (from generics and legislators) and unpredictable clinical results have put pressure on process development and manufacturing operations to accelerate the commercialization of approved products with higher quality and  better margins. Many life sciences companies are realizing that investments in manufacturing technologies, the adoption of a Process Intelligence culture and initiatives such as QbD and PAT can help them achieve their objectives. In a recent PharmTech Talk article, Patricia van Arnum discusses the state of pharmaceutical manufacturing based on her recent Interphex experience:

“Driven by quality-by-design principles, companies are seeking ways to enhance their process understanding and build better control strategies based on that understanding in their manufacturing processes. Strategies for risk assessment and risk mitigation have been an important theme among the conference sessions as well as the tools, such as process analytical technology (PAT), which can be used to better that understanding.”

Biopharma companies are using IT tools such as Aegis’ Discoverant to help meet the afore-mentioned goals. As Gartner recently noted in its recent report on Cool vendors in Manufacturing Operations:

“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 access to disparate sources of process and product data…For life science companies, it helps enable a more efficient technology transfer and process scale-up.”

Learn more about the Discoverant data aggregation, analytics and reporting platform in Aegis’ presentation, A Process Development and Manufacturing Intelligence Platform to Enable Process Understanding, Continuous Quality Verification (CQV) & Real Time Release.

-- Bob Di Scipio

Process Intelligence Outlook: New Process Validation Guidance Fuels Need for QbD

It’s been more than a year since the FDA issued its new guidance, “Process Validation: General  Principles and Practices,” which describes process validation in three stages–Process Design, Process Qualification and Continued Process Verification. The guidance helps pave the way for companies struggling to implement a Quality by Design (QbD) framework, providing the necessary justification.  As stated in a recent PharmPro article: “To support a final quality assurance approach to manufacturing, it is the information and knowledge gained from pharmaceutical development studies and process characterization studies that lead to an effective quality control strategy, based on scientific understanding.”

This guidance represents a dramatic shift from the practice of demonstrating process validation with “three batches” to a much more programmatic, scientific-based approach to establish process understanding and apply it to greatly enhance product predictability and reduce risks. The article shares a useful framework to help guide companies through the transition to a QbD approach.

Companies that adopt QbD and implement best practices for data analysis to achieve a culture of process understanding will reap greater rewards, including:

• Reduced batch failures
• Lower final product testing and release costs and reduced operating costs from fewer deviations and investigations
• Reduced raw material and finished product inventory costs
• Faster tech transfer and regulatory approval of new products and process changes
• Fewer and shorter regulatory inspections of manufacturing sites

-- Justin Neway

We're Hiring - Analytics Intern

Aegis is looking for a talented, enthusiastic intern to join our Consulting Team as a full-time intern or contractor from June 1, 2012 to Dec. 31, 2012. The Analytics Intern will assist with implementing process monitoring systems, statistical investigation and provide general analytic consulting support using Aegis’ Discoverant software. This individual will work collaboratively with Aegis’ Consulting Team, under the supervision of a Senior Analytics Specialist, and may provide an entry-level individual with the unique opportunity to gain applied analytic experience in the life sciences industry.

Click here for full job description.

Good Reads: FierceBiotech’s Future of BioPharma Manufacturing report

Process and automation. These two themes jumped out at me when I read a recent report from FierceBiotech titled “The Future of BioPharma Manufacturing.” It highlights pharma’s slow pace of adopting practices and technologies that help “…control quality, save money and increase efficiency,” then notes areas of achievement and opportunity. The report points to semiconductor manufacturing as an example of an industry that has embraced process modeling and data analysis for manufacturing improvements and suggests pharma companies can follow suit:

“Ideally, companies can combine process modeling with other software and equipment that monitors processes in real time and offers ongoing advice about when and how to intervene to ensure quality output.”

The report also discusses the problem with paper – and I would add “spreadsheet madness” – which impairs quality control due to human error and a slow, reactive response time. With automation tools and practices in place business users reduce risk, save time, boost productivity – and avoid costly mistakes – through real-time process understanding. McKinsey & Co. notes: “Advanced real-time process automation technology – combining computer software, hardware, and machine controls – will help to deliver greater manufacturing flexibility, improved yields and better quality.”

Companies that embrace a process intelligence culture focused on people, process and automation represent the future of biopharma manufacturing characterized by risk reduction, leaner supply chains, science-based decision making and, ultimately, greater profitability.

-- Justin Neway