Inference for Quality-by-Design

10/6/2008

Companies shifting to science-based manufacturing are taking their first steps by transitioning from quality-by-inspection to quality-by-design (QbD). From the FDA's viewpoint, the principles of QbD in pharmaceutical development are fairly straightforward. It requires establishing a clear linkage between the safety and efficacy of the drug product in the patient with its quality as defined by the attributes of the drug product and then linking it all the way back to the process for preparing the drug product. Specifically, QbD requires achievement of understanding at two levels as illustrated below:

Clinical Understanding, which establishes a link between the attributes of the drug product and safety and efficacy in humans; and
Process Understanding, which establishes a link between the attributes of the drug product and process parameters, process attributes and material attributes of the active pharmaceutical ingredient (API) and excipients that go into the drug product.

From a practical standpoint, process understanding, and the associated design space, entails:

identifying and explaining all critical sources of variability;
managing variability by the process via measurement and control of critical process variables; and
reliably and accurately predicting and controlling product attributes within specifications (achieve quality).

Sounds simple enough, but arriving at process understanding and putting it to work remains a tall order. Towards that end, the FDA suggests that modeling and simulation must play an increasingly important role in science-based manufacturing-that is, from its current supportive role in empirical-based pharmaceutical development to a central role in QbD-based pharmaceutical development. For modeling and simulation to fulfill this promise will require development and deployment of three critical elements:

A QbD Modeling and Simulation Infrastructure. An easy-to-use system for assembling, transforming, exploring, analyzing and reporting on QbD data.
A QbD Work Process. Guidelines and work processes for deciding where and how modeling and simulation should be carried out in QbD. Such work processes typically involves risk assessment, experimental planning, prioritization, and data analysis and documentation.
Organization and Culture. Establishment of integrated, multidisciplinary, multifunctional development teams trained in the use of QbD modeling and simulation for decision making.

Transitioning to QbD is reflective of a larger business trend aimed at fact-based decision making. In Competing On Analytics, Thomas Davenport of Babson College and Jeanne Harris of the Accenture Institute of High Performance Business outline a roadmap to company competitiveness by wielding analytics. Access to data is not enough. "Even if an organization has some quality data available, it must also have executives who are predisposed to fact-based decision-making." And, management support is critical. "A data-allergic management team that prides itself on making gut-based decisions is unlikely to be supporting. Any analytical initiatives in such an organization will be tactical and limited in impact."

Posted at 1:11 PM by Paul van Eikeren | Category: Process Understanding; Quality by Design; Design Space | Permalink | Email this Post | Comments (0)

1/28/2008

FDA Breathes New Life into Quality Issues

The FDA has recognized that product development is now the weak link in the “critical path” from scientific discovery to commercial product. In response, the FDA has instituted sweeping changes that are beginning to have a tangible impact on the way pharmaceutical developers and manufacturers conduct their business. FDA’s “Pharmaceutical Quality for the 21^st Century—A Risk-Based Approach” squarely takes aim at the current state and replaces it by the desired QbD state focused on product and process understanding.

Aspect	Current State	“Pain” to industry	Desired QbD State
Pharmaceutical development	empirical; typically univariate experiments	unscientific; relies on intuition; high risk of failure	systematic; multivariate experiments
Manufacturing process	locked down; validation on 3 batches; focus on reproducibility	ignores variation; high risk in moving to manufacturing	adjustable within design space; continuous verification within design space; focus on control strategy
Process control	in-process testing for go/no-go; offline analysis	slow response; lost batches; drug development delay	PAT utilized for feedback and feed forward in real time
Product specification	primary means of quality control; based on batch data	difficult to achieve right-first-time production	part of overall quality control strategy; based on product performance
Control strategy	mainly by intermediate and end product testing	delays batch release; low equipment utilization rate	risk-based; controls shifted upstream; real-time release
Lifecycle management	reactive to problems & OOS; post-approval changes needed	inefficient, costly processes; discourages changes	continual improvement enabled within design space

Quality by Design (QbD) provides “a framework for allowing regulatory processes to more readily-adopt state-of-the-art technological advances in drug development, production and quality assurance” and shifts focus from “quality by testing” to “quality by design”—that is, build quality into the process rather than rely on resource-intensive quality control systems to prevent defective products from leaving the factory. QbD practices are implemented at four levels:

· QbD Level 1: Process Understanding. Develop end-to-end process understanding based on multivariate analysis of designed experiments and/or historical data. Includes: (a) identification and characterization of critical-to-quality process parameters (CPP) and (b) identification of root causes of variability.

· QbD Level 2: Quality by Design. Design a process defined by a design space that is robust and where the variability is controlled.

· QbD Level 3: Monitor, Predict and Control. Monitor CPPs via off-line, in-line or on-line analyzers. Use real-time monitor feedback in conjunction with prediction to achieve process performance/quality supervision via real-time intervention of CPPs.

· QbD Level 4: Continuous Improvement. Use accumulating manufacturing data as the basis to modify and improve the process within the design space.

At this stage, the underlying concepts and rationale for implementing quality-by-design practices are well understood and accepted. Despite this progress, there remain critical impediments to QbD implementation including the following:

· From a practical standpoint, what comprises and how does one acquire process understanding?

· From a practical standpoint, how does one decide that a process parameter is critical?

· And most importantly, how do we know that the identified design space of the process links to the clinical design space of the patient? After all, the aim is to design a process that meets the needs of safety and efficacy for the patient.

These key questions will be the subject of subsequent posting.

Posted at 2:35 AM by Paul van Eikeren | Category: Critical Process Parameters; Design Space; Process Understanding; Quality by Design | Permalink | Email this Post | Comments (0)

12/13/2007

Pharmaceutical Manufacturing Wastes $50 Billion per Year

According to findings of the largest empirical study ever performed on the interplay of pharmaceutical manufacturing and the Food and Drug Administration (FDA), the pharmaceutical industry is wasting more than $50 billion a year in manufacturing costs—costs that could be better applied to lower prices or increased research and development. The study, conducted jointly by Olin School of Business at Washington University and McDonough School of Business at Georgetown University, received no funding from either the pharmaceutical industry or the FDA.

The goal of the study was to understand how the FDA regulates pharmaceutical production and to see where there may be conflicts that inhibit advances in manufacturing. The researchers collected data from 42 manufacturing facilities owned by 19 manufacturers. They studied the companies’ manufacturing performance in terms of cycle time, frequency of deviations, reasons for deviations, yield, and improvement rates on key manufacturing metrics.

The FDA, to its credit, has openly acknowledged that historical compliance prescriptions have had unintended side effects—namely, discouraging manufacturers from embracing new technologies and process improvements that address the findings above. The costs and risks associated with change, as a consequence of stringent validation and regulatory re-filing requirements, have been perceived by manufacturers as being too high. In response, the FDA has recently instituted sweeping changes that are transforming the way life science companies look at their research, development and manufacturing process engineering organizations. Central to these changes is the overarching Quality-by-Design paradigm, a centerpiece of the FDA’s “Pharmaceutical Quality for the 21^st Century—A Risk-Based Approach.”

However, despite the bad news regarding manufacturing inefficiency, the study did identify two positive factors that temper manufacturing inefficiency: lending support for implementation of Quality-by-Design initiatives by using a comprehensive solution like Inference for QbD. Specifically,

· Application of information technology correlated with superior manufacturing metrics. Companies that employed information technology to electronically track and report on manufacturing (people, processes and deviations) and centrally stored all their data, uniformly displayed superior manufacturing performance relative to those not using such information technology.

· Driving decision-making down the ranks results in higher overall manufacturing performance. This important goal is supported by Inference for QbD. Increased capacity for employees in lower ranks to make decisions directly correlates to gains in manufacturing performance. This is especially true when considering matters related to deviation management, lot failure, lot review and process validation.

Reference: J. Macher and J. Nickerson, “Pharmaceutical Manufacturing Research Report: Final Benchmarking Report,” McDonough School of Business (Georgetown University) and Olin School of Business (Washington University in St Louis), September 2006.

Posted at 1:35 PM by Paul van Eikeren | Category: Process Understanding; Quality by Design | Permalink | Email this Post | Comments (0)

QbD Viewpoint

10/6/2008

1/28/2008

12/13/2007