2003NA How New Technologies Can Improve Compliance in ...€¦ · mentality on dealing with compliance. As U.S. demographics shift with the aging Baby Boomer population, drug prices

Copyright © 2003 World Batch Forum. All rights reserved. Page 1

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North American Conference Woodcliff Lake, NJ April 13-16, 2003

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How New Technologies Can Improve Compliance

in Pharmaceutical and Biotech Manufacturing

Justin O. Neway, Ph.D. Executive Vice President and Chief Science Officer Aegis Analytical Corporation 1376 Miners Drive, Suite 106 Lafayette, CO 80026 United States 303.926.0317 (phone) 303.926.1161 (fax) [email protected]

KEY WORDS

Quality, Manufacturing Intelligence, FDA, Compliance, 21 CFR Part 11, Process Analytical Technology, Profitability, Efficiency, Productivity, Technology

ABSTRACT While U.S. drug products are of high quality, there has been an increasing trend towards manufacturing problems, resulting in recalls, disruption of operations and drug shortages. Companies are missing billions of dollars of potential additional revenue, and necessary medications are sometimes unavailable to the public. By enforcing 21 CFR Part 11 and introducing additional new quality-related initiatives, the FDA is signaling a need for better compliance to ensure consistency and safety. The industry wants to comply, but many manufacturers lack the necessary capabilities, or are unaware that new technologies are available to meet the beefed-up regulatory demands and still operate efficiently and profitably.

Justin Neway, Ph.D., executive vice president and chief science officer of Aegis Analytical Corporation examines why these problems occur and how new technology can improve the manufacturing process. Drawing from more than 20 years of experience in pharmaceutical and biotechnology manufacturing and in the development and application of software solutions to quality compliance and operational efficiency, Neway offers insight into how new technology expands manufacturing intelligence to speed up critical decision-making tasks, improve operational efficiency and increase manufacturers’ bottom lines without compromising quality compliance.


Introduction In today’s pharmaceutical and biotechnology manufacturing environments, compliance has taken on new meaning. It once implied a system of warnings that required attention. Today, the Food and Drug Administration (FDA) is demanding a new focus on compliance. Recent headlines reveal continuing industry problems and new efforts by the FDA to reduce them. And when you look beneath the regulation jargon, there are new opportunities to improve manufacturing efficiencies as well as compliance in ways that benefit the bottom line rather than cut into it.

This paper explores the recent regulatory and industry changes that place new demands on manufacturers today. Then, it looks at newer technology solutions that exist to help navigate the current and future needs of life sciences manufacturing.

The FDA Perspective Since the FDAs last major revision of the regulations for Good Manufacturing Practices (GMPs) in the late ‘70s, several changes have occurred. The agency’s role in healthcare has grown as more drugs have been approved for the market, dramatically increasing the agency’s resource constraints. Advances in pharmaceutical sciences and manufacturing technologies require advanced knowledge and scrutiny of processes to ensure quality, so the need to train inspectors has grown exponentially.

As the industry has become more and more global, pressure has mounted for the FDA to monitor drug imports and work more closely with the European Union. The reliance on foreign imports is increasing, posing potential threats to product quality that the FDA must address.

Although U.S. drug product quality is high overall, there are increasing trends toward manufacturing problems, resulting in drug recalls, disruptions in operations and drug shortages that affect revenues and consumer confidence. In 2001, in fact, 300 prescription drugs were recalled. Compliance creates huge challenges for manufacturing operations, and, unfortunately, this is often viewed as a hindrance to efficiency rather than as a way of improving it.

As a result of its resource constraints and the new industry environment, the FDA has introduced two important new initiatives. The first is an effort to guide the industry in applying Process Analytical Technologies (PAT) to manufacturing, and the second is a new quality initiative focused on the GMP regulation. Other noteworthy changes include moving some responsibility for approval of biological compounds to the Center for Drug Evaluation and Research (CDER) to ensure more uniformity of reviews and approvals, and welcoming new FDA commissioner Mark McClellan, who brings a history of innovation that is supported by the Bush administration.

Interestingly, when we look at trends in FDA enforcement since its systems-based inspection approach began in February of 2002, we see that 59 percent of the citations (483s) were issued for quality systems, production systems and lab systems. Fully 70 percent of the 153 observations in subsequent warning letters were issued in the same categories.1 The implication is that manufacturing is under FDA scrutiny, because the problems too often don’t get adequately addressed, especially in the areas of quality, production and lab systems. The data demonstrates an urgent need for the industry’s focus to turn to manufacturing.

1 Famulare, J., “PDA/FDA Meeting,” September 2002.


PAT In 2001, the FDA formed a subcommittee to design a philosophical and regulatory framework to guide the industry in applying PAT to measure and use quality indicators in real time during the manufacturing process. PAT allows on-line, non-destructive measurement, even potentially for individual tablets. Multivariate statistical treatment of manufacturing data is required to better understanding and control manufacturing processes. Thus, PAT has the potential to get more information into the hands of operating staff and decision makers more quickly, empowering them to improve quality compliance and global operational efficiency in a time frame that’s relevant to making the adjustments that can improve outcomes.

As a result of PAT implementation, greater control of product uniformity is expected. This will result in FDA inspection resource savings and some regulatory relief for manufacturing changes, which traditionally have been discouraged once a process has been validated. Another potential benefit is shorter cycle and batch release times thanks to less dependence on wet chemistry techniques, a capability to conduct multivariate specification setting and the cost savings associated with supply chain predictability, provided that the right supporting software technology is in place. PAT also has the potential to enable parametric product release, which can save costs and reduce inventory across the industry, as well improve process control and make continuous operations more practical. .

Pharmaceutical cGMPs for the 21st Century The PAT framework fits into an overall quality initiative designed to help better manage the FDA’s limited inspection resources, while assuring drug quality to the American public. The initiative goes beyond current Good Manufacturing Practices (cGMPs) to regulate product quality through a more scientific and risk-based approach.

The FDA, with participation from the industry, is evaluating systems for standards setting, submission, and review and inspections to determine objectives and effectiveness in achieving goals. Part of this evaluation involves assessing criteria used to prioritize FDA responsibilities, so that the most resources are applied to those areas with the greatest risk to public health. Combining a science-based risk management approach with quality systems inspection represents a new way of working for the FDA. This approach will force the industry to more tightly link product development to manufacturing. For example, capturing batch records early in the discovery process can provide data to strengthen control in manufacturing. Both the FDA and manufacturers will benefit from fewer post-approval surprises.

Another result is likely to be more intensive review of injectables manufacturers, with an emphasis on biotech, since this class of medications represents a higher risk to the public than oral dosage forms. The implications of the FDA’s focus on critical process control points as a greater risk area creates a need to better understand parameter interactions. And science-based review and enforcement necessitates that manufacturers be better able to show data and support conclusions on demand – not a simple challenge to address.

The Industry’s View Pharmaceutical and biotech companies are sizing up the FDA’s new approach and heeding warnings from competitors’ missteps. After a $500 million fine was handed to an industry powerhouse last year (2002), other companies are recognizing that return on investment in R&D and sales and marketing is


only realized when you can give a very high assurance to the FDA that a drug remains effective and safe during commercial operations. The new systems approach to inspections implies there will be less explicit guidance from multiple 483 citations, because the FDA will most likely only inspect one manufacturing site and it will be up to the manufacturer from there. If the same problem occurs at a different site, the FDA will treat this as cause for a warning letter even though the other plants in the system might not have been inspected or cited with a 483 for the same deficiency before. So a response to a 483 in one plant is expected to be carried out across the entire manufacturing organization, with no exceptions for other locations.

As if the regulatory environment weren’t difficult enough, pricing pressures impose a bottom line mentality on dealing with compliance. As U.S. demographics shift with the aging Baby Boomer population, drug prices are rising (e.g., a 17 percent increase occurred between 1999 and 2000). Reimbursement pressure on consumers from insurance companies favors generic drugs, and pressure from Congress favors competition, exemplified by President Bush’s support of the bill to limit patent extensions. Imported drugs increasingly add competition, when, for example, drugs from Canada can be purchased for up to 60 percent less than domestic products.

As these external forces impact product margins, companies also must cope with shrinking drug pipelines. They’re expected to get more profit out of fewer winning candidates. One solution that appeared to have potential was the promise of mergers and acquisitions to gain increased efficiency from operations. An enormous pressure exists to create centers of excellence, so that fewer manufacturing facilities are producing equal or greater revenue. This creates a burden on manufacturers in addition to that already imposed by the regulatory environment.

Technology offers both answers and challenges to the tougher environment global manufacturers now face. Advanced technology encouraged by the FDA holds promise, but with this promise comes a recent history of costly implementations and headaches with little near term ROI.

Closing the Gap The first major challenge as pharmaceutical and biotech manufacturers strive to comply with FDA regulations is the belief that a company cannot achieve operational efficiency without compromising consistent quality compliance. One must not come at the cost of the other, and today’s technology can help ensure that this is not the case.

Closing “the gap” between compliance and operational efficiency first requires understanding that although technology plays an important role, other commitments are also required. Imperatives that must exist within an organization include senior management’s philosophy, commitment and follow-through, staff-level commitment and follow-through, training and documentation, correct materials and workflows and, finally, compliant equipment and utility systems.

A familiar example of the wrong mentality within a corporate culture was brought to life last year, when one drug manufacturer was fined $500 million by the FDA. According to a now infamous whistleblower, “They indicated that there has been in the past a continual push for increased production and decreased downtime, sometimes at the expense of quality work and [FDA] compliance.”2

2 Simons, John, Fortune, “Schering Plough; Bitter Medicine,” Oct. 14, 2002.


This view of a trade-off creates “the gap”, which can be closed, in part, by technology. Software available today can be leveraged to close the part of the gap that includes document generation, tracking and control, “infrastructure” data gathering systems, 21 CFR Part 11 implementation and compliance and, most importantly, data-intensive decision-making.

Data-intensive decision-making is one of the most challenging aspects for the industry today. Data is everywhere, but very little of it is useful information. Most plants generate mountains of data, but without the right software capabilities to make the information content quickly available to the right people in a time frame that’s relevant to the manufacturing process, the data remains essentially untapped. How can manufacturers possibly handle the mountains of data that relate to both focus areas – quality with regulatory compliance (GMP) and operational/process stability?

On the quality side, easy access to the data in a useful form is necessary for parameter review for batch release, setting defensible specifications, investigation of out-of-specification (OOS) batches, manufacturing process validation, production trend analysis and annual product reviews (APRs), etc. Easy access to the data also can help shorten process start-up and scale-up times as well as trouble-shooting and adverse trend reversal times, improve productivity and quality as well as return on net assets (RONA) and finally, leverage existing technology investments, such as enterprise resource planning (ERP) systems, laboratory information management (LIMS) databases, data historians and batch records.

In today’s typical plant, there are numerous “inefficiencies” hindering the use of data for fast, effective decision-making. It takes several weeks, and sometimes months, to manually retrieve and align data of multiple types from multiple sources where it is stored. In most cases, manufacturing data is not a top priority for corporate IT. Once the data is gathered, combinations of “Excel add-ins”, discrete, continuous and replicate data create spreadsheet madness that must be endured. While many software programs exist that potentially could help make this data more useful, the choices are bewildering and usually inadequate for the exact tasks at hand. Programming is most often required to utilize these packages, and the users – those who need data to make informed decisions – aren’t likely to be professional programmers or statisticians. Once programming is accomplished, results are communicated in old-fashioned, ineffective ways. Tables of highlighted numbers and traditional two-dimensional plots seem to be the limits of most programs’ sophistication.

These inefficiencies cost companies millions of dollars in missed opportunities, representing the cost of not leveraging data-intensive decision-making for the organization. The table below shows the estimated bottom line impact for a $250-500 million per year product in a single manufacturing plant.

Typical Pharmaceutical Plant – Single Product

Challenge Annual Cost

Capacity underutilization (10% less yield) $25 – 50M

Lost batches (2/month out of 250/year) $24 – 48M

Delayed market entry (1 month/2 years) $10 – 20 M

Supply chain overburden (10% on 60 days) $4 – 8 M

Regulatory actions (every 2 years) $$$$$$$


Decision-making inefficiencies also leave companies more vulnerable to compliance issues. GMP-related FDA warnings include failure to investigate the root causes of OOS results, failure to adequately validate the process, failure to properly justify specifications and inadequate/incomplete APRs. A specific citation being made more frequently in recent times specifically cites “failure to establish and follow control procedures to monitor the output and to validate the performance of those manufacturing processes that may be responsible for causing variability in the characteristics of in-process material and the drug product.”3

So, whether a company chooses the side of “the gap” that favors operational efficiency or the side that strives to maximize compliance, data-intensive decision-making is required for success. And, the best news is, with today’s available technology, you don’t have to make a choice that sacrifices one or the other.

Technology Solutions More than 150 technology vendors claim they can help with Part 11 compliance alone. So how can manufacturers effectively evaluate and implement solutions that will truly enhance compliance while improving the manufacturing process?

It’s important to review the various types of data in the real manufacturing environment and the disparate systems that gather it. Discrete data is measured once per batch. Continuous data includes strip charts and other time series profiles stored in SCADA historians, DCS and PLC systems. Replicate data includes several measurements from the same sample and/or time. Finally, paper records include operator, equipment and quality records.

3 FDA, 21 CFR 211.110(a)


Slide 1

Data collection in the manufacturing process begins during the early steps – from bringing in raw materials, to lab tests and utility systems. Data is collected in different departments and stored in database systems ranging from ERP and LIMS to electronic and paper batch and other record systems. (See slide 1.) In fact, AMR Research estimates that paper records can often contain as much as 80 percent of pharmaceutical and biotech manufacturing data. As material moves through the manufacturing process, these disparate data sources are filled with more data from mixing, granulating and milling equipment, tablet presses and capsule fillers and final steps like coating and packaging,

With all of this data captured either in electronic or paper systems, the challenge lies in the practical requirements of getting to the data and extracting its information content so that it can be used for decision-making. The concept of relevant time is of utmost importance. In order to be useful, information must be available in time to impact at least the next batch – whether that’s in seconds, minutes, hours or weeks. In most plants today, the wait time for getting data to work with is typically months.

In a typical pharmaceutical or biotech manufacturing organization, both simple and complex decision-making problems must be addressed. Data must be easily available for both types of problems and must be readily useable by professionals who are neither programmers nor statisticians.


Slide 2

Different parts of the process may take place in different parts of the world, so one needs a global view of dispersed data from a single point of access, and the use of the system must become a widespread, cross-functional habit across the entire global organization. (See slide 2.)

In summary, if data is to be useful it must provide the right view of the entire manufacturing process in front of the right people at the right time.

Fortunately, a new bridging technology exists to convert the data on paper records into electronic form efficiently and in full compliance with 21 CFR Part 11. This software allows retrieval of data from paper records and capture in useful form without having to use a full-blown electronic batch record system that requires a much larger implementation effort because of the additional workflow requirements. Manufacturing professionals already have to use data from paper records today for trending, investigations and batch release, but they’re doing so using antiquated systems that require typing the data laboriously into un-validated spreadsheets, where it tends to become lost or discarded after a short time. This creates a risk of not being compliant, making fragmented decisions and losing the investment in previous data entry when the next data-intensive problem arises. It also requires more senior-level skills for taking care of these problems. By converting the data on paper records into electronic form using an enterprise manufacturing intelligence system that includes on-screen replicas of the batch records, admin-level skills can be utilized to get the data into an easily accessible form that models the process and is available company-wide going forward. Moreover, double blind data can be done routinely and the data automatically checked for accuracy, a step that is most often omitted in ad hoc spreadsheet approaches.


So how is this data made available to any user who needs access? It can be done with a point-and-click view of the entire manufacturing process, parameter-by-parameter in an expandable hierarchy. This allows users anywhere in the world to get value from the data by selecting any view of the process based on a specific organizing principle, such as the batch, operator, equipment or materials.

Slide 3

Regardless of where the data is stored and whether it’s discrete, continuous, replicate or paper-based, immediate access to the data from a personal terminal is available without having to type in command lines, write queries or request someone else to retrieve the data. (See slide 3.)

Once there is immediate data availability in a single point of immediate access, the choices can be unlimited, but they must be structured in the way that’s most useful for pharmaceutical and biotech decision-making. All parameters can be seen for each batch in a process-centric view that is filtered by selecting only the parameters needed for a particular decision or investigation.


Slide 4


Slide 5

Once data has been assembled and filtered, an analysis group is created for doing work ranging from fast batch release (See slide 4.) and root cause investigation (See slide 5.) to production trending – with customized alerts set to match individual process requirements. Specification limits can be set wide enough to ensure higher batch success based on acceptable failure rates, but within a range that will be acceptable for regulatory authorities. Prior to now, rules of thumb were used, because the available tools were not useable enough. Today’s technology puts more control in manufacturers’ hands, enabling them to quickly predict outcomes using available data.

Continuous data can often be “dirty” – misrepresenting what is actually happening in a fermentation process, for example. Filtering out this “noise” and preserving fine detail is possible using new technology that incorporates a properly designed data-conditioning interface. Feature extraction allows manufacturers to select a region of continuous data they want to understand in more detail, simply by clicking on the region of interest directly in the screen. By selecting the correct values for plotting and excluding irrelevant data, more meaningful interpretations can be made more quickly.


Making the Business Case In today’s pharmaceutical and biotech corporations, gaining approval for new software technology can be a tremendous challenge. Unfulfilled expectations from past enterprise implementations haunt executives. Execution nightmares due to separate departmental systems and skilled resource limitations create a fear-of-change mentality. The most hindering factor is disbelief that new software technology can at last enable what management really wants – a complete environment for data-intensive decision-making that achieves ongoing manufacturing compliance and lasting operational efficiency improvement.

Decision makers and their influencers evaluate new technology based on how well they achieve the following key manufacturing metrics: 1) sustained improvements in operational efficiency, 2) predictable yield and quality, 3) shortest time to revenue and 4) lowest FDA risk. Technology that can’t deliver these metrics won’t get past the initial demo, so it’s important to identify systems that truly deliver these benefits and emphasize them in business cases which also meet the following criteria:

o Application to existing priorities

o Leveraging existing investment

o Clear project definition and scope

o Near-term return to the bottom line

o Significant positive impact on corporate goals

Simulated Case Study – Dissolution Rates The Challenge

A major pharmaceutical manufacturer wanted to reduce lot failures and increase process predictability to improve profitability. The manufacturer needed to identify the combination of critical process drivers across the process that was determining the process outcome, namely the Tablet Dissolution Rate.

The Solution

Using a properly designed manufacturing software system with immediate enterprise data access, and user-centric investigational and visualization capabilities, this leading manufacturer was quickly able to work with data from more than 60 product batches from one plant.


Slide 6

Using Principal Component Regression (PCR), the smallest combination of controllable process parameters that had the greatest effect on the process outcomes was quickly found. (See slide 6.) There were only four such parameters and they were located in widely disparate parts of the process. Specific recommendations for process improvements were then implemented based on the best combination of ranges of these parameters that were predicted to give the best process outcomes.

The Result – Improved Outcome With Lower Cost

For all the data used in this study, the process parameters had been operated within their approved ranges. Therefore, the manufacturer was able to test these findings directly within the manufacturing process without the need for additional small-scale experimentation – a major cost advantage. In other words, the process improvement recommendations were derived from the variations that occurred in the manufacturing process when operated under approved conditions. When implemented, the recommended adjustments allowed the manufacturing staff to bias its process toward the best possible outcomes without the need to change the manufacturing technology or get it re-approved by the FDA – a tremendous savings in time and money.

The bottom line impact of this implementation included a significant reduction in atypicals by stabilizing tablet dissolution rates, lowering failure rates to increase yield by 25 percent – ultimately improving the bottom line by more than $5 million per year per plant.

Simulated Case Study – Bioprocess Yield


The Challenge

A similar example from the biotechnology field is found in the predicament of a well-known biotechnology manufacturer with unstable fermentation yields (0.5g/l – 1.5 g/l). Preliminary analysis of the final process yields suggested that the variation was random and the company didn’t know why.

The Solution

Looking at dissolved oxygen profiles in two-dimensional plots didn’t reveal whether an observed variation in the initial oxygen uptake rate was random or systematic.

Slide 7

When viewed in a three-surface plot, it became clear that the variation was systematic. Five cycles out of 20 batches showed a repeating pattern of slow uptake rates followed by fast uptake rates. Viewing the three-dimensional plot with the final yield superimposed on each batch as a shade of blue (a four-dimensional plot) demonstrated that high yield (light blue) lined up with the slow initial oxygen uptake rates. (See slide 7.) It thus became clear that the manufacturer needed to control oxygen uptake at the slower rate in order to achieve higher final yields. But how could the process be operated that way?


Slide 8

To determine what was driving the oxygen uptake rate, an on-screen feature extraction was done to derive the slope of each dissolved oxygen plot on the screen. (See slide 8.) The slopes were then correlated with several variables and they were determined to be best correlated with the age of the trace metal concentrate that was added to each fermentation. The clear implication was that the length of time a concentrate had been standing around before it was added to the fermentation medium determined the slope of the dissolved oxygen plot, and, therefore, the yield of the fermentation.

The Result – Higher Yield in Shorter Time

This manufacturer proved that using younger concentrate in the fermentation led to higher yield. In fact, the average yield was also increased from 1.0 g/l to 1.4 g/l – a 40 percent boost with a corresponding decrease in process variability. Two new interacting Critical Process Parameters were identified as root causes, and the problem was solved in just a few hours. This might have taken weeks relying on traditional manual data work and without utilizing on-screen feature extraction capabilities. The bottom line impact was greater than $20 million per year per plant.

Summary While the pharmaceutical and biotech industry faces formidable challenges to meet the demands of today’s marketplace as well as existing and forthcoming FDA requirements, new and existing technology investments can be leveraged to take a new approach – one that must be accepted by global organizations as the new reality. The right data-intensive decision-making technology can lead to better


quality compliance balanced with increased operational efficiency and profitability. The advances that technology can deliver to the manufacturing environment are substantial and require only a small investment to deliver an enormous return.

Documents

2003NA How New Technologies Can Improve Compliance in ...€¦ · mentality on dealing with compliance. As U.S. demographics shift with the aging Baby Boomer population, drug prices