0. Plug and Play PAT Anyone?
Presented at AutomationXchange
August 2005

1. Outline Thought for the day! Acknowledgements Top issues
Why GMPs for the 21st Century?
PAT at Pfizer
Impacts on Architecture/Infrastructure
Current Challenges
Common PAT Software
Call for action
Desired outcome: Plug and Play PAT Everyone!
I am sure you are aware of the buzz related to Process Analytical Technology and how it can help improve Pharma Mfg. I would like to take opportunity to issue a call for action related to strategic gaps to make PAT adoption easier. Before I do that, I will spend a few minutes on Pharmaceutical GMP’s for the 21st century mean to us all, and how we are adopting PAT within Pfizer. I will spend a few minutes on current challenges and what we are doing about them. In the end, I would like to outline what we can all do to get there.

2. Thought for the day
“…Be the change you want to see in the world…” M.K.Gandhi

3. Acknowledgements Martin Warman Jeff Miller
Sr. Manager/Team Leader of Process Analytical Support Group, Pfizer Global Manufacturing
Jeff Miller
Strategic Sourcing Manager, Pfizer Global Sourcing
Common PAT Software Project Core Team Member

4. Unprecedented Need For Change
Top issues
Patent expiries
Pressure to reduce drug costs
Challenges to IP
Counterfeiting & Piracy
Unmet needs
Regulatory Pressures
I work in the Pharmaceutical Industry supporting Manufacturing. Our company, Pfizer like others in our industry has grown through mergers and acquisitions in the last six years. We have some unique issues that require revolutionary changes in Manufacturing. Let’s look at some of the top issues;
The most pressing issue facing the Pharma industry is patent expiries of drugs. In the next three years, several key drugs will be coming off patent, leaving holes in the product portfolios. A drug on an average takes about eight years to reach the market, and can cost about $800 million to develop. Pharma industry has the highest of R&D budgets (12 to 15% of sales). In 2003 alone the industry invested $33.2 billion in research.
In 2002, US was the only country to spend more than 10% of its GDP on healthcare. There is tremendous social pressure to reduce drug costs. All aspects of the cost structure are being evaluated.
Despite the high cost of developing drugs, there are always challenges to the intellectual property that is the basis for the drugs. Some simply do not respect the patents or rush to file patents for copies before the original patent owner can assert the rights to it.
A far more serious issue is the counterfeiting of name brand drugs. This can undermine the reputation of a company as the fake drugs may not contain the active ingredients the patients rely on. Worse yet it is even more difficult to trace if fakes get commingled with good products.
Today the life-expectancy has improved, but there are unmet medical needs. These require innovative solutions in new therapeutic categories involving complex drug delivery systems that are more expensive to develop and deliver. Good example is gene therapy, where annual cost can be very high.
The regulatory landscape is also constantly changing, be it be with new interpretation of existing regulations or introduction of new regulations.
All these are driving our industry need for unprecedented change as Manufacturing is becoming a critical link in the business value chain. Shortening the time to market is still the imperative, but improvements in effectiveness and responsiveness to the needs of the business also are becoming critical.
Unprecedented Need For Change

5. What is GMPs for the 21st Century?
FDA focus on manufacturing
Manufacturing is inefficient
Industry has failed to innovate
Compliance record is unacceptable
Recognition that barriers to adoption of innovation and continuous improvement
PAT framework
Advances in quality systems & science
GMP regulations has not changed
Food and Drug Administration is chartered with regulating the manufacture of medicines to ensure safe and effective medicines. In the last five years, FDA has focused on the manufacturing regulatory compliance. The number of consent decrees, warning letters, stiff penalties and delayed drug approvals increased. If you look at some of the recent pronouncements from FDA, pharma manufacturing has been characterized as inefficient and costly. The innovation in manufacturing has been compared to potato chip manufacturers. The FDA implored the manufacturers to improve and innovate.
While it has been common practice that manufacturing processes remained static, any improvements made were done only in a reactive or corrective mode. The industry feels that there are regulatory burdens with no clear guidelines. The industry and the agency recognized that barriers exist for the adoption of innovation exist and was examining for ways to facilitate continuous improvement. There were a series of meetings since 2001 starting with the FDA science board. A consensus evolved with a mutual recognition that understanding processes and reducing variability offers best opportunity for continuous improvement. We all know that you can not understand something that you can not measure. This led to naming Process Analytical technology (PAT) as a good example of innovative technology, a test case.
Continuous improvement is a critical element in a sound quality system. This however needs a systems approach. FDA calls this the PAT framework, which includes continuous improvement, risk assessment, knowledge management, at/online sensors.
The FDA also has published report on Pharmaceutical cGMP’s for the 21st century as the framework to enable innovation and continuous improvement. However we need to note that the GMP regulations have not yet changed, but FDA is providing science and risk based guidance related to GMPs.
Report on Pharmaceutical GMPs for 21st Century
was published September 2004

6. FDA is providing science and risk based guidance documents
GMPs for the 21st century
Focus on risks to public health
Risk-based orientation
Mfg. Science based policies and standards
Integrated quality systems approach
Harmonization & alignment with other regulations
FDA would like to improve ability to make decisions based on risk. Most of the Pharma manufacturing processes are multi-variate . Understanding these relationships provides a sound basis for understanding interaction of critical to quality variables. This will help in developing robust internal limits, process controls.
As our level of process understanding goes up, the risk of producing poor quality product should go down. This should lead to less restrictive regulatory approaches.
The framework also includes introduction of Manufacturing science based policies and standards. This guidance focuses introduction of new technologies to improve efficiency and effectiveness of mfg. process development and control. Gains are expected from reducing cycle time, preventing rejects, real-time release, better understanding to automate, and ability of the process to manage variability.
FDA is focusing on a quality systems approach towards inspection. Reviewers, investigators and compliance officers work together on PAT applications.
FDA also is working with other regulatory bodies to harmonize regulations. Notable being the International Congress on Harmonization
FDA is providing science and risk based guidance documents
related to GMPs

7. Fundamental shifts Corrective action to continuous improvement
Continuous quality verification
Quality by testing to Quality by design
Diverse supply chains
New measurements
More information about the process
Role based
Event based
This guidance marks a fundamental shift for pharma manufacturing. The focus now shifts from corrective action to continuous improvement. Designing quality in the product vs. testing for it. While this is happening, other shifts are also occurring. Newer therapies and adapting to scale are resulting in diverse supply chains. Understanding of measurement variability is improving while at the same time, new measurements facilitated by adoption of PAT is coming to the forefront.
All of this is generating more information about the process. Users of this information would like to view and act on only information relevant to their role. To facilitate better understanding all events need to be captured during the mfg of the product. Why is this such a big change? How can we get to the desired state from the current state?
This strongly underscores the need for scalable, reliable, flexible, secure and timely information.
Scalable, reliable, flexible, secure and timely information
required in Pharmaceutical Manufacturing

8. PAT at Pfizer
Analytical technology used to gain more information on the process to identify sources of variability
Not lab based, although measurement techniques similar
Not just regular measurement
Temperature, pH, Pressure
Complex data and measurement technology
At-line
In-line
Now let’s look at what PAT means at Pfizer.
PAT refers to analytical technology to gain more process knowledge in order to remove sources of variability. This is an extension of our technical expertise acquired over more than 20 years. Pfizer considers PAT will help gain truly competitive advantage.
Most of the measurements are not lab based, but may be performed at line or in-line using measurement techniques such as using Near infrared spectroscopy.
At-line measurements involve extraction of a representative sample and performing analysis in a self-contained instrument. In-line means that sampling system is integrated into the process.

9. Why PAT? Key enabler for Mfg. Science
Establish Product/Process Knowledge- CtQA
Process capability Data – Ability to meet CtQA
Process understanding…Know all variability
Technology barriers are dissolving
What took hours or days now is possible in real-time
Real-time feedback and control now using PAT for all CtQA’s is feasible.
New measurements being characterized everyday
Why is Pfizer pursuing PAT?
We recognize that PAT is a key enabler for Manufacturing Science. If we understand all sources of variability, we can predict our process performance. This leads to a high level of process understanding. This will reduce process risk, regulatory scrutiny and puts us on a path for active continuous improvement.
Some of the technology barriers are dissolving. An off-line sample took hours sometimes days to provide a result. Now some of these measurements are possible in real time. Our Process Analytical scientists are working to develop new measurement systems every day.
Pfizer’s interest is to make processes measurement ready or PAT ready. What does this capability really look like? Let’s look at a typical PAT system.

10. Block diagram PAT measurement
Control spectrometer
Acquire data
Run predictions
Displays prediction
Store data
Maintain calibration, event log etc
Generate methods
Pre-treat spectral data
Extract constituent data
Data Analysis
Method
Spectrometer
Configuration
System Functions
Hist
PAS
Tank DB PC AT
A typical system has several functions. The system needs to control spectrometer, acquire data, run prediction models, display the results. A typical dataset for an operation involves 500 sample values or 3000 floating point values.
Now this is easy to handle if we have one instrument. We have tens of different application types, each having its own unique process and instrument configuration, data modeling, method builder and data analysis capability.
We also need to recognize that we need more process measurements before we fully characterize a process.
Detector
Data Modelling
Process
Configuration
About 3000 floating point values for 500 samples
PAT is a tool as a part of a system to verify process robustness

11. Impacts on Infrastructure/Architecture
R&D Information
Multi-factorial
Analysis
Shared Knowledge
Management
Over Product Lifecycle
Process
Understanding
Risk Analysis
& Mitigation
Justify
Innovation
Design
Continuous
Improvement
Customers
Lab Analysis
Weigh & Dispense
Processing
Packaging
Raw
Materials
Final
Product
On-line
Measurements
Process Control
Distribution
Let’s look at the impact on Infrastructure and architecture. In traditional Pharma Manufacturing (automated or not), raw materials are transformed into final drug product. In a drug product plant, raw materials are weighed and dispensed before they are fully processed into drug product, ready for packaging. The final product is distributed to customers. For the processing activity procedural controls may be automated or governed by Standard Operating Procedures. Usually quality is tested at end of each stage using samples in a lab. The process control strategy is pre-determined during the process development and is validated for concordant validation batches for a range of the specification. On-line measurements monitor if there is a deviation.
In the new paradigm, information from the process in its whole context is needed for a variety of purposes over the life-cycle of the product. For example, for risk analysis & mitigation, we need to understand the root cause of all deviations, so we do not treat all deviations equal. For process design, use of measurements to determine what is critical to quality using design of experiments, and correlations is necessary. Process understanding requires that knowledge about the process be available for easier analysis.
New infrastructure to handle this knowledge is needed at manufacturing plants.
©ARC Advisory Group. Used with permission
This requirement poses new challenges to current infrastructure
New requirements

12. Current IT Challenges Islands of Automation Platform based initiatives
Infrastructure planning driven by point application needs
Domain silos (Process control, IT)
Tactical Solutions archipelago
Measurement System Capabilities
Need for to manage and facilitate collaborative views on process information in near real-time
If you think current challenges are daunting, we add two more challenges to a long list.
Processes well understood are much more amenable to automation. However in a Pharma plant you will see several islands of automation.
Often mfg. plants choose point solutions to meet a pressing need, for example, one for batch control, another for manufacturing execution. Integration becomes someone else’s problem. As the urge is to meet the immediate needs for production, all these point solutions and ad-hoc integration efforts lead to a messy fragmented portfolio of systems. Most solutions are dropped into plant without thinking about how solutions impact or fit business strategy.
Our plant leaders ask the question, how come we can not get the information when we need it? In this mix, we need collaborative views for process information that our operations, technologists, manufacturing and QO teams need to use in product introductions, process improvement and product release.
This is not about technology alone.
Collaboration is critical to overcome these challenges.

13. What can we do till we get there?
Standardize measurements
Standardize data
Identify process owners and uses for data
Master plan: Impact assessment on architecture, applications, infrastructure
Leverage existing infrastructure
Slipstream new capabilities
Lay the foundation first, build as you go
There are several steps that we can take;
To make information exchange meaningful, we need to standardize these complex measurements. Spectroscopy methods are routinely being adapted for PAT. Examples are Near infra-red, Raman, UV-Visible, Flourescence, Acoustic. Type of measurements technologies suitable for measurement needs to be identified. This will help our partners to address technology implementation challenges to simplify deployment. Today we spend a lot of time in specifying a measurement for a process application. We would like to see this time reduced and become predictable.
Some of PAT systems are used not only for monitoring process but to control and predict process performance. Data needs to be exchanged with systems that perform analysis, control, decisioning and reporting. Start with standard ways of exchanging data across these systems.
In this new environment, the process communicates about its performance. Who really owns this information, what are the immediate uses for this data. Any exercise to determine this should involve process owners.
We have heard about several efforts to build master plans for deployment of innovative measurement technologies. But most of them are done in isolation without considering the impact on architecture, existing applications and infrastructure needs. Any master planning effort has to consider these impacts. The Master planning effort has to align strategies and derive specific objectives for infrastructure, applications, integration and PAT systems.
To do this effectively, all the functions need to be mapped to domains. New functions such as process optimization and process improvement should be considered in Mfg. system architectures. For each of the functions, impacts on level and approach to automation must be considered. This master planning exercise if done well, will help understand the business readiness for embracing continuous improvement.
In manufacturing each application focuses on its immediate needs to make it work. Leverage the infrastructure capabilities that already exist in the larger enterprise. Storage Area Networks, Server virtualization, Wireless networks, Information Portals, Networks are examples of existing infrastructure that can be leveraged to provide additional value. When an opportunity presents, slipstream newer capabilities.
The master plan should help identify foundational capabilities.
This should help in starting to layout that bridge to the “Desired State”
Build bridges to cross the chasm

14. Call for action Common PAT Software Standardize Data
Standard modelers, interfaces
Standardize Data
Recipes, Item masters, Measurement data, Production Information
Develop life-cycle management models for data
Standardize schema to exchange information
Migrate to Single Integrated Architecture
We have deployed several PAT systems. What we are seeing is that each PAT system has custom configurators, interfaces, proprietary modelers and data storage. While we can standardize measurement and build a core solution to deploy, we still run into deployment issues such as predictive delivery. If a new measurement takes six months to come on line and involves custom integration to qualified systems, plants begin to question the value. Also, PAT measurements are now being identified and used in the process development extensively. The scientists would like the information about how a scaled process is performing, being able to study it and use new predictive models from existing measurements to understand the variability. The updated model may call for newer measurements from the same PAT system. All this leads to the industry requirement to have common PAT software. Just like SCADA united PLCs, we believe a common application providing common modeler functions, a single environment to build and configure and manage all PAT systems is required. Components already exist in the marketplace. We believe that this will open up the adoption of new measurements on a widespread basis. We see this to be a market based product.
One of the major issues limiting PAT today is the lack of open standards and interfaces between instrumentation and software. Each individual instrument vendor offers its own proprietary software, and, as a result, each PAT installation is unique. While we tolerated this situation when PAT was in its infancy, it has become a major problem, since PAT applications need to operate and acquire data from the process in real-time to monitor, control and optimize processes.
As PAT applications proliferate, so are implementations with custom interfaces, point-to-point interfaces, custom modelers, and data islands. These are no longer acceptable. If PAT applications are to help in multi-factorial analysis, they must interoperate in control and modeling space.
Most of you are familiar with the S88 & S95 standards. The models and terminology defined helped us build better plants and processes, and communicate capabilities to others. Now some of the standards should help in structuring this process knowledge itself. Process knowledge can exist in different levels, the simplest being descriptive knowledge, the what. The next level is correlative knowledge that helps us understand the basic correlations among variables. The next level is casual knowledge that helps us understand what causes what, The next level is mechanistic knowledge describing how, and next level being first principles. Standards to define production information data is already in development. Extensions to some of these standards to include failure modes to equipment entities is required. When structuring process knowledge, this information needs to be managed over the product’s entire life-cycle. This life-cycle could be as long as thirty years. How to manage this information?
We will have to deal with reality of existing systems, infrastructure for a long time. We will have to exchange information with existing process control systems, ERP and MES systems. Unified schema to exchange information becomes critical. Several organizations have developed schema focusing on standard data related to functions of relevance, example the analytical markup language, batch manufacturing language and B2MML. While the schemas have been around a while, corporate schema needs to be developed to use them effectively. Schema validation services for entire vertical industry is a requirement.
To facilitate process improvement and innovation, the application architecture geared towards corrective action has to orient towards continuous improvement. This requires that application architecture that separated testing functions will have to be integrated. Application architecture domain models to address this needs to evolve.
We are not in this alone.

15. Common PAT Software Set up and run Process Analyzers
Provide a common modeling environment
Single environment to build and configure Process Analyzers
Facilitate archiving of PAT data into storage systems

16. Common PAT Software Functional Architecture
Instrument and Sensor Interface
P R O C E S S
Sensors/Control
Devices
Future
Inst
Protocol Adapter
Instrument
Prop. Inst. Software
Unit
Operation
Transient
PAT Data
Sensor
PAT Common User Interface
On-line
Prediction & Monitoring
PAT
Instrument Control
Off-line
Method Builder
Common Data Access
Retrieval, Analysis
Storage
Process
Control
System
Design of Experiment

17. PAT Paradigms of Use PAT Mode: Monitor PAT Mode: Control
PAT Mode: Optimize
Method
Modeller
Instrument Control
Data Analysis
Method Builder
Predictive Modeling
Controller Interface
Instrument Control
Data Analysis
Controller Interface
Interface
Instrument Control
Data Analysis
Interface
Interface
Analyzer
Analyzer
Controller
Analyzer
Controller
Process
Process
Process
Method