> DEPARTMENT OF HEALTH AND HUMAN SERVICES
>
> FOOD AND DRUG ADMINISTRATION
>
> CENTER FOR DRUG EVALUATION AND RESEARCH
>
> **PROCESS ANALYTICAL TECHNOLOGIES SUBCOMMITTEE**
>
> **OF THE**
>
> **ADVISORY COMMITTEE FOR PHARMACEUTICAL SCIENCE**
>
> Monday, February 25, 2002
>
> 8:30 a.m.
>
> Holiday Inn Gaithersburg
>
> Two Montgomery Village Avenue
>
> Gaithersburg, Maryland
>
> PARTICIPANTS
>
> Thomas Layloff, Ph.D., Acting Chairperson
>
> Kathleen Reedy, Executive Secretary
>
> MEMBERS
>
> Gloria L. Anderson, Ph.D.
>
> Joseph Bloom, Ph.D.
>
> Judy P. Boehlert, Ph.D.
>
> Arthur H. Kibbe, Ph.D.
>
> SGE CONSULTANT
>
> Melvin V. Koch, Ph.D.
>
> GOVERNMENT PARTICIPANT
>
> William F. Koch, Ph.D.
>
> OTHER GUESTS/SPEAKERS PARTICIPANTS
>
> Thomas J. Hale
>
> Leon Lachman, Ph.D.
>
> Kenneth R. Morris, Ph.D.
>
> G.K. Raju, Ph.D.
>
> Eva M. Sevick-Muraca, Ph.D.
>
> INDUSTRY GUESTS/PARTICIPANTS
>
> Robert S. Chisholm
>
> Rick E. Cooley
>
> Doug Dean, Ph.D.
>
> Steve Hammond
>
> John C. James, Ph.D.
>
> Ronald W. Miller, Ph.D.
>
> David Richard Rudd, Ph.D.
>
> John G. Shabushnig, Ph.D.
>
> Leon Shargel, Ph.D., R.Ph.
>
> Efraim Shek, Ph.D.
>
> Jozef H.M.T. Timmermans, Ph.D.
>
> Judy Wong, M.S.
>
> Jerome Workman, Jr., Ph.D.
>
> FDA
>
> Yuan-yuan Chiu, Ph.D. (Sessions I, II, IV)
>
> Douglas I. Ellsworth (Sessions I, III)
>
> Joseph Famulare (Sessions II, III)
>
> Ajaz S. Hussain, Ph.D. (Sessions I, II, IV)
>
> Moheb M. Nasr, Ph.D. (Session III)
>
> Michael C. Olson (Session IV)
>
> [C O N T E N T S]{.underline}
>
> [Page No.]{.underline}
>
> Call to Order: Thomas Layloff, Ph.D. 4
>
> Conflict of Interest: Kathleen Reedy 4
>
> Introduction, Overview and Objectives for
>
> Subcommittee
>
> Ajaz Hussain, Ph.D. 8
>
> **Session I: Process Analytical Technologies**
>
> **Applications and Benefits**
>
> Perspective 1: Steve Hammond, 33
>
> Pfizer
>
> Perspective 2: Doug Dean, Ph.D., 50
>
> PricewaterhouseCoopers
>
> Subcommittee Discussion 64
>
> **Session II: Product and Process Development**
>
> Perspective 1: John G. Shabushnig, Ph.D., 92
>
> Pharmacia
>
> Perspective 2: David R. Rudd, Ph.D., 108
>
> GlaxoSmithKline
>
> Subcommittee Discussion 126
>
> Open Public Hearing
>
> Gabor J. Kemeny, Ph.D. 163
>
> **Session III: Process and Analytical Validation**
>
> Perspective 1: Robert S. Chisholm, 168
>
> AstraZeneca
>
> Perspective 2: Leon Lachman, Ph.D., 187
>
> Lachman Consulting
>
> Subcommittee Discussion 201
>
> **Session IV: Chemometrics**
>
> Perspective 1: Jerry Workman Jr., Ph.D. 230
>
> Kimberly-Clark
>
> Perspective 2: Dwight S. Walker, Ph.D. 248
>
> GlaxoSmithKline
>
> Subcommittee Discussion 271
>
> [P]{.underline} [R]{.underline} [O]{.underline} [C]{.underline}
> [E]{.underline} [E]{.underline} [D]{.underline} [I]{.underline}
> [N]{.underline} [G]{.underline} [S]{.underline}
>
> **Call to Order**
>
> DR. LAYLOFF: This is the Process Analytical Technologies Subcommittee
> of the Advisory Committee for Pharmaceutical Science\'s meeting. If
> attendance of that program is not on your agenda, you can leave now.
>
> My name is Tom Layloff. I am a Special Government Employee with the
> Center for Drug Evaluation and Research. My day job is with Management
> Sciences for Health.
>
> To start off, I am going to call on Kathleen to give you a briefing on
> conflict of interest.
>
> **Conflict of Interest**
>
> MS. REEDY: Acknowledgement Related to General Matters Waivers for the
> Process Analytical Technologies Subcommittee of the Advisory Committee
> for Pharmaceutical Science on February 25, 2002.
>
> The Food and Drug Administration has prepared general matters waivers
> for the following special government employees, Drs. Judy Boehlert,
> Gloria Anderson, Joseph Bloom, Thomas Layloff, Robert Lodder, Melvin
> Koch, and Arthur Kibbe, which permits their participation in today\'s
> meeting of the Process Analytical Technologies Subcommittee of the
> Advisory Committee for Pharmaceutical Science.
>
> The Subcommittee will: (1) identify and define technology and
> regulatory uncertainties and hurdles, possible solutions, and
> strategies for the successful implementation of Process Analytical
> Technologies or PATs in pharmaceutical development and manufacturing;
> (2) discuss general principles for regulatory application of PATs
> including principles of method validation, specification, use and
> validation of chemometric tools, and feasibility of parametric release
> concept; and (3) discuss the need for a general FDA guidance to
> facilitate the implementation of Process Analytical Technologies being
> held by the Center for Drug Evaluation and Research.
>
> Unlike issues before a committee in which a particular product is
> discussed, issues of broader applicability, such as the topic of
> today\'s meeting, involve many industrial sponsors and academic
> institutions.
>
> The committee members have been screened for their financial interests
> as they may apply to the general topic at hand. Because general topics
> impact on so many institutions, it is not prudent to recite all
> potential conflicts of interest as they apply to each member.
>
> FDA acknowledges that there may be potential conflicts of interest,
> but because of the general nature of the discussion before the
> committee, these potential conflicts are mitigated.
>
> We would also like to note for the record that Leon Shargel, Ph.D., of
> Eon Labs Manufacturing, and Efraim Shek, Ph.D., of Abbott
> Laboratories, are participating in this meeting as Industry
> Representatives, acting on behalf of regulated industry. As such, they
> have not been screened for any conflicts of interest.
>
> With respect to FDA\'s invited guests, there are reported interests
> which we believe should be made public to allow the participants to
> objectively evaluate their comments.
>
> We would like to disclose that Dr. Leon Lachman is president of
> Lachman Consultant Services, Inc., a firm which provides consulting
> services to pharmaceutical and allied industries.
>
> Dr. Kenneth Morris would like to disclose that his department receives
> funding from pharmaceutical companies directly or in consortia
> programs.
>
> Dr. G.K. Raju would like to disclose that he has contracts and grants
> from Pfizer and the Consortium for the Advancement of Manufacturing of
> Pharmaceuticals. Dr. Raju also serves as a consultant and speaker for
> these firms. In addition, Dr. Raju is employed by and has a fiduciary
> relationship with Light Pharma, Inc. Finally, Dr. Raju has
> affiliations with MIT and Purdue University.
>
> In the event that the discussions involve any other products or firms
> not already on the agenda for which FDA participants have a financial
> interest, the participants are aware of the need to exclude themselves
> from such involvement and their exclusion will be noted for the
> record.
>
> With respect to all other participants, we ask in the interest of
> fairness that they address any current or previous financial
> involvement with any firm whose product they may wish to comment upon.
>
> DR. LAYLOFF: Any questions for Kathleen?
>
> Okay. I would like to call on Ajaz Hussain, who will give us an
> overview of the PAT and some FDA perspectives.
>
> I would like to comment on the speakers. The agenda indicates the
> speaker\'s time, and we will rigorously hold to those time slots.
> Thank you.
>
> **Introduction, Overview, and Objectives**
>
> **for Subcommittee**
>
> **Ajaz Hussain, Ph.D.**
>
> DR. HUSSAIN: Good morning and welcome on behalf of the Office of
> Pharmaceutical Science, Center for Drug Evaluation and Research. It is
> a pleasure to have all of you participate in this initiative and thank
> you again for being here.
>
> I wanted to share with you a couple of things. One is Helen Winkle
> could not be here, and she may just join us for a few minutes now and
> then, so Dr. Janet Woodcock, so they may be coming through and
> attending part of the meeting.
>
> \[Slide.\]
>
> Let me share with you some thoughts on the Process Analytic Technology
> in terms of an overview and objectives of this meeting. To do this,
> what I would like to do is trace back some history of when we got
> started, what it is and when we got started, and so forth, and then
> focus my presentation on goals and objectives of the subcommittee and
> working groups, what does FDA need or expect from you.
>
> \[Slide.\]
>
> Here is sort of my view of Process Analytical Technology. I am hoping
> that you would come up with a better definition of PATs by the end of
> this meeting.
>
> From my perspective, PATs are systems for continuous analysis and
> control of manufacturing processes based on real-time measurements, or
> rapid measurements during processing, of quality and performance
> attributes of raw and in-process materials and processes to assure
> acceptable end product quality at the completion of the process.
>
> We selected the term \"PAT\" because I think it is more than process
> analytical chemistry. It involves information management tools,
> feedback process control strategies, product and process design and
> optimization strategies, so there is a whole host of activities that
> constitute PATs in our mind, and I would like to get your thoughts on
> whether this is the right phrase and the right way to define PATs.
>
> \[Slide.\]
>
> Why PATs for pharmaceuticals? We believe optimal applications of PAT
> can improve the capability and the efficiency of pharmaceutical
> processing while maintaining or improving product quality.
>
> We achieve this through improved process understanding and this
> concept will help us to ensure quality was \"built in.\" That is our
> GMP term, building quality in, or quality \"by design.\" No matter how
> you say it, it is the same thing.
>
> It also will help us reduce risk of scrap and recalls, reduce
> production cycle times and enhance capacity utilization, and in the
> long run, we hope this will reduce product development time, because
> the science of formulation design emerges more rapidly by having an
> ability to measure the right thing at the right time, and this should
> help in the long run to have more science-based formulation
> development strategies that can lead to computer-aided design, for
> example.
>
> \[Slide.\]
>
> One of the questions that always comes is why, from a regulatory
> perspective, are we pushing for this or why we are promoting this. We
> believe the current level of product quality is generally adequate for
> intended use.
>
> The question that we are trying to address is the process itself. The
> process by which we achieve this level of quality in many ways is
> often inefficient. The reason we view it that way is we feel that the
> current manufacturing paradigm is skewed towards testing to document
> product quality and rejecting or recalling products of unacceptable
> quality. That is the paradigm that has sort of evolved over the last
> 30 years or 40 years.
>
> \[Slide.\]
>
> We believe that bringing focus on manufacturing is important to ensure
> high efficiency of the U.S. pharmaceutical manufacturing sector. This
> is needed to provide high quality drugs to the U.S. public in a timely
> manner by taking advantage of the many new drug development
> opportunities offered by advances in biology and chemistry.
>
> The point I am trying to make here is product development is now
> tending towards becoming a rate-limiting step, drug discovery is not.
> I think the high throughput screening and communitorial chemistry have
> provided a far greater number of molecules, interesting molecules,
> that need to be developed as drugs, so development itself is becoming
> a bottleneck.
>
> Also, we want to ensure optimal utilization of public and private
> resources to meet the growing healthcare needs of the U.S. public, and
> I will elaborate on that in a few minutes.
>
> Also, equally important, we would like to minimize risks due to
> suboptimal pharmaceutical process quality, so the focus here is on
> process by which we manufacture our products.
>
> \[Slide.\]
>
> Low manufacturing efficiency, waste, and high cost of compliance are
> some of the aspects that you will hear today from different speakers,
> and we heard a number of interesting presentations and data from the
> MIT program at our Science Board and from PriceWaterhouseCoopers, and
> I think you will see some of that again today.
>
> Because of the paradigm of testing to document quality, we feel that
> there is a very high need for high level of regulatory scrutiny from
> both review and inspection that is needed to assure quality, and high
> proportion of our resources are needed to maintain that quality.
>
> Also, there are recurring problems in manufacturing sector that do not
> seem to get resolved on a permanent basis, and also, we continue to
> debate on many fundamental issues between industry and FDA, and we
> generally don\'t come to permanent resolution.
>
> So, there is a need for fundamental technology to come in and a need
> for science to come into manufacturing in a much greater rate than it
> has in the last 30 years.
>
> \[Slide.\]
>
> Let me take a few minutes and sort of explain to you what I mean by
> \"risks due to suboptimal pharmaceutical process quality.\" There are
> many sources of risk that come into the system. You could look at that
> from the development perspective, how do you set the quality
> specification, how do you assure manufacturing capability, and how you
> would approve and inspect those processes.
>
> It could be a circular argument, it could be an argument saying that
> all these three elements have to come together to resolve and manage
> the risk associated with suboptimal process quality.
>
> \[Slide.\]
>
> When I mention that the quality of products is high, but the processes
> by which we achieve that is not as good as that can be, that means we
> are rejecting the throwing away a lot of material.
>
> Here is a sort of analysis that I modified from Doug Dean\'s
> presentation at the FDA Science Board. The modification is trying to
> overlay the 6 sigma concept on the pharmaceutical manufacturing. The
> present defect rates that you are seeing are more statistical defects
> rates, not the 6 sigma type of defect rates.
>
> Based on some of the information we have, the sigma level of
> pharmaceuticals is around 2.5 or 2.0, whereas, in other sectors, it is
> far superior in terms of the defect rates that you have.
>
> Under cGMP, for example, one way of looking at that would be when
> failures and recalls exceed 10 percent, we generally would say that
> process is no longer validated, and that would translate to a sigma of
> 1.65 in a statistical term, not in terms of the 6 sigma concept that
> is very popular out there.
>
> \[Slide.\]
>
> Also, if you look at the challenges that we face is pharmaceutical
> out-of-specification and batch failure rates, I think we generally
> plan for 5 to 10 percent, but we tend to accept that as necessary.
>
> The data that we have seen from MIT tends to suggest that exceptions
> of out of specification are very dominant in terms of the long
> production cycle times that you see, because investigations have to be
> completed, and it is not uncommon to see cycle times exceeding one
> year or reaching one year when you have out-of-specification results.
>
> This has always been there for discussion, and I just want to share
> one experience that was published in Pharmaceutical Development and
> Technology, have repeated that several times, but in light of the data
> that we have, this is very telling.
>
> I quote from this publication, \"It is authors\' experience that
> validation exercise precedes a trouble-free time period in the
> manufacturing area only to be followed by many hours, possibly days or
> weeks, of troubleshooting and experimental work after a batch or two
> of product fails to meet specifications. This become a never-ending
> task.\"
>
> I think this is one of the things which we want to try to address is
> bring more science, so that we can have resolution to some of this out
> of specification from a more scientific perspective.
>
> \[Slide.\]
>
> So, looking at the risks of suboptimal process quality, what are the
> risks? The risks are risk of releasing a poor quality product, recalls
> are not effective quality control tools.
>
> Drug shortages. First of all, delay in approval of important drugs due
> to manufacturing problems, there is a high potential for disruption in
> the availability of important drugs. We are facing that on a regular
> basis nowadays.
>
> Production of low volume. Essential drugs is also adversely affected
> because all the manufacturing focus tends to be on the large volume
> products, and some of the low volume products are getting neglected.
>
> \[Slide.\]
>
> Without clear understanding of how one optimizes formulation processes
> and how do you define that at the early stage in drug development,
> there is a tendency to have regulatory commitment on inefficient
> manufacturing processes.
>
> That leads to continued optimization activities in the post-approval
> phase, and we have a number of post-approval supplements that come
> through because of that, or on the other hand, there is a tendency to
> live with validated, but inefficient processes.
>
> Recurring manufacturing difficulties lead to very low efficiency and
> capacity utilization, and clearly, high manufacturing and regulatory
> compliance costs are locked in at very early stages.
>
> \[Slide.\]
>
> Continuing on those risks, increased risk of non-approval or delayed
> regulatory approval.
>
> These are some of them, sort of repeating it, but each slide is from a
> different perspective.
>
> There is increased potential for quality problems confounding the
> clinical safety and efficacy databases. I believe this is much
> under-appreciated. More and more because of the development crunch,
> optimization, in fact, development of formulation is being delayed,
> and the tendency is to use drug powder in a bottle for early clinical
> trials.
>
> That raises a risk which is a very significant risk, but
> under-appreciated, the very safety and efficacy database that you are
> developing for approval could be confounded with quality problems, and
> we have seen some examples of that occurring.
>
> Past quality problems can delay new drug approval, and clearly,
> industry and FDA resources are being spent on recurring problems. We
> need to get away from this.
>
> \[Slide.\]
>
> The question is when did we get started on this. This has been a long,
> sort of a project long time ago Tom Layloff had started something
> similar, and before he retired and left the Agency, he and I had
> several discussions on this topic, so in my mind at least, the third
> quarter of 1999, when things started crystallizing that there is a
> need for doing this, and Tom and I co-authored a presentation on this
> topic at the FIP\'s Millennium Congress in San Francisco, and there
> were several other meetings.
>
> One specifically that I want to mention is the New Technology Forum
> that the Royal Pharmaceutical Society had a lot to do with
> crystallizing some of the thought process here, the PhRMA Technical
> Meeting, that is where actually I met Dr. Raju and saw some of his
> data that added to the thought process here.
>
> But the first meeting that we had discussion was on the 19th of July
> at the Advisory Committee for Pharmaceutical Science Meeting where we
> got strong endorsement from this committee to move forward.
>
> Then, we took this concept to the FDA Science Board on the 16th of
> November, and that led to another discussion and formation of the
> Subcommittee.
>
> \[Slide.\]
>
> I can ask the question when\--from a different perspective now\--when
> can companies submit PAT-based applications or submissions to FDA? We
> have never actually objected to this, they could do it any time.
>
> So, any time a company is ready to do so, they can do it. However,
> there are many hurdles that seem to hold back PAT applications. It is
> widely perceived that FDA will not accept PAT-based applications, and
> this is not true.
>
> \[Slide.\]
>
> The hesitation is from uncertainty, so industry is hesitant to
> introduce PAT in the U.S., and the reasons being cited are regulatory
> uncertainty and risks that leads to a \"Don\'t Tell\" and \"Don\'t
> Use\" practice.
>
> Some of these are due to new questions that we don\'t have consensus
> on how to address. New technology results in new questions, is the
> method suitable, how do you deal with chemometric-based decisions, how
> do you validate process and analytical methods that are combined
> together, and also, clearly, old products plus new technology can
> raise new regulatory concerns.
>
> Some of the inherent problems that are on in the currently marketed
> products, how will we address those when they become visible when you
> are applying new technology to those processes?
>
> I think, most importantly, the biggest hurdle I think we face is the
> mindset, why change? PAT applications will add to current regulatory
> requirements, and manufacturing is not really on the high agenda of
> many companies in terms of manufacturing is generally taken for
> granted.
>
> \[Slide.\]
>
> So, how we plan to facilitate introduction of PAT? What we can do from
> FDA perspective is to eliminate regulatory uncertainty. Our position
> has been that FDA will accept PAT applications that are based on good
> science, and the key attribute is good science and how do we define
> good science, and that is where you come in is how do we develop
> standards for PATs.
>
> We need information on how would we define method suitability and
> validation, multivariate statistical and computer pattern recognition,
> how would you rethink your critical process control points and
> specifications, changes, and then out of specifications.
>
> We do not wish to have PAT and add to the list of out of specification
> because some of these can be very sensitive tools and you might just
> increase the out of specification rate because of the sensor drift,
> and so forth, so how do we do this without adding to the problems we
> face.
>
> Our position has been, and will be, the current system is adequate for
> intended use, and that allows PAT to be introduced, not as a
> requirement, but as an option that each company can decide for
> themselves is this the right technology for their products, do they
> have the technology and knowledge base, do they have the capabilities
> of doing this, so this is not a requirement, this is an option to
> improve your processes.
>
> \[Slide.\]
>
> We also would like to define conditions under which PAT may replace
> current end product release testing. We are moving, improving process
> controls to a point that end product testing in many ways will be
> redundant.
>
> The concept of parametric release is often used, but I don\'t like the
> term, first of all, but I think it is much more than parametric
> release that we are talking about, and I look to you to help define
> what that concept should be.
>
> We have to address invisible problems, as I mentioned earlier, and
> also I think one of the key issues here is the review and inspection
> practices. We need to have some clarity, so that you have more
> certainty when you come to FDA how we would look at the data and how
> would we evaluate the data, and last, but not the least, international
> harmonization.
>
> This is not part of the ICH process right now, but down the road we
> will have to think about it.
>
> \[Slide.\]
>
> We are currently moving on two tracks. One track focuses on the
> General Guidance on PAT. The information source for this guidance is
> you, and we have planned two meetings. Meeting one is this one, and
> there is a meeting being planned sometime in June. We haven\'t set a
> firm date yet, and as soon as we have, we will let you know.
>
> This activity will lead to a Draft Guidance, which would then be
> published for comment, and then finalized. The implementation process
> would be a team approach for review and inspection, so we will have a
> Center for Drugs and Office of Regulatory Affairs team looking at
> this.
>
> On the other end, we have the parallel track to this. We have been
> inviting companies to propose submissions now. We expect to receive
> proposals for submissions, I am guessing three by the end of this
> year.
>
> We will plan to have a review and inspection plan for these
> submissions and work with the companies for some sort of a review and
> inspection process to the development effort, so that we can help them
> answer questions as they come about, so that they don\'t have to do
> all, then come to FDA and say this is not acceptable, so we want to
> help and partner in that way.
>
> This will help us bring more information into the Agency and actually
> help the guidance process down the road also.
>
> \[Slide.\]
>
> So, the general guidance on PAT has the following goals and
> objectives. We want to clearly delineate general principles and
> terminology to bring the community on the same page, address issues
> related to regulatory uncertainties, clarify the regulatory process
> from the review and inspection side, and we also hope this will have
> other tangible benefits.
>
> We hope it will serve as a tool for building within-company consensus.
> The last several months, I have visited about five or six companies,
> and one of the challenges I see within companies is different groups
> have no clue what PAT is, and I think there are segments in the
> companies which have done a tremendous amount of work, but other parts
> of the companies don\'t even realize what is happening, so how do you
> bring, say, the R&D, the regulatory affairs, and the manufacturing
> folks together to have consensus within the company is important also.
>
> We also hope to promote research and development activities in this
> area. I think there is much more to be done.
>
> \[Slide.\]
>
> For the guidelines development process, what we are doing at FDA is we
> have formed a PAT Steering Committee, and this is a CDER and ORA
> committee. It is not just Center for Drugs, it is Office of Regulatory
> Affairs, so you are bringing inspection and review side together,
> working together.
>
> The Steering Committee members who are with us today are Doug
> Ellsworth from the New Jersey District, Mike Olson from the field
> labs, Joe Famulare from Office of Compliance, Frank Holcombe from
> Office of Generic Drugs, Moheb Nasr from research side of CDER,
> Yuan-yuan Chiu from Office of New Drug Chemistry, and myself.
>
> We have identified Raj Uppoor, a review chemist, to write this
> guidance, and the project management would be Chris Cole.
>
> We are also developing several communication tools which have not
> fully been implemented yet. We have a web-based system for internal
> communication, but we also have a website on PAT on the FDA\'s
> website. Also, we have set up an e-mail address for PAT-related. It is
> PAT@CDER.FDA.gov, so we hope to get some communication going using
> some of these tools.
>
> \[Slide.\]
>
> The draft guidance that we hope to develop will focus on applications
> related to use of process analytical technologies in drug substance
> and drug product manufacturing.
>
> The point I want to make here is we are not focused only on tablets,
> we are focused on all manufacturing processes, we are focused on all
> technologies, not just near infrared, so this guidance will not be a
> near infrared guidance, and it will not focus on any technology.
>
> We believe that if we focus too much on one technology, that will be
> detrimental to other technology areas, and that is not the right thing
> to do. So, this would be a general guidance covering all manufacturing
> aspects from drug substance to drug product.
>
> \[Slide.\]
>
> What I am hoping is at the end of this meeting, you will get a sense
> of what should be in this guidance. We have started drafting this, and
> these are some of the outline or sections we think should be in the
> guidance.
>
> I wish you would take a look at that and towards the end of this
> meeting, provide us your input on what this guidance should cover. I
> am not going to walk through those sections. I want you to come up
> with your suggestions of what should be in that.
>
> \[Slide.\]
>
> I see there are several options for introducing PATs. This is the
> additional page in your handout that I added this morning or last
> night. I see several options.
>
> Option one is a company might decide to apply PAT to a currently
> marketed product, and for that, they will choose one of the robust
> formulations or products, and apply PAT to improve efficiency, or, for
> example, it would be from a safety concern for the operators. It might
> be a potent drug, it might be a very toxic drug that needs this
> application.
>
> Here, the benefits are improvement in quality will be marginal, but
> the focus would be on efficiency, focus would be on protecting the
> operators, and so forth.
>
> Option two could be you would apply to a currently marketed product
> that needs improvement, there is a lot of problems associated with
> that, and here, I believe a step-wise PAT approach might be
> applicable.
>
> What I mean by \"step-wise,\" is you start focusing on the critical
> process variables that might be creating the problem, and just apply
> PAT tools for a particular unit operation, not for the entire thing,
> and do it step-wise until you get a handle on the manufacturing of
> that product, and then you would move towards a complete on-line
> analysis for that.
>
> A third option, new products. PAT utilized throughout development and
> scale-up, and lab-based tests are not only there to ensure shelf-life
> and/or for establishing public standards. Once you have that system
> set up, you would rely on on-line controls, and not end product
> testing, so that dashed line says you may not have to do routine
> testing, but only for stability and only for public standard-setting
> purposes.
>
> \[Slide.\]
>
> You are a major source of information for us, and I am hoping at the
> end of this meeting, you would be able to give us feedback on topics
> to be covered in this guidance, hopefully start laying out general
> principles for setting specifications, validation, and chemometrics,
> and at least reach consensus on benefits, definitions, and
> terminology.
>
> I don\'t expect to have the whole list of terminology. I think we just
> want to get started, but if we all agree that this is the right thing
> to do, the benefits are there, I think that that will help us move
> forward more quickly.
>
> We plan a second meeting where we hope to have more detailed
> discussion on optimal applications, identification and control of
> critical formulation and process variables, how do you set
> specifications.
>
> What I want to make sure is we think out of the box here, when we set
> specifications, for example, for blending, the current control would
> be time. Instead of going from time, we could move towards blending is
> homogeneous, so we want to think of more performance-based
> specifications, so that you don\'t have to deal with changes much
> more.
>
> We also look to you for illustrative examples for inclusion in the
> guidance, and we hope you will share some of that with us.
>
> \[Slide.\]
>
> The meeting is organized today starting with industry presentations
> for this morning and afternoon. We hope this will focus the
> discussion. We have provided to you several questions, which are in
> your background packet to stimulate and focus our discussion.
>
> We have four working groups, Benefits, Technology,
> Definition/Terminology. There is a general working group, which I hope
> will come to consensus at this meeting, and the next meeting we can
> look at the option of disbanding that working group and merging the
> membership with the other working groups. That is the hope. I am not
> sure we will reach that or not.
>
> Then, we have a working process and analytical validation,
> chemometrics, product and process development, and we have planned
> only two meetings. The challenge there is I hope we can do this in two
> meetings.
>
> \[Slide.\]
>
> I just wanted to say a few things about chemometrics. I am just
> focusing on that topic because I think it needs some clarification.
> Chemometrics, the term, the fathers of chemometrics are the two
> listed. We have one of them in this room.
>
> Multivariate data collection and analysis is what we are focused on. I
> think chemometrics can be much broader than that, but I think our
> focus is on multivariate data collection and analysis.
>
> We are looking at issues related to design of experiments, principal
> component analysis, partial least squares, non-linear partial least
> squares, neural networks as a toolbox set, but also focus on
> multivariate calibration, process modeling, patent recognition and
> classification, signal correction and compression, multivariate
> statistical process control, and other issues.
>
> I think what we are looking for is the type of tools we should prepare
> ourselves to deal with, general principles for validation, and there
> are several things here that I just want to bring to your attention.
>
> Software validation, there are many different approaches to that. One
> of the approaches that I am looking at is Center for Devices, their
> approach to process validation of computer software, I think would be
> a good model. I will try to get you a copy of that guidance that was
> recently published, and it is very logical guidance of how you
> validate software.
>
> But I want to leave this podium with the following challenges. In this
> room, we have very different perspectives, different expertise and
> affiliations. The challenge is I think we can come to the same page at
> the end of this meeting.
>
> If we are able to do that, I think I will consider this meeting to be
> successful and get ready for the second meeting, but the question I
> think I would leave here with is are two meetings sufficient to gather
> information necessary to develop the general guidance.
>
> We think it is because the scope of this guidance is so general and
> the processes related, we can do a lot. By the time we come back next
> time, we would have drafted that guidance.
>
> Also, is the general guidance proposed the most effective approach? I
> would like to hear from you on that.
>
> Thank you.
>
> DR. LAYLOFF: Thank you, Ajaz. I would like to point out to all the
> other speakers that Ajaz was on time.
>
> I have a couple of comments. If you look back, if you have been around
> the business of pharmaceutical analysis for a while, and you look at
> innovations and analytical technology and the invisible findings, I
> don\'t think that PAT will bring to us the invisible findings that the
> introduction of GC and HPLC brought to us when we switched from
> measuring things by UV measurement and composite analysis when we went
> to individual unit analysis by HPLC and GC. That moved us to a new
> plane, and there were lots of invisible problems out there that we
> encountered.
>
> Similarly, RIA, radioimmunoassay brought to us a lot of invisible
> problems in bioavailability that we didn\'t know were there. I don\'t
> think PAT is going to bring us things of that scope. I don\'t think
> there are that many things hidden under the rocks right now that HPLC
> brought to us with impurities and which RIA brought to us with
> bioavailability.
>
> Our next speaker now is Steve Hammond from Pfizer.
>
> **Session I: Process Analytical Technologies**
>
> **Applications and Benefits**
>
> **Perspective 1**
>
> **Steve Hammond, Pfizer**
>
> MR. HAMMOND: Good morning.
>
> \[Slide.\]
>
> I am going to speak about applications and benefits of Process
> Analytical Technology.
>
> \[Slide.\]
>
> I am going to work my way through six examples, three from API
> manufacture, three from drug product manufacture. I am going to sort
> of skip through what I regard as a process. There are a number of
> other things that we have done, but I hope these six examples
> illustrate some of the things that can be done and the benefits of
> these systems.
>
> I have to say that nowadays, there almost is a technology out there to
> do measurements if it is required. You can almost ask me to do
> something, and given a few months, I can probably find a measurement
> technology to do it. So, the technology is generally there to do most
> things that we need to do.
>
> \[Slide.\]
>
> The first example is the use of mid-infrared for action monitoring,
> just simply studying a reaction in real-time, inserting in this case a
> probe actually into the reactor, and you can find selective peaks in
> the mid-infrared spectrum and watch the disappearance of the reactants
> and the appearance of the product you are looking for.
>
> The big benefit of this on-line system is that you don\'t have to
> sample it, so plant operators don\'t need to go near the reactor. We
> can get an accurate measure of the endpoint, and that actually allows
> us to control impurities. We can balance when we want the maximum
> against minimum amounts of impurity formation.
>
> \[Slide.\]
>
> Having made an API, one of the critical process steps is the
> crystallization of the material before it\'s dried. We regard this as
> a big opportunity with this sort of device that we can insert this
> probe into a crystallizer and actually look at the crystals as they
> are forming and measure their size.
>
> This system has a fast-moving beam of light that comes out the end of
> the probe, and it just shines across the particles, and it is able to
> detect when it hits one side of a particle and when it hits the other
> side of the particle, essentially measures what we call a cord length,
> but it is the diameter of the particles. This is manufactured by a
> company called Lasentec.
>
> \[Slide.\]
>
> This is the sort of data that you can get watching your
> crystallization happen, is the safe point, and then you can see these
> size fractions of crystals forming. For this particular product, what
> is really of interest to us is the number of particles or crystals
> that we have between naught and 10 microns.
>
> This material later on goes into a process where the amounts of fines
> in there really does matter, and we found that by altering the speed
> at which we crystallize or even putting in cooling and warming steps,
> we can move these naught to 10 micron particles up to here. That
> actually removes downstream processing problems.
>
> But the use of this technology, we think will allow us almost, for a
> lot of APIs, to avoid milling all together. If we can control the size
> of the particles we produce in a crystallizer, we can avoid a lot of
> problems later on.
>
> \[Slide.\]
>
> What is also very useful when you are doing that sort of measurement
> is to put an endoscope into the crystallizer and actually look at the
> crystals, as well, because with that product, what we know is that
> these little side crystals forming are a problem, and what we really
> want is these nice, big, well-formed crystals in the middle, so
> actually looking in the crystallizer, as well as doing the
> measurements, gives you a lot of process knowledge about what you are
> doing, so we control fines, we can avoid agglomerates, we can reduce
> the need to mill, and generally we can control the particle size of an
> API.
>
> \[Slide.\]
>
> Having got the API, one of the common steps that we use is to dry the
> material. This is a typical dryer that we use. It\'s a pan dryer. We
> have inserted a near infrared probe into the base of the pan dryer.
> The near infrared is outside of the flameproof area, and we use fiber
> optics to interface to the dryer.
>
> \[Slide.\]
>
> This is a typical sort of profile that we get of drawing this
> material. We are actually removing the solvent acetonitrile. It is
> where the dryer is charged. You can see this large drop here, that is
> increasing the intensity of the absorption of acetonitrile, and this
> is the process which we flash off the acetonitrile, and then the
> gradual creep of the acetonitrile out of the crystals, because a
> certain amount is actually entrained in the crystals.
>
> What is of interest here is this step motion you can see here is a
> function of the dryer\'s agitator. So, this not only gives us a great
> deal of control over that drying process, we can stop it early. This
> is all wasted production capacity because the material was actually in
> spec here, but we can gain a lot of information about how the stirrer
> is actually working for this particular product.
>
> So, the benefits of this are improved capacity, which is cost, but
> again we can also control this process and make sure that we don\'t
> damage the product by overdrawing it.
>
> \[Slide.\]
>
> I am now going to leap forward to drug product manufacturing, but
> staying with the theme of drying. Within Pfizer, we have a new fluid
> bed dryer system that we are working on.
>
> Instead of having one very large tower, we have three sequential small
> towers. The resonance time for each of these towers is only about five
> minutes, so we do crude drying of large amounts of the water in the
> first tower, a partially dry product moves to a second tower, and
> there is actually a third tower here to do the final polishing of the
> drying.
>
> We have mounted these near infrared instruments on each of these
> towers, so that we can accurately offload one tower to the next based
> on a measurement, not just on a time.
>
> \[Slide.\]
>
> I just want to show you a drawing profile for one of the towers. This
> should in theory be a smooth curve as you go from a wet material here
> to a dry material here, but we found that isn\'t actually, it\'s
> sawtooth.
>
> These sawtooths actually relate to the filter cleaning. In fluid bed
> dryers, they have filters on the outlet to stop the product escaping.
> Periodically, in this system, the filter is backflushed, so you get
> material that\'s on the filters being pushed back into the dryer bowl.
>
> The material on the filters is actually wetter than most of the
> material in the bowl, so these, you have a nice drying curve, and
> suddenly you add wet material from the filters back into the bowl, and
> then that dries and you get the same process.
>
> Not only can you control that drying process to get to an endpoint,
> but you are getting knowledge about the function of the dryer, what
> are the vagaries of it, and timing your offload of the dryer relative
> to the filter cleaning actually becomes important. The on-line
> technology allows you to control that, before that even gives you the
> information to know that is happening.
>
> \[Slide.\]
>
> I would now like to talk about on-line blending. This has been driven
> by a new product that we have where the API is highly potent, and so
> has exposure limitations for our operators.
>
> We have mounted a small diode array instrument actually on the
> blender. The instrument is battery powered, and it communicates with
> its controlling computer via radio modems, which actually allows us to
> have the instrument in one room and the computer that is controlling
> it somewhere else, usually in another room, but can be up to 100
> meters away without any problems.
>
> \[Slide.\]
>
> This is the system. This is the battery for the unit plus the radio
> modems are in this box here. The instrument itself, the diode array,
> it\'s an in-gas diode array from Zeiss is in this box, and then we
> have a fiber optic connecting to a reading head, which collects
> spectrum through a sapphire window that is mounted into the lid of the
> blender.
>
> The head does not come into contact with the product, and this whole
> installation is permanent. You can just detach the reading head and
> take the bin off the system.
>
> \[Slide.\]
>
> The structure of the reading head is one of the vital points in the
> design of this instrument in that we collect a spectrum from a circle,
> a diameter of 30 millimeters.
>
> The size of this diameter has been very carefully worked out from
> experimentation on the depth of penetration and the density of the
> blend, so that we know that this reading head collects a spectrum from
> a weight of sample of around 300 milligrams.
>
> So, what we have done is to design the technology, so it collects what
> we usually regard as a sensible unit dose weight. This is very, very
> important in these sorts of measurements that you design the
> technology to collect what are really sensible GMP weights.
>
> \[Slide.\]
>
> The sorts of data that this instrument collects look like this. These
> are typical near infrared spectra. These are absorptions of saccharin.
> We have done a number of draw batches in this system using just
> saccharin in typical pharmaceutical ingredients to just shake down the
> system all together.
>
> This is the absorption of saccharin, its aromatic absorption, and this
> is typical of the change that we see in near infrared spectra during a
> blending process. We can use the spectra in two ways.
>
> One, we can look to see when the spectrum stops changing, because that
> gives us a blending endpoint, but we also need to look at the
> variation in groups of these spectra collected sequentially to get a
> measure of mixing, how homogeneous is the blend.
>
> These are the typical sorts of absorptions we look for that are
> specific to an ingredient. In this case, we have an absorption here.
> The aromatic is specific to saccharin.
>
> \[Slide.\]
>
> We can also find absorptions like this one that is specific to
> magnesium stearate, so not only can we monitor the uniformity of the
> active, but we can monitor the uniformity of things like magnesium
> stearate, the lubricant, and this is the change in the lubricant as
> that is mixed into the blend.
>
> \[Slide.\]
>
> An easy way to look for an endpoint is simply to plot the change in
> absorbance of each of these ingredients. In fact, what I am showing
> you here is saccharin that we regard as the active plus lactose and
> avicel, typical pharmaceutical ingredients.
>
> We are looking at the uniformity of all those ingredients, not just
> the active. So, that can give us an endpoint, but that is not enough.
>
> \[Slide.\]
>
> We need to know what is the uniformity of the mixture, and the way we
> do that is we take eight points, eight sequential spectra, and we
> calculate the standard deviation across those eight points.
>
> So, during a run, we may very well take 60 measurements, but they will
> be used. We can plot those in groups of eight and watch the change in
> variance. What that tells us is that we start off with a decrease in
> uniformity and then we reach a point when we start to gain uniformity,
> and that is very typically these blending operations. This is a
> uniformity curve for magnesium stearate.
>
> \[Slide.\]
>
> I just wanted to show you one example of this system on a full
> production blender. This is 1,000 kilos of blend in this particular
> unit in the plant in Sandwich in the UK.
>
> \[Slide.\]
>
> It is interesting, the active for this particular product is loaded in
> the middle of all the other excipients.
>
> \[Slide.\]
>
> This is the change in the aromatic absorption of the active ingredient
> during the blending process, so it starts here, and the process moves
> down to here.
>
> \[Slide.\]
>
> If we plot a cross-section through that absorption specific to the
> aromatic, we see three phases, a phase here where we don\'t actually
> pick up the absorbance of the active at all, because it is actually
> still in the center of the blend, and gradually migrating its way out.
>
> Here is the migration phase. We also have a third phase that we are
> pretty sure is the active actually starting to coalesce. This active
> has a tendency to form balls within the blend.
>
> But the point is that with that technology, we can get an
> understanding of the process of that blend by looking at what is going
> on inside that blender in real time.
>
> \[Slide.\]
>
> The benefits of this system, one of the key ones for us is no operator
> intervention is needed, the system is totally automated. For some of
> the new highly potent actives, that has become very important.
>
> You avoid sampling the bland. There is no error due to a thief. You
> get this information in real-time. We can look at multi-ingredients,
> the uniformity of them, how fast does one ingredient blend relative to
> another.
>
> We get an enormous amount of process understanding. We can fingerprint
> the process from stage to stage during scale-up. It gives us the
> ability to maybe blend to uniformity rather than a set time. We can
> actually adjust the blend time to get to the quality endpoint. That
> allows us we think to move closer to right first time.
>
> We also get fast release of the blend, which reduces their cycle times
> during manufacture.
>
> \[Slide.\]
>
> I just want to mention NIR analysis of tablet cores. We have for
> several years now been using a manual system to pass near infrared
> light through the center of the tablet cores after they are pressed.
>
> This is quite a simple device. A fiber optic just passes the near
> infrared light through the center of the tablet, and we collect the
> light that has come through.
>
> \[Slide.\]
>
> Just to show you somewhat of problem that this system detected in our
> plant in Australia. The plant operators once an hour take a collection
> of tablets. They take 10 tablets and they scan them on that device and
> look at the average potency and the content uniformity.
>
> Each of the dots you see on this plot are once an hour, a plant
> operator has checked the potency of the tablets being produced. You
> can see towards the end of this batch we have super-potent tablets.
>
> That was identified as blend segregation in the bin, and the problem
> is easily cured with changing the flow characteristics of the system,
> but the important point here is that that amount of scrutiny, this
> continuous monitoring of this process gives us the ability to detect
> these problems, to know they are there, and to cure them.
>
> \[Slide.\]
>
> What we are trying to do now is to move that testing into an automated
> fashion. On a lot of our tabletting machines, we already have weight,
> thickness, and hardness measurement systems, and what we are going to
> do is to combine a near infrared transmission measurement into that
> box, so that the tablet press has this near infrared capability.
>
> We are going to sample usually around 200 tablets per batch to check
> for content uniformity and potency across the batch.
>
> \[Slide.\]
>
> That is a picture of the reading system that we are going to use.
>
> \[Slide.\]
>
> I just wanted to show you the spectral change that we can see in a
> product. In this case, this product has the concentration of the API
> is 0.2 percent, and we are looking here of changes from 0.05 percent
> to 2 percent. This is a placebo, and these are the changes in
> concentration. So, even at that very low level of API, this system is
> more than capable of performing the measurements we require.
>
> \[Slide.\]
>
> In fact, this is a correlation between HPLC measurement for these
> tablets and a near infrared measurement across the range of 0.1
> percent to 2 percent.
>
> \[Slide.\]
>
> Again, the benefits for the on-line analysis of tablet cores are very
> similar to on-line analysis of blends, but the one thing we can do is
> use this system to, in an automated way, comply with PQRI
> recommendations on sampling unit doses.
>
> \[Slide.\]
>
> I just want to end by talking about our vision for the future of this
> sort of technology, because in our opinion, the best way to look at
> content uniformity of a blend is to look at it under the microscope in
> this sort of way, or with a tablet, again, to look at the matrix you
> have actually made and look at the uniformity of that matrix.
>
> We have been developing lab systems to do this.
>
> \[Slide.\]
>
> What we would like to do is to take the system I have already shown
> you with these components, remove them, and put imaging technology
> onto this blender, and actually look at the matrix that we have made
> in detail, and use that for judging the quality of the mixture we
> have.
>
> \[Slide.\]
>
> In summary, the benefits of the improved control we feel give us an
> enhancement on the conventional testing that we already do. The
> conventional methods do provide a product that is fit for intended
> use, but certainly advanced control gives us a better batch-to-batch
> consistency, better quality. In the case of APIs, it can give you less
> impurities and a much better controlled particle size.
>
> It should eliminate reworks/rejects, all of the re\'s that we are used
> to in our industry, improved understanding, faster response times to
> customer demands, certainly better productivity, and, in the end,
> lower cost.
>
> Thank you for your attention.
>
> DR. LAYLOFF: Thank you very much, Steve, and for staying on time. It
> was a very exciting presentation, very, very interesting new
> technologies.
>
> I would like to call on now Doug Dean, who will give us a Perspective
> 2.
>
> **Perspective 2**
>
> **Doug Dean, Ph.D., PricewaterhouseCoopers**
>
> DR. DEAN: Thanks, Tom.
>
> Once again, my name is Doug Dean. I am a Canadian living in Basel. I
> was worried yesterday that as a result of the Olympic hockey results,
> that Canadian weren\'t going to be allowed into the country, but I did
> make it in after all, so thank you for that.
>
> \[Slide.\]
>
> Ajaz asked me to emphasize two things in this short perspective for
> you. One is the potential win-win and benefits that are actually out
> there, and the second is to link back to some of the basic criteria,
> the motivation for change and the need to do things differently.
>
> \[Slide.\]
>
> I think if we look at where we are right now as an industry, two
> things become fairly clear, that we can\'t continue the way that we
> have in the past, we have seen a number of examples of that, and that
> the potential for change probably relies on slightly different
> approaches than we may have taken in the past.
>
> I think the third point is that there is quite a significant potential
> for benefit, both to consumers and to the industry and regulators
> here, as well.
>
> \[Slide.\]
>
> We look at where those benefits will come from. I see chiefly that it
> is going to be from a combination of factors - reduction in risk and
> in concomitant increase in compliance effectiveness, and that will be
> a win for regulators and for consumers, and as we have seen already in
> a number of examples, significant potential for reduction of cost and
> that then leading to an increase in shareholder return. That will
> certainly be a win for the business and provide additional resources
> to be put back into research and development for the creation of new
> products.
>
> \[Slide.\]
>
> Just take a moment here to look at the challenges that are facing the
> industry. We are all well aware of that, but I would like to very,
> very briefly link back to some of the macroeconomic factors here.
>
> First of all is that in the past 30 years, we have seen a dramatic
> slowing in the rate of growth in the industry, and that is apt to
> continue, probably looking at single-digit growth in the foreseeable
> future.
>
> \[Slide.\]
>
> When we look at the total annualized shareholder return of the top 20
> pharmaceutical companies, we see that that has been steadily falling,
> the implication of this, of course, being that we look to the
> shareholders for providing capital that we can invest internally to do
> new research, look for new products, and that is very important to
> raise this, but yet we have seen it falling consistently over the past
> number of years.
>
> \[Slide.\]
>
> When we look at really the engine room of the industry, what is
> happening in research and development, we have seen a couple of
> disturbing trends there. One is that in spite of the dramatic and
> steady increase in investment in research and development over the
> last 15 years, the output from that process as measured in new
> entities has been pretty consistently falling.
>
> I think the figures that I have seen for 2001 indicate a slight
> uptake. There were about 32 new entities released last year, but
> overall, this seems to be a steadily decreasing output from the R&D
> process.
>
> \[Slide.\]
>
> As if that is not enough, when we look at areas of exclusivity within
> a given therapeutic category, over the past 30 years and more, we have
> seen that steadily decrease. It is getting more competitive, and the
> implications there are that there are reduced windows of exclusivity
> to get a return on the investment that has been made to produce the
> new entity, and really, no matter what category we look at, that is a
> very consistent and ongoing trend.
>
> \[Slide.\]
>
> Within that macrocontext, when we look at what we, as manufacturing
> professionals, have delivered to the pharmaceutical enterprise, we see
> that there are some unmet performance expectations, chiefly four
> points that we can look at.
>
> The ability to utilize the assets and get a return on the investment
> that is made in those assets is actually quite low, and we typically
> see 15 percent or less being a fairly normal figure for asset
> utilization in the industry.
>
> It has been said a number of times already, I would emphasize it
> again, we generally begin every new financial year by assuming that we
> will scrap or rework between 5 to 10 percent of everything that is
> produced in a facility.
>
> If we look at what happens in the new product introduction process, it
> generally takes years as opposed to months to get a new process and a
> new facility fully effective, up to speed, and producing at project
> commercial scales.
>
> In conjunction with all of this, we see a very, very consistent cost
> of quality across the industry of between 20 to 25 percent. So, I
> think we can all agree that there is some significant opportunity for
> improving and changing some of these performance figures.
>
> \[Slide.\]
>
> The basic conclusion here is that the industry is under pressure, as
> many industries are. That means that there is going to be more
> competition for resource, and manufacturing will have to contribute
> positively to helping take the organizations forward.
>
> The good news about this is that there is a lot of room for
> improvement, and I think when we look at the main areas where we are
> going to see a contribution coming from manufacturing, it will be in
> reducing the level of cost to achieve the required level of compliance
> and quality, reducing the amount of time that it takes us to become
> fully operationally effective, and dramatically compressing the time
> to introduce new products at commercial scale.
>
> \[Slide.\]
>
> We do a bit of root cause analysis here and look at where the problems
> really start. We see that it begins far before they ever get to
> manufacturing, and a lot of the problems that we face in manufacturing
> are related to processes that are transferred, that really aren\'t
> capable or are not completely understood, and therefore very difficult
> to make them operate at commercial scales.
>
> The current approach to new product introduction creates a tremendous
> volume of data. Often it is not the critical information that we need
> to achieve the level of process capability that we require in
> manufacturing. We need to look at that.
>
> That leads to a phenomenon that we have uncovered in a number of
> studies, that approximately 50 percent of manufacturing costs are
> locked in around about the end of Phase II clinical trials\'
> production, and that means that there is really no scope for improving
> the cost structure when we get to full-scale operational production.
> Clearly, that is not a good situation.
>
> With the emphasis on new product introduction and time to market,
> often there is no basis to trade off the need for better process
> understanding in exchange for a little bit more time to achieve that
> process understanding, and I think this is something that needs to be
> better understood.
>
> \[Slide.\]
>
> So, when we try to link this back to PAT, we will see that there are
> three key factors here that consistently come up. One is that
> improving potential means that we need a better visibility of
> value-added versus non-value-added activities in manufacturing, and I
> will show you what I mean by that in a moment, but we will find that
> process analytical technologies will help to eliminate a lot of the
> non-value-adding activities.
>
> The way that we are currently measuring production effectiveness is
> usually MRP II driven, and frankly, the metrics are most often
> produced for accountants rather than for improving productivity, and
> we will see that the kind of data and information that we get from
> PAT-like technologies will enable a better window into the measurement
> of the production processes.
>
> A lot of this is linked back in reducing cost, to getting it right the
> first time, and I think we will see, and we probably all agree here,
> that PAT will definitely support this and allow us to move to a model
> that is more oriented towards productive quality management rather
> than reactive quality.
>
> \[Slide.\]
>
> Just as an example here, looking at a step in production of a solid
> dosage form, this happens to be a dispensing activity. It takes three
> days. When we look at the value-added time, the actual measuring out
> of the material that is required there, it is actually a relatively
> small proportion of the total time taken in that step, all the other
> activities adding no value to the conversion of those raw materials,
> but consuming a lot of time.
>
> \[Slide.\]
>
> If we proceed in this particular example\--again this is all the
> dosage form\--looking at the concatenation of all those various steps,
> looking at the way cost and time were aggregated as we go from
> dispensing through to packaging and final release, 35-day process, of
> which only three days of the process are actually adding value in the
> conversion of raw material to finished goods.
>
> \[Slide.\]
>
> What we generally see is that there is tremendous scope for reducing a
> lot of this non-value-added time, and we would generally expect that
> if one knows what the actual value-adding portion of the cycle time
> is, roughly, about two times that is the length or the maximum
> compression that you can expect to achieve, so for a three-day
> value-added cycle time, we can probably get that total process down to
> six days at best.
>
> You eliminate a lot of activities and get a lot of things right the
> first time to do that. That means that there is an associated cost
> reduction that comes with that, and these figures that I am showing
> here are by no means out of the ordinary. I think that is a fairly
> representative situation.
>
> \[Slide.\]
>
> Just for a moment, look at the way we measure things in manufacturing.
> There is usually a great allocation of losses and unexpected
> activities, that we really don\'t have much visibility over, and if we
> look at trying to quantify better what is happening in unscheduled
> down time, what happens when we lose time operationally, and how much
> time are we actually spending producing materials that are scrapped or
> reprocessing materials that were not done right the first time.
>
> If we could actually get better visibility of that, it would help to
> eliminate the root causes, understand the root cases and eliminate the
> problems that lead to those inefficiencies in the first place. Process
> Analytical Technologies will help greatly to achieve that.
>
> \[Slide.\]
>
> Ajaz has spoken already today about the sigma metrics, measuring the
> ability of a process to be right the first time. I think this is a
> critical thing to consider. We see that in some industries, the
> aggregate sigma level of production facilities is somewhere around 5,
> 5 1/2 sigma, and we typically see it is a function of dosage form, but
> average and generally speaking, about 2.5 sigma in the industry as a
> whole.
>
> That correlates very well with our observed levels of the cost of
> quality in most dosage form production facilities of about 20 to 25
> percent.
>
> \[Slide.\]
>
> Where that variability comes from, due to two things. The inability to
> maintain a process within its upper and lower specification limits,
> and the inability to maintain a process stability. It may be producing
> very tight output, but it may not be stable and it may wander a bit.
>
> So, if we can understand what is causing that and measure that in a
> real-time or a near real-time environmental, it does help to control
> it much more effectively.
>
> \[Slide.\]
>
> We look at where the benefits will come from. It all rolls up to the
> unit cost of production, and benefits will accrue in a number of
> different areas. If we can get it right the first time, and reduce
> scrap, we will reduce material cost, and if we are more effective in
> assuring quality, we will reduce period costs and expenses.
>
> If we get it right the first time, there is an overall effective
> increase in process capacity, and if we are scrapping and reworking
> less material, then, there is an effective increase in the process
> efficiency overall, leading to a fairly dramatic drop potentially in
> the unit costs of production.
>
> \[Slide.\]
>
> If we look at what a 5 sigma pharmaceutical production facility could
> be like, cost of quality and compliance would come down from about 20
> percent of period costs to about 3 percent. That would be more than 50
> percent lower than a typical facility in operation today, but a 6 full
> compression in cycle time and with a better process understanding,
> hopefully, newly introduced processes that are effective almost
> immediately rather than taking a number of years to understand the
> process and get it right.
>
> The key enablers that we would see in all of this, better process
> understanding and some sort of a parametric profiling, and some
> ability to trade off the need for process understanding versus time in
> the development process. These are all prerequisites to appropriately
> using PATs as we would see it.
>
> Then, the application of Process Analytical Technologies in production
> itself, all based on probably, as Ajaz has already commented on, the
> need for some basic IT-enabling technologies to tie all of this
> together.
>
> \[Slide.\]
>
> The big benefits are going to come in terms of the improvements in the
> compliance infrastructure and increasing the effectiveness of that
> compliance infrastructure. Looking here at a 2 sigma compliance and
> quality cost curve, which aggregates the cost of internal and external
> failure, the cost of appraisal and prevention.
>
> If we had a facility that was capable of operating at 5 sigma, what
> that would mean is we could move our operating point potentially at a
> significant reduction in operating cost, achieve a much higher level
> of compliance and quality.
>
> \[Slide.\]
>
> In summary, then, I think one of the things from a business
> perspective what we will see going forward is to improve productivity.
> It is going to be absolutely necessary to measure things in a
> different way. I believe that the application of Process Analytical
> Technologies are fundamental in enabling us to do that.
>
> We will need, however, more than just an aggregation of technologies
> that are applied in various points in a production process. It will
> need to be tied together and linked with different ways of working,
> particularly in the discovery and development process.
>
> There is, in my view, very, very definitely a significant win-win here
> both for the industry and for the consumers and for the regulators,
> and I think that is what we should be focusing on as we deliberate the
> various things that have been put forward for us here in the meeting
> today and tomorrow, and going forward for the meeting potentially in
> June.
>
> Thank you very much, Mr. Chairman.
>
> DR. LAYLOFF: Thank you, Doug, for keeping on time. Thank you very
> much.
>
> Now we are going to have time for subcommittee discussion. I would
> comment, Doug, we think the hockey win once every 50 years is about
> 2.7 sigma, and that is acceptable.
>
> I would remind the Subcommittee members if you would like to speak,
> that you push down on the microphone switch until it turns red, and if
> it\'s red, it is active. When you are through speaking, push the
> button to turn it off.
>
> I open the discussion now to the Subcommittee.
>
> Any questions?
>
> **Subcommittee Discussion**
>
> DR. MORRIS: Actually, this is more by way of comment. I think the
> win-win potential is, of course, outrageously high. Two comments,
> though.
>
> One is that comparing the semiconductor industry to pharmaceutical
> industry does have a couple of inherent problems in that the
> complexity of the systems we work with are quite different, I mean in
> terms of understanding of the physics of the raw materials, there is
> quite a big difference, not that that can\'t be addressed, but it gets
> addressed at one level, at the level it can, and you still will
> probably never get to the point of taking an organic molecular system
> and characterizing it as well as you can in an atomic system.
>
> The other thing, and this is to Steve\'s point, is that if I go to you
> and say I need a sensor for something, you can find the sensor. The
> question is what should I be monitoring, and that is the other
> difference.
>
> There are some things that if you need to monitor moisture, you
> monitor moisture and that\'s done, but there are other things,
> electrostatic charge, for instance, if I tell you I need to monitor
> that, it is not at all clear how you would do that or what it is that
> really contributes to the generation of it or its problem.
>
> So, this is a little bit in terms of, my comments, that is, are a sort
> of directed towards making sure that we look at the raw material
> variations which are very often the major cause of these problems even
> if you have a process that is well defined, change the raw materials,
> and there you are, out the door, which has been much more fully
> addressed in the semiconductor industry, for instance.
>
> The level of R&D, that your plot has actually included discovery R&D,
> as well, so if you look at the process R&D, the question is what is
> the return there, and I suspect it will be sort of similar, though, in
> the sense that we haven\'t really put the kind of basic R&D money
> against understanding the raw materials as well as we might.
>
> So, just to sort of frame the under side of this whole issue, I guess,
> I think we need to make sure we keep all of this in our heads.
>
> DR. LAYLOFF: I would ask as anyone speaks, to identify themselves, and
> that was Ken Morris.
>
> DR. MORRIS: And it still is.
>
> DR. LAYLOFF: Any other comments or questions?
>
> DR. BOEHLERT: Judy Boehlert. I guess I direct this question to Mr.
> Hammond. I don\'t know whether you did the PAT studies on old products
> or new products, but my question is probably the same.
>
> Can you tell me, did your focus change, did you spend more time
> looking at a product development formulation and quality of raw
> material issues or process development, you know, control of the
> process and transfer, is there one area where you put more of your
> focus?
>
> MR. HAMMOND: For that particular product, I was asked to focus on
> monitoring the process and controlling it, but I would add that we
> have an extensive near infrared database that we use for raw material
> conformance, not just identification.
>
> We do actually track trends of raw materials to look for rogue batches
> that will give us some problems in manufacturing. So, under a separate
> program, we are doing that as a global initiative, tracking raw
> materials in terms of consistency.
>
> I mean you are absolutely right, you install this sort of technology,
> but if the raw materials change a lot, well, you will see that it is,
> but you really want to eliminate that before you ever get that into
> the process, and that is a huge part of right the first time, so we
> are addressing that.
>
> DR. BOEHLERT: I would agree. I have long believed that the quality of
> the raw materials we used in process is the critical factor that
> perhaps hasn\'t been studied enough, particularly when it comes to
> physical properties.
>
> DR. LAYLOFF: I think that is what Ken was discussing, that there are
> critical control points that you may or may not have identified, and
> some of them are associated with. I think it was quite interesting,
> though, that crystallization monitoring, so that you could assure
> better consistency of incoming material streams.
>
> DR. LACHMAN: Leon Lachman. On the same subject, on control of
> materials, what about potential contamination of materials, will you
> pick that up?
>
> MR. HAMMOND: We will pick up certain contamination, particularly
> chemical contamination, but in most of the systems that we use, we
> wouldn\'t pick up biological contamination, I think, which is an
> issue, but that is something we are researching at the moment, looking
> for rapid biological testing systems actually that we can install in a
> warehouse, and have warehouse operators looking for biological
> contamination.
>
> Metal contamination is another one where at present, the types of
> technology we are using does not pick it up very well, and we are
> looking at advanced metal detection systems.
>
> So, there is a lot going on in terms of looking at the quality of the
> raw materials, because obviously, it is key to being able to do it
> right the first time.
>
> DR. LACHMAN: I think what this sort of indicates to me that there was
> a lot of effort going into the R part of R&D, but I think there is
> going to be a greater effort that has to go into the D part of R&D
> now, when you get into these new technologies, and this has not been
> existent in the past.
>
> I think before you can get to using these routine in-process controls,
> validation controls, you are going to have to do a lot more
> development effort, and I think that is where there is a big lag or
> lapse in this whole R&D effort.
>
> DR. LAYLOFF: Thank you. I think I agree, Leon, there is going to have
> to be more development work going with it. I think what we see a lot
> of is consistency assessment for the process control where you are
> actually looking at consistencies rather than the incoming quality
> stream.
>
> I think the incoming quality stream will have to be addressed with
> other technologies, and that most of the PAT areas are consistency
> assessments, and I think only the added contamination of bacterial
> contamination or metal contamination, which can occur in the process,
> or stability problems would not show up there, but the consistency is
> what we are looking at.
>
> I think that dimension has not been addressed well by the current
> technologies, but these other aspects are not hidden rocks. They have
> been there all the time also.
>
> DR. MELVIN KOCH: Mel Koch. I guess I was going to make a couple of
> points, the tone of the two discussions here, one, and the importance
> of improving development, I think is becoming apparent.
>
> I have had the impression that the cost of marketing, of formulation,
> of registration were always dominant relative to the percentage of
> total cost of manufacturing, and that is changing, as it has to as the
> industry is facing some of the problems we have heard.
>
> Now addressing 6 sigma and certainly trying to identify with the
> achievements that have occurred in the semiconductor industry is still
> a stretch, and I think most people who have gone along the 6 sigma
> route have found out that maybe they can only achieve a 3 or 4,
> approaching 5 sigma result.
>
> The next phase is I think even more important than recognizing the
> importance of 6 sigma, and that is in the design for 6 sigma, that
> most people have assumed, say, in the discovery process or even in the
> early development process, that chemistry done at a one liter scale is
> the real chemistry, when, in fact, there is a lot that occurs in
> getting to first principles of what is chemistry and getting into
> often miniaturization, diffusion-based controls, et cetera.
>
> So, improving in the understanding or the principles of putting the
> early stages of the process together and monitoring at that phase, I
> think is what is going to show the real results.
>
> DR. HUSSAIN: A couple of comments. I think the point that was made
> with respect to physical attributes of raw material is a critical one,
> and I think that was the first thing that attracted me to PAT.
>
> I think controlling crystallization of a drug substance is one part of
> the story, but the raw material excipients, we generally don\'t have
> that level of control on those, and are unlikely to have that control
> because of the nature of that segment.
>
> But having technologies that can give you valuable information on both
> physics and chemistry of that material is important, so starting with
> PAT applications of the raw material, processing itself is critical.
> That was one point I wanted to make.
>
> The second point, I was a bit surprised to see in Steve Hammond\'s
> presentation, reference to PQRI, and I think it makes sense, but I
> would sort of position that from a different perspective.
>
> In PQRI, the stratified blend sampling proposal that is being proposed
> focuses on the product itself, so again, it is still in the concept of
> testing for quality. I think with PAT, you are doing it much ahead of
> time. So, that is where I would put PAT application.
>
> DR. LAYLOFF: I agree. If the excipients are a key factor and since
> most of them come from the food industry, they are not going to put
> the control on them that you could exert on the other pharmaceutical
> components.
>
> I am not sure what it is as a value-added, though, in terms of
> clinically, you know, how important the added control would be in
> terms of cost and clinical effectiveness if you did control it,
> because the clinicians, the way they prescribe the stuff is really
> quite sloppy compared to the way it is produced in the industry, but I
> think there are a lot of cost saving factors that could be introduced
> here by adducing the consistency, but I think it is clinically
> significant as a factor also.
>
> DR. LACHMAN: I would say it also impacts on processing significantly.
> That is where it is going to play a major role, because most of your
> solid dosage forms are excipients with the rare exceptions when a drug
> is a major portion of the product.
>
> DR. HUSSAIN: Tom, I think the point I had tried to make was quality
> problems confounding safe and efficacious database. I think linking
> quality to safety and efficacy is always a challenge, and how we do
> that, I think we will always face that challenge.
>
> But one perspective on that issue is when we develop our products for
> clinical testing, clinical trials, the fundamental foundation is the
> quality. If you don\'t have a quality product, then, how do you get
> safety and efficacy? So, it\'s a circle of argument.
>
> DR. LAYLOFF: If the pivotal lot is sloppy, then, you are up a creek.
>
> DR. WILLIAM KOCH: I am Bill Koch from NIST.
>
> I am seeing two challenges facing the whole Process Analytical
> Technologies. One, that is the knowledge of the molecular properties
> of both the reactants and the products that we hope to achieve.
>
> I think for a long time, the sciences decided we know all the
> thermodynamics and kinetics that we need to know. I think we need to
> rethink that and go back and look at thermodynamics and kinetics and
> get the data that we need, so that we can understand the molecular
> properties.
>
> I agree, looking at Adams is relatively simple. Looking at complex
> molecules become more of a challenge particularly exasperated now that
> we have high throughput screening and communitorial techniques, and we
> are making new molecules, thousands and millions of new molecules a
> year. We don\'t really understand all the properties, both chemical
> and structural, which then begs the question of how you are going to
> measure all these things, and puts another challenge, developmental
> sensors, that can measure the properties that we need.
>
> Until I think those two research aspects are addressed and recognized,
> we are going to have a little difficulty going forward with process
> analytical.
>
> DR. LAYLOFF: Then, we throw into the box, differential glycosylation
> on proteins, and then you are whole another box.
>
> DR. MORRIS: I think the structural aspects in particular, which is
> more in my area of interest, become challenges to be measured, but
> first, you have to know what it is to measure.
>
> I didn\'t want to say anything, but since Steve has already said it, I
> mean if you look at the sort of databases that are being generated by
> companies, like Pfizer and others, it is really those data that are
> going to ultimately tell us what it is we have to measure when we
> cycle back through actual experiences with failures, because the idea
> is that it is not enough just to be able to very accurately document
> when your process failed.
>
> It is to be able to generate formulation and process development that
> keeps it from failing and at scale, as we were saying, and, of course,
> Tom, you have been preaching this for a long time, but just a
> clarification.
>
> DR. KIBBE: I have got a couple of questions for Mr. Hammond, more on
> the regulatory end of what is going to happen down the road, because
> we are supposed to be advising the FDA about how to regulate.
>
> The question first is you went to an in-process PAT in which location
> in your worldwide net of locations, and why did you go there in that
> location instead of a different one, what was the environment that
> made it worthwhile to do it in that location?
>
> MR. HAMMOND: The on-line blending system, the location that that would
> be installed in is in Germany, Tanquiller-tissen. We went there
> because of the safety issues of handling the API in that product. It
> has essentially got to be made in a containment facility, and there
> can be no operator intervention at all with the blends or the tablet
> cores. Until you have coated them, they are essentially a real safety
> risk.
>
> So, the driver for the PAT there was most entirely safety, so we
> needed to control the process without operators going near it.
>
> DR. KIBBE: So, the company makes that product only in that one
> location?
>
> MR. HAMMOND: It will do, it\'s a new product.
>
> DR. KIBBE: But you selected a location to match. The question I really
> want to get at is, was there a regulatory aspect to your decision to
> go to that location to be the plant to make that product using this
> process, how was that linked?
>
> MR. HAMMOND: I don\'t know that there was any particular regulatory
> reason for going to that plant. I think that plant was chosen because
> they felt that that plant was fairly advanced in PATs and could handle
> that technology.
>
> They were also a fairly high-tech plant that would handle that
> product, but in terms of the regulatory issue, they are going to be a
> worldwide source for that product, so they have every regulator in the
> world to worry about.
>
> So, I don\'t think that the site was chosen for any regulatory
> perspective.
>
> DR. KIBBE: Let me just follow up. Your company then is comfortable
> that our agency would accept that product here using this technology,
> right?
>
> MR. HAMMOND: Yes. Well, I mean at this stage, we are talking to the
> FDA about what we are going to do with production of that particularly
> difficult to manufacture, very safety issue product. I mean one thing
> we are hoping to do is to partner with Ajaz and show CDER everything
> that we are doing in terms of that monitoring technology, so we are
> hoping to work with the FDA on that.
>
> DR. RAJU: Just to add on to that, kind of push that question a little
> further, is it fair to say that in many ways the FDA is considered to
> be one of the tougher regulatory bodies in terms of bringing in new
> PAT technology on their examples, such as Australia, where they have
> made more progress?
>
> MR. HAMMOND: Yes. I mean this product is a case to point with. The
> biggest opposition to using the new technology on the product was not
> that people think the technology would work, everyone is pretty well
> convinced it will, but internal regulatory groups were very worried
> about what the FDA would say, simply because it wasn\'t conventional
> sample to blend and do HPLC, it was sample to tablet cause and do
> HPLC.
>
> Internally, there was fear that the FDA would be a problem.
>
> DR. RAJU: I forgot to introduce myself. G.K. Raju. Sorry.
>
> DR. LAYLOFF: I would like to comment on that. I think it probably is
> true that the FDA is one of the stronger drug regulatory authorities
> in the world and representing a very significant market where
> everybody eventually will want to come with their product, so they are
> going to have to come through FDA one way or the other.
>
> It may be that in PAT, we will have to go to something like a team
> PAT, like team BIO, where we actually team individuals together to
> bring more expertise in to help bring the training levels up, but
> ultimately, if you want to come to this big market, you are going to
> have to come through FDA one way or the other.
>
> DR. BOEHLERT: I might add that it is not just the reviewers at FDA
> that are going to have to be part of the team, but perhaps the
> inspectors, as well, because both of them are going to be looking at
> that new process, and we don\'t want them looking at it in different
> ways.
>
> DR. LAYLOFF: Team BIO is ORAM, CBER Biologics, we are looking at
> biological products, and that is the concept I was saying that maybe
> we need a Team PAT concept where you have more engineering and
> statistician type people coming from along with the GMP type people,
> so that I think that if our people\'s teams are not properly educated,
> then, we start looking at what is possible rather than what is
> probable, and when you move outside the probable box, move into the
> possible area, you are paralyzed.
>
> DR. MORRIS: Actually, to come back to a point I was interested in
> earlier, Doug, in your presentation, do you have statistics that
> correlate the R&D money spent on nonclinical and nondiscovery versus
> time to market, or at least time to IND or something?
>
> DR. DEAN: No, we don\'t really. There has been some useful work done
> out of MIT in that area. Wheelwright and\--and G.K., help me with
> this, I forget\--
>
> DR. RAJU: Wheelwright at Harvard, but I think it was Laskmi Sham and
> Stu Myers who did the finance and the R&D, and Rebecca Henderson who
> published in Harvard Business Review.
>
> DR. MORRIS: So, is it that broken down like I asked, though?
>
> DR. RAJU: The focus is usually on product research and not necessarily
> on process research, and where you should allocate your money in the
> different phases based on the different levels of risks.
>
> This again brings up the issue that the industry in general, and so
> the academia as a result of sometimes leading tends to do all their
> research on the product side of the research in terms of where you put
> your money, in terms of where your priorities are, and so when we look
> and we say that process development is where we should bring in all
> this new technology, and the understanding opportunity is, we also
> have to look at the bigger tradeoff in terms of the overall
> corporation\'s priorities in time to market where the cost of goods
> sold is 25 percent and the gross margin is 75, so there is a natural
> predisposition to say that we will always have to choose more often
> than not to go to market quickly rather than that process
> understanding incremental improvement.
>
> As Doug was saying, that tradeoff is not 100 and zero, it is 25 and
> 75, which not necessarily makes it a clear answer always. The tradeoff
> has to be better defined, and the answers will come out as a result, I
> think.
>
> DR. MORRIS: I guess just to follow up before I let you defend
> yourself, because this isn\'t any reflection on your data, but I guess
> in terms of framing the idea of justification of PAT, it would be
> helpful to have statistics or metrics that are more directly
> reflective of the potential benefits. I mean the potential time to
> market and folded in with everything else is also important, all these
> statistics are necessary, but I was thinking of an earlier assessment
> of the potential benefits, not that I know, by the way.
>
> DR. DEAN: Two comments on that, Ken. First of all, you may slightly be
> misunderstanding the point of raising the case. There was a
> productivity problem in R&D, the point being that I think we are going
> to see that turn around, and we are going to see a dramatic increase
> in the n number of new product introductions, so the issue there is
> that we have a very compelling need to get it right.
>
> If we think this is an issue for us now, it is going to be an even
> bigger issue in the future because it\'s fundamental to the long-term
> health and stability of this industry, so that is just going to
> happen.
>
> I guess I am suffering from a jet lag and a brain cramp here, but
> there has been a tremendous study done called the development factory,
> and just for the life of me, right at this moment I cannot remember
> the author of that study.
>
> DR. RAJU: Gary Pisano.
>
> DR. DEAN: Gary Pisano, thank you very much. I think a lot of the kind
> of fundamental work that you are talking about there in looking at
> tradeoff and where the benefits come from, we can take some of that
> from Pisano\'s work.
>
> DR. RAJU: Gary Pisano did a very interesting study, and he talked
> about the need to do more development and the need to do learning
> before doing, and I think the PAT framework, he obviously didn\'t
> necessarily think through PAT specifically, but I think the conference
> that we have and the two discussion days that we have today and
> tomorrow, fit very beautifully.
>
> Every time you can measure faster and see more, it only makes this
> argument that much stronger, so I think that is a very complementary
> thing.
>
> DR. LAYLOFF: I think in this case we shift the process assessment from
> analysis to consistency, because right now we are locked into analysis
> all the time. We are constantly looking at the process in terms of
> analysis of components in the process rather than consistency of the
> process.
>
> DR. SHEK: I am just looking at the topic of this section, which is
> basically looking at application and benefits for PAT, and that is
> what we are trying to assess, and if you look at it from the
> perspective that we are trying to automate aspects, so there is the
> quantity and there is the quality, so we can collect more data, but I
> think what is the important part is the quality, what do we see if we
> take samples manually and then run an assay, or we have sensors at the
> right place, do we collect better data.
>
> I am basically referring to this aspect, you know, to utilize it
> during the development process, to develop better products, and it is
> my belief there where, you know, the benefit will come, if we will be
> smart enough to do that.
>
> One of the issues that I see there, you know, the evolution of the
> type of products we are developing is going to change because as we
> discover more complex molecules, which are more difficult to deliver,
> and to ensure that they are efficacious and effective, we might see
> dosage forms which will be different than, you know, the tablets we
> have today, and we have to keep somehow in mind that there will be a
> shift there, too, to develop and commercialize such products and will
> the system, at the same time we are trying to find, let\'s say, more
> efficient ways to see and measure what is happening during the
> manufacturing process itself, to develop, and will we able to do the
> other part to adapt it to new type of dosage forms to more complex
> molecules.
>
> DR. KIBBE: I have got a couple of questions I would love to have
> somebody respond to.
>
> First, on the comments of the quality of the excipients, I think if
> the demand for a specific characteristic of excipients went up,
> excipient manufacturers would attempt to meet it, so while we might
> not have the excipients at the same standards that we want because our
> PATs are going to be better than at standards, I think Dow and some of
> the others would want to come along with us.
>
> The question I really have is we are moving in this direction, and
> there are some companies that are going to come forward with
> in-process activities that would then be acceptable to the FDA. The
> FDA is saying that this is not mandatory, and the question is how long
> before that shifts, because the tendency has always been with current
> good manufacturing practices and current good laboratory practices for
> the Agency to keep holding everyone to the standard that is being set
> by the leaders in the industry.
>
> DR. MORRIS: Can I just make a couple of comments, one on your first
> point, and to Dr. Lachman\'s point, the excipient manufacturers of a
> certain magnitude, if they are producing a certain magnitude, the
> starch industry doesn\'t really care much what we tell them, the sugar
> industry doesn\'t really care much, they are not going to change their
> processes significantly.
>
> Commodity, chemicals, it depends on if it\'s commodity drug, maybe you
> will get them to be more responsive. This isn\'t an insensitivity on
> their part, it\'s just numbers.
>
> The other point, to try to address your question, though, with respect
> to how the technology gets filtered down as a regulation, hopefully,
> if we are successful enough in instituting the technology
> successfully, the use will increase enough to up the amount of sales
> for each of the types of technologies, and so the prices become
> competitive enough, so that they can be substituted for traditional
> analysis.
>
> We have seen this certainly in NIR. I don\'t know, Tom, what they cost
> when you started doing your work, but they are relatively inexpensive
> now, and other sensors right now, and I don\'t know what yours is
> doing for, but there are sensors now that are higher priced literally
> because of the volume, and I think that is true of the LIF, as well.
> When the volume goes up, they will be cheaper than doing the wet
> chemistry, I think.
>
> DR. SEVICK-MURACA: My name is Eva Sevick from Texas A&M in Chemistry
> and Chemical Engineering. We are in sensor development. There is a
> couple of phrases that caught my attention where we are talking about
> regulating the technology.
>
> That is scary to the technology developers. I find that to be impeding
> some of the work that we are doing. We are not really regulating the
> technology. What we are trying to do is regulate the performance of a
> process that we use the technology to get that information.
>
> One of the things that when we are working with companies to try to
> commercialize technologies, they are scared out of their wits because
> of this comment of regulating the technology, because that is not what
> we want to do.
>
> If we put the guidances together, so that we say we need to make such
> and such a measurement in such a way, and leave it open to whatever
> technology, that is what we really need to do, because I think that we
> were styling technology development when we start talking about
> regulating technologies.
>
> DR. LAYLOFF: I think that is Ajaz\'s comment, you know, not NIR
> guidance, we are looking at it more broadly, so you can address any
> kind of technology, what areas do you need to apply the technology,
> but basically, you are looking at different assessment tools, what do
> you require for those assessment tools to perform, how they perform.
>
> DR. SEVICK-MURACA: Right, so if we could somehow state that this
> technology, you can use this technology to assess performance, that
> the technology has this accuracy, this precision, and our guidance
> says that rather than talking about the technologies itself, the NIRs,
> so we can make them very, very broad, then that would work well.
>
> But right now I think that in my dealings with companies trying to
> commercialize our technology, this is the thing that has been scaring
> people off.
>
> DR. LAYLOFF: You will have an opportunity tomorrow to get your
> thoughts down on paper.
>
> A comment on Efraim, we have talked here primarily about drugs, and we
> have talked about tabletted, I guess capsule type formulations, but I
> think this would also extend to biological products and to vaccines
> where I think there are already alternate technologies for assessment
> of consistency is used, because they can\'t do them any other way.
>
> DR. SHEK: My point was with regard to the effectiveness of the drugs.
> Some of the dosage forms are quite complex, and the way you put them
> together, the way you manufacture them might make a difference, and
> then we will have to find a way that you can test it, that you
> haven\'t changed anything during the process.
>
> MR. HALE: Tom Hale. I think another aspect that we need to think
> about, that has been alluded to, is that we can measure a lot of
> things, but if we don\'t also look at the process unit operations and
> the design of the unit operations at the same time, we may be
> measuring something that is inherently unmeasurable and that the
> critical part of implementation of this sort of technology is thinking
> about in the design phase and the scale-up phase, whether not only can
> we measure product and process or the process itself and the equipment
> itself is inherently measurable and scalable, and it will be critical
> to the implementation in parallel to the measurement activity itself.
>
> DR. LAYLOFF: Another thought is does it relate further downstream to
> the process.
>
> DR. LACHMAN: I think this is the main crux. I think you don\'t do
> adequate design work and don\'t do adequate scale-up during
> development, what you are trying to measure for consistency is routine
> process control may be doing the wrong thing for you.
>
> So, I think the investment has to be upstream before you go
> downstream, and I don\'t think that is being done enough.
>
> DR. LAYLOFF: I guess if there are no further questions, comments, we
> will take a break now and we will reconvene in a half-hour. Kathleen
> runs the meeting, and she tells me what to do, like Charlie McCarthy,
> so she says you have a 20-minute break. See you in 20 minutes.
>
> \[Break.\]
>
> DR. LAYLOFF: I think the presentations were very interesting this
> morning. In a sidebar conversation I had on product assessment using
> PAT, I was reminded that what we currently do with product releases,
> we take six tablets and do dissolution, maybe 10 or 20 or 30, and do
> content uniformity, and we release a batch that may be 3 million
> tablets or 3 million units based on an analysis of maybe 20 or 30
> tablets without demonstrating that the batch, in fact, is represented
> by a continuous statistical function, nor do we have a statistically
> representative sample that we use to make the release.
>
> I think PAT brings us to a higher level of quality than we currently
> have because of the lack of good statistics with our product release.
>
> Moving on to the agenda, our next speaker is John Shabushnig from
> Pharmacia.
>
> John.
>
> **Session II: Product and Process Development**
>
> **Perspective 1**
>
> **John G. Shabushnig, Ph.D., Pharmacia**
>
> DR. SHABUSHNIG: I would like to thank the FDA for the opportunity to
> participate in this subcommittee, and look forward to our continued
> effort in this area.
>
> \[Slide.\]
>
> In 1985, I came to the Upjohn Company. At that time, we had a vision
> in terms of what we would like to see in terms of analytical testing.
> We talked about at that time what we thought the laboratory of the
> future would look like, the QC laboratory, the future, and our vision
> was that that laboratory be an empty room, that there be no point in
> bringing samples back to a laboratory, but that all of the data
> necessary to control a process and make decisions about product
> quality would be obtained on-line or near-line, close to the process
> where it would do the most good.
>
> So, really, that vision was to go from a laboratory-based, finished
> product testing to truly on-line or in-process testing.
>
> \[Slide.\]
>
> Well, why use this technology? I think we have heard a lot of good
> comments already this morning, but I think the key drivers for us are
> improved process control, the opportunity to reduce our testing cost,
> reduce cycle time, and from that reduced cycle time, the opportunity
> to reduce our in-process inventory.
>
> \[Slide.\]
>
> What is it? We have heard a lot of different talk about the technology
> itself and a lot of talk around spectroscopic methods particularly
> near infrared and laser induced fluorescence, but there are also
> physical measurements like viscosity and specific gravity, optical
> measures of refractive index, and a number of electrical measurements,
> impedance resistance, dielectric constant, specific ion measurements,
> temperature, pressure.
>
> My point in putting this up\--and these are all measurements that we
> have made within Pharmacia\--is that don\'t ignore the simple
> measurements, don\'t get too focused on the gee-whiz applications, and
> near infrared is a very powerful tool, laser-induced fluorescence is a
> very powerful tool, but there are also some very simple in-process
> measurements that can give us a lot of information, as well. So,
> don\'t lose sight of those when we talk about process analytical
> technologies.
>
> \[Slide.\]
>
> Well, what are the common attributes of these measurements? First of
> all, they are non-destructive measurements, they tend to require
> limited or, ideally, no sample preparation. They provide for a
> convenient process interface. You saw the applications using fiber
> optics, and fiber optics then often lead to the ability to make
> multipoint measurements, again to provide more information about the
> process.
>
> They have rapid response times, and they have adequate dynamic range
> for the measurements that we are trying to make, the concentration
> ranges of which we are interested.
>
> \[Slide.\]
>
> Some familiar applications and some things that worked on within
> Pharmacia, and that is to look at moisture and, in particular, I
> wanted to point out that we have talked a lot about oral compressed
> tablets, we have talked about dry products and granulations, but this
> technology is certainly applicable to injectable products, as well,
> and we have used it to good success when looking at lyophilized
> powders and looking at sterile aqueous suspensions.
>
> Again, we have looked at moisture, again, something that has a strong
> absorbance in the near infrared lends itself to a good, robust
> measurement. We have looked at granulations and compressed tablets as
> have already been talked about.
>
> We talked about looking at and worked on potency, in this case sterile
> aqueous suspensions, and looked at other blend uniformity applications
> there, as well.
>
> We have also used the technology for identification of raw materials,
> packaging materials, and of the finisher product itself.
>
> When we talk about in-process measurement, we have talked about
> parametric release, but again, things like sterilization processes,
> like steam sterilization or using vaporized hydrogen peroxide, and
> using optical measurements of the vaporized hydrogen peroxide
> concentration as an indicator of controlling that sanitization or
> sterilization process.
>
> \[Slide.\]
>
> Well, how is it used? One is to support process development, and I
> think that is one key area that we want to see. I think moving
> upstream in the development process will help us in terms of
> implementing Process Analytical Technologies and ultimately,
> implementing more robust processes. Again, the opportunity there is to
> reduce the amount of laboratory testing that would be required.
>
> An example here is with our sterile aqueous suspension. It isn\'t in
> necessarily the development of the product formulation, but rather the
> development of the process or the process equipment.
>
> In this case, as we were developing the filling process, a suspension
> is a difficult product to fill, we used near infrared measurements of
> the potency of that sterile aqueous suspension to look at content
> uniformity, look at segregation that may occur in the filler, look at
> optimizing the recirculation process of that filler.
>
> The analytical test, that is, the registered test for that product and
> the release test, is an HPLC assay with a fairly extensive prep time
> and turnaround time, and we still rely on that assay for release of
> the product, but by using the near infrared method, we could take many
> more samples and do much more in terms of the optimization of that
> equipment and that process, and then confirm those results when we did
> our final validation testing for that process.
>
> So, it allowed us to gather more data, it allowed us to gather that
> data in a real-time manner, and to optimize the equipment in the
> filling process much more rapidly and to explore more variables than
> we would have been able to had we gone with the traditional HPLC
> method used in the laboratory. Yet, in terms of the actual registered
> test, we still were using that registered test.
>
> So, in terms of that parallel testing, if you will, I think it allows
> us to have more rapid confirmation of process performance, and to take
> larger samples that may more meaningfully represent the process that
> we are interested in.
>
> I have seen in some of Steve Hammond\'s earlier talks the idea of the
> \"don\'t ask, don\'t tell,\" and I think that really is pretty
> representative of the situation that we find ourselves in, at least on
> the process side, and that is, we have a registered test using more
> conventional analytical technology, but that we can run an alternative
> test, an in-process test, that gives us more information about the
> process and supports process development, but yet this is not a
> registered test and is not used for product release. So, we do operate
> in that \"don\'t ask, don\'t tell\" mode.
>
> Finally, there are limited applications where a process analytical
> test is actually used for the release of the product. Very early on,
> at least in the Upjohn Company, prior to mergers that became the
> Pharmacia Company, we had developed and registered a test for a
> veterinary product that used near infrared technology for product
> release, looking at moisture content, looking at potency, and looking
> at identification.
>
> So, those applications have been successfully registered with the
> Agency, however, that is not the norm. It is really the exception in
> most cases.
>
> \[Slide.\]
>
> Where do I think we are now? If I liken the technology development
> here to the drug development process, I would say that we are in Phase
> II, and that is, I think we have demonstrated the efficacy of Process
> Analytical Technologies. There has been a lot of good science that has
> gone into the development of these technologies, and I think we have a
> very solid foundation on which to proceed, but I don\'t think we are
> ready yet to release this as a product, if you will, that we are ready
> for approval.
>
> I believe our moving into Phase III, where we need to have broader
> application of the technology, and work out what I consider to be the
> engineering and development details, those process interfaces and more
> specifically, the ruggedness and reliability of the methods as we go
> forward.
>
> I think those are very achievable. I think we have the right people to
> do that, and I think with appropriate Agency support of that
> technology, we will have the incentives to move forward in that area.
>
> \[Slide.\]
>
> What I believe are the obstacles to broader use, and we have talked
> about a little bit, and I believe we will talk about it more today, is
> a little bit of the catch-22 situation that we find ourselves in
> today.
>
> Ideally, these methods should be developed during the product
> development process and transferred as part of technology transfer,
> but today, it is perceived that there is a risk in delay or product
> approval when there is a different method that is used or not a widely
> accepted method, and so that risk, and that risk is not only in terms
> of the delay of the approval and the cost of that delay on a sales
> basis, but also the loss of the limited lifetime of exclusivity, the
> patent lifetime for a particular product.
>
> So, there is a high cost to delay, and therefore, there is more drive
> to implement an acceptable process, but not necessarily an optimized
> process. So, I think the opportunity is to move back in the
> development process, and in doing that, we will see both improvements
> in the process itself and improved use of Process Analytical
> Technologies.
>
> If, on the other hand, we wait until the product is introduced, now we
> have duplicate method development cost if we implement after approval.
> Again, at that point, you need to essentially duplicate an investment
> that has already been made, and so you justify that on the incremental
> improvement as opposed to the first time benefit that would be
> achieved with that additional control.
>
> Again, there is the supplement filing and the review process that goes
> with that. So, it is a relatively long cycle even if it is done
> post-approval.
>
> I think the uncertainties around regulatory acceptance, we tend to be
> fairly risk averse, and so any uncertainties will cause us to think
> our position and be very cautious in terms of implementing this
> technology.
>
> Finally, one that I think is very important to recognize, and that is
> issues around complexity and reliability. Here we have I think again
> very good science behind the instrumentation that has been developed,
> but I think we need additional ruggedness and reliability in that
> instrumentation in order to use it effectively and use it widely.
>
> The example that I would use today is when we pull a sample, take it
> back to the lab, and we may make a potency measurement using HPLC, if
> we have a failure with that HPLC, it\'s a relatively straightforward
> matter of retesting, either to re-prep the sample and reinject the
> sample, and there is adequate control over that process, but if we now
> get to the point where we are dependent in terms of the data that we
> are going to use in order to make a release decision on a given batch,
> is dependent upon in-process measurements, and if we have a failure of
> an in-process instrument, then, we have essentially upped the ante,
> and we have a higher likelihood of losing that batch if indeed we lose
> the instrument independent of whether the process is performing as we
> had intended it to.
>
> So, I think again we have to think through the strategies in which we
> are going to employ the technology, and we need the ruggedness in that
> technology. Not all of that is a regulatory issue. Some of it I
> believe is an engineering issue.
>
> \[Slide.\]
>
> Well, where do we go from here? Along that same theme, I think we need
> to improve the measurement equipment, we need to make it more rugged,
> we need to make it more reliable, and certainly smaller, faster,
> cheaper doesn\'t hurt either.
>
> Those things, if we make them smaller, faster, cheaper, open up the
> doors for redundant instruments and therefore getting back to the idea
> of additional reliability in the data stream and the information
> stream.
>
> We would like to see an improved regulatory climate, and I think this
> subcommittee is an excellent example of changes in that area, and I am
> very optimistic that we will come to a win-win solution.
>
> Again, I think the goal here is to reduce uncertainty around the
> regulatory environment and to support PAT as an option with respect to
> process control.
>
> I also think that our best way forward is to identify those
> high-value, high-access applications to model. Look for those examples
> that we can point to as real successes with respect to Process
> Analytical Technology, and use those for broader dissemination of this
> technology.
>
> Finally, developing guidelines for development and validation will
> again help move this process upstream.
>
> \[Slide.\]
>
> I would just like to close by acknowledging the contributions of my
> co-workers at Pharmacia - Lloyd Fox, Bob Leasure, Jackie White, Rick
> Whitfield, and Steve Doherty, who have done much work in the
> development of the applications that I had pointed out earlier.
>
> Again, I would be happy to discuss any of those applications in more
> detail specifically, but wanted to use my time this morning to talk
> about what I believe were the general issues before us.
>
> Thank you very much for your attention.
>
> DR. LAYLOFF: Thank you very much, John. You are under schedule
> significantly.
>
> DR. SHABUSHNIG: I thought I would keep it short and get to the point.
>
> DR. LAYLOFF: You are an outlier on the short side.
>
> Since we do have a few minutes, I would like to go around the table
> and introduce everybody before Kathleen hits me. If we could start
> with John James, introduce yourself, and give us your day job, and we
> will move around the table this way.
>
> DR. JAMES: John James, Director of Analytical R&D for Teva
> Pharmaceuticals.
>
> DR. SHABUSHNIG: I am John Shabushnig. I am the Director of the Center
> for Advanced Sterile Technology at Pharmacia Corporation.
>
> DR. DEAN: I am Doug Dean. I am a managing partner in a global
> pharmaceutical practice, PricewaterhouseCoopers Consulting.
>
> MR. HAMMOND: Steve Hammond, Manager, Process Analytical Support, at
> Pfizer.
>
> MR. COOLEY: Rick Cooley. I am an analytical chemist in the process
> analytical chemistry area of Eli Lilly.
>
> MR. CHISHOLM: I am Bob Chisholm, International Technology Manager with
> AstraZeneca based in the UK.
>
> DR. TIMMERMANS: Hugh Timmermans from Merck and Company, Manager,
> Pharmaceutical Technical Operations.
>
> DR. WORKMAN: Jerry Workman, Kimberly-Clark Corporation, Senior
> Research Fellow.
>
> MS. WONG: Judy Wong, Senior Engineer, Process Development, Schering
> Plough.
>
> DR. RUDD: David Rudd, head of Process Technology in GlaxoSmithKline
> R&D in the UK.
>
> DR. MILLER: Ron Miller, Bristol-Myers Squibb, Associate Director of
> Pharmaceutical Technology and Development.
>
> DR. SHEK: Efraim Shek, Vice President, Pharmaceutical and Analytical
> R&D at Abbott.
>
> DR. SHARGEL: Leon Shargel, Vice President, Biopharmaceutics at Eon
> Labs, a generic drug manufacturer.
>
> DR. BLOOM: Joseph Bloom, University of Puerto Rico, Professor.
>
> DR. ANDERSON: Gloria Anderson, Morris Brown College, Callaway
> Professor of Chemistry.
>
> DR. KIBBE: Art Kibbe, Professor of Pharmaceutics, Wilkes University
> School of Pharmacy.
>
> MS. REEDY: Kathleen Reedy, Food and Drug Administration.
>
> DR. BOEHLERT: Judy Boehlert. I have my own consulting business in the
> consulting areas of quality systems, R&D, and CMC submissions.
>
> DR. MELVIN KOCH: Mel Koch, Director of the Center for Process
> Analytical Chemistry at the University of Washington.
>
> DR. RAJU: G.K. Raju, Executive Director of the Pharmaceutical
> Manufacturing Initiative at MIT.
>
> MR. HALE: Tom Hale. I consult to the pharmaceutical industry out of
> Chicago.
>
> DR. MORRIS: Ken Morris, Professor in Industrial and Physical Pharmacy
> at Purdue University.
>
> DR. SEVICK-MURACA: Eva Sevick, Professor of Chemistry and Chemical
> Engineering at Texas A&M.
>
> DR. LACHMAN: Leon Lachman, consultant to the pharmaceutical industry,
> regulatory compliance, and regulatory affairs.
>
> DR. WILLIAM KOCH: I am Bill Koch, Deputy Director for Chemical Science
> and Technology at the National Institute of Standards and Technology.
>
> DR. HUSSAIN: Ajaz Hussain, FDA.
>
> MR. FAMULARE: Joe Famulare from FDA, CDER, Office of Compliance,
> Director, Division of Manufacturing and Product Quality.
>
> DR. CHIU: Yuan-yuan Chiu, Director, Office of New Drug Chemistry, FDA.
>
> DR. LAYLOFF: Now we will move on with Dave Rudd.
>
> **Perspective 2**
>
> **David R. Rudd, Ph.D., GlaxoSmithKline**
>
> DR. RUDD: Thanks very much. Let me start just by thanking you for the
> opportunity to come and tell you a bit about the sort of process
> control and measurement strategy that we are starting to introduce now
> in GlaxoSmithKline both within R&D and in manufacturing.
>
> \[Slide.\]
>
> I thought we would get started a little bit around the business case.
> I don\'t want to spend too much time on this, but I found this very
> interesting set of data on the UK Department of Trade and Industry
> website, and it just shows UK\--and I stress UK, this is not meant to
> be a slur on the manufacturing industry and the rest of the
> world\--but the UK manufacturing profitability by industry sector for
> the period \'95 to \'99. That is just where the data takes this.
>
> You see some very interesting things here. I think this is
> manufacturing profitability based on the return for every pound or
> every dollar invested. So, you can see all of these sectors actually
> make a profit, but pharmaceuticals somewhere in midstream.
>
> You can see some quite interesting factors coming through there. For
> example, in the UK in 1997, we smoked very heavily. We smoked
> particularly heavily I think based on concern of our national soccer
> team qualifying for the world championships, but mercifully, you can
> see in \'98, if you look in the beverage column, we see celebrated in
> the traditional British way, when the team did qualify.
>
> The single thing coming out here, though, there is room for
> improvement in terms of our industry sector, and I want to look at
> briefly why that might be. The profitability in our industry ought to
> be good, and it clearly isn\'t as good as it should be.
>
> \[Slide.\]
>
> One reason I think for that is that we are locked into conventional
> manufacturing approaches. We are still a batchwise processing
> industry. This is how we manufacture. We feed, we operate our process,
> we get some kind of output, we store and hold.
>
> \[Slide.\]
>
> In truth, we do have process control, but it\'s based on some closed
> loop measurement of parameters that we can measure - temperature,
> time, pressure, things that may not necessarily be quite interesting
> or revealing, but what the hell, we can measure them, so let\'s
> measure them anyway and put them in to a database that we might look
> or never look at in the fullness of time.
>
> So, there is a word of warning for us. Let\'s make sure that any PATs
> that we develop and using new technologies do not fall into the same
> trap. Let\'s not simply measure things because we can measure them.
> The message is make sure we can make measurements and use those
> measurements as controls when they are critical and when they are
> useful.
>
> \[Slide.\]
>
> So, here is our approach now, our policing function as I will call it.
> We do off-line, lab-based review of product quality parameters and we
> hope that quality is good.
>
> \[Slide.\]
>
> Well, the case for improvement has been made already, and I was very
> pleased to see some of these major points appearing in previous
> presentations. I am very pleased to see some of these points, and I
> won\'t reiterate them.
>
> The one extra one that I want to make, though, is that we have the
> capability with PATs to move more towards continuous manufacturing
> processes in our industry. If you go back to the first slide and look
> at why foods and petrochemicals and the motor industry and the
> aircraft industry are more efficient than we are, one reason, maybe
> not the only reason, but one reason is they do use something closer to
> a continuous manufacturing approach, but in those circumstances, you
> don\'t have the luxury of end product testing. You absolutely have to
> get on-line measurement in there if you are going to guarantee your
> process stays under control.
>
> So, I would like to think about PATs. Maybe the \"T\" in PAT should
> stand for tool, and not technology. It\'s a tool, it\'s a means to an
> end. What we are really interested in is developing high-quality and
> robust processes, and the measurement capability allows us to achieve
> that. The big danger is that we just get locked into the measurement
> for the sake of it.
>
> \[Slide.\]
>
> So, if I look at the objectives, we have agreed, within GSK at least,
> and I think within industry, when we are developing products and
> processes, these are the sorts of watch words, the key words that
> repeatedly come out. You have heard some of these before, and some of
> these will come out a little bit later as we speak, but I think this
> is sort of the charter that we sign up to, the contract that we sign
> up to during product and process development, and in particular, in
> conjunction with manufacturing, I made this point very clearly, I
> hope.
>
> \[Slide.\]
>
> This is not just about development, this is about development with
> manufacturing in mind. I believe that one of the hurdles we have to
> overcome in our industry is this first point, the provision of
> manufacturing and monitoring equipment and technical expertise at the
> development scale, at the development stage, which can also be used by
> manufacturing or which manufacturing can relate to.
>
> We have a major problem in our industry whereby manufacturing is
> saying we want to reduce cycle times, eliminate waste, give us new
> manufacturing technologies or give us improved manufacturing
> technologies, and that is perfectly understandable and perfectly
> supportable except in R&D, we have product development teams who are
> developing using traditional approaches and traditional manufacturing
> equipment because that is the manufacturing equipment we are going to
> be using worldwide for several years.
>
> There is an imbalance there, there is, if you like, a barrier we have
> to overcome. How do we provide R&D with a development capability that
> is also matched to what manufacturing need? The answer is you have to
> build some kind of pilot scale facility or some kind of prototype
> factory of the future that is both R&D accessible and also utilizable
> by manufacturing.
>
> The whole theme of all of this is developing the process
> understanding, identifying the critical process parameters, not just
> the parameters we think we can measure, implementing controls where
> you need them.
>
> One thing about PATs is that you may make the measurement during
> development and discover you don\'t need to make that measurement
> routinely because the process is well controlled in that respect.
>
> Conversely, if it isn\'t well controlled, you had better make sure you
> make that measurement and use the process feedback to modify the
> process on the fly, and then the question is what is the
> decisionmaking process that you need to use based on the PAT
> measurement and based on the knowledge of the process. This
> information is in people\'s heads at the moment, and we need to bring
> it out and document and articulate that.
>
> \[Slide.\]
>
> I thought I would illustrate that by showing a couple of things that
> we are up to within GSK at the moment, and I picked a classical tablet
> manufacturing process and the various unit processes there, and I
> thought I would just show you a couple of things around blending and
> granulation.
>
> \[Slide.\]
>
> Blending, we have heard a lot about, homogeneity of powder blending.
> Clearly, it is a prerequisite of a good product, content uniformity of
> tablets. You had better make sure you have got a good blend, and I am
> interested to open a PQRI debate later.
>
> \[Slide.\]
>
> We can measure a number of things. You can do it a number of ways.
> Steve Hammond showed something like this earlier, tracking assay of
> drug. This example is just using near infrared, but tracking assay of
> drug in a powder blend, and you can monitor that with time and
> clearly, you have a decision that says once you reach a predetermined
> assay level and it looks fairly stable, it looks fairly consistent,
> then you have a uniform system.
>
> I have used near infrared as an example. C.K. will tell us that the
> LIF light-induced fluorescence is equally applicable, and the answer
> is correct. It is about spectroscopy, the spectroscopy matching the
> analyte, of course.
>
> \[Slide.\]
>
> But you can do it in other ways. Notice I have got the same weight
> here. That is fine. I have a calibrated system here, but actually, and
> Steve showed something similar earlier, it is all about monitoring
> change, and if I look at replicate spectra against time, here is the
> consistent signal because I just have the excipient blend, add the
> active. We get variability, and as the system mixes, the RSD of
> replicate spectra reduces down to a predetermined minimum.
>
> Notice, no calibration, no assay, but as an indicator of change, I
> have a good indicator of homogeneity.
>
> \[Slide.\]
>
> Imaging. Steve Hammond also talked about imaging, and this allows us
> to look at powder systems or other systems, of course, in a different
> way. This is a three-component mixture. The blue trace is the major
> excipient. The green is the principal active component, and the red,
> if you can see that, is the minor active component. Now, you tell me
> if that is a homogeneous mixture.
>
> If I have multiple pictures like this, and they all look pretty much
> the same, maybe I do have a homogeneous mixture, or if I have multiple
> pictures like this, and the red spot is missing occasionally, then, I
> have a problem. It\'s not quantifiable although you could, you could
> turn that into a series of numbers, pixel counts, spreadsheet, et
> cetera.
>
> We have to start thinking about process understanding in a visual way
> as much as a measured way.
>
> \[Slide.\]
>
> Powder blend dynamics. It was very heartening earlier to hear about
> let\'s just use some old-fashioned testing, let\'s just look at
> things. These are stills from a video film. We are videoing a powder
> blend mixing here at the 200- or 300-gram scale incidentally, and it
> is very revealing.
>
> You know, when the pattern of behavior is different to this, we know
> we have a mixing problem. We can do fundamental mixing studies on our
> materials at this level, the influence of particle size and shape and
> density, and any other parameters.
>
> These are crucial parameters, and it was good to hear about raw
> material specifications earlier, particle size, granularity, density.
> These are all critical factors that need to be studied at the
> development stage, and need to be understood.
>
> \[Slide.\]
>
> Granulation. Well, a number of properties are important in
> granulation, and there are things that we rarely measure in the
> laboratory. If you talk to process operators and formulators, they are
> interested in flow characteristics, bulk density of the granule.
> Particle size, maybe we can measure that.
>
> Let\'s get some technology that allows us to track granulations.
>
> \[Slide.\]
>
> Here is power consumption during granulation. The power consumption of
> the impeller motor will change as the granule quality changes. It is a
> picture. It is possible to quantify these sorts of things, but I leave
> you more with the image and the features of that image rather than the
> numbers associated with it.
>
> \[Slide.\]
>
> Near infrared can be used to monitor granulation. Here is good
> correlation and prediction of water content and particle size. So, a
> combination perhaps of those two measurement techniques is giving you
> much more depth, much more information about the process and the
> characteristics of the process as it operates.
>
> \[Slide.\]
>
> We have been doing a lot of work in GSK in recent years using
> ultrasound to monitor granulations. The logic is very clear. Small
> particles banging together will make a different sound to large
> particles banging together, so let\'s listen to the ultrasound
> emission as particles hit each other.
>
> \[Slide.\]
>
> Here is the sort of information you get. You can see very clearly the
> granulation process in there and the features as we add water, for
> example, as we affect the balance by drawing gradually, and even when
> we turn the machine off. But this is data that is not immediately
> intelligible to the human eye.
>
> \[Slide.\]
>
> So, we simplify it and we can make some predictions using that data.
> Here is some prediction from that same acoustic data on the mass
> median particle size of the granule. Not a bad correlation.
>
> \[Slide.\]
>
> On the same data, we have got a prediction of flowability as measured
> by Carr\'s index, for example, and again we have from the same
> acoustic data, a prediction of a physical attribute of the granule,
> and important attribute of the granule for subsequent processing.
>
> \[Slide.\]
>
> This one I find the most amazing piece of data of all. If you see
> nothing else in these next two days, remember this one. This is a
> prediction of the maximum crushing strength from tablets made from the
> granule on which the measurement is made. Let me just reiterate that.
> We are measuring the acoustic signal on the granule, and we are
> predicting crushing strength of tablets made from that granule. It is
> the first indication I think of an on-line or an in-process
> measurement that could be predictive of end product quality, for
> example, dissolution testing.
>
> \[Slide.\]
>
> If you look at the acoustic signal and the effect on scale, you can
> see that here we have a number of traces of the same process, but
> operating at different scales in a PMA blender, and what I hope you
> can see from that is that certain salient features of the trace are
> always there, and then other features differ.
>
> I won\'t go into great detail about that other than, for example, to
> point out that the blue or the green trace there is significantly
> different to the others, and this is because we deliberately
> over-granulated in that case. So, it\'s about characteristics, it\'s
> about pictures.
>
> \[Slide.\]
>
> I summarize that really by saying that I believe we need to develop
> something that allows us to describe the process, a process signature
> I have called it here, which may actually be based on a combination of
> multi-technique measurements. There is no single technology that will
> do everything you want.
>
> It\'s about building up the picture from power consumption, from NIR,
> from LIF, from video film, whatever it might be, but being able to
> characterize a process and to recognize when that process is operating
> well, and hence, you have an endpoint to work towards when you
> transfer that process either in terms of scale or from manufacturing
> site, whatever the variation might be. It gives you something to work
> towards, and I think this concept is an important one.
>
> We have heard a lot about the PAT\'s applicability, and I think this
> is the major one, developing that process signature.
>
> \[Slide.\]
>
> There is a natural corollary really, if you like. We are talking about
> moving the end product testing away and moving more upstream. I
> believe that what we are talking about is transferring the
> specification perhaps from the product to the process, and when you
> achieve that process specification, you have a process that is under
> control, reproducible, reliable, et cetera.
>
> \[Slide.\]
>
> So, the future control philosophy might look something like this,
> whereby we have our manufacturing process exactly as before, but now
> we have on-line monitoring of critical process parameters which we
> then feed back to use to control that process and to make sure that
> process stays within control.
>
> \[Slide.\]
>
> I have exemplified that in the example here for a continuous blending
> process, and I have included the PATs down here, and this could
> incorporate whatever you really want. It could be an IR, imaging, it
> could be LIF, it could be absolutely anything, but you are able to
> control critical process parameters in the case of a blending
> operation, maybe it\'s speed or maybe it\'s the rate of addition of
> materials, et cetera.
>
> \[Slide.\]
>
> There are some implications from that. I have introduced here just a
> couple of novel areas of research that need development, particularly
> around the third point, the data processing methods that might be
> required to build up this composite picture that I have talked about.
>
> For manufacturing and for R&D, I think we could be talking about a
> capability that says you do the same things at development that you do
> at the manufacturing scale. What we are looking to do here is to
> eliminate some of the issues of scale and technology transfer, and if
> we are able to move towards something closer to continuous processing,
> what we might have is a scale factor that says just run that process
> for longer or replicate that process rather than change, for example,
> scale of manufacturing equipment.
>
> \[Slide.\]
>
> So development equaling manufacturing scale could be an important
> benefit of the PAT approach.
>
> What we are trying to do is establish the relationship between the
> traditional end-product quality parameters, the classical release in
> end-product testing, content uniformity information, assay,
> dissolution, these things will not go away.
>
> These things are still important to us, but can we arrive at critical
> in-process measurements like I showed with the acoustic data, that are
> perhaps predictive of those end-product qualities, so that we can
> infer content uniformity, dissolution characteristics, whatever it
> might be, without necessarily using the tradition lab-based testing
> approach.
>
> Obviously, the onus in development is to be able to identify those
> parameters and to demonstrate and validate the predictive capability
> of those measurements or combinations of measurements, and, of course,
> the bottom line would be, having hinted at the notion of a process
> specification, is the development of that specification in just the
> same way that we develop the end-product specification at the moment.
>
> \[Slide.\]
>
> I have offered really here just a few final thoughts to kind of
> capture and summarize the theme there. I think what we are talking
> about is using PATs as a means to an end. I don\'t want to devalue the
> initiative, that I am very happy that the FDA has shown, but I think
> we mustn\'t simply think about analytical.
>
> We have to think about the processes that we are measuring and the
> analytical is there as a means to an end, as I said earlier, perhaps a
> set of tools that allow us to achieve what we are trying to do, which
> is actually improve our manufacturing strategy and overcome some of
> the inefficiencies, particularly associated with batch manufacture as
> opposed to continuous processing.
>
> Of course, the theme all the way through here is about understanding
> the process. It is using the measurement technologies at the
> development stage to understand what the critical factors in that
> process might be.
>
> If that, in turn, means we need to specify raw materials differently,
> or it means we need to change our manufacturing processes
> substantially, then, we had better go ahead and do that. If we do
> that, then, things like parametric release will simply fall out at the
> end, because we have built a quality by design philosophy, and
> parametric release is a benefit of that philosophy.
>
> I have hinted a couple of times that perhaps the move towards
> continuous processing, going back to my very first slide, I believe
> one of the reasons that we are not as efficient as we might be in this
> industry is because we are still thinking generally along batch
> processing lines. That is still the traditional approach that we use,
> and many of the other industry sectors, foods, beverages, et cetera,
> have gained an advantage on us in terms of efficiency by moving
> towards continuous manufacturing processes.
>
> I would like to perhaps leave it on that thought as to where this
> group might be able to take things using PATs as a facilitating tool.
>
> Thanks very much indeed. Thank you.
>
> DR. LAYLOFF: Thank you very much, Dave, and again we are on time.
> It\'s wonderful, just wonderful. Another exciting set of
> presentations, I mean really exciting, regulatory issues, production
> issues, speculations, perhaps end product testing is a consumer issue
> rather than a manufacturing issue. It is something that consumers
> should do to make sure they have the right drug or bought the right
> amount rather than a manufacturing issue.
>
> I would like to open it up now for discussion on these topics to the
> committee.
>
> **Subcommittee Discussion**
>
> DR. BOEHLERT: I think David Rudd made a very important distinction
> when you talked about using something like acoustic technology to
> infer a final result, and that is a little bit different than I think
> what many of us think of as using PAT to yield on-line what would have
> been equivalent to a final result, and if there is going to be
> guidance developed by the FDA using that kind of technology and how
> one might be validated, is going to be an important concept because
> you are not talking about generating the result on-line, you are
> talking about inferring quality from a measurement you make on-line.
>
> DR. HUSSAIN: I think that is a very important point. If you remember
> the presentation I gave to the Advisory Committee for Pharmaceutical
> Science on the 28th of November, the point I tried to make there was
> there are many test methods, like dissolution, we can infer
> dissolution is within specification by focusing and controlling all
> the critical variables that affect dissolution.
>
> For example, the data set I showed you at that meeting was dissolution
> failure at the end and towards the earlier part of the lot, and that
> was due to non-homogeneous distribution of magnesium stearate.
>
> Currently, we don\'t have a test for homogeneity of magnesium
> stearate, but now we can actually control that. If that is the
> critical variable, then, essentially you are assuring dissolution, and
> you essentially would establish a correlative or predictive model for
> that, and on that basis, you may not have to do dissolution test every
> time. So, that is the thought process there.
>
> DR. RAJU: I think this is kind of a very important presentation to
> figure out what our messages are going to be for today and the rest of
> tomorrow.
>
> Clearly, the important highlight is PAT, guidelines for PAT on one
> extreme dimension. On the other extreme dimension is guidelines for
> systematic process understanding is the other dimension.
>
> I think maybe, as a committee, maybe our plan is since we can\'t do
> everything, is to look at how we can use PAT for systematic process
> understanding. If you look at quality testing or process
> understanding, simplifying it, there are two dimensions of it.
>
> One is effectiveness, and the other is efficiency. That is, how well
> do we do it, that is effectiveness, and efficiency, how much resources
> do we consume when we do it. I think although the spirit of parametric
> release was always quite beautiful, the interpretations ended up being
> independent and discussed in terms of an efficiency argument, and when
> the effectiveness, that is, the process understanding has moved to the
> 3-, to 4-, to 5-sigma, the efficiency argument will take care of
> itself.
>
> The efficiency argument by itself is kind of a dangerous argument, so
> in the true spirit of parametric release is quite a powerful point.
> So, the question then is if we are going to look at the whole process
> understanding, and the sensors is one aspect of it, and there is
> analysis, and then this, design, we are going to start bringing up
> issues of what is validation, what is a specification, and now we are
> going to move sensors to the beginning, to the end of the process,
> back in time, back in space, and then we ask ourselves where do we
> draw the line in terms of where we draw the boundary, in terms of our
> goals for today and tomorrow, because this is an unbelievably big
> opportunity, at the same time it has got an unbelievable amount of
> dimensions.
>
> So, maybe, Tom, you can give us some guidance around that. It was just
> some suggestions. This is a good discussion, so that we can take
> David\'s presentation somewhere.
>
> DR. LAYLOFF: I think you brought up some very interesting points, Dave
> and John also. I think the acoustic measurement brought in a new
> assessment dimension that I had not considered. I mean I was looking
> at reflectivity and hardness issues, and things like that, but this is
> a projection out to more of a hardness from particle size, and then
> the question is how does that relax after you have compressed it, what
> are the things like stability testing, those that reflect out further.
>
> But efficiency and efficacy are critical dimensions that we need to
> look at, but I think we can make our guidance broad enough, so that
> there is room to work in. I think if we make the guidance too narrow,
> then, it is going to stifle things.
>
> I think Dave wanted to say something.
>
> DR. RUDD: I just wanted to make the point around the acoustic
> measurements, that actually is a very generally applicable technique.
> I mean I showed one example there where we were able to correlate
> acoustic data on granule to the tablets made from that granule, but it
> is much more than that.
>
> I think it is a way of getting particularly physical information,
> mechanical strength of the granule, mechanical strength of tablets. We
> have actually been using it, too, to look at the compression stage of
> tabletting to see whether we can characterize the actual portion of
> powder that is being compressed, because we spend a great deal of time
> during blending and granulation looking at chemical composition. We
> don\'t look at physical composition. I hesitate to open this door.
>
> But you could argue that one of the critical parameters during
> compression is, for example, the ratio of fine to large particles.
> Now, how on earth do we measure that unless you do particle sizing
> routinely on each portion of powder as it is being compressed?
>
> The answer is that with acoustics, you can actually get\--and again
> it\'s a trace, it\'s not necessarily numerical although it could be
> made numerical\--but you can get a profile that shows you during
> compression, the characteristics of the powder being compressed.
>
> I think the best way I have tried to visualize this is, it has been
> like if you take a pack of breakfast cereal, you know, if you apply
> pressure to the top of that pack, you will get a phase whereby the
> particles just settle down, but they don\'t actually fragment or
> rupture.
>
> Well, that gives a particular acoustic signal, it gives an audio
> signal, as well. If you continue compressing that pack of breakfast
> cereal, you will start to break the particles themselves, and that
> gives a whole different signal.
>
> So, you have two regions there that are indicative of two different
> physical aspects. One is the composition, the physical composition of
> the particles, and secondly, is the mechanical characteristics of the
> particles.
>
> Now, acoustics is giving you a lead into that, that I don\'t believe
> other technologies can easily do, so I just really wanted to make sure
> it was regarded as potentially a more universal technique than just a
> predictor of tablet hardness.
>
> DR. SEVICK-MURACA: May I make a comment? We actually look at the
> scattered signal, so that we can get particle size information, and if
> in the blend and if you are transporting powders, and you get this
> segregation based upon particle size or charge, or whatever reason,
> then, this change in particle size can give you an indication of
> downstream problems.
>
> So, the question is\--I think this is quite exciting, it confuses me
> as to your NIR signal change if it is due to change in particle size
> or the active ingredient, that needs to be resolved\--but the question
> is, do we include particle size, is it a reasonable validation measure
> to say that in your whole entire process as the stream goes through
> the process, that you don\'t have desegregation effects that could
> later on impact when your powder is sitting in the warehouse.
>
> I mean is particle size a reasonable parameter to measure, is it a
> critical one?
>
> DR. LAYLOFF: I think we just heard it is important to product quality.
> If you want to assure product quality, it is one of the process
> elements which is important.
>
> DR. SEVICK-MURACA: So, today, we will basically include this as one of
> the critical parameters in our guidances?
>
> DR. LAYLOFF: We can include whatever we want, can\'t we, Kathleen?
> Yes, Kathleen said we can.
>
> I think one of the things that has stifled us in pharmaceutical
> analysis has been that we have been stuck with a technology that we
> build in discovery. We start looking at trace impurities, and we take
> those technologies that we build to assure product for Phase I/Phase
> II, and then we just shove it down into development, and then we are
> such a big hurry to get it into production, we just shove it down into
> the control, and said let it fall where it may.
>
> We are stuck with the technology that came from discovery, that is
> very important in discovery, but doesn\'t really have a lot of meaning
> in manufacturing, but we are just stuck with it. It sort of hangs on
> all the way down the line.
>
> DR. HUSSAIN: Tom, I think it is very exciting to see the technology,
> but I just want to sort of bring the committee back to the questions
> that we will struggle with, and that is the scope of the guidance,
> because I am not going to write a guidance on acoustics, but any
> technology, how do we bring that into a regulatory framework from a
> validation perspective, from a specification-setting perspective, and
> this part is dealing with process and product development angle of it.
>
> DR. MILLER: I like the comment on Dave\'s points about observation and
> to particle size ever, but there has been some work in other dry
> technologies, roller compaction, with acoustic observation to the
> point of powder flows of the raw materials to the consistency of a
> roller compact in the middle nineties, and while it didn\'t gain a lot
> of support and acceptance of the rationale for that, was they didn\'t
> know where to go with that kind of work.
>
> So, it goes to other aspects other than particle size. It goes to
> powder flow and to consistency of a process. So, I think it\'s just a
> little bigger, there are other elements than just particle size. It is
> a technology or it is a piece of science that really hasn\'t evolved
> so much because they don\'t know where to go with it in our industry.
>
> DR. SEVICK-MURACA: I could be a devil\'s advocate and say we are
> looking at blend content uniformity, and you can say that you are
> going to assess blend content uniformity on a spectroscopic signature,
> but if the particles are of a different size, why not use that as a
> means of assessing the blend content uniformity.
>
> It also provide some indication, you know, you talk about flow\--I am
> trying to be a little bit broader in the fact that we do not
> necessarily have to be stuck with the spectroscopic signature
> especially when there are compounds that don\'t have one that is
> amenable.
>
> DR. RUDD: It was the point, hopefully, that I brought out. I mean I
> think the answer to your question really is that it is the
> combination. If the spectroscopic properties are important, that is
> fine, but equally, if they are not detecting or not revealing critical
> physical properties, and, for example, the acoustic seeds, you have
> got to put the two together.
>
> It is just like the way we deal with end-product specifications. We
> look at the combination of attributes. We don\'t look at each in
> isolation, but it is that concept, bringing things together to get the
> big picture.
>
> DR. SEVICK-MURACA: Again, I am going to point out the presentation
> that we saw, when we saw the change in the NIR signal, and you have
> got to convince me that that change in the NIR signal is not because
> of particle packing or particle size, or the absorbent signature.
>
> So, I see these two as mutually complementary.
>
> DR. RUDD: That is part of the validation.
>
> DR. LAYLOFF: That could be the fingerprint he was talking about.
>
> DR. RUDD: Yes, it\'s a diverse array of assessment measures which you
> put together into a fuzzy logic to say is the product consistent or
> not, and then you fish out the ones that are critical, and then start
> dropping the ones that are not critical.
>
> DR. SHABUSHNIG: Maybe the way, though, for the subcommittee to look at
> this kind of in terms of what kind of guidance, is really to talk
> about correlation-based measurements in general, and then what that
> does is it means that we have a very large toolbox, and I think the
> presentation here was very good in pointing out that we have more
> tools in that toolbox than maybe many of us had considered before, and
> we should keep our eyes open to look widely at what sensor technology,
> what measurement technology.
>
> I think, in particular, I would like what you were talking about, what
> would a good operator be able to tell you about the process using all
> of that person\'s senses, and what we can do is amplify those tools
> and provide additional information. So don\'t just focus on one site
> or one sense, that of vision, but use the other senses as well.
>
> But I think in terms of what this subcommittee can do, is to go back
> and talk about correlation-based measurements in general, because we
> are on that continuum already. We are still essentially, in the end,
> correlating some specific measurements that we make today with product
> quality, and relating that to how that product is actually going to
> behave for an individual patient.
>
> So, we are already making those kinds of decisions, and I think if we
> put what we are doing today in that context, we can come up with some
> meaningful guidance without limiting the technologies that would be
> available to us.
>
> DR. MORRIS: Am I wrong, or is it basically the charge of the
> subcommittee is essentially to do that, right, it is not to focus on a
> specific technology?
>
> DR. LAYLOFF: It is not technology-specific.
>
> DR. MORRIS: Right, and as you point out in your presentation, John,
> the regulatory buy-in in essence is a key, but in this particular case
> I was talking to Chuck at break, if you look at the genesis of a lot
> of the mentality that has been generated around sensor-based
> monitoring, a lot of it started, a disproportionate amount of this
> started I think in terms of what was done in the Agency with Tom and
> others.
>
> I think the energy barrier is much lower for that particular thing. I
> think a lot of the industrial angst about that, and I shared it when I
> was in industry, is perception rather than actual demonstrated
> reluctance, and, in fact, a lot of the work that we have done at
> Purdue was either suggested or supported by Tom and Ajaz over the
> years.
>
> So, I think that is a lower barrier than we are making it. Is that
> fair, you can\'t speak for where you aren\'t, but\--
>
> DR. LAYLOFF: Since I aren\'t there anymore, I can say whatever I want
> to.
>
> \[Laughter.\]
>
> DR. LAYLOFF: But I think certainly Ajaz\'s background is more hard
> science and engineering oriented, mathematics oriented, so that makes
> it easier, and that threshold goes down.
>
> Again, I think that one of the problems is that the Agency, in the
> review process, focuses on discovery, the discovery development area,
> because that is what you are looking at when you look at drug
> approvals. You are basically looking at the technologies that are
> associated with the discovery development and those kinds of
> assessments rather than these kinds of assessments, which are more
> downstream in the manufacturing area, which is more in the GMP area.
>
> DR. MELVIN KOCH: I would like to inject something here, building on
> what Tom said earlier, in the discovery phase. If we assume that there
> are other industries, and I can kind of guarantee that assumption,
> that other industries are truly using these type of techniques, it is
> not rocket science.
>
> The petrochemical industry has applied many of these, starting at
> similar stages here. Within the pharmaceutical industry and earlier in
> the chemical industry, it was assumed that the analytical profile,
> which was gathered primarily for composition and stability reasons,
> that those are the first techniques you want to run in the process.
>
> I think it has matured to the inferential type technologies, the
> acoustics, the scattering of thermal, you get into dielectric, surface
> tension, a number of things that are not profile itself, and you pull
> together for properties.
>
> The polymer processing industry dealing with melt flows and
> formulation, and all the imaging concepts, that has been applied for a
> number of years now. So, I think it would certainly be well worth it
> to try and make some analogies. The technology being applied across
> industries is not unique to the product. It is more of how it can be
> applied to a particular area.
>
> I believe what we are seeing here is something of applying all these
> developed technologies and the data handling, which eventually we will
> get into in terms of making more sense out of it, applying that to the
> problems we are talking about.
>
> DR. MILLER: In reference to changes of components, site, or batch size
> or manufacturing equipment, they are handled, Tom, through SUPAC, IR,
> for example, and I think companies would like to be able to use sensor
> technologies to reduce workloads and redefine how this could impact on
> SUPAC, its guidance in cooperation, because it is a post-approval
> change, and that is what we are talking about here.
>
> If it is not going to be done upfront, then, it is going to be done
> later, and I think that would have to be melded in, and it was one of
> my speaking points to this committee, that we think about that as a
> part of the PAT guidance.
>
> I also have other point that goes to an interesting concern that John
> presented to us, and it fits, in my view, to a regulatory small hurdle
> or GMP issue, more the GMP, and that is, well, what happens\--your
> question\--what happens if the equipment fails during a process.
>
> I think PAT would have to give guidance about, well, what kind of
> in-house protocol would have to be in place to handle something via an
> act of God comes into place. So, we know where the time of this
> failure is, but, okay, can we go to a shelf and pull off another
> instrument and come back and redo or recheck from that point in time
> to get us back on track for compliance and GMP issues.
>
> This is a fundamental issue that must be addressed and answered in a
> way that is meaningful for manufacturers. That would have to be part
> of that.
>
> DR. CHIU: I would like to make a few comments. First of all, I would
> like to demystify this so-called regulatory acceptability from the new
> drugs perspective.
>
> We have been dealing over the years with a lot of new dosage forms in
> the past, and Orsinger \[ph\] was the first one to approve the first
> biotech product, which is totally new technology, nobody had any
> experience.
>
> So, our philosophy of review is we always be open-minded, we will
> accept new technology as long as there are adequate data to show the
> technology will yield consistency of product quality.
>
> Recently, we approved a microsphere suspension dosage form. We
> approved also rapid disintegrated disk, and a few years ago, when the
> transdermal patches were around, we approved them with solid, valid
> data.
>
> So, we are always open-minded, and we would put the culture, this
> philosophy into our first guidance, so our first guidance will not
> talk about specific technology, because any technology will be
> accepted as long as they are feasible, so therefore, our guidance will
> discuss the mechanism of introducing new technology, and it will be
> more like what type of guidance rather than how.
>
> We don\'t want to narrow it down, you know, the foreign technologies
> are the acceptable ones, and how you are going to implement those,
> because that is not our purpose.
>
> I would also like to make a comment about uniform release,
> specification, shelf life specification, whether you need to do
> in-process testing in lieu of release testing. I think the Agency will
> be really accommodating those kind of concepts.
>
> Actually, if you look at a Q6A, you know, we have introduced the
> concept, so-called periodical testing, skip lots, so it is not
> necessarily all the tests need to be down for every lot at the
> release.
>
> However, traditional test specifications still has its place because,
> you know, you need to monitor the stability of the products and when
> we introduce generic drugs, we want to make sure that the two products
> are pharmaceutically equivalent.
>
> There is no way to compare in-process testing of one company to
> another company, because those are all confidential information not
> shared by companies. I think we know in the specifications, standard
> conventional test still has its place, however, the skip lot testing
> or even samples of the testing within a product can be accommodated.
>
> The last thing I would like to comment on is on SUPAC. Over the past
> few weeks now, I have been thinking about, because of the compressed
> development time we are facing now, and optimization often will be
> done post-approval, and our SUPAC guidances are a different type of
> guidances, it\'s more prescriptive. It tells you what you need to do,
> and it gives you sort of like a protocol.
>
> So, if in the future, we have specific tests or specific way to do
> on-line testing, maybe we could introduce those concepts in SUPAC, if
> you can demonstrate your process is robust by some kind of critical
> in-process testing on-line technology, maybe we can reduce the filing
> requirement in terms of whether you need a prior approval supplement,
> a CB supplement or even you can put in annual report once we know your
> process is robust.
>
> I think all those ideas are good, and we can incorporate into our
> regulatory scheme.
>
> MR. COOLEY: I would just like to make a comment on the mention of the
> inferential techniques. I think that is a real important thing to
> capture, and it is important for the reason that, as you start writing
> a document for validation of Process Analytical Technologies, that we
> do that with a clean sheet of paper, and not take a laboratory
> validation guideline and try and attempt to apply that to process
> instrumentation, because I think it is probably going to be the death
> knell of the technology if we attempt to do that.
>
> It is very important, as you mentioned, that these may not be
> measuring the critical parameter directly, it is inferring them a lot
> of times, and the means of how to validate that will be drastically
> different than how you validate a laboratory method.
>
> You may not be able to assess accuracy and specificity in the same way
> with an on-line measurement as you would in a laboratory measurement,
> so I think it is real important that we capture that.
>
> DR. CHIU: I think that is a very important point and we should discuss
> in the breakout session by the subgroups and come up with
> recommendation.
>
> MR. COOLEY: Another thing that I think that Ajaz kind of touched on
> was the use of artificial intelligence, and if you look at what the
> chemical industry has been doing, where they are taking measurements
> that may not be a direct reflection of the product at all, and
> combining those through software algorithms to produce soft sensors
> that they are using to control the process kind of ties into all that,
> and is applicable in this also.
>
> One quick comment also on David\'s introduction of Process Analytical
> Technology being an enabling technology, is one that I have used many
> time through the years at our company because I feel that very
> strongly that it is an enabling technology.
>
> To give a quick example, when we started producing biosynthetic
> insulin in 1980, to run a purification column manually and do off-line
> analysis really limits the scale that you can run in chromatography
> steps.
>
> When we were able to implement on-line HPLCs and do closed loop
> control of those purification columns, we were able to increase scale
> over 5-fold, and really became limited by the scale of equipment that
> was available or we could have gone even larger yet, so it is very
> definitely an enabling technology that is important to capture from
> the business case.
>
> MR. FAMULARE: I just wanted to bring up some of the GMP concerns that
> have been raised in terms of just the most recent concern was if the
> instrument fails, how will you react to that from a compliance and GMP
> standpoint.
>
> I think with the full deployment and development of this technology, I
> think you will be at an advantage as opposed to other types of
> failures that you may come into in terms of basic equipment failures,
> because in a sense, you have knowledge on every batch where in a
> traditional validation scheme using standard analytical methods, you
> basically do the first three batches and hope to keep that validation
> going consistently from thereon.
>
> So, I think there is a lot of measures, I don\'t know how specific we
> will be in this guidance that is coming out of this meeting on that
> particular topic, but I think there are more advantages that you will
> have and almost in essence doing validation almost on every batch,
> which this technology holds the potential for doing as opposed to the
> first three batches.
>
> So, I think we could find that to be advantageous as opposed to a
> disadvantage in the previous paradigm.
>
> The other thing I wanted to comment on was I guess the relationship of
> PAT testing to the official tests, and as Yuan-yuan said, it is
> important to having reference to it especially for stability, and the
> concept of skip lot testing.
>
> Basically, in terms of GMP, as long as you perform a test on every
> batch, that test, where it occurs is not important, particularly if
> the test if more valuable than a remote chemical test, so you will
> have met the GMP requirement and how you correlate that to the
> official test will be again something that I think we could work out
> in more detail.
>
> As Ajaz has pointed out in his discussion, I think you may be focusing
> on those issues which you can control now rather than the result of
> that, particle size or distribution of certain excipients versus
> trying to determine dissolution at a later stage.
>
> DR. LAYLOFF: I would like to say I studied two level a long time, too,
> in the Agency, and I think skip lot testing probably is not possible,
> but you can do alternate, I mean there are various testing parameters
> that go along the process that would be acceptable.
>
> I think skip lot testing that some people talk about, we are not going
> to do any testing at all. That is not going to work.
>
> DR. CHIU: I disagree. I think skip lot testing will be possible as
> long as you have valid data to support it.
>
> DR. LAYLOFF: But there will be valid data somewhere. There will be
> testing on the lot, there will be some kind of testing.
>
> DR. CHIU: It will be based on process control.
>
> DR. LAYLOFF: Yes, that is still testing.
>
> DR. CHIU: Not, not necessarily testing.
>
> DR. LAYLOFF: It\'s not skip lot, end-product testing.
>
> MR. FAMULARE: It depends on what you call the definition of a test.
>
> DR. CHIU: For example, we have in the past required hardness test, and
> now you don\'t need to do process hardness test if you have good
> compression measure in the process, so you control your process more
> rather than you do a hardness test.
>
> MR. FAMULARE: That measure we consider the test in terms of GMP,
> right?
>
> DR. CHIU: That\'s right. In GMP, you consider that as replacement of
> hardness test. We cannot do it as a skip lot testing for the batch
> release.
>
> DR. HUSSAIN: Tom, I think the other aspect which I wanted out of this
> segment of the discussion was I think some of the concept of
> fingerprint or signature. How can signature become a specification,
> how you build controls around that signature, I think, and how do you
> use that and justify that, I think as you break out into working
> groups for product development, you ought to start thinking of how we
> would rethink regulatory specifications. Signature is becoming one,
> and then up-line chemometric base to predict something else.
>
> So, I think all those discussions need to occur probably in the
> working groups.
>
> DR. LAYLOFF: I was very interested in polyvariate, I mean we always
> looked at the drug substance as being the active pharmaceutical
> ingredients as they anchor through the whole process, but now if you
> start looking at alternate assessment technologies of looking at
> consistency, then, the question is how do you deal with a polyvariate
> system like that.
>
> If the incoming materials are always the same identical, then, you can
> deal with it easier. If you don\'t, if the incoming materials gave a
> variance also, then, the fingerprint variance has to be investigated
> more broadly.
>
> I think it can be handled with a polyvariate signature or fingerprint,
> but you are going to have to test robustness bounds very well, define
> the robustness bounds.
>
> DR. HUSSAIN: The other aspect I think which needs to be considered is
> this, in the sense at least based on my knowledge, a lot of these
> things may not be stability indicating, so we really need traditional
> test for stability assessment.
>
> But that gives us a dual system, there is duplication, but I think
> there is an advantage to that, and the advantage being you have a
> built-in redundancy. If you have a sense of failure, you have a
> back-up system to check on.
>
> I think Sonja had made a presentation to us at our CMC annual day, and
> I think she had devised a protocol. If you have a question regarding
> the sensor, you have a back-up system to base your batch release on.
>
> But at the same time, I think what is also important to keep in mind
> is in my way of thinking, you have the public standard that becomes
> the floor, and with PAT you actually improve quality, and so you have
> a better quality assurance, and a second back-up system. That is one
> way of looking at it.
>
> DR. LAYLOFF: The legal standard will always have to be there. I think
> what you will do is actually, the patent will put you at a tighter
> domain on it, on meeting it.
>
> DR. MORRIS: So, are we going to frame this in terms of post-approval,
> prior approval, and prior approval with and without taking the
> technology through development, is it going to be that broad a
> guidance?
>
> DR. HUSSAIN: No, I think that that is a question for you, and this is
> what will be recommended. My thoughts were, as I said, there are three
> options. Option 1 would be in the sense you have take an existing,
> currently marketed product and do this for a reason of either safety
> or for improving efficiency, where the quality improvement may be
> marginal, but yet, I think that would be a post-approval example, but
> it can also have a submission example, which is part of NDA, so I
> think we have to cover both ends.
>
> DR. MORRIS: I guess my question is more if the technology is included
> in the NDA, but the sensor involvement in the development train
> didn\'t start with product development as opposed to manufacturing, do
> we have to then have dual techniques in the filing.
>
> DR. CHIU: That all depends whether you have correlation data because I
> think that is crucial. If your development is based on traditional wet
> chemistry tests, now your filing will be based on on-line testing with
> some kind of physical measurement, so you must generate that data to
> show the correlation, and I think while the working group is working
> on chemometrics, we will address how you deal with correlation.
>
> Once the correlation data is there, then, we do not expect you would
> have a dual process. You can just use the new one.
>
> DR. MORRIS: I guess one of the problems that you run into sometimes is
> that the on-line technique is a lot better than the gold standard, so
> it is difficult. If I have a much more sensitive method\--this is
> particularly true in blending\--my CV that I might accept with a few
> thief samples versus the level I can watch it in process may be quite
> different.
>
> DR. CHIU: I think there is a way to do that. I will just give you an
> example. In the past, when we deal with biological assay, very
> variable, huge variance, and then we move to HPLC, which is much more
> precise, we generate types of correlation data.
>
> So, therefore, there are other technologies there will be a way to
> address. I think this is probably the subgroup on chemometrics needs
> to discuss.
>
> DR. HUSSAIN: Just to add to what Yuan-yuan just mentioned, in addition
> to that approach, I think you also need to think of past principles.
> Validating something by comparing it to an existing method is
> definitely one approach, but if you can think of validating on its own
> merit also, I think that would serve some thought processes.
>
> DR. WORKMAN: I have just a short comment related to how to break this
> down possibly into usable bites. One would be to look at just the
> sensor technologies in general and the guidelines relative to using
> those sensor technologies.
>
> Another one would be to then look at the data processing because you
> produce a signal, how should that data processing chemometrics\'
> statistics be done, and then once that information is provided,
> whatever that information is, then, how is that going to be used
> process controlwise.
>
> In other industries, there have been some of these issues tackled.
> ASTM is one group that has looked at this rather carefully and tried
> to look at breaking that up in terms of the sensor development
> chemometrics, and then the process section a little differently,
> because each one of these aspects is well understood in terms of
> applying them to get good science. Just a comment.
>
> DR. LACHMAN: I think one of the approaches to use here would be to
> start early in the game, in the PAT, in the development phase. We are
> still not having enough time in development to really determine to PAT
> as a process understanding. If we can control the process, define the
> criteria that we need to control a process, then use the PAT, then it
> easy to extend it right into production.
>
> If you do it afterwards, then, there is a lot of correlation. You get
> into a lot of statistics, and it gets a little bit more complicated I
> would say.
>
> MR. HAMMOND: I just wanted to make a comment about shelf life testing.
> I am being asked to set in my sites, a totally automated,
> non-destructive stability testing system. So, I think the guidelines
> need to take into account the stability testing is well in the sites
> of PAT.
>
> DR. LACHMAN: I think if you can justify it, I don\'t see why that
> won\'t work. Here again, it is validating.
>
> DR. CHIU: I think that is correct. What tests need to be done to
> assure, you know, it is stability indicating, not necessarily needs to
> be a wet chemistry test, and if you have a physical test, you can
> detect degradation, deterioration of the product. We would accept
> that.
>
> DR. RUDD: I just wanted to endorse the comments that Leon made about
> the implementation of PATs at the development stage. Clearly and
> hopefully, it came through from what I said. That is the major
> benefit. It\'s a process understanding exercise.
>
> There is, if you like, a risk if we do start trying to apply PATs
> retrospectively to establish products. You know, simply instrumenting
> and making different measurements doesn\'t actually improve the
> process. It improves process understanding, but, of course, what you
> may then discover is that you now understand you have got a pretty
> lousy process.
>
> I would think from the GSK perspective, we have been looking at
> implementing primarily during new product development, and the
> retrospective application, the damage actually can often be done, and
> measuring more and more will not help you.
>
> DR. TIMMERMANS: I just wanted to make one or two comments. We have at
> Merck also explored the implementation of Process Analytical
> Technologies during the development phase, and I think there should be
> an important realization in the development phase. I see two important
> functions of Process Analytical Technologies.
>
> One is to support the development process in itself to better
> understand or unit operations to better understand our processes. The
> second one is to help control, monitor dose parameters that are
> ultimately deemed important to the process, and carrying those forward
> into the manufacturing facility, into manufacturing stage.
>
> Those are two very different things, and the subcommittee should
> consider to what extent they want to provide guidance on both of
> those.
>
> Also, I think from experience I wholeheartedly support the development
> process implementation, the early phase implementation throughout the
> development process, but I think there should be the realization,
> particularly if we start talking about fingerprinting of processes,
> that early one we have very little, you know, we only run very few
> batches actually at manufacturing scale, so a fingerprint may consist
> of five or 10 snapshots, and we may actually need 20 to 50 or 100 in
> order to actually capture a true fingerprint.
>
> So, while Process Analytical Technologies may provide us with a
> fingerprint, to capture the whole picture may be a very lengthy
> process, and we need to realize how we actually put that picture
> together.
>
> What we use early on in the development stage of the fingerprint is
> our back-up, as our primary control to ensuring ultimate product
> quality.
>
> DR. LACHMAN: I think what you have to do is get into the development
> phase earlier than we normally do right now in developing new drug
> products, because the \"R\" moves along, and development just supports
> the \"R,\" and I think development has to now come in sooner, and you
> do get that additional information and scale-up, and you probably
> would have to scale up sooner to get those numbers that you are
> looking for to do a statistical analysis of what is the meaningfulness
> of all this information, and that is going to be very critical.
>
> DR. LAYLOFF: I think that is what Jozef was pointing out, that when
> you move into early development, you don\'t have enough robustness
> data to really define the fingerprint.
>
> DR. MORRIS: But it is sort of incumbent on you at that stage to
> identify the parameters that you have to monitor. Even if you don\'t
> have a fingerprint, you should know what is important, at least to the
> level you can, based on the understanding of the material.
>
> By the time you get to full scale, even if you have sort of monitored
> a few things during development, and you get to full scale and realize
> that you have a crappy process, after all, if that is all it tells
> you, it is sort of the antithesis of fail fast.
>
> I mean if you identify the key physical-chemical parameters of the
> process that are important, and they have the sensors, as Eva was
> saying, look at the fundamental enough process, so that you know you
> are looking at the process with the level of resolution you need to,
> then, at least you know when you get to full scale, what eyeballs you
> have to have, because if you get to full scale with the wrong
> eyeballs, it doesn\'t make any difference.
>
> DR. TIMMERMANS: I totally agree. The only realization you should have
> is that in some cases\--and again speaking from experience\--something
> that is important at small scale, may not be at large scale, or vice
> versa.
>
> DR. MORRIS: Absolutely.
>
> DR. LAYLOFF: We are going to break for lunch now. We are on schedule,
> a little bit over, but this was very exciting and the Chair got
> excited also, so we ran over schedule. We will reconvene at 1 o\'clock
> for open public hearing. Thank you.
>
> \[Whereupon, at 12:05 p.m., the proceedings were recessed, to be
> resumed at 1:00 p.m.\]
>
> AFTERNOON PROCEEDINGS
>
> \[1:00 p.m.\]
>
> DR. LAYLOFF: We are at the open public hearing section of our meeting.
> I am going to turn the chair over the Kathleen, who will run it.
>
> **Open Public Hearing**
>
> MS. REEDY: The first speaker who has registered for the open public
> hearing is Gabor Kemeny.
>
> DR. KEMENY: Thank you. I have five minutes, so I will be jumping in
> the middle. I am very interested in all of these correlation-based
> technologies and all of the subjects that you touched upon.
>
> Within this five minutes, I would like to focus on one very narrow
> aspect of validating equipment, which is wavelength standardization.
>
> \[Slide.\]
>
> If you look at reflectance spectrum of materials, for example, I just
> pulled out a set of steroid spectra, there is a reflectance wavelength
> standard that the NIST puts out. It\'s the SRM 1920a, which has bands
> up to about 5,000 wave number, which is 2 microns.
>
> So, technically, beyond that, you cannot use that range for
> calibration or identification of materials.
>
> \[Slide.\]
>
> If you magnify out that region, it is very rich and that\'s the
> combination region which should be used. Therefore, I think there is a
> need for a standard to extend to that region of the spectrum, as well.
> This is not a very specific sample, just 6 steroids, and you can see
> how different they are, how characteristic they are, so it would be a
> waste not using that wavelength region.
>
> \[Slide.\]
>
> The NIST standard has three rare earth oxides mixture, erbium,
> holmium, and dysprosium oxides. You can see that above about 2,000
> nanometers, there is virtually no bands in the upper blue trace.
>
> So, we did a small incremental improvement on that standard, added
> another inorganic material to it, which just so happens has a band in
> 1,400 where the other standard is totally empty, where the other rare
> earth oxides do not have an absorption and also fills up the 2 to 2.5
> micron wavelength region.
>
> \[Slide.\]
>
> We proceeded to look at the standard in more detail in a
> inter-laboratory collaborative effort because the previous standard
> was calibrated in a dispersive instrument in the mid to late eighties,
> so the precision of the bands were not established very well.
>
> So, we got together University National Laboratory and private
> industry effort that involved five different FD NIR instruments, a
> dispersive instrument for reference purposes, and we looked at
> different optical arrangements, integrating spheres, diffuse
> reflectance accessories, fiber optics, measured spectrum on those five
> instruments.
>
> We look at the effects of the various algorithms for peak picking. We
> looked at first the effects of baseline and the derivative treatments
> that most of the near infrared techniques use, and then looked at also
> the center of momentum or polymonial fittings of these peaks, and
> looked at which are the most reliable, and also looked at the effects
> of different instruments and optical arrangements.
>
> Furthermore, one other thing we did, we looked for standard\--it is
> important what is the useful temperature range. This has not been
> established in the past, so this standard, we looked at a quite wide
> range from 7 degrees Celsius to all the way up to 60 degrees Celsius
> and found that the temperature coefficients are very low, so the
> standard is useful in a very wide range in the laboratory.
>
> What is very interesting, I don\'t want to bore you with just numbers.
> It will be published in the spring in a couple of peer-reviewed
> journals. There is also this work.
>
> The square root of the mean variance across the five instruments, we
> were able to reduce to about a quarter of a wave number, the
> differences between these various instruments, so this standard is
> very useful.
>
> The physical format is similar to the NIST standard in its physical
> size, and it has a sapphire window, so it is scratchproof and stable.
>
> \[Slide.\]
>
> In summary, I would like to mention that the standard, because it has
> an extended wavelength region, it could supersede the 1920a, which can
> only be used up to 2 microns.
>
> We have established these instruments to 0.03 wave number that
> presents themselves in a solid phase as only to a quarter of wave
> number. Temperature dependence was very minimal.
>
> Finally, I would like to ask any of you, or your companies, or
> somebody you know, who would be interested in partnering in getting
> these standards and other standards that we are working on into the
> hands of the users. I would be more than happy to talk to you, and my
> e-mail and other contacts are in the handout that I placed outside.
>
> Thank you very much.
>
> MS. REEDY: Thank you, Dr. Kemeny.
>
> The next speaker is Ronald Miller.
>
> DR. MILLER: I am going to yield my time to the next speaker. The
> discussion points would be handled during the forum today. Thank you.
>
> MS. REEDY: Thank you, Dr. Miller.
>
> The third and final registered speaker is Howard Mark of Mark
> Electronics, and he is not present. In your folders, the next document
> on the slide side is his submitted statement, so at some point you may
> like to peruse that.
>
> This ends the open public hearing.
>
> DR. LAYLOFF: We are going to go on to Process and Analytical
> Validation. Bob Chisholm from AstraZeneca will be our speaker.
>
> Before he gets up, I would urge all of you to pick up your questions
> that were handed out earlier on Process and Analytical Validation
> Working Group. We will try and focus our discussions on those topics.
> They are on the right side of your folder.
>
> **Session III: Process and Analytical Validation**
>
> **Perspective 1: Robert S. Chisholm, AstraZeneca**
>
> MR. CHISHOLM: Good afternoon, everybody. This has caught me completely
> unawares. I thought I had a whole hour to prepare for this, and no one
> has turned up for the public meeting, which comes as a bit of a shock
> to me. So, I may have to bluff my way through some of this.
>
> \[Slide.\]
>
> Firstly, I would like to thank the FDA for inviting me onto the
> committee, and to say it is a great pleasure to be back in the U.S.
> and particularly in the Washington area.
>
> I am supposed to today give a talk on the perspective on process and
> analytical validation. Maybe I had better start, giving a little bit
> of background, some context. The teams that I lead in the UK for what
> was Zeneca, now AstraZeneca, basically, it\'s the development of
> pharmaceutical engineering technology and pharmaceutical engineering
> science for the benefit of the industry, so we do quite a wide range
> of things.
>
> About three years ago, we decided to move into process analytical
> technology primarily in the form of things like Raman spectroscopy and
> near infrared analysis. This culminad on a sanctioning a plant in
> Germany, Plankstadt near Heidelberg, which is an important tablet
> facility PTF, and it is totally equipped with PAT and does real-time
> quality control on real-time quality assurance in using these
> techniques.
>
> I will try and keep the presentation general because it is a general
> gate that we are having. It will have very much a manufacturing flavor
> because that is my background for all the years I have been in the
> industry, so you will have to bear with me. There won\'t be much of
> process development from me, because I know nothing about it
> basically, so I won\'t talk about it.
>
> \[Slide.\]
>
> I think to understand the issues involved in validation, we have to
> look at the way that the pharmaceutical industry operates now, the way
> it will operate, and then what I would like to do is show you a
> generalized model of a PAT-based system, discuss that with you, and
> let you see where the validation issues have come from.
>
> What I will do is I will pose a number of questions without giving the
> answers to try and provoke some discussion that will help us when we
> are in the validation working party tomorrow.
>
> \[Slide.\]
>
> If we look at the traditional approach, I think it has been partly
> discussed already this morning. Processes are validated usually over
> three batches, at the life cycle commencement, then run for the whole
> of the life cycle. Sometimes companies revalidate them, sometimes they
> don\'t. They are operated, controlled by standard operating
> procedures, i.e., the operators have to always set the same
> parameters. There are no automatic controls or feedbacks in the
> system.
>
> QA, quality assurance is based on off-line testing of a small sample
> or product to the end of the each batch, is the old 620 rule, so very
> small sample data systems, not statistically based.
>
> If we look at the new approach, and I have used the word part because
> it is an accepted word in the industry, really, what I would call this
> is total quality management. You have got on-line analyzers for
> quality control of each unit operation, like your process control
> throughout the batch, continues process control and monitoring.
>
> You have got real-time, statistically-based quality assurance
> throughout the batch. This is a solid dosage facility. We actually
> have NIR analyzers actually on the tablet presses statistically
> sampling throughout the batch.
>
> What you have actually done is you have increased statistically-based
> testing regimes, and this given you the potential for release of
> product without further off-line testing, the so-called parametric
> release, which is not a term I like very much because I think it is
> totally unrepresentative of what we are actually trying to do.
>
> So, two totally different approaches, and the first one, small sample
> set at the end of the batch, and the second one, we statistically test
> throughout the batch, and increase the testing frequencies, and then
> can release the product.
>
> \[Slide.\]
>
> Everybody worries about statistics. I remember getting 19 percent at
> university in statistics. There is two different kinds of statistics.
> When I talk about statistical control, what I am saying is that we
> monitor throughout the batch. This gets rid of the problem that you
> get in traditional systems where you may have different profiles at
> the beginning and the end of the batch, which you may or may not pick
> up by simply taking some samples at the end of the batch.
>
> \[Slide.\]
>
> H.G. Wells obviously saw this coming, because in 1925, that is a quote
> from H.G. Wells, \"Statistical thinking will one day be as necessary
> for efficient citizenship as the ability to read and write.\" So, this
> guy clearly saw that we would all be sitting here today, because you
> looked at time and things like that, and decided to send us this
> quotation, I think.
>
> \[Slide.\]
>
> In terms of implementation of such a strategy, what we are actually
> doing is we are identifying and specifying all incoming raw materials
> in the dispensaries as they happen. Also, in the warehouse it happens.
>
> If you have a fluid bed drive, it will clearly control that. That has
> already been discussed this morning. We also control the granulator.
> Continuous on-line monitoring of blending, as Steve was pointing out
> earlier on, and end point control of blends, so you have a different
> blend every time if you need it.
>
> In-line monitoring of tablet quality parameters against registered
> specifications. That is your quality assurance throughout the batch as
> they come off the tablet press.
>
> We have this in a 21 CFR 11 compliant data management system.
>
> So, real-time continues quality assurance, which provides a platform
> for parametric release.
>
> \[Slide.\]
>
> That is a typical plant, solid dosage again I am afraid, but what has
> actually happened in this is, for some reason, the analyzers haven\'t
> come up on the overhead, so I don\'t know how that has happened. But
> everything coming in to dispense.
>
> Each dispensary is equipped with NIR analyzers, fluid bed drives,
> controlled end points and we have the blender under continuous
> control, and as we come off the tablet press, we are sampling tablets,
> not every tablet, but we are sampling tablets throughout the batch to
> check for conformity.
>
> We could also do the coating. It is not necessary for this particular
> product because the coating is actually cosmetic.
>
> \[Slide.\]
>
> That is, in fact, the actual plant.
>
> \[Slide.\]
>
> If I move now onto generalized model of a Process Analytical
> Technology-based system, so we can get a little bit more into the
> depth perhaps of these systems.
>
> What sort of modules would you need in such a system, what are the
> functionalities you actually get into here?
>
> Well, for a start, you are going to have to have long-term spectral
> data storage. You are also going to have to have long-term model
> storage, or, indeed, any other data that you are putting into the
> system, if it\'s not a spectroscopy-based system.
>
> You have got to remember you have also got to have analytical or other
> data storage also, because at sometime in the future, the regulatory
> authority is going to want to come and see all this data.
>
> You are going to have to have to do your modeling, so some module for
> that functionality. Reporting becomes very, very important, so you are
> going to have to have validation records, batch records, manufacturing
> records, and long-term storage of these, so you need a functionality
> there.
>
> You will also require an SPC, statistical process control module with
> the ability to historic trend and actually correlate across your
> processes, and that is the so-called management execution system, of
> course.
>
> We have to really look at these systems in terms of three modes of
> operation - modeling, validation of the modeling process, and then
> manufacturing itself.
>
> \[Slide.\]
>
> I am sorry, that has not come up very clearly on the overhead for some
> reason, but what you actually have there is just such a system. It is
> drawn more in a computer fashion, but these are actually the
> functionalities.
>
> At the top lefthand corner, you have got your spectral and model
> storage, the action storage. Next to that you have your modeling
> module, and on the righthand side you have your analytical storage
> module with all your data from HPLCs or whatever coming in there.
>
> You always have to have a central control module. In this case, it
> would be some sort of server managing the whole thing. On the right of
> that is actually the reporting module, which is sitting there for your
> validation reports, long term, and also for your manufacturing batch
> reports.
>
> As we come down at the bottom, you will see I have drawn a
> manufacturing execution system module with statistical process control
> and long-term trending.
>
> The analyzers are down at the bottom here, and the process is down at
> the bottom. So, that system represents any PAT system. In this
> particular case, it happens to be spectroscopic. For the modeling
> module, it would be based on chemometrics, but that does not
> necessarily need to be the case. It could be some other correlation
> module for different technologies.
>
> \[Slide.\]
>
> If we actually look at what happens in practice, and as I say, I do
> apologize, it is very, very hard to clearly see what is up there, the
> first thing that we have to do with such a system is obviously to
> create a model in the first place.
>
> The way we would actually create that model is let\'s take an example,
> say, of tablet active content. You would be taking the spectra. These
> would go into the spectral model up here for long-term storage. You
> would then take the tablets, and you would have to probably
> HPLC-analyze it, so that would come into your analytical data storage,
> and both sets of data\--and there would be quite a large data
> set\--would then, in fact, go into the modeling module to create your
> model.
>
> That would then have to be long-term stored because that is what you
> are going to use in your manufacturing.
>
> I think the first point that I would put to the group really and to
> the working group is how much of this data do we need to keep. There
> are people who think, well, you only actually have to keep the model
> itself because you are then going to validate the model.
>
> I think regulatory authorities would say that you have to keep the
> source data. That is something we need to discuss, and see sort of
> high-level recommendations I think we need to be making to the
> industry, because I am quite sure that an inspector would come along
> and say, well, prove how you did that model, show me again, and you
> can only do that if you have kept all the data you used to build that
> version of that particular model.
>
> So, there is a question: Do we keep all the source data and in what
> form?
>
> That is why I actually talk about long-term storage, both of the
> analytical data, as well as the actual spectral data in this case.
> These are important points, I think.
>
> You have then got to validate your model, so you are actually
> operating in a slightly different mode. What you would then be doing,
> you would still be taking spectra, you would then use the spectra in
> the model to predict whether or not you actually had good product.
>
> Then, you would have to take that tablet again and actually validate
> that you have good product by putting it through a normal register
> test and correlate the two. So, you have actually now validated your
> model by saying these are the analytical results, this is the spectral
> result with its prediction. They are both the same, in other words,
> parallel dossiers.
>
> This is an approach that you would certainly have to use for an
> existing product, and I believe actually, probably for any product at
> the end of the day, because I think it is probably what the regulatory
> authority would be happy with, but again, open to discussion, I think.
>
> What I would say there is that this time you have no choice. This is
> validation data, you have created your validation reports. This is
> long-term storage and has to be available, I would suggest the
> regulatory authorities, how did we do it, because they will want to
> see that that model has been validated and, in fact, is meaningful.
>
> So, some issues in there about these sort of areas, the practicalities
> of all the storage, et cetera, how did we do it. I think you will see
> what I am heading for here. The amount of data handled by these
> systems is so complex and so large, that almost certainly what we are
> heading for is a computer-based electronic record system with all the
> attendant difficulties that that will have.
>
> So, that is that. If we now say okay, we are into manufacturing,
> basically, all we are doing now, of course, is we are taking spectra
> of the tablets in this particular case. We are running them against
> the model, we are predicting, and saying pass or fail.
>
> The fundamental question I think that the working parties have to
> consider is what does a batch report know, what on earth is a batch
> report, how does the qualified person in Europe or the QA person
> actually decide it can release that or she can release that product. I
> mean what constitutes a batch report in these circumstances, what
> constitutes in statistical terms the pass or the fail.
>
> I think these are essential validation issues. I think they have to be
> discussed ultimately with regulatory authorities because we are in a
> whole different ball game from a simple analytical test.
>
> In some way, we have to have documentation that allows an inspector to
> come along, take what we would have known as a batch report, which is
> going to be a very different document now, and say, okay, take me
> through this, justify how you got that prediction, show me where the
> model is, how did you make up that model, and how did you validate it.
> All that information is going to have to be available, and I really
> don\'t see how it can be available in anything but a large data
> handling system, such as this.
>
> I don\'t think these things are particularly easy, but I think these
> are the sort of high-level issues that we really have to discuss, and
> these are the sort of things we should be giving guidance on rather
> than on the specific technologies.
>
> I just mentioned the regulatory status of model source data, spectral
> and analytical, traceability and long-term storage. I have mentioned
> traceability of spectral data, related analytical data, and model
> predictions for the model validation phase, and its long-term storage.
>
> In manufacturing, what form will the supposed PAT batch record and
> release data take? How can it be used by QA to release product, and
> how would a regulatory body inspector find an audit path from it for
> verification, because all these things will still have to happen.
>
> I find myself talking glibly, even I talk glibly about batch records,
> but we don\'t actually know really what it means, and I think we have
> to gain some agreement with regulatory authorities.
>
> The last thing I mentioned there, it is probably as well to go back to
> the previous slide. Down on the bottom righthand side, I have put in
> an SPC module and their long-term trending. What I am really putting
> there is a manufacturing execution system.
>
> I do believe that such data may well have to find its way into the
> batch report for product release, but there is a fundamental question
> here. Since this is a manufacturing execution system to help us
> improve and head for manufacturing excellence, is it really an issue
> for registration or inspection by regulatory authorities?
>
> The immediate answer that comes to mind is no, that is company
> business, not regulatory business, but if you actually think what you
> are doing here, to make these systems really effective in the way that
> we and I think the regulatory authorities want, SPC, statistical
> process control, will look on a batch-by-batch basis and make sure you
> are not turning out of compliance.
>
> Basically, behind the statistical process control, you will have
> long-term data trending, because you will wish to know, for instance,
> if you blend sames are varying, is it to do with raw material
> variance, which means you have to be correlating between any changes
> you are finding in your raw materials when you are using NIR on them,
> and, in fact, changes in blend times and changes in tablet quality.
>
> This is your complete management system that you are manufacturing for
> excellence. From the point of view of validation, should or should
> that not be in the realm of a regulatory authority? What we have to
> remember is this may cause us to take some critical manufacturing
> decisions, so there may be a case for it being certainly discussed
> with the regulatory authority if we use such systems.
>
> I will go on the next one again. The very last point I will just
> reiterate again. The MES/SPC activities provide process understanding,
> long-term knowledge, increase what regulatory status, if any, is
> associated with them.
>
> Again, I think we have to think of that as a high-level
> recommendation.
>
> If we come on to perhaps follow areas of discussion that the group
> could discuss in validation, the first thing I would like them to
> consider, I think, is registered processes versus statistically
> quality control processes.
>
> What we actually do in the industry at the moment, of course, is we do
> this validation, we have registered the process, and the operators
> will hopefully run that process to these parameters for the next 20
> years. They are more worried about running to these parameters and
> perhaps the end result, because it is the end result that matters.
>
> Once you go into statistical process control, you will actually want
> to vary parameters to keep your processors in control and compliance,
> and improve as your knowledge bases increases, what does that mean for
> registration with regulatory authorities, what, in fact, do we
> register now, because we are moving into a completely different
> paradigm from the one that we exist in at the moment.
>
> Myself, Dave, Steve have all talked about varying blend times based on
> some results, be it from acoustics, be it from NIRA, and in fact, our
> plant actually has variable blend times, we don\'t use them at the
> moment, because there will be a registered blend time for that
> process, and if a facility manager says to me, well, look, Bob, there
> is not point in me doing that, I have got to run to the registered
> process even if it\'s wrong.
>
> We are moving into a totally different world where we do not want to
> register things like that anymore, we want to keep a process under
> control. That is something else I think that could well be debated in
> terms of a high-level recommendation.
>
> There are issues involved here of what I would call fundamental
> science and validation. I don\'t want to go into these too deeply
> because once you go into these, you are becoming technology specific,
> of course.
>
> I just want to warn everybody that I think these sort of gades \[?\],
> if we are not careful, will be left empty and bereft if we don\'t have
> something about some of these issues in there, because there are a lot
> of fundamental science issues, especially in the areas of transfer
> between analyzers, et cetera.
>
> That more or less brings me to the very last thing that I wanted to
> say, going back to the earlier diagram, I mentioned that these change
> because of the amount of data and complexity to be big data systems,
> and these would require a lot of work in 21 CFR 11, the computer
> validation areas.
>
> Just to give you an example of this, this is actually the upside-down
> version of the Plankstadt facility. That is the actual system
> architecture for that facility. It is ethernet based. You have the
> analyzers at the bottom throughout the plant, all connected to
> ethernet, to servers, which go up to the spectral data storage, et
> cetera.
>
> I will not go into that because I have run out of time. Clearly, up as
> far as the tablet pressure of quality control, and the complete thing
> is a quality assurance system. I can assure you we validated the
> system. The amount of validation and work is hard to go into. It was
> quite extraordinary, as it is with all these big data systems, and I
> think people have to be aware of that, because there will be these
> kind of data systems that we will have to use.
>
> One question that I think is a question for the FDA, as well as the
> working group. The FDA does not really like and no regulatory
> authority likes open systems. They would much prefer a closed system
> where they can actually see everything that is going on, and nothing
> from outside can interfere.
>
> The very nature of these systems quite often means they are open
> systems because they have to be ethernet-based, usually on plant
> ethernet systems, and, indeed, in the future, may even be accessed
> directly by FDA through modems to check if a company is in compliance.
>
> This may be a direction we will go in, which means they are an open
> system. This brings in a lot of validation difficulties.
>
> So, I will leave you with that picture. This is actually the PTF
> architecture at Plankstadt, so it is being done, we have done it, but
> it is extremely difficult.
>
> Thank you very much.
>
> DR. LAYLOFF: Thank you, Bob.
>
> We will moving on now to Leon Lachman.
>
> **Perspective 2**
>
> **Leon Lachman, Ph.D., Lachman Consulting**
>
> DR. LACHMAN: The first slide I am going to show is the common
> definition for process validation.
>
> \[Slide.\]
>
> What we have been talking about with regards to inference testing and
> modeling, and so on, doesn\'t conflict with this definition. It is
> mainly to show that the specific process will consistently produce a
> product meeting its pre-determined specifications and quality
> attributes.
>
> Now, how you accomplish that could be done by modeling and by
> inference testing.
>
> \[Slide.\]
>
> We also have to keep in mind that we have to think of the equipment we
> are using to accomplish the modeling and the inference testing to come
> up with the validated process, and equipment we are using is not one
> piece of equipment usually in a process. It is multiple equipment, and
> we have to first show that the equipment is reproducible as part of
> the process.
>
> If the equipment is not qualified to show it repeats itself, then, the
> process is not going to be able to be validated.
>
> \[Slide.\]
>
> This is a similar definition by a European agency, and I think we can
> pass that up.
>
> \[Slide.\]
>
> Change control, we haven\'t heard about change control, but that is
> very, very critical in validation. You can\'t just go ahead and change
> modeling or inference testing without having a very strong change
> control process in place, and that is going to govern your
> effectiveness of your validation.
>
> \[Slide.\]
>
> We have been talking mostly about solid dosage forms by doing these
> inference testings that we talked about. We also have to consider
> solutions and more difficult ones are the suspensions and emulsions to
> monitor by modeling and inference testing. Lyophilization probably
> could be handled fairly well. Ointments and creams then become a
> little more complex, as well.
>
> \[Slide.\]
>
> We talked about the various steps in the development and design of the
> process, and this is where we have to get involved with the PAT
> testing, is in the design of the process, and we talk about size
> reduction, we talk about blending, granulating, compression,
> encapsulating, and coating, and these are the areas that we need to
> test out the various PAT parameters and how they effectively handle
> the process as we are developing it and scaling up.
>
> My concern is not enough of this is done during development. We have a
> big time period for \"R,\" the research part, but we have a small time
> period for the development, and it may be worthwhile to backtrack and
> start development sooner and do your scale-up, as well as your
> in-process optimization. That is going to be very critical is the
> optimization studies.
>
> \[Slide.\]
>
> For an example, we have equipment that we need to consider for
> blending, and the blender geometry, the intensifier bars, operating
> principles, the completeness of the volume of the blender, how much
> powder do you put in there, the order of addition, the RPMs, the time,
> all these play a role as to the homogeneity of the blend.
>
> So, you have got a number of variables just with the blender before
> you talk about milling, about granulating. Each step has multiple
> variables that we have to keep in mind. This is just example in
> blending.
>
> \[Slide.\]
>
> Liquids, we have other concerns. You know, for solution liquids, you
> have the regular materials go in solution. You have got the fill
> uniformity we get concerned with, filter compatibility, the tubing
> interaction that you have with the preservative active ingredient. You
> have got different flush volumes. So, you have got of background work
> to develop before you can really go into this modeling and the testing
> on a routine basis with inference testing.
>
> \[Slide.\]
>
> Suspensions. Here again we have the milling, the mixing. We have
> viscosity, resuspendability, agglomeration, and caking. What
> parameters do you measure, do you measure viscosity? Do you measure
> size? Do you measure agglomeration? What is critical in the process
> control? That is going to be very important to come up with early in
> the game.
>
> \[Slide.\]
>
> Here, we have got emulsions, and this is not an easy one to monitor
> again, because you have viscosity, you have got the creaming
> potential, who well does it reemulsify when it is used? You have got
> coalescence, globule growth, what do you keep looking for? Viscosity
> may not be the ideal parameter.
>
> \[Slide.\]
>
> We talked about lyophilization. That is not too difficult to control
> because you are freezing, you are looking at temperature and rate of
> cooling, and drying, you are looking at temperature rate of heating
> and vacuum, and then you have got the end product. You want to verify
> the dissolution rate of the cake is adequate.
>
> I am just showing you numerous variabilities and parameters we have to
> consider for these inference programs, modeling of the control system.
>
> \[Slide.\]
>
> Similar for ointments and creams.
>
> \[Slide.\]
>
> Methods validation, I think we all know the definition pretty well.
> This is one of the definitions, that procedures are suitable for their
> intended use and that they support the identity, strength, quality,
> and purity and potency of the drug substance and drug products on a
> repeatable basis.
>
> \[Slide.\]
>
> Now, there is a number of guidelines that has been issued in this, and
> they are ICH and the CDER, the USP.
>
> \[Slide.\]
>
> The ICH has a definition, too. We don\'t have to go through that.
>
> \[Slide.\]
>
> Now, considerations prior to validation. Before you go into methods
> validation, you have got to look at the suitability of the instrument,
> the qualification and calibration of the instrument.
>
> Suitability of materials, the reference standards, reagents, placebo
> lots, and so on. The suitability of the analyst, has the analyst been
> trained adequately for the procedures, and the documentation. These
> are all factors that contribute to the methods validation.
>
> \[Slide.\]
>
> These examples for different methods. You know, chromatographic
> methods, you have got a while slue of those, and then you have got
> spectrophotometric methods, capillary electrophoresis methods,
> particle size analysis by laser or microscope. You have got
> dissolution methods, titration, automated analytical methods, robotic
> automated analysis.
>
> I think for some of the testing we looked at just now, we are talking
> about automated analysis one way or another when we are looking at
> measuring performance through various analytical techniques.
>
> \[Slide.\]
>
> Now, we all know the general characteristics for methods validation.
> These are listed here, but if you are going to use inference testing,
> you can\'t do all these. You are going to have to select what is most
> important to assure the product is going to meet end-product quality
> attributes, so you are probably going to look at, what, accuracy,
> robustness, and specificity will be for stability, but you are
> probably not going to use the method for stability testing anyway, so
> you just want to show the reproducibility of the process, and I think
> accuracy and robustness probably of the inference method is going to
> be very critical.
>
> I am not going to go through all these. These were definitions, and
> everybody knows these, so we will just go fast through these and
> forget about them.
>
> \[Slide.\]
>
> Now, impurities is a very critical area, we have got to talk about a
> little bit. The method that we use, inference method has to also be
> able to detect impurities. You have to have some kind of mass balance
> to be shown, and the USP is the minimal standard with regards to
> degradation products or impurities or related substances.
>
> \[Slide.\]
>
> Now, the Compendial Analytical Procedures is a regulatory procedure in
> that it is listed in 501(b) of the Federal Food, Drug and Cosmetic Act
> as a regulatory analytical procedure for compendial items, however,
> this is somewhat of a disclaimer. \"The suitability of these
> procedures must be verified under actual conditions of use\" because
> the methods in the USPNF may not reflect the formulation that you
> have.
>
> \[Slide.\]
>
> Also, there is a disclaimer with regards to stability, so you have got
> to verify whatever of the compendial methods where they are stability
> indicating for your formulation when it has no interference.
>
> \[Slide.\]
>
> We get into the inference testing and modeling. Really, we are talking
> about automation. One way or another it is going to be in-process
> controls, there is going to be statistical controls and automation,
> computer involvement.
>
> We know it is going to reduce the variability, it eliminates the human
> interaction, increases knowledge of the process if you begin this
> process of inference testing, the PAT in the development phase.
>
> It will improve monitoring and control and decisionmaking because you
> are going to have a lot more data to do it with. You will improve
> process and product consistency because here again, you have a lot
> more data to analyze and determine your consistency of the process
> statistically, improving the documentation reporting capabilities
> because you are accumulating all this information in the computer, and
> it should reduce cost because you are going to have less rejects or
> less rework, or whatever.
>
> \[Slide.\]
>
> It also provides expanded real-time monitoring and adjustment of the
> process. This is the feedback, but you need a feedback for the
> controls. So, you are going to have to have a feedback system, not
> just for in-process monitoring, but a feedback when you do slightly
> show a trend out, you have to bring it back in control.
>
> You have this enhanced ability to statistically evaluate the process
> performance and product variables because this happens on-line
> continuously. You have enhanced data and evaluation capabilities and
> increased confidence about the process reproducibility and product
> quality.
>
> You also have the improved ability to set target parameters and
> control limits for routine production, correlating with validation
> results. Here again, this is very critical to start in a development
> phase and during scale-up, and so on, because your critical parameters
> and your range around those parameters are normally set during
> scale-up, during the development phase, and optimization during those
> studies are very important before you go into the validation.
>
> Then, you have enhanced reporting capabilities, and we just heard we
> are going to have a lot of stuff to report, and what do we report, and
> how do we report it, how does it get in the batch record, and so on.
>
> \[Slide.\]
>
> Then, we have the consequences of inadequate automation. The acquired
> data may not be complete or accurate and/or representative.
>
> Improper evaluation and process assurance and adjustments based on
> inadequate information, process deviations, product quality problems.
> You have got down time, rejection of in-process and finished product,
> product recalls and eroded goodwill.
>
> So, the automation component, the computer component of the inference
> program of on-line monitoring is going to be very critical for that
> entire effort because there will be a lot of data generated, and it is
> going to have to be handled somehow.
>
> \[Slide.\]
>
> The sensors must be calibrated. They just don\'t run by itself and
> calibrate by themselves usually. The controllers must be qualified,
> calibrated, and maintained at appropriate intervals, so there is going
> to be a maintenance program that is going to be different than you are
> accustomed to.
>
> The environmental requirements for a computerized system needs to be
> defined, maintained, and documented.
>
> \[Slide.\]
>
> We just heard my colleague here is going to be on the working group
> with me. System for reporting and evaluating deviations. You have got
> hardware, you have got software, you have got security, you have got
> life cycle management, you have got the equipment maintenance, you
> have got the calibration, you have target and control limits versus
> validated parameters versus historical performance.
>
> So, there is a whole slue of things that come into play that we don\'t
> think about. We hear these terms thrown out, but there is a lot of
> things behind those terms that need to be addressed.
>
> \[Slide.\]
>
> The operating environment, the in-process control data, use and
> retention, we just talked about that, how long do you keep it, SOPs,
> there is going to be a lot of new procedures, people have to be
> trained. We have data integrity concerns, and we have legacy systems,
> how are we going to treat those.
>
> \[Slide.\]
>
> Closed system controls is probably one of the things that we need to
> consider here, is the validation. We have the electronic and human
> readable formats, protection to ensure accurate and ready retrieval,
> authorized access. We need to have audit trails. We need device checks
> to determine validity of input, operational system checks as
> appropriate.
>
> \[Slide.\]
>
> We have to have written policies and procedures. We have to have
> controls over system documentation, operational system checks as
> appropriate, control over access to system operation and maintenance,
> revision and change control procedures, documented evolution of
> changes, and qualified personnel. That is going to be the biggest
> factor is get the appropriate qualified personnel.
>
> That does it. Thank you.
>
> DR. LAYLOFF: Thank you, Leon.
>
> I would like to open the meeting for discussion now from the
> subcommittee.
>
> **Subcommittee Discussion**
>
> DR. RAJU: I think we have had three sessions today in the morning. I
> think the kind of description of the potential benefits was quite
> huge, and I think we should be all excited by that.
>
> In the development and process and product development session, we
> began to see we need to go back in time and look at development
> because that is where the most reward would be, the lot of the
> flexibilities are there in terms of regulation. It is clear we need to
> do a lot of validation.
>
> In terms of adding another kind of perspective to the guidelines that
> want to form, how do we think about it in terms of one of our primary
> goals has been risk management and risk understanding in some ways,
> because it is clear the return was higher if we started off way back
> in time, if we did it in development because you would get a lot more
> impact over a longer period of time.
>
> What about the risk of doing it compared to that reward? Early in
> process development, we might agree that we want a better
> understanding of processes, but the rest of the company, the CEO, the
> marketing and the research would say don\'t be on the critical path,
> don\'t take a risk at that point, because it is about the 75 percent
> gross margin, not on saving on the 25 percent cost of goods sold.
>
> On the other side, assuming that we have to alter to look at PAT and
> manufacturing, yes, development might be high leverage, but we also do
> manufacturing. What is the risk there and how do we manage it in the
> sense, and I think Ajaz had three guidelines for those three cases, in
> terms of reducing regulatory uncertainty.
>
> One was good science, the second was it is an option but not a
> requirement, but the middle one was we presume your current processes
> are okay as validated, but when you bring in a new sensor, and it
> brings up segregation issues or something you haven\'t seen before,
> you have a new set of eyes. What do we do now in terms of the
> manufacturing?
>
> A new sensor would take you from a process capability of 2.5 to 1.5
> suddenly. The definition of process capability depends on the sensor
> you are using. What about the consequence on the validated processes
> of today? How do we manage the risk there?
>
> The risk about in-process development is slightly different, and the
> risk in manufacturing is slightly different. What would be our
> perspective, working together, what would be the FDA\'s perspective?
>
> If it\'s an approved process that is very safe, efficacious, saving
> people\'s lives, it is approved, it is within specification, but I
> bring in a new sensor and I find segregation, but it is still meeting
> the specifications of the past, what should I do? What is my
> accountability in terms of information risk, and what is my
> accountability to the investigator who is visiting my plant and
> looking at that data?
>
> DR. LAYLOFF: There is a couple things there. I am going to just make a
> few comments. You might gain more information on the process and bring
> it into better control, but the final product change might be
> improved. I don\'t think the additional data necessarily is going to
> tighten down the process requirements, because the bottom line, is the
> product suitable for its intended use.
>
> I do see a problem when you start talking about sensors, because if
> the technology is not mature and well understood, then, there is an
> inherent risk about bringing it in, is it going to address critical
> issues well.
>
> I think one of the things is going to be is having mature technology.
> The assessment tools have to be mature. If they are not mature, then,
> the risks are going to be relatively high.
>
> DR. RAJU: The technology is probably not the bottleneck. The
> technology might be mature. The mechanical aspects of linking it to
> the blender may not be, but they are pretty fixable, but the
> consequence of dealing with it may not be mature.
>
> MR. FAMULARE: I think the issue is what will happen, I think, as it
> was posed, if an FDA investigator comes in to a well-established
> process under the existing paradigm, and now with the addition of more
> information, finds things, whether it be less consistency throughout
> the batch or towards the end of the batch, that weren\'t apparent
> before under the old paradigm, and that is the important thing that we
> have to work through, why Ajaz mentioned it even in his original
> presentation here this morning, is that we are working with compliance
> in the field to make sure that we allow for process improvement to do
> that, improve the process, and not cause that to bring more regulatory
> concern or enforcement, because now we know something that we didn\'t
> know before.
>
> It is important to remember the baseline, that what is going on and
> passing under the current system is adequate for its intended use, so
> that we will work in our compliance and with the field to make sure
> that our investigators are trained to see that, to understand what
> that means, and as we are moving from a baseline to something that
> could bring you to a higher quality, that shouldn\'t be an area for
> penalization, but an area for encouragement.
>
> DR. LAYLOFF: I don\'t think there is going to be an issue of changing
> the specifications on final product. I think the final product
> specifications like USP limits, 85-115, things like that are not going
> to change.
>
> So, the process delivers that.
>
> DR. RAJU: You may or may not change your specification. That is the
> result of what you are about to learn as you go to 6 sigma. In the
> meantime, you have some information. You have taken a risk. The case
> one that Ajaz had put forward is fine. It is already well understood.
> It is about efficiency, all sensors going to new sensors, no problem.
>
> The case three was about process development, and it has a lot of
> merit, there are different kinds of risks, but those are
> organizational risks, and those are time-to-market benefits of those
> risks.
>
> But case two is about today\'s processes, and most of what we do is
> today\'s processes. We either have to give up on those or we must have
> a systematic way of dealing with, finding out what we didn\'t know,
> because almost by definition, by saying that we are not measuring
> important things and that we are 2.5 sigma tells us before we go to 6
> sigma, we are going to start measuring things that we have to explain
> before we have done the analysis, and the understanding to be able to
> explain.
>
> DR. KIBBE: If I might, I think you have raised a really interesting
> issue for a lot of different companies in different stages of the
> process, be they ready to bring a new product on the market or one
> that is already on the market that they have decided to go back and
> look at improving their own internal controls.
>
> There are lots of opportunities for using that information for their
> own benefit or to be punished by it if the Agency thinks that they
> should get all the data and therefore apply new things. So, some of
> that balance has to be worked out I think within the Agency and
> between the companies, but there is another step that can be put in
> place.
>
> What if they put a new process control system in, and they find small
> problems, and even though it is not problems that are significantly
> affecting the therapeutic efficacy of their product, they go ahead and
> improve their process and tighten down their controls, and now they
> have a much tighter product coming out the line.
>
> Then, they go back to the Agency and say we would like to request a
> change in the specifications on our product because we think that
> tighter is better for the patient, and the Agency does that, and they
> close out the four competing generics.
>
> DR. RAJU: I think tightening up the specifications is a win-win for
> everybody, but in the meantime, they are going to challenge the
> current specification\--the consequences are huge for the brand name
> companies if they understand their processes, but in the meantime,
> almost by definition, you have got to know what you are don\'t
> understand before you begin to get understanding, and what is the
> consequence of that in the meantime.
>
> DR. LAYLOFF: If you focus a product, content uniformity is really the
> issue, and that is plus or minus 15 percent, so a CV of 5 percent,
> that is plus or minus 3, you get 1 per thousand failing.
>
> You go to plus or minus 2 percent, you get 1 in a million failing. But
> the acceptance is still 85 to 115, so if you move your process control
> to CD plus or minus 2 percent, 2.5 percent, then, you are well within
> it. Your product is going to consistently make it.
>
> If you start working with a 5 percent CV, then, 1 in 1,000 is going to
> fail. If you get down to a 7 percent CV, then, you are in the business
> of having rejects.
>
> DR. RAJU: That is clearly an example, but if you look, the CV there is
> measured with teving \[ph\], for example, which is the convention
> technology that is inherently variable. As you look at your on-line
> sensors in that example, you would start seeing deeper levels of
> heterogeneity that you wouldn\'t be able to pick up by measuring only
> one thing.
>
> You might see that you have phases of lack of segregation. When you
> look at more, you might be able to see more kinds of issues. That is
> one. In dissolution, the six tables per batch might be fine, but when
> you start looking at more issues, you might find that they are not.
> With on-line technology, some other correlations may not work.
>
> How do we manage the risks, so that everybody wins on that middle
> case?
>
> DR. LAYLOFF: I think you are reducing the risk in the long run. You
> are reducing the likelihood of product failing the existing limits by
> bringing better control in, because we all agree that the current
> model is statistically unsound.
>
> You have nonstatistical sampling of unknown batches. When you talk
> about it failing, but I mean it may fail now, and if you go to FDA and
> you take another sample and run it, and it passes, then, FDA says you
> are testing it into compliance, the batch failed.
>
> DR. RAJU: I think that\'s true. In the end, I think it will be a
> win-win.
>
> DR. LAYLOFF: I don\'t think the risk with this technology change is
> significant compared to the one that we encounter with HPLC and GC,
> when we start seeing all those impurities in it, or when RIA showed
> differences in the bioavailability. Those were startling changes.
> There was a lot laying under those rocks. I don\'t think there is that
> much laying under this rock, because we have in place already the
> standards for the product, and that is what the bottom line is, it\'s
> getting a quality product out, and we have defined what that product
> quality is.
>
> DR. MILLER: I share GK\'s concerns in a similar way. There appears
> that there is a possibility of a gray zone and how do we handle that.
> Typically, when you have new drug, a part of the regulatory
> information is the system of methods used to determine the test.
>
> If we were to go to other systems of measurement, sensory systems,
> that would require filing information, because I haven\'t heard a
> change to that approach. So, it would seem to me that the current
> system of testing would obviously be in place, and that there would be
> a period of time where the new model sensors would be testing and put
> to the process to evaluate the effectiveness of the system.
>
> That being said then, well, now, in this interim period where it is
> not a filed methodology, how do we handle that data? That goes to more
> specifically of the reality of what exists today, documentationwise
> and systemwise.
>
> Let me just expand your concern because that is kind of where I see
> that as a concern, bridging the gap with the current methodologies,
> which are filed for testing to a scaled-up process using the new
> sensor technology, whatever it may be.
>
> So, how do we handle that data that may come to fit GKS\'s
> circumstance?
>
> DR. LACHMAN: During a phase you are talking about, you are still
> developing the method that you are going to use in a filing
> subsequently. Right now you are still using a filed method as the
> regulatory method.
>
> Now, you are not going to file, this method has to show correlation
> that is equal or better than the current method. So, you have got to
> show that, right, at some point in time before you are going to file
> that.
>
> DR. MILLER: Yes, but if it shows something that is a peculiar, how do
> I\--
>
> DR. MORRIS: Do you want me to say something since I don\'t have any
> industry tries to worry about? Let\'s say for the sake of argument
> that it is passing by the compendial method or by the approved method,
> I should say, but fails by the sensor method even though the product,
> as G.K. has said, is efficacious and meets all specs, what is the
> action going to be, is that a fair paraphrase?
>
> DR. LACHMAN: But in a sense, it hasn\'t been validated yet.
>
> MR. FAMULARE: If you are dealing with products that are already
> validated under existing methodology, that will still exist. It is
> suitable for its intended use, and I think we should just bring the
> discussion back to this basic validation, which we are not wiping off
> the table with this technology.
>
> As this technology shows you things that you were not able to
> illustrate before, the regulatory authorities and industry are going
> to have to learn together how to deal with this. We are going to have
> to learn to deal with it as regulatory authorities in terms of in the
> GMP realm, that this falls within GMP, and it may be, as somebody
> suggested earlier, changing of the process on a more frequent paradigm
> than we are used to as opposed to validating something and letting it
> go for 20 years.
>
> I think that if the sensors show you that there is a way to improve
> your process, then, we have an obligation as regulators to recognize
> that, to accept that, and to work that with our reviewers with the
> filing and under GMP.
>
> So, that is the strong thing that we should emphasize, that we will be
> able to accommodate these changes under validation, and we may see
> more changes than we have seen in the past, and our regulatory systems
> will have to accommodate that under this program.
>
> I think we should start thinking more as to how we could give a
> general guidance as we get into our discussion groups as to how best
> to accommodate these scenarios that we are bringing up here, I think
> as opposed to trying to solve each one of these scenarios here.
>
> DR. LAYLOFF: There is a critical control point, and you have an
> acceptance target for a critical control point, and right now you are
> using an assessment technology which might be inefficient, and you are
> talking about changing it to a more efficient technology which will
> better assess that acceptance target.
>
> Now, the target I don\'t think changes, because you do have a target
> at the end of the game, there is a target, and that target is not
> going to change. So, if your assessment technology gives you a tighter
> bound on that assessment point, at that critical point, I don\'t see
> how it is going to have any effect except improve things.
>
> DR. NASR: I think we are here today and tomorrow to gather information
> that we can use in drafting a guidance, so I would like to go back to
> the guidance, and that is the reason we are here.
>
> I would like to ask the question, can we go with a general guidance
> that does little except telling the industry that we will encourage
> you all to utilize new technology, and it will not be technology
> specific, where we give you specific information, what is needed in
> order to validate every aspect of the methodology, information like we
> have seen now, or do you need a specific instruction about each
> technology which we are not planning on providing you at this point,
> can a general guidance like that be useful to you, and if it is, and
> that is our intention, what are the major validation criteria since
> this session is on process and analytical validation, that you need us
> to address to encourage you to start implementing these technologies?
>
> DR. MORRIS: Just one point if I could. I think for those who have
> worked at full scale with sensors, I don\'t think that the fear factor
> is quite as large as it is for the unknown, but that doesn\'t, to your
> point, I think the guidance has to be not only nonspecific with
> respect to technology, but also it has to foster or promote the use of
> the sensors, however, so issues like G.K. and Ron have brought up, it
> may not be a question of whether or not we could write a guidance, but
> whether or not the guidance stimulates the use of the technology.
>
> That is really the issue, because the guidance is obviously our first
> goal, but if it doesn\'t stimulate the use of it, it is not of that
> much use.
>
> DR. KIBBE: I think that there is two extremes that we could go to, and
> both of them would be a mistake. One is to write it so broad, that
> there is no guidance, it is just an invitation to submit something.
>
> Well, industry, where do they go, what do I have to do to have an
> assurance that when I do submit something, it is going to be received
> well, unless I have got a track record, and they have track records
> for other submissions over the last 30 years, they know what to do.
>
> So, unless we give them something that they can hang their hat on,
> they are not coming forward. If we make it so specific that it fits
> them into a very tight niche, then, 80 percent of them aren\'t going
> to be there because they won\'t fit the niche, and we won\'t get
> anywhere.
>
> So, I think our struggle is to get in the middle somewhere, and part
> of it is exactly what we have been talking about, and that is, what is
> the down side for them of taking the risk, and how can we mitigate
> that, and what is the unintended implications.
>
> We are not trying to punish things and have things happen that we
> don\'t intend, but they will be there. Every time there is a
> regulation, there are unintended effects of that regulation, however
> benevolently we put it forward.
>
> So, I think one of the things we need to discuss is what are the
> possible ways that that regulation could have been twisted by
> somebody, because there will be somebody who will try, and pervert
> what we intend as a good outcome.
>
> DR. SHABUSHNIG: Maybe one way to break this out is to look at some
> different classes of situations and kind of thinking a little bit
> along some of the comments that were made earlier.
>
> One would be in the sense almost a like for like kind of substitution
> where you are taking a laboratory test and now you are going to make
> essentially an equivalent measurement on line, and that may have a
> certain level of guidance associated with it.
>
> In that case, you might say I have an HPLC method in the laboratory,
> and I am going to take a process chromatograph and put it on line, so
> I am essentially changing the location of the test, but the chemistry
> of the test remains the same.
>
> The next might be a class where you substitute a spectroscopic test
> for a chromatographic test, so there is a change in the measurement,
> but in terms of the basic information, you are still measuring the
> concentration of a particular species.
>
> Then, I think we need to make sure that we leave things open enough
> for where we think that there is the most opportunity, and that is
> whether it be fingerprinting or some other kinds of methodology that
> there isn\'t an equivalent laboratory test for today, that we have
> left the door open for that because there isn\'t really much of a
> reference point from a guidance standpoint today to go, but we want to
> go ahead and at least have that opportunity.
>
> There, I think we have to have at least more flexibility at this point
> in time, because there isn\'t as good a reference, but rather than
> lumping them all together, if we would have some broad classes in that
> regard, we might be able to help ourselves in terms of how we would
> address those situations and provide at least a foundation in terms of
> how the Agency would look at that and how as a company, we would
> approach those kinds of situations.
>
> DR. LAYLOFF: I think the transition is moving away from focusing on
> the active pharmaceutical ingredient as a unique analyte through the
> whole process stream, the marker through the process stream, to where
> you have the analysis and impurities assessment at the front end, and
> then you move to consistency assessment technologies downstream, so it
> is a change in focus on the blend rather than the active
> pharmaceutical ingredient as a single data stream through the process.
>
> I have difficulty thinking that there is a big risk in shifting from
> monitoring a single variable through the process stream, which is
> active pharmaceutical ingredient, to looking at uniformity, a
> consistency of the process stream, but that is what we are mostly
> talking about. The sensors are looking at consistency of the process
> stream rather than the single variable, so you are looking at it from
> a univariate part, you are looking at a polyvariate point.
>
> But if you are not changing the acceptance range or the univariate
> component by shifting to the consistency assessments, I don\'t think
> there is a risk.
>
> DR. SHABUSHNIG: I think the only question here, though it is still the
> unknown in a sense if you are not actually measuring the same active
> ingredient, and I agree entirely with what you are saying in terms of
> where we are looking to go, that the range that you set before may not
> mean anything anymore, in other words, that range is no longer an
> appropriate measure because you are measuring something entire
> different.
>
> You are still focusing on the same ultimate endpoint, but you may have
> to establish a new interpretation of what that range should be, and I
> think the risk is in the unknown of that at this point in time,
> because you don\'t have enough history.
>
> In general, I think all of us as we have looked at these technologies
> recognize that there is a period of time where you are probably going
> to end up running both of these in parallel to develop that baseline,
> to have that confidence that where you are going is going to be
> acceptable, and that is probably the belt and suspenders approach that
> most of us would recommend taking at this point in time, but I think
> without that, there is that risk of the unknown, that you will have
> insufficient data at this point in time to set an appropriate new
> specification because it is really a new variable that you are
> measuring.
>
> DR. DEAN: Tom, surely, some of the things we have been talking about
> here, looking for the guidances and making it workable, it does have
> to get back to what is good science.
>
> Now, regardless of what the new measurement technologies are, the
> critical quality attributes of the products will remain the same. We
> are just talking about how we are going to measure them.
>
> So, surely, as we start fingerprinting some of these processes and
> begin to understand, what we are really talking about is using new
> technologies to give access to new process variables, new things that
> we can measure, that will be accurate reflectors of the state of a
> critical quality attribute in an on-line environment.
>
> Surely, the guidance we are looking for is something about how we
> achieve that linkage, and surely the validation issues that are around
> that are related to how we can demonstrate that we can maintain
> control of those parameters within the stated upper and lower limits.
>
> I feel fairly confident that we can get some kind of a sensible
> guidance on this by getting back to the basics of what we are trying
> to accomplish here, and I can\'t imagine that we need to have
> scenarios that apply to a large number of different scenarios that
> really would be quite difficult to anticipate and adequately cover.
>
> DR. LAYLOFF: That is moving into what is possible rather than what is
> probable.
>
> DR. MORRIS: I may be misunderstanding this a little. I think I
> basically agree with what you are saying, but I guess\--I have to
> reduce everything to an example\--but if I am looking at blend
> uniformity and I can\'t remember if it was Steve talking about you are
> looking at a unit dose size sample, but let\'s say for the sake of
> argument that my sensor doesn\'t look at a unit dose, and it is very
> low dose, and sometimes I have volumes that have no active in it at
> all, so my CV is really very high.
>
> But, in fact, the product is fine because when I discharge it, each
> unit dose does have the proper amount, and I know that because I have
> correlated the two as you suggest, and as Tom has always suggested as
> backing into the validation, I think the only thing we have to make
> sure of in the guidance is that there is recognition of the fact that
> that sort of reconciliation will have to be allowable, I can\'t
> remember who was saying it down at the other end, but that the
> regulatory burden is to recognize those sort of reconciliations are
> part and parcel of the guidance.
>
> DR. RAJU: I agree with Ken. I think there is a large fraction of cases
> where you are going to be fine among those two case scenarios where
> you are going to be fine. One some of these middle case scenarios, you
> might choose not to even touch them, say we choose not to touch it,
> that is how we manage the new technology.
>
> We choose the classes of where we apply it and what kinds of products
> we apply, and we may or may not aim to do it, but if we do, there is a
> way to do it in a structured way based on the kinds of products and
> the cases, and probably the most important, we heard that they are
> going to work together with the FDA, and the FDA says yeah, we know
> that you are going to go through that phase, and we know that we are
> going to be conscious about it, so we are going to win when we
> ultimately come at the end.
>
> Somewhere in the use or in the guideline maybe, maybe not, but outside
> in the use of the guideline, we have some structure to follow up on
> that case or those classes of cases.
>
> DR. DEAN: I think we need to separate what makes business sense from
> the guidance that defines how we would execute against a scenario
> where it does make business sense to do this, and I don\'t think we
> want to get those things mixed up.
>
> DR. RAJU: But if it\'s the business sense that is preventing us from
> going forward\--
>
> DR. DEAN: That\'s a business decision. I mean that\'s too bad.
>
> DR. RAJU: But then the guidelines, we got the most benefit if they
> help us address the reasons for the technologies incorporation.
>
> DR. SEVICK-MURACA: I think it is going to cost money. The new
> technology is going to cost money so it is going to cost somebody some
> money. If you are going to invest money, you want to make certain that
> you lower the risk. You need to be certain that your investment is
> going to lower your risk. You are going to make good investments. So,
> we are doing this new technology on line. There is going to have to be
> some assumed risk. With profit margins\--and, Don, using your case,
> people are not going to necessarily want to take that risk. If we are
> going to try to encourage new technologies, somehow we have to have
> maybe a probational period that we took these new technologies\-- when
> we are looking at these new technologies, maybe there is a probational
> period where\--I am trying to think of ways that there is no reporting
> to the FDA, get it out of the regulatory area.
>
> Okay; I am an academician. I am trying to minimize the risk because
> someone is going to be making investments. We are not going to get rid
> of all risk, but I am trying to minimize that risk, and is there a
> period of time where there is sort of a probationary period for trying
> out new technologies.
>
> This is where the pharmaceutical industry is different than the other
> industries. When you put a new sensor on titanium dioxide plant, for
> example, you are going to have a period of time where you can take the
> data and you are not going to do anything with it. You are going to
> just look at it and assess it.
>
> But if this data is available on a pharmaceutical process, then, that
> data is there for the regulatory inspection, so we need to find some
> way that we can encourage process technologies.
>
> DR. LAYLOFF: Don\'t ask, don\'t tell, that\'s what the story is,
> right? They run parallel. They run parallel processes until you have a
> high level of confidence that you can make an effective transition
> without blowing the place out of the water. That is what they are
> doing.
>
> Now, I think that there is some parts of the sensor technology. The
> sensor technologies, I think will bring a lot to cost reduction in
> terms of dwell and lost wasted time. If you go in-line instead of
> sampling and testing, you improve your flow of material through and
> you reduce your inventory, and you have actually more accurate
> assessments because if you go to thieves and you go to analysis, you
> are stuck with a much higher variance than if you go with on-line
> assessments.
>
> DR. DEAN: Once again, I think we need to be careful about mixing up
> the business issues with the technology issues, and I think the best
> thing we can do to encourage the adoption of this is to have simple
> and relatively straightforward guidelines on how it is going to be
> used, and we should not confuse trying to precreate some business
> cases that will allow companies to take those decisions. They will do
> it themselves.
>
> DR. MORRIS: I don\'t think that was really the point of that
> discussion. I understand what you are saying, and I agree, but I
> don\'t think that was the point of G.K. and Ron\'s discussion either.
>
> I think the key is that if we would write the guideline so that it is
> clear, that the burden of the responsibility is always on industry to
> make sure that everything is done with proper scientific care and
> implemented properly, and on the regulatory side to accept
> reconciliation whether it be couched in the probationary period or
> whether it is just as you are doing it parallel, it is fine, and then
> the companies ultimately have to feel free to make the choice
> obviously, and it\'s best left in their hands, but they have to be
> assured at some level that they regulatory side is open to the
> concept, and I think by virtue of the fact that we are here, and the
> genesis of many of these ideas, I think that is true.
>
> We just have to make sure it is reflected in the guidance and then, as
> you say, not address the business directly.
>
> DR. DEAN: We could agree to agree here.
>
> DR. MORRIS: Absolutely.
>
> MR. FAMULARE: I think it is important to recognize, as I said earlier,
> and as Ajaz said in his slides, we are not wiping off what exists now,
> so if a product meets today\'s paradigm, it is good for its intended
> use, so in terms of a special period, you know, that period will
> always exist in terms of the current process, but as this new
> technology shows chances to improve the product, to improve the
> process, we are hoping to encourage industry to go in that direction,
> and at the same time recognize where process improvements can be made,
> because the whole idea of the win-win, as we have been talking about
> is that yes, there will be a better quality product, we hope, to the
> consumer.
>
> We are not mitigating that the product today isn\'t good, and at the
> same time, we are hoping that any company that potentially looks at
> this, will see the long-term economies and going to this type of
> operation after the upfront investment, and reducing the rejects,
> recalls, et cetera, all again the basic tenets that were brought up by
> Ajaz first thing this morning.
>
> DR. SHABUSHNIG: Isn\'t it fair to say that\--I mean we are looking at
> a fairly simple risk-benefit ratio here, and how do we improve that,
> well, you could improve it on the risk side or on the benefit side. I
> mean there is two pieces to work on.
>
> I think we have all said in terms of benefit, there is a broad range
> of benefits from a win-win standpoint, from the standpoint of the
> regulators, from the standpoint of the manufacturers, both focusing on
> product quality, that there is a potential product quality improvement
> there, as well as cost benefits that would go with that.
>
> On the risk side, I think what we are talking about, whether it\'s
> real or perceived, there is a regulatory risk and there is a
> technological risk, and within the scope of what we are trying to
> accomplish here, I think we are trying to manage the regulatory risk
> part of that equation.
>
> I mean the technological risk isn\'t going to be solved necessarily by
> this guidance. It is going to be solved by the additional development
> work that is done by the manufacturers, by the equipment builders, by
> the academicians, et cetera, but within this forum, we have the
> opportunity to, in that whole equation, reduce the regulatory risk or
> at least manage that regulatory risk, and therefore improve the
> overall risk-benefit ratio.
>
> So, I think that is our opportunity at least as I see it in the next
> couple of days and when we complete our task as a subcommittee.
>
> DR. LAYLOFF: On that note, regulatory is the issue. That is why we are
> here, and we are going to take a break now, and we will reconvene in
> 20 minutes, and we will bring regulatory back into the picture.
>
> \[Break.\]
>
> DR. LAYLOFF: Jerry Workman is ready to go.
>
> **Session IV: Chemometrics**
>
> **Perspective 1**
>
> **Jerry Workman, Jr., Ph.D., Kimberly Clark**
>
> DR. WORKMAN: My talk this afternoon is really about an overview of
> what chemometrics is and a philosophy of how chemometrics, as an
> emerging technology, faces difficulties in implementation, and so
> it\'s a philosophical discussion. At the end of this point, I would
> like to make a recommendation based on what the food and petrochemical
> industry in some sense did to implement chemometrics.
>
> \[Slide.\]
>
> The first thing we really have to deal with here is that no matter how
> logical and elegant this all looks on paper, it has really got to
> work, so let\'s keep that in mind as we go along here with this
> philosophical argument is all of these things have to work, and in
> order to know that they work, there has to be an experience base
> there, there has to be people with good experience and theoretical
> background, enabled and cooperating in order to put together the right
> kind of guidelines.
>
> \[Slide.\]
>
> Let\'s look at a few chemometric definitions to get started here
> because there have been several. The first one just is unsatisfying.
> \"Chemometrics is what chemometricians do.\" So, we have to go a
> little farther than that, and just go into, \"The application of
> mathematical and statistical methods to chemical measurements.\"
>
> \"Mathematical and statistical methods for the obtention in the
> optimal way of relevant information on material systems.
>
> \"Means to convert raw data into information, information into
> knowledge, and finally, knowledge into intelligence.\"
>
> \"It\'s a technique using mathematics and statistics to yield maximum
> information.\"
>
> \"It\'s statistical and mathematical methods applied in chemistry to
> application of statistics and mathematical methods, as well as those
> methods based on mathematical logic to chemistry.\"
>
> \"Application of mathematics and statistics to one improved chemical
> measurement processes to extract more useful information from chemical
> and physical measurement data.\"
>
> \"Measurements related to the chemical composition of a substance are
> taken and the value of property of interest is inferred from them in
> some mathematical relation.\"
>
> We have talked about all of these at some point during the day today.
> It is also defined as, \"A chemical discipline that uses mathematical
> and statistical methods to design or select optimal measurement
> procedures and experiments, to provide maximum chemical information by
> analyzing chemical data.\"
>
> According to Kowalski recently it\'s, \"The discovery of the
> development of new and sophisticated analytical methods for use in
> line as an integral part of automated chemical processes.\"
>
> Some have said that, \"Process analytical chemistry is 90 percent
> hardware and 10 percent chemometrics, but, of course, to an engineer,
> that means you don\'t need the chemometrics all, and that is not what
> we are talking about here.
>
> So, what do we have here overall through this definition? We have a
> process, we make some measurements, we collect data, and we use
> chemometrics to analyze the data to get information. So, we are really
> focusing on information content from data.
>
> The sensors and sensor technology can give us good data, but the
> information comes from the chemometrics. We review the information and
> attain some real knowledge. The real knowledge comes in the process
> control issues. The sensor guys and gals and the chemometricians can
> give good data, good information, but what is the value of that
> information? That really is integral with the process group, and it
> has often been a separate issue.
>
> We were just talking briefly before this. In order to implement some
> of these things, you need to be a champion of the technology, know how
> to do the technology, migrate through the mine field of your
> organization, actually implement and pull it off, and if you can do
> that, you will be a success.
>
> Without any one of those, the thing blows up. So, it is not easy to
> get these things done in a practical sense. The advantages of
> chemometrics, it gives you speed and real-time information.
>
> It can be really high-quality information if it is done properly. You
> get clear information resolution. That can be from first order, which
> we have been talking about, like spectra, second order, a time domain
> spectra, third order, it could be like 2-D methods over time, and even
> higher order data potentially, so you get amazing resolution
> information if you want that.
>
> You can also use chemometrics to clone sensors, so they look just like
> another sensor. So, it has a lot of promise.
>
> Provides diagnostic capability, so that you can monitor the sensor,
> and the biggest question that comes up is, is it the sensor or is it
> the process that is out of specification. You need to know that
> instantaneously. So, the diagnostics have to be there, and there are
> good recipes for diagnostic in chemometrics.
>
> It can improve measurement quality, improve knowledge, and it really
> does involve low capital requirements because math is cheaper than
> physics.
>
> \[Slide.\]
>
> So, in the case studies, we have safer plant and process operations,
> assurance that the process is in compliance, an increase in process
> plant operability. These are all the things that you read in the
> journal articles.
>
> \[Slide.\]
>
> Improved product quality, minimization of waste, cost minimization,
> optimization of production capacity. These are all possible, and these
> have been done in various industries.
>
> \[Slide.\]
>
> Elimination of possibly the greatest challenge to 100 percent
> compliance in that sampling. You can sample whenever and as often as
> needed, and you have that real-time feedback for learning and control.
>
> \[Slide.\]
>
> What is the disadvantage of chemometrics? Anyone with a computer can
> generate the solutions. There is plenty of room for misinterpretation,
> and chemometrics requires a change in one\'s approach to problem
> solving from a univariate thinking to multivariate thinking.
>
> \[Slide.\]
>
> Requires a \"paradime\" and, for some, even a \"paraquarter,\" very
> large change, in understanding that most of the processes we look at
> are multivariate, not univariate, and so you have got all the data,
> you have got the information, what do you do with it? That is very
> difficult.
>
> Most best practices still need to be collected and codified and to use
> full standards. There is an amazing amount of information and
> expertise in this room, however, getting all of that together and
> putting that in documentation or code or sensor development is an
> extremely difficult part of this.
>
> \[Slide.\]
>
> Here is the old versus the scientific method. A new method requires
> not a thought ritual, but rather a method involving many inexpensive
> measurements, possibly a few simulations, and chemometric analysis.
>
> The new method looks at all the data from a multivariate approach. The
> old method requires a scientist assume powers of observation from a
> univariate standpoint to be the key data processor.
>
> \[Slide.\]
>
> And so the old method is stating the problem, forming the hypothesis,
> observing and experimenting, interpreting data, traditionally
> univariate. It\'s the ponder and grimace stage where you do that often
> enough, the idea comes out like the golden egg, and then drawing
> overly simplistic conclusions related to complex processes, and then
> you assume the process is in control.
>
> \[Slide.\]
>
> The new scientific method for problem solving involving chemometrics
> would be to measure the process, analyze the data, iterate, create and
> test and verify the model, and look at this from a more multivariate
> understanding approach, make sufficient controls to verify the process
> is in control. The good science exists to do these kinds of things.
>
> Now, if you get good data and good information again, what you are
> going to do with it is another problem all together.
>
> \[Slide.\]
>
> So, to just keep going, one designed experiment is worth a thousand
> educated opinions, and real-time information gives you the real
> experiment, the design experiment.
>
> \[Slide.\]
>
> So, the information content of a thousand well measured results, how
> does that stand up to a presumed process model with a few selected
> measurements?
>
> I know in petrochemicals and foods and some other areas, it doesn\'t
> stand up. It is the presumed process model doesn\'t stand up very
> well.
>
> \[Slide.\]
>
> There is a reluctance to change, however. There is not very many
> standard methods involving chemometrics and sensors. There is the ASTM
> E1655, AOAC Official Methods of Analysis, and a couple of other things
> in the food and agricultural arena.
>
> \[Slide.\]
>
> There are some things going on in the pharmaceutical industry. Some of
> you are involved in those, Guideline for Development and Validation of
> Near Infrared Spectrometric methods, Spectroscopic Methods, Note for
> Guidance on the Use of NIRS by the Pharmaceutical Industry.
>
> \[Slide.\]
>
> Here is the typical process chemometrics project. Process decisions
> are in the domain of the chemical engineer, plant manager, and quality
> group. Their process decisions are based upon their process modeling
> and understanding. Decisions are made in the plant through various
> engineering groups. The decisions are made based upon past experience
> and current academic training.
>
> The reason that changes are slow and that most resist the changes
> involving chemometric-based sensors is due to resource deficiencies in
> time, talent, attention, motivation, and economic incentive, and it is
> not generally there in the understanding of those that control the
> processes themselves, the process engineers.
>
> \[Slide.\]
>
> The process engineer and manufacturing personnel require motivators,
> so we need recognition for accomplishment, demonstrated process
> improvement, no risk, convenience, economical choices. This was
> discussed a lot earlier. The risk-to-reward ratio must be near zero.
>
> The company has a separate list of requirements, improved process
> performance, increased profits, maintenance or improvements in
> quality, convenience, economics, and low risk, thus, the ratio of the
> rewards to risk plus the cost ratio is a very large number. It has to
> be very large. These are difficult conditions to find.
>
> \[Slide.\]
>
> Chemometrics supplies a perfect fit by providing the expertise and
> time and talent into the resource equation, minimizes cost and data
> analysis techniques. It requires some sensor and computer time, and
> demonstrates a potential benefit in understanding.
>
> The risk is minimized due to the flow of real-time information, at
> least it can be, but the risk that was talked about before is finding
> out your old processes aren\'t worth anything. That is a big risk. So,
> there is risk in that way, but if you are starting from scratch, now
> you know a lot about what your process is. At least you have the
> information.
>
> \[Slide.\]
>
> You have to meet certain requirements to make chemometric sensors
> work. You have to test your underlying assumptions, things like this,
> prepare multiple alternatives. You commit to implementing the
> technology for not one particular application of the technology. You
> look for multiple technologies, multiple uses, and here is a thing
> that doesn\'t happen very often, you avoid overload of the staff.
>
> You know, two substantial projects is enough, but you can\'t
> chemometrics onto someone\'s current load, because it is very
> user-intensive.
>
> \[Slide.\]
>
> Is there an internal customer market for the technology? Can we
> deliver the technology reliably and cost effectively? Can we take
> small exploratory forays into less challenging opportunities, and how
> do we continually codify and diffuse the information that exists out
> there somewhere into an applied method in our own plant?
>
> \[Slide.\]
>
> Here are some examples of things you could do. You have to look at the
> attributes, industrial chemometrics attribute map, something like
> this. You have to meet all the basic requirements for your sensor and
> analytical techniques. Some are non-negotiable, quality, efficacy, you
> know, conformity, and all the compliance issues.
>
> A discriminator or differentiator may be something that is a little
> bit attractive. For example, you can reduce cost of production or
> reduce time during production.
>
> A real exciter might be reducing the costs by 20 percent and reducing
> the amount of time it takes to produce the product by 50 percent, but
> taking a look at why and when would you apply these techniques.
>
> \[Slide.\]
>
> Here is another way to look at it. Along the abscissa, you have the
> technical risk, low, medium or high, and on the ordinate, low, medium,
> high cost of project. So, you can rate these things numerically.
>
> \[Slide.\]
>
> Then, you can apply a numerical map like this onto another numerical
> map, which is the cost versus risk score versus the value to the
> corporation or the value to implement, and you may only want to work
> in specific areas here where there is a low technical risk and maybe a
> little bit of high commercial risk to your organization. These are
> just examples. You can set these things up in any way and make scoring
> and ranking systems on where to go with this.
>
> \[Slide.\]
>
> The new value rules in technology. Really, if we look at where things
> are going, the information age is substituting information for energy
> to produce knowledge-intensive goods. Pentium chip requires less
> energy than a clock, but has a lot greater information. We are going
> more and more to information, how to deal with information.
>
> This is just the way the world is going.
>
> \[Slide.\]
>
> Here are some problems with going forward with new technology. New
> technologies are usually inferior to present state of the art because
> there is not as many experts around, and you don\'t really fully
> understand the entire nature of the new technology, so there has to be
> a learning curve allowed on this.
>
> Today\'s technology leaders dismiss the new technology because they
> are not familiar with it, so it is automatically, they are hesitant to
> use it.
>
> New technology moves forward very rapidly after some initial takeoff.
> It can if it\'s facilitated. Success creates the seeds of complacency
> due to arrogance. People have been successful in the past, they are
> not liable to change or want to change.
>
> Right here we are talking about some of the psychological or issues
> related to hesitancy to move towards change. The competency traps
> itself in the status quo, and to survive, the competent must seek to
> replace themselves with new competencies. In other words, there is a
> lot of inertia, what is going to be the driver that pushes chemometric
> sensors, and there has to be real significant drivers.
>
> \[Slide.\]
>
> Old technology insists on improved execution of the wrong thing, not
> an emphasis on doing the right thing. Making slide rules better and
> better out of titanium and having one more decimal place with a better
> whole grain leather holder didn\'t really do anything. The whole idea
> was going into the computer age in a digital technology.
>
> The technology is there to make the sensors, to validate and verify
> the sensors. It is there to do good chemometrics and provide
> information, what are people going to do with it, and why do they want
> it.
>
> \[Slide.\]
>
> Stages of change. First, denial, resistance, negotiation, and
> acceptance.
>
> \[Slide.\]
>
> There needs to be a real empathy, and this committee is a great step
> in that direction towards helping those that want to push new
> technology for the benefit of the company and for the benefit of their
> customers, for the benefit of technology. There needs to be champions
> out there pulling this, and there needs to be involvement of those
> that know.
>
> In an ASTM committee, which I have been part of, it is very difficult
> to get the people involved that have the knowledge base, what\'s in it
> for them.
>
> \[Slide.\]
>
> In leading the changes, we first need to gather fast, cheap
> information and corrective problems. We need to get lots of
> information, not data, which gives us the potential learning for
> success, and really, the size of our information pile is going to
> indicate the learning potential for information for future successes.
> Yet, I have seen over and over in certain industries where both the
> sensor and the chemometric technology provides the information, yet,
> there is no pull for the information.
>
> Again, to expose processes in other industries, and I haven\'t had
> much experience in pharmaceutical industry, to expose that there is
> process problems is not a popular stance for sensor people in
> corporations or analytical people. You almost have to start new
> because dealing with the old issues is very difficult.
>
> \[Slide.\]
>
> What was required in the petrochemical industry to put together a
> document? Well, some will argue with this, but really, for a specific
> document, because there were so many algorithms out there, and so many
> approaches, and so many software codes, and so many opinions, is that
> gathering this together allowed a group to standardize the algorithm
> codes for calibration, also, to produce standard samples for
> instrument monitoring, calibration transfer, to produce standard
> outlier detection methods, and standard analyzer functionality tests,
> and standard calibration and validation protocols based on sound
> principles of experimental design.
>
> These things are all codified into a document.
>
> \[Slide.\]
>
> To gather the expertise to write useful consensus standards with
> periodic revision was the only solution in a petrochemical industry.
>
> \[Slide.\]
>
> Note that standard methods will lag somewhat behind new technologies
> until the experience base is gathered.
>
> \[Slide.\]
>
> Here is an example, E1655-00. It\'s 2000. It\'s an ASTM document. It
> was peer reviewed by approximately 100 skilled in the art. It includes
> aspects of scope and use descriptions, instrument requirements,
> calibration mathematics, statistics, pre- and post-processing.
>
> Outlier statistics, calibration and validation protocols,
> troubleshooting guidelines, quality statistics, protocols for updating
> models, terminology, and a questionnaire to check compliance with the
> Standard, because when the Standard first came out, everybody say
> yeah, we are using it, so it had to say wait a minute, you have to
> answer all these questions in order for you to be able to say you were
> in compliance with this Standard.
>
> So, it was a substantial amount of work, and this covered MLR-PLS-1
> and PCR and the use for near infrared and infrared continuous process,
> but it\'s a lot of work.
>
> Thank you.
>
> DR. LAYLOFF: Thank you, Jerry.
>
> Our next presentation is by Dwight Walker.
>
> **Perspective 2**
>
> **Dwight S. Walker, Ph.D., GlaxoSmithKline**
>
> DR. WALKER: Again, the previous speaker, if you have already looked
> through some of my slides, has answered some of the questions I pose,
> but what I would like to bring is a little bit more attention to where
> we see some of these issues in the pharmaceutical industry.
>
> \[Slide.\]
>
> Sort of picking up, where are we starting from? Fortunately, we are
> not starting from scratch. As you can my ASTM, I need to get a new
> copy of it because we are up to 00, I have E1655-97, and as the
> previous speaker inferred, it is the Standard Practice for Infrared,
> Multivariate, Quantitative Analysis.
>
> There is also the USP Chapter on the use of near infrared, which is
> scheduled for the Second Supplement hopefully, and the issue date now
> I believe is June 2002, and for those who are familiar with the
> process, this has been a really, really long and dragged-out issuance
> of this document. This has been kicked around for quite a number of
> years.
>
> \[Slide.\]
>
> I like this quote. I picked this up from an older Science article.
> \"When provided with identical information, statistical procedures
> achieve greater empirical accuracy than do professionals. This remains
> true when one provides professionals with information not available to
> the statistical procedures.\"
>
> This has nothing to do with the pharmaceutical industry. This actually
> comes from the medical field where they actually looked at clinical
> versus actual procedures, and they found that using a rigorous
> mathematical model always gave a better answer than the practice
> clinician. I guess we should all believe what our doctors tell us, but
> there is room and there is sort of a growing\--I mean chemometrics and
> pharmaceuticals always lagged everything else it seems. It lags
> petrochemical quite substantially actually.
>
> \[Slide.\]
>
> First things. Fortunately, the previous speaker really answered this
> one. We do need a clear definition of what chemometrics encompasses.
> Jerry went through this. Does MLR constitute chemometrics? According
> to the ASTM Standard, it does.
>
> Also, is this strictly for higher order techniques, such as PLS and
> PCR? This is really important because if you go out and talk to an
> organic chemist or talk to an engineer in a plant, they can usually
> grasp linear regression. You can almost draw MLR on the board, but,
> boy, you get to PLS and PCR, and just watch the room glaze over.
>
> We have presented this to a number of groups, and it is really, really
> difficult. Are we approaching this as a date independent study? Do we
> need to consider the source of the data also?
>
> \[Slide.\]
>
> There is a number of general classes of chemometrics methods. There is
> an on-line determination of composition. I have gone through the
> slides I missed this morning. There has been quite a bit of talk about
> that specifically around the near infrared.
>
> One other thing I would like to throw out there is perhaps using
> pattern recognition and classification techniques. I don\'t believe
> anybody has spoken about that yet, where it is less of a hard modeling
> approach, and multivariate statistical process control, which is what
> I think everyone here is used to.
>
> \[Slide.\]
>
> Again, my ASTM Standard. I guess I need to get a new version of it.
> It\'s the \'97 release, but it does arise from the petrochemical
> industry, and again, they are well ahead of us, but they are somewhat
> different than us, too. I mean you talk to the people from BP, and
> they have something called Octane Giveaway.
>
> They would rather give you 94 octane gas than 93, but, boy, in the
> pharmaceutical industry, if we gave a little extra in that pill, boy,
> it can make some people really unhappy\--well, maybe it will make them
> really happy, it depends what the medicine is.
>
> This specifically addresses issues around infrared, although it does
> mention near infrared, and I guess from what the previous speaker was
> saying, maybe it has been updated to more reflect near infrared also.
> I don\'t know, I have not looked at the new release of it. Maybe you
> can speak to that, I don\'t know.
>
> It does define the term \"multivariate mathematical techniques\" to be
> all-inclusive. Again, this slide may be out of date. I have not seen a
> 00 release of this.
>
> It also defines many of the terms that we have been referencing, and
> people have sort of thrown up different chemometric terms. It is a
> good document as a basic starting point.
>
> \[Slide.\]
>
> Again, what separates the ASTM document from the needs of the pharma
> industry? I have a typo there. It should be, \"ASTM document describes
> the methods for processes that run continuously.\"
>
> Typically, pharmaceutical companies run in batch mode. That is
> probably not a revelation to anybody in this room, but we don\'t
> usually have the huge volume, and we are more of a high dollar/low
> volume as opposed to petrochemical, which is high volume/low dollar.
> Again, we don\'t have the number of batches to meet the requirements.
>
> That is something that we need to look in the validation of processes,
> too, is do we have\--and somebody threw out the number or they said
> they used three, I believe it was one of the earlier speakers used
> three batches to validate a process. I don\'t know if that would be
> considered enough.
>
> \[Slide.\]
>
> What separates again. A large sample set is required to span between 3
> to 5 standard deviations of all constituents. That is a pretty
> rigorous, if we were going to look at pharmaceutical formulation,
> there could be 5 to 20 things actually in a tablet. Do we need to have
> a large sample set for everything? Does it have to be all-inclusive,
> or can we just be looking at the active ingredient? Again, that has
> been tossed around a little bit today, too.
>
> Again, generating these out-of-spec samples is difficult\--this comes
> out of validation\--as they should be prepared using the same
> equipment as used in the process. For a pharmaceutical company, that
> represents big dollars. You talk about going to a production facility
> and running an out-of-spec batch, and, boy, you will get some really
> funny looks from the operators. One had nothing to do with that.
>
> Then, again, if Process Analytical Technology to be used upon product
> launch, the amount of active ingredient required may exceed what is
> actually existing. Again, the return on investment. Again, new
> pharmaceutical entities, chemical entities are usually really
> expensive when they come out, but they are just at that point going
> from the kilo lab to production, so to say you want enough material to
> actually ruin it to do this technology, again, there is the return on
> investment question that has been sort of thrown around, batted around
> quite a bit today.
>
> \[Slide.\]
>
> I think it was also referred to as USP Chapter on Near-infrared
> Spectrophotometry.
>
> It is again in the process of revision for a large number of years. It
> defines terms for both reflectance and transmittance. It does define
> the PQ/IQ frequency, which is just the instrument qualification and
> the performance qualification. It does rely on the Wavelength
> Standard, the SRM 1920 for reflectance only, so there is actually a
> gap there. There is no transmittance standards right, and I am not
> sure if anybody here from the NIST wants to speak to that or not.
>
> Again, it only refers to MSC. MSC is multiplicative scatter
> correction. There is no mention of chemometric techniques for data
> analysis. So, again, it begins to broach the subject, but it doesn\'t
> go too deeply into it.
>
> \[Slide.\]
>
> So, what technologies have been or may be used for Process Analytical
> Technologies? As you have seen today, most of this is around
> spectroscopic methods. There may be some payback to taking a
> chromatographic method and putting it on-line. Well, it\'s not really
> on-line, it\'s out-line. There is a big focus on spectroscopy. Again,
> it offers the advantage of bringing the measurement system to the
> sample, which is where the real value we believe is.
>
> I don\'t think anyone has spoken about UV/vis today. It is sort of the
> forgotten child of spectroscopy, but we actually use it fairly
> widespread. It is a well understood technology. There is a USP
> guidance for it, but the spectra tend to be highly overlapped due to
> the broad nature of the absorbance, so you have low specificity.
>
> UV/vis will rely heavily on chemometrics, and it does. We actually
> have release methods for some of our products, our two component
> products, where we do a chemometric analysis to release the product
> for a multi-component tablet.
>
> Commercial and validatable hardware/software are available. This is
> the old technology. The vendors have been doing this for a long time.
> They are very familiar with what needs to be in place, and Zymark and
> HP are more than happy to help you with that process.
>
> \[Slide.\]
>
> Infrared. Again, it is well understood. Spectra have a very high
> specificity. It is difficult making truly on-line measurements. That
> is just the physical nature of the equipment, the hardware.
>
> Commercial hardware is available, but the software is not written to
> be validated. That is something again the validation group needs to
> wrestle with, and industry, the instrument vendors also need to be
> aware of it. At least what I have seen for the process, infrared
> software, it is probably not validatable.
>
> Then, there is Raman spectroscopy, it has also been mentioned today.
> It\'s not well understood by manufacturing groups. Raman has been
> around for a long time, but only recently has come into the commercial
> forefront. There are safety concerns although some people claim they
> can get around them. You are basically using a laser to make the
> measurement. You know, there is ignition source, so there is whole
> other area of safety you have to e aware of.
>
> The spectra have high specificity, so it is again like infrared.
> Commercial hardware available, but again the software is not written
> to be validated. This is something, I am not sure if the
> pharmaceutical industry needs to take on themselves, or whether we can
> push some of this back onto the instrument industry.
>
> \[Slide.\]
>
> Near infrared. That is sort of like the workhorse of where PAT stands,
> I believe right now. It is well understood technology, USP guidance
> hopefully soon. The spectra are overlapped, not as badly as the
> UV/vis. The near infrared, no question, will rely on chemometrics.
> Commercial and validatable hardware/software are available, and there
> are a number of vendors that do provide validation documentation, and
> they provide it in large, large binders.
>
> Unfortunately, this is something we still hit, and I don\'t know if
> anyone has hit on this yet today, is the technology was over-sold in
> the eighties, and we still have this problem going to manufacturing
> sites, do you want to bring near infrared, they will point to an old
> brown elube in the corner and say, well, here, use that. That is a
> problem for us.
>
> \[Slide.\]
>
> So, what steps do we need to take to ensure success? I think the
> previous speaker really hit on that. It has also been mentioned
> before. First and foremost, we must ensure that we are doing good
> science. We saw this in one of the examples where she did eventually
> get the technology in place, but we went, we did the installation, and
> we went away, and they developed a model for two components that had
> like 16 factors. Oh, we are getting, geez, 100 percent fit, it is
> great. Well, is truly good science? Probably not.
>
> This will require that any that any candidate process for Process
> Analytical Technologies/chemometrics be well understood, and this gets
> back to the expert. You have to have some champion, some local
> champion expert.
>
> This, in turn, will require a rigorous calibration effort with real
> process samples and generation of data from referee methods. So, yes,
> you are going to have to make the on-line measurements, and somebody
> actually alluded to this before. You may have these two processes
> going on at the same time where you are running the standard process
> and building your on-line technique.
>
> This effort will take a considerable amount of time and effort, and
> does the return on investment exist? I think the feeling, we have seen
> release within GSK, if you have an old, established process, probably
> not, even worse, you have an old, established process in an older
> plant.
>
> \[Slide.\]
>
> Again, things for success. Are we targeting existing processes or new
> processes and products? The former has the advantage of being
> established, validated process, but often these things are not well
> suited to automation or PAT technology. The latter may be easier to
> generate required sample sets. So, it is a real tradeoff, and you have
> to find a site where you really have to pull, you are not trying to
> push the technology in on them.
>
> \[Slide.\]
>
> So, on-line or at-line determination of composition issues, or
> calibration issues. You have heard those beat around today. Again,
> there is maintenance of calibration. That\'s a big one. How do we
> maintain our calibration sets? Someone even brought up the point of
> how we know it is not the process failing, but the sensor failing.
>
> Sampling issues, what is a representative sample? I guess Steve hit on
> this, too, about a representative sample for doing blend analysis.
> Software issues and process control, other process control issues.
>
> \[Slide.\]
>
> Calibration. People talk about chemometrics, but it comes down to
> somebody has to do the calibration, and it is going to require a large
> number of batches. These again will need to include
> out-of-specification batches to properly span the desired range. You
> don\'t want to have a model that is so tightly around your release
> number that you get a number outside, and it passes it anyway.
>
> Who will generate these, and who is physically going to make these? Is
> it going to be somebody in production, is it going to be a pilot
> facility, is it going to be the researcher in the lab?
>
> The cost, especially if it\'s a new product. Again, these things can
> be thousands of dollars per gram for some of these new molecules. Will
> they be generated on the actual production equipment? Are you willing
> to take the time and actually tie up a manufacturing site for a few
> days making out-of-spec batches?
>
> What group within the company will perform the validation? Boy, I
> don\'t want it to be me. I have done it once, it\'s a lot of work. For
> those of you who have done it, you know what I am talking about.
>
> \[Slide.\]
>
> How often must the calibration be checked? Is daily suitability
> performed with some reference material? That is what they do in the
> petrochemical industry is they run every two or three hours like for
> gasoline, they will run a known octane sample through and make a
> calibration measurement. We don\'t probably have the luxury of doing
> that in our existing equipment.
>
> Does it depend on what type of measurement? Are you going to have
> different calibration routines depending on your near infrared,
> UV/vis, Raman?
>
> If the method is fiber optic based, does the probe need to be removed
> for this test? I mean you basically breach the system at that point or
> can you develop something where you can do the calibration in place.
>
> Again, for example, the near infrared for octane in motor fuel, they
> need to do a daily check with verification from lab testing also. So,
> they have a big drum of known material. They run that every three
> hours through the system, because they have a continuous process, they
> generate it, they have a calibration point then and there, and
> basically, somebody, when the 200 gallons is gone, has to regenerate,
> but they also always have to take a sample and run in the lab every
> three hours also. So, again, it\'s a volume argument for them.
>
> \[Slide.\]
>
> What if the check reveals an out-of-spec result? We heard a lot about
> that. Do you shut the process down at that point? Can you shut the
> process down? Does it bring into question the previous results? Do you
> have to go back and look at historical values? Again, who is
> responsible for this check?
>
> Do you have a local champion? Do you have somebody that knows, like a
> chemometrician on site that says, I can go back and say you varied a
> little bit, but you are not out of spec at that point, or do you have
> the operator looking at the red light saying oh, do I push the stop
> button? We have seen examples of both.
>
> \[Slide.\]
>
> Is there room for things like pattern recognition and classification
> techniques? I don\'t think anyone has talked about this today. It is
> to identify and assess the quality of raw materials and products, and
> to develop a library of spectra for acceptable lots. Again, it is a
> different approach, but it does use some multivariate approaches.
>
> Develop a multivariate statistical model of the library. Compare
> future samples to predict identity and quality. Can you start doing
> predictive work?
>
> Demonstrate sensitivity to known expected impurities, degradation
> products and foreign materials. Again, we start doing spectroscopy, we
> are probably not going to be picking up trace impurities. Is that
> going to be an issue? We don\'t know.
>
> There is the up-front investment in calibration, is it voided?
> Basically, you have a history, and this may work better for an
> established process where you can generate sort of a history, and you
> don\'t have to go through the big, up-front calibration, or also with
> ongoing calibration, maintenance costs are avoided. This may be a new
> approach and something we are actually looking at, and I will allude
> to how to do this at the end.
>
> \[Slide.\]
>
> Again, for multivariate statistical process control. Develop a
> statistical model of an existing process. Use rapid, low-cost on-line
> or in-situ spectroscopic measurements. Use multivariate
> statistics/chemometrics to characterize the process from relevant,
> sensitive measurements.
>
> Again, generate control limits. Generate your control that the
> operators are used to seeing based on historical database. Again,
> up-front calibration is gone, some of the other maintenance issues are
> gone.
>
> \[Slide.\]
>
> Statistical judgment of a process is superior to unaided. This is sort
> of the quote, and these are things I pulled out of that Science
> article. I can give you the reference if anybody is interested.
>
> Again, there are extremely effective tools for detecting correlation
> amidst significant noise. In the reference, it is basically in
> conducting interviews with people, how do they pull the relevant
> material out of that.
>
> Probabilistic relationships are more readily obtained than casual
> understandings.
>
> Methodical mechanical approach is more thorough, encompasses
> heuristics and intuition. But there are some potential issues that
> need to be addressed. This is again still in the research state. The
> volume, does the pharmaceutical industry have the number of batches to
> do this kind of process control.
>
> \[Slide.\]
>
> Sampling issues. How is the sample measured? Is the process sample
> collected the same way as the validation data was collected? Can you
> use a thief sample to generate your calibration, and then put a probe
> that actually doesn\'t require a thief?
>
> Again, a fiber optic break, what if the fiber/probe break or you get a
> crack in the fiber, is that out of spec? Other issues are probe
> fouling. Some of the papers have actually published, have shown there
> can be issues of probe fouling.
>
> Sample presentation. These can be an issue for solids or turbid
> samples, again, as I have spoken to you before. Is particle size an
> issue that could be a big thing for near infrared?
>
> \[Slide.\]
>
> Environmental issues need to be considered, and we have seen this in
> manufacturing sites. You know, is it summer or winter? Is it dry? Is
> it humid? We have seen differences in manufacturing in our Montrose
> site and Singapore site. You know, we can get some subtle differences,
> and again, the source of raw materials.
>
> \[Slide.\]
>
> Software. Again, who does the burden of validation fall on? The
> vendor, can they provide a validation package? Some of them say they
> can, but is it good enough? It basically falls on the end user, and
> what degree of testing is required?
>
> Do we need to ensure 21 CFR 11 compliance? Probably so. Vendors are
> more aware of these issues and have begun to address it. Some examples
> are the Bomem with the process FT-NIR and the Enabler software, the
> SpectrAlliance, process UV/vis software with the NovaPack.
>
> \[Slide.\]
>
> What are some of the current software packages that we are all so
> happy with? GRAMS/IQ, we are expecting release 8. It is supposed to be
> 21 CFR 11 compliant. Those of us that like can use Matlab. Well, I
> don\'t think it is ever going to be validatable. It just historically
> has not been written that way. LabView, we have seen a surge in the
> use of LabView. Could it be validated? Maybe so. Is there a big enough
> push for National Instruments to do it? Maybe if we all get up and
> yell and scream on our chair.
>
> \[Slide.\]
>
> Process control. Now that we have all these tools in place, what can
> we do with the information? Can we make process variations\--this is a
> big one\--can we make process variations based on the data from this
> Process Analytical Technologies? Can we do it if the chemical industry
> and petrochemical industry does? Can we vary our process based on this
> information? I am waiting for an answer on that one.
>
> These are validated processes. If a change is warranted, does this
> imply that the process was out of control? Or do we use this
> information to trigger a manual sampling? It would be really nice if
> we could alter our process, but that is what we have registered.
>
> \[Slide.\]
>
> For example, for those of you who have seen me speak before, dryer
> monitoring is a big one. We actually have this working in two
> different GS case sites.
>
> We are measuring the effluent from an oven. We are looking at the
> solvent vapors coming off, so we are not looking at the material in
> the oven. It is independent of what is actually in the dryer.
>
> It is a reasonably clean sample stream. We do see material deposited
> over time on the optics. We are using a PLS model to model multiple
> gases when appropriate. That is my example before where they generated
> PLS model for two gases with way too many factors.
>
> The data is used to signal manual sampling and off-line testing. We
> are not using it to release anything. We are just telling the operator
> now is the time to sample, and you will probably get a good
> measurement.
>
> \[Slide.\]
>
> Again, what was learned? Not going to be used as final release of
> material. Manufacturing is pretty darn conservative.
>
> Using chemometrics requires training local staff. Boy, that was an
> experience. Manufacturing sites often don\'t have technical expertise
> in these things. This is not what they do.
>
> Anything beyond linear regression was initially confusing. Boy, that
> was a big one, too. The first calibrations were generated off-site,
> and they were just not accepted. They did not believe the data, the
> calibrations had to be done there. They had to see the data generated.
>
> Again, the methodology for generating calibration was used. They use
> our methodology, but they didn\'t use our data.
>
> \[Slide.\]
>
> Need to access instrument manufacturer support worldwide. Boy, that
> can really come and bite you. If the manufacturer is not well
> represented where your manufacturing sites are, that can be a problem.
>
> Validation was not required because we are not using it to release the
> material. That is one way we skirted the issue.
>
> \[Slide.\]
>
> What can ease this in the future? We have heard some of these before.
> Advanced training of staff, easier to use software. Validation of
> software is going to be a big one, and some guidelines for
> chemometrics, which is why we are actually here.
>
> \[Slide.\]
>
> Other issues. Pattern recognition, can we use it? Based on historical
> data, can the process be monitored? Need enough history to account for
> all possible conditions, you know, can we ensure that.
>
> Here is another one. Can consortia help with some of these issues? I
> have seen other pharmaceutical industry members here, CPAC, MCEC,
> CPACT, can we use those to maybe leverage some technology and some
> ideas.
>
> Regulatory approval of new approaches. I mean the current is causal,
> understanding every aspect via conventional means or techniques,
> basically understand absolutely everything, or can we go to a
> probabilistic where we compare good batches to in-situ measurements to
> develop a history.
>
> I see I am flashing red, and I am done.
>
> DR. LAYLOFF: Thank you very much, Dwight.
>
> I would like to open this topic up for discussion of the subcommittee.
>
> **Subcommittee Discussion**
>
> MR. COOLEY: One of the things that Dwight and Jerry both made some
> inference to is calibration and the difficulty of calibrating a
> multivariate technique. Something that I don\'t think was mentioned
> was calibration transfer, and that is a big issue. Obviously, it would
> be nice to be able to do these calibrations in laboratory environment,
> and then be able to transfer those calibrations out to the process
> plant.
>
> These is a consortium that has been recently formed called COLI. Mel,
> I don\'t remember what the acronym is for, maybe you can tell us, I
> don\'t recall.
>
> DR. MELVIN KOCH: Chemometrics On Line Initiative.
>
> MR. COOLEY: That group, a large part of that is dealing with
> calibration transfer, so that is another resource that might be useful
> to the group.
>
> DR. MELVIN KOCH: That is one we started within CPAC and are making it
> into an open initiative, and a number of people have bought on to try
> to do things in addition to the calibration transfer things that have
> to do with out-lining with what methodologies are rugged and ready for
> incorporation in industrial processes.
>
> I know the calibration transfer, particularly from lab to production,
> and then from production, instrument to instruments has been
> accomplished within some individual instruments. Jerry, you were
> involved with one of those.
>
> DR. WORKMAN: Yes, that is a big issue, and it is an issue with every
> instrument because if you replace major components, you have a new
> instrument. There are a number of approaches that seem to work quite
> well, statistically evaluating transfer. I think that the technology
> is there. There are some new approaches that have been tried
> academically, but there are some things that have worked pretty well.
> They involve also an attempt to more or less clone instruments, make
> them very much alike.
>
> DR. HUSSAIN: Going back to the validation discussion that we had, and
> Bob made an excellent presentation and raised some questions. Bob,
> would you like to comment on your approach to validating some of the
> chemometric issues at Plankstadt?
>
> MR. CHISHOLM: To be honest with you, I haven\'t done much work in that
> area because what we are actually doing, just for people\'s
> information, is we are running a project where we have basically, we
> have been making this particular drug for five years, so we have a lot
> of QA samples, and we are using these samples to create the
> chemometric models, and that is actually being done just now, so I
> have not actually addressed the validation issues as yet in that sort
> of area, but you get also some problems because people tend, when they
> are modeling, if you have analytical results, they tend to enter these
> manually, and there can be quite a lot of these, and that causes a lot
> of difficulties.
>
> You line them up with spectra, and that is what I was saying in my
> presentation, I think unless you have got good data management systems
> in the future, it will be very, very difficult to validate such
> systems at all.
>
> But we as yet do not have a lot of experience because we have only
> just started modeling, and, in fact, we will be bringing in Professor
> Jim Drennan to help us with modeling.
>
> DR. HUSSAIN: I think the concept of validation and what is the meaning
> of validation in terms of chemometrics and modeling, I think there has
> to be a framework for discussion. I could start with what our current
> practice is, not in the chemistry area, but in the clinical
> pharmacology area, we use modeling quite extensively. In fact, we have
> a guidance out on how to validated pharmacokinetic/pharmacodynamic
> models.
>
> It is rather straightforward. We base our validation on predictive
> capability, and essentially, you need an external data set to validate
> that model, and we make our regulatory decisions today on that basis.
>
> There is another model for validation of chemometrics and pattern
> recognition, and that is the Center for Devices, the engineering
> approach, which is much more simpler. So, I think tomorrow I will try
> to bring copies of some of those guidance for the working group to
> take a look at, because a lot of concern gets raised with validating
> chemometric models, and the way we are handling that is pretty
> straightforward right now.
>
> I think the main issues from my perspective in chemometrics is
> calibration, transfer calibration and sensor variability is more of an
> issue.
>
> MR. CHISHOLM: Maybe just to finish that off, I think because we are
> dealing with an existing product and we would intend to validate
> against existing registered testing methodologies, it is much easier
> for us because we could run two parallel processes and have two
> parallel dossiers and demonstrate equivalence, which is what we would
> intend to do for this particular model. So, that does make life a lot
> easier.
>
> DR. MELVIN KOCH: I would like to address one of the things that came
> up in both the presentations, and that is the difficulty in training.
> Often, I believe a mistake on the part of those in chemometrics is
> that they feel they have to bring the engineer or the chemist, or
> whoever, up to speed in chemometrics.
>
> If we could just learn from what the computer science people have done
> in the trust me, this model is better than the last one, they have
> gained some level of acceptance in their field that when they come out
> with a new program or something that enhances that which people have
> been accustomed to in the past is somewhat accepted.
>
> There is still too many questions and wanting to understand some of
> the basics rather than to dwell on what the results are, and the
> results are overwhelming in terms of the capability. The field itself
> is moving from the spectroscopy into multidimensional techniques in
> their chromatographies, and some of the new developments on putting
> algorithms and things together for image analysis are going to enhance
> most of what we are talking about even further.
>
> It will be forced, I believe, because the speed at which most of your
> clients want data is increasing, and there is a point at which
> traditional methodology, no matter which way you run it, is not going
> to give you the data at the speed you need, so you have to incorporate
> mathematical models and predictions to keep up with the demand.
>
> DR. WORKMAN: There are methods of incorporating the sensor variation
> itself as part of the calibration space, so that what you have is you
> force requirements on the sensor to be with a sensor space, as it
> were, so it will fit a given calibration.
>
> There is many approaches, a few of which actually work.
>
> MR. HAMMOND: I would like to make a point about the use of
> chemometrics all together. I think a lot of these techniques could be
> over-complicated by overindulging in chemometrics when you don\'t
> actually need to. In fact, I would say that our policy is only
> calibrated if you absolutely have to, because of the issues that have
> been talked about here.
>
> There are many ways of using the spectra in very simple ways. I mean
> my favorite chemometric is a standard deviation. You don\'t need to
> indulge in heavyweight chemometrics if you are just looking for
> endpoints or if you just want to do really a patent recognition of
> when have I got to the same place. So, I think overindulging in
> calibration techniques when you don\'t need to is one thing that got
> the whole technology a bad name in the eighties.
>
> DR. HUSSAIN: I think you raised the issue of training. From the two
> perspectives there in the sense at least from an FDA perspective, I am
> looking at down the road, what would we need.
>
> In many ways you are looking at probably a group of experts,
> chemometricians would be in Office of New Drug Chemistry or wherever
> as consultants to handle all of these issues, but I think the concern
> would be the training capabilities in a general sense, are we
> producing enough people with the right training in this area.
>
> DR. MELVIN KOCH: No, and there is not enough academic groups that are
> turning out those who are advancing the field, however, I do feel that
> the techniques are available enough, so that it is becoming rather
> well understood in practice technology for people to use, principal
> components, and some of the other things in their actual
> interpretation of data.
>
> I would like to see it stressed more within the vendor community, so
> that it becomes part of the instrumentation, and not something that
> someone necessarily has to learn in advance. But I am more concerned
> about those who are being trained academically to continually advance
> the field. It is always going to be a concern to have educators who
> are keeping the present student group up, but so far it seems to be
> adequate.
>
> MR. COOLEY: Ajaz, I was kind of holding off bringing that topic up to
> make sure we were finished talking about chemometrics, but you kind of
> made an opportune time. I think that is a big issue. I mean the
> interest of analytical chemists in general and wanting to put a hard
> hat on and go out and work in the plant on an analyzer is relatively
> small compared to the number of people who want to work in the
> laboratory.
>
> I think that is an issue, of having sufficient people that are trained
> and experienced and have a desire to work in this area is one that
> needs to be considered, and wasn\'t really brought up as an issue
> anyplace.
>
> Another part of that is that it is a specialized field of training.
> Putting the process analyzer out in the plant is significantly
> different than putting an analyzer in the laboratory, and there are a
> lot of things that you have to think about to properly put them in,
> that you don\'t have to consider when you are putting an analyzer in
> the lab, and obviously, that all can be captured in a design
> qualification document, but people have to be aware of them, so that
> they can even be brought there.
>
> Dwight kind of touched on one, you know, putting a Raman instrument
> out in a plant, people think of fiber optics as just light, you know,
> it\'s intrinsically safe, it is not a problem to put it out in the
> plant, but yet there has been a lot of publications showing that you
> do produce enough energy from fiber optic probes that you can produce
> an explosion hazard when you have got it out in a solvent hazard area
> in a plant.
>
> So, you know, there is a lot of little \"gotchas\" that are not
> necessarily part of a normal bench chemist\'s training that needs to
> be thought of, and I think Eva would probably agree with that, and
> some experience that we have had in collaborations with her when the
> students came out, putting their instrument in the plant, there were a
> lot of things that you just didn\'t think of when you were working on
> it in the lab.
>
> Some of those things even get into sampling systems. You know, fiber
> optic probes, and that sort of thing, you know, what is the focal path
> length for the probe, are you really looking at the bulk of the
> product in that dryer versus what is close to the edge of the piece of
> apparatus.
>
> Dwight mentioned things sticking on probes. You may think, boy, I have
> got a really reproducible process here, and then come to find out, it
> is just a nice piece of cake that is stuck on the end of the fiber
> optic probe, and nothing was really changing.
>
> So, those are all issues again that you don\'t have in the laboratory
> environment that you have to deal with in the production environment.
>
> DR. TIMMERMANS: I also think that the issue is not necessarily, as
> Rick alluded to, bringing the process analytical chemist into the
> manufacturing area. Speaking from experience, I think one of the more
> difficult things is actually convincing the operators and educating
> the operators, not only in chemometrics, but on the technology itself,
> and putting in near infrared or any other spectroscopic analyzer on
> the wall.
>
> If they don\'t understand it, it\'s a black box to them, and if the
> black box, for whatever reason, malfunctions or gives them a result
> that they don\'t trust, the probe may get pulled from the process and
> hung on the wall, never to be used again.
>
> I think we have all seen maybe or heard of instances where this has
> caused an experience that may have occurred a number of years ago,
> that still carries through into the areas right now. So, I think
> education, not only bringing process analytical people into the
> manufacturing area, but actually getting the people at the
> manufacturing, at the operator level, to understand and have a first
> line of defense there is as important.
>
> DR. MELVIN KOCH: I wonder if I could add something to that. Having
> some experience in industry before moving to academia, we actually
> started to plot how long it would take from a failed experience to get
> a second chance within a production environment.
>
> It came out to be three generations of supervision, and the only
> positive about that is that they are reorganizing and changing more
> often, so that the time is decreasing from seven years down to maybe
> three and a half, but none of that is necessarily positive.
>
> But another point that I would make on the training from an academic
> point of view, and it is an analogous thing which is happening with
> the organic synthesis field as we are finding in chemometrics, but
> there is not much federal funding that is going into fields like
> these, because it doesn\'t tend to identify with those things that
> fall under the general umbrella of biotech or nanotechnology.
>
> So, from an academic point of view, it has been a difficult sell to
> get principal investigators to spend their career in this field and
> develop people in this. So, there is a point at which the momentum
> built up in organizations like this, that show the value of doing
> research in these fields to the point where maybe there is some
> bootstrap activity coming from industry to emphasize this.
>
> In the organic synthesis area, it is kind of interesting because the
> demand is increasing rapidly in industry, and those being trained is
> going in the other direction.
>
> DR. HUSSAIN: I think the interesting point you made in terms of how
> long it takes to recover from a bad experience, but that reminded me
> of why we are here in the first place. We are here in the first place
> because of a lot of manufacturing problems, but that seemed to be so
> accepted now that it\'s a way of life.
>
> Taking a year to manufacture a batch of tablets is routine. I mean we
> don\'t consider that as bad at all. So, there are a lot of bad
> experiences that have become part of the practice. We are trying to
> change that, so that is the challenge here.
>
> The other aspect I think which is important to keep in mind here is in
> terms of our draft guidance, I think there are a lot of issues with
> respect to different parts of the guidance, but what level of
> information would there be on chemometrics, and that is the question I
> am grappling with in this.
>
> Clearly, many of the applications would be straightforward. You really
> would not be modeling, so that is not an issue. The correlation-based
> or inferential type of testing or control, that is where the modeling
> comes in, and can we rely on our current practices of modeling or
> dealing with correlation-based system on predictive capability as a
> means. I think that is probably the limit of what we can do in this
> guidance, not go to anything beyond that.
>
> DR. RUDD: I just wonder if there is something more basic, maybe a
> general question. We have heard quite a bit about developing novel
> techniques, but if I just think about statistical methods, basic
> statistical methods, do you think there is enough awareness out there
> for potential users in terms of distinguishing between available
> techniques?
>
> You know, if I think back to classical statistical training that I had
> during my degree, which is three or four years ago at least now, one
> of the things you learn very quickly is the choice of method, you
> know, when do I use a one-sided, paired T test, or whatever.
>
> I think the same principle is here. We have heard about principal
> component, we have heard about MLR, you know, the list is endless. Is
> there enough guidance out there just to indicate to people when you
> should use one technique as opposed to another, and is there, hence, a
> role for any guidance document we might present just to clarify the
> mine field?
>
> DR. WORKMAN: I think that is very true in a sense of a baseline series
> of algorithms and also statistical approaches to validate those
> algorithms. However, chemometrics is a very creative field, so you
> have many flavors of some of the basic algorithms.
>
> What we did with the ASTM is we backed off to look at actually
> providing matrix notation for the description of the algorithms and
> the statistics themselves, so that there is at least a basis for
> action that was just a generalized form of those algorithms.
>
> They do obviously exist, but there is intellectual property issues
> where people are creating new algorithms, new approaches, slight
> variations to other algorithms, that there is not a lot of historical
> basis for implementing those in a process possibly.
>
> DR. HUSSAIN: Well, I think in terms of pharmaceutical industry, they
> probably will not adopt some of the new ones anyway. I think with
> respect to the general guidance, my thoughts are our expectations of
> the decision process, when does one arrive at a decision that a model
> is sufficient for use.
>
> I think regardless of how you get to that model, I don\'t think we
> will try to address that part of the thing, let\'s say, these are our
> standards for acceptability of a correlation or principal component
> model for use, not discuss how you get there, but this is our
> requirements of predictability and reproducibility, and so forth.
>
> DR. MORRIS: Could I just interject, Ajaz. I guess maybe to Steve\'s
> point, I mean if it is enough to run a simple calibration curve
> straight away and use it, then, God bless you, and if it\'s not, then,
> certainly you would want to take advantage of the more advanced
> techniques that we were just discussing.
>
> If you say you need to have a training set or you need to have some
> sort of demonstration that you have met the validation criteria for
> the process itself, is that not sufficient, I mean based on cycling
> back through the data.
>
> I mean I don\'t know how you go about it in terms of the statistics,
> but in terms of comparing it to the results, is that not the same
> process, is it not enough just to say that, and then let the business
> decisions lie with the companies?
>
> Somebody who knows more about chemometrics may want to speak, which
> wouldn\'t really rule many people out here.
>
> DR. MELVIN KOCH: I guess it is not really addressing that question,
> but what David brings up is the point that is behind the formation of
> this discussion group right now, and chemometrics on-line, because the
> other industries, even those that are very successful in the
> implementation of chemometrics are wrestling with what approaches to
> use based on what time and what is the level of implementation
> capability of some of these systems.
>
> So, it is at earlier enough stage that they are trying to pull some
> recognized recommendation approach. So, it is early. You know, Jerry
> mentioned this is still in a research phase.
>
> I would like to think we are past that because it has been
> demonstrated, it is definitely a proof of concept and moving on, but
> there is a huge need to try to have people better understand when to
> select it.
>
> As Steve pointed out, there is a tremendous negative feeling, because
> in the eighties, people ran and started using it very strongly.
>
> I happened to be involved in a situation where I had folks trying to
> get us involved in the chemometrics, and some of the senior scientists
> resisted making the jump until people understood what was good data,
> and did they really understand what their instruments were doing.
>
> It worked out very, very well because we were forced into preparing
> good data sets before we started to work with them, and there is
> something maybe we are not addressing, is that if your instrumentation
> or source of analytic data has not passed certain rigors, you are
> jumping into something that is really unknown when you start to apply
> math handling to it.
>
> DR. RAJU: I wanted to support and agree with the discussion that was
> taking place. We have looked at data and data analysis in a number of
> pharmaceutical companies, and we find that very little data analysis
> is done.
>
> If you go down to the drivers of why, then, I would say 4 out of that
> probably list of 10 is one. The information of relevance takes a long
> time to get. Testing takes 25 days at the end, it\'s at the end of the
> process, and so the cause and effect are very separated in time, and
> so you can\'t use that information. It takes a lot of time to get that
> information, so the value of the analysis at the end is less.
>
> Two, usually, the information is on paper in a QC lab, so it is not
> easily accessible and, hence, not easy to analyze.
>
> Three, as we discussed here, we don\'t necessarily measure all the
> process and product variables of interest that measure process and
> product quality, so we don\'t necessarily have enough information
> content in the data that we get to be able to connect it back. That is
> No. 3.
>
> Four, almost the definition of manufacturing is to try to do the same
> run again and again. As a result, you get a lot of data of again and
> again. So, the information content of the data, although the data
> quantity is higher, the quality is low.
>
> Those are the four of the 10 probably reasons why we don\'t use our
> data as a bottleneck, but if you look at process understanding as
> being a gap, our goal, it is clear we have to ultimately do it because
> in the end, understanding comes from first measuring, then analyzing,
> then interpreting and understanding, and then you get the model, which
> is your understanding.
>
> So, we have to do it. That is the bad news. The good news is that
> everything that we are doing with the PAT guidelines, and we plan to
> do today and tomorrow, is going to help us.
>
> One, we are going to measure faster; two, we are going to measure
> on-line; three, we might even measure more and better things; four, if
> we connect it back to development, might actually include the design
> and the development and the information content.
>
> The fourth, I am not so sure. The first three I am sure about. So, it
> is okay to keep chemometrics on the boundary for now, and will
> beautifully fit in for our next move, as long as we are conscious of
> it, we have to do it to get the process understanding and the 6-sigma
> at the end.
>
> So, I just want to compliment that we are on the right track, I agree
> in that sense.
>
> DR. LACHMAN: I think one thing we still have to keep in mind is the
> control of the data that you are developing. You have people
> variability, you have instrument variability, you have a lot of
> variability there, and how is that going to impact on your analysis in
> the chemometrics. That basic information needs to be well designed.
>
> DR. RAJU: There are also consequences of getting bad data. That is
> another barrier.
>
> DR. CHIU: I think, you know, in my simple minded way of thinking, it
> would be very helpful for the Agency, for the guidance, if the
> subgroup can develop a decision tree, and that the decision tree will
> define attributes and the criteria.
>
> If you look at what attributes one should look when you implement the
> on-line testing, and then if, under certain criteria, then, you have
> to do chemometrics, under certain criteria, you don\'t need to.
>
> I was thinking if you are looking at a univariate test, you don\'t
> need probably modeling, you don\'t need the chemometrics, you are just
> replacing, determining off a concentration by HPLC, now you are using
> NIR to determine the concentration. It\'s a univariate.
>
> But if you are looking at the multivariate attributes, to look at the
> solution profile, you need the chemometrics. So, if we can have a
> decision tree, clearly define the attributes, the criteria, and then
> to help the Agency to make the decision when and help the industry, as
> well, when and how we should approach this.
>
> MR. CHISHOLM: I think, returning to Ajaz\'s point, when is a model
> robust enough, certainly in our experience, one of the problems is
> that the data sets you obtain are in a very, very narrow part of a
> specification envelope, and, in fact, you don\'t actually obtain data
> sets which will give you confidence levels right across the breadth of
> the specification span.
>
> So, what you end up with in reality will probably be a model which
> reflects a much tighter controlled process than you have heretofore
> had, and a lot of pharmaceutical companies see that as a threat,
> because they are actually going to have to operate where we want to
> be, which is better quality processes, of course, but they see it as a
> threat.
>
> I think there is an example in Australia, it may even have been Glaxo,
> I can\'t remember, were asked to tighten a specification when they
> went forward with such a method, so it is about getting confidence
> levels on the outriders of your specification envelope is very, very
> difficult.
>
> You can make designer tablets and try it that way, but you are not
> going to make that many, so your confidence levels, once you move away
> from the specification, are going to drop quite significantly, and
> these are problems that I think will have to be addressed, and that is
> the sort of problem that I think the standard may well have to
> eventually address, because we have to put some measures on these
> things and agree them with regulator authorities.
>
> DR. HUSSAIN: Also, I think one aspect, especially in the
> pharmaceutical sector, would be the scale effect. I think there are
> ways of addressing the scale effect. Even with vibration spectroscopy,
> the differences that you see as a result of scale can be accounted
> for, and I think using small-scale batches to develop your chemometric
> models is feasible in certain conditions. So, we don\'t want to give
> that part up also.
>
> DR. LACHMAN: I think on the small-scale batches, that is good for
> development purposes, but when you scale-up, your statistics are
> changed. In one case, you have normal distribution, in another case
> you have non-normal distribution, so you have to be careful how you
> use the statistics.
>
> DR. HUSSAIN: That is exactly the point in the sense that the way we
> scale-up now, in a totally blind fashion, I think that with the probes
> on, you actually get inside, into the scale factors, and actually, you
> can pick those up and use that as the collection factors.
>
> DR. MORRIS: Just to that point, with the multivariate or I should say
> the analogy to the univariate solution model, the problem is that if
> you are looking, for instance, even at just the active in a blend, it
> is not really univariate, and that is really where chemometrics finds
> its strength, when it is rigorously applied.
>
> So, I think there really is a place to do it, because we say\--I
> can\'t remember who said this\--that spectroscopy was well understood.
> You say that spectroscopy and solutions is well understood, not in
> powders. I mean now you are really talking about scattering and a lot
> of other things other than just the spectroscopy.
>
> Clearly, chemometrics has a huge role to play in helping elucidate
> that, but you must elucidate it at some point or else you can\'t
> really rigorously define it. So, you still have to know where to put
> your sensors and what their levels of sensitivity and resolution have
> to be. Just to muddy the water a bit.
>
> DR. WORKMAN: I think that decision trees is a great idea for a first
> approach. There is a lot of \"gotchas\" in chemometrics, though, and
> somewhere along the line, somebody has to make, I believe, a good list
> of the \"gotchas,\" so that people can do a good diagnostic on what
> they have just completed using chemometrics, and make sure they are at
> least in the framework of valid methods.
>
> DR. HUSSAIN: I think since we have some time, if you want, you could
> open up for some questions from the floor and the working group.
>
> DR. LAYLOFF: Do any of the members of the working group have
> questions, comments? Sonja, stand up and say something.
>
> DR. SEKULIC: Specifically on the question of chemometrics, I like the
> flowchart idea, however, I think that if we provide a flow chart on
> what sort of chemometrics algorithms you are using in a guidance
> document, I think that might end up being a little bit restrictive.
>
> If we take into consideration the variety in products and the
> manufacturing processes that we are thinking of regulating, I think
> the chemometrician is a rather energetic and enterprising beasty, so
> we tend to generate new permutations and combinations of algorithms to
> cope with each and every situation, and so I think from that
> perspective, I don\'t have a problem providing a flowchart that
> defines this particular process and this particular algorithm and
> model that I put together.
>
> I think that is a legitimate request, and I think that should be done.
> I really would be challenged to try and figure out how to put a
> flowchart together that is general enough to be applicable in a
> guidance document, so that was my concern.
>
> DR. CHIU: I don\'t think as a first step we want a comprehensive
> flowchart to cover everything, every dosage form, every possible
> technology. We could start small. For example, you could use a solid
> dosage form immediate release, which is the most common dosage form,
> and start from there and see what we can do.
>
> I think the working group tomorrow probably can discuss this and to
> see what is the best approach.
>
> DR. LAYLOFF: Any other working group members have a comment?
>
> DR. WOLD: I am Svante Wold, Umetrics, one of the founders of at least
> the word chemometrics.
>
> I don\'t think that chemometrics needs any different approach to
> validation than any other method. There is no difference between, say,
> a combination of an instrument and evaluation of the data if you take
> HPLC and drawing a standard univariate standard curve.
>
> There was a lot of hullabaloo 20 or 30 years ago when biologists
> started to use standard curve, so there was a lot of confusion, but in
> the end, it is the same criteria as always. As Ajaz says, we need to
> check out for the activity, but also one needs to have validation data
> that are representative of the situation. That is very easy to create
> with design.
>
> So, a combination of design to set up the space you want to evaluate,
> and that should, of course, cover what you want to evaluate, and not
> make it too narrow, then, you cause trouble for yourself, then, see
> that things behave.
>
> Now, the problem I think with chemometrics is that when you do things
> right, the methods become sensitive, so sensitive that you see a lot
> of new things, and that is confusing. We have to learn to live and use
> the new type of information, but we shouldn\'t confuse that with
> validating the old. That is two different issues.
>
> We have to understand what happens and appreciate the new type of
> information, but we shouldn\'t see that as a burden, we should see it
> as an opportunity.
>
> DR. RAJU: Ajaz, the one place where chemometrics could be central is
> if you want to push or formulate the process signature idea upfront,
> if you want to do that now, chemometrics would be really pretty
> upfront then.
>
> DR. HUSSAIN: I was looking at some of the acoustic signatures. There
> is two ways of handling that. One would be trying to use that and sort
> of get some numbers out of it, or simply use that or certain parts of
> that as a spectra. So, we may want to use chemometrics, we may not
> want to use chemometrics, depending on the application you are
> seeking.
>
> But I think what Yuan-yuan was getting at, I think it is an important
> point. If, for example, we can clearly delineate what are the direct
> measurements that really do not need any sophisticated analysis, and
> inferential and indirect measurements, like predicting dissolution,
> and how one goes about doing that, so at least if we have a decision
> tree that charts out, we know where we need chemometrics, where we
> don\'t need chemometrics, and so forth. So, I think that would be very
> helpful for us.
>
> DR. CHIBWE: My name is Kennedy Chibwe from Wyeth Pharmaceuticals.
>
> I just have a comment and observation. I think there has been a lot of
> talk about process development control or process control, in-process
> technologies, as well as opposed to laboratory technologies.
>
> One of the points that I would like to make is that maybe if industry
> could be given some leeway, there should be some learning curve, such
> that\--I mean I know the characterization is, \"Don\'t ask, don\'t
> tell.\"
>
> It should be allowed to have a learning curve. They don\'t have to
> necessarily submit some of the parameters that are going to come up in
> terms of optimization, and that could be done during chemical
> development. The FDA doesn\'t necessarily have to request information
> on all the parameters, because one of the points that I would really
> have to be careful about, all the technologies we are going to be
> talking about have limitations.
>
> Good example. Raman is not going to see exactly what near infrared is
> going to see. Raman wants seawater, NRO seawater. So, you have all
> those limitations. But if industry is given sufficient leeway to
> actually do the learning curve, at the same time I think it is very
> good idea that FDA is already moving on for PATs.
>
> It is definitely very encouraging. We are involved in new
> technologies, and we really would want to have some room, if you see
> what I mean.
>
> Thank you.
>
> DR. HUSSAIN: What I have learned through some of the visits to
> companies, and so forth, there is even hesitation\--I think the Pfizer
> term was, \"Don\'t tell, don\'t use, don\'t ask,\" was not the
> phrase\--but regardless, even there is a hesitation to do something in
> addition to the required testing.
>
> What I mean by that is, for example, if a company wants to investigate
> use of on-line, if they put it on line and start collecting data, they
> fear that an investigator might look at the data and see some trends
> in that, and penalize them for that.
>
> If that is the meaning of that in the sense if you want to do
> something without having a need to submit and be penalized, I think we
> probably should discuss and probably address that.
>
> DR. CHIU: I think our guidance can address that. When you have
> parallel processes, one is conventional, one, you are trying to
> develop new ones, then, the guidance document could say, you know, the
> approved conventional traditional process is the regulatory process,
> the other one is just developmental until it is finalized and refined.
>
> DR. LAYLOFF: That is a good idea, I think.
>
> DR. RAJU: It should be part of the guidance discussions, as well, you
> think?
>
> DR. CHIU: Yes, this is what I am proposing, you know, our guidance can
> cover that point.
>
> DR. HUSSAIN: One of the disheartening things for me was even that is
> sort of inhibiting any innovation to some degree, and even if
> companies do it, they do it in a hidden way, so that the investigators
> are not there, and then move everything off\--I am just kidding.
>
> DR. LAYLOFF: If you move it into a guidance and then do it into a
> training program, it should be helpful.
>
> DR. CHIU: Any guidance, we always have internal training and external
> training.
>
> MR. HALE: I think there is a difficulty, though, in the idea that
> adding a sensor for the sake of adding a sensor is going to do
> anything, because we already have sensors. We measure temperature, we
> measure pressure, we measure humidity. We do all this already, and we
> use them to relate a variable to something in the process, as was
> described earlier, and a final product.
>
> I think one of the large difficulties especially with existing
> products is that it is very difficult and of minimizing importance to
> look only at a specific in-process unit operation without looking at
> the final product.
>
> The reality is we don\'t test the final product very much, and to
> start sensing and collecting data on something where you can\'t
> compare it against the product characteristic fundamentally, is always
> difficult, and I think that is a big hurdle to overcome in
> implementing these technologies.
>
> It is easy to say that we can look at segregation or we can look at
> humidity or we can look at drying curves and perhaps do that better,
> but we can\'t compare that with the performance issue, because we
> don\'t take the data, so we are adding on to data collection in the
> end, and that is a huge risk and difficulty in implementing these
> things.
>
> I think we have to remember that adding a sensor for the sake of a
> sensor doesn\'t give us anything, that we have to have the process
> understanding and we have to have the product understanding and data
> to implement anything in the statistical methods.
>
> DR. HUSSAIN: If I could just sort of paraphrase that, if I understood
> that correctly, the challenge is in the sense to understand your
> process and its impact on your product performance\--correct me if I
> am wrong, Tom\--what you are saying is that routine testing that we
> do, say, six tablets for dissolution, really is not going to give you
> that information. You really need far more sampling and analysis of
> end product to get that information.
>
> Is that correct?
>
> MR. HALE: Our current concept of product validation is that by doing
> validation, we can do reduced testing, and therefore, we do reduced
> testing, and that is our concept and definition of the current state
> and why it is good to release product.
>
> If we are looking at statistical process control or all of these other
> ideas, you want to look at the product coming out, and doing that,
> there is a product release issue, but having enough data to correlate
> and have data for the chemometrics or whatever statistical or process
> understanding we have, and that is where it becomes difficult, because
> our look at process optimization is the same as our look at release.
>
> By looking at release, we have some very practical issues to overcome
> in all reality, and I think that is a huge burden that this guidance
> is going to have to sort through.
>
> DR. HUSSAIN: I totally agree. I actually have an example of that
> scenario. I sort of presented that to the Advisory Committee on two
> occasions. The PQRI effort was trying to get some data for stratified
> sampling, and one of the companies wanted to provide data, and they
> actually did the stratified sampling and found a problem.
>
> That is what the fear is, I think, if you do extensive testing, then,
> you find problems, how do you deal with that. You have to correct that
> problem.
>
> DR. LAYLOFF: Just don\'t look, don\'t tell.
>
> MR. HALE: But I think that may result in\--there was talk about tiered
> systems. The tiered system may be old product and new product, because
> the process of collecting data and understanding is different pre- and
> post-registration.
>
> The other thing is that we could look at expanding the time of
> development is probably unrealistic in the scope of the economics of
> the industry, but one thing that we can do besides measuring is
> looking at processes a priori, that are inherently measurable and are
> designed to fit into some of these models that aren\'t so complicated,
> that don\'t have the history that some of our processes do, that work,
> but are inherently difficult to scale.
>
> They are inherently difficult to understand without complicated
> measurement techniques and a lot of gut feel. So, if part of the
> design exercise is not doing more work, but doing better work in the
> design phase by changing the way we measure it, but also changing the
> way we process it, we could have huge improvements, I think.
>
> DR. WOLD: To this question about adding sensors, I think that one
> should start with the data you already have, and the production data
> in all industry including pharmaceutical industry is very little used
> for process understanding. It is used for process control.
>
> If you start to use that to get a better picture, look at the
> dynamics, for instance, in batch processes, you see a lot of things.
> We are amazed, both with the paper industry and with the
> pharmaceutical industry, when you take a very simple batch process
> with just five variables, you start to be able to do diagnostics of
> things and problems that people haven\'t even dreamt about, and that
> is without additional sensors.
>
> Now, if you find that this doesn\'t work, then, we can discuss
> additional sensors, but I think this PAT should include the technology
> to do better with the data as they come already, and there is a huge
> gain there.
>
> DR. LAYLOFF: I think we have run out of steam on this one. A couple of
> things. I would like to remind you all that tomorrow morning we start
> at 8 o\'clock. We are adjourning early so you can get to be early.
>
> Also, I think Mel was commenting that if there is a problem, it takes
> about three generations before it clears. I think we see a different
> FDA sitting at the table who has come here to work with you to help
> move the technology. So those three generations must have gone away. I
> think that was one of them.
>
> We are adjourned for today. We will see you tomorrow morning at 8:00.
>
> \[Whereupon, the meeting was recessed, to be resumed at 8:00 a.m.,
> Tuesday, February 26, 2002.\]
>
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