SGDEPARTMENT OF HEALTH AND HUMAN SERVICES

> FOOD AND DRUG ADMINISTRATION
>
> CENTER FOR DRUG EVALUATION AND RESEARCH
>
> **ADVISORY COMMITTEE FOR PHARMACEUTICAL SCIENCE**
>
> **MANUFACTURING SUBCOMMITTEE**
>
> Wednesday, September 17, 2003
>
> 8:30 a.m.
>
> 5630 Fishers Lane
>
> Rockville, Maryland
>
> PARTICIPANTS
>
> Judy P. Boehlert, Ph.D., Chair
>
> Hilda F. Scharen, M.S.
>
> MEMBERS:
>
> Patrick P. DeLuca, Ph.D.
>
> Robert Gary Hollenbeck, Ph.D.
>
> AD HOC MEMBERS:
>
> Daniel H. Gold, Ph.D.
>
> Thomas P. Layloff, Jurisdiction., Ph.D.
>
> Garnet Peck, Ph.D.
>
> G.K. Raju, Ph.D.
>
> Nozer Singpurwalla, Ph.D.
>
> GUESTS AND GUEST SPEAKERS (Non-Voting):
>
> Efraim Shek, Ph.D., Acting Industry Representative
>
> Colin Gardner
>
> Edmund Fry
>
> Greg Guyer, Ph.D.
>
> Tobias Massa, Ph.D.
>
> Gerry Migliaccio
>
> Kenneth M. Morris, Ph.D.
>
> FDA STAFF:
>
> Diana Koliatis
>
> Joseph Famulare
>
> Ajaz Hussain, Ph.D.
>
> Norman Schmuff, Ph.D.
>
> Janet Woodcock, M.D.
>
> [C O N T E N T S]{.underline}
>
> [PAGE]{.underline}
>
> Call to Order and Introductions, Judy P. Boehlert,
>
> Ph.D., Chair, Manufacturing Subcommittee 5
>
> Conflict of Interest Statement,
>
> Hilda F. Scharen, M.S., Executive Secretary 7
>
> Introduction, Ajaz Hussain, Ph.D.,
>
> Deputy Director, Office of Pharmaceutical
>
> Science, CDER, FDA 9
>
> PQRI/FDA Workshop Report Summary, Tobias Massa,
>
> Ph.D., Executive Director, Manufacturing and
>
> Controls, Eli Lilly & Co. 12
>
> Defining Quality, Janet Woodcock, M.D.,
>
> Director, CDER, FDA 20
>
> Considerations for Quality by Design, G.K. Raju,
>
> Ph.D. Department of Chemical Engineering,
>
> Massachusetts Institute of Technology 49
>
> Current Regulatory Challenges in Assessing
>
> Quality by Design, Norman Schmuff, Ph.D., Office
>
> of Pharmaceutical Science, CDER, FDA 70
>
> Proposals for Regulatory Asses of Quality
>
> by Design:
>
> Industry, PhRMA, Gerry Migliaccio, Pfizer, Inc. 91
>
> Industry, GPhA, Edmund Fry, IVAX Corp. 103
>
> Academic, Kenneth Morris, Ph.D.,
>
> School of Pharmacy, Purdue University 112
>
> Regulatory, GMP Perspective, Joe Famulare,
>
> Director, Division of Manufacturing and Product
>
> Quality, Office of Compliance, CDER, FDA 132
>
> Regulatory, CMC Perspective, Ajaz Hussain, Ph.D.
>
> Deputy Director, Office of Pharmaceutical
>
> Science, CDER, FDA 143
>
> Open Public Hearing:
>
> Robert C. Menson, Ph.D., Menson Associates 166
>
> Committee Discussion and Recommendations 185
>
> [C O N T E N T S]{.underline}
>
> [PAGE]{.underline}
>
> Quality by Design and Risk-Based Regulatory
>
> Scrutiny CMC: Specifications and Post-Approval
>
> Changes, Colin R. Gardner, Transform
>
> Pharmaceuticals, Inc. 228
>
> GMP, Greg Buyer, Ph.D., Merck & Co., Inc. 257
>
> [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 and Introductions**
>
> DR. BOEHLERT: I would ask everybody to start taking their seats so we
> can get started properly at 8:30. I think it is close enough to 8:30.
> I would like to call this meeting to order.
>
> Good morning, everybody. I am Judy Boehlert. I would like to welcome
> you to the second meeting of the Manufacturing Subcommittee. This
> meeting will perhaps be a little bit different than the first one we
> had because today we are looking for definite input from the
> committee; the first one was more introductory in view. So, today we
> are going to be asked to address a number of questions around the
> topic of quality by design and the concept of risk, and how the two
> fit together.
>
> To get the meeting started, I would like us to introduce ourselves. I
> will start off by saying I am Judy Boehlert. I have my own consulting
> business to the pharmaceutical industry. We will start at the end of
> the table with Efraim.
>
> DR. SHEK: Efraim Shek, from Abbott Laboratories.
>
> DR. GOLD: I am Dan Gold and I also have my own consulting business.
>
> DR. LAYLOFF: I am Tom Layloff. I work for Management Sciences for
> Health, which is a health sector NGO, working primarily in Africa.
>
> DR. SINGPURWALLA: I am Nozer Singpurwalla. I am a professor.
>
> DR. HOLLENBECK: I am Gary Hollenbeck, professor from the University of
> Maryland.
>
> DR. DELUCA: Pat DeLuca, professor at the University of Kentucky.
>
> MS. SCHAREN: Hilda Scharen, Executive Secretary for the Advisory
> Committee for Pharmaceutical Science.
>
> DR. RAJU: G.K. Raju, Executive Director of the MIT Pharmaceutical
> Manufacturing Initiative.
>
> DR. PECK: Garnet Peck, professor, Purdue University.
>
> DR. WOODCOCK: I am Janet Woodcock. I am the head of the Center for
> Drugs. I am also the Chair of the Product Quality Steering Committee
> Initiative for the FDA.
>
> MS. KOLIATIS: Diana Koliatis, Regional Director, Northeast Region,
> Office of Regulatory Affairs.
>
> DR. HUSSAIN: Ajaz Hussain, Office of Pharmaceutical Science, CDER.
>
> DR. BOEHLERT: I would like to ask Hilda Scharen to read the conflict
> of interest statement.
>
> **Conflict of Interest Statement**
>
> MS. SCHAREN: The following announcement addresses the issue of
> conflict of interest with respect to this meeting, and is made a part
> of the record to preclude even the appearance of such at this meeting.
>
> The topics of this meeting are issues of broad applicability. Unlike
> issues before the committee in which a particular product is
> discussed, issues of broader applicability involve many industrial
> sponsors and academic institutions. All special government employees
> have been screened for their financial interests as they may apply to
> the general topics at hand.
>
> Because they have reported interests in pharmaceutical companies, the
> Food and Drug Administration has granted general matters waivers to
> the following SGEs which permits them to participate in these
> discussions: Dr. Judy Boehlert, Dr. Patrick DeLuca, Dr. Daniel Gold,
> Dr. Gary Hollenbeck, Dr. Thomas Layloff, Dr. Garnet Peck, Dr. G.K.
> Raju.
>
> A copy of the waiver statements may be obtained by submitting a
> written request to the agency\'s Freedom of Information Office, Room
> 12A-30 of the Parklawn Building.
>
> In addition, Dr. Nozer Singpurwalla does not require a general matters
> waiver because he does not have any personal and imputed financial
> interest in any pharmacology firms.
>
> Because general topics impact so many institutions it is not prudent
> to recite all potential conflicts of interest as they apply to each
> member and consultant. 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.
>
> In addition, we would like to disclose that Dr. Efraim Shek is
> participating in this meeting as an acting industry representative,
> acting on behalf of regulated industry. Dr. Shek is employed with
> Abbott Labs.
>
> 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 participant\'s 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. Thank you.
>
> DR. BOEHLERT: Thank you, Hilda. Just by way of further introduction,
> the meeting today will be structured with a number of presentations,
> followed by committee discussion, followed by another group of
> presentations, followed by committee discussion. We have two topics
> that we have been asked to address. One is quality by design and the
> other is relationship between quality by design and risk-based
> regulatory scrutiny. So, with that introduction, I will ask Ajaz to
> get us started.
>
> **Introduction**
>
> DR. HUSSAIN: Good morning. Madam Chairperson, we would like to sort of
> welcome everyone here, the subcommittee members and invited guests, to
> Rockville and, hopefully, Isabel is not on your mind today.
>
> As you have already mentioned, this is the second meeting of this
> subcommittee of the Advisory Committee for Pharmaceutical Science and
> we would like to, as was said earlier, at the first meeting, move away
> from \"blue sky\" to some \"blue collar\" work here. To do this we
> have posed several questions to you in the memo I sent out to the
> committee. In particular with respect to quality by design, we seek
> your comments and recommendations on how do we define quality by
> design and how does one achieve quality by design, and then how one
> should assess quality by design in a regulatory setting such that we
> do not interfere with the development programs of a company. That is
> one set of questions that we posed to you.
>
> To support the discussion and facilitate the discussion we have
> invited several speakers with several different perspectives, and I
> would really hope that the speakers invited would focus on providing
> some proposals to you and different perspectives. This will be
> followed by committee discussions and please feel free to ask the
> invited guests the questions that you have, as well as provide us with
> your recommendations on the questions that we have posed to you.
>
> The second part of the discussion focuses on linking quality by design
> to risk. Now, if you go through the presentations you will see that
> the risks we are talking about are focused on the CMC review process.
> So, I think there is a general feeling that there are opportunities to
> reduce the burden that we have in terms of managing post-approval
> changes. For that aspect we have invited Dr. Colin Gardner back. He
> had introduced to you the concept of make your own SUPAC and calling
> it custom SUPAC. Based on the development knowledge, can we find
> better ways of developing a regulatory framework that recognizes that
> level of science and allows companies to benefit from the high level
> of science that has already occurred, has been done for a product. So,
> how does one link to that? We have invited Greg Guyer also to focus on
> aspects of that.
>
> We also have a couple of presentations in the open public session. I
> think one particular presentation focuses on risk. I think that will
> be very valuable. With that, I would sort of let you know that Helen
> is on a well-deserved vacation somewhere on the West Coast, away from
> Isabel, and I again welcome all of you and look forward to discussing
> these important topics with you today.
>
> DR. BOEHLERT: Thank you, Ajaz. Our first speaker this morning with be
> Dr. Tobias Massa, who will be updating us on the PQRI/FDA workshop.
>
> **PQRI/FDA Workshop Report Summary**
>
> DR. MASSA: Good morning. Last April PQRI co-sponsored a rather large
> workshop with FDA on many of the topics within the context of quality
> for the 21st Century. This meeting had approximately 500 attendees
> from a broad swath of industry and included approximately 70
> representatives from FDA. We had people from the innovator as well as
> generic industry, small molecules as well as biotech companies
> represented. The human as well as veterinary segments of the industry
> were there as well. We had international participation as well. We had
> industry representation from EFPIA and JPMA, our European and Japanese
> counterparts of PhRMA, as well as some EU regulators.
>
> The main focus of this meeting was the discussion groups. The topics
> that were covered are listed on this slide, specifically focusing on
> manufacturing changes and how we can change the regulation to make
> manufacturing changes easier to achieve manufacturing science to try
> to define the body of information upon which we make decisions; how to
> define risk and how risk ties into the issues of manufacturing science
> and changes; and then also trying to integrate CMC review, and that
> includes development through the review and inspection process.
>
> With regard to risk, I don\'t think it was any surprise that
> everybody, or most people\--we had a consensus opinion that risk and
> science-based approaches to GMPs and regulations is the desired state.
> Tiered regulatory oversight was deemed to be considered appropriate.
> The lower the risk, the lower the regulatory oversight. The more
> information you know about your product, the better you know your
> product, the lower the level of regulatory oversight.
>
> There was also consensus around the concept that risk is dynamic and
> changes over the product lifetime. The more commercial experience you
> gain from your manufacturing, the better your body of manufacturing
> science is and, therefore, the lower your risk is.
>
> Now, we were not able to get consensus on some items and clear
> definition of risk, risk assessment or risk management, just was not
> there. When you have that many people involved it is a little
> difficult to reach consensus. Also, how risk is related to fitness for
> use and how you tie that to manufacturing, what happens on the
> manufacturing floor, was not something that could be agreed on
> although there were certainly a lot of opinions there.
>
> Within the manufacturing science discussion group, this definition was
> agreed to. You will hear more about that from Gerry Migliaccio later.
> But within the body of manufacturing science it was felt that there
> should be identification of risk at various points in the
> manufacturing control process and how that risk is mitigated. Again,
> the concept that risk is dynamic and manufacturing science is dynamic
> was discussed in this group as well.
>
> What started to emerge were concerns about what should be shared with
> regard to manufacturing science and how it should be shared. We have
> not achieved, I think, that culture of trust that we need between
> industry and the agency in terms of how we are going to handle this.
> There is a lot of concern from the industry that this might result in
> more regulation instead of less regulation. So, I think both within
> the agency and within industry we have a lot of cultural barriers to
> overcome with some of these concepts and, again, the concept that
> manufacturing science would be inversely proportional to the level of
> risk and manufacturing oversight.
>
> The issues that were discussed here were exactly what I just
> mentioned\--what data should be shared and how will that data be used.
> The overall goal, obviously, is assurance of product quality by design
> rather than by testing. This group came up with some very specific
> recommendations and these are enumerated here. Basically, what we are
> talking about is having more discussion between industry and the
> agency on the topics of what is the body of data to be shared; how do
> we collect that; how do we format that in a way that makes sense for
> the agency; how do we identify a risk classification system based on
> that body of data; how do we use technology to mitigate risk and also
> providing guidance on broader interpretation of current regulation.
> Note that I am saying \"interpretation\" of the regulation, not
> changing the regulation, as they pertain to filing supplements and
> inspections.
>
> With regard to integration of the process, the review and inspection
> process, a lot of the same comments were made. It was felt that if you
> had the appropriate body of data you could have tiered regulation or
> tiered regulatory oversight.
>
> With regard to inspections, there was a general consensus that PAI
> should be conducted where warranted, i.e., in higher risk situations,
> and higher risk could mean a new technology that has not been approved
> before or a new plant that may not have been previously inspected.
> There should be a risk-based focus on the most critical issues during
> any inspection,
>
> The CMP inspections or the biannual inspections should be focused, it
> was felt, on quality systems as opposed to being product specific.
>
> Probably the one item that will probably be very difficult to attain,
> based on past history, is mutual recognition of inspections and
> industry has raised that at ICH as well, not only mutual recognition
> of inspections but, maybe a little too \"blue sky\" mutual recognition
> of the review of CMC sections of applications.
>
> People also saw a lot of value in the proposed pharmaceutical
> inspectorate that FDA has in their plans. Industry would like to
> participate in trying to put together a training program so that we
> can draw on some of the expertise that industry has to help put that
> program together.
>
> Again, the concerns are what data and how much data should be shared;
> how will it be reviewed and by whom; concerns about more, rather than
> less, regulatory oversight; a lot of concern about what is the impact
> on the review timeline. People do not want to have their reviews held
> up by submitting additional data.
>
> This is kind of further out on the fringe, but there are also concerns
> about FDA dictating pharmaceutical development. We are going to be
> submitting a lot more data and what we don\'t want to have happen is
> for the agency to say, \"well, I like company A\'s development
> paradigm better than I like company B\'s. Therefore, company A\'s
> ought to be the one that\'s used.\"
>
> There is also concern about the role of reviewers, technical experts
> and inspectors. If we are talking about an integrated approach here,
> how will this work? I think industry wants to hear more specifics
> about who will be responsible for what parts of this process.
>
> With regard to manufacturing changes, a lot of the comments that were
> made on development reports, and risk, and manufacturing science also
> showed up here. The two comments that I will make here are that the
> comparability protocol, as was proposed in the draft for small
> molecules, and at this point in time we had not seen the large
> molecule or the protein comparability protocol guidance, that proposal
> was too narrow. It did not allow enough breadth of scope to allow for
> a manufacturer to make changes in an expedited manner. We think the
> scope of that can be enhanced.
>
> Also, we need to have global, not US-centric, change regulations based
> on risk and science. We manufacture\--many of us manufacture for a
> global customer base and we can\'t be operating, and we have been
> operating based on regional regulation or interpretation of that
> regulation. We have to get to a harmonized set of regulations or
> interpretation of those regulations so that we are not trying satisfy
> three different regions with the same body of data.
>
> In terms of next steps, what can be summarized from this is that we
> need to have further discussions on what is the definition of risk,
> risk assessment and risk management. What is the appropriate body of
> manufacturing science and how should it be shared with the regulators?
> How do we marry the concepts of risk and manufacturing science to come
> up with tiered regulatory oversight? How can we achieve global
> standards and mutual recognition for inspections, as well as
> manufacturing changes? And, how can we define the roles of and
> training for reviewers, experts and inspectors in the process and in
> the review of manufacturing science data? Thank you.
>
> DR. BOEHLERT: Thank you, Dr. Massa. Are there questions from the
> committee members? Yes, Nozer?
>
> DR. SINGPURWALLA: One point of information, Product Quality Research
> Institute, is its focus strictly for drugs or is it across the board,
> including all kinds of manufacturing?
>
> DR. MASSA: We are only concerned with pharmaceutical manufacturing.
>
> DR. SINGPURWALLA: That helps explain the next question, namely the
> last slide that you put up, definition of risk, risk assessment and
> risk management. Now, all this is pretty standard outside this
> particular community. Why is that particular knowledge not absorbed?
> Why start defining things that have already been defined?
>
> DR. MASSA: I think based on some of the discussions we have had,
> particularly based on the last meeting of this group, we are exactly
> trying to do what you are implying, and that is learning from other
> segments of industry in terms of how they apply identification
> assessment, management of risk, and that is I think what we are trying
> to achieve. I think your point is well taken.
>
> DR. BOEHLERT: Are there other questions or comments from committee
> members? If not, thank you, Dr. Massa. Our second speaker this morning
> is Dr. Janet Woodcock, who will speak to us on defining quality.
>
> **Defining Quality**
>
> DR. WOODCOCK: Thank you. Good morning. This talk that I am going to
> give bears directly on the point that was just raised by the
> committee, which is, of course, there is a framework for definition of
> risk, and a framework for risk management and how to do risk
> assessment, and so forth. The question that we have really been
> dealing with in PQRI and in our whole initiative is how does that
> specifically apply to the manufacture of pharmaceuticals?
>
> I am reporting on deliberations that have been going on with the
> steering committee for the FDA, Pharmaceutical Product Quality
> Initiative Steering Committee. What we determined is that we really
> have to have a common definition of what is quality for a
> pharmaceutical product and then we can start talking about what is a
> risk to quality.
>
> So, my talk is going to take you through some of these reasoning. It
> may seem peripheral at first to your deliberations, but I think by the
> time I get to the end of my talk you will see how this links to
> classic definition of risk, risk assessment and so forth.
>
> Now, when we looked into this, when we looked into the issue of
> quality and how it is applied in other sectors there is really a very
> common understanding in the world of what quality is. From a quality
> person\'s standpoint it is a product or service that meets or exceeds
> customer needs. So, over the years, over the last fifty years,
> whatever, people have recognized that if you are in the business of a
> product, a service, or whatever, your obligation really is to
> determine what your customers needs are and meet those needs or exceed
> those needs, and quality is really a customer-centric definition. So,
> that is the outside world.
>
> Now, in the regulatory context of pharmaceutical quality, I think it
> has been agreed that the customer or the market cannot easily or
> rapidly evaluate the attributes of performance that are critical to
> them, which are the safety and efficacy of the drug. That is due to
> the nature of medicines; it is not easy to tell, obviously, whether or
> not they work. That is why we have these extensive clinical trials on
> side effects. It is not easy to link whether you have had a side
> effect due to a quality problem or not in many cases, although not
> always. By economists this is called a \"market failure.\" The market
> isn\'t able to sort out these characteristics.
>
> But I think our society has decided that regardless of this much is at
> stake with medicines\--your life maybe; your health. So, don\'t just
> let the market sort it out. That was the impetus for the statutes that
> were established in the last hundred years requiring pharmaceuticals
> to be safe and effective before their marketing. By the Food, Drug and
> Cosmetic Act governing statute, FDA actually stands in for the
> customer. The way we are establishing and enforcing these quality
> standards that will ensure the clinical performance, as I am defining
> it here, of the product, we are defining quality for those attributes.
>
> I am defining that tentatively\--more discussion will be had about
> this\--as clinical performance, which is delivery of the effectiveness
> and the safety as described in the label, which is derived from the
> data and information in the clinical trials of that product. That is
> sort of the contract that is made and enshrined in the label is that
> this product has been tested in people and it will deliver this
> effectiveness; it will deliver this kind of safety profile. We rely
> upon the manufacturing controls and standards to ensure that time and
> time again, lot after lot, year after year the same clinical profile
> will be delivered because the product will be the same in its quality
> in this narrow sense of the word.
>
> So, I am defining quality almost as clinical performance of the
> product, that it will deliver the clinical performance, but that is
> not aesthetics of the product; it is not the price of the product; not
> other kind of consumer-defined attributes. So, there are other aspects
> to quality that consumers may have that FDA does not regulate and will
> leave out of this discussion because we are not concerned too much
> about the risks there.
>
> That leaves the open question then of who are the customers, say, of
> the FDA standing in for the customers? Who are we standing in for? We
> agree, and the Center for Drugs has agreed for a long time, customers
> are people who take medicine. They are our customers because we have a
> pact with them that we will make sure that they get that medicine that
> they need. Also, of course, their parents when we are talking about
> children or caregivers, relatives, etc. are all people who take
> medicine, which ends up being most of the public.
>
> Now, obviously there is the public health stake in this in
> pharmaceutical quality. So, in that sense, the whole public has a
> stake. Also, a very strong customer for this are the health
> professionals. They prescribe and dispense these medicines. They are
> relying on this system, quality ensuring system to make sure the
> medicines they prescribe and dispense deliver the quality that they
> expect. Then there are many, many other customers, including Congress
> and the administration. The pharmaceutical industry has a certain kind
> of relationship to the FDA, and so forth. But I think when we think
> about quality and risk to quality we have to think of the primary
> customers as people consuming that medicine and we have to think of
> the statute and what we are guaranteeing in there, that the drug will
> continue to be safe and effective and perform as described in the
> label.
>
> We can debate all this when I am done, but let me get through the
> argument. So, this is how we proact as regulators. We want
> bioavailability studies to make sure that new twists or tweaks of
> formulation continue to deliver the drug delivery in the same way. If
> there are major changes in the drug, we might ask for clinical or
> additional safety studies. We want to make sure that clinical
> performance continues the same or, if it is different, that the
> changes are reflected in the label.
>
> A surrogate for this has been proposed and that is fitness for use. In
> the next part of my talk I am going to discuss using a surrogate like
> fitness for use and its relationship to clinical performance which
> actually, in my mind\--and I am going to be talking from a clinical
> perspective here, this is a somewhat tenuous relationship,
> unfortunately.
>
> I don\'t think we are disagreeing that fitness for use is a surrogate
> that is used for quality. We define that via the standards that are
> established in regulation, in guidance, internationally, and so on, as
> well as the attributes we regulate which are basically the
> specifications of a product, in-process controls, and so forth. So,
> there is a body of items or quality attributes that, if a product
> passes those, conforms to those, then we consider the product \"fit
> for use\" and it is released.
>
> The question is if you are talking about risk and you are talking
> about risk to quality, which is clinical performance, how do these two
> things relate to one another, these specifications and everything on
> one hand and performance on another?
>
> We define, as you know and you know this better than I, a product
> \"fit for use\" if it meets its established quality attribute
> standards, including all these and often many more. There are
> in-process standards; there are all kinds of things. I don\'t have
> sterility on here. There are a lot of things, stability, all sorts of
> things that a product has to meet to be \"fit for use.\"
>
> This includes attributes of the label and packaging that might
> influence the performance of the product. It also includes aspects of
> physical performance. For example, if a metered dose inhaler isn\'t
> delivered with a plume properly would not be \"fit for use,\" and so
> forth, and there are a lot of physical performance aspects. Adherence
> of a patch, for example, is a very important. If a patch falls off it
> is not going to deliver the drug to the person. So, those also are
> attributes that we look at.
>
> But another regulatory quality attribute, one that is going to be
> discussed here this afternoon, is made in compliance with current Good
> Manufacturing Practices. That is a surrogate in its own right. That is
> a process problem, processing problems. There is a quality system
> surrounding this product so that the probability that processing
> problems have influenced the quality negatively is low, and that is
> made in compliance with cGMPs. I am going to talk about this a little
> bit as a surrogate.
>
> I also have to remind everyone, and this is something I don\'t think
> that FDA was always focused on by everyone although clinically we have
> always been aware of this, an important quality metric from the point
> of view of the customers\--if you talk about people who take
> medicines, people who prescribe or dispense medicine\--is
> availability. It is a key attribute. If a product is not available
> people can\'t use it and we act as if this is a very critical
> variable. We go to extreme lengths often for medically necessary
> products at the FDA. We go to extreme lengths to make them available
> to the customers if things have happened so they are not available.
> So, by our actions we have clearly signaled that, obviously,
> availability is a very important point. Actually, the mission
> statement for the Center for Drugs says that we assure that safe and
> effective drugs are available to the public, and that has been our
> mission statement for a long time.
>
> Obviously, availability is important and, as with all quality measures
> and efforts, you have to factor this in its importance compared to
> some of the other attributes and risk to the other attributes. Risk to
> availability is a risk to quality.
>
> The issue I want to raise here and the rest of the talk for you is how
> does this surrogate that we all use, and that you are probably going
> to be discussing, the fitness for use surrogate with the associated
> specifications and so forth, complaints, good manufacturing, how does
> this relate to the real quality metric of clinical performance and
> what do we know about that? And, you all may debate me about this but
> this is what I think. This is the view of a clinician about this.
>
> The relationship has really several dimensions like any surrogate.
> There is a qualitative dimension. There is a quantitative dimension
> and then, particularly in this case, there is probabilistic dimension
> of the relationship.
>
> First of all, and I think we really have to focus on this and you can
> see where I am going with this, to quality by design at the end of
> this talk\--first of all, you have to think about what do you select
> for a given drug? What attributes do you select as critical to
> performance, and on what basis do you select them?
>
> I would propose right now, you know, we generally select them on the
> basis of tradition but some of our traditions are really good. For
> example, we feel that content uniformity is an important attribute. I
> agree with that. It is critical to performance probably\--it is
> critical to performance but I don\'t think we always go through a
> conscious process of deciding what attributes are critical to
> performance and how do we decide this, and that really determines
> whether or not your ultimate fitness for use surrogate and your risk
> analysis is going to be useful or not. So, that is point one. You have
> to get the right attributes. I am sure you all agree with this.
>
> But once you select an attribute there is going to be a relationship
> between the value that you get for the attribute and clinical
> performance, safety and effective. Maybe. Maybe there is going to be a
> quantitative relationship and maybe there isn\'t. But whatever the
> relationship is, it is usually nonlinear and my observation of this is
> that we usually treat it as linear.
>
> Let me give you some examples. For example, with content uniformity,
> we have all agreed this is an important attribute. Right? So, you get
> increasing content uniformity and at some level you are going to start
> getting diminishing returns as far as better safety and effectiveness.
> I think we all agree with that. Right? But the problem is because the
> clinical readout of this is so coarse, much coarser than the assays
> you do of content, you really don\'t have a very good idea of where
> the minimal acceptable level is. I think what we end up doing usually
> is that we look at what was achieved in the clinical trials that the
> process can achieve and we say, \"well, that looks pretty good. We\'ll
> tighten it a little bit beyond that,\" make it a little tighter so you
> fail five percent of the time or whatever, and that is the spec. Now,
> I may be wrong. I mean, you guys can tell me I am wrong but that is my
> idea of what happens.
>
> Here is an example, we kind of set arbitrarily to some extent, of the
> minimal acceptable level based on what the USP has traditionally set,
> or whatever, but you have to agree with me it can\'t be the same for
> all drugs. That makes absolutely no sense. Right? But it is to a large
> extent. That is my understanding, anyway.
>
> Then, this is theoretical 001 but you can see that with increasing
> rigor of a particular attribute you get a big gain, and then you can
> have much increasing rigor after that but, don\'t forget, this is a
> quantitative relationship between the attribute and the ultimate
> performance in the person. You can have no improvement in performance.
>
> Why am I going over this? Well, this is very important if you are
> going to construct a risk model because on the right side of this
> graph you don\'t change risk. You aren\'t having any influence on
> ultimate risk. But, depending on how you set up your attribute, you
> may think you are having an impact on risk because you haven\'t looked
> at the relationship of the surrogate to the ultimate safety and
> effectiveness.
>
> A lot of times what we do is we set an arbitrary limit, and this is
> fine. Again, this is due to the coarse readout in the clinic, in the
> animals or whatever. We just don\'t have a lot of information to bring
> to this. We decide, okay, we have qualified some level of impurity by
> a toxicology study and maybe in clinical trials, a lower level, and
> anything below this is fine and anything above it is unknown and,
> therefore, not acceptable. So, there is a dichotomous relationship.
>
> You know, sometimes we see that a product may have an inactive
> contaminant in it that is a metabolite. Of course, much of the drug is
> converted to the metabolite inside a person\'s body but you want to
> control how much is going in. Is that an inactive metabolite? So, we
> develop an arbitrary limit, and that is it.
>
> That is fine, and that is a very pragmatic and reasonable way to
> proceed but I am trying to point out that it has no relationship to
> risk that I can tell, at least no quantitative relationship to risk
> which is what I am talking about right now. It is arbitrary. All
> right?
>
> Then, there is an example here where you might have used the wrong
> color ink\--it is still readable and everything; it is not what you
> said you were going to do but it is still readable. There is no
> relationship at all to clinical performance but it is a manufacturing
> defect of some sort or other.
>
> So, there is a whole spectrum here and my point is that in very few of
> these, and it doesn\'t have anything to do with people who manufacture
> drugs but has to do with the nature of pharmacology and our inability
> to distinguish the impact of small changes in the clinic\--we have
> very little understanding of the relationships of these attributes to
> what we have already decided, if you agree with the opening premise,
> is the ultimate quality measure for these products.
>
> Finally, and I am glad Dr. Layloff is here; he can correct me on this
> if I am wrong, there is a probabilistic relationship between the
> measurements we take on the surrogate because we don\'t just have an
> absolute value of the surrogate; we get our surrogate by doing
> measurements and between that and the medical performance.
>
> I want to go through two examples. One is the testing surrogate, a
> measurement, and then GMP compliance. For testing, of course, we
> ordinarily evaluate whatever attribute it is for each unit that is
> released. We take a sample, a very small sample usually and then we
> extrapolate to the whole batch, or whatever. We are then doing a
> probabilistic exercise. We are saying if this sample passed, well, how
> probable is it that the whole batch would pass if you tested it. If
> the sample failed, you say, well, there is a probability that this
> batch is different than other batches but certainly by no means are
> either of these 100 percent probability.
>
> So, we have a surrogate marker and we are one step back from the
> surrogate marker because we are taking a sample and we are doing a
> probabilistic evaluation based on our testing of that sample.
>
> Now, the same thing, in my mind, is true to inspections. It is
> analogous to testing. You do an inspection and you get a set of
> observations, which is a sample about the quality practices of the
> organization. I haven\'t gone through that in terms of the graphs and
> what the quantitative relationship might be because I have even less
> idea. I think the world has even less idea about how those practices
> quantitatively might relate to the probability of making a quality
> product in terms of performance in the clinic.
>
> Then, when you get this set of observations you ask how does this set
> of facts that you have observed about GMP compliance or lack of
> compliance relate to the probability that you are going to either
> produce a high quality product that performs well in the clinic or you
> are not? That is the task that we have when we evaluate inspection
> reports, as I hear from folks who are engaged in that. They take a
> holistic look at this set of observations and say does this set of
> observations lead to the conclusion that the control of manufacturing
> process is either out of control or in control and, therefore, likely
> to have a certain outcome?
>
> So, the relationship of the proposed surrogate, the point we have been
> talking about all day, which is fitness for use\--the relationship of
> that clinical performance is what I have been discussing. I think we
> generally lack information about that except at the extremes. So, we
> know if you have really bad potency or content uniformity that is
> going to have an impact on performance. If you have an extremely high
> contaminant, it may but it doesn\'t seem good. But in the middle,
> where we are talking about much of this risk analysis, and so forth, I
> don\'t think we have information.
>
> So, fitness for use in the medical world is not a complete surrogate
> because of this lack of information. So, should we just give up and
> not have any more discussions about this? No, I don\'t think so. First
> of all, I believe that we can use fitness for use. We just have to
> keep in the back of our mind these issues. All right? We shouldn\'t be
> paralyzed. We realize we don\'t have a complete link all the way to
> the person in the clinic and we have to live with that because we have
> lack of information. We have to move forward.
>
> Second of all, I think that quality by design makes a lot of sense.
> This would be in prospectively designing a product but I think you are
> also going to talk about changes, process changes and everything
> during the day and I think it also makes a lot of sense there. If you
> can prospectively design or designate the critical quality parameters
> during your development for the product and the process, and evaluate
> and refine those, then you are testing in the clinic something that is
> controlled on these parameters. You still will never get this clinical
> link because it is just not really doable yet.
>
> But you can create a robust link that is hypothesis driven between the
> process parameter, the specs that come out of that and the clinical
> performance of the drug and we can all have, and I think that is what
> we have been talking about a lot, more confidence about changes, and
> so forth, if you have gone through this quality by design exercise.
> But I think none of us should mislead ourselves as we talk about this.
> To a great extent this is still empirical at the clinical end because
> of the limitations of the medical science that feeds the information
> back about product quality.
>
> To close and to get back to the question you asked earlier, we are
> thinking now about how you would apply risk models to this definition
> of quality; how do you think about risk to quality. Because when we
> think about risk to quality we have to think about what is the risk
> that a patient will suffer from failure of medical performance of a
> drug. That is the real bottom line here, if that is our definition of
> quality. But we can\'t think about that because it is too hard because
> we don\'t have the data linking it, except in the extreme cases.
>
> So, we can use, I think, the fitness for use surrogate and we can move
> pretty far using traditional techniques of risk assessment, risk
> management and so forth. We can move pretty far along in this area. At
> the same time, this is why we think we need to develop the quality by
> design part of this exercise because that really has the potential to
> make the link much stronger from the beginning of the manufacture and
> development of the product. So, thank you very much.
>
> DR. BOEHLERT: Thank you, Dr. Woodcock. Are there questions from the
> committee members? Tom?
>
> DR. LAYLOFF: First of all, I would like to thank Janet for an
> excellent presentation of the subject. I think the tradition of
> quality in the FDA stretches back to 1906 before safety and efficacy
> so it was adulteration, misbranding and labeling. I think that that
> tradition has carried forward into drugs so when we buy a bottle of
> pills and it says it contains 100, we expect it to be between 90 and
> 100 regardless of what the clinical aspect is. So, I think that
> tradition has rolled on into content uniformity and all of our
> concepts. There is a tradition of commodity sales rather than the
> quality issue of clinical performance. But I agree that we should
> bring that clinical performance into the risk issue rather than the
> commodity issue. It is time to walk away from that one.
>
> DR. WOODCOCK: The customer still expects the commodity properties to
> be there, and we do regulate many of those, as you pointed out, based
> on our tradition. And, I think we still should but that is not the
> be-all and end-all anymore. I mean, those should really be pretty much
> no-brainers. You should have the number of tablets in there, and so
> forth. I agree with you and that is a good comment.
>
> DR. BOEHLERT: Are there other questions? Yes?
>
> DR. SINGPURWALLA: I have two comments and a question. The first
> comment is on your graph A and graph B. I presume these are just
> illustrative because how do you measure content uniformity and how do
> you measure increased rigor? Those are not measurable things. I
> presume you are just showing them for illustration.
>
> Perhaps the more germane comment is about what is the probability that
> X test result will predict Y outcome? I just want to alert you and
> alert everyone that probability is subjective and adversarial,
> particularly in an industry and government situation like the one you
> have. My probability is not your probability and there is a potential
> adversarial scenario evolving.
>
> The second thing is that many times probabilities are calculated based
> on prior beliefs. So, you start with a prior probability and you
> collect information and you come up with a posterior probability.
> Again, there is a potential conflict because of the adversarial
> scenarios that you have. So, I think you want to be aware of those
> obstacles that you may face.
>
> The question that I have is can you, in one or two sentences, try to
> enlighten me as to what is the focus in which you want us to think
> vis-a-vis your presentation? There is a lot of information there and,
> as Dr. Layloff said, it is pretty good but I need a sharper focus for
> me to be able to focus on it.
>
> DR. WOODCOCK: With respect to your comment about the probabilities and
> the adversary relationship, nobody disagrees with that. That is why we
> would like to develop a model that is commonly understood of what is
> the probabilistic relationship, or at least define some greater level
> of specificity than what we have right now. And, it is not a
> regulator\'s model, an academic model and an industry model but a
> single model that we can discuss and agree upon and we can use data.
> And, we are trying to do that. We are trying to construct models,
> mathematical models and see how the data look in those models. So, we
> agree but things are only adversarial and only value-driven if you
> don\'t use concrete models; if you use mental models. That is what we
> are trying to get away from.
>
> Your question is where am I going with this and why did I give you
> this information when you are supposed to be talking about quality by
> design in the GMP process? I think the reason is that it relates to
> what you raised earlier. Okay? If you are only talking within a
> self-referential system where you are saying quality is defined as
> whatever we say the specs are, that is not really right and we have to
> remember that. That is my basic message, that we don\'t know the
> relationship of the process controls and the specs totally to the
> clinical outcome, what their quantitative or even whether they should
> be a measure in the case of a particular drug.
>
> As you go through your discussion I think you have to remember this,
> otherwise, as I said, you get into a circular self-referential system
> where you say, well, risk is risk to the specs, and that is actually
> what has been proposed already. fitness for use is defined as meeting
> specifications and whatever process parameters and GMP parameters. You
> can easily get into a situation where you can\'t get back to the
> underlying scientific principles, I think, if you just stick to that
> definition, and that is why I presented this.
>
> On the other hand, I am also saying that you can\'t use the clinical
> readout as your measure because it is too coarse and we don\'t
> understand these relationships well enough. But you have to keep in
> your mind that the ultimate measure of quality is how it performs for
> the patient and that these surrogates are not that good a fit, in my
> mind. You may disagree with me though. Partly I raised all this to get
> some disagreement maybe.
>
> DR. BOEHLERT: Dr. Gold?
>
> DR. GOLD: Dr. Woodcock, I am a little confused now. Are you
> challenging fitness for use or are you really challenging the setting
> of specifications when we establish the quality parameters for the
> product, the method of establishing the specifications?
>
> DR. WOODCOCK: Well, I think that is what you are going to talk about
> in quality by design. Right? Isn\'t that part of it? How do you set
> those specifications? How do you go about the process of determining
> what this product should look like? Is it an empirical process that is
> sort of post hoc, or is it built into the development?
>
> So, am I challenging fitness for use? No. I am saying that is as good
> as we have right now but we have to be mindful as we build those
> attributes that go into fitness for use. They can\'t just be what we
> have had for 100 years because I know that is not right. Every single
> drug can\'t have the same requirements. So, we need to move forward
> with what we can do and what we have, but we have to remember the
> bigger picture. That is what I was trying to say.
>
> DR. PECK: Concerning fitness for use we are not dealing,
> unfortunately, with a single response. We do have patients who do not
> act the same as another group of patients and we have these side
> effects which cause us to have some sort of limits and a deviation, if
> you will. So, if we look at the clinical side of it, we already have
> some sort of deviation from what we would call the norm and we are
> trying now to match this, I am assuming, with quality attributes and
> at the moment we are still dealing with some band of attributes.
>
> DR. WOODCOCK: Well, I agree. The hypothesis, going into this, is that
> most of the side effects that are experienced by humans with today\'s
> drug supply, which is very high quality, are not related to quality
> attributes of the drug. It is related to the pharmacologic attributes
> to the drug and genetic and other variability in the people\--drug
> metabolism, all sorts of things. So, that is why the clinical readout
> isn\'t very useful for us in determining many of the quality
> attributes because they don\'t lead to the side effects that we see.
> In many cases in the clinic in doubling the dose we can\'t
> distinguish. We can\'t distinguish a double dose in the clinic. Well,
> a double dose off the line would be a horrible thing if you didn\'t
> intend to do that. So, that is what I am saying, that those readouts
> are very coarse and it is hard to know which matters; sometimes it
> would probably matter a lot. That is quality by design, thinking all
> the way from the functional use of the product and thinking backwards,
> I think, to what do you need for this product to perform that way.
>
> DR. BOEHLERT: Any other questions or comments? First we will start
> with Gary and then Tom.
>
> DR. HOLLENBECK: I think your remarks are right on. I really appreciate
> your last slide because it says three things to me. I think, first of
> all, we are not going to find a probe that will measure clinical
> performance. As we are looking at PAT and in-process measurements, you
> are not suggesting that we blow up the system that we have. It is the
> best that we have.
>
> DR. WOODCOCK: Right.
>
> DR. HOLLENBECK: The second thing that you said that really impressed
> me is that we have for years developed this portfolio of information
> that includes many useless tests. So, we are going to look in this
> process critically at what tests have the best information available
> for us to make decisions.
>
> Then the third thing, with reference to PAT, is that we are going to
> look for strong links between in-process measurements to those
> specific critical parameters. I think those three points are very
> important.
>
> DR. WOODCOCK: Thank you. You said it better than I did.
>
> DR. BOEHLERT: Tom?
>
> DR. LAYLOFF: Yes, I think we have a couple of other traditions, and
> one tradition is that we typically push fitness for use as to what is
> technically feasible so that as our technologies improve we change the
> definition of fitness for use by what technologies are available.
>
> There are a couple of other risks that are involved also, another side
> bar. That is, a useless test is useless to whom? Because if you are
> the one performing that test it is a risk to your job. So, risk is in
> the eyes of the beholder and useless is in the eyes of the beholder
> also. But we tend to move specifications and fitness for use by what
> technologies are available, and that certainly was the case in digoxin
> as we moved on, that and others. It is the available technologies
> which drove the whole thing, starting first with RIA and then
> fluorescence as we shifted to try and deal with it better, but it was
> clearly that the changes in technology drove the standards.
>
> DR. BOEHLERT: Other questions or comments? Yes, Nozer?
>
> DR. SINGPURWALLA: At some point in time you mentioned fitness for use
> as a key factor. Now, I agree with you, if I understood you correctly,
> that fitness for use should be defined in terms of how effective the
> particular drug is to a patient or to a taker.
>
> However, the issue here is that this is a manufacturing subcommittee
> that we are talking about, and if I was a manufacturer, my job is to
> produce the product to the specifications, whereas what you seem to be
> saying is question the specification itself because it is the
> specification that determines whether a headache is going to be cured
> or not. If the drug is even manufactured to specification, I may still
> not be cured.
>
> So, there is a potential conflict in my mind vis-a-vis the charge of
> this committee, namely manufacturing. So, from a manufacturer\'s point
> of view you simply say, \"I did what you asked me to do; it\'s within
> standards.\" Whereas, you are questioning the standard at a much
> higher level, and perhaps correctly so. How do we resolve this
> conflict? Am I clear?
>
> DR. WOODCOCK: Yes, you are very clear. I think what I am saying is the
> fitness for use\--I am proposing we should define as meeting
> applicable specs. That is how the regulators behave. Right? That is
> how the manufacturers behave. But we can\'t lose sight of the fact
> that we have accepted a surrogate for clinical performance because we
> don\'t have anything much better. I am not saying that this committee
> has to find something better and define that link. That is going to
> take probably ten or twenty years of biomedical science, for us to
> make that link better. But what I am saying is although we say fitness
> for use is a set of specs we ought to look very critically at the
> specs, and there ought to be reasons that those specs are there, not
> just commercial commodity reasons, as Tom was saying, or tradition, or
> whatever. They ought to be something we believe in because that is
> what we are making a product to.
>
> DR. BOEHLERT: I would like to cut off comments, if I could, so we can
> stay on schedule. I think we will have plenty of time for discussion
> later today. Thank you, Dr. Woodcock. Our next speaker is Dr. G.K.
> Raju who is going to be talking to us on quality by design.
>
> **Considerations for Quality by Design**
>
> DR. RAJU: Considerations for quality by design, and I will attempt to
> do that in the next half an hour or so. It is not possible to do a
> complete job in half an hour and I will try to give a very high level
> set of components for us to discuss in the afternoon. I hope I will be
> able to do that.
>
> To me, I see quality by design and the extent of quality by design
> being very much about the extent and about manufacturing science. I
> don\'t see them as being in terms of descriptions, how they go along
> together to be that different. As you listen to my talk, you will find
> that I say that multiple times.
>
> From what I understand, I guess in some ways why and how the setup
> comes to talk about quality by design, and to do that let\'s look at
> our manufacturing system. If you define a manufacturing system to be a
> set of processes and systems bound by common material and information
> flow, this is what a manufacturing system looks like today. We have a
> set of steps at the beginning and the end and little or no in-process
> testing. As Gary suggests, the question then becomes is that the place
> to be testing and how are these correlated with the in-process testing
> that we are doing or could be doing?
>
> But if that is the way our manufacturing system is today, what are the
> consequences of that manufacturing system\'s performance given that
> the products are predominantly, by far and almost exclusively, safe
> and efficacious?
>
> Given the interests of time, let\'s just take a look at time and ask
> what are the consequences of our manufacturing system today in terms
> of our motivation for quality by design. The testing that we do at the
> end of our process is exactly the same set of tests that Janet
> Woodcock put up on her set of CFR 210, 211 kind of tests. These tests
> are done and they ensure safety and efficacy.
>
> But what are the consequences of doing these tests at that point in
> time in this way, using this technology? A consequence, and measured
> in time, is that this testing demonstrating safety and effective given
> a set of presumed specifications for a drug product in this case seems
> to take at least as much time as making the product. Clearly, we are
> not building quality in this by design and that is why we are all
> here. We seem to be testing, not sure whether we are testing in
> quality. We may not be. We may have built in the quality and we
> certainly are putting a lot of effort into testing the quality at the
> back end.
>
> Now, if we tested at the back end, and put so much effort into it, and
> our products and processes were not variable, then there would be
> inefficiency at the end. But despite that, we would have to also have
> to bring to the table the fact that while we take a lot of time
> testing we continue to have issues around how do we bring technology
> to be able to address the reasons for that testing. Taking that long
> test at the back makes it difficult to understand exceptions.
>
> If you look at multiple companies over multiple years doing these
> operations, all are safe and efficacious. The consequence of it is
> that it takes half a year to do this safe and efficacious product for
> the world, and the question then becomes what can we do as a
> manufacturing subcommittee to enable and maybe continue to enhance the
> safety and efficacy but ask questions about how we get to getting to
> that safety and efficacy.
>
> If you argue the motivation is not about safety and efficacy but how
> we get to safety and efficacy, let\'s look at manufacturing science in
> terms of this definition as being a body of laws, knowledge,
> principles involved in the transformation of materials and information
> into goods for the satisfaction of human needs. That is, we want to
> ensure safety and efficacy but what is the body of knowledge, laws and
> principles with which we do it today and with which we can do it
> tomorrow? That, we would argue, is the extent of manufacturing science
> and, in many ways, the extent of manufacturing science is nothing but
> the extent of quality by design from a philosophical point of view and
> in many of the measurements point of view and the kind of knowledge
> that you capture at different points as you go forward.
>
> I would argue that we are very much talking about how we get to that
> safety and efficacy in terms of a manufacturing system, and how we can
> work together to enable us as a society to move from knowledge that is
> descriptive, correlative, sometimes causal but rarely, in my opinion,
> mechanistic and, hence, rarely predictive. And, if we cannot be
> predictive, we would not have designed in quality.
>
> Yes, we would like to go to ultimately predicting everything, but if
> we can predict the qualitative trends I think that would be a huge
> achievement for us. So, in many ways I see this opportunity for us as
> we ensure the safety and efficacy, how do we go from a set of bodies,
> laws and principles that are mostly descriptive and correlative to
> those that are mechanistic and may be beginning to be qualitatively
> predictive. That is, how are we going to work together whether this Y
> axis is the extent or manufacturing science or the extent of quality
> by design to, depending on your business choice, do a lot of the
> mechanistic knowledge development even before you make a submission.
>
> If you do that, you have now enabled yourself to be quite independent
> and very much able to make changes during the regulatory period of
> your manufacturing, and maybe you have bought yourself the ability to
> be quite independent of the regulator. The alternative is to have a
> minimum level when are at commercial manufacturing and work within
> your company and within the regulator, interactions you share with the
> regulator, to enable you to make this transition.
>
> The reality, in my opinion, is while this is the desired set of
> profiles, whether we are going to build in quality towards a quality
> by design state during development or during routine commercial
> manufacturing. There is, I believe, a state of today, which is very
> much the correlative and causal knowledge, and a state of tomorrow,
> which is the mechanistic and first principles knowledge, and between
> these two states of today and tomorrow is the cost, quality, time,
> opportunity for the social structure.
>
> If those are the dimensions of manufacturing science and quality by
> design, I think I begin to lay the foundation of two general classes
> of leverages to go from here to there. While each is a powerful
> leverage and not mutually exclusive, it seems clear that the strategic
> level or the leverage that has the biggest impact is the one during
> development because that is when you decide what your specifications
> are. That is when you decide what your information sources are, what
> your experiments are at a small scale in your collaborations in the
> laboratory. That is your ability to make yourself independent of the
> regulator to a large extent. However, there are costs and
> organizational consequences of that.
>
> A second, tactical leverage is to do that quality by design
> development around learning from each lot, particularly the lots that
> are the exception lots.
>
> So, the strategic leverage to get to quality by design in terms of
> learning by doing is a significantly enhanced level of product and
> process understanding before commercial manufacturing. Doing so
> enables potentially a mechanistic basis for setting product and
> process quality specifications that allow us to get out of this
> discussion that we had earlier today.
>
> It has an impact over the whole life cycle. You do the development
> much before the manufacturing and makes it easier at this stage to
> make basic process design changes between wet granulation, dry
> granulation and blending, for example, and few, if any, regulatory
> barriers at that point in time.
>
> The tactical leverage is an enhanced level of product and process
> understanding during commercial manufacturing. The good news is that
> there is a potential to use large amounts of production data, much
> more data than you do for many of your experiments, but much of that
> data is large in quantity but low in quantity \[sic\] because in
> commercial manufacturing most of your runs are about trying to meet
> specifications rather than trying to do experiments to gain
> information about the process.
>
> Investigations and exceptions are the ones here that provide
> opportunities to learn. It is difficult, however, to make significant
> product and process changes because you now are making a product that
> has been approved given a set of bioequivalency, given a set of
> submissions that you have made to the FDA, and it is rarely an
> environment to develop a mechanistic understanding. I would argue that
> this is a very difficult place to do mechanistic understanding despite
> the fact that you have huge quantities of data and you have
> opportunities where the data has some information contact.
>
> Depending on which of these two leverages you use or what combination
> of leverages you use, we have an opportunity together as we go from
> correlative and causal knowledge kind of process, which is represented
> here, which is the diagram I gave you as an example, to one that has a
> much more simple, much more automatically controlled, and much less
> quality by testing focused technology, manufacturing system, process
> flow diagram.
>
> The question then becomes how are we going to go from here to there.
> The strategic leverage is to go from here to there during development,
> and the tactical leverage is to go from here to as far as you want to
> go or have to go during manufacturing.
>
> If you look carefully at this diagram, one point to make is that you
> sometimes have to measure more than we measure today to figure out
> what your critical to quality process variables are for process
> understanding. To a large extent, the critical to quality variables
> for safety and efficacy are very much in place. The critical to
> quality measurements about process understanding are not necessarily
> in place and so we have to measure to figure out what we have to
> measure, and this is a significant investment of time and resources
> both for organizations and development and in manufacturing. So, we
> bring in the question of what is the cost-benefit tradeoff to make
> this transition together. It also brings up the point that this has to
> be a choice of the companies rather than a requirement.
>
> So, those are the two big leverages and that is what the picture could
> look like in the future. What do I see as being the components of
> getting to quality by design to be able to get to the top of that
> pyramid in terms of first principles?
>
> I believe that going to the top of the pyramid is a learning, is an
> improvement, is a change goal or a change exercise, and I believe that
> all learning opportunities/problems have five components to them.
>
> One is the thing you are learning about, called the application
> domain. It can be the process and its relationship to the product, and
> that is its relationship that is somewhere in the world and you are
> trying to learn that relationship and how much of that relationship
> you have learned is measured by the extent of your manufacturing
> science or your quality by design.
>
> This then would be where you are at any point in time. But where you
> are at any point in time depends on three pieces. One, where you
> started, which is your prior knowledge. You may have started here; you
> may have started there and that very much determines where you are
> going to be given the time that you have.
>
> Two, what your relationship is between you as a learner and the
> application and the process you are trying to learn about. That is,
> are you really trying to learn, or are you simply trying to conform?
> Are you simply trying to comply? If you relationship as an
> organization, as a site, as a plant is about compliance only you will
> learn very little after you have complied. You will not challenge your
> specifications. You will not see the need to go to a mechanistic basis
> because you complied. The cost, and quality, and human life
> consequences of doing so are significant. So, we must ask ourselves
> what is our relationship between the process and ourselves as an
> organization. Is this an opportunity to learn or is this a
> demonstration of compliance?
>
> Given these two determinants of this place in the learning curve, we
> can measure our place on that learning curve given a set of
> performance measures. I will tell you a couple of examples of each of
> these components.
>
> Given that in this case our organizations are fixed in terms of their
> names at least for now, and the focus is on the processes, those two
> parts of our learning structure are fixed. Let\'s then talk about the
> other three parts that help us define where we are relative to where
> we started and where we can be.
>
> The first step is a priori knowledge. As we interact with the FDA and
> track within our companies and we want to communicate to each other
> and the FDA what our level of quality by design is, I actually think
> it would be quite difficult to make that communication in terms of a
> set of numbers at this point in time.
>
> If your level of process understanding is at the correlative and
> causal level, you need to also share knowledge about your prior
> knowledge, where that comes from about your materials, your
> excipients, your APIs, how much was known before you started, how much
> was known in your development programs, how much was known across the
> industry, and what does this a priori knowledge look like in terms of
> the extent of it being first principle, mechanistic, causal,
> correlative and descriptive knowledge.
>
> The second piece, what was the basis on which the experiments, the
> data, the runs were done for you to say what your performance is? If I
> did the same thing again and again by having all my variability
> outside my process system, I could have a very high process capability
> number for a while but it really wasn\'t that capable. For example,
> what was the extent, how much data and what kind of data you have.
> That is the question, how much of this target space have you really
> explored, not was I able to do three batches. This is not necessarily
> a good thing.
>
> Then the question then becomes, in terms of relationship between the
> organization and its process, what were your experiments? How much of
> this space did you explore? And, what are the basic failure modes
> around the edges? That is the next piece of information that I believe
> should be communicated as long as we are not yet at a mechanistic
> level of understanding.
>
> The important point to bring up in the case of the role of information
> exchange between the process and the organization is that the
> measurement system that is in place very much determines your measures
> of variability and what your critical to quality performance is, and
> it is very difficult to do that because often the process, the cause
> of your performance and the actual test in our current testing
> paradigm has a lot of built-in variability. That is a big factor in
> determining the role of information exchange between the process and
> the organization and how fast they can go down that learning curve to
> head towards quality by design. So, that positions beautifully the
> role of bringing in the different measurement systems into the
> information exchange between the process and the organization.
>
> The third piece of getting to quality by design, the third component
> is how far have you got and how well do you perform in terms of the
> extent of quality by design. Here I would like to suggest four, but
> really three new or maybe additional variables as potential things for
> us to discuss today as performance measures of extent of quality by
> design or extent of manufacturing science.
>
> Safety and efficacy in terms of what it is in the outside world will
> always be a measure of performance, of quality by design. That is the
> ultimate performance. When you have a recall or a complaint about
> safety and efficacy, that will always be a measure and it is always
> going to be in our current system.
>
> In addition, I want to put on the table three metrics, each of which I
> will also have significant complaints with as I put them on the table.
> First, process capability associated with critical to quality
> attributes. Two, variability of critical to quality attributes and,
> three, predictive ability of performance.
>
> With that suggestion to put them on the table, now let me criticize
> why these have to be discussed in great detail and a significant
> amount of thought has to be given to keeping them on the table for
> very long.
>
> First, I like variability. It is one divided by process understanding.
> But given our performance measures of today beyond safety and
> efficacy, it is not clear that we know what critical to quality
> variables are and so putting it as a measure without having an
> understanding that this is not yet in place could be a source of great
> friction if we don\'t lay the groundwork in place for research
> exemption, or safe harbor, or the reason why we are doing the whole
> thing being process understanding and not necessarily safety and
> efficacy.
>
> The second variable up here is called process capability, which is the
> variability of the process relative to the customer specifications.
> Again, you have the question of critical to quality variable in it but
> you also have a presumption of a specification in it. As you put it up
> there and, yes, you are safe and efficacious, just because you have a
> low process capability doesn\'t mean your process is that bad. It may
> actually be good. The question is really all about your specifications
> and it comes back to what Janet said earlier today, we are in this
> exercise of challenging our specifications, and that is the
> mechanistic piece, and that is the first principle piece. I know it is
> a lot of work to get there but as we develop these pieces we are going
> to make sure we have all those pieces in place as we talk between
> regulator and regulated.
>
> However, in many cases these are about mathematics. Mathematics is in
> the end trying to describe physics and chemistry. In the end, however,
> the physics have to be represented if you want to go beyond
> correlative to causal to mechanistic understanding. To do that, this
> would be the ultimate test of performance, and this will really be the
> indicator because you cannot necessarily define your a priori
> knowledge if you have this piece already. You don\'t necessarily have
> to define your relationship with the process if you have this piece
> already because that piece is embodied in your mechanistic model. If
> you don\'t have it, you will always have to add those other two
> components of your learning paradigm, which is what was the data you
> generated; what did you know before; what does all that other
> knowledge look like? So, it is going to be very difficult to have one
> or two variables, these two being the only two variables where a lot
> more context has to be given to them. In the end we will come to this
> highest state but we would have to go through quite a transition to
> get there.
>
> So, my complaints with these three suggestions that I put out is, one,
> we don\'t necessarily know the specifications here. We don\'t
> necessarily know the critical to quality pieces here. In many cases we
> are far away from here. So, this is not necessarily immediately useful
> either, although it is a desired state and it is in place for some
> cases in my opinion.
>
> So, given those three performance measures, on top of the safety and
> efficacy implications and presumptions that I think we very much do
> very successfully on, I believe that is the opportunity to decide
> where, as an organization, we are going to go between two, to three,
> to four.
>
> To end my presentation today, ask yourself if those three components
> make sense. Ask yourself if those performance measures make sense. If
> they do, and even if they don\'t and we find better ones, which is the
> whole point, in many ways quality by design is simply the extent to
> which we do things right first time. That is, if we are going to do
> quality by design by bringing in these changes here in development,
> yes, we have reduced the burden on the testing on the end of the plant
> but we have reduced the burden of the testing outside of a plant in
> society, and with the building quality, philosophically we have laid
> down a social structure for us to go to be designers and developers
> rather than testers, and for the regulators, instead of being
> evaluators and investigators, to maybe be facilitators and
> accelerators. That is the quality by design consequence to the society
> at large.
>
> But, of course, how can\'t quality by design and manufacturing science
> not be about lowered risk, process understanding, lower variability
> which is one of the majors and, of course, lower costs? We want to go
> to a physical understanding and a chemical understanding that goes
> beyond \"I can correlate; I see a relationship; but I can\'t
> extrapolate because I don\'t know if this is the cause.\" I have some
> causal knowledge. I can extrapolate a little bit now but I don\'t
> really know if there is a linear relationship or a nonlinear
> relationship. I know the basic forms of the relationship so I can
> extrapolate and I know the basic bounds. I don\'t necessarily know the
> individual parameters to the dream land of \"right first time\" and in
> many ways the extent to which we do things right first time is in many
> cases the extent to which it is quality by design.
>
> As we begin to understand our mechanistic knowledge, I think this
> committee, probably not even this committee but industry and the FDA
> together can lay down a foundation for a classification, a separation
> of social tasks as to when the FDA no longer needs to be involved in
> the process at all. If you go back to the cGMPs of 1978, maybe there
> is an opportunity, as we measure better, as we look at more product
> and the product is connected to a mechanistic understanding and the
> manufacturing system has more presumed mechanistic understanding,
> maybe we don\'t need to go into the process at all one day in the
> distant future. That is my last slide.
>
> DR. BOEHLERT: Thank you, Dr. Raju. Are there any questions? We will
> have an opportunity for further discussions, since Dr. Raju is a
> member of the committee, later this morning. Janet?
>
> DR. WOODCOCK: Yes, I would just like to make one comment because I
> feel that perhaps my presentation or focus might be confusing as far
> as how it is related to this, but it shouldn\'t be. I think there are
> several pieces of quality we are talking about here. When I was
> talking about definition of quality for the regulator, as I said, that
> is what the patient ultimately deals with.
>
> I think G.K. is talking about the process quality, quality of the
> process. That is different. It should lead into the quality but there
> is another step there, which is the step I was trying to talk about,
> which is how you set the specifications that the manufacturing process
> is aimed at some goal, and that goal would be the safety and efficacy.
>
> So, I think my point was you need to understand the goal as well as
> understand your process, and understanding the goal might be even
> earlier. The earlier you do that, the better off you probably will be,
> although I know it is very hard. At least, the earlier you develop a
> hypothesis about what your objectives are, then you can design the
> process and a formulation that is intended to achieve those. So, there
> are two different kinds of quality and you are talking about different
> kinds of risks when you are talking about each of these. So, I just
> didn\'t want to confuse people. We will be developing better models as
> we go ahead so you don\'t have the feeling you have to solve all these
> issues today.
>
> DR. BOEHLERT: Thank you. Now we can all relax.
>
> \[Laughter\]
>
> Our next speaker before we take a break is Dr. Norman Schmuff, from
> the Office of Pharmaceutical Science.
>
> **Current Regulatory Challenges in Assessing**
>
> **Quality by Design**
>
> DR. SCHMUFF: Well, I can\'t relax because, I mean, I am up here.
>
> From the high level perspective that G.K. presented, I would like to
> give you some thoughts, if not from the trenches, at least from one of
> the commanders of the troops in the trenches. As a team leader, I see
> all secondary review from the CMC in our Division and, as well, I have
> been involved in CTD-Q and in those ICH negotiations where that was
> drafted, and in the drafting of the P.2 section in our Drug Product
> Guidance which we are now revising for final Drug Product Guidance. I
> can tell you, we have plenty of comments on the P.2 section.
>
> So, this is my outline here. I would say the current model, if we
> divided up in sort of the typical ways in the IND, we heard from
> industry in the past that they don\'t like to hear a lot of comments
> about their subsequent development. So, generally we should stick to
> issues related to safety. That is the way our Phase I guidance and our
> Phase II/III CMC guidance have been drafted. The emphasis is on
> safety.
>
> It is somewhat peripheral that product consistency and quality are
> also aspects that should be addressed, and during the IND process the
> CMC amendments that we see are usually pretty brief.
>
> Now, the NDA \'87 model for NDA submission is that, as I see it, we
> only had a couple of places where this development data could have
> snuck in. That is, there are investigational formulations which
> typically is just a table saying here are the components and
> composition of a product that we used in our earlier clinical trial.
> There is, however, a section for in-process control.
>
> In supplements these two, as with IND amendments are generally not
> very substantial documents and don\'t contain really much development
> information. Annual reports, which are becoming more important in
> post-approval changes, many post-approval changes, we still don\'t see
> there much development data.
>
> So, the conclusion is currently available information, we don\'t see a
> lot of development data. Traditionally, much of this data was not
> shared. There have been some cases where firms have shared with us the
> European pharmaceutical development report and I think people
> generally have found that to be helpful, but there are regulatory
> concerns and concerns about increasing resources and increasing sizes
> of the submission.
>
> But I guess it does provide an opportunity to down-regulate
> post-approval changes if we can feel more confident about the quality
> that you built into your product; if we can feel confident that your
> development program has identified critical issues, and you can make
> changes, and we would then know what was critical and what was not
> critical.
>
> The existing development reports, really the P.2 section of the CTD
> owes its history to the European development pharmaceutics report, and
> there is this guidance that is still on the web for the pre-CTD
> development pharmaceutics. They subsequently have issued a post-CTD
> development pharmaceutics report which really is not much more than
> what is in the CTD. There also is a development chemistry section that
> you will find i you look at this notice to applicants.
>
> FDA\--there is a thing mentioned in an ORA guidance called a product
> development report, which is not obligatory but the items are
> mentioned that should be included, if not in a development report,
> should somehow be available for inspections.
>
> Of course, we have the P.2 pharmaceutical development section which
> was in the CTD. Now we are trying to draft out some drug product
> guidance about what that would be and there are, of course, the ICH
> initiatives in that area.
>
> Here are the broad-brush headings and subheadings of the
> pharmaceutical development section. P. is the product section. So, you
> see that even in the product section there is some drug substance
> information. Then there is drug product information.
>
> Perhaps the section where there is the most opportunity to educate us
> about your process knowledge is this 2.3 section, manufacturing
> process development.
>
> This is verbatim what the CTD-Q says. Essentially, I have summarized
> it in the next slide. It is compatibility of the drug substance with
> excipients; the physicochemical properties that can influence the
> performance; and the compatibility of the substances with each other
> if you have more than one drug substance in a dosage form.
>
> There are opportunities to put this information in different sections
> of the application, and we have typically seen it in different
> sections of the application. So, the drug substance and product group
> have struggled with the \"what goes where\" question and how these
> sections differ from similar sections. Here I have just listed out,
> for example, where polymorphism is mentioned. It is mentioned in the
> pharmaceutical development section but it is also mentioned in these
> two drugs substance sections.
>
> So, one proposal would be that testing on a drug substance still be in
> the substance section, and the drug product testing would be in the
> pharmaceutical development section. Then, data in the P.2 section can
> be used to justify drug substance specifications. So, it seems a
> little bit the reverse, that is, you have a section in product that
> points to justification of drug substance specifications.
>
> Here is the Q6A drug substance particle size decision tree. I just
> thought I would point out that in answering these questions in this
> box, many of these would probably be in the pharmaceutical development
> section, in this P.2 section. So, that is how P.2 would relate to the
> Q6A decision tree.
>
> If we look at the polymorph decision tree, conduct a screen\--you
> know, there is some question of how to conduct a screen. Can
> polymorphs be formed in characterizing the polymorphs? I guess the
> current thinking is that this actually would be in the drug substance
> section.
>
> Now, if you go further in this tree you will find these items. Is the
> product safety or performance enhanced? In that case, we see that it
> would go in the drug substance part of the pharmaceutical development
> report. But the justification for no further testing and the
> justification and the setting of the specification would go in the
> drug substance section.
>
> The remainder of the polymorph decision tree\--we see that more
> product testing, that is, does the product performance provide
> adequate control if polymorph ratio changes, that would be in this
> physicochemical-biological property section of pharmaceutical
> development.
>
> An alternative proposal would be that any kind of one-time testing
> should be in P.2 and that all stress testing, for example, be in P.2.
> So, this was discussed during the ICH negotiations and it is not
> explicitly written into CTD-Q but it was one proposal.
>
> The excipients\--what kind of data should we expect since, to some
> extent, this is new to us, and this is kind of your pre-formulation
> studies? Should we always expect to see this kind of compatibility
> testing? Test all of them at once? And, drug product stress testing,
> should that be performed or would that be covered if you did adequate
> pre-formulation development?
>
> So, CTD-Q indicates that you should essentially justify, based on
> function, why you used the excipients that you used, and we have sort
> of added in the draft of our product guidance that ranges should be
> justified; that functional excipient performance be mentioned; that
> there be additional information on novel excipients; that if you use
> an excipient with some biological activity, inherent biological
> activity, that you tell us something about that, that you rationalize
> that; and that you give us the tracer information in that particular
> section.
>
> There are other excipient sections that are listed here that deal with
> control of the excipients. Really it is control in your product. So,
> the one-time testing is in P.2 and the control is in this particular
> section of the CTD.
>
> The novel excipients appendix really was designed to provide a place
> for providing extensive information should you have an excipient that
> has never been used in an FDA-approved product.
>
> So, now we get more into the development-related issues. The CTD says
> that the development history should be included in this formulations
> development section, including route of administration and use. Here
> is where you should lay out what were the differences in the clinical
> versus to-be-marketed product. So, you would give us the information
> about the composition that was maybe used in a Phase I study or maybe
> in one of the Phase II studies, and you would lay out what the
> difference in manufacture was and, if it appropriate, you would give
> us bioequivalence, at least a summary of bioequivalence data there.
> Generally most of the bioequivalence stuff is in the clinical part of
> the CTD.
>
> In the drug product guidance we added a few other things about scored
> tablet; about overfill. We actually didn\'t put in anything about drug
> product studies and the polymorph decision tree because I think,
> frankly, we are still thinking about it. And, diluent selection, that
> is, why did you select the diluent that you selected. Compatibility is
> in a subsequent part of the P.2 section.
>
> So, this is the entire manufacturing process development statement
> description in CTD-Q. I am not going to read it but it is relatively
> brief and certainly open to a lot of interpretation in terms of what
> went in, what would go in and how much would go into that particular
> section.
>
> So, we sort of laid out some additional information, although really
> not a lot beyond what is in the CTD, that says you should describe the
> manufacturing in-process controls. You should at least mention that
> thing that you mentioned in the previous section about changes that is
> in the clinical trials. You should explain selection and optimization
> of the manufacturing process and define critical aspects of the
> manufacturing process.
>
> This criticality issue comes up a lot. It has been mentioned several
> times today and also it is a CTD-Q heading in control of critical
> steps and intermediates. That is a heading in both the drug substance
> and the drug product section. Actually, we did get some comments on
> the draft guidance that maybe we should define what we mean by
> critical.
>
> One of the ICH guidances in which critical is defined is the Q7A GMP
> API guidance, in which it indicates that what is critical is any step
> that, should it lack control, would affect the specification of the
> drug substance. So, that is one sort of way to get at criticality but,
> it seems to me, it is perhaps a bit incomplete.
>
> The development data\--these are some general thoughts on the
> development data. That is, you should identify the critical steps and
> variables. I can tell you that we had a discussion in my Division
> about drug substance. We have a CTD-Q application and the applicant
> has finally decided that there are no critical steps in the drug
> substance manufacture and I guess we are kind of struggling with that
> concept. That is, could that be, or would it maybe even generally be
> true that you don\'t have critical steps in drug substance
> manufacture? You can argue that, after all, with drug substance
> probably a lot of the quality attributes can be tested, end-product
> testing probably does tell you quite a lot about the quality
> attributes at least for drug substance.
>
> I think that science-based specifications, that is, specifications
> based on what you know about the manufacturing process, what you know
> about any clinical data, should allow us to focus on the high risk
> steps and the controls on these high risk or critical steps.
>
> I guess lack of adequate development data would suggest that there may
> be critical things that you didn\'t uncover and suggest that maybe
> there is a higher risk in any post-approval changes, so maybe the
> reporting category should be higher. But when best practices are
> employed, I think most people would agree it would minimize the risk
> of poor product quality, and it would allow us to down-regulate any
> post-approval changes.
>
> That is kind of where we are now, where we have been in \'87. We are
> still trying to work out where exactly we are going with the P.2
> pharmaceutical development. There is another concept paper that will
> be presented in Osaka to the steering committee, or actually perhaps
> before Osaka, who will then have the opportunity to adopt P.2 as an
> ICH topic. So, there may be some substantive P.2 discussions at Osaka.
>
> So, we are trying to refine this in the drug product guidance and we
> would be anxious, since we are currently rewriting that, that is, we
> are taking the draft and writing the final guidance, we would still be
> interested in hearing your comments. I can tell you we got maybe 200
> pages printed out of comments from not a large number of people, but
> the people who did comment had many comments.
>
> Closer cooperation between ORA and the Center review chemists\--I can
> tell you that I have been here for 15 years and it is still not
> completely clear to me, even after having taken some GMP training
> recently, what exactly it is that the ORA folks look for. I mean, if
> you ask me to write out, for example, the elements for a validation
> protocol for a wet granulation, I have some sort of general idea about
> what that would be but I think I really lack some specific knowledge
> in that area. I think that generally reflects this sort of division
> between the field and the Center in that we don\'t really understand
> what the field folks do and I think the field folks are not completely
> clear on what we do. So, you can see that these current initiatives
> that we have are going to bring us closer together on that.
>
> Now, P.2, when there were initial discussions, was initially seen as a
> one-time only report that would go into the initial submission. When
> discussions turned to well, what about post-approval changes, there
> was general agreement that for CTD-Q we should focus on the original
> NDA submission, and we shouldn\'t focus on any subsequent submissions.
> So, I think the thinking was there that P.2 would maybe be submitted
> once but now I think the idea certainly comes about that once you have
> established your manufacturing process you learn a lot in that first
> year on drug product manufacture. So, maybe it is appropriate in some
> sort of subsequent filing to update that and to tell us what you have
> learned subsequently, maybe in a first supplement or something like
> that.
>
> Could portions of the GMP product development report be included in
> P.2? I say that because, you know, there are issues of resources,
> resources devoted to putting together this P.2 report that you didn\'t
> have to put together previously. But I think now, if you think about
> it, we have the opportunity for data reuse so you can imagine reusing
> some or all of this GMP product development report in the P.2 section,
> thereby minimizing the amount of resources for this seemingly new
> section.
>
> I think really the XML-based document management that probably will be
> necessitated by the XML-based eCTD will promote this kind of
> information reuse, this reuse of various modules so I think, for
> example, one thing that occurred to me is that information from the
> annual product review, which typically we don\'t see. So, in the
> Center we don\'t see the number of batches that you made during the
> year, you know, what the specifications were like and what the
> acceptance criteria were. We don\'t see control charts that I
> understand are typically in the annual product review. So, maybe there
> would be an opportunity, with little additional resources, to provide
> that to us, telling us the number of batches manufactured and the
> observed trend.
>
> I guess I have to point out that FDA actually was sort of in the
> forefront in using this kind of scheme and that ten years ago we had
> the Morris project which had a CTD for chemistry, which we worked on
> with several other regulatory agencies, and I think that was a thought
> that we had at that time, that if you used this kind of XML model it
> really would promote the reuse of information and minimize the
> resources and redrafting what essentially was the same thing. That is
> all the comments that I have.
>
> DR. BOEHLERT: Any questions or comments? First Dr. Gold and then Dr.
> Layloff.
>
> DR. GOLD: Dr. Schmuff, you mentioned that you had difficulty with the
> Q7A definition of critical. You did give the definition and, to my
> memory, it is correct. How would you modify that? The definition of
> critical is a very important aspect of what we are talking about
> today.
>
> DR. SCHMUFF: Well, I guess in my reading of it, I mean, critical says
> that\--I will put it this ways, it says that if it doesn\'t affect the
> drug substance specification it is not critical. At least the model
> that i still have in mind is that product quality is built on
> specifications and GMPs and what happens along the way and
> specifications don\'t cover all of the aspects of drug product
> quality. So, in that same way there should be critical elements that
> are not covered by specifications.
>
> DR. GOLD: So, you are saying that \"fitness of use\" involves more
> than the specifications that we have currently in our files?
>
> DR. SCHMUFF: Well, I would say it involves some aspects of the current
> model, which are GMPs and product specifications. I mean, that is the
> model for drug product quality. If drug substance specifications were
> the only story, then GMPs would not be important for drug substance
> and I think most people agree they would be important. So, there must
> be something related to criticality that relates also to these GMP
> aspects or attributes that simply aren\'t tested.
>
> DR. GOLD: Aren\'t the GMPs a surrogate or an examination for lack of
> quality? That is, lack of adherence to GMP implies the product may be
> adulterated and the GMPs say it is adulterated if you don\'t adhere to
> GMP. But if you don\'t adhere to GMP, you can still make a perfectly
> good product under various circumstances. So, I am not clear why you
> are involving GMPs in this issue.
>
> DR. SCHMUFF: Yes, I mean, this is just my personal view on this but if
> you can make a product that meets the specification, and the current
> model is if you have a non-satisfactory GMP inspection, then we don\'t
> approve the product without a GMP inspection. So, it seems to me that
> there must be something about criticality, there must be some critical
> aspects built into the GMP inspection.
>
> DR. GOLD: Well, if there are I would like to hear about them. But let
> me end this conversation. I think I now understand what your viewpoint
> is.
>
> DR. LAYLOFF: I think I share some of Dan\'s hang-ups. On a drug
> substance, is there a dimension other than the characterization of the
> drug substance itself? In other words, are there process steps that
> are critical to the drug substance? If you take an ICH model and you
> say that you have to identify or qualify every impurity over a tenth
> percent you define the product quality around those analytical
> parameters rather than critical steps in the process of obtaining it.
> The question is are there critical steps in the process apart from
> those that you find out analytically?
>
> DR. SCHMUFF: Well, I would say that, yes, there probably are some
> process control issues related to yield. I still believe there are
> some GMP aspects that are important in determining the quality of the
> drug substance. I did say that I thought that end-product testing took
> care of most of the drug substance quality issues, but I don\'t think
> it takes care of all of them. I think otherwise we wouldn\'t have had
> this big effort aimed at developing a Q7A and having an MRA related to
> API inspections.
>
> DR. BOEHLERT: I think Ajaz wants to make a comment.
>
> DR. HUSSAIN: Yes, I think there are several aspects to this
> discussion. One is do specifications tell the whole story is that
> argument, and if your is against that, the specifications often do not
> tell the whole story because of a number of other elements that go
> beyond that, for example, one is the probabilistic aspect that Janet
> talked about. I think to be a representative sample for your
> decision-making you really have to approach it from a control
> perspective, understanding the process and bringing that to the
> forefront from that aspect because the fundamental basis of GMP is
> that quality cannot be tested into a product; it has to be built in.
> Just reliance on a set of specifications often is insufficient from
> that argument.
>
> Also, I think the other argument that I would like to sort of present
> is that specifications are a test method, an attribute of
> interest\--the next step is criteria. The test method is related to
> the a given process and you cannot look at that in isolation often.
> So, I think you have to approach it from that angle. So.
>
> DR. BOEHLERT: Dan?
>
> DR. GOLD: Well, I still have a problem, Ajaz. The GMPs are inferential
> in determining product quality. Now, I agree with you that if the
> batch is not uniform you may take a portion of the batch for your
> sample and measure quality that is good and, yet, there are parts of
> the batch that are not good. But that is a matter of the processing
> methodology which is what you presumably control when you approve the
> application. Now, it may well be that we are not using all of our
> knowledge in approving applications and making certain that the
> procedures we use for manufacturing give us quality through the batch.
> Isn\'t that why we started the validation activities, to show that the
> process was robust and the batch was uniform, and the sampling that we
> do is truly representative of the entire batch?
>
> DR. HUSSAIN: Correct, and I think that goes to the heart of what I
> think Norman was getting at. If we have uncertainty with what are
> critical attributes, what are critical quality attributes, what are
> you validating against? So, that is the discussion.
>
> DR. BOEHLERT: One more brief comment and then we will take a break. I
> don\'t want to put any pressure on you, Tom!
>
> \[Laughter\]
>
> DR. LAYLOFF: There is an interesting example, sugar, sucrose. We have
> beet sugar and we have cane sugar that are different processes,
> completely different processes. Yet, if you look at NF and any other
> food chemical codex, any process, we look at sucrose as a chemical
> entity. I think in the case of process, process is very critical for
> ill-defined or non-homogeneous materials. If it is a unique
> homogeneous material, then the end-product test actually does define
> it I think. It is the critical aspect. Certainly, in case of sugar
> that is true.
>
> If you want to go further than that, then you can talk about a
> consumer view and then we would say sweetness, and then we would say a
> high fructose corn syrup is a sweetener also and we have then a
> different behavioral problem.
>
> DR. BOEHLERT: Ajaz, very brief.
>
> DR. HUSSAIN: Tom, under ESP they may be the same but when you come to
> processing physical attributes, they won\'t process the same way and
> that is the point I wanted to make.
>
> DR. SCHMUFF: If I could just make one point about the GMPs for APIs,
> if a firm used a reactor that was previously used for a toxic
> pesticide, and we have seen this case and, of course, the pesticide
> residue is not going to be tested into the drug substance, then that
> is something that clearly cannot be picked up on the review side and
> can only be picked up by GMPs, but I would defer to my field
> colleagues to further define the importance of GMPs for APIs.
>
> DR. BOEHLERT: Thank you. This was a very interesting discussion. It
> sounds like we could go on for quite some time. We will take a
> 15-minute break and reconvene at 10:50. Thank you.
>
> \[Brief recess\]
>
> DR. BOEHLERT: I would like to get started again. Our next speaker is
> Gerry Migliaccio.
>
> **Proposals for Regulatory Asses of Quality by Design**
>
> **Industry, PhRMA**
>
> MR. MIGLIACCIO: Thank you. What I would like to try to do is advance
> this discussion of quality by design and try to dig into a bit more
> detail and really talk about using manufacturing science and risk
> management principles to achieve quality by design.
>
> I am not going to talk about specifications. Specification is what is
> developed during the NDA process for us and it is what we need to
> achieve in whatever we design. So, acknowledging the limitations and
> setting those specs, as Janet pointed out, we certainly support a
> science-based specification development process and, hopefully, we
> will achieve that in the future but the specification is what we need
> to design to at the present time. So, I won\'t be talking about the
> design specifications.
>
> What we are talking about is designing quality into the pharmaceutical
> management process and, at the same time, encouraging innovation and
> encouraging flexibility in the associated regulatory processes. So,
> those are the overall objectives, as we see it, of quality by design.
>
> A couple of key definitions, and this is a classical definition of
> risk just applied to manufacturing processes, which is that it is the
> probability of a manufacturing event occurring and having an impact on
> fitness for use, safety and efficacy, factored by the potential
> severity of that impact.
>
> My definition of manufacturing science is slightly different than
> G.K.\'s. It starts with a body of knowledge but then it gets into some
> specifics about that body of knowledge that, hopefully, will lead to
> the definition that G.K. uses. But it is getting into the critical to
> qualities and the capabilities of the process, technologies used and,
> importantly, and this pertains to the last discussion before the
> break, the quality systems infrastructure. Because the specification
> doesn\'t define everything, the quality systems infrastructure is
> critical to ensure that those things which cannot be measured, like
> cross-contamination in many cases\--those things that cannot be
> measured are being addressed properly.
>
> This is slightly modified from the last meeting when I presented to
> you but it is the correlation or the conceptual correlation. That is,
> as manufacturing science, as that body of knowledge increases the risk
> associated with the product or process so that the risk of that event
> occurring decreases. Then, what we are advocating is a tiered
> regulatory approach, certainly in the post-approval change management
> arena, a tiered regulatory approach that as manufacturing science, as
> that body of knowledge goes up the ability to make changes is more
> streamlined.
>
> Now, the key issue is how do we get a product or process on that
> manufacturing science curve? How do we quantitate it? Where we think
> we need to go, from a PhRMA perspective is to move into developing a
> quantitative measure, developing a method to place a specific product
> on that curve.
>
> Now, this is going to be somewhat repetitive of what Norman just
> talked about, but that body of knowledge\--getting into some more
> specifics, what are we talking about? What are we talking about
> having, developing, sharing with the FDA? On the API, certainly the
> critical attributes, both physical and chemical, and compatibility
> with excipients and, obviously in a combination product, compatibility
> between APIs. Excipients, the critical attributes associated with
> excipients.
>
> Drug product formulation, what is the rationale for the dosage form
> that we are using? Why did we decide on a tablet, capsule, liquid,
> whatever? The formulation development. And, the key physicochemical
> attributes and the relationship of those attributes to the finished
> product quality or the surrogate, as Janet is talking about, for
> quality and, of course, performance testing.
>
> In a drug product manufacturing process, what are the critical to
> quality steps? What are the manufacturing technologies used for those
> critical to quality steps? What are the critical to quality process
> parameters? Importantly, what is the relationship of those parameters
> to product quality? What process control technologies are used for the
> critical to quality parameters? And, where sterilization is involved,
> aseptic manufacturing, terminal sterilization, etc., what method are
> you using to achieve that?
>
> Then, the manufacturing facility, what is the quality systems
> infrastructure? That is generally measured by inspectional
> performance.
>
> So, that is a little bit more specific about what we are talking
> about, this body of knowledge that should be shared and should be used
> to determine what level of manufacturing science a given product is
> at.
>
> Now, what we are recommending is that we take that body of knowledge,
> that manufacturing science, and we turn it into some metrics. These
> are potential metrics. Others could come up with a different set but
> let\'s use these for an example. Three potential metrics, first,
> process complexity. Complexity can be determined by the number, the
> nature of critical to quality attributes in a process or critical to
> quality parameters in a process, but also the inter-relationship of
> those critical to qualities. So, that can be a measure of complexity.
>
> The robustness, process robustness, how much tolerance do those
> critical to qualities, how much variability can you have in those
> critical to quality parameters without impacting safety or efficacy?
> Finally, a well-established statistical analysis process capability.
>
> So, from that body of knowledge we could convert that into three
> metrics, or more, where we have a measure of complexity, of
> robustness, of capability of a given process.
>
> What do we do with those metrics? Well, first of all, there are some
> intuitive correlations. You know, lower complexity should mean lower
> risk. Higher robustness should mean lower risk and higher capability
> should mean lower risk.
>
> Then we can mitigate risk. Once we know where we are on the curve and
> whether we have a higher or lower risk product we can take steps to
> mitigate risk. For higher risk products and processes we have talked
> about advanced technologies over the last couple of years, and process
> control technologies to mitigate risk. But for inherently low risk
> products it really is important to point out, and I think Janet showed
> that in one of her curves, that more technology, more control doesn\'t
> necessarily lead to any lowering of risk or any real benefit.
>
> Examples of risk mitigation\--process automation, eliminate or at
> least reduce the potential of human error. Isolators and closed
> systems for aseptic manufacturing. Dedicated equipment and closed
> systems for highly potent compounds. I mean, a perfect example of the
> risk equation is the potential severity of penicillin
> cross-contamination is very high. I mean, it could be fatal. So, the
> severity is very high. So, we use dedicated facilities to reduce the
> probability to zero that you will have penicillin cross-contamination.
> Okay? So, that is a risk mitigation strategy.
>
> Process analytical technology, learning more about the process,
> monitoring the process, real time, real time feedback\--that is risk
> mitigation. And, vision systems on packaging lines, ensuring that the
> cavity has a tablet in it; that the label is there, the lot number and
> expiration date are all there. Those are examples of risk mitigation.
>
> So, getting back to the quantitative method, we believe that an
> algorithm can be developed to assign some manufacturing science factor
> to get us on that curve, and it is a relationship of the complexity,
> the robustness, the capability and the risk mitigation strategies.
> Okay? Again, this needs to be scoped further. Maybe there are other
> metrics that you would use besides these four, but this is the
> direction we believe we need to move in.
>
> That algorithm will get us onto this curve and, hopefully, achieve the
> tiered regulatory approach that we are discussing.
>
> When you look at this again there are three elements here. There is
> the manufacturing science element. There is the risk management
> element and then there is the regulatory process element. So, what we
> are really recommending to operationalize this is we believe, through
> the successes that we have seen through PQRI recently, that we should
> have three working groups where academia, FDA and the industry come to
> the table with a focus on manufacturing science to develop these
> metrics with a focus on risk management, on how to model using this
> information to truly classify the risk associated with a product, and
> then a regulatory process focus which is really related to changes,
> process change, and also is related to the inspectional process.
>
> For the sake of time since there are several speakers after me, I am
> not going to go through what I believe the focal areas are for each of
> these three. Clearly, we need to scope this out more clearly before we
> send these working groups off. If we can do this, the benefits of
> quality by design I think are manyfold. G.K. talked about some of
> them; Janet talked about some of them.
>
> Certainly, enhanced quality assurance will encourage the sharing of
> knowledge between the industry and FDA. It certainly will promote this
> mechanistic view, more process understanding and a mechanistic view of
> our products. It is going to promote the effective use, and this is
> one of the underlying drivers here\--the effective use of both the FDA
> and industry resources on what is important. Certainly, it is going to
> facilitate innovation and continuous improvement.
>
> So, our drive here is really to encourage FDA and the industry to
> support the establishment of PQRI working groups to operationalize
> quality by design and, again, to bring it to an operational stage we
> believe we need to start to quantitate what we are talking about,
> bring it from the conceptual to the quantitative. Thank you.
>
> DR. BOEHLERT: Thank you, Gerry. Are there any questions? Yes, Nozer?
>
> DR. SINGPURWALLA: I have a few comments. On your first slide you had
> objectives, the very first slide. I am sure you agree with me that
> these objectives are conflicting.
>
> MR. MIGLIACCIO: I don\'t.
>
> DR. SINGPURWALLA: You can\'t get all three.
>
> MR. MIGLIACCIO: Why?
>
> DR. SINGPURWALLA: Well, we can have a long discussion on that, but you
> can\'t have your cake and eat it. That is why. All right?
>
> MR. MIGLIACCIO: I believe in the genius of the end.
>
> DR. SINGPURWALLA: I don\'t agree with you. Because you have these
> conflicting objectives, you are going to strike a compromise and I
> don\'t know where. If you agree\--of course, you don\'t\--
>
> \[Laughter\]
>
> \--but if you agree that these objectives are conflicting there is
> going to be some form of a compromise and I don\'t know where it is
> going to be.
>
> Let me go to something else. I said I am just going to make some
> comments. The second comment I want to make pertains to this algorithm
> that you would like to develop, page eight, first slide. That sounds
> like a good idea, except I don\'t know how to do it. Part of my
> difficulty is that those four factors that you have put up perhaps are
> interdependent. Therefore, doing one is tantamount to eliminating the
> other. For example, robustness and capability may be very highly
> correlated.
>
> MR. MIGLIACCIO: They are.
>
> DR. SINGPURWALLA: All right. So, how are you going to incorporate the
> interdependency? And, my major concern is how do you define
> complexity? A lot of people have struggled with the definition of
> complexity and there does not seem to be a satisfactory definition,
> other than when we talk socially\--
>
> MR. MIGLIACCIO: Yes.
>
> DR. SINGPURWALLA: \--about what we mean by complex.
>
> MR. MIGLIACCIO: If you want an algorithm, it is going to be very
> subjective and that is why we believe we need to have the right
> people, the right scientists in the room to discuss and define
> complexity.
>
> DR. SINGPURWALLA: Do you think this can be done, not complexity but do
> you think this algorithm can be done?
>
> MR. MIGLIACCIO: Yes, I do.
>
> DR. SINGPURWALLA: You do?
>
> MR. MIGLIACCIO: Yes.
>
> DR. SINGPURWALLA: Well, thank you. Good luck!
>
> DR. BOEHLERT: Thank you. Our next speaker is Edmund Fry.
>
> **Industry, GPhA**
>
> MR. FRY: Thanks. Good morning. It is a pleasure to be able to speak
> here, to meet with the subcommittee today and, also, I am speaking on
> behalf of GPhA. My comments represent my personal understanding of the
> general views and concerns of the generic pharmaceutical industry and
> don\'t necessarily represent the views of all member companies.
>
> What I am going to try to do today is add some little practical
> aspects to the discussion and raise some issues and suggestions. GPhA
> members exist to make affordable drug therapies available to all.
> Although our companies are generally smaller than the brand-name
> companies, we believe it is completely appropriate that the same
> regulatory requirements apply to all companies. Recognizing the range
> of companies that will be affected, we have confidence that FDA will
> provide needed flexibility in its requirements and guidance arising
> from this initiative. The bottom line is that we fully support the FDA
> initiative.
>
> I have been involved with the implementation of GMPs for a long time,
> both inside the agency and in my subsequent career, and the slogan
> \"you can\'t test quality in\" has been the justification for good
> manufacturing practice for as long as I can remember. What is new
> about the current initiative is that it seems to recognize that
> quality by design goes beyond the traditional manufacturing and
> quality control unit organizational silos. It works if it becomes the
> company\'s culture. It is a way of focusing on factors that are
> important to the customer in assuring that products and processes
> address these factors. To me, it is a much more rational approach than
> the traditional and sometimes arbitrary approach to good manufacturing
> practice.
>
> Compared to the modern and widely known quality approaches, there are
> some limitations on the pharmaceutical industry. For example, the
> methods of Tagucci and others encourage continuous improvement in the
> product. As has been stated, design changes during manufacture can
> result in the last product produced being different from the first
> product. When it comes to pharmaceuticals you have to be very careful
> of improvements. You can improve stability, purity, tightness of
> specifications, etc., but not necessarily the way the product works in
> the patient. Of all the variables that physicians face when treating
> patients, one variable they probably don\'t want is batch-to-batch
> differences in the performance of the drug products.
>
> In the generic industry there are additional constraints. The
> innovator reference drug is a fixed target which the generic
> manufacturer must not improve on. The biological performance of the
> reference drug is a target to be duplicated even if not optimal, and
> the generic manufacturer has no access to information about how the
> reference product was developed. In the case of certain dosage forms
> even the inactive ingredients must be the same as the reference
> product.
>
> The successful implementation of quality by design is going to require
> the regulatory environment to change. Quality by design requires
> adequate resources, both in number and quality, and currently there is
> little guidance in that area.
>
> In quality by design self-assessments play a key role. However, they
> have little regulatory significance. They are suspected to be
> self-serving and, therefore, not worthy of much attention.
>
> Continuous analysis and improvement is another key. Although the
> product itself is amenable to improvement only in some areas,
> continuous analysis is important to understanding the process.
> Currently the focus is on annual product reviews instead of continuous
> analysis.
>
> Hand-in-hand with continuous analysis is good change practices. As we
> all know, there are formidable barriers to change. We are a
> conservative industry, having learned well that we are pervasively
> regulated by a conservative agency. We would like to see the barriers
> loosen, including a reorientation of the emphasis on enforcement.
>
> The last bullet sums up the concept. Focus on what is important. Some
> things are more important than others. Therefore, we should
> acknowledge that there may be some things that are simply not
> important and we can let go of them. An example, the GMP section that
> requires recording the lot number of every single bottle of expired or
> near-expired product that is returned by customers to our distribution
> centers for credit. People have to be hired to do that work which is
> of no discernible benefit.
>
> Some suggested actions\--in an effort to be constructive, I have
> listed some actions that I think would be welcome by many in the
> industry. Most, if not all of them, are already under way: Give credit
> for good performance; continue to reduce unnecessary supplements;
> develop the pharmaceutical inspectorate; reward process innovation;
> eliminate unnecessary testing; and address some issues with oversight
> of API manufacturers.
>
> I would like to expand on these. We welcome the efforts to reduce the
> inspection burden for companies that have a proven record of good
> compliance. I believe this concept can be expanded and refined as time
> goes. The factors should become transparent so that companies
> understand their goals. There is no point in keeping it secret and
> generic companies should be rated on the same basis as larger
> companies. A system that would assign greater risk to generic
> companies than brand-name companies isn\'t rational and isn\'t fair,
> and it would be inconsistent with FDA\'s clear and long-held view that
> generic drugs meet the same standards as brand-name drugs.
>
> We know the agency is focusing a great deal of attention on reducing
> unnecessary supplements. I just want to add my encouragement to a
> couple of areas, which is new manufacturing plants and post-approval
> changes for sterile products. Launching a new plant, as I have
> learned, is extremely complicated, with certain pre-approval
> requirements appearing to be redundant to work done in the past and
> done in the field. As you might imagine, delays in commissioning a new
> plant can be extremely costly but they almost always result in
> improvements in both the process and potentially the product.
>
> In the case of sterile products, post-approval guidances never
> materialized resulting in quite a few pre-approval supplements that
> would appear to be unnecessary.
>
> FDA is doing an excellent thing with the pharmaceutical inspectorate.
> We encourage further integration of field and review activities, with
> more delegation of decisions to the field force.
>
> PAT is a large area of promise but is not the only area of innovation.
> Similar encouragement should be given to other promising areas. One
> example is advanced aseptic processing. Until PAT came along we
> sometimes felt that FDA was overly skeptical of new technology. We do
> not think that new technologies should necessarily be made mandatory
> but they do deserve encouragement.
>
> the other side of \"the you can\'t test quality in\" coin is the
> elimination of arbitrary and unnecessary testing requirements. This is
> just a couple of examples, sterility testing where experts have for
> years pointed out its lack of usefulness, and blend uniformity testing
> in which we look forward to the FDA guidance that we understand is
> coming, guidance based on input from PQRI.
>
> Our industry, large and small, is dependent on overseas API suppliers.
> We are concerned that regulatory scrutiny may not be on a par with
> domestic companies for a variety of reasons. Variation and physical
> quality of APIs is a practical concern in the industry and is an area
> where FDA could help.
>
> In the software field there is an industry program, operated by PDA,
> that pulls qualified audit data for use by all pharmaceutical
> companies. FDA support for such a program for APIs would help increase
> the level of information available to all, agency and industry, and
> could potentially increase the quality of products from API suppliers.
>
> The generic industry is very interested in participating in such vital
> activities as ICH, although we are concerned that we have not been
> able to participate to such a degree as the brand-name companies. We
> simply don\'t have the same level of resources. We do, however, need
> to be at the table. As ICH moves from drug development initiatives
> into GMP and other areas that affect all companies equally, it is very
> important that the generic industry be a full partner.
>
> Of all the adverse factors that a patient faces from drug therapy,
> manufacturing deficiencies are fortunately the least of his worries.
> He or she may face lack of efficacy, adverse reaction, misdiagnosis or
> dispensing error but it is very rare for a manufacturing deficiency to
> cause a discernible effect. The member companies of the associations
> represented here, along with the agency, can be proud of that.
> However, we understand that you can\'t rest, ever rest, when it comes
> to quality. Quality by design makes excellent sense. We support it
> fully and GPhA welcomes the opportunity to work with FDA. Thank you
> very much.
>
> DR. BOEHLERT: Questions or comments?
>
> DR. SHEK: Can I just comment on both of these particulars as well as
> Gerry\'s presentation? The comment on the objectives, as a matter of
> fact, those three objectives that Gerry presented, I believe, they are
> synergistic. They are not contradictive to each other because without
> having the flexibility and without having innovation, I think it would
> be very difficult, you know, to design quality into a product because
> there are restrictions there.
>
> Another point which was very interesting, talking about designing
> quality into the product, it looks like there are two phases. One
> phase is when, let\'s say, the innovator comes out the first time with
> a product and you want to make sure that it is efficacious and it goes
> into clinical testing. Once you have that, then you have the other
> part of the quality to ensure consistent manufacturing day in and day
> out.
>
> So, I think as we go into the discussion later on, we have to somehow
> separate it otherwise I believe we are going to confuse ourselves
> because there are really two parts of quality, as I see it here, and I
> think your presentation to me exemplifies it because you have the
> original product and now you are going to a situation where the same
> product is going to be manufactured by somebody else. So, what kind of
> design in quality do you do at that stage relative to the first stage?
>
> MR. FRY: It is a somewhat different challenge. Thank you for the
> comment, Dr. Shek.
>
> DR. BOEHLERT: Other questions or comments? No? Thank you, Edmund. We
> have now heard from two industry associations, PhRMA and GPhA. Our
> next presenters represent academia. Ken Morris?
>
> **Academic**
>
> DR. MORRIS: Thanks, Judy, and thanks for the invitation. I am here as
> the blue collar representative I think because what I am going to talk
> about is going to be more detailed with respect to an overview of
> tools that are currently available, and sort of keeping in concert
> with the idea that GMPs, although the specific regulations may kick in
> at different points during the development process, GMPs start from
> day zero or minus one.
>
> To that end, I am going to focus more on the two first primary goals
> in the cGMPs for the 21st century document, which are the risk-based
> orientation and the science-based policies. I know that is largely the
> focus of the committee as well. Obviously, the rest of them are
> important but a little beyond the scope of what I will discuss.
>
> The first question I think you have to ask yourself, as we all have
> been doing I think, is what is new. The idea that you use good science
> to develop compounds is not new. We are all presumably doing
> that\--let\'s be as generous as possible, we are all presumably doing
> that now to the extent that it is available and to the extent that it
> is reasonable. But there are some technologies, some techniques and
> models that are at least advanced, if not new.
>
> This certainly includes computers and the advent of the really high
> speed computation that allows the implementation of things that may
> have been known for a hundred years but have never been really fully
> implemented. I think G.K. Raju\'s work on bench-marking the
> pharmaceutical industry certainly shows this.
>
> Sensors have been developing at a frightening rate, which is to our
> advantage. Chemometrics, which was once the sort of domain of the few
> obscure Scandinavians, has now become the sort of mainstay of our
> curriculum as well as tool. Phenomenological and fundamental models
> still have to rule the day. The physics has to dominate if it is
> physical; chemistry if it is chemical; and biology if it is other.
>
> The last thing that is new, and I think this is probably the most
> important thing and is the reason why we are all here, is that the
> mutual FDA, industry and academic recognition of the technical way
> forward in the application of the state of the science is unique in my
> experience, and even the experience of those which is somewhat longer
> than my own.
>
> So, let\'s sort of use Janet\'s analogy and look backwards. Actually,
> these are data that Ajaz presented at Arden House but I think some of
> these are actually from the University of Maryland.
>
> DR. HUSSAIN: No.
>
> DR. MORRIS: No? No, these are not. In any case, what we have here is
> sort of the example or an example of how formulation and development
> variables can impact on dosage form performance, which is what we are
> after. This now is trying to bridge that gap, if you will that Janet
> described between the development process and actual therapeutic
> activity by drug plasma concentration.
>
> So, if we look at the sort of traditional development timeline,
> starting with early develop which bridges to discovery through
> pre-formulation in the product development and drug substance
> synthesis trying to come up with a commercial pathway, and
> formulation, design and development, you see that fairly quickly you
> can\'t really separate that from what comes next, nor should you, in
> this sort of historical disconnect that is in part I think
> disappearing, but this historical disconnect between analytical and
> formulation, between API and drug product is now I think something we
> can no longer tolerate as a community.
>
> The same is true upstream from that because what happens is that these
> minor changes that you think are occurring early on, that you are not
> really sure are important, turn out to make batches fail in the final
> analysis.
>
> With that, what I would like to do is sort of go through each of those
> three stages and point out a couple of important properties and the
> theory and method or the variables and methods that are used to
> address them, with the underlying theme, as I said at the outset, that
> the tools for many of the things that we want to do already exist, in
> an effort to sort of take the sting out of what looks like an entirely
> new paradigm, really is in many respects just the proper application
> and the more modern implementation of existing knowledge.
>
> So, in the initial drug substance characterization phase issues of
> purity, solubility/dissolution, partitioning, stability, solid state
> shape and form and hygroscopicity are all, of course, important. Each
> of these has their related theory and the method that is based on the
> theory for detection. In this case, purity, of course, the paper
> chemistry, if you will, coupled with the HPLC was in itself an
> innovation twenty years ago providing a very robust way of looking at
> purity.
>
> Solubility and dissolution still, no matter what changes, is based on
> the thermodynamics, the kinetics and that, irrespective of regulation,
> won\'t change.
>
> Similarly, with partitioning you are stuck with thermodynamics and
> there are various ways of monitoring that are all excellent, or many
> that are excellent, I should say.
>
> Stability, this is now solid state stability as well solution
> stability, relies both on chemistry and their associated HPLC but also
> solid state methods which are more on the edge of what we understand
> very well but in keeping with the adage that being able to detect
> changes may be the first step in being able to predict changes. We
> have nice methods for detecting them; predicting is a little further
> down the road.
>
> Solid state form, crystallography, solid state physics are the sort of
> bulwarks that underlie that issue, and screening, prediction and
> control are the tools that we use.
>
> Hygroscopicity itself, although almost a non-defined word\--the extent
> to which something will interact with water, if you will, can be
> described in terms of classical surface energetics, and measured by
> automated systems.
>
> I will try to show you quick examples of a couple of these. I want to
> refer quickly to Peter York\'s paper from \'94 where it was pretty
> clear to Peter, even long before this, that raw materials\--here he
> has \"new solutions to old problems\"\--are sort of the fundamental
> non-controllable variable for many formulations and processes. The
> statement I make in class is that formulations and processes are only
> as robust as their ability to accommodate changes in the raw
> materials. If you have not taken into account what is going to happen
> over the breadth of possible change in the raw materials, you can\'t
> possibly formulate around what you don\'t know. The techniques that
> are trying to eliminate the differences through mechanical or
> solution-based approaches have an effect but they are not certainly
> complete.
>
> Here is just an example of screening and controlling forms. There are
> many ways to do this. They are certainly accessible to all folks these
> days where you can relate the frequency of a particular form. We chose
> colored polymorphs because you didn\'t have to analyze them very
> carefully; you could just look at them. Plus, Steve likes them. But,
> in any case, what we have here is an example of the frequency of
> distribution following the supersaturation ratio at which certain
> compounds will come out of solution. This is fairly well described by
> traditional nucleation theory and is, therefore, certainly something
> that can be placed under the column of being able to be handled.
>
> Advances in hygroscopicity\--even though, as we have said, you can
> describe the energetics of moisture absorption, the advent now of new
> instruments\--this one is a 10- or 11-point simultaneous multi-sample
> instrument that will measure the same material over a broad range. So,
> if you really want to know what the variation is in your material,
> instead of taking a sample and couching everything in terms of the
> results from one sample, you put a dozen samples on from the same lot
> and look at your innate variation in what it is that you are going to
> do in the future with modeling or with formulating.
>
> A simple method for looking at crystal shape is a handy tool to be
> had. You can certainly do this microscopically and there are other
> ways to do it. If you do it through looking at powder diffraction and
> x-ray diffraction in combination you can also get crystallographic
> information. So, we have made, as a community, fairly large advances
> in terms of at least understanding what the morphologies of these
> crystals are.
>
> The advantage to having a technique that gives you this sort of
> information is that you not only get the information about the shape
> itself but you get representations of the moieties, the chemical
> moieties that are going to be exposed during processing. In addition,
> the crystal structures give you, as they have for many years, a lot of
> information with respect to how they will respond to mechanical
> stress. How something responds to mechanical stress is the problem
> that you don\'t think about in the reformulation stage. You don\'t
> think about it until your tablets start capping but you really have to
> start thinking about it earlier. I think, sort of in line with what
> Gerry was talking about with algorithms, I will say something at the
> end.
>
> For formulation design we have dosage form selection, of course, which
> we know is a combination of medical and processibility issues,
> excipient selections and stability to processing, as well as
> mechanical properties. A lot of these elements, as Norman said, can be
> placed in different sections of the development timeline. I have sort
> of placed them where I think they have the most impact, and finally,
> initial processing during this period.
>
> I think here we have sort of a mixed bag. We have pretty decent ways
> for looking at mechanical properties. Certainly, Hancock and Houston
> initially, of course, looked at those and found very nice correlations
> between what goes on in small samples in the laboratory and what
> happens ultimately during processing.
>
> Initial processing includes some process models that we will talk
> about in a moment, and process analytical technologies in order to
> monitor these processes, much like we discussed earlier, in terms of
> implementing things that have been known for a long time but not
> usable due to the lack of technology.
>
> With excipients, on the other hand, it is not so hard maybe to choose
> an excipient for its functionality but it may be very difficult to get
> anything that really expresses or manifests real-time excipient
> interaction potential liability. That is still an open question.
>
> Powder flow has really made a lot of advances in recent years. Here
> are a few flavors of powder flow instrumentation. The important part
> here is that each of these has an underlying theory that allows you to
> extract the information that is specific to your use. So, that is the
> bottom line here, that in a sense it matters less what instrument you
> use than that you know what the data that it is giving you say.
>
> For compaction and mechanical properties\--I won\'t go through the
> derivation of the Heckel analysis. I think a lot of the people here
> know it as well. But, certainly, there is a lot of information to be
> had in terms of compactibility of material from Heckel analysis. This
> is a routine measurement. You can get these data off any instrument
> these days.
>
> Shape and flow, we talked earlier about determining shape and it
> sounds more or less like an academic exercise when you do it early on,
> but when you look at the real impact of shape on everything from
> capping, edge erosion and flow, you see that there can be quite
> significant differences. Here is an example that we have from Bristol
> Myers Squibb that shows the impact of the shape on the mass flow rate
> where the shape differences were detected by x-ray. But that is less
> important than the fact that when you are above this threshold of the
> responses you have absolutely no flow. This is faithfully preserved
> when you go on to mixtures and causes essentially complete failure.
>
> Then we come to the processing stage and process analytical
> technology. Here, ironically, a lot of the advances that we have been
> using to elucidate very fundamental questions were championed by the
> processing and technical operations in manufacturing sectors, which is
> part of the reason that this committee exists. But if you look at
> this, and I won\'t read the list, if you go through essentially every
> unit operation, there is some modeling aspect to be applied that will
> at least give you the beginnings of understanding of the process and
> in many cases will give you the ability to control the process given
> the proper eyeball.
>
> If you start down the road, particle size reduction models have been
> around for years that relate the particle size reduction to the energy
> put into a system.
>
> Powder charging, which is perhaps one of the most elusive
> characteristics to be tracked, you can see that although
> quantitatively there are going to be issues for a long time, there are
> mechanisms to measure this and anticipate charging problems a priori
> and a priori means \"a blender.\" I don\'t know exactly what the term
> in Latin is for before you go into the blender but before you go into
> the blender.
>
> Blending itself, I think we have seen a fair advance in modeling of
> blending. This particular example is phenomenological modeling that we
> developed but there are really dozens of models, reaching from
> discrete element methods through very applied models but, clearly,
> with the proper modeling and being able to monitor real time, you can
> scale from relatively small to normal batch sizes using these models
> by the use, in this case, of scale-up coefficients and in other models
> other variables that are important.
>
> Granulation, here is a quick slide of one of the students that Garnet
> Peck and I advise, showing the impact of the force of breaking of a
> ribbon out of a roller compactor versus the roll speed, and showing
> the response for the NIR IR versus the same force showing that you can
> now, real time, monitor the ribbon and then predict the force on
> breaking, which is mildly interesting. What is more interesting is
> that you can also predict the post-milling particle size distribution.
> So, there is no real disconnect between the measurement and what you
> are ultimately going to have. Now, compatibility is down the road.
>
> Fluid bed granulation is well modeled and monitored using NIR IR,
> showing that you can simultaneously predict the size increase as well
> as the moisture content so that you can stop when you get to an
> optimal condition. This is again a real-time process.
>
> Wet granulation and high sheer is one of the most problematic in terms
> of determining endpoint and there are a lot of people working on this.
> The point here is that by understanding the basic phenomenon of
> wetting and over-wetting and the characteristics of the water
> molecules themselves you can at least spectroscopically make an
> attempt at following, and in the first stages now\--this is a little
> bit blinded because it is not yet published, but it shows that during
> the wet massing stage, using signals treated from the NIR, you can
> control to a particle size now post drying, a particle size mean and,
> in fact, particle size distribution.
>
> Drying was the lowest-hanging fruit for PAT but it is certainly one of
> the most ubiquitous processes we deal with, very well handled by the
> same sorts of technologies that we have already been discussing, which
> will include the heat and mass transfer engineering essentially that
> underlies the process, and can be modeled a priori, \"a dryer\" before
> you start and come up with the drying cycle that is appropriate for
> your system.
>
> You can take advantage of that to increase the cycle throughput, or
> decrease the cycle time; increase throughput but following through
> evaporative cooling stages to protect your product.
>
> Just an example of monitoring and seeing excursions in drying, you can
> see that this will now, in this particular case, correlate to
> excursions or deviations in dissolution rate, so a drying monitoring
> process that results in the ability to essentially eliminate
> dissolution testing.
>
> The last example I have is one that is perhaps more germane\--I can\'t
> remember who was talking about this, whether it was Janet, but talking
> about the idea of the statistics, the probabilistics I guess, that is,
> if you take ten tablets out of a million no statistician can keep a
> straight face if you tell them that that is how you are doing content
> uniformity. The idea that we can improve our statistics I think is
> today closer than it ever was because you can do real-time monitoring
> in a realistic way. A lot of people are working on this. We have
> developed just the statistical justification for the process of
> real-time monitoring of portions of tablets, but the point is that the
> limitations imposed on us by the small statistics with which we
> typically deal are in part relievable.
>
> I have one more slide, just showing that you can also monitor coating.
> When I say monitor, in every case I mean monitor in model because the
> reason we can monitor is, in part, because we know what to look for
> and what eyeball to use. The second part of that becomes the
> mathematics that describe the process. Together this makes a very
> powerful set.
>
> If you look at it individually, these theories and techniques that
> look independent\--it looks like you are looking at one unit of
> operation here and one unit of operation here and one aspect of the
> API, but together they show a really concerted effort to describe, I
> would say, contributions to the overall process of drug development.
> What this is saying is that you really have now, by going at it
> piecemeal, the tools you need to link these together into some sort of
> an algorithmic approach. These are applicable to batch as well as
> continuous. Ultimately, the multivariate linkage through chemometrics
> will be replaced. The univariate approach is typically used first to
> make sure that you have the right variable.
>
> So, what does a multivariate approach look like? Well, there is the
> multi-block PLS approach that Paul Gimperline is developing based on
> MacGregor\'s work. What that says is that as you go through the
> stages, as you go through pre-formulation into early development, you
> can link these processes chemometrically by identifying principle
> components and doing partial lee squares on top of this to link. If
> you do this at each stage, much as Gerry is saying, then when you get
> to the next stage you add that to the model and eventually, in \"the
> blue sky\" sense, which is not my forte\--in \"the blue sky\" you
> eventually link it to the clinical data. But at the very least there
> are active projects to link it at least through the development
> process.
>
> Ultimately, this gives you the ability to understand how the
> development variables interact to influence the final product and the
> product quality, of course, which is the goal.
>
> The business case is that essentially the earlier you start collecting
> information, the more you will know and the more comfortable everyone
> will be, as Norman said. Given that level of knowledge and assuming, I
> would say, facile communications with the agency, you have to be at
> the lowest risk as we propose it. Obviously, if you don\'t have the
> data there is nothing to communicate. There is no value in the risk;
> there is no lowering of risk.
>
> On the other hand, if you do show variability the sooner you know the
> better. I don\'t believe, and certainly in my years in industry it was
> never communicated to me that if you don\'t know, it won\'t hurt you.
> That is just not a viable stance; never has been and, hopefully, never
> will be.
>
> I guess the bottom line here is that the companies really right now
> have most of the tools in their possession. This should improve with
> research but almost all of the companies that I interact with, and I
> have to thank CAMP and NSF for letting me present a lot of these data
> and Purdue, Michigan, but most of these companies are way ahead in
> terms of having the science. It is a question of implementing it and
> it is a question of having the internal regulatory environment
> conducive to communicating that to the agency. That is all I have.
>
> DR. BOEHLERT: Thank you, Ken, a lot of good information presented this
> morning. Any questions or comments?
>
> DR. MORRIS: And they said I had too many slides!
>
> DR. BOEHLERT: We are running a little short on time, I don\'t know
> why.
>
> DR. MORRIS: I have no idea.
>
> DR. SHEK: Ken, if I may? I think, as you have indicated with regard to
> the API and you showed the examples and, you know, the synthetic
> chemists as well as the formulation scientists and the process
> engineers realize some attributes of the API\--to show the shape, you
> know, not just the size\--are important.
>
> DR. MORRIS: Sure.
>
> DR. SHEK: As a matter of fact, what you see is happening is you design
> your API to fit into your process, and you see it more and more. My
> question to you, and I think you mentioned it, is what do we
> do\--because I am concerned that the next wall will be basically the
> other ingredient, the excipients that we have less control over. In
> your opinion, what can be done? You know, you can check so many
> variables, so many suppliers of excipients but things are changing
> with time and that might affect your process and the quality that you
> designed in. I don\'t have a good solution.
>
> DR. MORRIS: I don\'t have a good solution. I think the sort of medium
> term approach to that can well be designing the processes using some
> of the more multivariate models so that you can build in the variation
> that you expect to see. So, if you can work early on to get the kind
> of products that are representative of the variation that you might
> expect to see, then if you can build that in so that you can formulate
> against these models, knowing that this variation may occur, then you
> have a shot. It is not too different than, you know, when you are
> trying to qualify a vendor and you always ask for multiple lots. But
> the problem with that is that multiple lots to a vendor may often be
> just subsets of the same batch. So, it is not trivial to do but, to
> the extent that you can, if you can get this variation in material,
> built that in to a multivariate model after having established the
> univariate dependence, then I think that is the best medium term
> solution. Obviously, granulation and the other techniques that are
> used to try to sort of blank out the differences are also viable, as
> we have known as a community for years.
>
> DR. BOEHLERT: Thank you.
>
> DR. MORRIS: Thank you.
>
> DR. BOEHLERT: Our next two speakers are going to talk to the
> regulatory assessment of quality by design, starting with Joe Famulare
> from the GMP perspective.
>
> **Regulatory**
>
> **GMP Perspective**
>
> MR. FAMULARE: Thank you. Good morning. I am going to try to briefly
> give a GMP perspective and how that plays into quality by design. I
> know we have had some preceding discussion this morning so, hopefully,
> we can elaborate on that somewhat.
>
> I will start out by looking at the quality system as a whole.
> Actually, this is a definition we wrote in the program for our
> investigators conducting GMP inspections. It is from our compliance
> program. We basically say that the quality system assures the overall
> compliance with GMPs, internal specs; encompasses not only the quality
> control unit and its approval duties but all aspects of drug product
> defect evaluations and evaluations, and the various sub-parts of the
> GMP. So, it is very broad as we put it there. Actually, we put that
> system, as you will see later, as the center of our inspection program
> because it is the basis for many of the things that we are talking
> about for the quality system.
>
> We have several presentations internally now going on about quality
> systems, as was noted in our cGMP for the 21st century most recent
> announcement. I think Dr. Woodcock was inspired by the first speaker
> we had from the Malcolm Bladridge program in terms of looking at
> quality from various aspects, as we announced just several weeks ago.
>
> But summing it up, say what you do; do what you say, proving it and
> improving it. Of course, one of the earlier speakers, Ed Fry, even
> said, well, improving it sometimes gives you somewhat of a challenge
> in the pharmaceutical element. What are all the regulatory
> inhibitions? What are all the things about the pharmaceutical product
> that may affect how it works on the patient? So, these are the
> challenges that we are facing in terms of overall quality systems.
>
> So, what does building quality in mean? Here are some suggested
> proposals, at least from a cGMP perspective and it should overlap with
> other perspectives: Developing a product that meets the patient\'s
> needs; identifying and developing appropriate specifications; then
> developing a process that can reliably reproduce a product meeting
> these specifications and a mechanism for translating process knowledge
> to maintain and improve that state of control.
>
> So, these are the challenges that work into quality by design that we
> have been talking about. What is the state of control? At least one
> theory has been proposed by Russ Madsen in his article, \"Real
> Compliance\" in the PDA Journal.
>
> Some of these issues are important as we talk about not only existing
> systems but in moving forward\--processes that are well characterized
> and understood; process checks that are essentially confirmatory
> rather than controlling, again, based on understanding the process;
> feedback loops; feed forward indicators and failure alarms;
> instructions and procedures; verification of critical operations and
> documentation and, again, \"critical\" being important; and an immune
> system which has root cause determination, corrections, etc., and
> consistency. So, some of these themes we are seeing now. I guess he
> was evolutionary or, you know, preceding many of these goals in the
> GMPs for the 21st century.
>
> When we look at the overall issue in terms of pharmaceuticals, we very
> much look at product design and process design from the company
> perspective and from the regulator\'s perspective. In product design,
> of course, there is the desire to have product specifications that
> reflect the formulation and the desired safety and efficacy. From the
> process paradigm, we want operating parameters that evolve from
> process development knowledge, action limits that reflect the process
> capability knowledge, and suitable equipment and measurement tools.
>
> We have been talking about these through various committees, through
> PAT subcommittee, not only doing these but trying to integrate these
> processes, which is very important.
>
> From the GMP perspective, these things are consistent with cGMP
> requirements and even those elements that maybe go beyond that
> somewhat in terms of the design of the facility, the design of
> equipment, to have a facility or equipment that won\'t bring in
> unknown elements that cGMP, in terms of the quality system, is there
> to cover. For example, a pesticide that maybe was processed in another
> part of the plant in a contract manufacturing type of facility,
> situations we have run into in reality. Equipment that won\'t affect
> the process in a negative way, but is also designed to accomplish the
> process.
>
> The design of production and control procedures\--very often cited
> situation in terms of validation and where we go beyond what may be
> traditionally have been thought about in terms of process validation,
> and to convert that into process knowledge.
>
> And, the requirement in GMP that you have a development of laboratory
> controls that come from scientifically sound specifications. That can
> only come from engineering the process in such a way that those will
> be scientifically sound.
>
> So, these elements are there in the GMP. How do we activate them now
> with modern technology and our modern ways of thinking that we have
> been talking about? Going back to what I said earlier, in terms of how
> we looked at conducting cGMP inspections and looking at the various
> aspects that will be important on an inspection, the quality system is
> the underlying base that makes all these things happen, and that is
> why we made that central to the program. Without that underlying
> quality system for consistent procedures, processes and
> controls\--looking at critical design mechanisms for facilities and
> equipment, production, the laboratory, those things won\'t be
> effective without the underlying quality system.
>
> In looking at a CGMP quality system the focus has to be on patient
> safety, product quality through sound science and technology. An
> important element of the quality system approach, as we have seen in
> modern quality system paradigms, is the ultimate management
> responsibility and that connection with management to make that
> happen, and that being applied to the design, execution, review and
> inspection of the product and the processes. Just to reflect a comment
> that Ken made, you know, it has to start not only at time zero but
> even maybe at negative points in terms of where that quality system
> begins.
>
> The challenge, of course, that we have been discussing over time, not
> only in our GMP initiative but even in the PAT initiative as a whole,
> is to have a regulatory process that encourages new technology to
> improve product quality and process control. We are trying to meet
> that challenge through the GMP initiative, through the guidances that
> we are issuing to not only have clear expectations but also to provide
> flexibility where it is needed and to emphasize critical areas. We
> hope that Part 11, for example, might be one way where we are
> exercising that flexibility.
>
> Again as I said earlier, the ultimate goal is to have an integrated
> quality system, to have a systems approach across design, execution,
> review and inspection. The ability to control quality within your
> system, and that was that element that Ed Fry was talking about
> earlier\--how do you make that variation towards quality improvement,
> and it is a challenge in terms of how does that in the end affect the
> patient. Focusing on critical process parameters, measurements and
> product performance, again, documentation that focuses on critical
> product and process parameters; and science-based inspections
> resulting in increased consistency. These are the goals that we are,
> hopefully, on the road to achieving at least from the regulatory side
> and, again, integrating these things on the industry side.
>
> So, in terms of cGMPs for the 21st century, again these themes come
> through in terms of being science based, risk based, encouraging the
> use of modern technology, quality management techniques in involvement
> of management of making that happen, clear guidance, and at least in
> terms of the beginning stages of harmonization discussions where we
> already have two groups formulated for the next ICH meeting in Osaka,
> which will bring these elements in the risk-based group in a way and
> the common technical document group that is expanding to take on
> quality by design.
>
> It is important to bear in mind that in the future pharmaceutical
> manufacturing, and this comes from the cGMP for the 21st century
> announcement, will need to employ the best principles of quality
> management to respond to the challenges of new discoveries and ways of
> doing business such as individualized therapies and genetically
> tailored treatments. So, these are the challenges that we face, and I
> think we are in the midst of meeting those and we realize there is a
> long way to go in terms of the GMP perspective. Thank you.
>
> DR. BOEHLERT: Thank you, Joe. Questions or comments? Nozer?
>
> DR. SINGPURWALLA: Thank you. Somewhere on your slides, one of which
> was quite intriguing, it said \"say what you do and do what you say.\"
> Below that you have another bullet that says \"improve it.\"
> Subsequent to saying that, you made the comment, ah, but that is a
> little difficult because of the regulatory process\--I don\'t know
> exactly what verbiage you used. So, the question I have is the
> following, does regulation impinge on continuous improvement?
>
> MR. FAMULARE: In terms of regulation, we have seen over time that it
> has posed a challenge, at least as we have been told by industry and
> in looking at what are the issues that face pharmaceutical quality in
> the 21st century, and this has been very much an operating theme in
> the cGMP for the 21st century initiative. We are self-examining
> ourselves in terms of regulators. Is regulation posing a challenge in
> that? Does GMP provide enough flexibility, for example, to allow you
> to make those changes? We would hope so but is our implementation of
> GMP allowing for that as well in terms of how do we interpret things
> on inspections, etc.? I think when Ajaz talks about the CMC process
> can also talk about that in terms of the review process, how is the
> regulator affecting that. So, our challenge and our goal, as we put
> forth this initiative, is to try and make sure that we are at least
> not the stopper of innovation.
>
> DR. SINGPURWALLA: No, thank you. You have clarified my question but
> you have also reasserted one of my previous comments, that some of the
> objectives are rather conflicting. Thank you.
>
> MR. FAMULARE: Okay.
>
> DR. BOEHLERT: Tom?
>
> DR. LAYLOFF: I think there is no problem with continuous improvement
> but continuous change is a threat. Any change which is not documented
> to be an important could be a negative thing. I mean, when you say it
> is continuous, when you say continuous you mean change. But does
> change result in improvement? And, that is a documentation issue, a
> demonstration issue.
>
> DR. SINGPURWALLA: I think any time there is supervision from one group
> to another there is the sense of inhibiting the supervised group from
> being completely innovative, or open, or whatever have-you. But, at
> the same time, if you don\'t do that things could go amok too, and
> that is basically what my comment was, that some of these objectives
> tend to be conflicting and when you strike a compromise the optimum of
> everything is going to change, and where is the biggest change going
> to come? That is all I was saying.
>
> DR. LAYLOFF: I am not sure if they are conflicting, but they may be
> restraining.
>
> DR. SINGPURWALLA: Okay.
>
> DR. BOEHLERT: Thank you, Joe. Our next speaker is Ajaz, who is going
> to talk about the CMC perspectives.
>
> DR. HUSSAIN: Madam Chairperson, just sort of for clarification, we
> have two speakers in the open session so I can give a briefer
> presentation\--
>
> DR. BOEHLERT: Yes, I have been watching the clock, we have from now
> till 12:45 for three presentations.
>
> DR. HUSSAIN: Yes, so I will probably be briefer than I was planning to
> be to allow the two open speakers to have their time.
>
> DR. BOEHLERT: Thank you.
>
> **CMC Perspective**
>
> DR. HUSSAIN: Well, I think I do want to focus the discussion on change
> and innovation for the afternoon session and how quality by design can
> improve that.
>
> I believe the regulatory process is intended to meet the patient\'s
> need, that is, to have a safe and efficacious product available all
> the time. In absence of knowledge, in absence of good methodologies
> often, change, which is often necessary to keep the product on the
> market, is difficult to implement. The unintended consequences often,
> from the regulatory system, is that we do inhibit good change. I think
> that is true. I do want to sort of build on that.
>
> My talk was designed to sort of pose some questions in addition to the
> broader things that I outlined in my memo to you, but I don\'t expect
> you to focus on every question that I have posed here but to sort of
> focus on the broad things of helping us define what is quality by
> design and help us take the next steps.
>
> So, the outline for my presentation is quality by design, QbD and I
> hope you like the small \"b\" there. What is quality by design from a
> pharmaceutical science perspective? Here what I would like to sort of
> share with you is that we do achieve quality by design and one of the
> biggest challenges we have I think is reflected in Norman Schmuff\'s
> presentation this morning. Before I joined the agency I used to
> consult with many companies, and so forth, they have a lot of
> information which is the basis of their development, and so forth.
>
> When I came to the agency, one of the contrasts that I saw was looking
> at the submissions and looking at the scientists in the companies, I
> said those folks would not have done this. So, what we see in the
> submission, there is a big disconnect with what it takes to develop
> that product. I think that is the issue that we are dealing with, the
> opportunity exists today, without doing any new technology, to do a
> better job on our part to ask the right question. If we ask the right
> question industry has to have the right answer. So, if we are not able
> to ask the right question, then it builds in inefficient systems and
> processes which are not adding value from the public perspective. So,
> I think that is the theme of my talk.
>
> So, what is quality by design from a pharmaceutical science
> perspective? How is or should this be achieved? When should this be
> achieved? How should the level of quality by design be evaluated and
> measured? How should quality by design be communicated, especially to
> the agency? What is the relationship between quality by design and
> risk? What are or should be the regulatory benefits of quality by
> design? And, what steps should FDA take to realize the benefits of
> quality by design?
>
> Now, with respect to the second to last question, regulatory benefits,
> for the afternoon presentations we have invited Colin Gardner, and so
> forth, to focus on change management, what is the most efficient
> change management? At the previous meeting we had discussed that
> because we spent ten years working with the University of Maryland
> developing our SUPAC guidances and we do have that knowledge base of
> experience. So, quality by design overlaid on that will probably
> provide an easier task of moving forward in that direction.
>
> In some ways, what I would like to sort of present to you is that what
> we are talking about is not new. It is new from the perspective of
> regulatory decision-making to a large degree. Now, if we take a look
> at traditional dosage forms, tablets are a hundred years old now. We
> have often forgotten to think about how we make these products in
> terms of design. Say, for example, when a decision is made to make an
> immediate-release dosage form of a tablet, that is a design decision.
> Then, how you make that is a process decision, process design
> decision. So, design features of these conventional products and
> processes have essentially been defined over the last several decades
> and today we often do not consider these as design issues. In many
> ways, because of lack of thinking of that as a design issue we often
> jump in and just do things by tradition, and I think that is the
> challenge that we face.
>
> Thinking or rethinking in terms of quality by design offers
> significant opportunities. So, this is I think one thing of my
> presentation. I am not going to go through each of the slides but just
> to make my point, here are certain book chapters from the Encyclopedia
> of Pharmaceutical Technology, and the title is \"Dosage Form Designs\"
> So, we have always considered that from that perspective, at least in
> academia and in the industrial setting but not in a regulatory setting
> per se although, as Joe pointed out, our regulations do emphasize that
> but the questions we ask and the decisions we make do not fully bring
> this into consideration.
>
> Here is a definition: A rational approach to dosage form design
> requires a complete understanding of the physicochemical and
> biopharmaceutical properties of the drug substance. This happens to be
> from the University of Kentucky. Then, from the University of
> Maryland, and if two academic centers agree then we have a
> consensus\--
>
> \[Laughter\]
>
> Again, just the same thing, tablet dosage forms have to satisfy a
> unique design compromise. You know, dissolution versus hardness, and
> so forth, but the same emphasis is pre-formulation characterizing and
> learning about the aspects for moving forward.
>
> Just to sort of emphasize for design features, optimal drug
> dissolution and, hence, availability from the dosage form for
> absorption consistent with the intended use, and these are actual
> quotes; I should have put quotation marks. I have cut and pasted this
> from this book chapter. Accuracy and uniformity of drug content.
> Stability, patient acceptability and manufacturability. So, all the
> discussions we are having are actually simply bringing in the
> disciplines of industrial pharmacy and pharmaceutics to bear on some
> of the decisions we make.
>
> Those are two academic perspectives. Here is a perspective from Chris
> Sinko, who came to us in September of 2001 at our CMC workshop,
> internal workshop, and talked to us about achieving quality by design.
> The design aspects are integrity, uniformity, weight control, chemical
> purity and stability over the entire shelf life. How do you achieve
> that? You focus on your ingredients; you focus on your manufacturing
> process. You actually then design these things through a pharmaceutics
> profile, selecting the right salt, deciding what the right particle
> size is, making sure compatibility exists, understanding the
> degradation pathway of the molecule, doing process simulations, and
> using material property characterizations, and so forth. That is one
> way of getting there.
>
> So, what we are talking about essentially exists today. I did have a
> plan of sort of explaining some of the challenges with an example. Let
> me see if I can at least touch upon this. If we take one attribute,
> bioavailability, you can make a beautiful tablet but if the tablet
> does not disintegrate or dissolve it comes out the next day and it is
> in your toilet. So, that is not a bioavailable formulation. So, that
> is one aspect which is important, essential.
>
> So, if the design objective is to maximize bioavailability and make it
> reproducible, you approach it from first understanding what are the
> absorption mechanisms. If you don\'t, then a lot of your formulation
> strategies take you to where you don\'t get a return on your
> investment. Then you focus on what are the physicochemical attributes
> related to the release of the drug from the product and its
> absorption; designing a formulation, making sure you have the right
> disintegrating agent, if you need a solubilizer a wetting agent, and
> so forth. Then, designing a process whether wet or dry. Just keep in
> mind that wet granulation and dry compression of the same formulation
> will not give you the same bioavailability. So, you have to bring that
> in. Then you design your specifications and controls that will make
> sure the process remains reproducible.
>
> Now, here is one example. This again happens to be from Chris Sinko\'s
> presentation. How does one sort of arrive at a critical variable? In
> this case, if particle size is important so if dissolution is rate
> limiting in the absorption process, it is likely that particle size
> will be important of the drug material. So, there are elaborate
> procedures in place today, which are not shared with the agency, which
> sort of go in a step-wise structured way of arriving at a decision of
> what the particle size should be.
>
> Here, again, I don\'t want to take time to get to the decision tree
> criteria in this case but it essentially goes to early studies,
> including animal studies, looking at information that sort of signals
> whether particle size is important; doing simulation work; and then
> arriving at a decision\--if particle size is important, can it be
> achieved to the level needed, or what needs to be done.
>
> Also, I think one important element that is in this decision tree is a
> decision on particle size with respect to dissolution also impacts on
> uniformity, content uniformity. So, you have to sort of decide on all
> aspects together.
>
> Just as an example, the aspect that I would like to sort of focus on
> is that I think today there is a lot of inefficiency built in. Now,
> formulation and process design generally starts at a small scale but,
> I will sort of share with you, continues on pilot scale and then
> continues with clinical materials too. In the pre-approval world you
> then have to face the bridging studies so you have to qualify changes
> during development for bridging studies.
>
> If I take the example of bioavailability, we often use [in
> vitro]{.underline} dissolution tests as a tool to screen and evaluate
> various design prototypes. Now, often when an [in vitro]{.underline}
> dissolution test is deemed not sufficiently reliable many companies
> might do [in vivo]{.underline} studies either to provide some
> relevance to that [in vitro]{.underline} test or just qualify those
> changes based on [in vivo]{.underline} dissolution.
>
> My personal observation before I came to the agency, and I think it
> just reinforces this, is that I have seen development programs that
> have developed 50, 60 prototypes and have used an [in
> vitro]{.underline} dissolution test to screen but never asked the
> question was that screen meaningful or not, and then start the cycle
> again. So, often the dissolution test is used to screen and evaluate
> experimental formulations without sufficient consideration or
> verification of its [in vivo]{.underline} predictability or relevance.
>
> The experience I gained a lot from looking at all the submissions that
> I could lay my hands on where we had problems, and this was when I had
> to lead the development of this guidance on waiver of [in
> vivo]{.underline} bioavailability and bioequivalence studies for
> immediate-release dosage forms, and what we have tried to do with this
> guidance, that we published in 2000, was to bring the physiology, the
> physical chemistry, the chemistry together along with the test method
> to see when is this test method reliable and when can we rely on that.
>
> In this case, I think what we have done is we have connected
> pre-formulation information to all the decisions that occur later on.
> The work I did in this case led to a very interesting sort of
> observation. On the new drug side, where we have data for failed
> studies\--on the generic side we don\'t have failed studies so this is
> biased in some regard\--when you have to make a decision to say how
> good is this [in vitro]{.underline} dissolution test for
> immediate-release dosage forms often this gives you false positives or
> false negatives. Very rarely does it give you the right answer. So, it
> is on either side.
>
> From a regulatory perspective we have been very happy, saying
> dissolution generally is over-discriminating so you can see big
> differences that do not translate to differences [in
> vivo]{.underline}. At least from our perspective, we have said we are
> comfortable saying if there is a difference we won\'t allow that
> change to happen but that becomes too restrictive. But there are
> situations, and we estimated about 30 percent of the time, where
> dissolution actually gives you the wrong answer.
>
> Now, why is that? Here is just an example from a published work where
> you can get false positives and false negatives. If you look at
> formulation \"C\" compared to the reference formulation, which is the
> top line, if you look at the dissolution at 45 minutes, it is 92
> percent. It meets the specification. But if you look at the maximum
> concentration, it is 55 compared to 100 of the reference. But if you
> look at formulation \"F\" the dissolution is only 53 percent. So, this
> often can be not a reliable test of you don\'t qualify and if you
> don\'t approach it from a scientific perspective.
>
> An example of over-discriminating test\--here are our research
> examples from the University of Maryland plus all the ANDAs and the
> innovative product on a drug called metoprolol. All these products are
> bioequivalent. They meet the criteria. But one doesn\'t meet the
> specification. So, this is an example of what we call
> over-discriminating.
>
> At the same time, here is an example of product \"B\" which we
> actually withdrew from the market. Product \"A\" is the innovator
> product. It meets the specification. This is a pre-\'62 drug so the
> only criteria for marketing was dissolution meeting USP
> specifications. So, it met the specification. Product \"A\" being the
> innovator, the innovator company did a bio study and submitted a
> petition saying that product \"B\" is not bioequal and shouldn\'t be
> on there and so we actually withdrew product \"B\" from the market. In
> this case, it is inappropriate acceptance criteria. If you just look
> at one point of the curve, it gives you the wrong signal.
>
> Here is an example. I was surprised that Ken would show this, but more
> and more, if you don\'t select your dissolution test in accordance to
> the physicochemical properties of the drug substance you get
> misleading information. Here is an example where a drug is a weak acid
> and if you do the dissolution in slightly alkaline conditions you do
> not get the right answer. The company actually used that as a basis
> for development and landed up with a tablet 2 formulation which was
> supposed to be the marketed formulation and was not ready.
>
> I do not want to go through this but I think it goes to the same
> point. Just to sort of emphasize, the point I am trying to make here
> is this, as we think about design, if you change the mind set to a
> design mind set you first start off evaluating what is appropriate. If
> the dissolution test is a method by which you screen your
> formulations, then you have to think about whether it is appropriate
> first or not. There are many reasons why this may not give you the
> right answers.
>
> I will skip this but I just want to hone in on one point. Changes are
> reality. Changes happen every day. On average, in a new drug
> application we estimate that there are three to six bioequivalence
> studies, clinical studies done just to qualify that.
>
> Here is an example of what a major company does, on average seven
> bioequivalence studies for each product. But here is an example. This
> is an actual case study. Each start that you see is a bioequivalence
> study done during development to qualify those changes. Phase I was
> dealing with a capsule. They went to granulation. They qualified with
> a bioequivalence test, and each change was qualified using a
> bioequivalence study. At the very last minute, ready for approval, the
> test failed. So, what do you do? So, here is an example where not
> thinking through the process actually delayed the approval process.
>
> So, the aspect I think of what I would like to say is, in a sense,
> that as we think about design you have to approach it in a holistic
> way, looking at the reliability of your methodologies that give you.
> One aspect which is important and we have quite a little bit of
> experience with, working with the University of Maryland, is what we
> did as a model for development. The University of Maryland has been
> collaborative with such a model which we used to support our SUPAC
> program. We start with pre-formulation, focusing on the right
> pre-formulation attributes, physicochemical characteristic, adding the
> critical variables through a design of experiment. Now, I know the
> design of experiment concept to some companies is fairly new because
> they still do the trial and error type. Again, if I call back Prof.
> Shangru, he published a paper in \'93 of the survey he did and only
> six to eight percent of the companies surveyed actually used a formal
> design of experiments in their development. That was in \'93; I don\'t
> know what the situation is. But in a multifactorial world you have to
> design your experiments to identify your critical variables and do it
> step-wise and that can be easily achieved. I know many companies which
> do that. So, for many companies this is a low-hanging fruit.
>
> Let me just skip to this slide. This is again from the University of
> Maryland. For example, the general tendency has been that this is the
> dissolution test. We will test it and screen it on the formulation.
> But if you don\'t pay attention to what that information is telling
> you, then you are missing half the point.
>
> Here is an example of the experimental formulations that we
> manufactured at the University of Maryland. If you analyze this at
> different points on the curve, you know where the formulation of
> process factor impacted the dissolution profile. For example, if
> magnesium stearate is a critical attribute the dissolution profile
> picks up the changes in magnesium stearate and has a negative impact
> when you look at time, about 10 minutes to about 15 minutes. But if
> you make decisions only on time 30 minutes\--that is what the
> specification focuses on\--it does not pick up the differences in the
> variability in magnesium stearate. So, the point I am trying to make
> here is if we have to identify critical variables, we need to know
> what the test is telling us and not simply blindly follow\--this is
> the target specification. I am going to do this; I don\'t want to know
> what it is telling me.
>
> This is our own research study on the products we made at the
> University of Iowa. This happens to be flurosomide, a Class IV drug.
> Any minor change in composition today will require a prior approval
> supplement for this; will require three batches of stability studies;
> will require a bio study. All right?
>
> In this particular case, for example, one of the ingredients our SUPAC
> guidance identifies as critical is magnesium stearate. For this
> particular formulation the changes in the level of magnesium stearate
> have no impact because the product was designed to be robust to
> changes in magnesium stearate. The only aspect which was critical here
> is the disintigrant level in the formulation. Even the processing
> conditions were not important or critical. The reason is that the
> right level, the right disintigrant takes care of all other things. It
> makes all other variables less critical. But our guidelines do not
> recognize that today. So, even if a company understood that, they face
> quite a significant challenge for getting any change approved today.
>
> Wrapping up quickly, what is quality by design? I think that is the
> key question. I think if design decisions are based on thorough
> formulation and process understanding as these relate to the intended
> use, I think that is what you are trying to achieve by quality by
> design.
>
> So, what are my thoughts on what should be the relationship between
> quality by design and risk? I want to emphasize this, and this is how
> we have emphasized this in our draft PAT guidance. Within a given
> quality system and for a given product, there is should be an inverse
> relationship between the level of quality by design and risk. I think
> that is the framework. So, we have to sort of think about that and
> mature that part further as we go along.
>
> So, how should quality by design be achieved? I think in a structured
> manner, guided by scientific information and knowledge gathered during
> pre-formulation, development, scale-up and production.
>
> When is or should quality by design be achieved? Ideally, before you
> get into your pivotal clinical trials. If not, you pose the risk of
> confounding your safety and efficacy studies with quality problems.
> However, you have to recognize this is a continuum. So, for all
> critical material attributes and other aspects this should be done
> before we get to pivotal trials, but then fine-tune this over the life
> cycle of a product.
>
> How is or should the level of quality by design be evaluated and
> measured? This is a very sensitive sort of topic, and I think this is
> where we seek your help. The sensitivity is industry is hesitant,
> largely rightly so, to share this information in the review process.
> The reason is if we are not asking the right question having this
> information will create a nightmare for the companies, resulting in
> delayed approval. So, we need to learn to ask the right question, and
> we have to be ready for that.
>
> One aspect is\--this is sort of my thinking, if, for example, we
> don\'t want to interfere with the development activities, how does one
> evaluate the value of the knowledge content of the information that we
> have? I think one possible way is if we have established
> relationships, especially mathematical quantitative relationships with
> product and process variables and the quality attributes, then the
> predictability of those relationships could be one way of saying, yes,
> you have sufficient coverage, sufficient data density and your
> predictability is acceptable so we can actually make decisions without
> having to sort of scrutinize every step of the way. That is one
> possible scenario. Hopefully, you will consider that.
>
> How should this be communicated? Preferably as part of the original
> submission. Normal provided the sections where this can come in so
> there is no need to create new sections. I think the sections are
> there; they need to be filled with the right information.
>
> But I think what I have proposed, and that is the reason why I invited
> the speakers for this afternoon\'s session, we need to probably think
> about this in the post-approval stage first. There are two reasons.
> One, I think the agency needs time to be ready to sort of ask the
> right questions, learn how to ask the right questions to a large
> degree. Second, I think we have taken this to the ICH process and I
> think that will continue in that mode. So, I think the post-approval
> world offers a very forward process. The information could be shared
> in the form of a supplement or a comparability protocol.
>
> What should be the regulatory benefits? I think in my mind the most
> important benefit is more rational, science-based, mechanistic-driven
> specifications. For that, it has to come in the NDA submissions but
> that is the ultimate goal.
>
> At the same time, I think risk-based regulatory approaches that
> recognize the level of scientific understanding and the capability of
> process control strategies is our desired state statement. I think we
> can move that from a post-approval situation of thinking about
> customized SUPAC or SUPAC-C, whatever you want to call that.
>
> So, what steps should FDA be taking to realize the benefits of quality
> by design? What we are trying to do is start to build elements of
> pharmaceutical development in all relevant guidance documents. One
> such guidance document was included in your background packet. Some of
> the comments we have received are interesting.
>
> Support development of ICH guideline on pharmaceutical development.
> This process has already begun. Train FDA staff on how to evaluate the
> knowledge content of pharmaceutical development reports. We already
> have a set of activities planned, and we invited Ken to come and
> brainstorm with us in a number of sessions to help the leadership in
> the Office of New Drug Chemistry and Office of Generic Drugs and
> Office of Biotechnology Products. We want to sort of start thinking
> about this in terms of how we approach this.
>
> I think while the ICH process on pharmaceutical development is
> ongoing, and this will start in Osaka next month or month after that,
> what I think we should do, and this is open for your comments and
> suggestions and I think we need some feedback, is focus on the SUPAC-C
> concept, customized SUPAC concept. One option is to work with or
> within the draft comparability protocol guidance. But we have heard
> already that this will probably be too restrictive.
>
> So, in addition to the comparability protocol concept, one thought
> could be to develop additional guidance on SUPAC-C. This could be not
> an elaborate guidance but be part of an appendix to the comparability
> protocol guidance or planned revision of the SUPAC guidances that we
> have already started because 314-70 is to be reissued and I think we
> have to revise all of our SUPAC guidances anyway, or it could be an
> independent SUPAC-C guidance.
>
> I want to sort of share some thoughts on level of quality by design
> metrics. Again, to measure this you have to sort of begin with an end
> in mind. Achievement of predetermined product and process performance
> characteristics that are adequate for the intended use on every batch
> and in an established cycle time. So, that is my way of thinking about
> it.
>
> So, performance characteristics are selected of developed through
> scientific studies to identify the target characteristics of all
> relevant sources of variability in the target characteristics, and to
> evaluate the effectiveness of test and control strategies to mitigate
> the risks. So, that becomes part of quality by design.
>
> Metrics\--I think this is the key. We really need to have the right
> metrics because we do what we measure. So, if we measure the right
> stuff we will be doing the right thing. If we don\'t ask the right
> question this will not get there.
>
> One proposal is right first time. Percentage batches manufactured
> right first time could be a metric. Process time over cycle time, the
> ratio of process time over cycle time and its improvement. And,
> ability to reliably predict impact of changes. That gives you an
> ability to say, all right, this is a low risk. If we require a product
> supplement for this, we probably don\'t have to. It may not mean that
> you don\'t have to do all the qualifying tests. The qualifying tests
> could be done and kept on site, and the integrated approach that we
> have talked about with CMC review and inspection can address that. So.
>
> So, I will stop with these questions again for you to think about.
> Thank you.
>
> DR. BOEHLERT: Thank you, Ajaz. In the interest of time I will defer
> questions and comments on Ajaz\' presentation because he will be here
> for the discussion. We are going to go to the open public hearing
> section, and we had two speakers scheduled and only one will present
> and that will be Rob Menson.
>
> **Open Public Hearing**
>
> DR. MENSON: I need to thank Fred for giving me some of his time here,
> and I hope that I can prove to him that he did the right thing. I also
> know that if all of you who are sitting out there like I was, the
> seats are getting hard and you are getting hungry. So, I will try and
> make this as quick and dry as possible.
>
> My name is Robert Menson. I am a consultant. I have my own company,
> Menson Associates and I work with QRC Associates, also a consultant in
> the pharmaceutical industry.
>
> We are going to briefly talk about a risk model today. We heard a lot
> of discussion about risk, risk models, risk management in the
> pharmaceutical industry. Today I am going to present a model that we
> have been using for a couple of years now. It is described, to a
> certain extent, in a different iteration in the [In Vitro]{.underline}
> Diagnostic magazine, March 2003. It is a model in which we are going
> today to talk about application to the perfect product and a perfect
> process to make sure we mitigate any potential event disturbing that
> situation.
>
> By changing the decision trees and the rules, the model can be applied
> to such things as where is the best way to put our resources in
> designing a process to make a product? How can we balance off changes
> of our product or process and looking at what the impacts are?
>
> We all heard that, of course, the FDA\'s mandate is a risk to safety
> of patients, users or potentially handlers. Now, the handlers is more
> in the medical device industry. We also have business and regulatory
> risks and we have product liability risks.
>
> I borrowed the discussion of intended use or intended purpose from ISO
> 14-971, which is the application of risk management techniques to
> medical devices. It fits in pretty closely with the earlier ones
> presented by Dr. Woodcock on the surrogate fitness for use. It is a
> pretty general statement. Intended use is use of a product, process or
> service in accordance with the specifications, instructions and
> information provided by the manufacturer.
>
> That can be a fairly general application as we go forward. I bring
> this forward because when we begin to look at the failure analysis in
> the process, the model currently ties all failures to implicit or
> explicit fitness for intended use. Implicit fitness for intended use
> would be something where the customer doesn\'t know they are not
> supposed to expect particulate matter maybe in an injectable;
> explicit, the customer would be expected probably to know that he is
> expecting a sterile product that is not going to give him any problem.
> So, when we look at the failures we need to consider both of those.
> Also, the model helps us identify critical quality parameters by tying
> them to process steps which impact the fitness for use.
>
> The elements of risk management process, these again come from ISO
> 14-971. We all talked about risk analysis, risk evaluation. The
> addition of control and post-production feedback into your risk
> assessment situation model is the important aspects because we do the
> best we can when we do risk assessment, but we need to have the
> feedback because we all know we have recalls. We all know we have
> discrepancies, as showed by earlier slides. So, what did we miss when
> we get those when we look at the control of our process and integrate
> the aspects forward?
>
> There are various risk assessment tools out there in the industry, as
> was brought out again earlier. These are standard tools used in a lot
> of different industries. They are just beginning to be new things to
> the pharmaceutical industry and we are going to present shortly a
> modification in which we are combining two of these tools in a model
> that we feel is beneficial to the industry.
>
> I will briefly talk about some of them. Fault tree analysis most of
> you have heard about it. Our National Safety Transportation Board uses
> that after the fact, unfortunately, for crashes to figure out what
> could go wrong. So, typically a fault tree analysis is done after the
> fact, though you can also use it to determine reliability of a product
> by mathematical formulas and you can also use it to see whether you
> have conflicting design criteria.
>
> The standard one most of us have heard about is FMEA or FME(C)A. We
> typically use these terms interchangeably today. I am going to briefly
> talk about the FMEA model to lay the groundwork for the model that we
> want to talk about in more detail.
>
> HAZOP was developed for the chemical industry, particularly to make
> sure that any of the things they did didn\'t blow people up or create
> other hazards. But it also has its applicability to certain aspects of
> the pharmaceutical industry, particularly maybe in manufacturing and
> API or during formulation to look at the impact of what might happen
> if an operation didn\'t conform to its specifications during the
> manufacture of a product.
>
> HACCP, which was started in the food industry\--actually, it started
> earlier in applying the technique to developing food for astronauts.
> It was promulgated in the seafood industry, and we will go through
> briefly what the HACCP process is.
>
> The FMEA model typically looks at the device or the function, If we
> are looking at the design of a product we start out with what is the
> component and what is its function. If we are looking at a process
> FMEA, we look at the functional step in the process and try and
> understand what the process is supposed to do. At each step we
> identify the potential failure modes. In this particular case I used
> an example, because it is fairly easy to work through, of visible
> treatment field indicator at an x-ray machine where there can be
> several different failure modes. We look at those and what happens if
> a product does fail.
>
> Once we do that, we assign it a severity level. Behind all this is a
> lot of work defining severity, occurrence and detection tables which
> we don\'t have time to go into today. Once we look at what is the
> severity, we say, well, what could cause this and how often would it
> happen. That is the occurrence column. What do we have in current
> controls that we can do to mitigate this and are we able to detect it?
> In detection here, we are looking at detecting before the event
> occurs. Obviously, we can detect it after the event occurs most of the
> time.
>
> In this particular case we looked at, well, if it happens really in
> this case it is increasing the setup time and probably in 99 out of
> 100 times that has no severe impact upon a patient. But if that one
> time is when that patient needs that x-ray because something severe is
> going to happen to them in an emergency room, we may have to change
> our severity level.
>
> So, this is really the FMEA model and, as I said, I don\'t have enough
> time to go through it in detail today but look at the overall graphic
> and we will come back to this when we look at the model I am going to
> present in a few minutes.
>
> The HAZOP model starts with a design statement. We have an activity;
> we have the material; we have a destination and we going to transfer a
> powder to a hopper.
>
> We then come down and say apply a set number of words that we use in
> the HAZOP criteria. These are just some examples and the examples are
> \"no material,\" \"more than,\" \"not greater than,\" \"less than.\"
> We apply it to each one of these statements in our design criteria. As
> we apply it to each one of the statements in design criteria, if you
> look down here and it says \"no transfer\" what could have caused no
> transfer? A valve closed, line blocked, pump broken\--no material.
> This then allows us to systematically walk through our process,
> understand what would happen if various standard HAZOP words are
> applied to each one of the design criteria. Then we take this forward
> and say, well, if we had no transfer of the material because the valve
> was closed, was there a risk? If there is a risk, then we put together
> a plan to mitigate the risk. We may want to check on the valve that is
> open or that is functional before we do something.
>
> HACCP started with the criteria, again, that we had something that was
> particular in food. We had a natural product and what we wanted to do
> was make sure that we didn\'t add any additional hazards to the
> product. So, at each step of the way in the process we looked to see
> whether we added a biological hazard, a chemical hazard or a physical
> hazard. Again, you can take this to some aspects of pharmaceutical
> manufacturing where you can look at could I possibly add particles to
> a tablet during tableting operations? The answer is possibly yes
> because we could have particles coming off the punch press. So, we
> need to say, well, how would we control that?
>
> The HACCP process requires that you have a prerequisite quality system
> program because all it is doing is providing you a method to analyze
> things, and it says that I have a process that I am going to go
> forward to maintain this. Traditionally it is something like a GMP.
>
> I have to apologize for the printouts because one of the things that
> happens if you don\'t print this in pure black and white\--you guys
> have the same product that I have; all the red boxes are black on
> yours. So, we will go through this and correct it as we go forward.
>
> The most important part of the risk assessment process we are going to
> talk about today is to make sure you map the process. You really need
> to use the map to walk through the process as we go through it. Now,
> as I said, this process that we are presenting today is a combination
> of a FMEA analysis and a HACCP decision type tree. This is what adds
> to the model beyond the FMEA and we are combining the two of them.
>
> In a classic FMEA, if you can remember back three slides, you have
> severity, occurrence and detection and different models multiply those
> and get what is called a risk priority number and they generally set a
> cut-off and work forward. What this model does by using the HACCP
> technique is it emphasizes severity of the problem before it goes any
> further.
>
> So, once we map the process, then what we are going to do is use the
> FMEA type model to do a risk assessment. We use our decision tree to
> decide whether we have an ECP, in this particular case an essential
> control point; some people call it a critical control point, there are
> various names for the same type of thing. We move those things forward
> to a review matrix and then an action plan.
>
> Now, what this model does is it integrates all the things that a
> manufacturer has done in the pharmaceutical industry from test method
> validation or process validation and puts it together in one analysis.
>
> So, the first thing we have to do is create what we call the SOD
> tables. In the model we typically link the severity to the end product
> functional failure. I talked to you earlier about both implicit and
> explicit. We get the medical department of the company involved to
> classify the severity of the functional failures. Obviously, as
> somebody brought up earlier, those will vary depending on the risk of
> the product. If you have a short fill for a product where somebody is
> trying to take 1 cc out of 5 cc, that doesn\'t necessarily have the
> same risk as if you are trying to give somebody 1 cc out of a 1 cc
> bottle and you need to have it right away.
>
> When we look at this, because the model as I am representing it today
> is looking retroactively at your current manufacturing process, we
> typically use historical data or data from similar processes and
> products to identify the possibility of the event to occur. If you
> have a product that you make very few times a year but you apply that
> same process to multiple products, what we do is assimilate the
> knowledge across the processes.
>
> For detection we can look at our method validation studies, in other
> words, can we detect it or can we not, assuming that it has been
> presented to us, which means sampling is an important part of
> detection capability. Again, we can look at historical data.
>
> So, the concept of the process is we assign an essential control point
> to steps in the process for a process that is in control\--and by our
> definition, it does not produce a significant defect and, again, we
> can spend a lot of time on what do you mean by significant
> defects\--but it is difficult to verify by testing. An example of a
> process here would be sterility. We can\'t possibly test sterility in.
> The corollary is a process that may have a higher level of defects
> than you want but we can always detect them.
>
> So, if severity is greater than 5, and when we set the model up
> anything greater than 5 we deal with in our table\--the model that I
> am currently working with does not allow anybody to give a 5 because
> when you do these analyses most people want to stay in the middle
> ground. So, we force them to make a decision. Anything above 5 we deal
> with; anything below 5 we have considered of a less impactful nature.
>
> Basically, as I said, we have a risk assessment tree. I am going to
> talk through some of these but basically if the severity is less than
> 5 we have judged that this is a low risk and we are not too concerned
> with that step of the process. Now, again, when you talk about low
> risk, as was brought up earlier, it could be that the color of the ink
> is slightly wrong. It is a low risk potential. Now, there is a quality
> issue. It may not be our standard so by definition we may be out of
> GMP compliance but it is a low risk issue.
>
> If, however, the severity is greater than 5, then we go through the
> analysis. If the severity is greater than 5, and if you go down and
> say the probability is greater than 5, then we go down and say can we
> detect it? By our definition, detection less than 5, because this is
> reverse, says we can. Typically we assign detection of less than 5 the
> fact that we have a high chance of catching both random and non-random
> events.
>
> Then what we do, we call the detection capability at that step the
> essential control point, and we want to make sure we spend our
> resources and effort on making sure we can detect it at that step and
> that our process and our testing method is robust and rigorous.
>
> In this particular model that I have up here the probability is less
> than 5 so we add can we also detect it? If it is less than 5, which
> means it doesn\'t happen very often and we can always detect it, we
> say we have a robust step because one or the other can potentially go
> out of control and we can still have fairly good assurance that we
> have mitigated the risk.
>
> On the particular model that I have up here\--I changed this at the
> last moment, I apologize\--if the probability is not greater than 5
> and I can\'t detect it if it happens, then I want to make sure I spend
> my time on the validation process. I want to make sure that I have a
> good process capability and it doesn\'t happen very often.
>
> If the model that I just talked about says that it happens but I can
> always detect it, then I make sure that my control point becomes my
> detection point and I want to make sure that I don\'t do anything to
> disturb the detection.
>
> Now, occasionally what happens is this, it happens more often than I
> want; I can detect it less than satisfactorily. That means that I need
> to do something about that process step. I either need to reduce the
> probability or increase my capability to detect.
>
> Now, one way you can increase the capability to detect is to add
> additional test methods, samples or use a different test method. Then,
> once I do that I assign the control point to the reduced parameter.
>
> This then comes into convening participants and beginning to fill out
> a form, as I showed you referring back to the FMEA form earlier. We go
> through each process step. We look at what would be the failure mode
> at that process step and I told you earlier we took those all to the
> implicit and explicit intended uses, fitness for use, whatever term we
> want to use. We look at, if stability failed, what could be the
> potential hazard. Some products subpotency can be just delayed medical
> treatment in some products subpotency can be fairly severe.
>
> We look at potential causes at a fairly high level. What are the
> controls? In other words, what methods do we have in place, what
> process step do we have in place, etc. How can we potentially find
> this failure if it happens? Then we go through our decision tree.
>
> Under the severity column, we have already decided with medical, based
> on our severity table and they listed the ratings of severity for the
> functional failures so that is kind of a given. So, if stability is
> the failure, in this particular case the medical department said
> subpotency will cause delayed medical treatment but we don\'t call
> that a high severity. Okay?
>
> Probability, we again base that on historical knowledge. Again, most
> pharmaceutical companies, because of the number of lots they make,
> they don\'t have the same amount of information we have on reliability
> before it happens that you might have on a chip from Intel. Detection,
> again as I said, is related to what we\--the confidence level we can
> determine something.
>
> We have gone through this. We have assigned these numbers to each one
> of these and then we go back to our ECP decision tree. In this case,
> because severity is less than 5 we say that we do not need to call
> this a critical control point.
>
> As you go down further, the next one says severity is 10, which means
> we automatically have to look at it. In this model we have said, gee,
> it happens more often than we think it should but we can detect it
> pretty easily and, therefore, what happens is the ECP becomes the
> detection point.
>
> Once we have done this whole assessment, we then bring the information
> down from that assessment to a work plan in which we now say, okay, we
> have brought the step down; we have brought down the failure mode for
> continuity; we have brought the potential cause down and now we begin
> to look at our existing controls and say what is the actual procedure
> step? What is the quality attribute we are going to be looking at?
> What is the test method we use? What equipment do we need to use for
> that test method? What documents do we currently have in place to
> support that? And, any related issue, and in this particular case we
> put down sampling because sampling in a water system is an important
> issue. Then we said this is owned by the quality control department.
>
> This then provides us with a method for compiling the information,
> because what you will do when you to through these, you will find that
> multiple control points are in the same place and what we want to do,
> rather than treat them individually\--you notice we brought a bunch of
> ECPs down, 4.1, 4.2, 4.3 in this model, and they are all related to
> looking at the same procedure. They are all related to looking at the
> same task. If we had any prerequisites we would put that in there. For
> instance, if we are going to look at the prerequisite for test method
> validation, qualified equipment, we would put that in there. We assign
> responsibility, completion date and then any particular links.
>
> So, what we have done is taken the process, taken each step, looked at
> the risk and linked it to all the information there that the
> organization has in process and other capability. This then becomes
> our remedial action plan if, of course, we have ECPs. I haven\'t done
> this with any manufacturer yet that hasn\'t had some ECPs to look at.
>
> Thank you for your time. I thank you for ten minutes of your lunch
> hour. I will be glad to answer questions either now or later.
>
> DR. BOEHLERT: Are there any brief questions right now?
>
> DR. SINGPURWALLA: I have a brief comment.
>
> DR. BOEHLERT: Okay.
>
> DR. SINGPURWALLA: Probabilities bigger than one are not allowed.
> Probability is always between zero and one. You show probabilities
> with four, five, seven, eight or nine.
>
> DR. MENSON: No, in the FMEA model you rank the probability from one to
> ten and then you assign a probability to each one of those numbers so
> that you can carry through the mathematics.
>
> DR. SINGPURWALLA: Those are rankings?
>
> DR. MENSON: Those are rankings, yes. Thank you.
>
> DR. BOEHLERT: Others? If not, it is time to break for lunch. We will
> try to reconvene as scheduled, at 1:45, for committee discussion.
>
> \[Whereupon, at 12:56 p.m., the proceedings were recessed for lunch,
> to resume at 1:45 p.m.\]
>
> [A F T E R N O O N P R O C E E D I N G S]{.underline}
>
> **Committee Discussion and Recommendations**
>
> DR. BOEHLERT: It is time to get started. FDA has asked us to answer
> several questions today. They are in your handout that has the agenda.
> There is no page number, but topic number one that we are to focus on
> is quality by design.
>
> There are three bullet points that we have been asked to address:
> Articulate a clear description of the term quality by design. Identify
> the type of information and knowledge most useful to assess quality by
> design. Regulatory approach for assessment of pharmaceutical
> development knowledge to maximize its value without impacting drug
> develop.
>
> We have an hour for this discussion. Our goal is to come up with some
> concrete proposals, not just to have a free-wheeling discussion but
> actually come to some proposals that we can leave with the agency.
> Would anybody like to get us started? I don\'t know why I am looking
> at this end of the table\--Pat?
>
> \[Laughter\]
>
> DR. DELUCA: I wanted to make some comments; what I heard today was
> very informative and to a great extent I think people were saying the
> same thing in a different way. I think Ed Fry, you know, indicated
> that for decades we have been talking about building quality into the
> product. I think it goes back to a conference that I was at in the
> 1970s and it evolved into an FDA handbook. I will have to send the
> reference to Ajaz. It was published in 1973.
>
> I guess what Ed had said about it being a culture, the new thing of
> building quality into the product is a culture work. Now it evolves
> that most of the elements in the pharmaceutical company, you know,
> from the research to development to the quality control, manufacturing
> and even the regulatory component and now having a tie-in with the
> regulatory agent, the FDA.
>
> I guess what I see here is quality design, as we have talked about,
> coming up with a description, is a dynamic process. It entails both
> learning before doing as well as learning by doing. I think there is a
> balance there and I think you have to, at some point, get on and learn
> by doing, by experience.
>
> So, I see as the definition of quality\--I know it was brought out too
> in discussion where the patient, you know, was involved here, and
> satisfying the patient. I think that, one, that is the pharmacological
> aspect of it, which is not easily clearly defined or measured. I think
> once we know that the product has a pharmacological effect and gives a
> therapeutic benefit, then I think when we talk about quality we are
> talking about the product and the process and specifications that go
> with that.
>
> I think also with regards, you know, to learning before doing and
> learning by doing we get into the clinical trials often with a
> product. Certainly, that is the same thing today, a company wants to
> get into the clinic as quickly as possible, and we get there by
> probably not development\--I mean, development is still ongoing while
> clinical testing is going on. The INDs, as was brought out, certainly
> lack all of the detail. There are things that aren\'t in there that
> should be in there. I know, from experience from conversations and
> going into the clinic and preparing INDs, that discussions such as we
> don\'t know this or we should know this but let\'s get to the clinic
> and we will do these other things later. But oftentimes that \"later\"
> never comes and there is reluctance to do anything later.
>
> I guess what I think that I would like to bring out here is that
> somehow that this design of quality has to be clearly expressed as a
> dynamic effort that continues. It continues in the development stages
> while the clinical testing is going on, and it must continue
> post-approval to a product. You know, as we talk about process
> improvement, however you define the process complexity or what-not,
> certainly I think the incentives to improve a process are either lower
> risk or lower cost so that a company is going to either reduce the
> cost or it is going to reduce the risk that is involved in that
> product. I think they should be encouraged to do this. I hope when we
> talk about lower cost that that lower cost doesn\'t mean just a
> savings to the company but it is passed on to the patient as well. So,
> I think there are some ethical and humanitarian issues that are
> involved here with regards to costs.
>
> There was one thing I would like to bring out which Janet had said,
> that FDA, in looking out for the safety of the patient, one of the
> things they weren\'t really concerned with is pricing. In some respect
> I think we can\'t say that entirely because oftentimes if the price
> gets too high there are a lot of people who can\'t afford the
> medications. So, I think price is something that should not be
> excluded from some of the considerations.
>
> But I think when we talk also about how much risk to accept and how
> fast to get a product to the market or to make changes, I guess one
> can\'t avoid\--and I think it was brought out by the last
> speaker\--legal issues. There are legal components that can play a
> role here. We know very well that products that are on the market, if
> they have a side effect, like the Viox, there are going to be legal
> issues and we will have the lawyers advertising to seek out patients
> to try to get them in class action suits.
>
> So, I think these are also things that probably come into this. But I
> guess one of the things that I wanted to stress here is that quality
> design is a dynamic process and that it should continue post-approval
> of a drug, and I think this should be brought out in any kind of
> description that we give of quality by design.
>
> DR. BOEHLERT: Thank you, Pat. Others? Tom?
>
> DR. LAYLOFF: I want to play too! First of all, I want to talk about
> quality and I am going to use it interchangeably with fitness for use.
> We will walk away from the safety and efficacy side because I don\'t
> play in that box. So, quality by design is establishing a formulation
> and manufacturing knowledge base which is sufficiently robust to allow
> manufacturing of product which consistently meets requirements, as
> sort of an over-arch. And, the type of information and knowledge most
> useful to assess quality by design is the identification of stressor
> elements in the critical control points and robustness of those
> critical control points.
>
> The regulatory approach for assessment would be an output orientation
> as a number of OOS or failures in the control systems. So, I see it
> more as establishing the dimensions for keeping the system in control
> and building it by identifying stressor elements at the control points
> so that you can see what the control dimensions are for incoming
> materials and manufacturing. Then, lastly, the way of assessing it
> would be how well is it staying in control. Those are my two cents.
> Nozer is not going to like that.
>
> DR. GOLD: Let me just ask Tom a question. I think you have a lot of
> very good elements but you talked about meeting requirements and that
> smacks of specifications, and I don\'t think specifications are really
> the same as fitness for use. Fitness for use includes specifications
> but it goes beyond specifications, from my perspective.
>
> The comment that was made earlier today, I think by Norman, was that,
> say, a product becomes contaminated, you can\'t test for it; it meets
> all the specifications but it is not fit for use. So, I would suggest
> perhaps we change the wording when you said meets requirements to
> meets customers\' needs or meets fitness for use.
>
> DR. LAYLOFF: I have a problem with untoward contaminants because I end
> up in a universe and I don\'t know how to deal with it. For example, I
> had a heated discussion with somebody from Food Chemicals Codex
> because they changed the limit on lead and sucrose to a tenth of a
> part per million lead. I asked them if they did that on the basis of a
> health risk and he said, no. And, I said why did you do it? he said
> because it was technically feasible and nobody objected. I said, well,
> what is your cadmium limit? He said we don\'t have one. I said, well,
> cadmium is toxic also. You really should have one and I doubt if
> anyone would object. How about bismuth? How about some other elements
> that are probably more toxic than lead? Plutonium for example? You
> might get a limit for plutonium. He thought I was being facetious but,
> of course, I thought he was nuts.
>
> \[Laughter\]
>
> DR. BOEHLERT: Nozer I think was next, and then Efraim.
>
> DR. SHEK: Yes, I just want to propose a definition, I think that is
> the first assignment, and basically using one of the slides that Ajaz
> has and modifying it. It will be a higher level than, Tom, what you
> are proposing. For example, it can read as follows: What is quality by
> design? Design based on pharmacokinetics, formulation and process
> understanding as it relates to the intended use of the drug product.
> We can then go into each one of those areas and ask what information
> do we need to know the pharmacokinetics. For example, do we want to
> know the clearance? Okay, if we know the clearance we know how to take
> the next step. Formulation, physicochemical properties of the drug
> substance. Then process, you know, we talk about the manufacturability
> once we decide on the dosage form. I feel that will be comprehensive,
> talking about both the quality with regard to clinical performance as
> well as translating it to a manufacturing environment.
>
> DR. BOEHLERT: Questions or comments?
>
> DR. LAYLOFF: I want to walk away from the clinical because it is so
> noisy.
>
> DR. SHEK: On the other hand, the product that we make is supposed to
> work and somehow, if I go and design a tablet and I don\'t get the
> blood level that I need, it doesn\'t matter how well I make the
> tablets. So, at some point in the design there has to be something to
> do with, you know, the efficacy. I don\'t know whether
> pharmacokinetics is the best but that is something that I can
> understand.
>
> DR. LAYLOFF: Okay, but then you play with a 30 percent window or more.
>
> DR. SHEK: Thirty percent window?
>
> DR. LAYLOFF: If you give solutions to people, how many people compare
> if you do multiple tests on different people?
>
> DR. SHEK: But I think that really will depend on the pharmacokinetic
> profile. There is a lot of variability there and you have to take it
> into account. If it is pretty robust and you don\'t see a lot of
> changes\--it depends on the biology of the compound. You will have to
> play around with it. In some cases you will try to have tighter\--I
> won\'t say specification but tighter requirements.
>
> DR. BOEHLERT: Now, Nozer, did you have a comment?
>
> DR. SINGPURWALLA: I am going to speak what comes through my mind. The
> question that is asked is articulate a clear description of the term
> quality by design. That is what we are asked to articulate. We are
> also asked to articulate clearly.
>
> So, what goes through my mind are the following things. I took a
> course on quality control long, long ago. I also happen to have worked
> with Edward Demming and have written a paper with him. So, I have some
> idea of the history of what is going on. When I took a course on
> quality control and reliability the particular subject matter was an
> understanding and study of variability. How do you understand
> variability; how do you study it; how do you control it to whatever
> extent you can and, based on the variability that you observe, what
> kind of actions and decisions you make. That was the state of affairs
> for a long, long time.
>
> Then comes along Tagucci who essentially said the following, he
> introduced the slogan \"design by design\" and that became a slogan
> and what was he basically saying? He was basically saying two things.
> Now, whether this was his original statement or whether it trickled
> down from the likes of Demming and others or George Box, I am not
> sure. But basically his claim was the following, that quality control
> should be active, not proactive. The way the old books were written
> was that the designers designed; the manufacturers made; and then the
> quality control people came in at the end and watched everything and
> reported what they saw.
>
> So, Tagucci introduced this notion that quality should be more active
> and he said one way you can do quality control more actively is to use
> design of statistical experiments which were used in agriculture. That
> is why we have the slogan \"design by design.\"
>
> We now have this new verbiage, quality by design. The thought that
> goes through my mind is that whatever you do to produce good quality,
> you should think about it way in advance, use all possible
> methodologies that are available to you, which includes experimental
> design, pharmacokinetic experiments and so on and so forth. So, to me,
> the term quality by design simply means think about quality right from
> the word go.
>
> Now, I would like to suggest that this committee, if possible and
> others if possible, watch an excellent program on public television.
> It is called \"Building an Airplane for the 21st Century.\" It is a
> story of how they build and designed the 777. It is a seven-part
> program on PBS. Basically, they are essentially doing what I think you
> are trying to do. They start with a concept. They bring the designers,
> the manufacturers and the customer\--this happened to be United
> Airlines\--and essentially they designed this airplane, which ran for
> the first time successfully and, thank God, nothing has gone wrong
> with it as yet.
>
> But I think that gives me a signal of what one means by quality by
> design. I still cannot articulate it very carefully, other than the
> fact that it simply means think about everything. Otherwise it is just
> a slogan. Perhaps it is still a slogan.
>
> DR. BOEHLERT: Other questions? Comments? I think we have heard some
> variations on the same theme here, except perhaps for Nozer\'s last
> comment which was a little bit different. I guess Tom\'s comment, you
> know, meet requirements which includes specifications and other things
> that were brought out. I think Efraim brought in the concept of
> pharmacokinetics. We heard comments about involving nowadays most
> areas of companies. We talked about a dynamic process, you think about
> things early. You know, it is a concept that gets started\--you know,
> learn before doing kind of concept and we need to satisfy patient
> needs.
>
> So, I don\'t think we are saying completely different things here. I
> think we are all going around the same issue and I don\'t know if that
> is helpful to you, Ajaz.
>
> DR. HUSSAIN: No, I think I like the previous comments quite well. I
> think that is something we have thought about in a similar way,
> Tagucci\'s approach and so forth, and you will see a lot of those
> thoughts in our draft PAT guidance which are already sort of captured
> in that thought process.
>
> At the same time, I think I would like you to keep in mind\--at least
> my personal perspective is this, quality by design and all the effort
> that goes into designing a product, especially formulation and
> manufacturing, a lot of it already exists. One of the challenges we
> face is that we have to make decisions in absence of some of that
> information and our decisions, therefore, have to be extremely
> conservative. We essentially create this as an art. You may have just
> achieved a formulation and manufacturing process just by chance and
> then when you repeat it you don\'t understand anything more. So,
> somebody who does that is in the same box as somebody who has really
> put all their effort in designing experiments, doing an optimization,
> and so forth.
>
> So, one of the objectives of quality by design is to differentiate
> between those groups, so as to give advantage or give incentives for
> people who do the right things.
>
> DR. BOEHLERT: Tom?
>
> DR. LAYLOFF: I think I was very interested in Nozer\'s discussion
> because the customer could be identified and in the case of
> pharmaceutical products is the surrogate customer. In the case of 777
> also all of the operational definitions and variability issues could
> be previously identified and programmed; it was in control. So, the
> design was conducted in control with a known consumer. Now, the FDA is
> a known consumer, our surrogate consumer. But the knowledge base for
> it is available but not visible so you don\'t have the design under
> control as far as the consumer is concerned.
>
> DR. GOLD: Ajaz, what you need to elaborate on is if you follow the
> path that you just discussed, and that is the path that we have been
> talking about for a while now, how does one proceed unless guidance is
> also given as to what the requirements really must be to satisfy the
> objective that you discussed, that robust development has been
> performed, multivariate experiments have been run, independent
> effects, confounded effects have all been determined. How can you
> establish this without providing the requirements in clear form so
> that companies can follow them? I did not think our pathway was moving
> toward that type of regulatory guidance but it may be necessary so I
> would like you to elaborate, if you could.
>
> DR. HUSSAIN: Well, I think my thought processes were more focused on a
> change situation, and I think we can actually start defining things in
> that mode. For example, change in zip code can be a post-approval
> change and can be a prior-approval supplement if it is a
> modified-release dosage form. So, you are keeping everything the same.
> You are moving your factory to a different location. If the product is
> such, you may need a prior-approval supplement. You may need a bio
> study. You may need a stability study of three batches, and so forth.
> So, essentially the requirements are set.
>
> So, one could take that and say what are the concerns here. One
> approach could be that the dissolution test that you have, even if the
> product meets that criteria, is not giving the regulators the comfort
> they need to say, all right, the product has not changed. So, you have
> identified a limitation or perceived limitation in a test method that
> probably is holding a decision back and they would like to see
> additional testing done to make a decision.
>
> Similarly, I think with respect to stability testing you need three
> batches of stability data. Keep in mind that when you get a
> prior-approval supplement what you receive is maybe three months of
> axillary data and whatever real-time data you have. But the review
> process, and so forth, often is such that by the time we approve it
> you actually have more real-time data on that and often there is not
> enough shelf life left so you throw away the batch.
>
> I am going back a few years, in talking to the review chemists one of
> the concerns in terms of stability testing, one of the biggest
> concerns that comes up is the axillary stability studies are not fully
> indicative of, for example, the shelf life, especially when the shelf
> life is associated with physical attributes. The reason for that is
> the basis of the axillary stability studies truly may not be valid for
> predicting physical changes. So, I think you sort of start taking
> layers and layers of concerns out and then you can sort of structure
> the discussion.
>
> So, one aspect that I could think about is if we understood what are
> the critical variables and how they are impacted, how they control to
> a higher degree, then we could say, all right, this change is really
> not of major concern because the process is well understood. You may
> still do some of the qualifying and additional testing that may be
> necessary but that could be handled within a quality system within the
> GMP aspect, and not have to wait for a supplement and wait for the
> process, and so forth.
>
> So, it is easier for us to actually think in that mode because you at
> least have well defined endpoints that you can discuss. In the
> development it is a bigger question and that is the reason I proposed
> that we should probably start thinking about this in the post-approval
> world because I think there we can hone down to the key questions.
>
> DR. BOEHLERT: Nozer?
>
> DR. SINGPURWALLA: I would like to make a proposal to move forward
> regarding this first bullet, articulate a clear description of the
> term quality by design. Certain times in the sciences, particularly
> the mathematical sciences, you take certain things as axiomatic; you
> don\'t question them, one of which is your declaration independence by
> Jefferson, we take it to be true that everybody is equal, or
> something. Basically it is an axiom. You basically don\'t question the
> axiom.
>
> So my proposal is the following, take the term quality by design as an
> axiom. Don\'t try to articulate on it and don\'t try to explain it but
> go to the next step and try to see what kind of information and
> knowledge is the most useful to assess quality by design. Take it as
> an axiom and then start looking at its attributes. This way you will
> make progress, otherwise we can spend the next so many years trying to
> define something which is, to some extent, vague. You know, it is a
> catch-all expression and one could think of it as experimental design;
> one could think of it as specifying requirements; one could think of
> it in so many possible dimensions. So, my proposal is to just take it
> for what it is and then go further down the line and then see if we
> can come back and revise it.
>
> DR. BOEHLERT: Okay. Gary?
>
> DR. HOLLENBECK: I like that. It moves us to bullets two and three.
>
> \[Laughter\]
>
> I think that is where we have been spending most of our time talking
> about, those two aspects. I am probably going to ramble a little bit
> here too but I think to proceed we need to think in the current
> context, the current system that we have and make progress within the
> things we understand. Some of the things we have been trying to debate
> here today we have debated even before the \'70s and we will debate
> long after we leave here.
>
> As I flash back, the term developing meaningful specifications has
> always rung really true to me, and I think that is a clear part of
> what we are talking about here. Your goal is to create incentives for
> a broader development context so that companies do it and communicate
> it to you. I think that is an essential part of this.
>
> The other essential part of that process is the identification of
> those things that really matter, whether you call them critical
> process parameters, critical variables, critical components. Within
> the experimental designs that you are doing the identification of
> those things that we really need to monitor and follow is the second
> step, it seems to me.
>
> Then, I can\'t help but link that second step to the PAT initiative,
> and that is to find better ways to efficiently determine whether or
> not you are meeting those specifications and not be redundant. So, I
> think those three things are really part of bullets two and three, you
> know, how we can accomplish quality by design, which is clear to
> everybody now.
>
> \[Laughter\]
>
> DR. BOEHLERT: Diana?
>
> MS. KOLIATIS: I like your comment about moving off to the second part
> of the question, but ultimately you have to decide what you are
> building here. I think you are either building a sports car or you are
> building a sedan for the family. Once you have made that decision,
> what you are building, then the content and format of your development
> data takes on a certain path, and that is what I think is what we are
> looking at, what is the content and what is the format that needs to
> be presented to the regulatory customer, as Tom said. That is the
> other customer that you want to keep in mind. What needs to be
> presented so that that regulatory customer can evaluate your thought
> process on how you came up with your process to manufacture either the
> sports car or the sedan. That choice is yours. We are not telling you
> what to manufacture, but once you come up with that decision, then
> what was the thought process to make the best sports car or the best
> sedan.
>
> So, I don\'t know if we can get away from defining the term quality by
> design. I think Janet tried to put out some concepts about what are we
> trying to make here. I think we should ultimately get back to defining
> it but I think what you are talking about is the content and the
> format to allow FDA to come in and make that assessment of the thought
> process, not to tell you what that thought process should be and not
> to tell you what the specs should be. That, I think, is what the
> company needs to do and then we need to sit down and look at that
> thought process together and say yes or no.
>
> DR. BOEHLERT: Ajaz and then Garnet.
>
> DR. HUSSAIN: I think I agree with Diana\'s comments. I think the
> discussion on quality by design, I think I would like to sort of point
> you to Gerry Migliaccio\'s slide. I think he presented that in the
> context of manufacturing and I think that has some relevance here.
>
> But I do want to go back to sort of the issue of quality by design,
> different levels of that. For example, if I have a choice between an
> immediate-release tablet versus a controlled-release tablet, if I know
> that the features of an immediate-release tablet will lead to certain
> adverse effects because of the high peak concentration, and so forth,
> then there is an advantage for doing it in the controlled-release
> product or a transdermal product. Then, that is a design feature. I
> think that clearly is sort of one element of the design.
>
> I am not sure if we are sort of talking at that level yet. I think our
> discussion has been that this is the product feature that we have
> selected so there is a clinical link to that, and I think clearly that
> is a very important discussion but for this committee I think we have
> made the decision; we are making this particular product; it is going
> to be an immediate-release dosage form. Now let\'s design the
> formulation and the manufacturing process to produce that in a
> consistent, reliable way. So, that is the part of the discussion that
> I think we need to focus on.
>
> DR. BOEHLERT: Garnet?
>
> DR. PECK: We have heard a number of comments about understanding the
> process. We have heard a few comments made on what I am going to say
> now. I think we have delved into manufacturing science. I am still
> concerned about material science. I think the elements certainly for
> obtaining quality by design are achievable but we have to also
> remember that the material we are starting with has to be well
> defined, both the API and the excipient.
>
> The excipients are very dear to me because many of them are commodity
> items and we make some judgment based on small sampling of very large
> amounts of material. I was reminded of this in a presentation I made
> in June to Food and Drug inspectors and reviewing chemists. They
> reminded me about looking at just a small sample of an excipient that
> is available in extremely large quantities.
>
> I am still concerned about the material science of everything that we
> are trying to put into a particular dosage form. We know by processing
> that we can modify these various substances, but do we know everything
> that we can know about either the active or the excipient? I think we
> have a long way to go yet in pharmaceutical material science and we
> need more effort in this particular area to stabilize what we are
> going to propose from the processing concerns that we have. I think
> understanding our materials is going to contribute to quality by
> design.
>
> DR. BOEHLERT: Other questions or comments? Ajaz?
>
> DR. HUSSAIN: I think I would like to sort of respond to Garnet on
> that. I think that is a very important point and I think the task
> ahead in terms of how you want to do material characterization is a
> significant task.
>
> My concern, as I think Garnet also pointed out, is that for many of
> the materials, the commodity items, and so forth, the resources needed
> to characterize the relevant functionality and develop measures and
> test methods for measuring functionality I think\--USP wanted to sort
> of pursue that. But in my mind, that will take twenty years for us to
> really get there. That is a bigger societal issue and we don\'t even
> have the infrastructure, academic infrastructure to even start
> tackling that problem. So, that is one piece.
>
> The API, on the other hand, is quite well characterized. We have that
> information. So, I think my way of thinking over the last couple of
> years has moved to saying we know this is a highly variable material.
> We take that as an axiom and say this is highly variable. This is a
> commodity material and we mix commodity material with our API which is
> so well characterized. Therefore, the current paradigm that we have
> that we manufacture to a fixed time, I don\'t know whether that really
> is fully supported. It is a dichotomous situation.
>
> So, that is the reason the PAT guidance emphasizes that, for instance,
> if we learn how to understand the variability and them manage that
> variability, this variability will remain. So, if you move to a
> process approach or process design that gets to endpoints that are
> more meaningful, instead of time as an endpoint, then that is one way
> forward. So, I think I wanted to add that.
>
> DR. BOEHLERT: Tom?
>
> DR. LAYLOFF: Yes, I agree with Garnet that material science is really
> critical to the whole manufacturing process, and I agree also with
> Ajaz that these critical processes have\--the endpoint identification
> has to be sufficiently robust to deal with that spectrum. So, part of
> the process design is looking at the robustness of the process after
> the assault with different materials.
>
> DR. BOEHLERT: I think that is going to be one of the challenges that
> you are going to face, do we have good tools to assess\--you know, at
> a multi-component mixture that may be changing, a number of the
> components changing at the same time\--to reach a defined endpoint?
> And, are those tools available today? We may need to develop some new
> tools.
>
> DR. HUSSAIN: But I also want to sort of point out that I think there
> is a whole spectrum of options and tools available. One of those
> options is a well-tested option. I actually have written on that also
> myself. It is based on the experience from the University of Maryland.
> Just to give you an example, magnesium stearate is present in 97
> percent of all products and it is a big culprit in the problems it
> creates. So, the way we control that is most of us buy it from one
> source. If the source changes we really would run into some
> difficulty.
>
> The monograph approach to that in the USP does not even get to the key
> functionality even from the purity perspective. So, just because
> somebody qualifies magnesium stearate just on the basis of USP, I
> think that is a high risk situation. So, how does one address that? I
> think there are many formulation strategies to address that, one of
> those being use of a wetting agent within the formulation to make it
> robust and less dependent on the effect of magnesium stearate on
> dissolution.
>
> So, there are formulation design strategies that can overcome some of
> the variability. So, I do want to sort of point out to you that there
> are many options. I think if we know there is a source of variability,
> one way would be new technology to sort of manage that. The other way
> would be to try proven approaches. But we don\'t have a means to
> recognize that as a robust formulation.
>
> To go back to Gerry\'s presentation, one of the criteria could be that
> the process or formulation is robust to these sources of variability.
> If we can generalize about how we get a robust formulation, that
> becomes one additional option that becomes available. So.
>
> DR. BOEHLERT: Other questions or comments dealing with the type of
> information and knowledge most useful to assess, or any of the other
> bullets?
>
> DR. SINGPURWALLA: I am not sure if we have moved forward from the
> first bullet\--
>
> DR. BOEHLERT: I am not sure either.
>
> DR. SINGPURWALLA: Assuming that we have not, I am going to take a
> second crack at an attempt to move away from it. So, I am going to
> propose a definition. Quality by design is the process of achieving
> acceptable quality by a methodical and systematic scrutiny of all
> elements that go into characterizing quality from inception to end
> use. That is sufficiently general; sufficiently nebulous; sufficiently
> meaningless.
>
> \[Laughter\]
>
> DR. DELUCA: I would accept Nozer\'s definition as one alternative. I
> always like to have a clear description of something but as I heard
> the discussion I really think we ought to get on with item two because
> we can always come back, and after we know what information we need we
> can always come up with some sort of a definition. You know, the type
> of information is going to vary by the product.
>
> DR. HUSSAIN: I would agree with that and I think that is where it is
> more fruitful and more useful for us because I think the type of
> information then gets associated with the intended use, the risk, and
> everything and I think fine-tuning and definition can come later on.
>
> DR. BOEHLERT: Gary?
>
> DR. HOLLENBECK: I am going to phrase things slightly differently. Here
> are things I don\'t want to do, I don\'t want to endorse a process
> where we are trying to find out everything. I think Garnet said you
> would like to know everything about the materials and we all know that
> is impossible and we really can\'t afford to do that. I want to know
> more about the things that really matter. I think that is what we are
> focusing on.
>
> The second part of that is I also don\'t want to wait for FDA to
> provide the kind of detailed guideline that I think Dan was asking
> for. I know you hate to hear these words but I think it is case by
> case pretty much, especially at this stage in the process. If you are
> building an SUV it may be different than if you are building a
> compact. I think you have to engage the industry on a case by case
> basis as you look at these development portfolios.
>
> DR. HUSSAIN: If I may, I think I agree with that and that is the
> reason I think my thought process sort of focused on post-approval
> changes because that provides a flexible means to sort of engage in
> that sort of a discussion and create some aspect of what Colin will be
> talking about later one. So, that was sort of my motivation of getting
> to that, not directly but indirectly. Then, once we learn a bit more,
> then bring it up in a later forum. So.
>
> DR. GOLD: Gary, I don\'t know how you could go out with a statement
> saying you want development information without defining the type of
> information because the industry is going to ask you this. I just
> cannot understand how we could not be prepared or say that this is
> going to be needed. I do agree with you it is a case by case, but the
> industry is certainly going to ask for guidance on what are the
> parameters that we should be looking at.
>
> DR. HOLLENBECK: But I think we know that. Maybe we could have our
> second axiom, we should do good experimental designs.\"
>
> DR. GOLD: And we should celebrate motherhood!
>
> DR. SINGPURWALLA: That should be a theorem!
>
> \[Laughter\]
>
> DR. HUSSAIN: I think the point is well taken. I think that is the
> challenge that we will have. I actually put on the table the FDA
> University of Maryland research model, which sort of starts on a small
> scale doing screening experiments and then do response process
> analysis to look at the response, and the impact of different
> variables on that response, what the impact is. So, it is a more
> structured approach to that. The draft PAT guidance is saying, all
> right, from a knowledge perspective what are we looking for?
>
> Now, the challenge that comes in any product development, whether it
> is pharmaceutical or any development, is that the developer or the
> formulator brings past knowledge to bear on this. Okay? So, that is
> one critical element that I think is very valuable because, in absence
> of that, if we suggest you have to do design of experiments, the
> number of variables that we have to deal with, the complexity of the
> designs would be out of reach. So, that is not what we are talking
> about. We are talking about bringing past knowledge to bear on
> decisions, which then become more rational and structured, to define a
> program that leads to a satisfactory outcome of what the intended use
> was. There is a structure to the information that then becomes
> knowledge. I think that is what we are looking for.
>
> To add to that because Dan did ask in a sense, my preference here is
> not to give a detailed guidance because if we do, the unintended
> consequence of that is that we will encroach on the development
> programs, and we don\'t want to do that. I think if we simply define
> the objectives that we seek, to understand the value of the controls
> that you have, the ability to mitigate risk, what is the relevance of
> the specification, when there is a process change meeting the same
> specification, what does that mean? Does it give us the satisfaction
> that the performance will remain the same, or was the acceptance
> criteria or the test method not sufficient to handle the changes that
> you may have?
>
> Just to give you an example, we established correlation between
> dissolution and bioavailability. All right? So, there is an
> established correlation. The way we accept that correlation, it is a
> type \"A\" correlation, point to point, that brings it closer to being
> causal but is not causal yet. So, if you change your manufacturing
> process significantly and you still qualify your change based on that
> correlation, that correlation may have been formulation specific and
> with certain changes in formulation the correlation will not hold. So,
> I think that is how we can sort of approach that.
>
> DR. BOEHLERT: Tom?
>
> DR. LAYLOFF: One more comment on the development issue, I like the
> idea of submitting post-approval because it gets around the risk of
> stalling at approval, which could be a big issue. The firm, of course,
> has to invest in the development to get a viable product for the
> approval. Then, going on beyond it into the design for any
> post-approval would I think be a more palatable option. I like that
> one better.
>
> DR. BOEHLERT: I have been thinking about that as well and I think that
> has barriers as well because post-approval assumes that you have very
> good knowledge of your current process. If, in fact, you don\'t and it
> isn\'t up to today\'s standards, perhaps then it is far more work and
> far more involvement to try to decide what it is you are going to give
> to the agency because, in fact, you put your prior process at risk.
>
> We have about ten minutes or a little less in our discussion period.
> Joe, did you have some comments?
>
> MR. FAMULARE: no, I agree with your concern on the post-approval
> changes. If it is not framed properly, post-approval changes could be
> effects for a product that is not well developed in the first place.
> So, we have to be careful how we frame post-approval. If it is an
> accretion of process knowledge that relates back to the original
> development work, that could be a logical progression. But if it is to
> try and fix what wasn\'t done properly in the first place, well,
> isn\'t that kind of where we are at?
>
> DR. HUSSAIN: But I will add to that. In fact, I won\'t say most but a
> large proportion of develop occurs after approval.
>
> DR. BOEHLERT: Tom?
>
> DR. LAYLOFF: I think that the manufacturing experience at the time of
> approval is very, very limited and that the knowledge base is
> increasing all the time. I actually sort of like the idea of interim
> specifications to allow that evolution to occur under a regulatory
> blanket. I put this under the same thing, after the approval occurs
> and the manufacturing is under way and you get more experience with
> different excipients, different material issues, you actually are
> redesigning to deal with the variable material science and process
> science.
>
> DR. DELUCA: I have a little bit of a problem with conveying the idea,
> you know, about the post-approval that you are trying to fix
> something. To me, okay, that may be true but so what? Let\'s fix it. I
> mean, whether it is improving the process or fixing it, let\'s do it.
>
> \[Laughter\]
>
> MR. FAMULARE: But fixing sometimes is not really fixing; it is just
> mitigating something and just going on till the next time something
> comes up as opposed to going back and finding a root cause, or really
> finding the problem. That is the sense that I meant it in. You should
> fix it and we should have a lifecycle approach to dealing with a
> product in terms of what you learn over time. But, you know, many of
> the paths of action that are taken, as we talked this morning about
> the regulatory framework, well, let me fix it so much that it stays
> within my approved specifications and filings, and so forth, because
> that may pose a bigger risk than really getting to the root cause of
> the problem.
>
> DR. DELUCA: I was only being critical because it may create the
> impression on the part of the manufacturer that I don\'t want to go in
> with this change because they will feel we are fixing something
> because we did something wrong in the first place, when that may not
> be the case. So, I think it is best to improve the product for the
> benefit of the patient and to lower cost.
>
> DR. FAMULARE: And I think the overriding question is how much of that
> latitude could be in the hands of the firm in terms of the regulatory
> filings. I think that is an important element in terms of what I was
> talking about this morning, improvement. How much can you improve and
> keep improving and keep on that paradigm without the regulatory
> scrutiny so that you can truly improve, and now much do we need to
> come back into that to make sure that the product does act the way we
> felt it acted when we approved the product in the first place.
>
> DR. BOEHLERT: G.K.?
>
> DR. RAJU: I agree that we need to have experimental design as an
> axiom, but even before we get there, in terms of the process
> development knowledge one of the first things I think we should have,
> whether it is in the record or not is to be debated, is the boundaries
> and the basic failure modes of this process in terms of its basic
> safety and efficacy issues and predictability issues. I think those
> come even before an experimental design. I don\'t know if that was
> talked about but they actually are the basis for the ranges of the
> variables and the specifications, and they don\'t have to be
> quantitative but I think the qualitative ranges are really priceless
> information and the investigations around them, even if they are under
> development, I think are very valuable information because the
> failures tell you the best relationships between the Xs and the Ys.
> Then you can do the optimization later but the big stuff is the
> failures. The successes and the better successes can happen later I
> think.
>
> MS. KOLIATIS: Just to follow-up on what Joe mentioned in terms of
> improvements to the process, and I want to get away from the
> terminology \"fixing\" but improving a process\--in many cases the
> individuals who are improving or tasked with improving the process are
> a little bit removed from those who actually developed process and the
> R&D that went into that process. So, they are trying to fix a process
> without all of the underlying scientific information, and the danger
> that we might see is that we are going to move away from that desired
> product which had a relationship to the product that was studied under
> a clinical trial and gave you that desired clinical performance.
>
> One of the goals that, hopefully, we can see through this discussion
> and through the process is to integrate production with R&D folks on a
> greater scale, and have them get together in the development phase so
> that there is less need to improve \"fix\" after the product is in
> post-approval and is out on the market.
>
> That is one of the things that we see in the field when we go out and
> do our post-approval inspections. A lot of what we see are problems
> and things companies have to deal with because of perhaps a lack of
> communication early on in R&D with production. So, one group is now
> trying to fix it without all that underlying information. So, I am
> hopeful that some part of this process will allow for that increased
> communication of these two groups.
>
> DR. SHEK: That is an interesting point because I believe industry,
> exactly because of this point, thinks of changing. If you follow up on
> the way companies are structured, and interaction between R&D and
> manufacturing, there is a big change, I would say, in most companies
> because some of the reasons you brought up.
>
> But we have to remember, you know, all of us were conditioned to some
> kind of rules and regulations, and that is what I think many of us are
> reacting to. So, now we are talking basically about a new approach and
> I think here we have to use the same and it should be basically
> encouragement.
>
> I would assume a company will go and say let me try to fix the process
> in the frame of specifications because it will be, you know, not today
> under regulation. It will be faster to go, you know, and fix the
> problem I have. But if there is another pathway where you can add this
> information, do the right thing which might take you longer in the lab
> but faster than to bring it to completion, I believe we, in industry,
> will be conditioned to do it differently.
>
> The information is there. Manufacturing and R&D are working much
> closer than in the past and this information is being shared. If that
> can also be encouraged by some kind of, you know, let\'s say friendly
> regulatory approach, I think it will, again, be a win-win situation.
> The environment is right; we just have to create it. The same thing
> has happened, you know, with the PAT. There is no question, if you
> read the guidance\--we just talked about it outside\--it is no
> different than other guidance that was ever published and that is
> refreshing. I think we can continue this approach also in talking, you
> know, about quality by design.
>
> DR. BOEHLERT: Ajaz?
>
> DR. HUSSAIN: Yes, I just want to sort of summarize what I heard and
> sort of help you sort of close this part of the discussion. Clearly, I
> think the phrase quality by design is a term that we sort of all have
> a grasp of what it is. It is difficult to define in words but I think
> I like the idea of defining what gets you to quality by design and the
> discussion was very helpful for us to sort of frame that. And, I think
> I was very pleased. Some of that was very consistent what we had
> articulated in the draft PAT guidance. I think that was very, very
> helpful for us.
>
> The aspect that I think we also heard was to move it at least in an
> interim step in the post-approval world because it makes more
> opportunities to collaborate and to work together to really hone in on
> how best to do this, and creating flexibility to achieve this in
> different ways. I think there are many different development
> approaches that can get you to the same end goal. So, we don\'t want
> to be directing which is the best development approach, and so forth.
>
> With that, I think that was very helpful and I think after you have
> listened to Colin and Greg, sort of give some more thought in your
> discussion in the post-approval world on how we can approach the next
> steps for quality by design, if you can consider that in the second
> part of the discussion that would really help us because what we plan
> to do is take this discussion and sort of structure some of the
> activities of our manufacturing science working group within the GMP
> initiative to sort of focus on how we move in this direction, keeping
> in mind that we already have two draft guidance on comparability
> protocol and keeping in mind we have the SUPAC revision thought
> process and how we can integrate some of this into those activities.
>
> DR. BOEHLERT: Tom, did you want to have the last work?
>
> DR. LAYLOFF: Penultimate.
>
> DR. BOEHLERT: Penultimate word?
>
> DR. LAYLOFF: I would say the most conscientious manufacturer is going
> to try and get availability, the safe and efficacious product out the
> door and on the market as soon as possible. The way the materials are
> manufactured, since the pharmaceutical industry doesn\'t swing the
> manufacturers of excipients, excipients are going to be a variable and
> manufacturing processes really should change to reduce cost and become
> more robust in time. That means post-approval. So, I think
> availability, having safe and efficacious drugs out there as quickly
> as possible is not going to allow you to explore all the critical
> dimensions of incoming material science because it will slow
> availability down and increase costs with no net gain.
>
> DR. BOEHLERT: Gary?
>
> DR. HOLLENBECK: This is not the last word. I like your summary, Ajaz,
> except I don\'t see the need to restrict to post-approval changes.
>
> DR. HUSSAIN: No, we are not restricting it but I think putting our
> efforts in that because I think that will yield results more quickly.
> I think the regulatory process actually is quite flexible enough if
> someone wants to do this right now, and some companies are already
> doing it. In fact we already have some proposals, and so forth. So,
> our system is flexible but getting to a formal guidance and other
> approaches, that is where our efforts could be placed. That is what I
> was saying.
>
> DR. BOEHLERT: Any other final comments? We are scheduled for a break
> right now. I would suggest we come back at three o\'clock. We might
> take a very brief break and get started again promptly at three
> o\'clock.
>
> \[Brief recess\]
>
> DR. BOEHLERT: We are ready to get started. We have two presentations
> scheduled. The first will be Colin Gardner.
>
> **Quality by Design and Risk-Based Regulatory**
>
> **Scrutiny CMC: Specifications and**
>
> **Post-Approval Changes**
>
> MR. GARDNER: Well, I have to thank you, Ajaz, for inviting me again. I
> thought I had come and done my bit in May but you insisted that I come
> back again today so I had to dream up some new slides to present. I
> also want to acknowledge Scott Reynolds, who is executive director of
> pharmaceutical development at Merck who got back in the early \'90s
> from a manufacturing division to come into pharmaceutical R&D as an
> engineer and bring a lot more engineering principles into the
> development of processes. I have continued to chat with Scott even in
> the time that I left Merck so part of what I present today with ideas
> from Scott.
>
> The CEO of our company always tells us you have to tell people at the
> beginning what you are going to tell them and then you come back and
> tell them at the end to make sure that they understood what you are
> going to tell them. So, here is what I hope to get across today, the
> continuum of process development activities really starts with the NCE
> selection and continues all the way through development and
> manufacturing process and post-approval.
>
> Fundamental new chemical entity characterization and process
> development really lead to meaningful control points. I agree with
> what Garnet said, you know, material science is absolutely critical. I
> have a colleague who is a professor at MIT for material science and he
> has dealt with the electronics industry all his life, and only
> recently became involved with the pharmaceutical industry and he is
> absolutely shocked at how poorly we define these multi-billion dollar
> products. That is his view, anyway.
>
> Success of the scale up exercise and also process changes and site
> transfer is really driven by rational comparison of meaningful process
> and product parameters that we have to define during development.
>
> Ultimately we have to have a fingerprint of parameters that are
> identified to be able to monitor process robustness, and these are not
> regulatory specifications but monitoring the robustness of the
> process, and drift in those parameters can be used to flag issues
> before you lose control of the process.
>
> So, that is what I would sort of like to get across today. Let me
> start and be a little bit on the social science rather than the hard
> sciences here because I think there are some aspects of that involved
> here as well. So, issues within the industry themselves\--this is data
> from PRTM, a consulting company in Boston, basically saying that it
> takes anywhere from 6.5 years to 13.5 years to develop a product, and
> it can cost up to 800 million dollars according to the latest figures
> from Tufts and Bob Ruffalo from Wyeth who quoted 2.4 billion last week
> so it is an even growing number.
>
> But the challenge really, and someone already said this, is to send
> safe and effective drugs on to market. These are the two things that
> the pharmaceutical industry is targeted towards and they haven\'t been
> terribly successful at doing that recently so they are trying to
> improve that.
>
> But what really are the products of a pharmaceutical company? Well,
> there are three products. There is the API itself. There is the
> marketed dosage form or dosage forms, and there is the approved label
> claim that is used to position the product in the market for the
> physician and the patient to use the product.
>
> But if you think about which one of those is most important, it is not
> the API; it is not the dosage form. What really rings the cash
> register is the approved label claim that is used to position the
> product on the market. That is what is important to the CEO.
>
> So, the consequences within the industry is that R&D tends to focus on
> potency and selectivity and safety and clinical response. These are
> the things they monitor. They don\'t uniformly recognize the
> importance of any investment in process chemistry and formulation
> development. Those are things that they figure will get done on the
> way to developing the product.
>
> They also tend to have inexperienced clinical staff who, you know,
> have come from an academic environment, they come into a company and
> they are put in charge of running the clinical program, and they set
> timelines and targets that are totally independent of the product
> development capabilities.
>
> The goals and the rewards of the various divisions in discovery,
> development and manufacturing adverse event not aligned. Discovery
> people get rewarded when they get when they get a compound into
> development. Development people get rewarded when it gets transferred
> to manufacturing, and manufacturing people are left to suffer the
> consequences of those rewards.
>
> \[Laughter\]
>
> And the CEOs really haven\'t regarding manufacturing excellence as a
> competitive advantage. So, the industry is not the only one to blame
> here. I think the regulatory agencies have their share of the blame
> too.
>
> Most of the people who are in the reviewing divisions of the
> FDA\--correct me if I am wrong\--tend to have analytical chemistry
> backgrounds and a lot of what we are talking about here is process
> engineering and if you don\'t have a background in process engineering
> how are you going to understand the information in a development
> report? That is partly the reason why companies don\'t sent those
> development reports in because the regulators in the companies are
> afraid of how it will be interpreted by the agency.
>
> Secondly, the timeframe to review and understand the regulatory filing
> is really limited. I am sure the reviewers of the agency are
> constantly working on different programs and very often it is right
> down to the wire before they get around to reviewing the product and
> they only have a few weeks to do that. Again, that doesn\'t mean that
> they have a fundamental understanding of what is happening.
>
> Then, I think the training of compliance inspectors, particularly in
> the early days of PAIs, was very, very poor. I think I hear that is
> improving but let me give you some examples that I encountered in my
> area. These are two very, very simple processes. I didn\'t go for
> complicated controlled release processes; these are really simple
> ones.
>
> Here s a case where we developed a biobatch and it was a simple mixing
> of excipients and drug in reasonably viscous but not terribly viscous
> environment. So, it was at a 10 liter scale and, if my memory serves
> me correctly, took about 15 minutes to achieve homogeneity by sampling
> that process. As we scaled that up to the commercial batch it was 100
> liters and it was 45 minutes to get to homogeneity.
>
> When we had a pre-approval inspection, the FDA inspector said the
> processes are different because in one case you used 15 minutes; in
> the other case it is 45 minutes. It is not the same process. This
> indicates, you know, the fundamental lack of understanding of the
> process engineering.
>
> Here is another one which was even more dramatic. In this case we have
> a 4,000 liter tank in which the drug is, again, being suspended. It is
> an oral suspension. It goes to a filling tank which feeds the filling
> line. In this pump the tubing is flexible, is capable of adsorbing
> things. Of course, since it is an oral suspension it contains a
> preservative.
>
> So, in development we asked ourselves if this line were shut down for
> one reason or another, how long would it have to set there before we
> started to adsorb the preservative and it would drop below
> specification? So, we did those experiments in the lab. Then we went
> to the manufacturing division and we ran an engineering run, not a
> validation run, an engineering run to figure out where we were going
> to go. We ran that for six hours and we showed we got a certain amount
> of adsorption in those six hours and we had time points all along. So,
> we established cardiopulmonary and if it was shut down for more than
> 15 minutes we would empty out this line and then continue the filling
> process.
>
> At the pre-approval inspection, the inspector\'s conclusion was that
> not only did we have to throw this out, but we had to throw this out
> as well. Since this was days before the approval of the drug there was
> no way we could argue. So, that is what the process has to do, they
> have to throw this out if there is a 15-minute shut down. Again, it
> doesn\'t explain good process engineering.
>
> So, what can we do about this situation? Manufacturing processes
> really have to start with the choice of the NCE, its form and its
> formulation. They have to link discovery, early development, process
> scale-up and manufacturing.
>
> Let me skin through this slide because I don\'t really have time to go
> through all of it, but the key part of this is really if we are going
> to do this we have to be able to demonstrate reduced regulatory risk
> to the agencies. As a result of that, we have to be able to get
> regulatory relief for companies that have done good process
> development and then demonstrate the value of that to the company
> management. That, to me, seems the fundamentals of what we are trying
> to do.
>
> How do we do that? First of all, we have to pick better development
> candidates. We have to build in developability. The processes that are
> used in discovery these days are targeted towards finding selectivity
> at various receptors and enzymes, and that results in very much more
> hydrophobic compounds that are much more difficult to formulate and
> prove bioavailability.
>
> So, we have to start somehow affecting that process. This is today\'s
> process using genomics and libraries of chemicals and high throughput
> selection processes to identify hits, mingling through synthetic
> chemistry, looking at selectivity, metabolism, some animal models, [in
> vitro]{.underline} tox and some small [in vivo]{.underline} tox
> studies, and only after selecting the candidate for development they
> bring the process chemists and the formulation people in to bear on
> the problem.
>
> With all of the constraints that are being put on the pharmaceutical
> development people in terms of the number of compounds that are coming
> forward, the time constraints and the results constraints, this really
> constrains these people. So, the best way to address this is to think
> the form and formulation back into this process to help build into the
> molecule that you are developing the physical properties that make it
> a better candidate.
>
> So, if you think of ways to do that, instead of just looking at
> potency and selectivity and metabolism and iterating through this
> until you get a lead candidate, you can actually build in ways to look
> at the physical properties much earlier which then, eventually, gives
> you a target which is a developable compound rather than one that just
> has an interesting chemical entity.
>
> How about form and formulation selection? This is a very, very busy
> slide and I don\'t intend to walk you through it, but the point I
> really want to make here is that, unlike developing an airplane, you
> are not designing an airplane and then testing it, coming back and
> testing it and flying the plane, and eventually having the final
> product. Here the final product is really defined at Phase IIB because
> by that point you have clinical data and you have the dose response.
> Anything after that that affects the performance of the product is not
> permitted. So, you are really investing in engineering to be able to
> do your process development and skill to meet the criteria that were
> established on the product in Phase I and Phase II. That is a very,
> very different kind of challenge.
>
> What it does mean is that you have to put more effort up front in
> terms of understanding your product and understanding your formulation
> and understanding your excipients so that you actually have something
> in Phase IIB that is the basis of a good Phase III development.
>
> Traditionally, this has been somewhat of a black box and you can use
> this black box to represent anything. I am just talking about solid
> forms here. If you only do a limited number of experiments you may
> only find a couple of solid forms in this box but, in fact, if you now
> move to using some of the high throughput technologies that are
> available, if you cast a flood light on this, you can find all of the
> forms that are in here whether these are polymorphs or salts or
> hydrates, and you know you have much, much more information, and you
> can gain this very much earlier in the process with very much smaller
> amounts of material.
>
> Let me show you an example that I used at Merck to say that we really
> had to have pharmaceutical people work with the discovery people to
> pick a candidate for development. Here is a compound that came
> forward. It was an antibiotic. It had great solubility but it was sort
> of weakly crystalline, and since it was going to be injected we needed
> 10 mg/mL solubility. Once it came into development and the process
> chemists got their hands on it, it converted to this beautiful
> crystalline trihydrate but the solubility was now less than a mg/mL so
> that project was dead.
>
> So, I think this demonstrated to management that it really was
> important for people with pharmaceutical and process capabilities to
> be working with the discovery people to pick the best candidates to
> come forward.
>
> At the other end of the spectrum, ritonavir is the one that is
> represented here and lots of companies have had this problem. They
> just haven\'t had it with an AIDS drug after it was on the market. But
> this compound, of course, threw up a new polymorph after it had been
> on the market for a year and a half, with the result that it had to be
> withdrawn from the market for a short of period of time and be
> reformulated.
>
> Again, using modern techniques you can actually do this kind of
> screening for all the different forms. In fact, we have done some work
> along these lines. In fact, there are five different forms of
> ritonavir. This can be done with very, very small amounts of material
> and in a very, very short time. So, this is the kind of activity you
> can do to build this information into the development process.
>
> Here is another example. This is the marketed product here. You can
> actually make a salt form of this drug, which had never been made
> before. It is a lot more soluble and when you put that into animals
> you can see what happens, you get a much, much faster onset compared
> to the green line, which is the marketed product. So, if you are
> looking at, say, pain then onset is much more important. So, the
> choice of that salt would be a much better development candidate than
> the original choice of the compound that is on the market.
>
> On the other hand, as Ajaz pointed out, you might get side effects
> from this peak. So, you might actually have to develop a
> controlled-release form. The form you take into the controlled release
> might not be the same form you would use for immediate release. But
> knowing all of this information allows you to do a much better job of
> selecting the candidate and the formulation.
>
> Another example, here is the product that is on the market. As you
> increase the dose you increase the area under the curve but you
> increase it in a nonlinear way. If you change the form of the
> formulation of that product you can get it to perform in a totally
> linear way, with a bioavailability that is 2.5 greater than the
> marketed product.
>
> So, doing that kind of search at an early stage results in much better
> products. Let me use the same analogy again of the black box. The
> current norm is to poke into this black box a little bit and figure
> out what is happening in the process. Well, a much better way is to
> really shed the flood light on this box and understand the process in
> depth.
>
> So, the objectives of the pharmaceutical process development really
> are to provide a continuous link from these early phase
> characterizations of the materials to the final manufacturing process;
> to define the process based on unit operations approach; to have a
> road map for tracking success so that as you scale up and you have
> transfer and site transfer you really know what you are doing; and
> enable effective process monitoring and improvements after you are on
> the market.
>
> So, an initial design is really important to identify the parts of the
> process which are most susceptible to failure upon scale-up. If you
> identify those and work on those, then you are going to have a much
> better process. The way you can do that is to conceptualize the scale
> down of the final manufacturing process to the pilot plant and to the
> lab and to carry out experiments there that will then direct you as to
> what the critical parameters should be to monitor at full scale.
>
> In terms of process understanding, you really need to determine the
> fundamental process constraints and, where appropriate, you can
> utilize unit operations which are the most forgiving. So, if you have
> a choice of two different processes and one is much higher risk than
> the other in terms of its ability to be controlled, you are going to
> go for the one that is most forgiving. And, if you can show that to
> the agency, then you can demonstrate that there is a much lower risk
> with that particular product.
>
> Identify the underlying principles which control the process. In other
> words, avoid this black box analysis and really understand what is in
> the black box so that you can make much better decisions. Then,
> identify appropriate process parameters to monitor and to control.
> That is where the value of the process analytics comes in, which can
> be done on-line and in real time. That will then provide confidence
> about the process robustness and, again, make the argument to the
> agency that you know what you are doing.
>
> In terms of process optimization, it is really important to find the
> regions of the process parameters where the process is most stable,
> and then to design the process to what was in this regions. If I show
> this schematically, what I am doing here is reducing a multi-component
> system to two dimensions, and saying that within this space here, this
> is the region where the process is unstable and these are the targets
> we are going to shoot for, and these will be the basis for our
> specifications for the product.
>
> But in order to demonstrate process robustness you have to stress the
> range of the variables. As Ajaz said, you have to find out where your
> plateaux are. Again, what Garnet said, you have to include the range
> of materials because the material properties of the excipients are
> going to play a very important role. Also, the environmental
> conditions and process parameters, and if we think back to the famous
> old days, Ajaz, of working on site stability, that turned out to be
> the issue of site stability, the environmental conditions. It had
> nothing to do with the site itself; it was poor control of the
> environmental conditions. It took us a long time to convince the
> agency of that.
>
> So, once you have done the process robustness, now you can find the
> region where the process is, in fact, robust. This is where your
> target is but the process is robust in this region. I don\'t mean to
> imply it is the same parameters we are looking at here, but you have a
> set of parameters to define the robustness of the process.
>
> Then, by going through your process design you can have measurable
> quantitative endpoints, again using PAT; eliminate any dependence upon
> qualitative endpoints; evaluate how the process can respond to
> variations in process equipment performance and ran material
> characterizations; and then provide a continuous fingerprint of
> process performance. Again, this should not be a regulatory
> requirement. These should be parameters that the company tracks to
> monitor whether or not the process is still in control.
>
> Also provide hooks for future process development. So, plan into your
> development program the collection of these fingerprints that you can
> use for future comparisons when you change site, or when you modify
> the process, or you change the excipients. Design a validation
> protocol to collect similar fingerprints. So, your validation protocol
> should be designed based on the process that you are validating. I
> once met someone from validation who said that his job was to read all
> the reports of recent FDA inspections and to be able to answer every
> one of those questions when they came in to check the validation. Our
> development person said we are validating a particular process. What
> FDA did last week at Wyeth of Pfizer has nothing to do with it; we
> have to define the validation protocol that is relevant for this
> particular process.
>
> Then use these parameters in manufacturing to continually monitor the
> process, monitor its operation and its status. When you do that you
> have a subset of these parameters that you can monitor and this become
> the fingerprint region so that you can see whether the process is
> robust and prospectively identify drifts before your specifications
> start to go out of control.
>
> I am sorry I had to race through all of that. I hope I have managed to
> get at least some of it across. So, I will come back to my summary
> slide again. It really is a continuum of process development all the
> way from the definition of a new chemical entity, all the way through
> manufacturing.
>
> We need to fundamentally characterize our new chemical entities and
> our excipients and process development as a consequence, and that will
> lead to meaningful control points.
>
> The success of any scale up, or tech transfer, or process change
> should be judged by rational comparison of meaningful process
> parameters that we have defined during the development stage. And,
> this idea of having a fingerprint of parameters, that are not
> regulatory specifications, that can be used to monitor process
> robustness, and then to flag issues before the process goes out of
> control. But that will only work if, in fact, FDA does not regard
> those as regulatory specifications. In my experience, it has not been
> the FDA that has been the problem, it has been the regulators in the
> company because they are afraid to have those specifications around
> because they think the FDA is going to come in and immediately assume
> that they are regulatory specifications. So, we have to change culture
> and mind set both within the agency and within the company.
>
> I wrote a few notes down here as I was listening to all the other
> presentations that were being made. I think the implication for the
> FDA is that we don\'t have a box-checking mentality, as it were, and
> we are talking about trying to define\--I think you said you couldn\'t
> see how the FDA would not have some guidelines for the product. But I
> think the guidelines have to be in the mind set of the regulators, not
> saying exactly how processes should be developed but a mind set of how
> to look at the development reports that the pharmaceutical companies
> send in so they can understand what those reports are about and then
> interpret them, not just a box to check off.
>
> I think Ajaz\' suggestion of starting with post-approval changes
> probably makes sense but I certainly would hope we would not end up
> there. I would hope that companies, in fact, are doing this all along
> but it might be easier to start there at least to get the message
> across that the cultures are changing and, in fact, this is a viable
> way to proceed. So, thank you very much for your attention.
>
> DR. BOEHLERT: Thank you, Colin. Questions? Comments?
>
> DR. GOLD: Colin, I don\'t want to beat a dead horse but I don\'t know
> of any initiative that FDA has taken where the industry has not said,
> well, please explain what you need. These are your requirements,
> please elaborate on what these requirements are. For example, if we
> expect analysis of variance to be done, statistical design, or
> whatever, certainly we need to look for interactions as well as main
> effects, do we not? Doesn\'t this have to get across to the
> practitioners?
>
> DR. GARDNER: I saw people use these kind of approaches when I went
> into industry at first and, you know, I have never been convinced that
> they are used correctly in the industry. I think people, you know,
> build these models, very many of them are linear, and they put in a
> bunch of parameters but often they don\'t put in the critical
> parameters. In general people have not used a process engineering
> approach to look at the process and understand the fundamentals of the
> process, and then you can define the process. I don\'t think the FDA
> should be defining the process for us. The pharmaceutical companies
> should be defining that and telling the agency what to expect of this
> process and what the parameters are that they will control. I think
> what the FDA has to say is that this has to be a mind set, that this
> is the kind of approach we are going to expect from you but we are not
> going to tell you what to do. It is your product; it is your process.
>
> DR. GOLD: Yes, I am not trying to imply that the agency should define
> the variables that are going to be applicable to any particular dosage
> formulation, but I am thinking that they will need to provide general
> guidance for how to develop these experimental programs. I may be
> wrong.
>
> DR. GARDNER: I think that is destroying innovation. I think the
> innovations come from the companies and they should be bringing
> forward concepts of how they develop they processes.
>
> DR. GOLD: In a perfect world, Colin, I think you are right.
>
> DR. GARDNER: Well, we disagree then.
>
> DR. BOEHLERT: Nozer?
>
> DR. SINGPURWALLA: Well, I was hoping not to ask any questions\--
>
> \[Laughter\]
>
> \--but this brings me in as an outsider who knows something about
> design of experiments and analysis of variance. Did I hear you say
> that the analysis of variance and the design of experiments that are
> done by industry don\'t take into account the true variables, and just
> takes canned variables into account?
>
> DR. GARDNER: I think many people have done that in the past.
>
> DR. SINGPURWALLA: Then the industry is lagging behind in terms of
> Baysian ideas because the Baysian ideas would essentially allow you to
> do it.
>
> DR. GARDNER: I am not disagreeing with you. I mean, I think it is
> changing but it certainly was like that 15 years ago.
>
> DR. SINGPURWALLA: Well, it is not that; I think the point is this, it
> is a different philosophy and a different paradigm of doing
> experimental design. The kind of old paradigm does exactly what you
> are saying. The kind of paradigm that you would like to see is now
> allowed by certain new methodologies, and what you are saying is that
> industry has not adapted to new methodologies.
>
> DR. GARDNER: I think that is about what I am saying.
>
> DR. SINGPURWALLA: Then it is the function of a committee like this to
> draw attention to that.
>
> DR. GARDNER: Right.
>
> DR. SHEK: I am a little concerned about generalization. I don\'t think
> it is generally correct\--you know, your experience, my experience is
> different; things are changing. Many companies have, you know, process
> engineers. My personal thought is you cannot separate the formulation
> from the process. Both things have to happen at the same time. You
> want to get people involved, process engineers, as you select your
> formulation because otherwise you put yourself in a box and you try to
> get a formulation that you cannot process of vice versa.
>
> DR. GARDNER: Absolutely.
>
> DR. SHEK: But it is true, like in any other business, some people are
> doing better in experimental design and some are not as good, but the
> concept of experimental design and training people\--it is happening
> in industry. At least that is my experience. I want to make sure that
> we don\'t have a generalization.
>
> DR. GARDNER: I probably have more process engineers than any other
> company. You know, our whole organization was chemists, process
> engineers and material scientists so I think we started that trend.
> So, I hope, you know, we understood what we were doing there. I still
> think that originally there was not that focus on trying to really
> understand the fundamentals but, rather, modeling around very, very
> standard parameters.
>
> DR. BOEHLERT: Ajaz?
>
> DR. HUSSAIN: Colin, you and even Diana were discussing the fingerprint
> concept or a signature concept, and that being used as a means of
> comparing and evaluating changes, and so forth. I think that is a very
> intriguing thing. I think that is a very viable option, and that not
> being a regulatory aspect, we agree with that. What challenge do you
> think there are in that mode?
>
> DR. GARDNER: Well, I think the challenges would be to identify what
> are the parameters that you are going to select to do that, and that
> involves\--I mean, the way I would see that is starting off in the
> development phase, conducting a lot of collection of data as you go
> through the elements of formulation design. I agree with you,
> formulation design and process development are indistinguishable but
> you are starting off usually with a few grams of material when you are
> starting to define the formulation and then you go into tens of kilos,
> hundreds of kilos. But you should be collecting that information as
> you go along and basically be building a database of parameters that
> you can measure. Then, as you scale the process up and you go into
> your Phase III studies, you will probably select a subset of those
> that you could continue to monitor. Some of those parameters will be
> selected eventually as your end specifications whether they be on the
> end product or specifications for intermediate steps. But you will
> still maintain a significant number of parameters that you are
> measuring. You will use a subset of those to do your tech transfer
> into the manufacturing and then you will use a subset of those perhaps
> to be these fingerprints that you continue to monitor, and they are
> the ones that you have shown are most significant in terms of
> monitoring when the process is going out of control.
>
> That gets back to the question I think you asked me last time, you
> know, the fact that you have a lot more variable in the excipients
> that you have in your API and, therefore, how can you control for
> that? I think you control for that by building that into the process.
>
> DR. SHEK: Yes, I think I agree that is very correct. For example, if
> you take a granulation process, today we have an endpoint which
> companies are using which is like power consumption, which really
> doesn\'t tell you anything about what you have inside. You know, you
> see an effect. Now, with looking with various others, you can maybe
> have some measurements which will tell you about the particle size,
> tell you how much water stays there. Then you can build some kind of a
> signature which, hopefully, will stop the process\--
>
> DR. GARDNER: What you just said about power consumption though was
> developed because up until that point, as Ajaz said earlier, it was
> time, and time was fixed and that was part of the NDA specification,
> and if you changed the time there was a difference in the process. As
> you change your excipients, your API, you have to change the time if
> you are going to make the same product. So, I agree with you, power
> consumption was one step along the way. As new technologies come
> forward we should definitely encourage people to try and use those. I
> mean, where PQRI can come in is by helping to define what the value of
> those measurements is, not for any one particular product but just in
> general.
>
> DR. HUSSAIN: In that concept is sort of the learning aspect in a
> sense, and actually collecting more information that is in your batch
> record. I think that is a major concern because people don\'t want to
> do that but, at the same time, I think the dilemma we have is when you
> have a specification you often don\'t get to the root cause because
> you are not measuring the right things that will get you to the root
> cause. So, that becomes a part of the continuous improvement.
>
> DR. GARDNER: And another thing\--I know there are differences in
> different companies but during the development part of the process you
> make a lot of batches for clinical supplies. Those absolutely should
> be part of your development program. I think to have a separate group
> that makes clinical supplies from the group that is doing development
> is actually a very, very big mistake because the amount of experience
> you get in making clinical supplies and building all of that into your
> database is just a huge advantage. If you think about how much time
> you might spend just developing the product and then maybe a hundred
> batches or so made for clinical supplies, if you don\'t capture that
> information you are losing an immense amount of knowledge.
>
> DR. BOEHLERT: Thank you. Our next speaker is Greg Guyer.
>
> **GMP**
>
> DR. GUYER: Well, the good news is I am the last speaker of the day.
> The bad news is I guess I am the last speaker of the day! I guess I
> would start off by saying what I am going to show you is obviously not
> a baked cake by any stretch of the imagination, but a lot of the
> things that you are challenging yourself on is exactly what we, in the
> industry, have been challenging ourselves on, how to actually get to a
> quantitative model that could be used conceptually in a way that would
> show the bridge between the body of evidence in your manufacturing
> science, and then somehow equate that to a level of risk. That has
> probably been one of the most significant challenges that we have had.
>
> We haven\'t had any challenges with a lot of the conceptual things,
> and I think the whole concept of quality by design\--again, no one
> disagrees with the concept of quality by design. It just makes good
> sense, business sense, regulatory sense. So, we haven\'t really
> challenged ourselves. Actually, we don\'t even have a definition for
> quality by design. It is just what we call all of the things that we
> have been working on.
>
> What I want to try to do is to maybe start to give you some ideas
> about how we might start to equate this body of evidence in terms of
> manufacturing science to risk. What I want to do is kind of pick some
> pieces out of different presentations that you have seen because you
> have seen a lot of different information and, again, I don\'t see any
> real differences in the objectives of what people want to accomplish.
> But there are different ways to get to that. So, I am going to pick
> pieces of what Gerry presented. I am also going to pick pieces of what
> Rob presented from a risk management standpoint and try to start to
> integrate well-validated risk models. Also, understanding better
> manufacturing science and using some of the core parameters that we
> talked about, and Gerry talked about earlier, as well as G.K. did, to
> kind of start to pull those together and see if there is some way in
> which at the end of this we can at least get some common systematic
> framework whereby this information could be collected, could be
> presented and could be agreed upon.
>
> Our goal is not to train either the reviewers or the investigators or
> even the industry on 40 different models of how to do this. It would
> be nice if we could use a common model. It doesn\'t mean we have the
> same collection of data, to Colin\'s point, but at least collected in
> such a framework that would be consistently applied.
>
> So, I want to talk about one way or one suggestion we might use that
> is a validated model and, again, this is not a baked cake but it is
> more of a conceptual presentation.
>
> So, let\'s start with a definition of risk management. This came from
> Australia. I apologize, I have no references here but this has been an
> evolving presentation and evolving thought because I am not a risk
> management expert but I am understanding that we all make risk-based
> decisions every day; we are just not aware of them. A lot of the
> things I am going to tell you are, you know, motherhood and apple pie,
> things that we all know. What I am trying to do is put those things
> that we use as attributes of risk into a quantitative model.
>
> So, we look at risk management. It is really a process consisting of
> well-defined steps which, when taken in sequence, support better
> decision-making by contributing to a greater insight into the risks
> and their impacts. That is a lot of jargon but basically what it is
> saying is that by using a very well-defined common process, if it is
> done in the right way, you can actually come to a set of decision
> elements which are predicated not only on science but also on the
> elements of risk.
>
> So, let me talk to you about what that might look like. If I get back
> to the famous model that Gerry presented, again, we all understand
> there is an inverse relationship of management science to risk. I
> don\'t think that is a debate. But the question really is how do we
> start to equate these two concepts together.
>
> So, when we start to think of developing relationship between these
> two, what does that algorithm look like? We talked about it earlier.
> Can you solve, can you create an algorithm? You know, I would argue
> that you have to. It is not a question of if, it is a question of how
> do you because it is so critical to what this whole initiative is
> about, in my mind, that we have to figure out a way to extract volume
> of information that a pharmaceutical company will develop, extract
> what is important so that when it is received by the agency they can
> understand that information; they can evaluate that information and
> they can understand the decision-making that was made in a very
> consistent and robust way. The bottom one is how can it be solved
> consistently and systematically using validated models?
>
> If we look at those primary attributes that we talked about earlier in
> terms of manufacturing science, and I am not going to argue that these
> are the only five but there are five so we will start there and I
> think some of the concepts we will talk about are equally applicable
> if we want to broaden this, constrict it, whatever.
>
> But we talked about process knowledge. We talked about process
> capability. We have talked about manufacturing technology. We have
> talked about process control technology. This is where PAT comes in.
> We have talked about quality systems infrastructure.
>
> What I want to do is to think about if we use those five main
> attributes, just for argument\'s sake, say those are the five main
> attributes of defining your body of management science or where you
> are in terms of your value of manufacturing science, I want to do is
> go through each individually and talk about how we might look at those
> a little bit more quantitatively than we might have historically.
> Then, at the end I don\'t have an algorithm for you. I don\'t have an
> equation that fits in but I think that is what a group of people
> should do. I think there is enough in the outline here that you might
> be able to think about how we move from a very conceptual state to a
> very quantitative state.
>
> So, let\'s talk about process knowledge. I apologize that it is kind
> of hard to read, but since I am not a risk management expert I am
> going to have to use some terminology. It was great that Rob went
> before me because he really explained to you what failure mode effect
> analysis is and he tried to do it in a short way. Some of you, I know,
> are more aware of it than others. We and Merck have had quite a bit of
> experience with it, not necessarily in this realm but in a lot of
> other areas. But I think that the concepts there which are clearly
> identifying failure modes, to G.K.\'s point earlier, basically use a
> systematic way of examining all the ways in which a failure can occur
> within a process.
>
> When you are thinking about this, this is higher level than probably
> what is in your mind right now. It really is looking at the process
> steps and trying to look at each process step and the failure modes
> beneath them, and then identify all the potential root causes of each
> failure. So, anything that can go wrong with that process step,
> identify that and then what are the controls that you have in place
> and what would be the root causes for those failures?
>
> For each failure you can estimate what the effect would be on the
> whole system. In the case we are talking about here, it is the effect
> on product quality. My definition of product quality is probably a
> little different from Janet\'s this morning because I start from a
> basic understanding that in process development and in the clinical
> programs we derive a set of experience with a range of parameters,
> excipients, different manufacturers of excipients. We have different
> parameters that we understand. There is a whole body of information
> that is going on while that clinical program is developed.
>
> The output of that is a synthesis of the development program, from a
> pharmaceutical research and development standpoint, to look at the
> parameters and to define what are the critical aspects that could
> impact the quality of your product. In doing that you do have a link
> to the clinical program, and that is the basis under which I make the
> supposition that the specifications that we have today are more than
> what we need. I don\'t know of specifications that could be challenged
> to say that we don\'t have sufficient specifications to say products
> are safe and efficacious. I would argue that they are. So, I would say
> that this argument is about constriction rather than growing it and
> that we are measuring the wrong things.
>
> In some cases I agree a lot with Colin in that we have jumped to some
> endpoint testing because that is what FDA expects. So, although we
> understand from a fingerprint standpoint what are the elements within
> the process that are important and, as you see, we go back to those
> over and over again when we make process changes and we make
> validations at new sites, we go back to those. But we all stand by the
> set of, you know, 12 tests that everyone has to do because that is
> just what FDA wants. So, there is a change that has to happen in the
> mentality for us to start to go to something. You know, those
> fingerprint items that we look at, those are really critical
> parameters and these tests, we know they will never change as long as
> these fingerprint items don\'t change.
>
> That is really the concept that Ajaz and G.K. and others have been
> talking about. A lot of that information is already there. It is now
> starting to try to leverage it in a little bit different way. So, once
> you have done this, this would include how often that failure could
> occur in the specific step; the severity of the failure, the impact of
> that failure; and the ability to detect it. So, if you get that
> failure, are you able to detect it readily so you can mitigate it?
>
> This is really the start in a way of defining critical quality
> attributes and parameters. If you think about it, if you look at your
> failure modes you start to understand in your process what is critical
> to defining the quality. Now, that doesn\'t necessarily mean all of
> those parameters or attributes are critical, and that is something I
> would like to discuss a little bit with you just conceptually.
>
> I can say that internally at Merck we have spent a lot of time
> wrestling with this concept of critical quality attributes and
> critical process parameters. But I would say FMEA gives us the first
> step in understanding what are the critical process steps. But
> deciding on whether something is a critical quality attribute or
> deciding whether it is a critical quality parameter will depend on
> some other variables.
>
> So, let\'s go through these manufacturing science attributes and at
> the end maybe you can see how all these attributes are interlinked;
> they aren\'t independent variables and they aren\'t independent
> assessments. They actually are very much linked. But I am going to
> talk about how that might be done in a way that might give you the
> right solution.
>
> To me, once you have used FMEA you can start to define potential
> critical quality attributes and parameters. But then you define the
> process capability to meet those accepted ranges. In other words, FMEA
> would say these are the ranges under which you can run your process
> and you won\'t have impact on quality. So, that is step one.
>
> Step two would be what is your process\' ability to continue to meet
> those ranges? So, that is step two. Then, if process capability is
> well within acceptable ranges, then additional risk mitigation may not
> be necessary. That might not be a place, even though it is a critical
> step\--let me give you an example because we have this all the time.
> It is easier to do it in the API world.
>
> If you think about it, you can almost drive any parameter to failure.
> I mean, think about pH when you are developing a chemical. Even though
> the reaction happens at a pH of 3, if you tried to do that reaction at
> a pH of 9 you are not going to get the chemical moiety you want. Well,
> that makes sense. That doesn\'t necessarily mean that is a critical
> step. If you can control your pH between 2.95 and 3.05 and you can
> show process capability to say whether it is 1.5 to 4.0 you get the
> same result, to me, that is not a critical process parameter because
> you can drive a truck through it and not screw up your process. So, to
> me, that doesn\'t tell my manufacturing people that you need to focus
> on that. You absolutely need to make sure it is always the ranges of
> 2.95 to 3.05. But that wouldn\'t drive, in my mind, necessarily to
> make it a critical process parameter.
>
> This is where the definition in Q7A has frustrated us. It states
> something along the lines of any parameter that could impact the
> product quality. Well, almost any parameter can impact product quality
> at some range. The question is what is its relationship to your
> ability to continue to meet that.
>
> I think the other thing is that when you think about the manufacturing
> technology you have to have the right technology to be able to control
> your process in a way to demonstrate you can control within that
> acceptable range reproducibly.
>
> So, one size doesn\'t fit all. It doesn\'t say you have to go to
> barrier technology. However, in some conditions you may have to go
> there because of the control necessary. So, that is why it is not a
> one size fits all but, again, the FMEA process sends you through a
> thought process that will make you ask those questions of yourself and
> start to define what is really critical.
>
> So, if the process capability cannot ensure process reliability within
> those acceptable ranges, I go back to my example and say your
> technology can only control between 2.5 and 3.5 and you know that at
> 2.4 and at 3.4 you start to get some changes in terms of whether it is
> the polymorphic form or some other impurity. If you start to
> understand that, then I think that is where you need to employ risk
> mitigation strategies and either look at a new technique, learn how to
> better control it, there is a whole host of things you can do. But
> what FMEA does is it drives you on a path to make decisions and
> understand what is important about your process, and then that is
> where you focus.
>
> So, that is kind of the way we have used it, again, in different areas
> but it really helps you get through the morass and start to focus
> really on what is critical.
>
> The next concept is process control technology. I would say that is a
> very important one if you are in that bottom bucket I just talked
> about. That is where your process capability cannot reliably keep you
> within those acceptable ranges. Then, I see PAT as a potential risk
> mitigation strategy which is considered when the critical attributes
> and parameters cannot be reliably ensured in the process to meet those
> acceptable ranges.
>
> That is one way. Again, when I think about process analytic
> technology, it is where you want value real-time data. Obviously, you
> want to focus it where there is a risk to not determining quality of
> your product, but you want to know absolutely that you are maintaining
> it within a range that is acceptable. That is where you have to deal
> with your process capability.
>
> This may mean that in some cases for new products you have to look at
> the technology you chose. You might even have to change the way you go
> about it if you can do these studies early enough. You might even
> change the technology you would use to make sure you can reliably stay
> within those ranges.
>
> Lastly, and very important, especially to people like Diana, is the
> quality systems infrastructure. It is a different attribute mainly
> than what I have talked about, but I look at it as the ability for a
> plant operation to reliably make any process you give them. If you
> think about process and product development, there is a series of
> studies which deliver a process to manufacturing that, hopefully, can
> reliably meet all the predetermined specifications and ranges and all
> of those fingerprint aspects that Colin also talked about.
>
> But that all has to go into a facility that has a quality systems
> infrastructure that can reliably make a product. In other words, you
> are trying to dampen the operator error input into the equation as you
> are raising your process capability. So, in other words, you are
> trying to control those variables better. To me, what quality systems
> infrastructure has done, and has really done this especially for us at
> Merck, is really demonstrate the ability for whatever process comes to
> be able to take out of the equation, to a large extent, the
> interdependencies on material controls, on product release, on
> manufacturing systems. If you have a good fundamental quality system
> it will set you up significantly to reduce the type of deviations and
> atypicals and things that you have that sometimes are deemed to be
> process problems when, in actuality, they don\'t have anything to do
> with the process. They are the way in which the facility actually
> operates your process.
>
> So, to me, those first four are very important together. The last one
> is a risk determination which really can be made by FDA. I mean, their
> inspections today are totally, or in most cases, quality systems
> related and really give us a good assessment about how good we
> integrate quality systems in the decision that we make. For the most
> part, I think FDA has a pretty good idea about the quality systems on
> a plant basis when they go in. I think this is something we have to
> work at to try to quantitatively let the agency decide on how that
> fits into the algorithm but my point here is that it is a critical
> part of the algorithm because that is a critical part of risk. The
> ability for us to control our operators, and our chemists, and
> everyone in a way that can allow us to manufacture processes that are
> reliable and robust is a critical ingredient to this risk equation at
> the far end.
>
> I think that is it\--no, the last point I wanted to make is if you
> think about it, risk should equal some aggregate evaluation. It is not
> additive, but some aggregate evaluation of the elements above as
> determined by the manufacturer, except for the last piece which is
> something I think we do collaboratively with FDA.
>
> But what I have tried to do is just give you some idea about how we
> might put all that data together in a very constructive way to start
> to weed through the stuff that is not as important. Again, FMEA is a
> very validated methodology.
>
> I can tell you, although I hadn\'t planned to, we have used it in a
> process that is not a manufacturing process but we have used it in a
> quality system process that had some defects and we were not happy
> with it. It is a very cross-country process. You might guess what that
> might be. But in applying FMEA we went from a defect level that was in
> our minds unacceptable to better than six-sigma. It was a methodology
> that wasn\'t over-tedious. It took us a couple of months to actually
> do this analysis. But you actually go through the critical steps and
> then what it tells you to do is where you focus your energy. How do
> you make sure that you don\'t have those defects get on the market?
> And we did that, and we don\'t see those defects anymore. So, it is a
> very robust methodology. I know Rob went through some detail of it,
> but it can be used at a very high level to start to weed through some
> of this.
>
> So, it is one approach we might want to think about when we start
> trying to collect this information in a way that is understandable.
> The other thing that I am concerned about, my ten years at FDA told me
> I don\'t want companies submitting 10,000 pages of some development
> activities they have been doing for the last 20 years. I would like to
> see it in some way where I can trust the methodology that was used
> that get me to the parts that are important for me. How do I know that
> you have done all the right steps, and how did you come to the
> conclusions that these are the critical quality attributes? FMEA does
> that for me. It gets you through a very methodical process that can
> you get to what is important about your process.
>
> Obviously, there are a lot of studies and a lot of infrastructure that
> has to be developed for you to use it effectively, and I think that
> that has evolved quite a bit. Even in my ten years at Merck I have
> seen that evolve quite a bit and I think it is time to start putting
> those kind of concepts together. Then I think that will create a nice
> algorithm, for lack of a better term, for FDA to start to assess.
>
> I think it might be better to start on the post-approval area because
> I think FMEA originally was set up after the fact. It works very well
> that way. So, to Ajaz; point, I think that is a great place to start
> but the concepts are very applicable in development as well; it is
> just not quite as robust yet. So, that is my presentation.
>
> DR. BOEHLERT: Thank you. Questions or comments?
>
> DR. GOLD: May I make one comment? Greg, one of the advantages of
> having a definition of the critical parameters, critical variables,
> however we want to express it, is that perhaps that leads you to the
> consideration of redundant instrumentation in the type of example you
> gave because, should you have a calibration failure of that
> instrument, your process is going to go off.
>
> DR. GUYER: Correct.
>
> DR. GOLD: So, there are some advantages to do this and they are not to
> be under-evaluated.
>
> DR. GUYER: Dan, if I take that example one step further, it was not a
> regulatory process. So, what it allowed us to do is to stop doing ten
> things and start doing three things, and those three things were the
> most critical pieces that we could control and now we don\'t have the
> defects. We were doing a shotgun approach; we were doing ten different
> and everyone thought they were accountable and no one was accountable.
> We were doing all this documentation, but the value at the end of the
> day was lost because people weren\'t focused on the right thing.
>
> So, I think your point is very well taken but it is an example of
> where you can move to that state very easily because it wasn\'t a
> regulatory process. It was one that we owned. Although the output of
> it is a regulated process, the design of it was not.
>
> DR. GOLD: Good presentation, Greg.
>
> DR. GUYER: Thanks, Dan.
>
> DR. BOEHLERT: Other questions? If not, thank you very much.
>
> DR. GUYER: Thank you.
>
> DR. BOEHLERT: We are getting towards the end of the day but we have
> one last topic to address. Ajaz, did you want to say something about
> it?
>
> DR. HUSSAIN: Sure. I think the thought process was, in a sense,
> quality by design and process understanding I think. In many ways you
> achieve quality by design through understanding, at least to a
> significant level, a fundamental level, the attributes that sort of
> lead to your quality, and so forth. So, process understand is the key
> framework.
>
> Post-approval change is a risk scenario because clearly, I think, we
> recognize that there are certain attributes when change will improve a
> product. But change brings risk. And, there are examples, clinical
> examples of a minor change leading to significant safety issues, and
> so forth. So, change is a risk scenario.
>
> I think the two concepts come together quite nicely and in our
> statute, Food and Drug Modernization Act, there are three risk
> categories that sort of came up, you know, the level of scrutiny that
> we apply to a changed scenario. For example, any change that requires
> a change in specification, the statutes require that to be a
> prior-approval supplement, and so forth. Any change that necessitates
> a clinical study or a bio study automatically is a prior-approval
> supplement type of a change.
>
> So, the concept of risk and the concept of process understanding
> essentially come together quite nicely in the post-approval world.
> What I presented to you, and I think that is how we defined it in the
> draft PAT guidance also is that within a quality system and for a
> given process or for a given product, the risk associated should be
> inversely proportionate to the level of process understanding. The
> process understanding of relevance will come on the basis of what type
> of changes you are likely to make and why you are making those
> changes, more so in what type of changes are necessary.
>
> In the post-approval world, and I think as part of continuous
> improvement, fine-tuning of a manufacturing process is often necessary
> and new technology has to come in, as well as changes in equipment,
> changes in site of manufacture. These are all necessary changes that
> need to occur. A product that is experiencing a lot of difficulties in
> manufacturing has to be changed too to improve that process.
>
> So, from that perspective, the two concepts come together and,
> therefore, I was hoping you would give us some feedback on the
> proposal I had in place, at least to move forward in the post-approval
> world to bring some more concrete steps that we can take to achieve
> some of these objectives.
>
> DR. BOEHLERT: I have two announcements I want to make before we get
> into the discussion, just so I don\'t forget them. First, the next
> meeting of this committee will be January 13th and 14th. So, if your
> calendars aren\'t marked, please do so.
>
> The other has to do with an announcement about another committee
> meeting. The Drug Safety and Risk Management Advisory Committee
> meeting scheduled for Thursday and Friday, September 18th and 19th has
> been postponed. So, for anybody in the room who might be interested in
> that meeting, it has been postponed. This is risk management I think
> at the highest level. I just wanted to get those off the table so I
> wouldn\'t forget them at the end of the day.
>
> Ajaz, I think you are looking for us to give you some feedback,
> relationship between quality by design and risk management.
>
> DR. HUSSAIN: Right. I think in this context, using the word process
> understanding sort of as a means for quality by design would be a way
> of sort of describing that. And process understanding sort of comes
> from different levels too. In the PAT guidance we define high level of
> process understanding is when you can actually predict the impact of a
> change. I think Greg sort of was getting to some of that in his
> example of PAT. If you have understood that and how well you have
> controlled that, then that leads to a risk assessment. So, any change
> associated that is necessary can be judged in that light.
>
> There are two things that occur. One is the type of filing that will
> be necessary, whether it is a change that can be managed within the
> company\'s quality system and reported in an annual report. So, that
> is one aspect. The other aspect also is what sort of test is necessary
> to qualify that. For example, Colin brought up the issue of site
> specific stability. That was a very controversial and heated
> discussion between us and industry. I think what we were expressing
> there is the elements of uncertainty that come because of the
> materials not being characterized and the physical aspects. So, I
> think that debate sort of occurred that way. How do we sort of move
> forward with a better level of process understanding to provide the
> least burdensome change management processes?
>
> DR. BOEHLERT: Would somebody like to initiate the discussion? Please,
> Gary.
>
> DR. HOLLENBECK: Ajaz, I have all sorts of risk things going through my
> head. What you really would like to do first I think is find a way to
> place things at those three levels. Is that the focus initially?
>
> DR. HUSSAIN: Well, I am thinking more in terms of a custom approach in
> a sense. If you look at the scale-up and post-approval changes
> guideline, I think clearly that was a step forward but, yet, I think
> the criticism there is that it is so conservative. I think what I have
> argued is based on the information that we gather through our
> research, there is a limit to generalization. Flexibility can come
> when a company can provide a level of process understanding and
> quality by design knowledge to sort of justify other changes. So, this
> could be as part of several options, as part of the comparability
> protocol. Although I have heard criticism that it is too narrow and
> too restricted, but I think the comparability protocol is flexible
> enough to allow that to happen. I think that was one of the intentions
> that we had, that people could use the comparability protocol to share
> this knowledge to justify change or justify a number of expected
> changes that could occur.
>
> For example, I won\'t to be very specific but we actually have a
> couple of good examples in the small molecule also where the product
> is fairly unique. We don\'t have a change guidance for that in terms
> of SUPAC, and the company said we will need to make these changes as
> we development, scale up and then produce this. So, this is our
> knowledge. These are the variables that we have assessed. Based on
> this information, we think this is high risk and we would like to
> report this in this way, and this is how we will qualify that, and so
> forth. So, it was a very novel proposal. The unfortunate thing is it
> came to us two years ago and we were not ready for it. We want to be
> ready for it next time.
>
> To help the committee, I would like to suggest this, from the
> perspective of reducing uncertainty, the fear, we will be working
> diligently in sort of trying to identify approaches to assess that
> information. As I mentioned to you, we have invited Ken Morris to come
> in and work with our chemistry leadership to sort of brainstorm and
> sort of identify a strategy for asking the right questions. That
> process is already starting.
>
> The second approach is in a sense ICH P.2 activities will get started
> in November. There are two aspects that we have requested and that I
> think we have agreed on. In the P.2 concept paper the activities will
> incorporate two elements, one element being quality by design. So,
> that group is going to address some of those challenges. The second
> element is risk. The risk aspect will be run as a parallel group to
> the P. 2 pharmaceutical development expert working group. Greg will
> chair that and John Barrett is going to chair the P.2 group. Diana and
> others will be part of the expert group working with that. So, that
> activity is already starting in November.
>
> What my proposal is, and I would like feedback from you, to move in
> parallel here. We are initiating the training aspects that will help
> us ask the right question. Now, there are proposals that we can take
> some of this in the PQRI world and actually start developing very
> focused activities. For example, one aspect could be definition of
> critical elements, and so forth. So, that is one element.
>
> The third aspect, which I really need your help on, is from a
> regulatory perspective, the comparability protocol, what are the
> challenges possibly with that? That is one element. Should we consider
> a separate guidance, it could be custom SUPAC or make your own SUPAC.
> It would not be a very extensive guidance. It would be more of a
> framework which sort of either becomes an appendix to a comparability
> protocol to expand its scope, or it becomes part of the other SUPAC
> guidances that we have to update anyway. So, there are many options.
> What would be the most useful from your perspective?
>
> DR. LAYLOFF: Do you envision like a template, product type template
> for identifying elements and the scope of the elements?
>
> DR. HUSSAIN: No. The PAT guidance is the framework and you will start
> seeing more general guidances rather than prescriptive guidances. The
> key element is consistency and in the guidances for the last ten years
> we have addressed the consistency issue. I think we can approach
> consistency issues from a training perspective and sort of creating
> procedures for assessment. That is the approach.
>
> In many of these aspects, when your goal is not to interfere and sort
> of have unintended consequences\--for example, with the PAT guidance
> we tried hard not to even have the work NIR in that. We did a few
> examples here and there because if we elaborate on that everybody will
> jump to that whether it works for the system or not, and we don\'t
> want that to happen. So.
>
> DR. LAYLOFF: I was thinking of leaving it more open, like you identify
> the critical parts and the fingerprint sensing, or something like
> that. You don\'t even want to go that far?
>
> DR. HUSSAIN: No.
>
> DR. LAYLOFF: Then I have difficulty, you are going to have a
> hodge-podge stream coming and, from a regulatory point of view, what
> do you look at, what elements do you look at?
>
> DR. HUSSAIN: Again, the elements that we need to look at\--one aspect
> is predictability, if you have understood and if you have the ability
> to predict and describe that change. For example, one approach could
> be what Colin provided, a fingerprint approach and areas of maps of
> the system which says this is a critical region and this is under
> various controls. So, the flexibility has to come and the suggestions
> have to come from industry. So.
>
> DR. SHEK: I believe we are talking about topic number two,
> relationship between quality by design and risk based. Is that the
> theme of this?
>
> DR. HUSSAIN: Yes.
>
> DR. SHEK: Okay, the question is what is the relationship between the
> two. Well, to me, at least to my understanding, good quality decreases
> risk. And good quality is a responsibility of the industry. Ensuring
> risk or scrutiny is a regulatory function. So, the relationship of one
> feeds to the other. However, the former is the responsibility of the
> industry and the latter is the responsibility of the regulator. So,
> that is the relationship. If that is not the case, then why even have
> a relationship?
>
> DR. HUSSAIN: No, that is the case. I think what we want to find is
> better ways to use the knowledge that drives quality to say how do we
> take steps, or what questions do we ask that actually lead to risk
> reduction and not lead to burden or constraints that lead to, say,
> lack of innovation, lack of improvement, and so forth. That is the
> basic theme.
>
> DR. SHEK: So, as a corollary to this, if the industry came and said
> that we have excellent quality and you were satisfied with it, then
> there is no need for you to do risk because good quality minimizes
> risk, and if you are satisfied with good quality then the question of
> evaluating risks is moot because even if the quality is excellent,
> certain inherent risks cannot be removed. For example, open heart
> surgery is an example. You could have an excellent surgeon but there
> is only so much the surgeon can do. There is a risk obviously of
> something going wrong. So, that is the answer.
>
> DR. HUSSAIN: True but, no, that is not the answer because even if you
> take the example of surgical procedures, unless the surgical
> procedures and techniques improve or the training is adequate in
> different hospital centers, you see different rates, and so forth. So,
> I think from a public health perspective you really have to keep an
> eye on is there an acceptable risk. Everything has risk. Therefore,
> for example, on the inspection side the quality system, is it
> adequately managed to get you to make sure the risks are minimum, is
> one aspect. But that is not what we are talking about here. If you
> understand the regulatory part of managing changes, if there is a
> change needed to improve a process, it is not done today. And, the
> fear of a change changes risk, but innovation is change and
> improvement is change. So, we have to reconcile some of the
> dichotomous and opposing forces that lead to that and try to find a
> better way to arrive at a least burdensome pathway that is shown by
> the level of process understanding. So.
>
> MR. FAMULARE: There are two concepts in terms of the regulator trying
> to evaluate that risk. Ajaz touched upon it in the hospital setting
> where they advertise their success rates. You know, is that the way to
> tell whoever regulates hospitals how to deal with them in terms of the
> level of scrutiny?
>
> If you go back to the successful example that Greg used in his talk,
> it was something that is not subject to regulatory scrutiny and Greg
> seemed to attribute that to some of their success in using that and
> making the change and challenging that.
>
> So, going back to the regulator quality paradigm, it is still leaves
> for us the open question, in a non-prescriptive way as Ajaz has been
> saying, how could we assess that quality or level of risk in such a
> way that allows for changes? For example, just the one example that
> Greg described?
>
> DR. HOLLENBECK: I am getting closer here. I think if we flash back to
> SUPAC, for instance, from a risk approach there that the agency was
> willing to take based on therapeutic index, solubility and
> permeability of a drug, so what you are talking about now is a
> different paradigm. You are talking about a risk assessment strategy
> based on process control and the kinds of attributes that are listed
> on Greg\'s last slide. Is that right?
>
> DR. HUSSAIN: Actually, this builds on the previous paradigm. That is
> the reason why I sort of brought the biopharm classification system
> into my discussion, which I did not fully expound on. The decision
> that we made in the SUPAC\--there are two aspects that we primarily
> focused on in SUPAC, unchanged shelf life and unchanged
> bioavailability in the event of a change. I mean, those are the two
> most prominent driving forces there.
>
> Now, in the case of unchanged bioavailability we are using a surrogate
> of an [in vitro]{.underline} dissolution test. In the
> immediate-release world we don\'t often have an [in vitro]{.underline}
> [in vivo]{.underline} correlation because often dissolution is not
> rate limited, and the dissolution test has built-in flaws that
> sometimes give you false positives and false negatives. So, the way we
> approached it there was saying identify what are the risks. The risk
> question here is what is the risk of bioinequivalence when a
> regulatory decision is made on the basis of similar [in
> vitro]{.underline} dissolution? That was the risk question.
> Essentially, what we found was that because of the inherent inability
> of the dissolution method, as well as lack of connection between
> formulation and dissolution, there are risks associated. So, the
> biopharm classification mitigates the risk by saying bio waivers are
> feasible under four conditions. One, the drug is of high solubility.
> Two, the drug is high permeability. The product has rapid dissolution
> under three different conditions. So, that is how we sort of
> structured that.
>
> So, that becomes sort of quite a nice model for making decisions and
> saying if you met those criteria, then there is no need to do a bio
> study. But now I think the same concept comes with respect to process
> understanding. If you have understood the process so well that a
> change that is necessary\--you are changing equipment and
> encapsulation, that raises a concern but since you have understood the
> process and you have understood the other change that you will be
> applying to make and its impact, and have said the change is not
> likely to change the performance, then that becomes a low risk.
>
> We don\'t have that in the SUPAC in a way that allows process
> understanding to come in. So, you have to sort of think of this as an
> extension of the current SUPAC.
>
> DR. HOLLENBECK: Then would the agency be willing to use those
> attributes listed on the last slide in Greg\'s presentation to make
> these judgments? Why not have the same kind of an aggregate conclusion
> to determine what level you are at? This would still be on a product
> by product basis. You are not taking about classifying Merck as a
> number one company for every product. You are still talking about
> individual products.
>
> DR. HUSSAIN: No. No, I think the way we are thinking about it is there
> are two pathways that we plan to take. One is in absence or process
> understanding because this information is not available to us. We will
> use the concept that Chiu proposed sometime back and that will be part
> of the discussion later on, and that would be a very conservative
> approach to saying that we don\'t have information. These are the
> critical elements. Anything beyond that is a prior-approval
> supplement.
>
> The second layer comes in if you have understood the process and are
> able to predict the impact of a change on the key attributes of shelf
> life and bioavailability. Then, the level of scrutiny could be
> reporting in an annual report, managing the change under the
> inspection program rather than having all the paperwork sent here, but
> that does not mean that you maybe will not do any additional test. The
> test would be done possibly and be managed under the GMP change
> system. That is how you make it less burdensome, more manageable
> change but, yet, you have the level of scrutiny that ensures the
> safety and efficacy.
>
> DR. GOLD: Ajaz, how do you define the difference between a
> comparability protocol and the concept you were talking about,
> SUPAC-C?
>
> DR. HUSSAIN: Well, in my mind, the way we started out, I think the
> comparability protocol is broad enough to accommodate \"make your own
> SUPAC\" concept. But I think I am hearing the comments that we are
> receiving which raises the concern that it probably will not. I think
> we will have that discussion elsewhere. But I think within the
> comparability protocol \"make your own SUPAC\" could be. We anticipate
> these type of changes to occur over the next several years of this
> product. The difference will be that these changes, say, the site
> change or change in equipment or change in scale or change in the type
> of quality control measures that you want to have, are not likely to
> impact on the critical attributes that we are concerned about, shelf
> life and so forth, and so forth. And we have arrived at that decision
> on the basis of this information that we have collected during our
> development, and so forth.
>
> So, that becomes a proposal to the agency as a protocol, saying that
> because of this information and knowledge that we have, these are low
> risk and this is how we plan to qualify the changes that will need to
> occur. The changes may not have occurred. So, the protocol gets
> submitted to the agency and the agency reviews the protocol and agrees
> or disagrees with that protocol and says, all right, when this change
> needs to be made this could be reported as an annual report and
> managed in the way the protocol outlines.
>
> DR. GOLD: So, the comparability protocol gives you a prior approval
> approach to agreement by the agency as to how you can make the change.
> Now, if you are going to request development documentation at the time
> a submission is made for making a change in the process based on
> robust knowledge of the process, that is going to require time to
> review and determine whether that information is sufficiently
> appropriate, is it not? So, how does it then differ from a
> prior-approval approach?
>
> DR. HUSSAIN: The difference is it is one time so that is a big
> difference because, for example, this is a case that we ran into and
> luckily it was not misunderstood. I will give you a very recent
> example. The first PAT protocol came in. It is a prior-approval
> supplement. It is for a new PAT-based approach. It applies to over 150
> NDAs. All right? So, the change is managed through one protocol for
> all those applications and it is a one-time use and all subsequent
> changes will be reported. So, that is one way of looking at it. The
> bundle supplement also gets there. So, it is a very similar concept.
>
> But the concept here is you are agreeing on a less burdensome change
> management system based on the information provided, development
> information provided, as well as the testing protocols that are
> necessary to qualify changes. So, it is a one-time supplement.
>
> DR. GOLD: Oh, I understand. What I am trying to fathom is why not
> extend the umbrella of the comparability protocol to cover SUPAC-C.
>
> DR. HUSSAIN: Maybe you misunderstood that. That is one of the aspects.
> We have the flexibility of doing this under the comparability protocol
> or creating a separate document of SUPAC-C. Which is a better option?
> I am not sure. That is one of the questions I posed to you. So.
>
> DR. GOLD: Well, personally, I don\'t see any advantage to creating a
> separate protocol if you simply enlarge the concept of the
> comparability protocol.
>
> DR. SHEK: The way the comparability protocol is today, it gives you
> one level of jump. Right? You go from one level, whether it is, you
> know, from reporting and what you are talking about is basically
> completely\--to me, it sounds like a new concept, a different concept.
>
> DR. HUSSAIN: No, the SUPAC-C is much broader. It is probably less
> restrictive than the way we have defined the current comparability
> protocol.
>
> DR. BOEHLERT: I think you are running out of new ideas. We have beat
> around the bush.
>
> DR. HUSSAIN: I think so. If I could just summarize, I think the aspect
> that we tried to bring to get some feedback today was I think some
> elements of quality by design. I think the key aspect is that we will
> focus on the knowledge necessary to achieve the type of risk
> assessment that needs to occur. I think many of the things we have
> heard we have already incorporated in the PAT draft guidance so that
> was sort of reconfirmation that I think we are on the right track, and
> that was very helpful.
>
> We also heard from our invited guests and others that, clearly, the
> post-approval change scenario offers a way forward to bring
> pharmaceutical development information to learn how to better use that
> information. That will give us not only the information coming in that
> will help us train ourselves, as well as I think will start building a
> culture of sharing this information. I think that is clearly an
> important aspect. Clearly, I think well-defined projects within PQRI
> can get us to that state quite rapidly.
>
> At the same time, since I think we already have certain aspects in
> ICH, the process will run in parallel but, at the same time, I do not
> want to give the impression that pharmaceutical development reports
> are only for post-approval change. I think there are many issues that
> I think you want to welcome and we want to sort of open up the process
> in an NDA and alleviate the fears of delayed approval. I think this
> exercise will help us get there.
>
> I think since we have a number of opportunities for meeting during the
> NDA process, the fear should not really be there. I think as we move
> towards a quality system for the CMC review process itself, that I
> think will address many of the concerns that I think were raised
> today. So, what I got I think from all the discussions is that, to a
> large degree, the thought processes that we have expressed in some of
> our draft guidances are probably on the right track already. We will
> continue with that process and we will focus on training. We will
> focus on creating some additional frameworks that will bring
> development knowledge. At the same time, these activities will support
> our delegates to the ICH process which will be working on the P.2
> section. I will invite Joe Famulare and Diana to say a few things.
>
> MR. FAMULARE: In summary today, I think the presentations were very
> good and enlightening in terms of the types of paths that we are
> looking to follow now in terms of the ICH groups, etc. So, as Ajaz
> says, I will say just briefly I think it just reconfirms that some of
> the thinking that we have is on track and, as I say, the presentations
> today I think were helpful to us.
>
> MS. KOLIATIS: We heard a lot of information from different folks and I
> think we have a very good basis to continue our discussions on the ICH
> front, and to be able to communicate all the concepts that we heard
> today.
>
> DR. HUSSAIN: We didn\'t hear about Isabel so have a safe trip back and
> thank you.
>
> DR. BOEHLERT: Thank you.
>
> \[Whereupon, at 4:26 p.m., the proceedings were adjourned.\]
>
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