**ANTIMICROBIAL RESISTANCE**

**PUBLIC MEETING**

**PRE-APPROVAL STUDIES AND PATHOGEN LOAD**

**STUDY CONCEPTS FOR MODELING RESISTANCE DEVELOPMENT**

**AND/OR PATHOGEN LOAD CHANGES**

**WEDNESDAY, FEBRUARY 23, 2000**

**8:30 A.M.**

**DOUBLETREE INN**

**1750 Rockville Pike**

**Rockville, Maryland**

**Regency Room**

[I]{.underline} [N]{.underline} [D]{.underline} [E]{.underline}
[X]{.underline}

**STUDY CONCEPTS FOR MODELING RESISTANCE DEVELOPMENT**

**AND/OR PATHOGEN LOAD CHANGES**

**February 23, 2000**

[Page]{.underline}

**INTRODUCTION**

by David G. White, Chairperson 3

**USE OF IN VITRO STUDIES**

by Dr. Susan F. Kotarski 4

**IN VITRO ANTIMICROBIAL SUSCEPTIBILITY DATA:**

**POINTS TO CONSIDER**

by Dr. Thomas Shryock 30

**USE OF MATHEMATICAL MODELS**

by Dr. Marc Lipsitch 46

**MODELING CHANGES IN PATHOGEN LOAD**

by Dr. Paula Fedorka-Cray 75

**PANEL COMMENTS ON QUESTIONS/ISSUES FOR**

**BREAKOUT SESSIONS** 111

**OPEN COMMENT PERIOD** 123

**DESCRIPTION OF AFTERNOON BREAKOUT GROUP PROCESS**

by Ms. Aleta Sindelar 143

Keynote: \"\-\--\" indicates inaudible in the transcript.

[M O R N I N G S E S S I O N]{.underline}

(8:40 a.m.)

CHAIRPERSON WHITE: I would like to welcome everyone back to day two of
our pre-approval meetings. And we would like to welcome all of the new
people that have joined us today as well. Today\'s meeting, the morning
session is a continuation of yesterday afternoon\'s session and it is
going to be focusing on study concepts for modeling resistance
development and/or pathogen load.

We are going to have several speakers from pharmaceutical, academia, and
government talk about their ideas about resistance development design
and pathogen loads. We are going to have a break and then we are also to
have a study concepts panel where the speakers from yesterday afternoon
and this morning will come up on stage and maybe talk for a few minutes
about the positive aspects of the model, what things they would change,
what things we need to take into consideration.

Then after that we will have an open comment period where people can
come up to the microphone. All I ask is when you go up to the microphone
please identify yourself and the organization you are with.

So, are first speaker this morning is Dr. Susan Kotarski. She received
her B.S. in Microbiology from Michigan State University, a Masters in
Microbiology from the University of Illinois, and a Ph.D. from the
University of Illinois.

She then did a post-doc at Walter Reed in D.C. and she is currently a
senior research scientist at Pharmacia & Upjohn. She is the project team
leader and lead scientist to address antimicrobial resistance issues of
a development of antibiotics for use in food-producing animals.

Please welcome Dr. Kotarski.

DR. KOTARSKI: Good morning everyone. I would like to thank the Center
for inviting me to speak this morning and compliment them on offering a
public session so we can discuss these concepts.

I am going to go right to my talk this morning.

(Slide)

As I understand it, my objective for today is to review in vitro systems
applicable to pre-approval studies. My emphasis is going to be on
bacterial system modeling. Specifically, continuous cultures and the use
of batch cultures and their potential applications.

(Slide)

In order to do that, I thought what I would do is start right out by
talking about continuous models. I think many of you have heard about
them but haven\'t been involved in working with these systems.

(Slide)

This is a systematic diagram of the system that we are using. It is
among the more simpler of systems that can be used. A continuous culture
basically can be defined by the use of a continuous input of nutrient
flow into a culture system. The objective being to maintain a bacterial
culture continuously in a steady state or semi-steady state of a
cultivated growth.

This is in contrast to a batch culture system in which the nutrient
content is defined. The organisms grow within a finite volume. They have
a build-up of toxicological end products. The organisms can die in this
batch system as a result of the build-up and the depletion of nutrients.
In the continuous culture on the other hand, there is a continuous
influx of medium.

Now, in our system we are using a set of pumps for input of that medium
and for removing the effluent to maintain a constant volume in that
continuous state. We are also using an anaerobic system.

And this is the same system \-- we are fortunate enough to have a number
of microbial ecologists in our group that have devised a system in which
we have a battery of eight replicates, each with its own nutrient input
system. And the model system that we have been working with is
developing continuous cultures of fecal flora to model the colonic flora
in humans.

(Slide)

Okay. Well, one of the nice features of these systems is that they can
maintain a fairly high level of bacteria with a fairly diverse
population. The colonic system that we are mimicking contains on the
order of 400 species, in feces typically. And a population of 10^11^ or
10^12^ cells per gram.

With these continuous culture systems, whether we are talking about the
ones that we have been working with or ruminal ecosystems, the organism
load that you can maintain is on the order of 10^9^ or 10^10^ cells per
mil. So, we are talking about two orders magnitude lower than what is in
an in vivo setting. For example, in a ruminal in vivo setting or in a
colonic in vivo setting.

And what I also wanted to point out is that with these systems, that by
virtue of the fact that we are using a sterile nutrient medium, we are
essentially defining the inoculum at the onset. We are using one sample
inoculum. We have a finite gene pool if you will.

We have, as the system evolves to equilibration state, we have a model
of one ecosystem. And that one ecosystem may or may not represent the in
vivo setting that it began to mimic at the tangent in which we sampled,
or the dynamics of the system that occur in vivo.

One nice feature of that system though is that the, as I mentioned
before, we had a growth substrate for semi-continuous or continuous
maintenance of those cultures. They do maintain a dynamic metabolic
state. Another good feature about this is that you can add antibiotics
continuously or at pulse-dose or semi-continuously.

And this system lends itself well to developing concepts of bug drug
interactions similar to the types of concepts that Dr. Papich was
talking about yesterday. This system could be used for that, either for
organisms grown in pure culture or more complex bacterial groups.

I also want to emphasize as a major point of this talk though, is that
the variability of these systems have not been very well defined.

(Slide)

Well, let me talk about one developmental test system that we have used
that has some parallels to pathogen loads and what we have found out in
working to develop a test system.

The objective of a test system was in fact to identify drug
concentrations that would not have an impact on the microbial ecosystem
that it mimicked, namely the colonic flora of human fecal bacteria. That
was our objective. To define a no-effect concentration for drug
antibiotics on an otherwise stable ecosystem.

We decided to use a fecal inoculum as I mentioned before, and for our
reference drug to develop the system we decided to use clindamycin.
Pharmacia & Upjohn produces clindamycin, so that was about one of the
major reasons for choosing that.

And secondly, another major reason is clindamycin is well characterized
with respect to its effects in vivo on the colonic flora and its
potential to disrupt the flora and the potential for overgrowth of
certain opportunistic pathogens, mainly clostridium difficile.

Clostridium difficile is not part of the normal flora of humans.
However, upon destruction by an antibiotic, if it is clindamycin or
another therapeutic that might be used, it can if it is present and if
it is ingested, it has a potential to overgrow. And if that strain
overgrows and it is toxigenic it can manifest itself clinically as
pseudomembranous colitis.

So, clostridium difficile because it was not an indigenous part of the
flora was a natural choice and clindamycin was an obvious drug choice
for reference for developing this in vitro system to test for a
no-effect drug concentrations on equilibrated flora.

(Slide)

Now, what I have here is a diagram, conceptually of what we expected to
see in the model. In any one experiment that we set up, what we
initially do is equilibrate the culture to get a stabilized dynamic and
as we work with this we use an inoculum of fecal flora such that it does
not contain clostridium difficile. Or at least it is below the detection
limits.

And what I am plotting here, conceptually, is the log base of the C.
difficile population in that continuous culture. So, first we go through
an equilibration phase to a point zero of the experiment. And on day
zero of the experiment, once we achieve equilibration as measured by
total population, we also can verify that the enteric populations are
\-- excuse me, the facultative populations are below the total anaerobic
populations in those fecal systems.

Once we achieve that equilibration, then in our model system we add
clindamycin at a level, at a particular test level, and we infuse that
on a constant basis for seven days. Which is typical of a drug treatment
therapy. And one other point that I might make is that the carryover
time in the system was one day.

Another important point of this experiment was is that we added C.
difficile on a daily basis starting on day zero. And what is different
for this experimental system as compared to other experimental systems
to detect C. difficile overgrowth either in vivo or in other in vitro
continuous culture systems was that we didn\'t use a bolus dose of C.
difficile.

Characteristically and the parallel that we spoke of yesterday in the
550-815 studies is that you give a large bolus dose of the organism, of
the challenge organisms. This is in contrast to the normal setting
either for C. difficile in the human situation or salmonella, as we
mentioned yesterday, in the animal setting. Usually these organisms,
unless they are in a disease state, don\'t see this level of organisms.

We reason that it would be better off in our test system to in fact
challenge daily with a low level of organisms that would be more
representative of the environmental setting. And this concept, if we are
going to do salmonella challenge studies, might be incorporated into in
vivo settings. Or may be one, you might want to think about it.

What is another driver considering this is a potential, is that we were
looking at \-- we were titrating the clindamycin concentration that we
would add on a daily basis and we reasoned that clindamycin, at high
enough concentrations, can inhibit the growth of C. difficile.

So therefore, there could be concentrations that we might add that would
inhibit the growth of C. difficile. And the if so, and if we only bolus
dose say for example on day zero, even though we might effect or disrupt
the gut flora, in fact we might not detect C. difficile overgrowth
because we are likewise inhibiting the organism.

So, it made sense then to challenge rather than on one large bolus dose
on one day to instead challenge at a level that was low on a daily
basis. And thereby, if we had a concentration that disrupted the gut but
it was at a concentration that inhibited the C. difficile concentration,
you would expect that once that clindamycin concentration declined then
there would become a point when the destruction was such and the
clindamycin was such that it permitted growth of the organism.

Likewise, if clindamycin at low concentrations that permitted growth of
the organism and disrupted the culture, you would be able to see
overgrowth at an earlier setting.

So basically we expected to see a type one response during clindamycin
concentrations at the lower levels and perhaps a type two response or a
delayed overgrowth with this type of set up.

And the other concept as well is, is that you might see no response if
there was no disruption of the clindamycin you would expect to have seen
no difference in C. difficile overgrowth.

So, what I want you to do for the next slide is just remember the colors
of type one and type two response. In fact, what you will see in the
next slides is that we were able to identify a concentration in this
test system that allowed overgrowth during the clindamycin
administration. That was at 2.6 microgram per mil.

We also found a type two response at the 260 microgram per mil
concentration of clindamycin. And that 260 microgram per mil
concentration is the concentrations that you might expect to see in
feces.

(Slide)

So, what I am demonstrating here is three separate experiments. I want
you to focus on the first experiment. Here again is the 2.6 microgram
per mil response, that is that type one that I spoke of. We have a
delayed reaction after the clindamycin infusion has stopped and we get
an overgrowth at 260. And then there was an intermediate concentration
that we tested as well.

The no-effect concentration that mimicked the zero dose was 0.26
micrograms per mil, shown in red. And that was great. That comprised one
experiment. In total that experiment took about one month to run and
three associates with a battery of six vestibules.

We tried the experiment again. This time we wanted to know if \-- at
that time we were using 1,000 cells per mil on a daily basis as our
challenge dose for C. difficile. We wanted to titrate down and find out
what was the minimum number of organisms that we could use to detect a
similar response.

As you can see the type one and the type two response again showed up.
This is one inoculum with the different treatment groups. And what we
identified in that experiment was that a 10 cell per mil concentration
in fact would not pick up the response but the 100 and the 1,000 cells
per mill appeared to do so.

Before we wanted to go forward, we wanted to see really how reproducible
this type of system was. You can see from one experiment to the next the
dynamics of the response that we were seeing was fairly reproducible and
it made us quite happy.

In the next experiment what you see is a representation of two sets of
fecal inoculum. We had four replicate vestibules with four different
treatment groups: 0.0, 0.26, 2.6, and 260. And the second set the fecal
inoculum are designated A and B.

And I wanted to emphasize as well we were using four inocula for
inoculum A and a different set of four inocula for the total inoculum
for the replicate B. An important point is here, is that these were run
simultaneously and in only one set of the four replicates were we able
to detect an overgrowth response at the 260.

In neither system were we able to detect the type one response. And with
the other response variables that we measure, we also saw a lesser
response in terms of total VFA, impact on total bacteria.

(Slide)

So, my point is that basically, if we start looking at these models and
start to reproduce them, we are likely to encounter some variation. That
variation could in fact be two inoculum as suggested in our last
experiment, but we haven\'t gone through the examination to identify
whether or not the test system that we were using, some unidentified
aspect of the test system was in fact accounting for that variation.

Ultimately though your test system is defined by that inoculum as I
mentioned earlier because we are having a constant input of a sterile
nutrient medium after the initial inoculum. And as well, the
equilibrated state that you do achieve in these in vitro test systems is
ultimately dependent on the culture conditions and the nutrient system
provided to that system.

If we think about doing gene transfer studies in these test systems,
these same concepts apply in terms of variability and the finite concept
of the inoculum. The genetic determinants that you put in the system is
dependent upon your inoculum. It is on that first day. It doesn\'t
include the genetic variation or the genetic input that you might, that
the animals might, receive in terms of the types of protocols we are
thinking about for this workshop.

This system does not incorporate the day-to-day variation in terms of
the organisms that are encountered by these animals. Ultimately, the
inoculum, once inoculated into a culture system takes on an evolution of
its own. The good thing is that it sets up a stable ecological system in
which we can tests concepts of drug bug interaction.

But, it does not necessarily mimic the bacterial flora that the sample
represented when it was inoculated and we have no information to the
extent to which it can mimic, and it has a high likelihood that it does
not mimic, the day-to-day changes and variations that an individual
animal will encounter or the population as a whole.

(Slide)

This following slide is not a continuous culture, but I just want to
bring to your attention the in vivo setting. This is a set of data that
was generated about 10 years ago by Denny Corpet and it is simply a plot
of total E. coli found in the fecal flora of humans on a day-to-day
basis.

These people were given first a normal diet and then switched to a
sterile diet. And the point of emphasis is, is that the darker \-- I am
having a hard time with this pointer \-- the darker lines represent the
tetracycline resistant coliforms that were present when the humans were
eating a non-sterile diet.

And as you can see, even with the change of the sterile diet it detected
resistant coliforms which represent in fact how a small portion
initially became even less then.

So if we think, going back to an in vitro continuous culture system and
modeling these systems it is woefully deficit in terms of modeling the
day-to-day infusion of resistant organisms.

(Slide)

And also, with respect to these in vitro systems, they do have a useful
potential which I will talk about in just a second. But, there are some
other concepts that we want to think about if we are going to use it for
any specific objectives.

And that is that the ecosystems that we are thinking about in terms of
this workshop, there is not just one ecosystem available either with the
animals or in the production setting.

(Slide)

A good example of this would be in the ruminant of course, and an
obvious example actually when you think about the ruminant animal. The
ruminant of course being the large organism it is and harboring a
dynamic population with a dynamic input of organisms associated with
this foodstuff presents a myriad of changing microbes with respect to
influx of genetic determinants of population diversity.

Yes, it does have an overall stabilization, but there is variation
associated post-randomly and over the age of the animal. Within this
animal then there is not only a ruminant microbial ecosystem, but a
colonic one as well.

(Slide)

And, if we want to think about monogastrics as maybe a more simplified
system in terms of ecosystems, I would challenge you to think again.
Within the colon, and I mean the different portions of the colon of
course you can expect to see different ecological systems.

And the cecum that essentially represents a blind sac where the influx
of nutrients and the dynamics of metabolism will be somewhat different.
And there is well, lesser colonizations of the upper part of the G-I
tract.

(Slide)

Another topic that came up yesterday, but is also apropos in terms of
thinking about in vitro ecosystems is the inoculum that is used with
respect to the health state of the animals.

(Slide)

If we are talking about animals that during treatment might be housed in
a hospital pen or if we are talking about animals that might be exposed
transiently to antibiotics during a healthy production state, these will
also influence ultimately the ecosystem that we might introduce into
that once in vitro system.

Another consideration in terms of ecosystems and the number of
ecosystems is the age of the animal. The neonate will have a microbial
flora. That microbial flora will change as it ages and that microbial
flora will have different population levels as it ages.

So, thinking again for an in vitro system as a model to mimic in an
animal, we are essentially taking one point in time for whatever
ecosystem we choose to use.

(Slide)

So, overall I would like to personally recommend these in vitro systems
have a predictive capacity at this point in time that is really unknown.
One point that I didn\'t mention is that obviously these glass systems
do not incorporate concepts of hosts metabolism and obviously the
metabolism may effect the potency of the drug that is introduced.

These systems are closed systems, they model one bacterial ecosystem. I
can\'t say that enough. The gene pool is defined and the test
variability has not been determined.

Another concept is that if you are able to identify a no-effect
concentration in these glass systems, the question then becomes how do
you translate that from microgram per mil to microgram per kilogram body
weight as dose function.

(Slide)

So, given that is the state in terms of modeling pathogen load studies
or the rate and extent of a resistance emergence, I am not really keen
on using an in vitro system to do so.

(Slide)

I would say that as we begin to look at in vivo systems, and I recommend
that we might think about these systems, I anticipate that through the
course of the discussions we will probably identify that there is no
perfect in vivo system as well.

(Slide)

And so, from that standpoint, I mentioned earlier that the in vitro
system has as a disadvantage that it doesn\'t take into account host
metabolism as an example. That is a disadvantage in the context of a
pathogen load study. It is a disadvantage, but it is an advantage.

And if your objective then is to better understand mechanistically the
dynamics of the interactions drug bug interactions, a microbial
ecosystem where you don\'t have variability of host metabolism entering
into your studies might be to your advantage.

And from that standpoint, then I would urge you to think of the in vitro
system more in terms of the context of your study objectives. Now at
this workshop we are not at a point where we have identified specific
objectives and I hope that at the close we will come closer to that
goal.

But, given the facts of the case at this state, I would rather like to
think about the in vitro systems in terms of a tool kit as we identify
those objectives.

(Slide)

And from that standpoint, let me go into just briefly concepts of batch
cultures. I emphasize again, batch cultures have a finite growth
substrate. They typically use short incubation times and bacterial
metabolism changes with times because the nutrient source is not
replenished, it is limited, and there is no removal of bacterial end
products typically.

Nonetheless, these batch cultures systems, whether they be pure culture
or whether they be complex in vitro systems do provide rapid screening
devices to get again information that is useful in our overall drug
evaluation.

(Slide)

Okay. With respect to a batch culture, typically we will think of pure
cultures. But, there is also concepts that we can use in terms of a
complex inoculum. When I say complex inoculum, I am talking about an
intestinal content: excreta, litter, or manure as the inoculum with no
nutrient addition and a short incubation period.

I mentioned before a glass tube systems can only achieve levels
typically of 10^9^ to 10^10^ cells per gram in which we are going to
test a drug concentration. If you take these samples and incubate them
for a short periods, two to three hours, aerobically or anaerobically as
appropriate, you can maintain that diversity for a short period of time
and at levels that the drug might see in an in vivo setting.

From that standpoint, those types of systems might be useful for
mimicking bacterial diversity insomuch as it is a short period of time,
but it can be useful for screening for drug concentrations that might
disrupt an ecosystem which might be detected by changes in fermentation
acids or the production of hydrogen or a number of different response
variables.

These also might be systems that can be used for detection of rapid drug
inactivation. For example, if a drug is inactivated simply by the fact
that it binds irreversibly to complex matrices within the inoculum, such
that it decreases its potency, this is good information to know.

(Slide)

Let\'s give you as an example studies that were done some 10 years ago
regarding ceftiofur degradation in feces. Our initial observation in
residue decline studies was such that we could detect ceftiofur residues
in the intestinal content of treated cattle. The concentrations were on
the order of 10 to 11 parts per million in feces. But we were unable to
detect any microbiological activity.

This in the face of the fact that we could also look at the tissue
concentrations of ceftiofur and show that there was active metabolites
present. The question then became well, if we can detect those residues
in intestinal content, why are we not able to see microbiological
activity? And in fact, we were not able to detect it. At least to the
detection assay of our potency assays.

So, what we elected to do was to do some short-term incubations in which
we took fecal material. We diluted it minimally and then asked the
question if you add ceftiofur to these fecal incubations, the bottom
line is that in a short period of incubation, on the order of four to
six hours, for an addition of say for example 80 parts per million, the
decline in microbiological activity, as is measured by microbiological
cylinder plate assay or by HBLC.

We were able to show that in fact that decline was quite rapid within
the space of literally hours. We have been able to demonstrate that in
other species as well, including humans.

(Slide)

Another concept \-- one more thing before I leave that. I realize that
most antibiotics are not going to have this property. On the other hand,
the property of inactivation does apply to other molecules, for example
the aminoglycosides or the flouroquinolones.

Now, the extent to which that occurs can be refined or better understood
in the context of using these as matrices. Likewise, this can be used as
a rapid screening device if we think about new molecules that we might
want to screen for this characteristic. It is going to be difficult to
find, but nonetheless we might want to use it.

(Slide)

So, another concept to think about in terms of in vitro systems and
rapid screening is just to better understand the frequency of mutation
within the zoonotic population. This is a classic type of experiment
that can be done very quickly. And initially, once a drug is under
evaluation \-- I am not going to go into the details of that.

(Slide)

And classically, a number of MIC tests have applications that many of
you are familiar with. Namely, these MIC tests defined the spectrum of
drug activity both in zoonotics and veterinary pathogens. Usually, we
don\'t emphasize the activity against commensals, but certainly we can
incorporate that early on in getting a better understanding of the
potential for causing changes in the bacterial flora.

We use these MIC tests to define potency. We use it for our interpretive
criteria for efficacy. Certainly to support clinical efficacy studies
for pharmacokinetics and pharmacodynamics as Dr. Papich mentioned
yesterday. They help us to define cross-resistance to other drugs. They
help us to characterize strains that are isolated from pathogen load and
resistance emergency studies.

This is typical of the types of studies Dr. Mevius presented to us
yesterday. Likewise, we use these MICs to understand the distribution of
MICs for any particular species of organisms whether it be a zoonotic
organism or a target pathogen. We would be best to understand what that
distribution is pre- and post-approval.

(Slide)

The use of these in vitro systems in terms of complex ecosystem models
we can then use to screen for drug concentration. They have that
potential either to drug concentrations that disrupt ecosystems or drug
concentrations select for antibiotic resistance. We can use them to
screen or confirm drug inactivation by ecosystems.

We can also model short-term drug exposure scenarios. Say for example,
in a continuous culture system, whether we are evaluating a one-time
dose, a pulse dose, a continuous dose, or \-\-- of antibiotics. That is
not to say that they are necessarily predictive, but we can better
understand the principles underlying the observations that we might have
in a more diverse or complex matrix.

(Slide)

The predictive capacity, I will emphasize again, is really unknown
though in terms of pathogen load studies and extent and rate of
resistance emergence for many of these models. They do not incorporate
host metabolism, they only model one bacterial ecosystem.

The result is defined by the culture condition. The gene pool is defined
by the inoculum and test conditions. The test variability has not been
determined. And the extrapolation is difficult.

(Slide)

So, overall my conclusions are that the in vitro test systems provide
useful but limited information regardless of whether or not you can type
supporting the processes of lead findings or drug evaluations or drug
registrations.

There is no in vitro predictive test that I am aware of that is in place
right now to provide pivotal registration data regarding the effect of
drugs per se on resistance emergence and pathogen load.

I thank you for your endurance. And I will entertain any questions.

CHAIRPERSON WHITE: Okay. We have time for one or two questions. Please
go to the microphone.

DR. GOOTZ: Tom Gootz from Pfizer. That was a good talk Susan. I was
wondering, in this system though one advantage is the fact that if we
are involved here, we are trying to assess the safety of animal health
drugs with respect to selection or resistance of campylobacter, E. coli,
gut organisms, it is true that the sponsor should know how much of their
drug is in the fecal matter of those use animals.

And a lot of that drug obviously has to be bound to fecal material. So
that only a fraction, let\'s say .1 percent is actually bio-available as
it passes through the gut. Your system actually would be very good for
trying to look at steady-state conditions at that level for each drug,
for each agent, because that is probably the only system where you can
look at population dynamics at concentrations of the drug that mimic
that which is available in the feces.

So, that could be an advantage of this type of system. Have you tried
\-- as I noticed, just trying to look at some of your models, your data,
when you have lower concentrations of a drug you tend to get less
resistance. And maybe what we are often seeing here is this low-level
free drug which is perhaps enough to induce certain types of resistance
like to macrolide, but is not really efficient at selecting permanent
mutations that cause resistance and then get into the environment.

Do you have any thoughts about that?

DR. KOTARSKI: A couple of thoughts. I agree with you that in this
setting because you can define what your inoculum is you can look and
try to develop model systems where you have low drug concentrations and
look at the impact overall where you do have that complex ecosystem
setting.

There are other models that have been used that are either plugged flow
that involve matrices that are more complex than just a fluid matrix. We
are using a fluid matrix here. Or they involve addition of substrates.
For example, real food substrates.

You can have hardware that will accommodate that so you can incorporate
not only the inactivation or binding that might occur for a drug to the
bacteria themselves, but also other matrices that might be present that
are not bacterial per se.

So, I absolutely agree with you. To develop mechanistic studies of bug
drug interactions either in pure culture or in complex matrices, these
have potential. Absolutely.

DR. SILLEY: Peter Silley, Don Whitley Scientific. Thanks for that Sue.
Really just carrying on from that, have you looked in your model at
actually putting any solid substrate into your system and seeing if
there are any differences?

DR. KOTARSKI: We have not done that. I know that Karl Signiglia has a
similar system and in fact we modeled our system on Karl\'s model in
which he is using food substrates to dump \-- excuse me, not dump in \--
but add on a systematic basis, twice a day basis. I haven\'t seen the
data that he\'s generated in that system with respect to this question,
but I would anticipate that we will see some data coming from his
laboratory in the future.

DR. SILLEY: We have been working with in effect a batch system which
uses some solid matrices as well, and have been able to actually
maintain from that a mixed fecal flora. Certainly a reason to study
\-\-- in terms of the bacterial position that appears over 14/15 days.
We have not really got any further than that.

But what is actually interesting, if you are looking at that sort of
gene transfer and then you will actually see differences if you look in
the liquid phase as opposed to actually in the solid phase, which is
probably not surprising. But, I think it is quite interesting if we are
then trying to maybe extrapolate to see what happens in vivo, because of
course obviously there is a solid phase and a liquid phase. But there
certainly are differences between the two.

DR. KOTARSKI: So, from what you are saying I understand that you can
actually develop more than one ecosystem within that one culture?

DR. SILLEY: Yes. Absolutely.

DR. KOTARSKI: Okay.

CHAIRPERSON WHITE: One last question.

MR. : Susan, just a comment. You didn\'t highlight or perhaps we missed
it, but the possibility of using the in vitro or lab-based studies to
see the frequency of transferrable elements. You know, transconjucants,
etc. like that which is something actually that might be quite useful
and is done all of the time of course, if you know there is a
transferrable element.

DR. KOTARSKI: Sure. Absolutely. I agree with you. I didn\'t want to
highlight too much the pure culture work that can be done. In terms of
characterizing the capacity for frequency of drug transfer, absolutely
the pure culture work is useful. And it is also useful in terms of
setting up systems to make comparisons to see that capacity pure culture
versus seeing that capacity in a more diverse matrix.

Say for example like in an in vitro system such as ours. And that is
easier to set up in an in vitro system than it is in vivo. On the other
hand, the next question becomes, with that observation is how do you
translate the in vitro observation to a capacity or put it specifically
in terms of what is the rate of resistance emergence. That is difficult
stuff. You know, translation of that in vitro data to in vivo data.

CHAIRPERSON WHITE: Thank you, Sue.

Just to let people know, we do have chairs in front. If you want to hold
up the wall back there, that would be great too. The next speaker is Dr.
Thomas Shryock. Tom got his B.S. in Biology from the University of
Toledo and his Ph.D. in Medical Microbiology from the Ohio State
University.

He then did a post-doc with Case Western Reserve University. He has been
a research scientist, a senior research scientist with Pfizer. He has
been an assistant professor in the Life Sciences Department at Indiana
State University. He is currently a technical advisor and research
scientist in the animal science discovery and development research
program at Elanco Animal Health.

He also chairs \-- I will keep going for Tom here \-- the new Division C
in American Study for Microbiology which is animal health microbiology.
And he is currently the chair also the Veterinary Antimicrobial
Susceptibility Testing Committee in NCCLS. Please welcome Tom.

DR. SHRYOCK: Thank you, David. Thanks to CVM for the invitation to
participate in the pre-approval studies workshop. Unlike David\'s
concluding slide yesterday with Apollo 13, I do agree that we do need
teamwork, but susceptibility testing is not rocket science.

So, I hope to share some of the considerations that we will have to take
into account, but keep in mind these are doable and certainly not that
highbrow science that we would associate with Apollo 13.

(Slide)

With regard to the objective of the antimicrobial susceptibility testing
or AST for shorthand, I think many of the previous speakers have already
covered just about every point that I was going to make so I can roll
through this fairly quickly. So, I will really try to emphasize some of
the points that I think are essential to keep in mind as we enter into
the break-out group discussions this afternoon.

Also, the handout that is available in the packet has many of these same
points in it so you can refer to that as needed.

One of the key points to keep in mind is that while we do a lot of the
susceptibility testing and relate that to in vivo outcomes, we really
are making some arbitrary assumptions. In that the in vitro conditions
are the in vivo conditions, and that is not true because we have host
factors that also interact.

We can spend a fair amount of time on that triad then between host,
bacterium, and antibiotic and we have to keep that in mind as we go
through some of these discussions.

(Slide)

A number of the speakers that have already talked about the jigsaw
puzzle as to how AST or susceptibility testing really is just the
centerpiece to support a number of the other pre-approval studies.

For example, it has got a very significant role in clinical efficacy
data that has a use for the PK/PD data as Mark Papich discussed
yesterday. Post-approval we use this data for NCCLS guideline
development. However, the data is actually developed during the
pre-approval phases.

One thing that hasn\'t been mentioned is that all companies do some sort
of field isolate survey for pre-existing resistance, usually in the
target animal pathogen. However, I think with the recent discussions
here some companies may be looking for some of the zoonotic to see what
kind of resistance reservoir or potential exists out there.

Obviously, a spectrum of activity and potency are assessed on the
typical battery of laboratory strains that companies have. All of this
is used to phenotypically evaluate the resistance selection potential.
And I will come back to that because phenotypic doesn\'t necessarily
line up with the genotypic. That is still something that we need to put
together in a much firmer way.

We can also use susceptibility testing to compare strains of known
resistance characteristics. For example, there is quite a number of
well-characterized resistance genes. A battery of those type strains can
be tested to see what kind of response you will get with a new test
agent.

And then lastly, the pathogen load and resistance selection studies that
we are discussing today can also be very directly supported through
susceptibility testing.

(Slide)

So, as I tried to put the talk together today I wanted to really try to
list out some of the factors that I consider as I design susceptibility
testing studies. As we do so we really come to the realization that a
lot of this is an interactive consideration where we have to really know
a lot about this type of specimen that we are going to be obtaining and
how that interacts with how you isolate a pure culture, how do you
identify it, to what level. Even some of the considerations on storage
and perhaps shipping.

A number of speakers have talked about antibiotic properties,
physicochemical characteristics that are important, as well as the
actual methodology. All of that has to be factored in, in one way or
another, in order to make an appropriate interpretation of the data.

(Slide)

With regard to the origin of the specimen, obviously this can take on
many forms. Animal isolates from a variety of sources, either fecal,
cecal tissue, etc. can be obtained at various time points throughout the
life or medication period of an animal. I think that is very
self-evident just by reading the slide.

Sue gave a very excellent talk on culture. Chemostat methods a sampling
can be taken in those types of situations as well. And Kathy Ewert
mentioned a battery of bugs yesterday, a reference culture type of
situation. Those could also be considered an origin for your specimen.

(Slide)

As we continue on this path, recognizing that to do susceptibility
testing we really need to, in my opinion, get down to some pure culture
situations. Consider the type, whether it is environmental samples,
fecal tissue, etc. and then that drives some of the sample size and
processing-type of activities.

Dik Mevius mentioned yesterday that if you use 25 grams of fecal
material versus one, your isolation rate goes up. Some of those kinds of
considerations have to be given a little thought.

We talked a little about what kind of bacteria should we be looking at,
we can discuss that in the break-out groups a little further. I think we
have all got some fairly structured ideas on that.

Pure cultures, marked challenge strains for example, yeah those can all
be done. Separating out mixed or contaminated cultures. There are some
issues that go there. If you have an extraneous milieu, such as organic
debris or blood that has to be taken into consideration.

And finally, a key point is the number of samples. Dik yesterday
mentioned that if you have a culture plate that has 300 colonies, how do
you choose which few or how many to take? Some real basic questions, but
they do enter into the need for some consideration that does impact on
the statistical design.

(Slide)

Obviously, isolation procedures are key in terms of trying to
standardize and consider how you really approach your topic. Enrichment
will pick up just one or two bugs, maybe as many as 10, depending on the
specificity and the sensitivity of the enrichment.

That can only be varied also with selective enrichment where you may
actually have antibiotics or other types of chemicals in there that
really enhance the growth of a particular type of bacterium.

All of this leads up to the expected recovery rate. How many samples do
you really need to take in order to get the desired number? If you need
100 samples and you anticipate you will get 40, that is a real factor
there.

One thing that we have not really discussed is what about damaged cells
or those that will be viable, but they are not cultivatable. That is
kind of an unexplored area and something, again, just to consider.

(Slide)

Identification: how far down do you want to take that? Is the use of
say, enterococci good enough? Or, do we need to speciate all of the
various species of enterococci? Do we need to get into serotyping in
some cases? Lots of considerations, lots of extra lab activity there.

(Slide)

As far as storage and shipping, some other considerations from real
practical aspects. If you take the cultures you may want to test them
all at once. It is a lot easier to do that. If you do that then there
can be some other unforeseen affects. For example on gene or plasmid
stability. Sometimes plasmids get lost, that can influence the
interpretation of your data.

Obviously, recovery of pure cultures as opposed to mixed: sometimes you
put away what you think is a pastorolida metastida and you get something
else out, it happens. Not often, but it happens.

Viability: if you store these samples away, not all of them come back.
And a lot of that has to do with the type of conditions that you choose
to store your cultures in.

This also leads then to the next question of banking. How many isolates
will you need to save for how long? How can you identify all of these
things? It is not rocket science, but it is a factor that you have got
to consider and order appropriate freezer or liquid nitrogen storage
space.

There may be some limitations on biohazard agents as far as shipping or
work in the lab that we might want to put forward.

(Slide)

Antibiotic properties is something that can be factored in fairly early
in the game. Whether you are dealing with a novel class, one that is
never been explored before or if it is an analog of an existing class,
the extent of knowledge will vary.

Obviously, the physicochemical attributes, such as solubility,
stability, potency, and purity requires a lot of chemistry support from
the analytical and formulations group to help the microbiologists sort
through that sort of thing.

Mark, yesterday discussed the mode of action: bacteriostatic and
bactericidals, some other important characteristics to be aware of. We
haven\'t really talked about testing related metabolites except in
general terms, but that is something that could be considered.
Especially if those have microbiological activity. We have already
mentioned spectrum, narrow versus broad, and protein binding as a couple
of other factors that could be considered.

(Slide)

With respect to the susceptibility test methods, there has been some
question should we use NCCLS or should we use something else? That is a
question that is certainly open and on the table.

The NCCLS manual here, which you can get from the NCCLS organization,
basically is an SOP to do susceptibility testing and quality control
development. There is another related document for sponsors that tells
you how to set these types of things up.

This really is designed for the clinical, diagnostic laboratories.
However, it can be used in research areas as well. So, while I am not
going to spend a lot of time on this I just wanted to draw your
attention that it can be useful, but there may be other methods that
would suffice to address the research issues that are at discussion
today.

No matter which of these methods, and they could include E-test, spiral
plater, replica plating, filter methods, etc. always have to have some
study to associate the conditions that affect the endpoint of those
studies. Otherwise, you can get falsely high or low MICs.

For example, macrolydes are notorious for their Ph influence. So you
have got to take that into consideration. For sulpha drugs, for example,
the thymidine content in the media can influence the results
tremendously.

Those sorts of things have to be considered and microbiologists are in a
situation where they can do that, but all of this has to go toward the
idea that you are going for validation, reproducibility, and the ability
then to generate quality data.

As an aside, it was mentioned yesterday that we might want to consider
testing human use antibiotics along with or in place of animal use
antibiotics. I will just make the comment that the availability of the
human use antibiotics may not always be there. Especially for companies
for which there may be human-pharma competitive counterparts.

So, it may not always be practical to obtain some of those particular
agents for comparative testing. At least in some cases.

(Slide)

Probably the key thing with all of the susceptibility testing data is
what do you do with it once you have it? We can generate all sorts of
numbers. Probably the most useful thing in the pre-approval mode would
be the histogram frequency data. The slides that Dik Mevius shared with
us yesterday would be a good example of that histogram frequency.

I think you can really start then to see some specific types of data and
how that can be used to support some of your interpretations. That is as
opposed to taking say an MIC 50, 90 or range, which is all right to do,
but that is perhaps not the best research use or summary of the data in
some of these types of studies.

For the interpretive criteria, which would be the susceptible
intermediate resistant, that really has more to do with the clinical
diagnostic laboratory utilization and interpretation of the data. And in
part that is still related to pre-approval studies, but that is a little
bit of a shift in emphasis.

Again, linkage that you have a phenotypically high MIC should be
associated with the genotypic characterization for that resistance gene.
The two do go hand-in-hand.

(Slide)

Just to list out some miscellaneous tests for susceptibility testing,
these are things that you find in the literature quite frequently. For
example, serum bactericidal activity attempts to look at the killing of
the antibiotics in the presence of serum.

Obviously that doesn\'t hold for all drugs. For example those that are
active against enterics. But, it does give you an idea of what happens
in vivo in some cases.

Post-antibiotic effect can be measured quite effectively. It is a
relatively simple and straightforward in vitro study to do. As well as
subMIC effects.

All of these then tend to reinforce, because they are part of the PD
characteristics, some of the uses of that whole discipline to support
rational dosing designs. The same for kill curves and in some cases \--
we really take this out a little bit further \-- effects on virulence
factors, for example toxin production, etc. could also be considered.

(Slide)

Some other considerations which may not be exclusively specific to
susceptibility testing or laboratory activities include the logistics.
Use of a single lab versus multiple can enter into this because of
variability, various technicians doing different things at different
times. So, that is factored in as I would design a study.

Obviously cost is a consideration. We have to be real about that.
Validation, we have always got to justify that these methods that we are
using are accurate, reproducible, and have some basis in fact.

Information technology or data capture and analysis, that is always a
part of this but it is never voiced very openly. But that is a big part
of it. You have got to take some kind of a record of your data and then
do something with it. That is a consideration.

We haven\'t heard anything as far as statistical design on these
experiments. Not being a statistician, I don\'t really want to get into
that, but that is a very important consideration that we should pay
attention to. Particularly, with sample numbers, statistical design,
etc. because that forms the best basis for the interpretation of the
studies.

If we are going to be using panels, obviously the manufacturer of those
panels has some considerations there.

The last thing here to consider that hasn\'t been mentioned is all these
studies need to be done under good laboratory practices which requires
certain specifications that the lab must adhere to. It makes it a little
bit more challenging, but by the same token it also makes the study that
much more believable when you have these kinds of practices in place.

Ultimately, whatever protocol is decided has to be approvable by the
CVM. That just goes without saying. That is all part of having NADAs and
that sort of thing.

We haven\'t mentioned anything as far as in the pre-approval studies how
that could relate to say a post-approval monitoring program. Certainly
some of these studies that are done pre-approval would have to be looked
at as foundations or background information to support some of the
post-approval monitoring programs that will be considered.

(Slide)

So, to wrap up here and to complete the jigsaw puzzle. Again, to put
into perspective, susceptibility data to me is the centerpiece. The
interpretation is really key. At this point we are using the data for
field surveys. Looking at reference collections. And sponsors are using
the data also to develop the NCCLS guidelines and interpretive criteria.

They are also using these to support the efficacy studies and in some
cases the rational dose selection through the use of PK and PD. The new
piece of the puzzle would be the lower right-hand corner there
(indicating) with the pre-approval studies and the resistance selection
where we can actually then start to use these very same tools to apply
in that regard.

(Slide)

So, once we have got the puzzle together we can get to work. And with
that I will close and open it for any questions.

MR. : Tom, could you explain what you mean that you sometimes have
difficulty getting human drugs for susceptibility testing?

DR. SHRYOCK: Sure. The comment had been made yesterday that we might
want to consider using human drugs to test for resistance selection.
Obviously, if a veterinary pharmaceutical company is developing
something that is in the pipeline in a different pharmaceutical company,
the request may or may not be fulfilled by that competing company.

MR. : You are talking about not approved human drugs, human drugs that
are in development?

DR. SHRYOCK: It can be either one. I wouldn\'t necessarily limit it.
There is no \--

MR. : Well, I would take exception to that because one part of that is
that there is no limitation. You can readily get, commercially get,
human drugs that are approved to do susceptibility testing. I agree that
if it is in human development it is hard to get. But, in actuality CDC
as we \-- as soon as a drug gets into Phase II trials, we often get the
human drug to do susceptibility testing.

For instance, we are testing against zerosin which is in Phase III
trials, etc. So, we can get the human drugs very easily, commercially
even once it is approved. It is the veterinary drugs that we cannot get.
The growth promoting veterinary drugs that we cannot get to do
susceptibility testing.

So, I just find it ironic that you had a different conclusion.

DR. SHRYOCK: Tom?

CHAIRPERSON WHITE: One more question.

DR. GOOTZ: Tom Gootz. One of the hot button organisms is campylobacter.
But I understand that there is not really an NCCLS approved or
standardized, I guess I would say, method for susceptibility testing
that. So, how dangerous is that for us to be doing a lot of pre-approval
studies with campylobacter and even monitoring resistance if the NCCLS
test isn\'t standardized yet?

DR. SHRYOCK: The NCCLS is working on that. Bob Walker could probably
speak to this more effectively than I, but we are in the process of
developing the quality control information for campylobacter against a
variety of different antibiotics.

So, that should be available shortly. At this point, life does go on and
people do use E-tests or other methods and come out with MICs. Chances
are that those values will be fairly close to what the NCCLS comes out
with. I can\'t foresee if they are going to be so different, that that
would invalidate or repudiate existing data.

It would be nice to have some agreed-upon standards, and that is what
Bob is working on and we should have those soon.

DR. WALKER: Yes. I think if you are going to be doing these studies, the
things that we are looking at right now is the QC organism will probably
be ATCC335660. So, campylobacter jejuni ATCC335660. Incubation
conditions will be 5 percent CO~2~ and the Mueller/Hinton broth for the
microdilution, Mueller/Hinton auger with 5 percent blood for the auger
dilution.

The disk diffusion, we are not going to recommend that testing method
because it is like reading a hologram. It all depends on how you hold
the platelet determines the zone size you get. Thirty-five to
thirty-seven degrees centigrade for 48 hours.

DR. GOOTZ: If you \--

CHAIRPERSON WHITE: Can you use the mic, sir.

DR. GOOTZ: Sorry. If you are to use an E-test on that in the interim for
any macrolide and then incubate it in CO~2~ to get the organism to grow,
you might likely get much higher MICs since the acid nature of the CO~2~
on the auger surface will artificially give you a higher MIC.

That is my only concern. But, no one is using that past this point. You
said your standardized test is a \-\-- broth?

DR. WALKER: Right. And we actually get better growth in 10 percent
CO~2~. The MICs don\'t change in 5 percent CO~2~ or 10 percent CO~2~,
except for the macrolydes. And the macrolydes always have a higher MIC
with the increased CO~2~. So, what we are doing is developing the
quality control ranges under these test conditions.

And so if you use this QC organism, use those test conditions, then any
other test you run, as long as your QC organism is in control the rest
of the testing method is, because you are defining those conditions, it
should be okay.

CHAIRPERSON WHITE: Thank you, Tom. Our next speaker is Dr. Marc
Lipsitch. Marc received his Ph.D. in Zoology from Oxford University. He
did a post-doc with Dr. Bruce Levin at Emory University and he is
currently an assistant professor of epidemiology at Harvard School of
Public Health.

His research focuses on mathematical modeling and experimental
approaches to study the population in evolutionary biology of bacterial
pathogens.

DR. LIPSITCH: I am sorry, I am subject to Microsoft\'s file size
inflation and wasn\'t able to get my entire presentation on a disk. It
is not that long, it is just fat.

CHAIRPERSON WHITE: Just to remind people that we will have both a study
concepts panel and an opportunity to ask questions at the end of this,
after our break. So any of these questions can be revisited for our
panel members.

DR. LIPSITCH: While we are waiting I will just tell you a little bit
what I am going to be talking about.

As the introduction said, I am a population evolutionary biologist and
have been interested recently in modeling antibiotic resistance in human
community-acquired and hospital-acquired organisms. And, I don\'t have
much first-hand knowledge about the farm animal situation, and am
therefore going to try to talk about it to predict two models.

So we used, recently, to look at in one case a hospital-acquired
pathogen and in another case a community-acquired viral pathogen. And
try to show by example how those sorts of models can be used in a human
situation and then try to draw parallels to where I think it might be
useful in the animal area. And then describe some of the limitations as
well.

So, we are getting close.

(Slide)

So, another outline of what I will talk about is I will briefly mention
some mechanisms by which antibiotic use may select for resistance as
those relate to the way those mechanisms are incorporated into
mathematical models.

I will then describe how these models can be used to look at infectious
disease transmission and give a couple of examples from our work. And
then talk a little bit about the applications to the veterinary
situation.

(Slide)

I have listed here three, what I think are distinct mechanisms by which
antibiotic use may select for resistance. And which of these mechanisms
is or are relevant depends on the organism and the drug that are in
question.

And, I list them explicitly here basically because when constructing a
mathematical models, one of the advantages is that you become explicit
about your assumptions about the way in which your drug is related to
your organism and to selection. And because of that advantage it is
important to construct your model appropriately for the drug and
organism in question.

So, in the human situation these are, the first of these is known as
acquired resistance. The idea there is that a subpopulation is already
resistant in the human host. Maybe a single mutant or a small population
of mutants or perhaps organisms carrying a plasmid.

And that upon treatment the susceptible population declines and that
resistant population overgrows, possibly leading to treatment failure.
So, that is an event within a single host and it is a direct effect on
the treated host.

A second mechanism is what is known as primary resistance. That is the
idea that you can become infected directly with a resistant organism.
That is why it is called primary resistance. And in this case, the use
of the antibiotic, it is a sort of indirect effect in promoting
resistance via primary resistance.

So, in this case the effective treatment of susceptible infections
reduces the transmissibility of those drug-susceptible infections making
it more likely that an individual who is infected will become infected
by a resistant organism rather than a susceptible organism.

So the difference, there is an important difference there between
primary and acquired resistance. Acquired resistance is associated with
a failure of treatment, whereas primary resistance is really associated
with a successful treatment. And in a way, the more successfully an
antimicrobial reduces the pathogen population in a treated host, the
more selection there is at the population level really on other hosts
for the spread of resistance.

You will see what I mean by that later, I hope.

The third is via the effect on the normal flora so the treatment of one
infection facilitates colonization by resistant organisms of another
species via the effects on the bystander organisms. I think that is a
process everyone here is familiar with.

(Slide)

The kind of mathematical modeling I am going to talk about today is what
is known as compartmental modeling and I am going to show you in a
little bit of detail what a compartmental model for a much simpler
system would be like. Then I am going to tell you fewer details about my
own models because they are a little more complicated than this and in
the interest of time. But I want to try to make it clear what these
models do and how they work.

They are called compartmental models because they track the progress of
hosts, individuals, people in my case, through a series of compartments
that refer to their state with respect to the infection you are
interested in.

So for a simple infection, for example measles, you consider people born
into a susceptible compartment. And then they may be infected at some
rate moving them into an infectious compartment. And it is those
infectious individuals who determine the rate at which new people become
infected.

And then individuals recover at a particular rate and become immune,
thereby making them no longer contribute to the process of making new
infections. People are removed by these compartments either by aging
past the age of susceptibility or, depending on the infection, or by
death.

And you can model, for example vaccination, in something like this, in a
model like this, by taking people directly from the susceptible category
into the recovered or immune category without going through the infected
state. And any model like this can be drawn as a diagram, but is
represented in the analysis or the computer simulations that you do as a
series of differential equations. And those are the equations for this
fairly simple mode.

(Slide)

These kinds of models have been applied for a variety of questions. In
the early part of this century models like this were used to evaluate
interventions for malaria control. Comparing vector control for example,
to preventing bites to various other interventions. They were targeted
in different ways to ask which of those kinds of things would be the
best to control the total burden of disease.

Another common use for these models is to calculate the critical
coverage for an immunization program. So what fraction of a community
has to be vaccinated by a particular vaccine in order to eliminate an
infection from that community?

Another very interesting kind of application of these is to try predict
and then prevent unintended effects of vaccination. The example for this
is the story with congenital rubella syndrome. And I will talk about
this just to illustrate how these indirect effects can work.

With rubella, the infection itself can be serious but the most serious
public health concern, especially in developed countries, with rubella
is the possibility of congenital rubella syndrome in which a pregnant
woman becomes infected and consequences ensue for her child.

When you vaccinate against an infection, one of the effects is to make
the average age in which that infection occurs increase. As a result of
that, if you start with a community in which the average age of
infection is long before puberty and you start to vaccinate, the number
of people who are infected at puberty or post-puberty at a time when CRS
may be a concern, will increase.

So, there is a possibility under certain circumstances for the actual
burden of congenital rubella syndrome to increase as a result of
vaccination transiently as the vaccine program is introduced.

That was recognized in the UK in the advance of the introduction of a
rubella vaccination program. The program was then designed specifically
in order to prevent that effect. So that is one of the really nice uses
of this kind of modeling.

More recently, similar models have gained the spotlight in looking
rather than at transmission in a community, looking at the dynamics of
viruses in infected cells in individuals and showing the dynamic process
of viral turnover in HIV infections.

(Slide)

So, how can these kinds of models be used to look at resistance? I think
there are really two kinds of applications which may shade into each
other, but really the approach is somewhat distinct and depends on the
kinds of data you have and the kinds of questions you are interested in.

The first, which I will give an example of in a minute, is trying to
make a very specific model to predict how fast resistance to one drug
will emerge in a particular organism following a change in use.

And the reason why you can\'t always do this is because there is a
requirement for fairly extensive data and a need to do fairly elaborate
analyses of the uncertainty and the predictions associated with the
uncertainty in your inputs. But sometimes that is possible and I will
explain one example of that.

The other application is to use general models which are not going to
give you an answer like resistance will double in 10 to 20 years or in
10 to 20 days. Instead, these models can be used to identify key
processes and parameters in the transmission dynamics of a particular
organism and the effect of the drug on resistance in that organism.

They can be used to suggest mechanisms that explain observations that
have been made but for which the explanation has been uncertain. And
they can be used to identify the approximate time scales for changes in
resistance, even when precise predictions are not possible. I will give
an example of that as well.

(Slide)

My first case study is a more predictive model. And this is a model that
we put together in order to look at the changes in resistance to
nucleoside analogs in herpes simplex virus type I, which is the cause of
cold sores in a large number of people and a cause of a much more severe
disease in immunocompromised people.

The maker of the topical cream for treatment, antiviral cream, for
treatment of this infection, applied for over the counter status for
this drug. This obviously raised serious issues of resistance.
Particularly because the topical pencylovir cream showed
cross-resistance to all of the major first line anti-herpes viral drugs.

So that if you select it for resistance to that cream it could quite
serious, particularly in the smaller but important population of persons
who got severe disease from the same organisms.

So we were asked to try to model the question what impact would increase
use of topical pencyclovir have on resistance? So, there was a lot of
data fortunately available for this situation. We knew a lot about the
base line levels. We knew the current prevalence of the infection and of
systematic recurrences of the infection.

We were able to calculate from these data that there is a period of
roughly 10 years or more on average between transmissions. So that a
person who is infected, on average it would be 10 years before they
would pass on the infection, even if they were in a community of totally
unexposed hosts.

We knew the current levels of anti-viral use and we knew that that was a
large number \-- and I am not giving numbers here because they are not
so important in understanding the generalities here \-- but, I can
answer questions about that later.

They are large in kilogram and daily-dose terms, and it looked as though
there was really a lot of use of the anti-viral class already. But, if
you then looked at the proportion of the total cases being treated it
was a very, very small number. There are a lot of people with recurrent
herpes labialis and a very small number of those were being treated. So,
as a selective pressure on the organism as a whole, the burden of
current treatment was quite low.

(Slide)

And that was reflective in current resistance levels which were about .3
percent in the immunocompetent hosts and higher in the immunocompromised
hosts, where the use was much higher and the emergence of resistance is
much easier in immunocompromised hosts.

(Slide)

So, how does anti-viral use in this case select for resistance? Well, I
described those three mechanisms and I think there are two of those
mechanisms that are relevant for the HSV1 case.

The first is that treatment with the pencyclovir cream reduces viral
shedding, the duration, by about 20 to 25 percent. And therefore may
promote resistance by reducing transmission of the sensitive virus,
leaving hosts susceptible to infection with resistant virus. Leaving the
other hosts who might have been exposed to that susceptible virus, but
aren\'t then susceptible to infection with resistant. So that is one
mechanism and that selection is proportional to the efficacy of the drug
in reducing shedding.

The other mechanism that might be involved is that in rare cases it may
cause acquired resistance. And treated immunocompetent hosts were the
ones to whom this OTC drug would have been marketed had it been
approved, which it wasn\'t.

The data on that showed that in about 1,800 patients in different
studies there were no clear reports of acquired resistance. There were
four case reports, not in studies but just people who had been
identified that might have been immunocompetent hosts in whom resistance
emerged but it wasn\'t clear.

So, our best quantitative data was zero out of 1,800. Which if you take
a confidence interval, the 95 percent confidence interval says that
number can give you confidence that the true rate of acquired resistance
is less than 1 in 625.

I am going through all of these numbers in this case for a reason.
Because it turns out the 1 in 625 is actually very different from zero,
which is what we initially thought. We in fact initially modeled it with
zero and our predictions were so optimistic that we then wanted to see
how close to zero is zero. And you will see that in a minute.

(Slide)

So we constructed a more complicated model than the one I just showed
you which basically mimics that idea of people being born susceptible
becoming infected. We put in two different kinds of infection: resistant
and sensitive. We considered the dynamics of the way in which herpes
works with period recurrences, some of which may be treated, thereby
reducing shedding.

(Slide)

And we expanded that model. Again, our initial predictions were really
very optimistic and we wanted to make sure that that was not an artifact
of the model. So we expanded the model to include immunocompromised
hosts and to include several other factors which we thought would be
likely to speed the development of resistance in a population.

(Slide)

The uncertainties that went in the model were really parameter values.
One was how much of this drug would be used? And based on parallels from
the UK situation with another drug, we suspected up to 30 percent of
episodes might be treated.

We didn\'t know, it is not known, how much of transmission comes from
symptomatic versus asymptomatic sources. And that is important because
if most transmission comes from symptomatic patients, then treatment of
those symptomatic patients will have a big selective effect on
resistance in the organism.

Whereas if most transmission is from asymptomatic patients then
treatment of the symptoms may be much lesser of a selective force. We
didn\'t know much about the immunocompromised in the transmission cycle.
We didn\'t know whether resistant viruses are less transmissible than
drug-sensitive viruses and we don\'t know the actual probability of
acquired resistance.

(Slide)

So, as I said, we made some educated guesses and estimates of those
parameters and we started by assuming no acquired resistance because we
thought zero out of 1,800 sounds like zero. What we found that the
scale, on the X axis there is years and the red line represents our most
pessimistic assumptions about everything except acquired resistance.

So we assumed that all transmission came from symptomatic patients,
meaning the selective effect would be greatest and we assumed that
resistant organisms were just as transmissible as drug-sensitive. We
made a variety of what we thought were pessimistic assumptions and the
model predicts then a very, very small rise from about .3 percent to
about .4 percent over 50 years.

So, that was wildly optimistic. We felt rather sheepish about this and a
little uncomfortable and therefore we wanted to \-- spent a lot of time
trying to figure out what is accounting for this slow increase.

(Slide)

And we went back to this acquired resistance point. And I summarized
most of what is on this slide earlier. The main thing being that we knew
it was rare, but we didn\'t know how rare. And the numbers from studies
that existed suggest that it was less than 1 in 600 patients.

(Slide)

So we then reran the model assuming that it was in fact 1 in 625
patients, which is at the top left (indicating). The slide I showed
before is at the bottom right (indicating) for comparison and
intermediate cases are shown in the other two.

And what you see at the top left is that making the assumption that
instead of zero acquired resistance, it was 1 in 600 treated patients
had acquired resistance, totally changed the dynamics of the system. And
under the pessimistic assumptions, instead of going from .3 to .4
percent in 50 years, it went from .3 percent to almost 3 percent in 50
years.

And even under less pessimistic, more realistic assumptions in the
yellow line, it went up to about 1.5 percent. Still relatively slow
compared to some other pathogens that you may be aware of. But, the sort
of wildly low numbers that we initially found were not verified once we
added in acquired resistance.

(Slide)

So, what we concluded from that was that under all sets of assumptions
the predicted increase in resistance would be slow compared to, for
example, just to take an example of vancomycin resistant enterococcus
where the doubling times have been in the year to two-year range and we
have seen a really rapid increase over 10 years. We are looking at a few
percentage points over 50 years.

But, we found that a small probability of acquired resistance
dramatically accelerates the spread in the community. And we found that
our conclusions were similar to predictions that were made a few years
earlier by Blower and colleagues for genital herpes.

(Slide)

For a little point of comparison, this is the output of another model by
Alan Pearlson\'s group in Los Alamos that looked at influenza A
resistance under treatment in an outbreak with romantadine and
amantadine. What they \-- the dotted line at the bottom shows the
resistance over this rather rapid breakout, over 30 days as the
horizontal scale.

While the numbers aren\'t all that impressive in terms of total number
of people, you see that the percentage resistance climbs rapidly and
reaches a substantial fraction of the epidemic really within two weeks.

And so we wanted to understand of course why we are talking about a few
percentage points in 50 years versus influenza with a couple of weeks.

(Slide)

And when we analyzed the differences between our assumptions and theirs,
and then started tweaking those assumptions unrealistically in our case,
but just to see if we could generate rapid increases in resistance like
theirs, it turns out you can if change the parameters to be of a
different disease.

And the parameters, such as influenza, the parameters that are most
important in determining those, are first of all determining the rate of
increase. First of all, the availability of resistant variance by
acquired resistance or by gene transfer.

Secondly, the level of selective pressure. The acquired resistance as we
saw made a big difference in our model and also the possibility for
primary resistance in transmission between sensitives. Or reducing
transmission of sensitives.

What is not important directly are things like kilogram usage, doses a
little bit. But especially kilogram usage is not important. The compound
we were studying, for example, was a topical compound and therefore the
total amount of drug in there we calculated the price on a gram of drug
basis and it is more expensive than gold as it turns out.

So that the total increase in kilogram usage compared to all the
perentro use that was going on in the community would have been
negligible had this drug been used at the levels contemplated under
over-the-counter use.

Nonetheless, the selective effect would have been important because it
was treating many more infections. So kilograms are not a very good
proxy for selective effect.

(Slide)

The generation time of the infection is probably the most important
difference between our results and the influenza results. In the case of
influenza, the meantime between transmission is on the order of a week
or two. In the case of herpes simplex infections type I, it is on the
order of 10 years.

And that is basically \-- natural selection works on the generation time
of the organism involved. And in this case the relevant generation is
not one viral replication, but it is the transmission of a case. And as
a result, the time scale is magnified from weeks to decades in our case
compared to the flu case.

Other factors include differences in transmissibility between resistant
and sensitive infections and other factors which I am not going to talk
about.

(Slide)

So, that is a case where we were able to make fairly quantitative
predictions with quite wide uncertainty. But we were able to say even
under the worst assumptions the rate of increase would not be rapid in
the scale of weeks, it would take decades and it would be a few
percentage points. Which might be very important and certainly
clinically could have important consequences, but just as a matter of
scale would not be the same as observed in some other infections.

What I want to turn to now is a second case study of some work we did on
antibiotic resistance in hospitals. Which really tried to do a different
thing. We weren\'t trying to make such specific predictions, but rather
to explain some of the observations that had been made in
hospital-acquired infections for which it was unclear the reason why.

One of those observations that had been made is that following an
intervention in a hospital resistance levels can change much faster than
they do in the community.

A second observation is that non-specific control measures, which are
not targeted particularly at resistant infections can nonetheless reduce
the frequency of resistant infections and maybe even more so than
drug-sensitive infections.

And finally, there was a puzzling observation that in certain studies
use of a single antibiotic could be a risk factor for carrying bacteria
resistant to another. Even in the absence of cross-resistance or linked
resistance determinants.

So for example, in a recent study flouroquinolone use was a risk factor
for receipt of extended spectrum betalactam base gram-negatives in a
hospital. And that is strange because what would the mechanism be?

(Slide)

So the first phenomenon I just described is a difference in rates of
change between community-acquired and hospital-acquired infections. This
is the data from a finish study supplied to me, kindly on a slide by
colleagues at CDC, in which erythromycin use was curtailed by about
six-fold in Finland.

And what happened following that was a gradual change in the levels of
resistance to erythromycin in Group A Strep, first an increase, then a
decline. Finally, about a 50 percent decline from the original level
from about 14 percent to about 7 percent.

But that took from 1988 when the program was put in place to \'96 when
they stopped measuring. Over eight years we saw a relatively, well a 50
percent decline, over almost a decade. A similar situation was seen in
strep pneumoniae in Iceland following interventions to reduce penicillin
use.

(Slide)

And those are two of the real success stories that are cited for the
benefits of controlling antibiotic resistance in the community, use in
the community to control resistance. But in these cases the reductions
take years.

Mathematical models by others, particularly Darren Austin and Roy
Anderson at Oxford, have shown that the rate of decline in resistance is
determined largely by the fitness cost of resistance. The difference
between the transmissibility of drug sensitive and drug resistant
infections.

Those models include some simplifications which may not be entirely
appropriate. And the reason for my question mark under the comment \"No
fitness costs/no decline\" is that I think there are ways in which
declines can happen, even if there is no difference in transmission
between resistant and sensitive. But, as a first approximation, that is
expected to be slow or non-existent unless there is a fitness
difference.

So, I think \-- this is really directed toward another question, but the
expectations for studies in the community, both previous experience and
now mathematical models suggest that you should have had moderate
expectations for the success of interventions to control resistance in
the community.

(Slide)

In contrast to what you observe in the hospital, this is the result of a
study from the early \'80s on Methicillin resistant or Gentamicin
resistant MRSA in a hospital. And as you see, following interventions in
August of 1979, the level of this organism went from 30 percent down to
zero in December and was really eliminated from the hospital.

(Slide)

So we wanted to understand what the mechanism of these differences
between communities and hospitals might be. We constructed another
compartmental model specific for a hospital. And, again I won\'t go into
details, but the idea is that individuals could be either colonized with
drug sensitive, colonized with drug resistant, or not colonized with a
particular bacterial species which we did not specify because we were
trying to do a general model in this case.

And, that individuals in contrast to a community where individuals are
born not originally carrying a particular infection, or colonizing
organism. In the hospital, individuals are admitted often carrying drug
sensitive and sometimes even carrying drug resistant versions of the
organism. E. coli or enterococcus for example. That turns out to be
extremely important for the dynamics of the system.

(Slide)

So, the first prediction that we found was that using realistic
parameters for a sort of compromise among several hospital acquired
infections, you see very rapid response to interventions. If you reduce
the use of a drug or switch it to another drug you see very rapid
reductions in resistance to that drug over a time scale of days to
weeks, in contrast to the community.

You also see that if you do infection control it disproportionately will
reduce the prevalence of resistant bacteria as we observed.

(Slide)

And finally, we find a very strange phenomenon which is that, as I said
earlier, sometimes in some studies if you look at the association
between having received one antibiotic and having bacteria resistant to
another you see a positive association. And that was a puzzling
observation.

In this figure, we show \-- the red lines show individuals who have
received a second drug, drug two, and their level of resistance to a
first drug, drug one, in those people. And the blue line shows those who
have not received drug two.

And no matter what the overall rate of treatment with drug two, in that
model we see that the persons who have received drug two are at higher
risk of resistance to drug one. But, the more drug two is used in the
hospital, at the whole hospital level, the association goes the other
way. The more of drug two is used, the less resistance to drug one there
is.

So, there are a lot of assumptions behind that which I am not going to
have time to explain now. I am happy to discuss in more detail over
coffee or during questions, but the point of this is simply to say that
in a very simple system, it really matters what you measure. If you
measure individual level associations or group level risks and that may
have a lesson for other situations.

(Slide)

I went through the predictions rather quickly and didn\'t explain why.
In all of those cases of those predictions, the reason for them and they
reason why they are unique to hospitals, is this idea of people flowing
through the hospital. Coming in colonized and staying for a short time
and then leaving.

And this influx of drug sensitive bacteria makes a big difference to the
dynamics of a hospital versus a community. And that may be relevant in
considering the animal situation as well.

(Slide)

So, what can these kinds of models possibly suggest for animals, and I
offer these tentatively in an audience full of people who know much more
about the animal situation than I do.

First is population processes in the host population may be important.
As I said, having an open system in which people are constantly coming
in or animals are constantly coming in colonized may change the
situation. And where the life span and the duration of stay in a
particular group, like in a particular herd for example, has a big
impact on the rates.

The second possible lesson is never say never. That comes from the
herpes example. And what I mean by that is that very rare events like
the one in 600 or less event of acquired resistance may have an
important impact on the determinants of changes in the resistant level.

The third thing is that indirect effects are important. All of these
models are really designed, and their strength is that they are good at
looking at how treating some individuals affects the flora of others.
And I think that if this kind of modeling can make a contribution to the
farm animal situation, this is probably how.

And finally, models like this, even with limited data can give a rough
idea of time scale thereby making a rational basis for measuring the
effects of changes in antibiotic use.

While we don\'t know precisely what time scale we expect things to
happen in a hospital, for example if you reduce the use of a particular
drug, we know that the dynamics should be on the order of weeks to
months, and not on the order of years. And that is done with very
general data.

(Slide)

So, what can these models do? They can predict the response of bacteria
to changes in antibiotic use if sufficient data are available. They can
make testable predictions about the factors influencing these time
scales, which analogy is the right one. So should we think of a
particular agricultural or farm animal situation as being more like the
herpes situation of decades or more like the influenza situation of
weeks?

They can suggest mechanisms to explain previously unexplained
observations and they can aid and study design by suggesting time frame,
sample size, and some of the key processes that should be measured.

(Slide)

What can they not do? One thing, unfortunately, is they can\'t make
reliable predictions based on limited data. The better your data,
obviously, the more specific the predictions can be.

The second thing they can\'t do which I think is really critical in
understanding the role of animal use of antibiotics and their possible
impacts on human health is no model can predict very rare stochastic
events which may be critical to the evolution of resistance, such as
genetic innovations.

(Slide)

This is a dendrogram from a group led by Marshall and colleagues showing
the Van-A, vancomycin glycopeptide resistance genes as found in
enterococcus. Which groups, as being very similar in sequence to those
from the glycopeptide producers, the organisms from which those drugs
were initially isolated.

And how that jump was made, probably through other intermediate species
is a mystery. And no model can predict that in any real sense because it
is a rare event which just happened to take some decades probably, at
least until it reached an important level.

And what role, use in animals plays in facilitating that sort of gene
transfer is really, it is open to discussion, but it is not something
that you can model.

(Slide)

And finally, I want to just make a brief comparison between what I have
been talking about, my compartmental models, and the sorts of models
that many of us saw presented. A very impressive example of last time we
were here in December by David Foes, the risk assessment model.

The compartmental models that I have described are deterministic, while
the other ones are probablistic or stochastic risk assessments.

One benefit of the kinds of models I talked about is that they are very
good at looking at direct plus indirect effects, while the risk
assessment models don\'t have population dynamics and therefore are much
better for looking at direct effects.

Another benefit of compartmental models is that they naturally handle
changes over time because of this dynamic aspect. Whereas the risk
assessment models can do that, but it adds another layer of complexity.

On the other hand, the compartmental models have a harder time
determining uncertainty because they are deterministic. You can add
uncertainty analyses, but that increases the complexity. While
uncertainty analyses is a quite natural part of risk assessment
modeling.

(Slide)

And finally, I just wanted to mention my collaborators on the herpes
work: Bruce Levin, Arista Mantia at Emory University and colleagues at
Smith Kline Beecham. And in the hospital work: Karl Berkstrom and Bruce
Levin at Emory.

Thanks!

CHAIRPERSON WHITE: We have time for one or two questions, real quick.

MR. : Marc, I think there are examples of some of the points that
you\'ve raised from agriculture and I think a really great example is
the slime facility at the University of Kentucky which has been without
tetracycline use for a decade, yet still has a prevalence of
tetracycline resistance amongst E. coliform.

And probably because they are of course giving their replacement pigs
from \-- it is not an entirely closed \-- and the second thing is,
interest in data from both the United States and from Denmark and Europe
of the transition of traditional dairies to organic dairies and how long
it takes for resistance to be impacted as they change to an organic
dairy, which organic dairies don\'t use any antibiotics.

And what is interesting is that until the organic farm \-- experience in
both those settings \-- until the organic farm changes to raising all
its own replacements, not having any \-- purchasing any \-- so in other
words, to become a certified organic you have to raise your own
replacements. But during that transitional period they are purchasing
replacement heifers.

They see no impact upon resistance until they have a closed cycle.

DR. LIPSITCH: Right.

MR. : In fact aren\'t bringing in external pressures that might apply.
And just \-- the last thing. We emphasize the idea of an unpredictable
event is really the most disconcerting thing because I think as we \--
those uncertain events, rare events are certain to occur, we just don\'t
know how long it will take until they occur. So, although uncertain they
are certain to eventually happen.

DR. LIPSITCH: I agree and thanks for both the suggestions of the data. I
would be interested in learning more.

CHAIRPERSON WHITE: Any other questions? If not we will move to our
break. We are about 10 minutes behind schedule, but we started 10
minutes late so being optimistic we are right on time.

So, why don\'t we \-- we have a 25 minute break scheduled. Why don\'t we
meet back here promptly at 10:40. We will start up again at 10:40. Thank
you.

(Break)

CHAIRPERSON WHITE: Thank you for coming back right on time. As Dr. Cray
mentioned, this is an analogy of trying to herd calves to try and get
everybody back in here, but I think it shows that we are just a very
social group and we enjoy talking about this issue so much. So,
hopefully this continues over in the breakout groups this afternoon.

Our next speaker is Dr. Paula \--

DR. CRAY: Pointer, pointer.

CHAIRPERSON WHITE: Oh yeah. I am sorry. If anyone has a pointer they
could donate for this talk it would be greatly appreciated. The ones we
have all of the batteries are dying. Anybody? Marc, do you have one?
Thank you.

Our next speaker is Dr. Paula Fedorka-Cray. She received her B.S. in
Microbiology from the best university around, Penn State.

DR. CRAY: Yea!

CHAIRPERSON WHITE: Sorry.

DR. CRAY: I like this guy.

CHAIRPERSON WHITE: Masters from North Dakota State University where I
also was. Is that a Masters of Administration?

DR. CRAY: Yes.

CHAIRPERSON WHITE: From Johns Hopkins University. And a Ph.D. in
Veterinary Microbiology from the University of Nebraska Medical School.

She has been employed by USDA-ARS since 1991 and is currently the
research leader of the antimicrobial research unit at Richard Russell
Research Center in Athens, Georgia. She is also the coordinator for the
veterinary arm of the National Antimicrobial Resistance Monitoring
System. Dr. Cray.

DR. CRAY: Thank you. Thank you, Dave. I always have a hard time at
football season: half of me is blue and half of me is red for Nebraska
and Penn State. North Dakota just gets cold.

I will go ahead and talk here. The title of the talk in the paper I
think talks about modeling. But what I am going to do is I am going to
show you some of the experiences that we have had.

I have been doing this for 20 years now, looking at vibrio, actinobus
solois, salmonella, and campylobacter now in various animal systems.
Actually looking at the pathogenesis of the disease and colonization
carrier state.

And also here from the laboratory is Scott Ladely. Scott and I have
worked together for 15 of those last 20 years and much to his credit he
has still lasted, I must be doing something right. He has as much
experience doing this as I do and hopefully we would be split up between
two different groups.

He is a wealth of information and I would ask him questions also in the
hall if you have any after this presentation.

(Slide)

And, before we start I would just like to acknowledge Scott and my
collaborators and most importantly the people in the lab who really make
all of this happen, who are still back, literally, working as we speak.

(Slide)

Well, I think one of the most important points from this whole talk, if
you don\'t take anything else and you can nod off now after coffee and
donuts, is that you have to ask the right question.

And the problem is what is the right question? The right question is the
question that you want to have answered. But, the interesting thing
about microbiology and animal models is that you can manipulate the
system to really achieve most of what you want to achieve.

Therein lies the confounding basis for some of these studies is that now
that we have done enough of these, we can challenge and we can set up a
system to observe either disease or shedding or colonization or the
carrier state under enough different parameters.

It gives us a really good idea then of what might be happening, not only
in a challenge, a laboratory design system. But also what might be
happening in the field too. And we have carried a number of these
studies out into the field so that we have a very good idea of whether
there is a synchronization going on between the information.

But again, I think that you really have to sit down and you have to ask
yourself what is the right question. And you really want to only ask one
question. Because once you start adding more variables in, it becomes
very difficult I think to achieve the answer that you are really looking
for.

(Slide)

So, where do we look? Some people would say we just want to look in the
animal. I think what we really need to do is look everywhere, somewhere,
and sometimes nowhere or what we perceive is nowhere. And I think it
interesting from the last talk, from Marc\'s last talk, is that one of
the things that I find to be most predictable about microbiology is its
unpredictability.

In that, I think that a lot of what you are seeing is that there are
these microorganisms that are survivalists and they are creating their
own niche in the environment. And so where you might not think to look
or where you might think that there is nothing residing anymore, in fact
the bacteria are still there.

Either they are viable, but not culturable or they are in such low
numbers that we don\'t have the sensitivity and specificity to find them
yet. And we will see how some of that fits in as we go on.

(Slide)

So, one of the first things that we need to go ahead and do is we need
to select a target organism. In addition to asking the right question
what do you want to test? What kind of target pathogen do we want to
look at? Do we want to look at the organism that drug might be indicated
for use against?

Are we going to look at a food-borne pathogen? Are we going to look at
salmonella or campylobacter or 015787? Are we going to look at a
commensal, E. coli? Are we going to look at enterococci? Are we going to
look at lactobacillus? Are we going to look at anaerobes? Are we going
to look at any of the other aerobes?

And you can develop list, upon list, upon list of organisms that you are
going to look for. And all of this is going to be limited by time,
money, and expertise of the lab that you actually have built up to look
at these model systems.

And, it may come down to a time how what we do in a lot of these
situations is that we actually select now, in our studies, many
different organisms and we freeze them. You talk about collections, if
anyone ever got into our freezers downstairs we probably have 60,000
cultures now in some various states of form.

We also have a lot of frozen feces too. It is not some place I would
want to be if we had a power outage and you were stuck in a tornado.
But, there are things in the freezer. It is just what is in the freezer.

And what we are doing now is we are trying to develop these studies so
that we are actually saving these organisms, taking them from the same
animal so that if we want to ask a different question for another
organism we can do that from the same test conditions retrospectively.
And, if you have the ability to do that, that is one thing that I would
encourage you to set up.

(Slide)

All right. We have gone down through here looking at aerobes. Do we
select anaerobes, enteric bugs, respiratory bugs, and now do we need to
consider mixed infections? I mean yesterday we heard a lot about \"take
it to the field\". Well, the problem with taking it to the field is that
you have a whole other complicating set of factors that are superimposed
now upon your original design.

And those in particular are mixed infections. Especially with the viral
pathogens which tend to relegate then some of the food-borne pathogens
to secondary infections. And, it also exacerbates clinical disease and
actually outcome. So survivability will be affected in these situations
too.

And you can have salmonella, especially PRRS, and we have a paper coming
out that clearly demonstrates in Vet Micro in the next month or so, that
if you have a PRRS infection with a salmonella infection you are going
to increase your mortality about five-fold.

And that would have tremendous impact on what you may be looking for,
what you may want to select, and how many animals you may have to select
to get the desired number of organisms that you want.

And E. coli also convicts with a variety of viral pathogens and other
bacteria too to then be relegated to a secondary pathogen. Now, the
other thing that we have observed over time is the effect of the
antibiotics on the actual bacteria as we screen for these.

If we look at \-- we looked at 420 isolates of salmonella from a study
and we looked at the effect of resistance to tetracyclines:
oxytetracycline, chlortetracycline, and tetracycline and we essentially
saw no difference between the three.

However, there had been reports where if you look at pasteurella or some
of the other E. coli or other bacteria and the tetracyclines that in
fact you will see a difference in resistance between the oxytets, the
chrlortets, and the tetracyclines.

So, in fact if you are reporting out that you just have resistance to
tetracyclines, and you are not very specific in your drug, you may in
fact be misrepresenting what you are actually going to need to be
looking at.

(Slide)

So, if we take salmonella for an example here and we look at three
different parameters. If we want to look at disease, then in fact we
need to have fairly high exposure doses in a laboratory situation.
Typically 10^9^ and up. And I liken this sometimes to just giving them a
paste. And, it is also a very strain dependent.

And I have UK\'s story here. And this is not a United Kingdom story,
this is a Roy Curtis universal killer story. He has a strain that he
calls UK, for universal killer, because he swore this strain would kill
anything. And so we said okay, well send it to us because he had never
tested it in pigs. This was in my other life in Iowa.

And so we put this Typhimurium into pigs and we thought well, we better
do a 10^8^ dose so we can just see how devastating it is and then we
will knock it down to about a 10^3^ dose so we at least have some
animals that survive. And we went in the next day and we went down there
with all of our necropsy gear on thinking that this was going to be one
of those mornings and in fact it looked like we had given them bubble
gum or candy.

I mean they were running around. I called Roy and I said you are not
going to believe this. We had to send him the isolate back again, but
the universal killer apparently doesn\'t kill pigs. Or these were super
pigs. These were Iowa pigs. They are good chops.

But, I think that what it points out is that even within the same
serotype that you can have differences that occur and especially between
animal species, if we take a Typhimurium from chickens say and we try to
infect swine we have a very different outcome then if we take that same
Typhimurium and pass it through a pig first.

So, there are all of these confounding factors now when you look at
exactly what you are going to try to reproduce the disease with. All
right.

Well, then if we just want to set up a model where we look at just
shedding and we don\'t have disease or we have very mild, transient,
clinical disease: some febrile response, inappetence, some other things
going on. Then we can cut the dose down to a range of about 10^6^/10^8^
CFU per pig.

And if we just want to look at colonization for a short period of time,
and by short I mean about six to eight weeks and not through to
slaughter age, then it was absolutely no disease. I mean it looks like
you have done nothing but annoy these guys after you give them the
challenge doses.

You give them about a 10^3^ and you have very minimal and short-term
shedding.

(Slide)

Now, if we look at salmonella heidelberg under these situations and we
look at two weeks and six weeks post-challenge, at 10^3^, 10^6^, and
10^9^ dose, and if we look at the tonsil, the ileo colic lymph node, the
ileo colic junction, and the cecal contents \-- and I just have to say
it is so nice to be talking about something besides antibiotics right
now. This reminds me of my other life \-- that at two weeks from the
tonsil and at six weeks we really don\'t have recovery at a 10^3^ dose.

However, these are log 10 CFUs, so we have about a one and one-quarter
log, a half a log, now if we boost it up to a 10^9^ challenge dose you
see we dramatically increase the number of bacteria that can be isolated
per gram of tissue. And this holds true now, looking at the tonsil, the
ileo colic lymph node, the ileo colic junction, and the cecal contents.
These are internasal inoculations and we will go into that in the next
couple of slides.

But you can see then that depending upon the tissue that you select and
depending upon the time that you are looking you can find very different
numbers residing in any of these tissues.

(Slide)

All right. If we have a lab-attenuated strain, I think that typically
they tend to be not very virulent. I think that this is part of the
problem that we find when people try to repeat our challenge studies and
they end up using an ATTC strain.

Or we find out that they have had a strain that they have passed for 52
and one-half years now because Salmon gave it to them, you know, and
they really like this strain. Well, it just doesn\'t work that way. And
what you have to do is that you have to pass it back through a mouse or
the species that you are working with at that particular time to boost
its virulence.

Essentially, I think that what you are doing is you are turning
everything back on. You are exciting the bug again and it is up for
doing battle now in the host system. The field isolates then we find to
be often more virulent. Especially those that we actually take from a
disease case.

However, going back to salmonella as an example, we have to look at the
serotype considerations. There are more than 2,400 different serotypes
and not all of them are equally virulent, although all of them have the
potential to cause disease. I think that is a very important point that
you don\'t want to forget.

However, we find that the virulence can differ dramatically within
animals. If you had Typhimurium in just about anything at the particular
dose you can induce disease. If you have Poona, Poona is very often
recovered from exotics. Iguanas love this stuff, which is why I will
never have my sons

\-- well, besides they are ugly. They will never have an iguana in the
house.

But Poon is often recovered from children. It is very virulent in
children, but it is almost like a commensal in exotics. Okay.

(Slide)

And then you have differences in resistance. And what do I mean by that?
Well, if we look at the top 12 serotypes \-- I must have been delirious
and couldn\'t count \-- you can see that from 1998 we had 557
Typhimurium and on down. So these are the first six and the next six.
And those are just the actual numbers that we have out of about 3,318.

(Slide)

If we look at beef on farm or cattle on farm, cattle diagnostic samples,
cattle slaughter, chicken diagnostic, and chicken slaughter and those
are the total number of isolates we had in each of those groups. We can
see then a specificity for some of the serotypes begins to emerge.

Certainly, the chickens at least compared to beef, you will find
Heidelberg more often than you will find Derby and you will find Derby
more often in swine \-- this is swine diagnostic, swine slaughter,
turkey diagnostic, turkey slaughter \-- then you will Heidelberg.

And then you find some of these other ones mixed in there depending upon
the situation. So you do find a significant association with some of
these serotypes with some of the species that you are going to be
isolating them from.

(Slide)

However, as serotypes vary within each of these production schemes, and
I will show that in the next two slides, so does the resistance between
these serotypes. Typhimurium regardless of species tends to have the
highest resistance associated with it and others would include
Heidelberg and Mbandaka.

While salmonella sero montevideo may show very, very little if any
resistance. The same with Poona and all of the ones that you get from
the exotics.

If you look at salmonella anatum and agona, which are most often
resistant to tetracycline and very little resistance to the other
antimicrobiotics. Well, those are the ones that are most often recovered
from swine operations, too.

(Slide)

Now, serotypes may also be related to clinical outcome. And we have
talked a little bit about that. If we look at a study that we just
conducted with poultry and we look at DT104 a sensitive strain versus
DT104 a resistant, the penna resistant, the typical. So we have a pan
sensitive DT104, then we have a penna resistant DT104.

And we expose these poultry on day of hatch to two seeders that were
challenged with 10^8^ CFU of either one of these bacteria. We find that
although there was absolutely no clinical disease between these groups,
in fact the resistant bug is shed in higher numbers, significantly
higher numbers, and it also significantly colonizes more birds.

So we have two same serotypes, two same fasche types, but we have a
resistance versus a sensitive phenotype essentially and we have a very
different outcome when it comes to shedding and colonization.

(Slide)

Now, some of the other considerations that we have to have our genetics.
Now we are trying to build the best \-- I can\'t say bug anymore \-- but
we are trying to build the best animal, all right. We want to have an
animal that is resistant to disease, that is resistant to drought,
resistant to McDonald\'s burgers, resistant to a lot of different
things.

So, you have animals now that you are actually trying to incorporate
some of these resistant strains into. However, the problem with
salmonella is that it is ubiquitous and it is very, very hard to find
any animals at all that aren\'t colonized with salmonella. Especially
when you look at conventional systems.

So, if we try to \-- one of the things that was pointed out in several
of these presentations was that you want to have animals that are free
of salmonella. That is no easy task. It took us several years to develop
a model where we have actually been able to routinely now take piglets
and maintain them free of salmonella.

But, in these early studies we looked at a group of 43 sows and we had
two sows positive for salmonella before farrowing. Seventy-two hours
after farrowing you had 27 of the 43 sows that were farrowing.

And then you always have your outlier. You had one that was negative,
negative. Obviously, she liked being pregnant and having kids. But here
you have 38 sows and we had six that were positive after farrowing and
we only had one that was negative, negative again.

If we wean these pigs at 10 to 14 days of age, especially from sows that
we know are negative from salmonella, then we can typically maintain
them free of salmonella. But, what happens is if you just wean at 10 to
14 days of age and take it from this type of scenario, we ended up with
41 animals out of 407, or about 10 percent, that were positive.

Now, not all of these serotypes interestingly matched the sow. So the
question is where were they coming from? Well, they were probably coming
from the environment or they were in such low numbers that we weren\'t
able to recover them in the first place.

Now, we also have to look at the age of the animal when we look at the
animal models because we know that for salmonella especially less than
four to six weeks of age it is very difficult to reproduce disease in a
lot of situations. And that is because you have the maternal antibody
coming through.

However, the most susceptible age is six to eight weeks of age. And then
you come into another window then of about 8 to 10 weeks of age where
literally you would have to give them a paste in a lot of instances to
get them sick again.

So, it is very, very important to look at the animals that you are using
in any of these experiments. How you are selecting them and are they
truly free of what you want to look at. And one of the other organisms I
think that is going to be just horrendous to try to work out a system
with is campylobacter.

(Slide)

That is why I am using salmonella in these slides. So, if we look on the
farm then at just some other confounders, well what are we going to find
in a typical farm situation? Well, we went on to this one farm, and
actually we repeated this twice, and if we looked at the farm and we
actually did field environmental samples.

So we took 150 samples every time we went out. We also looked at tissue.
We necropsied 10 pigs, all the way through slaughter. And if you don\'t
think necropsying 250 pound pigs in the middle of July in Iowa is a
picnic, boy, come see me this summer!

And what we saw was is that we had these seven serotypes that we were
able to recover, but not all the time. We never saw S. agona until nine
weeks of age and that persisted through until slaughter. We saw Agona
sporadically, more consistently missing the ninth week. Berta we only
saw once. Brandenberg we saw through nine weeks. Johannesburg we did not
see until slaughter. Monte Video, 18 weeks and Worthington one, nine and
18 weeks.

Now interestingly, we also followed these carcasses into the slaughter
plant. And if we did carcass swabs and if we looked at their lymph nodes
that we were able to take, all of the carcasses were negative.

So, I think that this tells you that when you go into a field situation
that you are going to have a lot of different scenarios and when you
look at trying to track your own organism or a marked organism through
the system, I think that you are going to encounter a monumental task
when you go ahead.

It is not impossible, but you have to be aware of a lot of the other
confounders because if you had a plate that was mixed at any one time
with two to four different organisms, you are going to have to pick many
more colonies to try to find what you are going to need to be looking
for.

(Slide)

Now, one of the favorite areas that I have is the rod of inoculation.
Some of the work that we did looking at internasal versus the gastric,
and this gastric was actually a gelatin capsule that \-- a big, big
gelatin capsule \-- that we filled with feed and then we inoculated it
with salmonella, put the top back on and you can let it dry down and it
hardens.

You can still recover the salmonella because it desiccates quite well.
And if you shove this down, and so you are inoculating it much like they
would be taking up feed, versus an internasal. And historically
salmonella is thought of as a fecal/oral contaminant.

Well, we could find with our internasal rod of inoculation that we have
a more significant clinical outcome. That humoral immunity is affected.
Although we have an IgM and an IgG response the gastric tends to produce
a greater IgG. We don\'t see IgA with either one of those.

Cellular immunity \-- the B cell response is greater with internasal
versus the gastric. However, shedding levels will be significantly
higher especially in the first three weeks of the experiment. Whereas
the gastric, we are only going to see shedding typically for the first
three days at a high level, and then it will decrease over time and you
will just find sporadic shedding.

Tissue distribution tends to be much higher in an internasal
inoculation. In gastric it tends to be much lower. The actual numbers in
the tissues tend to be two logs higher from an internasal inoculation
versus a gastric.

(Slide)

Now, why do I bring this up? It is partly because I don\'t necessarily
think that we can look at salmonella in the typical boxes of fecal/oral
pathogen. Especially with our confined facilities. Especially with the
evidence that is been presented by ourselves and other people, including
Cliff Wray from the U.K. and deJong from the Scandinavian countries that
aerosol transmission is highly probable.

If we look again at an internasal inoculation, this time with
Typhimurium, and a trans-thoracic where we actually went in between the
third and fourth rib, directly into the lungs, and then we look at our
typical gut challenge right into the stomach again with our pill popper,
we can see that in three hours the tonsil has 4.6 logs and 3.3 logs in
the trans-thoracic challenges. Nothing now in the gut.

The gut issue, the ileo colic lymph node is negative at three hours, but
at six hours it is positive. That is a rapid dissemination throughout
the host. And what we did was we actually esophagautimized these pigs so
we took out any possibility that they could have received any type of
gut challenge with our internasal model here.

And this suggests to us that in a transport situation where you are
going to commingle pigs at any point in time and they have a possibility
of being in contact with any other serotype, there is a high likelihood
that they will in fact become colonized, at least to low levels then
with another serotype.

If we look at 18 hours now, we can see that our gut is now just becoming
positive with non-quantifiable levels. But, we are still, especially
with the trans-thoracic at 4 and 5 logs of salmonella that we can
recover. I think this is something else that you have to consider though
when you are setting up your challenge models.

(Slide)

Now, you can also look at fasts: no food versus reduced food. This
typically occurs only after clinical illness has been initiated. I mean
pigs, chickens, everybody they all eat \-- I will leave that comment out
\-- and what we see though is that if you want to mimic some things then
I can\'t quite see why you would want to fast because you really don\'t
see that.

We don\'t stop eating, even as humans, until we are slightly depressed,
feeling a little yucky, taking some Tylenol. And that is the same
scenario that is happening in an animal situation, too.

So, if we don\'t fast pigs, then do we have a more natural exposure? I
would submit to you that we probably do. Some people will do a stomach
neutralization too and I would contend that that is even \-- I mean you
don\'t go in and take, before you are going out to eat, and say okay
let\'s take a dose of sodium bicarb. We are going to go out to eat and
see what we come home with.

That is not a natural scenario, in most cases I would guess. And so I
would say that no neutralization is probably the way to go too. So, if
you read a lot of the historical references and you are reading a lot of
the outcome based on animal models that have been presented, you have to
look and you have to evaluate exactly what they were doing and were they
manipulating the situation in any particular way to influence outcome?

(Slide)

All right. So not only do we have the internasal and the trans-thoracic
and the gut challenge and your typical per OS challenge, what we like to
use now are seeder animal models. Which is, what I consider to be one of
the most natural ways.

And again, we are still taking a guess. But, what essentially we like to
do is we like to take our pen, our unit, that we are going to challenge.
We like to challenge one or two other animals or some other ratio that
we pick. And we typically challenge those with our organism and then we
introduce those birds or pigs or cattle or whatever into our herd or
unit situation.

And we look at the dissemination of the bug then amongst the population.
And we believe that that is going to simulate in a much more natural
manner then exactly what would be happening under field-type conditions.

And Jeff Gray published a very nice paper a couple of years ago which
clearly demonstrates that by using this type of model system that we can
mimic a lot of the experimental and in fact the field conditions that
are going on.

We find that the serologic response is often the same, especially with
our challenge systems that you would be doing a direct challenge with
every animal. And the bacteriologic response is parallel.

And we can evaluate this spread within both the group and the
environment then. Because, not in all situations are we going to find
that all of the animals will become infected. We always have those
outliers in any one scenario.

(Slide)

So, the question then becomes if we set up a situation and we have done
some looking at the effect of drugs, is when do we treat that? Do we
treat when we see clinical illness? Well, what if we can\'t induce
clinical illness? Do we treat when we would expect clinical illness to
occur, which is typically 24 to 48 hours after exposure to the
salmonella?

These are questions that you have to ask. Now, how many times do you
treat? Do you treat by label indications? Or do you mega treat? I would
submit to you that if you are going to stand out there and inject or
expose drugs all of the times that you are going to influence the
outcome of the resistance population.

So, should we be looking at setting up models where we are only looking
at it under label indications. And then perhaps doing more screening of
the environment which may in fact be the seeder population for other
naive pigs that you may be bringing in. And that brings us to the
re-exposure route.

The environment plays a critical role, not so much in our isolation
units, but typically on the farm where we have a fecal and/or litter
build-up. It also raises the question about our naked DNA running around
there or laying around there or whatever around there, being available
for incorporation into all of the other bacteria that might be around.
So these again are all different parameters that we have to look at.

(Slide)

Well, since I forgot to bring the exact numbers with me and I couldn\'t
remember, I did remember the exact simulation, though. And what we did
was we looked at S. Heidelberg and we treated, we exposed two groups:
the red and black and the yellow group to S. Heidelberg on day zero to
about a 10^9^ dose. And we did start to see some clinical illness at
about 48 hours

So we treated which is what the stars indicate on days two, three, and
four with either drug A or drug B. And what we found was that with drug
B, by day five we absolutely had no shedding levels whatsoever. We
couldn\'t find it. And we looked extensively for any bacteria that we
could find.

And now our culture methods I can tell you are sensitive down to 1 CFU
per gram. And we couldn\'t find any salmonella on day five. But by day
six, or 48 hours after we stopped treatment, then we started to see
these levels come up. So this very clearly demonstrated the differences
in reducing a pathogen load, but not necessarily eliminating the
pathogen from the environment. And these are the types of scenarios that
you can probably expect with some bug drug combinations.

(Slide)

Now, having sat through a whole day\'s worth of talks, one of the nice
things about Powerpoint is that you can begin to critique everybody
else\'s and no one gets a chance to critique yours. So, yesterday a
comment was made that, you know, do you look at a single drug? And well,
I would submit to you that resistance to a single drug is just that, it
is resistant to a single drug.

And, we now have the dynamics of multiple resistance in our midst that
we have to consider, because we really don\'t know what that trigger is
for initiating the development of a cassette or the incorporation of a
cassette or how many drug-resisted genes will be incorporated in any one
cassette.

So, even though we look and we say, well this is going to be relatively
innocuous because we may only have low levels of resistance that we are
evaluating, we can\'t be remiss and at least think at the back of our
mind that in fact any particular drug or disinfectant or metal,
something is going to act as a trigger for setting off this movement or
incorporation of other genes into the chromosome or between different
bacteria.

And again, I think that goes back to the survival mechanism with
bacteria. They simply exist to survive.

(Slide)

So, what are we measuring? We are measuring clinical disease. Is that
what we want to measure as an outcome? Do you want to measure
performance? Which is a totally different question. Do you want to
measure average daily gain? Do you want to measure days to market? Do we
want to measure shedding?

Do we want to measure this elusive pathogen load that can change
depending upon what time of the day? We know that typically animals have
cycles too. They don\'t take U.S. News and World Report into any corner
or anything, but they have different times of the day when we find that
they shed higher numbers too.

How many people are you going to put out in the pen sampling over a
period of time? And all of these different parameters, asking the
question what are we going to measure will mean that we have to have
different approaches to setting up the models.

(Slide)

What do we test? If you do direct catch, you better have significant
back insurance for the person that you are out there asking to do these
direct catches. It is much easier to do a direct catch from cattle and
swine than it is from chickens.

So, you really have to think about what you are asking for over time.
This is where graduate students come in and be very invaluable. I hope
there are none out there.

All right. If we look at environmental samples then we have to ask
ourselves are we picking up extraneous contaminants and how do we sort
those out? Or, were they really just there anyhow and are they going to
have a significant effect on outcome? Are we going to ask is it just
plus/minus?

And, to me anymore plus/minus is becoming relatively meaningless because
we can find plus/minus for most things. Especially the more ubiquitous
organisms. So then we have to do quantitative bacteriology. And I would
submit that most of us don\'t have a pizza budget that can take that
kind of abuse. Trying to ask people to do MPNs or multiple plates over a
long period of time.

Then, what type of sample do we take? Do we take a swab? That tends to
be least sensitive. Well, then we have to take a grab sample or a fecal
loop or a chunk or a hunk or something else. So we have to look at
quantity then and that increases the cost, the amount of time, and the
manipulation.

(Slide)

Now, there are many culture methods and that has been alluded to several
times and I think that is a very significant point. As many culture
methods as there are investigators, and we are as passionate about what
we like as Fred is about this whole issue here. We like our own culture
methods and we don\'t like anybody else to tell us that they are not
good.

But, they tend to be specific for each bacterium. They are limited in
their usefulness in that while it does provide a colony for further
characterization, they have a sensitivity and a specificity range
associated with them.

It tends to be relatively easy which is one of the nice things. And, it
is inexpensive with the exception being the sample size, the number of
samples, and the media type. So, sometimes inexpensive doesn\'t really
equate to \-- it is like words don\'t really measure up here.

Now, it has been alluded to that we have to look at differences in
outcome. But we have spent an inordinant amount of time in our lab and
Peter Davies\' lab. One of the questions that I think every investigator
has to ask in every lab is how is your sample holding up against
everybody else\'s? That is critical.

That is absolutely critical to be able to evaluate what your data means
in relationship to anybody else\'s. If you don\'t have that information
then \-- most of the time I sit in the audience I listen to some people
and they say well, this is the way it is and I think well, time to go
have a beer, you know, because it is just not true, all right?

It is just not going to come out that way because they haven\'t done a
thorough evaluation saying how their standards measure up to every other
lab. Now, and I think Dr. Mevius yesterday is entirely right in saying
that the larger sample that you take the increased sensitivity and
specificity that you have, especially with fecal samples.

But, Peter Davies and I and our lab did an extensive comparison over
many years, many different farms, we sent technicians up, they sent
technicians down. And, what we found was that if you use our method and
we use two broth cultures which I will show you in the next slide, and
two plates, you can use one gram of sample.

Peter uses a 10 gram sample in 100 mils of culture media: one to ten
dilution in buffered peptone water overnight. Transfers in 100
microliters into 10 mils of Tetrathionate broth, and then plates on one
to two plates. We find that there is no significant difference between
recovery between labs.

So, we are perfectly comfortable saying that by our methods we know that
we can compare quite easily with what Peter\'s reporting in the
literature and that is stacking up. Okay? That took a lot of
coordination, a lot of work, a lot of field work, a lot of intra- and
inter-lab coordination to look at that.

Now, we can increase our recovery significantly and Peter can by using
Rappaport in his scheme too, which is a transfer after Tetrathionate
broth. And, what we like to do is for poultry samples or feeds or dry
matter, we will typically pre-enrich in buffered peptone water.
Otherwise we will just take the feces and do a direct enrichment in
either GN \-\-- or Tetrathionate broth.

We let that go for either 24 or 48 hours, transfer it into Rapport media
for another 24 hours and then we plate onto BGS or XLT4.

The other thing that I would point out is that your test is only going
to be as good as the people who are trained to do it. That your recovery
will be much less when you have new people coming into the lab or people
who need new glasses or somebody who doesn\'t care, as opposed to a
well-trained technician who cares about what they are doing.

So that influences outcome in all of these different situations.

(Slide)

Now we have to look at the plating media. We find that there are many
different types and typically you will find this overgrowth. So, picking
a pure culture oftentimes, especially like Dr. Shryock alluded to, is
difficult. And you might freezedown what you think is campylobacter and
pull out a staph.

There is probably campy in there, but it is overgrown now by a
contaminant. And, antibiotics will also confound what you are going to
be able to see.

(Slide)

Now, what do we take at necropsy? Everything, something, nothing, or
none of the above? Some people would like to say let\'s just not do it.
But, the pathogen load and the environment is also critical. But the
pathogen load in the host, as we have seen through some of the other
data that I have shown is also critical.

So, do we look at gut tissues, which are possible carcass contaminants
and will have various variable serotypes within the gut environment,
typically at any one point in time. Or, do we look at lymph nodes which
are probable meat contaminants. Especially those animals in which like
the head meats are used for sausages or other types of products.

The mandibular lymph nodes come particularly to mind because they tend
to stay colonized for an extraordinarily long period of time. Now, you
will find variable serotypes between the lymph nodes and the gut
tissues. And we are in the process of doing a study now to look to see
how the antibiograms will vary between the same serotypes coming from
the different tissues too.

(Slide)

Multiple serotypes are likely, especially for salmonella and
campylobacter. And we have the confounder by the use of the marked
strains. And a resistance difference we will also see between the
colonies. We have one sample of jejuni and we just take the same sample:
we picked five different colonies that we knew were campylobacter.

They were all campylobacter jejuni. Some had tet resistance and some
didn\'t have tet resistance. And this wasn\'t like where they
borderlined, this was some were resistant, some weren\'t. So, within a
single sample population you can have multiple resistance patterns
occurring.

(Slide)

Now, this is the question we asked about the gut and the different
between serotypes. And the implications with the age groups then are do
we look at suckling pigs versus older pigs or chicks with campy versus
the broilers that grow out? Do we look at grow out? Have we looked at
retreatments and environmental exposure?

And how does slaughter with transport and processing confound all of
these? Do we have a transport effect? Yes. Do we have a post-processing
effect? Yes. But we don\'t know what those levels necessarily are.

(Slide)

So, I would submit to you that one of the most critical things that you
can do in any of these studies is developing a pilot study where you
look at a very small number of animals. I would say that would be around
six to eight different animals.

And this is where we look. Well, if we give them a 10^3^, what do we
think we are going to et? If we give them a 10^6^, what tissues should
we be looking for? This is where we work out all of our details so that
when we go and we run the experiment then, that we know exactly what we
are looking for. Hoping not to find too many other variables that come
into it.

However, there is an expense associated with this. How does the
interpretation affect actual outcome? And can we scale up? I mean a lot
of times it is like a fermentation. You have a very small number,
you\'ve got it controlled. You move it up to a 20, 30-pig, 40-pig,
100/200-chick scenario and you might find very different outcomes.

So, some of my observations are that there are differences when animals
are penned individually versus commingled.

(Slide)

Having individual animals as a pen as a separate unit will give you
vastly different results as opposed to commingling animals in a much
more natural or field-type environment.

If you have isolation facilities versus field studies, it is really all
game \-- you\'ve got entirely different scenarios there too and you
can\'t necessarily extrapolate between the two unless you have a lot of
historical data to say that the transition is going to flow.

And then you have to look at the effects following withdrawal of drugs
as in Heidelberg.

(Slide)

Now yesterday, Dr. Ewert talked about her battery of bugs but that I
don\'t think would work with attenuated lab strains. It would only work
necessarily with field strains, but they may also be problematic because
resistance is a survival mechanism.

And you may have a competition between all of those going on too. So,
Dr. Angulo yesterday set up the scenario well, he would like a
surveillance program especially where we are looking in the field
situation. And, I would say we need to set up a surveillance program
which eliminates the field, but constantly screens for new resistance
mechanisms before releasing it into the environment where the
magnification can be rapid.

And that would come into some of the modeling talks that we had because
I think that that is what you see. That when you take it into the
environment you see this rapid magnification from environmental factors.

(Slide)

And what do I mean by that? Flies and insects can act as vectors caring
the pathogens on their body surfaces. On poultry, 4.6 percent of our
insects, both inside and outside the facilities, are positive; 27
percent of our fly strips are positive \-- don\'t like those at picnics
any more. And for salmonella, 6 percent were positive for campylobacter
of insects and fly strips.

So we have movement going on that can influence exposure at any one
point in time. Birds are known historically to cause and spread feces
contaminated with the pathogens. Geese have been implicated in the
spread of s. Mbandaka berry hurst \-- berry hurts. Nineteen percent of
feces from the birds we trapped; 70 percent of bird rinses, bird in a
baggy; and 47 percent carriage in the intestine were positive for birds
in poultry environments.

So I think there is significant outcome when you have a lot of these
different types of exposure.

(Slide)

And if you look at rodents, they may be having a good time running
around, but every fecal pellet has a potential of having 10^5^ CFU which
is an infectious dose. So any time a chicken pecks one pellet they have
given themselves a whopping dose now that can be used to disseminate the
bacteria throughout the whole herd.

Pigs can very easily take up more than one fecal pellet on any one
occasion. And this becomes a means for self-inoculation over a period of
time.

If we do mouse-in-a-bag we find that 33 percent of the outside of them
are positive for salmonella, 53 percent of the inside are carriage. And
we find 6 percent in both situations for campy are positive.

(Slide)

In organic matter, perhaps some of the most significant work that Jeff
did was looking at organic matter as potential sources of reinfection.
We know that salmonella survives well in the dirt. It also survives well
in desiccated feces greater than 13 months. We were able to take it, put
it back, expose it to pigs, and they were able to become infected again.

So, if you miss it cleaning in there, you are listening with your little
walkman on, you are power washing, but you miss that feces in the
corner, there is the potential then that any time the animal is rooting
or pecking they are going to become reinfected.

(Slide)

Salmonella survives quite well in dust. We had an experience looking and
pulling dust down from the rafters and blowing 10^2^ CFU over us. It
survives well in the dirt and there is rapid dissemination in the soil.

One of the things that Dr. Cliff Wray and I had a conversation with last
week was that a lot of these environments now, we find that once it gets
out into the field it actually percolates, especially through sandy-type
soils.

Eight percent of our dirt, 8 percent of standing water, 22 percent of
boot swabs can be positive at any one time in a poultry environment. And
certainly feed and feedstuff can be implicated.

(Slide)

And finally, I am no statistician, which is why I left it, but we have
statistical design that we have to worry about. Do we have a significant
effect from the pathogen? Is there a significant effect on the pathogen
load? Is the pathogen load significantly affecting the outcome? The
significance of the resistance and is there any intersection between all
of them?

How can we have enough numbers to adequately demonstrate that this is
going to happen in a model system? And can we really have a pass/fail
criteria that is clear cut? Unequivocable and we all can go and sleep
well at night? I sleep anyhow, but that is up to you guys to decide.

And, can companies comply with all of these types of restrictions? And
can regulatory agencies try to implement these types of models. I think
that those are the types of questions that we have to ask ourselves.

(Slide)

So in summary, we have to essentially go through all of these points:
asking the right questions; the statistics; the bugs; the serotype;
disease colonization; shedding; animal ages; animals themselves;
inoculation routes; kiteman regimes; effect of the environment.

What are you going to measure? How are you going to pick it? How many
are you going to pick? How many are you going to characterize? How many
tissues are you going to pick? The same type of thing, are you going to
do yes/no? Are you going to give numbers? How many isolates are you
going to look at? And the final outcome would be the antibiograms?

(Slide)

If that doesn\'t exhaust you, this is a quote that I thought was just
absolutely spectacular from my six-year old son who said after he was
doing his math homework: \"I am thinking so much my little brain
hurts.\" And I would say that all the questions that have been put up
here, my little, little brain hurts, too.

(Slide)

This is the cast of characters and I thank you for your time.

CHAIRPERSON WHITE: Okay. We have time for one or two questions again.

All right. If not then we are going to move to our study concepts panel.
I would like to ask our speakers for the last day and one-half to come
back up. That would be Dr. Ewert, Dr. Mevius, Dr. Papich, Dr. Shryock,
Dr. Kotarski, Dr. Lipsitch, Dr. Cray, and also we have a guest panel
member as well, that is Dr. Randy Singer from the University of
Illinois.

Randy completed his D.V.M. and M.P.V.M. from UC Davis in \'95, and his
Ph.D. in Epidemiology at UC Davis as well, in \'99. He is currently an
assistant professor of Epidemiology at the University of Illinois. We
appreciate him coming for this.

[STUDY CONCEPTS PANEL]{.underline}

CHAIRPERSON WHITE: I think what we\'d like to do at this session is have
our panel members maybe take about five minutes if they can to talk
about any comments on any questions or issues or factors to consider for
the breakout sessions after lunch.

I don\'t know if we wanted to start with Marc and then work our way
forward, if possible?

DR. LIPSITCH: I guess I have really only one comment which is to amplify
something I mentioned a little bit a few minutes ago. That is the sort
of limitation of any kind of pre-approval study, which is not to say
they shouldn\'t be done, but only to recognize that they can only
identify some of the hazards and not all of them.

What I am referring to is these genetic events. These rare genetic
events in terms of either creation or amplification or transfer of
resistance determinants from one species where they may be causing no
human problems into other species which down the line could.

And because those events are so rare, in fact it is very hard to observe
them in any pre-approval study. I think the example I described from the
HSV I, where you couldn\'t see the emergence of resistance in a single
patient, or at least not clearly, demonstrated pre-approval in the
clinical trials despite doing 2,000 or so patients.

Nonetheless, that becomes quite important probably down the road in the
emergence of resistance to that drug. So, the good news from
pre-approval studies shouldn\'t make us complacent, but only tell us
some things might not happen.

DR. MEVIUS: Okay. Thank you. I would just like to remark, continue what
I said yesterday that resistance is going to happen. We know that. It is
inevitable. So, I think we should use pre-approval studies not just to
ban all drugs, but to find optimum dosage regiments in order we will be
able to control resistance, which is also possible.

What is very important, and not just used, a look at data coming from
pre-approval studies to compare that to what is actually happening in
the field. Because these rare events that may have very important
effects on situations which will not take place in studies, need to be
studied as well. They need to be compared what was done in these kind of
pre-approval studies.

DR. SINGER: Well, I didn\'t get a chance to speak earlier, so I have a
few points I would like to make. They were eloquently stated, many of
them, by the speakers so far, so I will just quickly walk through them.

I would like to thank FDA/CVM for inviting me to participate. I am happy
to be here even though I had to miss last night\'s Illinois thrashing of
Indiana.

DR. SHRYOCK: I am Ohio State, that is life!

DR. SINGER: The first point I\'d like to consider is the selection of
our study groups and of the environment in which we are going to pursue
our studies. Clearly the in vitro study and some laboratory in vivo
studies have their use. Ideally, I\'d like to see these studies
conducted in the natural environment.

There are some unfortunate consequences though to that. First is what is
our unit of measurement? Is it the individual animal? Is it the herd? Is
it the flock? There are population variables that we know of such as
herd density, herd population size that will directly effect resistance
dissemination, resistance emergence. So we have to consider both.

The problem with that is if the herd becomes our unit, then we have to
conduct multiple or replicates of these herd-level experiments. So we
are not just talking now about multiple animals in the herd, but we are
talking about multiple herds and these studies become huge.

Related to this treatment group discussion, it was raised yesterday that
possibly we need multiple treatment groups for looking at the different
dosages. I would also suggest that we need multiple control groups. This
was suggested in some way by Dr. Mevius and also by Dr. Carnevale where
one group might be the group that receives no antibiotic and no
treatment control.

But we also need to consider the use of closer related, already approved
antibiotics as additional control groups. For instance with
Tetracycline. You can have cryptic Tetracycline resistance genes that
they up-regulate and become increased, their mobility increases in the
presence of Tetracycline.

So, by including multiple control groups with different antibiotics we
are able now to separate out, do the resistance mechanisms that we
observe, are they the result of a new mechanism or are we seeing the
amplification of a previously existing mechanism?

All right. Some of the additional points were raised, so I will just
move quickly. In terms of the pathogen we need to consider, obviously we
are going to think about monitoring the target pathogen for which the
antibiotic is directed, but I really feel strongly that we need to
consider the commensal.

It becomes a very laborious task, but commensals not only E. coli, and
maybe some of the gram-positives, but the anaerobes, some of the most
numerous bacteria in the intestinal microflora. What role are these
commensals playing as reservoirs of antibiotic resistance? That
mechanism becomes problematic if we are really trying to assess the rate
and extent of antibiotic resistance.

Sample size is a huge problem. It is hierarchical in nature. We have how
many treatment groups? How many animals per treatment group? How many
samples should we take per animal? How many bacterial genera should we
be collecting per individual? If we use a cultivation technique, how
many colonies are we going to pick?

This sample size question, while it is difficult is only answerable once
we figure out what the outcome we are trying to measure. What is it that
we actually want to detect? What is the outcome that we want to find a
measurable difference for? And to do that we need to think of the
difference between biological significance and statistical significance.

I will probably be insulting all of your intelligence by just walking
through this discussion briefly, but we have talked about statistical
power and obtaining an adequate sample size to achieve a desirable level
of effect or of measurement.

But remember that statistical significance or a low P value is dependent
upon both the measure of effect as well as sample size. So, we can
detect any difference we want as statistically significant if we just
increase the sample size high enough.

So that is why I am beginning to feel that in some of these studies we
need to be looking more at what is the biological relevance and think
\-- and I can\'t believe I am actually going to say this as an
epidemiologist, an analytical epidemiologist \-- that maybe we need to
be thinking of these studies as descriptive. Where we are trying to look
at trends and not as is there a significant difference between treatment
groups.

Because again, because there is so much variability in these systems I
am not sure we will be able to separate the effect from the sample size
issue.

Finally, and this is maybe my recent introduction to this field of
antibiotic resistance, also my naivete about pre-approval studies, but
my overlying concern and confusion is what the actual purpose of the
pre-approval study is. We have addressed many questions, many causal
mechanisms, many potential confounders, all these affect modifiers.

There is the inherent stochastic elements to the natural environment.
Are these pre-approval studies becoming these incredibly broad research
programs? Or, what is it that we are actually trying to get at here? If
it is the rate and extent of resistance development, these will evolve
into major research programs.

For the pre-approval study, I guess I would have to agree with some of
the points that were raised by Dr. Angulo and others that maybe what
would be the most useful is to look at what genetic mechanisms do we
know of currently that might confer resistance to this new
antimicrobial?

What are some of the determinants that that resistant determinant can be
transferred? What is the current prevalence of that genetic mechanism in
various environments and in maybe eight stratified groups? And use that
then to focus an active surveillance and monitoring system.

So really, emphasizing the natural experiment that will occur once the
antibiotic is approved. Otherwise I am not sure we would ever see
approval of new antibiotics. These pre-approval studies could turn into
extremely long-term, very expensive projects.

Ultimately we could then use the changes that we are observing in a
natural environment, changes in resistance frequency, along with the
framework document guidelines to help us manage the future of the use of
these antibiotics. So, kind of a dynamic system that changes as our
monitoring efforts continue.

That is all I wanted to say. Thanks.

DR. SHRYOCK: I agree with many of the points that Randy had to voice. I
guess what I would like to raise for consideration in the breakout
groups this afternoon would be from an industry perspective. That
whatever is decided this really has to fit within the confines of a late
phase discovery, early phase discovery scheme.

I agree that these really should not be the types of studies that become
an academic exercise to explore question after question. I am not sure
we would get very far in terms of getting anything approved if we take
that line of reasoning.

With respect to the animal modeling studies, these are things that I
have been involved with throughout a great portion of my industrial
career, both from the resistance as well as the efficacy and some of the
salmonella shedding studies.

I think Paula gave an excellent presentation today, as well as the other
speakers just outlining all of the issues and the confounding factors
that go into these sorts of studies.

No matter what way one wants to look at setting up these kinds of
studies, the key bottom line question is are they relevant to the
commercial situation? How predictive are they? And can they be
validated?

Those are some really significant questions if the whole drug approval
process is contingent upon getting a sufficient answer to those specific
questions. So, do these types of studies become rate limiting or the
critical success factor?

Are they the pass/fail? Or, perhaps could they be considered as
supportive, non-pivotal type of information that is generated that goes
into a dossier that really doesn\'t impact on whether the product
ultimately goes forward or fails. It is just supportive, additional
documentation.

I do agree that perhaps thinking outside the box in terms of enhancing
surveillance systems might be something to further explore. Obviously
there is a lot of in vitro data already existent with existing classes
of antibiotics. We don\'t have to go out and find a whole other alphabet
soup of irm genes for example. Those are out there, they are
characterized, we can do those sorts of things.

We can use those tools then in monitoring programs which are either
ongoing or could be initiated if we even want to expand that say to
target animal pathogens, etc.

And then, just to finally reinforce all these pre-approval studies with
some of the pharmacokinetic/ pharmacodynamic studies, to really get that
does narrowed in to the best of our ability, seems to me like an
opportunity to use data that is already collected, but to put it in the
appropriate context.

So, maybe we can move forward on trying to get to a point where we can
at least put these studies in a reasonable format of things that are
being done within the development scheme of things right now and
strengthen some of the potential in vitro contributions.

Thank you.

DR. EWERT: My comments will be brief. I do agree with a lot of the
comments that the other presenters have made. And, as you go into your
breakout sessions I would ask each group to think about identifying the
outcomes for the studies and the objectives for the studies.

Without an end we can\'t define the means. And so, if we know what the
objectives should be then all of these other questions that we have all
been asking would be much easier to answer.

So, I am not sure that one pre-approval study can answer any question
that is going to happen or any situation that is going to happen in the
real world, but until we come up with some kinds of outcomes to measure
or describe perhaps, without statistics, we really can\'t move forward.

So, as you go into your breakout groups, think about what the end is and
then we can work on the means later.

Thank you.

DR. PAPICH: I don\'t have much to add other than to reinforce what is
already been said. I would agree with the comment Tom just made that is
the relation to the topic I had yesterday that if we can define some of
these PK/PD parameters for some of these organisms, and some of that
information may already be available in work that is done during the
pre-approval process. We could use that to some advantage.

Other than that I don\'t have much to add other than, as I said, what is
already been commented on.

DR. KOTARSKI: I\'d like to reiterate that as a sponsor we are looking to
market drugs that are efficacious and are safe for use in food animals
for the public health. And in that process of our drug evaluations,
whether we are working with the CVM or we are in the early lead
evaluations, the important question for us with respect to resistance
emergences is if there are questions that we can ask to identify an
unsafe drug with respect to resistance emergence, help us identify that
objective. If there is a way to do that. Focus on that objective.

And the second is to help us focus on identifying an effective dosing,
efficacious dose in minimizing resistance emergence. But, this afternoon
I would just ask you to focus on the objectives for the safe
registration of the drugs.

DR. CRAY: I just reiterate everything that my colleagues say, too. I
think that one thing though that I would \-- I think we all have to be
really cognizant of, is that the study has \-- if you are going to
design a study that it really is only going to be able to answer one or
a very few number of questions.

And that these studies, there are many of you who haven\'t been in
lab-type situations, these are horrendous experiments to conduct. These
are horrendously expensive. They are very time-consuming and they
require a lot of people who know exactly what is going on.

And so in many ways these are going to end up being academic-type
situations and I think that, what I become more and more impressed with
is the amount of money that it really takes to build a national program
on any level where we are looking for the data that we need from a
historical basis.

One thing that we are really doing is we are building for the future
here. We are not going to have all of the answers now, but they are
going to come with the studies that we are doing. So the studies that we
are doing and we are designing now need to form a basis for what we are
going to be using to change models, to change dynamics, and principles
in the future.

I think that looking at trends over time versus the significance factor,
we will probably have more relevance in the real world. And I would
welcome all of you to come and spend some time in a microlab to see how
hard some of these things are to conduct if you are having trouble with
study design.

CHAIRPERSON WHITE: All right. Any final comments from any members of the
panel? Well, I think we are moving into an open comment period and I\'d
like to keep the panel members up there. And we introduce Jim Heslin
who\'s are moderator for this session.

CO-CHAIRPERSON HESLIN: Good morning. We have about 30 minutes for either
some additional questions for the panel or just comments you want to
make. After this session we will talk a little bit more about the
breakout sessions this afternoon: some of the logistics involved, some
of the ground rules. But I will get to that later.

So, if anyone has an additional question or comment if you\'d step to
the microphone, identify yourself and the organization you are with.

MR. MINORSMAN: I am Rick Minorsman from the ARS-USDA, and now that I
have told you who I am I have to admit that I am pretty ignorant about
pathogen load. So far all I know about pathogen load is that Fred Angulo
is against it.

Can you all, from the perspective of people that are working with
antimicrobial resistance, can somebody define it for me and tell me why
it was instituted to start with and why it is controversial?

CO-CHAIRPERSON HESLIN: Does someone want to take that on?

DR. SHRYOCK: Do you want to give it a shot too?

DR. : Tom is the pathogen load person.

DR. SHRYOCK: Yeah, I will it a shot and then Paula \-- if we can get the
feedback modulated here.

I will start and see if we can give some perspective on the pathogen
load. This really started as an issue with regard to resistance back
about the time the Swan Committee Report came out. The idea being that
gram-positive active antibiotics alter the gut flora which allowed a
niche to open for salmonella to come in, colonize. Potentially then
increasing the magnitude of salmonella prevalence, quantitation, and
duration.

Those types of considerations led to the 55815 studies which were
instituted in the \'70s, went through the \'80s and are still on the
books today for antibiotics which are administered in feed at 200 grams
per ton or less for more than 14 days. So, that is kind of where we are
at on that. The whole idea being that you are going to have more
salmonella if you use infeed antibiotics.

From my understanding now with the framework that whole concept is to be
expanded then to include other types of antibiotics. Not just by
administration in the feed, but water and possibly injection \-- well,
certainly injection \-- for therapeutic uses as well.

The idea being that if you enhance the population of pathogens in the
gut potentially those could obtain a resistance gene and then you\'ve
got a vehicle for massive dissemination of food-borne pathogens that now
are resistant.

Hopefully that is quick enough to kind of give you a flavor for what the
pathogen load issue is all about. I will turn it over to Paula, she can
add to that.

DR. CRAY: You have to spend more time in my office downstairs, Tom. I
think that part of that, I think that sometimes we are remiss and I
liken this group now to a marriage. I feel like we have all been
together and we all speak the same language and we forget that other new
people are coming in probably don\'t have the same sense of where that
historical perspective is.

But, I think that when we define, or when I define pathogen load, we are
looking at several different things. We are looking at numbers of
bacteria either residing in the animal host, and that would be either in
specific tissues or in the cecal contents. And we would also look at
pathogen load as being actual numbers that would be shed into the
environment.

And then we would look at, if you will, an environmental load where we
were looking at numbers that are in the environment that may also be
available for exposure. I think that, I look at this as two different
waves. I look at it as that there in fact would be a pathogen-type load
and then within the pathogen load there is probably a resistance load
also.

Sometimes I think that we need to \-\-- One of the main objectives that
we need to focus on from a research perspective, which is what our
mission is in the Agricultural Research Service is to just decrease
pathogens in general and then weave the resistance issue into that. And
by decreasing resistance within the pathogen load then we will be
reducing the exposure of human or animal populations to numbers and
numbers of bacteria that may be resistant.

So, we are really just talking about, at least from a microbiological
animal model-type person, we are talking a numbers game.

CO-CHAIRPERSON HESLIN: Was that helpful?

MR. MINORSMAN: Yes.

CO-CHAIRPERSON HESLIN: Okay. Yes?

DR. ANGULO: Fred Angulo from Center for Disease Control. Just to clarify
on pathogen load, obviously we are not \-- we are very concerned about
the load of pathogens in the food supply and prevalence of pathogens in
the food supply.

To be clear, we just don\'t believe that pre-approval studies will yield
much information that will help us mitigate the pathogen\'s frequency in
the food supply. And in fact we are quite excited about interventions
that are in place that are mitigating the prevalence of pathogens in
food supplies.

Particularly, with the possible implementation of irradiation on a
commercial basis. But, on pathogen load, turns to then exploring the
objective of the resistance portion, resistance testing portion of these
pre-approval studies. And certainly endorse the purpose or the objective
of the resistant study as put forward by CVM.

And that is to characterize the rate and extent of development of a
resistant bacteria, enteric bacteria. And so I think, I tried to think
of what people had stated as possibly the essential parts of
pre-approval studies that would allow the characterization of the rate
and extent.

I heard five different, what I thought would be essential parts of
pre-approval studies. One would be it would be useful to do studies that
determine the mutation rates or organisms in the laboratory. It also
would be useful to do studies to find the presence of resistance genes
to this new compound, or if it is a known class of compounds, than those
resistant genes as Tom said, are probably already known.

And then three, it would be useful to know the frequency of transfer of
resistant elements to this resistant gene, to this new compound.

Fourth, it would be good, as explained nicely from our colleagues in the
Netherlands, it would be useful to know what would be the optimum dosing
rate so that we can minimize resistance. Because of course all of the
dosing work done pre-approval is largely for therapeutic efficacy, but
not for resistance, so it would be nice to have that added to the
pre-approval process.

And then fifth and critical, we need to categorize drugs pre-approval.
So, unless we know what the resistant gene\'s consequences are and know
what the cross-resistance will be to those genes if it emerges, we
can\'t categorize the drugs and we can\'t implement safeguards.

So, the point is then what would be the safeguards? What are the
safeguards? How do pre-approval studies contribute to safeguards? And we
have to understand that there is recognition that both drugs are needed
to treat sick animals and then recognition that resistance emergence is
inevitable.

Also, recognition as pointed out nicely by Marc, that resistance is
difficult to mitigate once it is in the community, even when the drug is
withdrawn.

So, with those three recognitions we need to have safeguards to prevent
an impact on the clinical utility of these drugs in human medicine. And
where are those safeguards? They exist within the framework document.

Our first safeguard is to categorization of drugs. There should be
exclusions with the categorization of drugs, it seems obvious that a
Category I drug would not be allowed to be used in feed, high level
exposure.

You could probably extrapolate other exclusions. So, I would think the
concept of exclusion of certain exposure categories of drugs would be
required. And Category I drugs would not be approved for use in feed for
example.

And in the second then important safeguard that is necessary is some
sort of post-approval reaction to emergence of resistance. And that is
the whole concept of thresholds and corrective actions. Those thresholds
and corrective actions are where we really can mitigate the emergence of
resistance. That would be the true public health safeguard.

And so pre-approval studies would support those efforts of the
thresholds and corrective actions and the key linkage appears to be
pre-approval studies should help determine the categorization of the
drugs.

CO-CHAIRPERSON HESLIN: Thank you. Any other comments or questions at
this point? Yes?

DR. GOOTZ: Tom Gootz, Pfizer. I guess I agree, just to restate the
obvious, that it might be very difficult to have a one-size fits all
pre-approval model here for feed additives and therapeutic antibiotics
for animal health.

It may be very unfair to have to look at things differently, but unless
we begin to think that ways in terms of our concerns about resistance I
am not quite sure how you are really going to move forward. Hopefully
some of that can be discussed in the breakout groups.

In other antibiotic areas, and brevity can sometimes be seen as a virtue
in the sense that one way to sort of decrease the role of resistance and
maintain the livelihood of some drugs anyway is to not use them for a
long time. Try and develop drugs that are one shot or limited dosing in
duration.

Therefore, hopefully if you do of course pick the most efficacious dose
and optimum PK, but you hopefully decrease the risk of cause and
resistance.

So, I hope deliverables, which is a nice way of saying real action items
that come out of the breakout groups today and tomorrow, are things we
can actually get our hands around and we can actually do. As opposed to
coming up with a very long list of \-\-- bacterial types of experiments
which none of us can do in a fairly brief amount of time.

CO-CHAIRPERSON HESLIN: Thank you. Any other comments or questions? Yes?
If there is no other \-- I think this gentleman in the back.

DR. ANGULO: Thank you. I \-- just because this is a new idea that I was
discussing, I think it is critical to point out that unless there is
some agreed upon \-- if a sponsor can bring any drug that current rules,
or a sponsor can bring any drug for any application forward to the CVM
and then do pre-approval studies, etc.

And if that is the paradigm that is continued then we will have this
frustration because, as a public health official, I don\'t know any
study that possibly could be done to show that with the complete
confidence of public health officials, that any drug that is essential
for human medicine, or drug that is used in human medicine that is
judged to be critical medicine, could ever be deemed to be safe to be
given in feed.

And so, that is why this concept and I really think that when we do the
categorization of the drugs there should be some agreed upon exclusion
of certain use exposures or usages. Because otherwise if we have to do a
pre-approval study to really say with certainty that it would be safe to
give fluoroquinolones in feed, or as a subtherapeutic agent, I don\'t
see how we could ever design such a pre-approval study.

But, if we could get past that, then I think that we could have a fairly
low threshold on pre-approval studies as long as we do surveillance
afterwards to respond to resistance that emerges.

CO-CHAIRPERSON HESLIN: Yes?

DR. CRAY: I have a comment.

CO-CHAIRPERSON HESLIN: Okay. Go ahead.

DR. CRAY: I have a concern from what I am hearing, and maybe I am not
hearing it right, but it seems that your proposal is that you put
everything right now into boxes and that decisions be made without
evaluating how the dynamics, or how new drugs or what we might discover
in the next several years with efficacy and shedding and loads and all
of those types of issues might effect.

And so my concern would be that if you wanted a set of rules and
parameters that were set in stone now, that there would be less \-- that
the burden of proof might be, or the burden of doing anything different
would be too high because there\'d be no way you could crawl out of that
box.

And that all of the \-- it would seem that \-- I mean you named, as you
are talking about rate and extent and I am sitting here and I am
thinking mutation rate, in vitro, and the different \-\-- that you have
to do, not only just with one drug, but what about the combinations of
drugs? And what about multiple resistance? And what about all of these
different types of scenarios?

And the presence of resistance genes to new compounds and looking for
new mechanisms over time? And looking at ways that we might circumvent
those mechanisms. And so, everything really has to be looked at on an
individual basis. That any study is going to have to be customized to
the proposal that is put forth.

And that the box situation, and neat little packages of coming in and
saying for poultry these are going to be the types of pre-approval
studies that you are going to have to do, aren\'t really going to fit
for other things.

So, almost \-- to me the scenario that I would see is that it would be a
pull-down menu type of situation where you would have some studies that
would be set in place now that would give you an idea of what the
outcome might be. How you set that particular study up and what it is
going to tell you or what the expected outcome is based on experimental
design.

And then maybe in submission of X drugs, you are going to have to do
one, two, six, and eight. And in submission of Y drugs, you are going to
have to do two, nine, seven, and ten. And you are going to have these
types of scenarios. But \--

DR. ANGULO: You are just presenting a perspective, I mean we are not
engaging in a dialogue here I don\'t think.

DR. CRAY: Well, but part of that \--

DR. ANGULO: Well, I think \--

DR. CRAY: \-- no wait. Part of the concern that I have is that I think
sometimes that seeds are planted or that it comes across as one way and
that that is the only way and then if it isn\'t that way then it isn\'t
going to be satisfactory. And what we are dealing with here is some
very, very complex issues that are not going to be easy answers.

And, I agree with that at some point there is going to have to be a
categorization and all of that, but we are talking about how do you
design studies that will give you an outcome here. Okay. And I think if
you lose sight of that fact here, that the rest of it is going to become
too confusing because you are not going to know what to go after.

What can we do \-- just like you said \-- what can we do to look at the
rate and extent. And maybe there is an acceptable level of even a
Category I drug, not necessarily to be used in feed but to be used in
therapeutic, that will be acceptable. So don\'t preclude that fact now,
especially if we are designing these types of studies.

CO-CHAIRPERSON HESLIN: Could I ask that you keep it brief so that the
gentleman can have an opportunity to talk?

DR. ANGULO: Absolutely. Although I think this is useful dialogue. I
think that if we move the concept of pre-approval studies away from a
pass/fail and a clinical trial concept of only one variable and the
outcome is either P value X or P value Y and then the answer is yes
approved, yes or no; not approved.

Get away from that concept and deem the pre-approval studies as
information gathering that will support the post-approval monitoring
that is going to occur afterwards as much as explained by our colleague
from UC Davis, from Randy.

I think if we go to that paradigm, then we can deal with this problem in
the face of all of the uncertainty that you just \-- I mean the reality
is that in the face of all of this uncertainty we need to have adequate
safeguards.

But on top of all of that uncertainty I think there can be some agreed
upon certainty. And that is precious antibiotics should not be used on
heavy exposure potential which is in feed. So there probably can be
agreed upon some immediate exclusions, let\'s not waste our money trying
to do pre-approval studies for these obvious exclusions.

And then the other uncertainties, we will just deem the pre-approval
studies as information gathering and let the post-approval process
mitigate problems as they emerge.

CO-CHAIRPERSON HESLIN: Yes?

DR. CARNEVALE: Yes. Thank you. Rich Carnevale, Animal Health Institute.
I had two completely different remarks. One is to actually concur with
Dr. Angulo on a point, which is kind of nice for a change.

And that is that pathogen load studies, I think when one looks at what
USDA-FSIS has been reporting over the last couple of years with the
progress of Hassive and the continued reports that pathogen
contamination, at least by measuring salmonella in raw beef and poultry,
is going to go down.

And then when you further look at the most probable number counts on
those positive carcasses that are found, those are very low. In fact, to
the point where in beef carcasses you can hardly even find salmonella in
a slaughter plant.

The point is, when you look at that and then you think of doing a
pathogen load study for a single antibiotic and looking at the results
of that, you kind of wonder whether that has any impact at all. And
obviously, through all this period of time when we haven\'t been doing
pathogen load studies on antibiotics it hasn\'t made a difference.

It has really been the plant activities and the pathogen reduction
activities in the plant that is had the major effect. So, you really
have to put in perspective what do pathogen load studies give you on a
single antibiotic in a single species in the big picture of salmonella
or campylobacter or any other pathogen contamination.

So, I think the group needs to keep that in mind when you are looking at
the value of pathogen load studies.

The second comment I\'d make is completely independent of this
discussion. It is a response I wanted to make on comments yesterday
about antibiotic usage. And I wanted to let this audience know before we
break for lunch that AHI has released a report about two weeks ago on
antibiotic usage.

This is a survey that we have been conducting with our member companies
over the last year, trying to respond to continual requests for this
information. It has been a difficult exercise because of the
complexities of the animal health business and the distribution among
species.

There has also been some proprietary issues we have had to deal with,
not to mention the fact that we don\'t represent 100 percent of the
animal health industry. So we have had to make some extrapolation.

But, be that as it may, we have been successful in putting together a
report which looks at two issues. One is antibiotic usage by poundage of
active ingredient by various drug classes. And then we have also tried
to estimate how much of that total antibiotic is being used for
production purposes. That is, for promotion of feed efficiency claims
versus control and treatment of animal disease.

So that report is available and has been out for about two weeks. We
will make copies out on the table available to anyone that wants them. I
just wanted to let people know that that is available.

I hope that this report now will lead to a better understanding of
antibiotic use and put animal use of antibiotics more in context with
human use. So, thank you for that.

CO-CHAIRPERSON HESLIN: Question?

MR. : Can you get that by e-mail?

DR. CARNEVALE: We can send it by e-mail. Yes, it is on our home site. It
is WWW.AHI.ORG.

CO-CHAIRPERSON HESLIN: Thank you. Any other comments or questions at
this point? Okay.

DR. ANGULO: Since we have five minutes could I turn my comments into a
question? And my question is then to the panel. Does the panel agree
that the five essential elements that I put forward for pre-approval
studies are in your minds essential elements of a pre-approval study?

The five essential elements I would judge for a pre-approval study is
determine mutation rates for this new compound; to determine if there
are resistant genes to this new compound already prevalent in the normal
flora and environment; number three, determine the frequency of gene
transfer, of transferrable elements with this resistant gene.

Number four, to use the pre-approval studies to optimize the dose to
mitigate resistance; and number five, to determine cross-resistance to
human drugs so we can categorize this new drug that is coming forward
for approval.

CO-CHAIRPERSON HESLIN: I have a question. Is this something that the
panel should respond to or should be considered by the breakout
discussion groups?

DR. SHRYOCK: Okay. The feedback is going to get me, so I will skip that.
Perhaps it would be worthwhile considering on the spur of the moment to
have a fifth breakout group to discuss this particular approach about
the in vitro studies.

Obviously we have discussed many of these things in the species breakout
groups, but maybe we could consider bacteria the fifth species, if you
would, just as a way to go about that.

I think the point that you\'ve raised provides some good grounds for
further discussion. I think Paula raised a number of issues relative to
this that would have to be further explored.

But certainly, by limiting it to these particular five points that to me
implies that the animal studies would not necessarily be a part of that
package of the pre-approval type of scenario, at least the pathogen load
would be excludable and perhaps some of the studies for resistance
selection in animals.

Those types of studies as we have heard are very complex, of dubious
value for relevancy, etc. So, if we do choose to go more towards more of
an in vitro situation, I think that is worthy of more discussion and
might suggest a fifth breakout to address some of those points.

DR. ANGULO: Just to clarify Tom, that one of those essential elements
which is define resistant genes in the environment or the normal flora,
would have to be, I would think, under conditions of selective pressure
in a field environment. Although I don\'t envision having a huge
clinical trial, but I think that should be a field setting.

That might be the only animal study portion.

DR. CRAY: Which one?

DR. ANGULO: Essential element number four, maybe, or three. Just to find
the presence of resistant genes in the environment or normal flora of
the animal.

DR. CRAY: That wasn\'t what you said. You said frequency of transfer
resistant elements for number three.

DR. ANGULO: That was number three. But, I think that number two is just
existence of \--

DR. CRAY: Resistant genes for five, though.

DR. ANGULO: Right. But you can\'t find resistant genes in a sterile
laboratory. You have to go to the normal flora of animals, expose them
under selective pressure of the new compound and see if a resistance
gene pops up.

DR. CRAY: What about optimum dosing rate?

CO-CHAIRPERSON HESLIN: I think we need to address the suggestion or the
possible suggestion that was made about an additional breakout group. I
know that we have but four moderators and four venues, so I am not sure
what the logistics would be of adding another group.

MR. FLYNN: Yes. Bill Flynn, CVM. That is a good suggestion, I am not
sure logistically we can handle a fifth group. I think that the purpose
of this panel discussion and open comment period is to try and get input
on perhaps other questions or issues that should be considered in the
breakouts beyond just the questions that were put in the agenda.

So, obviously there has been a number of points brought up from the
panel in terms of additional items that should be discussed and I think
a number of people mentioned talking about the objectives. I think that
is an important issue.

I think what Dr. Angulo referred to really gets to the objectives as
well. So, I think certainly each of the breakout groups, and we can add
that basically as an agenda item for each of the groups, to talk about
the objectives of these studies.

And Dr. Angulo made a suggestion in terms of one way of looking at it
would be sort of a five-part objective. So, I think that could be added
as a point of discussion for the four groups.

CO-CHAIRPERSON HESLIN: So the discussion groups are not limited to these
five questions, but can also address the other questions?

MR. FLYNN: That is right. Those are just questions put there to
stimulate discussion. The reason we are having this session right now is
to try to get other things on the table while everyone\'s in the room
together so they know what each group will be talking about.

So we can certainly add some of these points basically to the agenda for
the breakout groups.

CO-CHAIRPERSON HESLIN: Okay. That would satisfy your recommendation?

DR. SHRYOCK: Yes.

THE COURT: Okay. I think we need to move on. We are going to invite
Aleta Sindelar up here to talk a little bit about the logistics for the
breakout groups. But, the first thing I want to say \-- come on up \-- a
little bit about the roles and responsibilities.

In each of the breakout groups there will be a moderator and that person
will be introduced here shortly. A CVM representative who is going to be
sort of a technical resource and a scribe. And also a process
facilitator. I will be in one of the groups and they will be others as
well.

The bottom line to all of this is tomorrow at 1:00 o\'clock the
moderators have to present the major discussion points and responses to
these questions to the larger group. So you will have to plan
accordingly. You will have this afternoon and most of tomorrow morning
to address the five plus questions that we have asked you to address.

In terms of the conduct of the group itself, some of the details can be
worked out for the particular group because we are not really sure how
many people are going to show up at each session. But, I would encourage
you to be attentive to the time, allow for a break, start on time, end
on time.

And also, to engage in the discussion everybody needs to or should
participate. A question had come up earlier about moving from one group
to another and that is fine. If you have multiple interests and you want
to hear and listen in on some other discussion group.

The only thing I would ask is that recognize that you may arrive after
certain things have been discussed and I am sure the group isn\'t going
to want to revisit everything for an individual to catch up. But
otherwise, to the extent that you can pick up from where you arrive, you
are free to move from group to group.

I think that about covers my piece of it. Aleta, do you want to tell
them a little bit more about where they are?

MS. SINDELAR: Luckily, I only have logistics for four rooms at this
point. Thanks Fred! I just am here to point out where some of the rooms
are and who is going to be in the room.

Group A is the ruminants. They are going to be here in the Regency Room.
Dr. Gatz Riddel will be the moderator for the Regency Room. There is a
CVM representative who will be transcribing the more salient points for
the purposes of our Friday discussion on Powerpoint. Susan Harper, if
she\'s here from CVM. Can you stand up Susan, that way Dr. Riddel can
see. And the facilitator for this group will be Jim Heslin.

Group B is the avian group. They will be in the Montrose Room which is
located around the corner here. You will see the poster, it will have a
turkey on and a chicken \-- rooster, excuse me. And the moderator is Dr.
Dennis Wages. And our CVM rep is Jeffrey Gilbert. David Garow will be
the facilitator for the group.

Group C is the monogastrics. They will be in the Twinbrook Room. That is
located right next to the Montrose Room. Again, around the corner. Dr.
Robert Morrison will be the moderator. Chuck Andres from CVM will be
using the Powerpoint. And I will be the facilitator for the room.

Group D is aquatics. They will be located right here in the Randolph
Room, the adjacent room to the Regency Room. Dr. Randy MacMillan will be
the moderator. And the CVM representative, Donald Prater, will be
transcribing on to the Powerpoint. And the group is small enough I think
you will be able to moderate the session successfully.

So, thanks very much. All of the breakout groups will convene at 2:00
o\'clock and thanks very much.

(Plenary Session Adjourned for Lunch at 12:30 p.m.)