Session V Transcripts
September 22nd (AM) Session V: Predictive Chemical Biology
Panel Discussion
Stuart Friedrich, Tom Raub, Geoffrey Ginsburg, and Richard Kim
Question: Especially with regard to Professor Kim, there has been some suggestion—
for example, William Pardridge has suggested that to get compounds into the brain we
might take advantage of uptake transporters. So, I'm wondering if you have an opinion
of the viability of that approach, first of all in general and second of all, perhaps from
some other members of the panel, whether that would be a productive or efficient
strategy to take in drug discovery to try to utilize these transporters to get compounds in
that we normally can't get in by passive diffusion.
Richard Kim Response: I think that's an excellent question. In reality, I think that drug
companies have been doing this without understanding transporter biology. If you look
at some of the most successful drugs ever marketed, which include the statins, the
reason the statins are so useful is that they're able to attain very high intrahepatic liver
levels and that's the target of the HMG-CoA and the companies must have iterated their
drugs against this intrahepatic concentration, which could only be obtained because of
the presence of these transporters. So that shows that these kinds of approaches have
worked even without really knowing about them. And now we can have a mechanism
for that. I think that in terms of CNS drug delivery you can certainly take advantage of
some of these nutrient transporters that some people have talked about, but we're also
starting to see that some of the transporters that we think of as xenobiotic, broad
substrate-specific transporters are also highly expressed at the level of the blood-brain
barrier. So, for example, in humans, OATPs passes the blood-brain barrier; it's a
transporter that can recognize anionic, neutral, and cationic compounds, and one
wonders whether you could do a proof of principle study. Optimize the drug against that
to see if in vivo you get a better response. The problem will be that there are marked
species differences between the OATPs. Therefore, you may not really know until you
actually give the drug to humans, but it's a great reminder that you may be able to
rescue what may look like a very polar, non-CNS penetrant drug to actually have
access to the CNS.
Tom Raub Response: That scenario actually scares me, because when we're working
through SAR and we're trying to eliminate an efflux like PGP, I oftentimes see
compounds I wouldn't expect to get in as rapidly as they do. And, not having the tools
in hand to sort through that I don't know what to do with the information. So, the species
differential is a potential problem.
Question: I'd like to follow up with that question and ask Richard, in patients that have
this variant OATP-C *15 *15, they're going to have unusually high blood levels of these
statins, right? Could that contribute to those patients ending up getting the side
reactions that you normally get with statins?
Richard Kim Response: Well, that's the implication so this is where again
understanding transporter biology is useful because given that OATP-C is highly
polymorphic, you start to wonder should you take advantage of more than one liver
transporter—is OATP-C the only pathway? Also, if OATP-C turns out to be the only
pathway for some of these statins then, yes, that is highly likely and it could contribute
to this problem of rhabdomyolysis. So, again, it's very important to understand not only
the type of transporter but the extent of different types of transporter involvement. And,
yes, there are a lot of questions regarding whether mutant transporters will give you a)
lack of efficacy because you're not getting the drug into the liver and b) also additional
toxicity because now you have high systemic exposure. No one has actually shown
that polymorphisms cause that, but a number of groups are trying to look at whether
people who are more prone to rhabdomyolysis are more likely to have these fits.
Question: I have two questions for Stuart about PK PD modeling. When you take
these sort of probabilistic approaches and add the uncertainly into each of the variables
and kind of do the stoichiastic simulation of what your outcomes might be, you tend to
get these log normal distributions in your probabilities—you know, you've got these tails
that go way out. So there's a fair amount of uncertainty in these models. Now I
understand the second example that you gave when you said the projected dose is 12 g,
we shouldn't eve n bother thinking about that. But in the first example it looked like the
projected dose was around 150 or 200 mg with maybe a fair probability of being a lot
less than that. When you've got that kind of information, how do you use that to make a
decision to go forward? It's easy to see when you kill it, but how do you use it to go
forward. The second question, that's kind of related, is when you've got these models
that you've developed, at what point is it worthwhile to actually take something into the
clinic to validate that? That's a large expense, but what's the thought process that you
would have to go through to say it's worthwhile doing this?
Stuart Friedrich Response: You're right, in the first example that 200 mg is really not
an unreasonable dose. It was really a combination of that dose estimate, which was
higher than what we expected, and every molecule has a cost of production and a price
that we think we're going to get in the marketplace. And that, of course, really
influences what a marketable dose really is. So, 200 mg itself is not a high dose, but
the overall assessment for the molecule, including the cost of production and the price
for the molecule, influenced the decision. But, also, it wasn't just that but a combination
of the results of the PK PD modeling and the toxicology, where we had difficult
establishing dose-limiting toxicity ADME properties that were not optimal and so on.
These all added together. It was really a combination of output from the different
functional areas on the product team that helped contribute to the decision to terminate
that molecule. And regarding the uncertainty as to how it is used, this is still related to
the first question, when you come up with a range of plausible doses you hopefully are
able to take into account that uncertainty when you're designing your early clinical
studies so that you fully study not only what the expected dose is, looking at the PK and
toxicity at very close to the expected dose, but also the full range of doses. So, if
possible, go right from your lowest dose to your highest dose such that it incorporates or
encompasses that total range of uncertainty. As to the second question, it depends on
how knowledge-rich you are in the therapeutic area or even in the target itself. If you,
for instance, are developing a compound where there are two or three other compounds
in the marketplace, then you can actually internally validate this method of this empirical
scaling. So you take those two or three other competitor products, you do the
experiments in your preclinical pharmacology model, you see how well it predicts
internally the response from one molecule to the next, both being in the clinical
marketplace. And then you can say with fair certainty how well that will work for your
own clinical candidate. That's one method and, of course, as you have less knowledge
you have more uncertainty and that has to be taken into account. If you, for example,
have a compound where you have no other clinical information for a competitor
compound, then you tend to fall back on more mechanistic-type modeling approaches
where there are more assumptions being made and more uncertainty. But, in the end,
you want to ask the question specifically as to, for example, dose—what's the
probability that the dose will be higher than this specific dose that is unmarketable—and
go from there.
Question: I'd like to follow up with Stuart on this question. At this meeting we have an
interesting mix of big pharm and smaller biotech-type companies. So, my question to
Stuart is, when Lilly looks to in-license compounds, do they actually do this kind of
analysis in determining whether they see this as a viable candidate? I ask this because
so many times I see small companies thinking they have a drug candidate when, in
reality, the industry looks at it as a lead.
Stuart Friedrich Response: I've been on due diligence teams at Lilly for in-licensing,
and part of my job is to understand what factors may influence the overall outcome for
the product. If there are missing pieces of information you have to understand what is
the possibility of that impacting the overall outcome. A good example is the tornado plot
I showed. If the missing piece of information does not contribute a lot to the uncertainty
in your overall predicted outcome, then it's not as critical in your in-licensing to have as
part of the package. But if it was a parameter that you thought, based on the modeling,
would heavily influence the outcome, then that would help make the decision on
whether it's a good idea to in-license the compound, how many resources would be
required to fill those uncertainties in the compound; those all come about in the inlicensing.
Question: Tom, esoteric question. In your autorads it really looked like there were
other structures in the head that were labeled that correlated with uptake into the brain.
Was one of those the salivary gland?
Tom Raub Response: Yes, the Harderian gland frequently labels. But even more
importantly, in the one brain image I showed there were actually two such regions that I
didn't discuss that you have to be cognizant of. One could involve transporters actively
accumulating compounds into the choroid plexus epithelium. It's ten times more
perfused than the brain endothelial vasculature and these compounds tend to get in
there and stick so you may have a background brain level if you don't eliminate that in
your analytical process. Thus you have to account for that or you may have a drug
that's sequestered all in one place and possibly not in the area of activity.
Question: The answer to that esoteric question allows me to ask one that may be
more interesting in this context. Since the salivary gland is such an active secretory
organ and likely contains a lot of these transporters, is this an opportunity for a
surrogate means to look at secretion of drug into the saliva as a way to understand how
transport function might be working in drug elimination? It would certainly be a very
accessible fluid.
Tom Raub Response: It's an interesting question. Tear drops have also been
suggested as a surrogate for free fraction with all the caveats, but I don't know of
anyone looking at that.
Question: This question is for Stuart. With your knowledge and experience in the kind
of case studies you talked about in PD/PK relationships, could you comment on the
experiment that's often done earlier in drug discovery where, especially if you have an
expensive or time-consuming efficacy animal model, you dose that model and then you
take a couple of samples, say at 1 hour and 4 hours, and try to correlate that exposure
level with a pharmacodynamic response you're getting. What are the pitfalls in that kind
of experiment? First of all, is that a good experiment to do and, secondly, what things
should we watch out for in looking at or interpreting that data in terms of selecting leads
or optimizing leads?
Stuart Friedrich Response: I think that any information that you get on exposure in
your pharmacological model is important, whether it be a single time point or multiple
time points. In the example I gave, the sampling that was possible in that pharmacology
animal model was sparse, and that required me to analyze the data using a population
PK PD modeling approach, which makes the assumption that all the animals behave as
a population of individuals that are centered on a population value with variations on
that. So, even with very few samples, I think it's still valuable to collect that information
and use the modeling tools that are available to estimate the exposure in each animal
and correlate that with your pharmacodynamic response. The other thing that I didn't
actually get into is there are different measures of exposure and in these cases we were
relating the average steady-state levels to the pharmacodynamic response, whereas in
other cases it could be that Cmax is more related to your pharmacodynamic response.
And those are different questions that have to be answered with different experiments,
but, getting back to the initial question, I think it's always useful to collect exposure
information in the animal species or in the pharmacology model where you're actually
collecting your dynamic data whenever possible.
Question: Is there a certain number of time points that would be advisable to take?
Stuart Friedrich Response: That all depends on the variability between animals and
also the time course and so on, but I've seen instances where even a single time point
will give you a reasonable estimate of the exposure in that animal. Say, if you want to
calculate an AUC for that animal over a particular time course, a single time point can
sometimes do that. A single time point across 20 animals in the experiment, say, will
sometimes do that. Because, again, that single time point is combined with all the other
data from the other animals in the population analysis approach to give you an estimate
of the population clearance and also the individual animal clearances.
Question: Would there be a minimum number of animals?
Stuart Friedrich Response: I can't really give a number because it's a case-by-case
basis. What you can do is do simulations and then analyze the simulated data to say,
OK, let's do a simulation to say we only collect one time point per animal, how
accurately are we able from that one time point to estimate the exposure in that animal?
And then you can keep adding time points until you get the accuracy that you require in
your estimate.
Question: A follow-up question for Stuart. You see this less and less frequently, but it
still occurs. You go into some smaller companies where there's not a lot of DM/PK
support and they will actually have a pharmacological model and they will have dosed
animals and know what the maximal response is and then they begin to try to use that
information to say that the bioavailability of the compound is 50%. They have an IV
dose response curve and an oral dose response curve and then they'll estimate and say
it's 25% bioavailable or 50% bioavailable without measuring anything. Is that a good
idea or a bad idea?
Stuart Friedrich Response: So they're basing it on the pharmacological response.
That's a reasonable first cut. I guess you'd have to understand where you are on the
dose-response curve when you're making that assessment to understand how accurate
that assessment is, and if you're on the steep part of the dose-response curve there's
probably more uncertainty in your estimate than if you're on the flat part of the doseresponse curve.
Ron Borchardt Response: I was hoping you'd say it was a bad idea. Because often I
see people getting in trouble doing that.
Stuart Friedrich Response: It's obviously not as good; the best is a true crossover PK
study to estimate your bioavailability. But any knowledge that will help you, I mean, if
you didn't even have that information available, you'd have a total uncertainty on
bioavailability, whereas that experiment decreases your uncertainty in what the
bioavailability could be and, therefore, it decreases your uncertainty in your outcomes.
Question: A question for Richard Kim. What is the real impact of transporters? You
have this great example with rifampin with OATP-C and induction of PXR and PEP to
eliminate it. I worked with intestinal tissue so P-gp and CYP-3A are proteins of interest.
Should we be looking at CYP-3A and P-gp expressed alone in cells or should we look
at these proteins expressed together in the same cell line?
Richard Kim Response: It's a difficult question. At least in the transporter field a lot of
people have tried to have multiple transporters expressing cell lines. The layer of
complexity becomes fairly severe with even a couple of systems. So, the problem with
trying to predict with key players, not with just having the key players alone, you have to
understand the relative expression of the key players and how do they vary in people.
And, of course, in the intestines there's regional differences in expression so I think
most people find P-gp is higher in the ileum compared to the duodenum and vice versa
for CYP-3A. We know very little about uptake transporter expression, although we
know some of the OATPs are expressed. So the model system would have to
incorporate a physiologically relevant level of these transporters, validate them, and
then you would have to use fairly robust and complex models to simulate various drug
concentrations outside the cell, inside the cell, in the presence or absence of a drugmetabolizing enzyme or transporter. Because the question that's still a daunting task is,
when you have more than one transporter or P450 present, is the clearance really 1 + 1
or is it something different depending on the extent of metabolite formation. These are
still important but very difficult questions. It's going to keep us in business for a long
time. As a first step you have to use systems that have the major players, and at least
multiple key systems are likely to give you better predictions than just studying
individual systems alone.
Question: This question is for Dr. Ginsburg. In your biomarker program you developed
at Millenium, I wonder if you considered the lack of appropriate efficacy models for
human responses and formulated a strategy very early to take a compound into the
clinic based on biomarker, ability to hit target in a preclinical efficacy model, as opposed
to a biological endpoint? Because we know we're losing efficacy late often because our
preclinical models are just very poor predictors of human outcome. And, if you've done
that, what has been the response of the agency and how did you shepherd that strategy
through, going forward toward those goals?
Geoffrey Ginsburg Response: The first question centered on taking compounds
forward but having something that was more of a surrogate?
Jim Stevens Response: Yes, say, for example, in oncology, you know that the
xenograft growth response is sometimes a very poor predictor of outcome in the clinic.
So if you use just the ability to hit the target in the xenograft, and say we have very good
data that this molecule is very selective for the target, we have a good biomarker
strategy. Have you tried to take a molecule forward into the clinic based on the
biomarker data as a surrogate for efficacy, arguing that the preclinical model is not a
good predictor of outcome in human.
Geoffrey Ginsburg Response: We have not taken that strategy, but I think in the area
of oncology you're on better ground to try to do that simply because the chances of
achieving efficacy and the consequences of not drive the decisions to move the
compounds forward into development. We think that, particularly for a number of the
molecules that are moving into clinical development, the biology is so unvalidated or
there's not sufficient data to even convince ourselves that we wouldn't go to the agency
unless we had the preclinical data to support the biomarker work as well the biomarker
strategy to support the clinical work. So, given that, we haven't really tried to move
things into clinical development in concert with the agency that didn't have those kinds
of data to support the program.
Question: So, oncology seems to be the example where there's some movement in
that direction. Since you have such a well-developed program in indications such as
RA, did you see flexibility? Because even in RA, particularly in neurosciences, we have
a very difficult time saying that the preclinical efficacy model is likely to predict outcome
in human. Yet, if we have good data on receptor occupancy or inhibition of a kinase,
phosphorylation, say, of a specific antibody, do you see movement in that direction and
other indications outside of oncology in your experience?
Geoffrey Ginsburg Response: I haven't seen enough of what the agency has seen
from other places to give you an answer of what their thinking is. They're staking the
ground on innovation and stagnation. The white paper that the agency put out earlier
this year indicates to me that their expectation is that it's more in the realm of human
studies where there might some more flexibility if there were appropriate tools to make
these disease measures. I believe that because there have been so few successes and
the number of NCEs approved has gone down so dramatically, that there will be, at
least in early stage development, more opportunities to do those types of studies and
not necessarily supported by a neat preclinical package.
Jim Stevens Response: It will be an interesting ongoing discussion to see if the
proposals in that stagnation paper are followed up by a regulatory path that allows
companies to move forward with different strategies.
Question: A philosophical questions for all of the panel and anyone else that wants to
pipe in. We tend to design drugs toward the mean values, you know, the mean
pharmacokinetic parameters, the mean response to a drug, pharmacodynamic
parameters, but also what ends up killing a drug is the variability. Dr. Kim centered on
SNPs as one source of variability; there were also questions that were brought up about
the composite effects of variability for different elements. I'm wondering when we
advance a drug, whether it's really a manageable thing to anticipate what the variability
is going to be when we get into a large population. We tend to go ahead with a
compound into safety based on what we anticipate its property to be. But what can kill
a drug or, even more important, do damage to people is what's going to happen in the
one in a thousand to one in ten thousand individuals that are out there in the population,
and variability is basically an aspect of the population, everybody's different.
Geoffrey Ginsburg Response: There are well-identified variants that are low
frequency in the population. Some companies maybe in the room as well as Millenium
are developing essentially cohorts of individuals that harbor these variants with a very
directed strategy of recruiting them into clinical trials so that we understand their
response to exposure. But even that may not get at the kind of gene frequencies that
you alluded to where idiosyncratic reactions may occur or where the real frequency of
some of these variants may be too low to detect, at least in the kinds of studies that we
would entertain. The NIH, Francis Collins specifically, is contemplating establishing a
cohort of 500,000 people for a variety of issues, including this one, which is to get at
some of the very low-frequency events and variants that could play a role in extremes of
toxicity and to be able to establish that as a resource for some studies such as this. So,
I think you raised and important issue; I think the FDA will be looking for companies to
address what's known about PK variability through genetics. If they're not already, they
will be in the not-too-distant future.
Question: Richard, I'm wondering if the polymorphisms that you're observing in the
OATs have translated into a clinical changes in the catalyzing enzymes. If you induce
with rifampin, do you see a 3A level difference in the polymorphisms? And, sort of
related to that, I'm wondering if you've had the opportunity to correlate the SNPs or
haplotypes that you're seeing in the OATs with haplotypes in P-gp because of the large
problems of functional observations on the P-gp side.
Richard Kim Response: The data relating to induction and transporters regulating
some of these things are really quite modest if there are any. Even with rifampin, for
example, there's really no effort by people to monitor rifampin levels, so we really don't
know. People are given 600 mg once a day and it's assumed that at that dose
everything will be maximally induced, and it may be. We're looking at some of our own
data as well as actually designing some studies with lower dose rifampin to see if we
can pick up kinetic differences as well as subtle 3A level differences.
Question: But now that you have the polymorphisms identified, you could do a
prospective study with people that have been selected.
Richard Kim Response: Right, and that's exactly what we're hoping to do. With
rifampin it is a bit of a bugger in that it is very light sensitive—the assays have not been
well validated—labile, and we think the 600 mg/day dose may be very high because,
typically, whatever has been published seems to reach concentrations in the low
micromolar even at trough levels, so you may have to lower the dose and get the proof
of principle. I think we may be seeing a lot of what we think are variations in induction
that may actually be due to variations in intracellular drug level because of either
polymorphisms or expression differences that dictate that. Some people have shown
some data in vitro where if you throw in with, for example, OATP-C, you can get totally
different gene expression patterns in the cells with and without this transporter because
a lot of the ligands are also hormones or hormone conjugates that are ligands for other
nuclear receptors as well. So you may have almost a master regulator of gene
expression because it will affect multiple nuclear receptors, but it hasn't been proven in
people.
Question: Any correlation between or linkage to equilibrium between haplotypes?
Richard Kim Response: We've not carefully looked at the OATP-C versus MDR1. We
have a lot of samples, some of them retrospective, but the frequencies are quite
different so I don’t know.
Question: Richard, has this field of transporters come to a point where investigators
like yourself can recommend to the industry that they should routinely screen certain
transporters, like they do CYP enzymes, like they screen the CYP 3A4 and 2D6 and
2C9? Has it reached a point where you would recommend people in DNPK groups
focus on certain transporters?
Richard Kim Response: Most of the companies, in fact, do this one way or another,
whether they're using a direct system or cells lines or knockouts or whatever, I think it
becomes kind of a de facto transporter. There are other efflux transporters and uptake
transporters, they way I view it is it's inevitable. The key thing is you have to have
people in industry who will have to understand the biology of transporters and pick the
key players and bring it in-house because it is clear the field at least over the past 5–10
years have not been told and some transporters are becoming much more important.
And you do have to remember that, unlike P450s, with transporters there's this tissuedependent expression of different types of transporters, a subset in the kidney, different
set in the brain, different set in the liver. And if you're making different types of
therapeutic targets one would think both of delivery of the compound or maybe making
the drug more favorable in terms of pharmacokinetic profile. One would start doing
designs for transporters. We have done studies with companies who have iteratively
removed the metabolism as a way of predicting enhanced or more favorable PK profile.
In fact, for some of those compounds, the clearance becomes greater because then the
transporters come in. So as you design away from 2D6, 3A4 and other P450s, I think
you'll see more transport problems.
Question: This question is directed to Tom, but I'm sure there are several other people
that would know about this. With regard to P-gp efflux blood-brain barrier, have you in
working with discovery teams seen situations where once you identify whether a
compound was a substrate for P-gp, and that was a major problem, they've been able to
actually structurally modify a series in order to take that efflux component away? And, if
so, what were some of the key structural changes that allowed that reduction in P-gp
efflux? And, then, kind of corollary question is, there's a number of P-gp inhibitors that
pharmaceutical companies have developed and they're very potent. So, why haven't
we seen the introduction of therapies where a P-gp inhibitor is coadministered with a
drug like taxol or something that we want to try to get into the brain in order to swamp
out the P-gp transporters to try to get the drug where we want it to be?
Tom Raub Response: With regard to the SAR, and Jerome spoke to this too so he
might want to add, it's more difficult to move away from P-gp when it's part of the
scaffold responsible for the activity-dependent portion. However, it's doable. I don't
believe you need to actually engineer away from P-gp interaction since you can also get
around it by improving passive diffusion. Here you're affecting pump efficiency, not
necessarily molecular interaction between pump and compound. So, success is easier
if it involves a non-critical component of the scaffold. It's like the example I gave where
you have a hydroxyl group that's not critical to the activity, but the substitution marked
changes physical-chemical behavior. I've also been in cases where we couldn't move
around it because of the complexity of the molecule, like a peptidomimetic, is such that
the global properties require you to move too far away. Having said that, I have another
issue to throw out for consideration. You can have P-gp effects and still certainly have
an effective CNS agent, and that may, in my opinion, actually improve it's activity. This
I'll suggest as a potentially controversial issue particularly with regard to PET ligands. If
P-gp is not rate-limiting for a compound to get into the brain, but rather is limiting in
removing it from the brain, then reducing its distribution volume via P-gp-mediated efflux
could be an advantage. Efflux could decrease nonspecific background without
impacting specific binding. I'm confused by the lack of success using inhibitors to
improve delivery, I'll admit. I think it's just been a combination of poor clinical design,
inability to attain efficacious exposure, and concerns about liabilities with respect to
letting other potentially harmful things in that have contributed to an underutilization of
such an approach.
Question: One last quick question for Tom. What I think you've done in terms of
developing this model for studying the brain barrier permeability is excellent. But I
wonder if you're at all concerned about the possibility that you're going to optimize the
delivery of drug candidates to mouse brain.
Tom Raub Response: I obviously don't have a lot of experience beyond mouse
excluding some correlation to rat and maybe even the dog, but I've not yet seen a
species difference excluding any kind of active transport. That's certainly where you
have to be careful, I think, in the implementation of such in vivo or in vitro assays at the
lead optimization phase. I remember an example years ago where somebody had
developed a CNS drug using Caco-2 to optimize brain exposure and they ended up
selecting a lead series that used an active transporter in Caco-2 cells that didn't exist at
the blood-brain barrier in vivo. So, you definitely have to be careful.
Ron Borchardt Response: I asked that question because we had a recent experience
where we've looked at P-gp substrates, and in rat they show very little brain exposure
and in guinea pig they show significant brain exposure.
Tom Raub Response: Assuming that species-dependent serum protein binding
differences are not at play here, I’m unsure why this difference would exist with regard
to passive diffusion. There certainly could be species differences with regard to active
transport, but less so for P-gp I would say. I’ve also seen big differences with certain
scaffolds with respect to the dose route —IV vs. PO or IP—so one also needs to be
careful there when interpreting data.