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Executives

Karl Mahler – Head of Investor Relations

Hal Barron – Head Global Product Development, CMO

Analysts

Michael Leacock – RBS

Keyur Parekh - Goldman Sachs

Roche Hldg Ltd Spons (OTCQX:RHHBY) Late-Stage Pipeline Conference Call November 7, 2011 9:00 AM ET

Karl Mahler

I think we can start now. So welcome in the name of Roche, for our investor event here. My name is Karl Mahler, I’m the Head of Investor Relations. And first of all I wanted to thank all of you for your interest in Roche. I think this is the second pipeline event with Hal Barron, basically, a year ago we met here, basically at the same point in time.

The purpose of this meeting today is to share with you important data we have generated in the course of this year. And having said that, the focus of today will be on certain selected trials on it.

So if I look at the trial outcome we had this year, if I counted correctly, we had about 23 data endpoints; Phase II and Phase III points. Twenty of them met the primary endpoints, and Hal Barron asked me to let you know that should we today focus on the signs and not on Greek bonds, or any kind of Italian debts. He felt that he may not even be the right person to address that.

So if you look at the track records of the pharmaceutical industry over the past 50 years, and you can see that on this slide here. You can see that the – and this is a long-term outlook, that the output of the industry in terms of new market entities actually was always in the range, some are between 15 and 20 new market entities per year.

So there was a kind of a constant rate of output. There was only a catchup in the 90s here in the United States, but in average, the today’s output is more less on historical levels with what we have seen in the past years.

If you now look at Roche specifically, again, over a longer period, and not just at a point in time, the Roche R&D productivity for new market entities was also above the [inaudible]. So in other words, if you look what we spent in terms of R&D, and what the success was, you can see that the Roche Group, actually over a longer period of time, trended above the industry trend line.

And if you just look at this one, last but not least, I think if you talk about R&D output, what we spent and what we get out of the system, it’s not only good enough to look at new market entities, but it’s also necessary to look at line extensions.

Line extensions usually have the same – or offer the same amount of phase. Usually, they do cost basically the same as a new market entity, but they definitely also enrich the portfolio over a longer period of time.

And here you also can see the event today will not be on line extensions, you’ll focus on our rich late-stage pipeline and new marketable entities. And here I can really say that we have a rich pipeline, lots of exciting stuff to come. And the focus of today will be basically on those assets which we have not so much shared with you in course of any kind of scientific meetings which we have this year, but also on those things which we could not share with you so far, but which you should also found that its interesting.

With this one, I would like to hand over to Hal. Before I do that, I wanted to thank [inaudible], and [inaudible] for their work here to make it instrumental, preparing the presentation, and to make this event happening. Thanks a lot.

Hal Barron

Okay, well, good morning. It’s actually great to be here today. Thanks for taking the time out of your busy schedules to listen to a few thoughts on our pipeline. I always enjoy doing the analyst presentations. I always find that it’s a very exciting, very bright group of folks that always push me and push us in interpreting our data and give us insights into the industry that we might not have seen. So hard questions are well received. So thank you very much for that.

And I also particularly enjoy coming here to London. I forgot how much I enjoyed coming to London. When I’m in San Francisco, no one really thinks I’m very smart. When I come here, everyone keeps saying “Brilliant, brilliant.” I give somebody my slides, “brilliant.” I order a steak for dinner; they said “brilliant.” So it’s so much fun to be here.

So what I’m going to do over the next 45 minutes or so is trying and convince you of two things. The first thing is, and hopefully the slides and the examples I show you will help you see why I believe this and hopefully convince you of this.

I think our pharma industry and particularly our company, is undergoing a significant transformation. A transformation from much more impured drug development, where the probabilities of successes are low, to one which is I think much more biologically driven, because of our increasing understanding of human disease and our appreciation for the heterogeneity of disease.

So much of clinical medicine, the diseases were described were based on clinical features. But with the molecular biology and the techniques we have, and our real focus on understanding what’s going on in the individual patient, I think we’ve become very, very savvy as to the heterogeneity in diseases. And by understanding in any given disease, whether it would be melanoma or basal cell, or even asthma, by understanding that each patient, and subsets of patients actually have their disease progress from different mechanisms and different biology, we can then use a personalized healthcare, a companion diagnostic to actually identify the patients most likely to benefit, least likely to experience toxicity, which increases the probability of success, and reduces the time to development. And it ultimately reduces the cost to success, which I think will make our industry significantly more viable. And I think we’re in the midst of a significant transformation where more and more of our development programs focus on this, and I think the impact will be quite substantial.

The second thing I’m hoping to do and maybe this will or won’t work, but I thought that it would be fun to spend 10 or 15 minutes towards the end of the presentation going deep on about three or four papers that have come out in the last ten months in the metabolism space that I think speak to what I just described, which is an increasing understanding of the biology and pathophysiology of certain diseases. And I want to share with you some papers that I thought were particularly helpful in understanding the role that reverse cholesterol transport HDL and remodeling of the plaque plays, and why dalceptrapib and aleglitazar may be addressing those in a very unique way. And we’ll see – it’s a deep dive in the science, hopefully a little teaching and discussion. So that’s the goal.

Oh, this a build. Okay, so you see, we’ve had a pretty good year. We’ve had 20 out of 23. Wouldn’t it be nice if that’s all you had to do for 20 to 23, is just click a button and all of sudden you get 20 positive trials. A lot of incredible work by a lot of people, that both designed and enrolled, and then interpreted these trials. But really, I think the key thing here is, these trials were designed and implemented with an intense focus on doing what’s best for patients and following the science.

And I think, as I say, and I’ll show you examples, when you really do that, when you really look at the data carefully and decide what not to advance and what to advance, you can really have some pretty impressive treatment effects. And these trials were not only positive, but I think many of them have the potential of completely transforming how medicine is practiced.

We have a very rich portfolio, you’ve seen this slide before. There’s around 90 unique molecular entities, all of which are being designed to be first or best-in-class. And we have a process through the drug development stages by which we assess whether that’s more or less likely to be true, and make bigger investments once we realize that’s highly likely. There’s 160 different programs for these 90 MNEs and ones that we’re extremely excited about.

They’re in not only oncology, which does represent about 60% of the portfolio, but also exciting molecules in Immunology and Virology and CNS, metabolism, Ophthalmology, et cetera; so a very diverse portfolio, both with large molecules, proteins, antibodies, as well as small molecules. And our small molecule program inside the cell for the cancer is growing quite rapidly. Over the last two or three years we’ve had a major advance in that. And I think that speaks very well for the future of our Oncology portfolio.

In Oncology in particular, I just thought I’d share with you some of the exciting strategies we’re taking. Many of the molecules you saw on the prior slide fit into this. And in Oncology in particular, one of the areas that I think has the greatest opportunity to completely transform the treatment of cancer patients is our focus on armed-antibodies, or antibody drug conjugates.

And T-DM-1, which I’ll show you some data on as an example of this, but we have 37 additional ADCs that are in early and late-stage research, and in fact, quite a few in the clinic already. And this raises the possibility of really re-thinking how we’re going to be treating cancer patients by selectively delivering the chemotherapy into the cell, to avoid the systemic toxicities that you get with systemic delivery. But also, as you’ll see, maybe being able to treat the patient longer because of this, resulting in greater efficacy and potentially even through other mechanisms, increasing the treatment effect.

And this is a really exciting area that I think you’ll see, not only us, but a lot of companies focused on because it does have a huge opportunity here.

The other thing that I think sets – I’m sorry, is there a question? Anyone’s allowed to ask a question anytime, so feel free to interrupt me.

The second area that I think is really exiting is the combination therapies. This isn’t any surprise to anybody. But one of the great things about having such a large oncology portfolio is our opportunity to combine them, very easily in both preclinical and clinical studies to try to determine whether there’s true synergy by antagonizing two independent pathways where the biology will tell you, you should get a pretty significant treatment effect, we can actually explore our dosing and regimens that would induce the synergy. And we’ve finding some pretty intesting things by doing that and then we’re translating that into the clinic to see if we could actually really substantially improve patient outcomes by doing that. And as I say, with having so many small molecules and antibodies in cancer, we have the luxury of being able to undertake many different combination strategies and some are listed here.

Also our focus on apoptosis and the modulating those pathways, particularly with DCL2 inhibitors, very exciting. We’ve got a number of programs earlier than that, but that whole area in collaboration with Abbott looks very exciting.

I think Avastin is great drug. It’s shown tremendous benefit for many patients but I think we’ve just scratched the surface on how to fully and maximally impact the vasculature in the tumor to really have the greatest impact.

It’s also a theme that we talked about when I was here, was it last year? Yeah, last year. It seems like a lot longer than that. And we can see how – what I was describing how the data’s evolved quite nicely. I think all of you are probably pretty familiar with the concept of the HER2 franchise.

MARIANNE is a very exciting trial where we have HER2 positive patients who progress to recurrent locally advanced breast cancer, but previously untreated. So the frontline setting here, primary endpoint PFS, where we’re going to look three ARMs, T-DM1 plus pertuzumab, which may someday become the best therapy HER2 positive breast cancer; T-DM1 versus the control of Herceptin Taxol. And we’ll be able to see if either of these ARMs is better than this control. And we expect to follow this data around 2014.

Now one of the things that I mentioned with the Neosphere trial, the importance that had not only for pertuzumab, but what it might signal for the future of drug development and breast cancer. Here’s the data from Neosphere. It’s a complicated trial in some respects, but very, very important. And this is the control arm Receptin docetaxel. You’ve got the addition of pertuzumab here, you’ve got the removal of docetaxel to see if chemo is needed, and you’ve got the removal of Herceptin.

Now, based on everything I’ve showed you about the preclinical data and actually the Phase II data, one would argue that in fact Herceptin is needed. But what this shows is in fact exactly that, that when you compare the control arm, Herceptin, docetaxel, and pertuzumab, you get about a 16, 17% increase in the pathologic CR. Pathologic CR is the observation that when a woman comes in with breast cancer, gets the chemotherapy before surgery, when she goes in to have the tumor removed, under the microscope there is no tumor. There is a pathologic complete response. And obviously, that woman who have that outcome, do very well. And increasing that rate is thought to represent a significant advance, and increase dramatically the probability that when studying the [inaudible] setting, the drug would work. So it’s a very nice surrogate for, we believe, for benefit in the [inaudible] setting.

And if you look at the treatment effect observed with adding pertuzumab to Herceptin, it’s around 16, 17%, which is about what we saw when we added Herceptin to taxaine, pretty amazing.

What we also learned is that as it relates to pathologic CR, that taxaine matters. We’d like to get rid of chemo, it’s not going to happen here anyway. And maybe pertuzumab, since it does bind to HER2, HER1, HER3, HER2, HER4. Maybe you don’t need Herceptin, but again, as I mentioned, all the data would suggest you do, and in fact this collaborates that. That when you lose Herceptin, you lose the treatment effect.

So again, it really confirms that Herceptin, pertuzumab is probably the regiment that’s going to create the greatest value on top of taxaine for women with HER2 positive breast cancer. And what it also did, as I mentioned, the better understanding of biology and understanding of clinical medicine has allowed us to make decisions in a more robust way than we would have. This is Herceptin, and it was about two years after the unblinding of the Phase III data, metastatic cancer to when we started the [inaudible] trial.

Now, we turn the clock up some time, a decade or so, and by adding the neoadjuvant study and having the data, we were able to take what I think was a smart risk, without knowing CLEOPATRA, without knowing if it worked in the metastatic situation, and begin the adjuvant program. And in fact, we will be presenting the CLEOPATRA data, and enrolling the first patient in the adjuvant around the same time; savings of about two years. And the impact that will have on all those women is pretty dramatic.

So again, understanding the biology, understanding surrogates in this case to understand the clinical medical is allowing us to make I think, better decisions, and certainly ones that result in shorter development programs, and which results in less cost.

This is the infinity trial design, this is the adjuvant program that was initiated from the neosphere data that I described, it’s relatively straightforward. We hope to have, as I said, first patient in this quarter. And there’ll be a three year follow-up and we expect filing will be sometime as described here, and around 4,000 patients.

Now one last comment, innovation comes in all flavors. One way to think about innovation is I’ve just described better efficacy, greater safety. Another way which is being driven by the subcutaneous Herceptin, is how to make it more convenient for patients, particularly as it relates to women who are taking Herceptin for 52 weeks in the adjuvant setting, being able to reduce this to a subcutaneous injection that takes five minutes, relative to the infusion in a hospital setting, not only is more convenient, but in fact might reduce healthcare cost, which again, is increasingly important in our society. And we are, the regulatory status Herceptin is being filed for the neoadjuvant indication through the NOAH trial, and the HANNAH study, which met its primary endpoint with looking at PCR rates and PK to be able to be comparable in the neoadjuvant setting. So we think we have a path forward, and we think you have a way of innovating it in a different way.

Now as it relates to T-DM1 in the adjuvant setting, one of the things that we are challenged by, and it’s a great challenge to have, is every time you improve the outcome of woman with HER2 positive breast cancer, or any disease for that matter, the next drug becomes even smaller because the unmet need becomes smaller, but the sample size to show us similar treatment effect become larger, the timeline is longer. And when we think about how great Herceptin was, and now how likely or you can – I’ll give you probabilities, but at least reasonably likely, that pertuzumab is going to add even further. Adding T-DM1 in to the adjuvant setting adjuvant is very complicated, the hurdle is high. The current standard could very well be pertuzumab Herceptin. So should that be the backbone.

You also have to remember that T-DM1 has the taxane width, the [inaudible] with the Herceptin for the entire period. So if you’re going to be giving that for the entire period Herceptin was given, which is one option, your patients will be experiencing incremental toxicity, because they will be getting some toxicity from the DM-1 component.

And other ways of thinking about this problem are to think about who they’re for are the high-risk patients who might benefit the most. So lots of different ways of thinking about how to insert T-DM1 in the [inaudible], but clearly, if it’s going to be adding value over Herceptin alone, and based on its mechanism we feel reasonably confident that there’s a way forward in the adjuvant setting.

And we will be looking at data to help us decide what’s the right strategy and path forward, and that’s through a number of trials, five on-going trials. Importantly, MARIANNE, there’s a Phase II neoadjuvant safety study that will help us understand the safety issues in the adjuvant setting, which are different than the metastatic. And the combination of T-DM1 with pertuzumab Phase I, big safety study. All of this will help us inform us. And we think by the next four to six months, we’ll probably have a very robust strategy on how to do this.

So here’s where we were last year, we sort of saw similar response rates, didn’t have the duration response with T-DM-1, and did have a better safety profile which I showed last year, and didn’t harp on this year. And we felt like T-DM1 was increasing tolerability at a relatively similar efficacy, maybe slightly this way. But that pertuzumab was moving the bar this way, greater efficacy.

Where you see us today is it looks that T-DM1 actually has not only a safety advantage, but an efficacy, and it could be that someday T-DM1 pertuzumab is the new standard of care for HER2 positive breast cancer.

Let me talk another example, Zelboraf. I think this again, I’m trying to highlight examples where the help you see, what I believe which is when we were in the midst of this transformation into a new way of developing drugs, one that’s faster, cheaper and more likely to work, and had dramatic impact for patients.

What I’m going to focus on are four things actually. First is updated overall survival data from BRIM3, I’ll show you quickly the Phase I survival rates, the two year survival rates which actually are amazingly 38% at two years, which is really higher than I think anyone ever thought was possible, and then hone in on the timelines.

So here’s the most recent March 31, cutoff of OS, and you can see some pretty impressive figures here. Estimated six months survival was 83%, the median follow-up here is 6.2 months. We have a hazard ratio of .44 for overall survival. These are numbers that we almost never see. Doubling in survival in a metastatic situation are even more than that, as really ushering in a new era. And once again, you can see that in the carbazine arm, the estimated 6 month survival is significantly less than the 83, at 63, the median follow-up here is 4.5 months. We’ve got a median OS of about 7.9 months. We haven’t reached the median here yet, but one can estimate it through hazards.

And again, a major advance, based on the science, based on finding out that there are patients with melanoma, who this disease is driven by this B-RAF mutation. The science tells you that’s what’s causing their disease, and focusing on them by selecting only the patients who have this abnormality allows you to see this incredible treatment effect.

If we had studied all [inaudible], this would mean that there’s no benefit in the B-RAF wild type, the treatment effect would be obviously half. And it would be a drug that worked, but of marginal maybe value to the patients in society. And this is the kind of programs I think you’re going to see more of.

This is the two year survival data from the Phase I again, very impressive. I won’t harp on this. But you’re seeing curves that were never seen before by intervening in this disease in a very impactful way.

Not only was this very, very quick in terms of its filings … I’ll throw out one fact that I find almost staggering. In, let’s see, Q2 of ’'09, we presented Phase I data. In November of 2010, in Sydney we presented Phase II data. In January, two months later, we stopped the Phase III at an interm analysis for extremely robust overall survival findings.

Three and a half months later, the drug was found, and three months after that essentially, it was approved. So when you think about the time from Phase I data presentation to approval, being on roughly two years, this is on-paralleled You don’t see this, but I think you will be seeing more of this kind of thing because the treatment effect is so huge, the diagnostic identifies the patients most likely to benefit. It’s just such a great advance for patients that the regulators are moving fast, safety becomes less of an issue when you have much more dramatic treatment effects than have ever been seen.

And not only is all that unbelievably impressive, the other wave for the future is, here’s this dramatic benefit, before Zelboraf, after Zelboraf, anybody can see that. But here’s, unfortunately, what happens after relapse. Like you might expect from a very smart cancer, these things have ways of evolving around the pathway you’re blocking, and combination strategies are needed because you need to be able to prevent whatever the resistance mechanism is as well to have the greatest effect.

And what’s amazing, as I said, November was Phase II data and January was Phase III unbinding. In between those two dates was a nature paper that elucidated the mechanism by which patients developed resistance. The timeframe for all of this, to have it in the midst of your Phase II, III program, to have the resistance mechanism identified, it’s truly staggering.

And what it allowed us to is in several months after this paper, start the Phase I study of the combination of a MEK inhibitor, GDC-0973 with a B-RAF inhibitor. To be able to take this story one further step. And again, it’s a story of one, but I think it’s a story that you’ll be seeing more of. Not from just us, but from lots of people, because the biology and the clinical medicine is evolving at a rate that really has never been seen in the history of medicine.

We also have some other programs where we’re using combination trials again with IPPY. We’re looking at the MEK inhibitor programs as I mentioned, BRIM 7, a cell called BRIM 7, and also exploring whether B-RAF inhibition would work in other tumors, particularly in thyroid cancer where we know the mutation is prevalent and probably in the cause of pathway.

I just wanted to give one quick other example, because it’s a little different than B-RAF, but I think it’s a similar concept. We have Vismodegib, which is a hedgehog pathway inhibitor. It’s being developed in Basal cell, why is that? Well the basic science of Basal cell has made enormous advances in the last five years, ten years.

And there’s a disease called Bornholm syndrome which is a hereditary disease where patients develop multiple basal cells, basically one or two a year. And the biology of this, it’s very rare. The genetics of this has been understood, and it’s a abnormality in the hedgehog smoothened pathway. And this data, this information allowed us to start exploring basal cell in general, not the genetic mutation basal cell, and realized that the vast majority of basal cell in general is an alteration of this genetically induced phenomenon in these cohorts of people with Cortland Syndrome. And that allowed us, without a companion diagnostic to identify disease which is relatively homogeneous.

So when diseases are heterogeneous, they really rely on a diagnostic. When diseases are more homogeneous with the pathway being used as predominately related to one abnormality, you can make a significant advance by just studying this homogeneous disease. And you’ve seen data, I think, about this. This is – is from the New England … These are pictures that show the dramatic benefit that you see in basal cells, these tumors can be extremely disfiguring, and the surgery can be equally disfiguring, particularly when on the face or in areas that are challenging. And you’re seeing basically, these tumors melting away.

And the treatment effects are enormous. My guess is that this will be viewed, in many respects like B-RAF, and that we’re really transforming this disease, and really benefiting patients by understanding the biology of the disease, developing reagents that block that pathway, and allowing a very, very expedited development program to get the patients fast.

So that was goal one, some examples of that. I thought I’d take ten minutes or so. How are we doing on time? Okay? And tell you, I’m a cardiologist, my background is more in lipid and Metabolism. So when I’m preparing for talk, I like to go back to my roots, and I thought this would be fun to show. I’ve got a number of questions from you over the years, on the metabolism franchise. And I think there’s been three or four papers that have come out over the last nine months that I thought were important. And I thought I’d just share with you those, and why I thought they were useful in understanding maybe how the field might involve.

The first one, which I think in many respects might become one of the line-mark papers on metabolism, was published in a New England journal in January, so about ten months ago. And essentially what these investigators did was explore a concept that many have talked about in pre-clinical models but done in people. And they basically had two different cohorts, one normals that underwent IVIS, and looked for atherosclerosis intimal changes. And others who were [inaudible] disease and they were comparing them to a cohort of controls.

It’s a complicated paper but let me try to walk you through it. What they found was, in terms of the odds ratio for coronary artery disease, typical things like diabetes made you twice as likely, well known hypertension, 1.8, smoking 1.3, [inaudible] cholesterol actually wasn’t in this model predictive, HDL, the higher your ratio the better off you were, 1.85. What was interesting is, they added another covariant, another way of assessing risk, called E-Flex capacity, cholesterol E-Flex capacity. And that basically is a system, a model where you can take any of your blood, put it into this system which I will only go into if there are questions, and determine how any of you E-Flexes removes cholesterol from plaque.

And most people thought that the E-Flex capacity was simply a reflection of how much HDL you had. The more HDL the better E-Flexible. What this paper identified is there’s a reproducible way of assessing E-Flexible, and while it correlates nicely with HDL, it only – HDL only explains about less than 40% of your E-Flex capacity, our E-Flex capacity.

In other words, two people with the same HDL might have very different E-Flex capacity. And it also suggest that if you look at by cortile, those with the greatest E-Flex capacity had as much as a 60% reduction in the risk of a cardiovascular disease. Even after correcting for all known risk factors, including HDL, you’re still up in the high 40s, so about a 50%, 55% reduction, if you were in the best E-Flexer.

Now, what was also really interesting in this paper, is they took some people before and after various therapies. Now, when you look at the statins whether you took pravastatin 40, Atorvastatin 10, or even atorvastatin 80, there was no significant reduction or improvement in cholesterol E-Flex capacity. Statins don’t improve E-Flex capacity.

The one drug they found that did, interestingly, was pioglitazone, small numbers, but about 11% improvement in E-Flex capacity. So not only does pioglitazone bump up your HDL a little bit, it does probably make all of your HDL also a little more functional, improving your E-Flex capacity.

Now I’ll tell you why I think that’s important in a second. So here’s one more study that I think is really important, and it’s going to again, maybe usher in a new era of how we use surrogates in cardiovascular and metabolic disease to figure out whether our drugs are active. It’s again, a study, October – okay, so this is three or four weeks old. I’m trying to present to you some of the most interesting stuff that’s hotel off the press. And in this investigation, Serial FDG PET Scans were done. This is looking to see what’s the metabolic activity as assessed by PET using FDG of a plaque. How metabolically active is a plaque. And it’s not that the more metabolically activity a plaque is, the more inflammatory changes, the more sort of verse modified macro pages eating up plaque and disrupting it potentially, [inaudible] of plaque rupture and event. So more inflammation, more PET uptake, the worse off you might be.

And what they found using PET is that unlike Glimepiride which actually reduces hemoglobin A1c almost equivalently to Pioglitazone, the PET was dramatically different. This seemed anti-inflammatory, anti-PET. It stabilized the plaque, however you want to say this. And I think that’s very consistent with the prior New England study which shows that pio is probably improving cholesterol E-Flex capacity and stabilizing, reducing the inflammation in the plaque.

One other study, because I told you both those drugs equally reduces hemoglobin A1c in a diabetic, you might say strange that they equally reduce glucose. But one has a much better effect on the metabolism or the plaque itself, might explain why reducing hemoglobin A1c in the diabetics patients has never been shown to reduce cardiovascular events. It may be that the cardiovascular events are caused by something else.

So [inaudible] colleagues looked at this and they said what are the predictors. By IVIS of a reduction in IVIS burden and basically in reduction in plaque with pioglitazone. Some people might argue, well, it’s not the people had the greatest reduction in hemoglobin A1c, the greatest reduction glucose, maybe those are the people, it’s not just the means but the greatest reduction of benefit. Turns out, hemoglobin A1c changes, we’re not at all correlated with improvement in atherosclerotic risk and vascular plaque. But the only thing, and the single most important thing was the HDL change divided by – the triglycerides change divided by the HDL change. So the more you make your triglycerides go down and the more you make your HDL up, if you looked at the people getting pio, some people didn’t have much, some people had moderate, some people had a lot of change in this ratio. And the more the ratio changed with pio, the better off you were from a vascular perspective.

Again, mirroring, I think all three studies are trying to tell you the same thing. It’s this alteration of the lipid profile and making the HDL healthier, the results in improved cholesterol E-Flex which translates in to a reduction in PET inflammation in the plaque.

So what all that important for us? Here’s the ALECARDIO Phase II trial. I don’t show hemoglobin A1c, I show you the triglycerides need shell changes, and I also show you the design of the Phase III.

Here is Placebo, this triglycerides, here’s the various doses of Aleglitazar and here’s the pio. As it relates to triglycerides, and this is the dose moving forward, you can see Aleglitazar has a much greater reduction in triglycerides than pio. Also, when you look at HDL increase, you see that Ale increases HDL moreso than pio. And if you think about that ratio, the ratio is significantly further improved, of course and control and probably than pio, and that might be an harbinger than in fact, more patients will benefit from a cardiovascular standpoint, and that’s why we designed Phase III to not be a diabetes drug per se, just lowering hemoglobin A1c in the typical diabetes like program. This is a acute corneal syndrome, this is being treated as a cardiovascular drug for diabetics. So the potential here is to completely transform how drug development is done in diabetes, to look to see how do you actually impact on the number one most important outcome that these patients experience, which is cardiovascular and stroke, morbidity and mortality. And if you can do that, you’ve really done something great for patients. And to date, no one’s done that. But we think we have a reasonable chance and the rationale is based on a greater and greater understanding of the biology behind lipid abnormalities in patients with diabetes and cardiovascular disease in general.

Now how does that relate to Dal. It’s mostly related to Ali story, but I it also I think, relates a little bit to Dal because the key thing for Dal that we’ve been talking about is will dalcetrapib, which I think you’re aware, raises the HDL quite nicely, 30%. The key issue here is not that because I think we’re all clear, it raises HDL by 30%, is that HDL functional. We know with pertuzumab it appeared not to be functional, there was no benefit. In fact there was toxicity. This is just a description of how we’re developing it. We’re focusing on a very high risk population. We’ve got this dalcetrapib trial which is event driven. The first interim has past and there was no safety concerns, which was helpful to know, given what happen with Tofacitnib.

And we are going to have a second interim when 70% of the events are accrued and that will be in the first half of 2012, with the final analysis being somewhere in the end of 2012 or ’13. So we’ve got some recent data, there was a ton of it, and very challenging to interpret. The Dal plaque study. I think there was a lot of important information in Dal-plaque, probably the most important was it’s very unlikely, at least from this study that dalcetrapib increases inflammatory changes in the plaque. It’s not likely to be causing an increased risk of cardiovascular events, which was its purpose.

Back in the days when we were designing this, tofacitnibs mechanism was off target, [inaudible] and stuff wasn’t really fully appreciated and it was really concerning whether we’d have the same effect. So it was designed to insure that we could get an early read on safety.

But there was some intriguing data. And if you step back and say what’s the most important thing we need to learn about Dal is whether the HDL generator is functional. And from the studies I just presented to you, one way one might look at that is to see whether those people with the greatest amount of HDL had an improvement in their PET, in their plaque. And what you can see here in this data that was presented at ASC is two things.

First of all, in [inaudible], whether you look at three or six months, relative placebo, there was at least at six months a trend towards a reduction in the absolute PET uptake in the carotid. But I think equally important, and maybe from a biologic perspective more important, is if you looked at them by tertile, so this is the group that had the lowest HDL bump, which is of course mostly placebos. Here’s the group that had the middle tertile which is mixed with some green, some placebos and some reds Dal. And then the highest HDL improvement which is all Dal.

And as you can see, there’s a linear improvement in the amount of PET reduction that you see with a R23 and a [inaudible] that hits statical significance. So to me, it’s just suggestion that in fact what we’re seeing might be functional HDL. Of course, how this translates into outcomes is unknown, but certainly it’s what we’re trying to figure out, is the HDL functional. And I thought the data presented at the recent ASC meetings were interesting as it relates to this whole story that’s evolving, and our understanding about metabolism basically in patients with cardiovascular disease.

So moving on past a little discussion of three or four interesting papers that came out in the literature. I thought I’d just share with you two more examples of things that are just super exciting in our portfolio. The first one is Ocrelizumab in MS. I think you’ve all seen this study design, it’s a little complicated on this chart, but essentially basically, people got placebo or two doses of Ocrelizumab and interferon beta for the for the first six months. And then they got crossed over into open label therapy. So we had information on the durability of the effect, if you will.

Now we’ve shown data before that at 24 weeks we saw this dramatic, and this is very dramatic reduction in gadolinium and half lesions of both doses. And relative to placebo, or to interferon for that matter, this was quite substantial. You can see here that if you look at gadolinium lesions, or even more importantly, annualized relapse rates which was the secondary end-point, you’re seeing somewhere between 70 and 80% reductions, relative to placebo, and possibly around 50 plus 60, 80% reduction relative to interferon therapy. So pretty impressive treatment effect. And what’s also very impressive is that now we have 96 week data, two year data. And this was recently presented. No patient receiving Ocrelizumab had a gadolinium lesion at week 96 by MR, none.

I mean, this thing is really turning off the disease. There were no new or enlarging T2 lesions from week 24 to 96. Again, a very important secondary endpoint that probably reflects neuro degeneration and the cognate of decline of the physical disability that the fall is somewhat related to this T2 as well. 78, 80% of the patients respectively, were relapse free on 6200 milligrams respectively, and 67 to 76% of patients were free of any clinical disease activity in the 600 and 2000 milligram groups respectively. So in terms of efficacy, this drug is really quite active. The question that comes up and the question that can’t be answered until you have large data set, is how safe will this be.

And as you know, we had discontinued the RA program due to an excess risk of serious infections. And this was at the high dose, didn’t really see that at the low dose, but at the high dose we saw what appeared to be an in balance in serious adverse events. But importantly in RA, we believe that these patients are older, their on concomitant, methotrexate, and steroids. And we think that combination may be what’s particularly distributions-advantageous to have. Where as in MS, people aren’t on concomitant immunosuppressant and their younger. And what we’ve observed so far, and this is the safety data generated, and unfortunately there’s no, for safety, there’s no placebo control beyond week 24, but you can get a sense of the rate.

Now, we’re constantly monitoring this and with more data, we’ll have better point estimates but so far the idea that we – by giving a lower dose to younger patients who aren’t on concomitant immunosuppressed therapy seems with small data sets now, it could be resulting in not only an incredibly effective therapy, but one that seems reasonably well tolerated.

And here is just, again, it’s not necessarily apples to apples because there are different studies, Phase II, Phase III, and cross trial comparisons are always flawed, but it does appear that the activity with ocalizamab is quite solid.

We have a number of trials I think you’re familiar with, the Phase III trials in relapsing remitting MS and one in primary progressive MS where based on some data of Rituxan, we also have some hopes that this will be active as well for this population that’s particularly debilitated.

So last example, this personalized healthcare, this approach to heterogeneity in clinical medicine clinical medicine is not isolated to oncology. This is a phenomena that I think this example highlighted as a phenomena that is in fact just as common outside of oncology whether it’s RA or asthma, or depression, or whatever the disease. Clinical medicine has evolved to describe these by the clinical symptoms, not the biology. And when you go deep on the biology, you start to realize it’s not one disease. Asthma in particular is not one disease. It’s a severe disease and one that increases with severity as you move up these so-called steps and the [inaudible] aisle 13 program is specifically targeting the patients in step six, the sytomatic patients in step four and five, who we believe have the highest need, they’re uncontrolled patients and have relatively limted treatment options.

We’ve believed for years in the field, the world has believe for years that it’s likely but aisle 13 is involved in asthma. There is genetic data, there is preclinical data, there’s knock-in, knock out, blah, blah, blah. There’s lots of data suggesting that aisle 13 appears to be the causal pathway for animal models and patients with asthma. A lot of different ways aisle 13 could be causing this problem. But it was thought very likely to be in the causal pathway.

Hal Barron

Well, I wasn’t around when there was 13, and there’s not 13 today. So if there were 13, I didn’t kow about them. I’m looking over here and everyone’s saying there weren’t 13. So I only know of two, those two. I think the technology is one that will have a lot of proof of concept for when we understand the data from HANNAH and the Rituxan subQ. Once we understand whether this technology is viable, there are plenty of other opportunities for antibodies that can’t be administered subcutaneously on their own.

If the dose of the antibody’s low enough, you can give it subcutaneous without having to get fancy. But when the volume of – the volume is so high that you need to employ this technology, those are areas that we’re actively looking at. And then we have a number of programs where we think, assuming the dose that we think is going to work works, we will leverage that technology to provide a subQ formulation.

Unidentified Analyst

I think [inaudible] just the number of targets that have been defined as we disclose what the [inaudible] balance. [Inaudible].

Unidentified Analyst

Hi. I’m [inaudible]. [Inaudible] asked this question this time last year and hopefully you’ll have more information on this. The infusion rates or reaction rate is very high, 35% on [inaudible] Now, understanding [inaudible] Rituxan, which has a – should have a better IR rate than Rituxan, but it’s the other way around. Can you explain why it’s so high?

Hal Barron

Well, two points. First of all, you’ve got to be a little careful when you’re looking at infusion rate reactions because I don’t know what the number is, but it’s quite substantial in the placebo group too. So infusion reactions of modest severity are possibly due to other things besides the antibody.

Now, the severe ones are unlikely – that’s due to the antibody. And the two things that – or the Grade 3-4s, the ones that are more significant than 1, 2, and what we found was there’s two predictors of, or at least one clear predictor of infusion reactions is simply the speed in which you infuse things. If you – if someone’s having an infusion reaction and you slow down the infusion, you can mitigate the reaction quite substantially. So it’s the rate. And you have to be careful when you compare things to make sure that the rate of the two drugs is given at the same time. And if you’re trying to increase the speed with which the Anti-CD-20 is administered, then you have to control for that in terms of it’s rate.

The second thing is, is that there’s probably different types of infusion reactions. There’s some that are due to the [inaudible] nature of an antibody. But some are due more to the ADCC and complement dependent affects. So I don’t think that you can necessarily always say that if something’s humanized, it’s guaranteed to have less infusion reactions. It’s really more about the severity which we don’t think is actually a problem as well as the infusion rate, which you might be willing to tolerate if they’re very modest in nature for convenience.

And also, it’s important to look at the second infusion. Sometimes you find that if the first infusion is only a phenomena and that the rates are negligible and similar to placebo even after the first injection And when you’re thinking about MS, where people are going to be on this for possibly a long period of time, you’re really focusing on what might be the rate over a five or six year period, it might be very different than just on the first infusion.

Unidentified Analyst

Just to follow up on this one patient who died of [inaudible] system reaction, was that anything to do with the first dose administration?

Hal Barron

No. I mean, we don’t think so. It didn’t seem to be. Yes.

Michael Leacock

Thank you. It’s Michael Leacock from the RBS team. Just on your [inaudible], you presented very nicely [inaudible] as to how these new trials give you nice [inaudible]. But of course, the decision that you made to put aleglitazar into Phase III was made some time ago, or before these studies were published.

So what was it that made you put the drug into Phase III? Was it the same rationale or was it in other factors in this case. What were they?

Hal Barron

Yeah, very good question. Two things. First of all, while I think the data that’s been – I presented in the last nine months, clearly after the decision, we do often have to decide how much more aggressive to be with the development of programs of our molecules. So there’s opportunities to expand further with Ali at some point in the future and this kind of data gives us more or sometimes less confidence in a molecule. But the idea that hemoglobin A1C reduction wasn’t the driver for the cardiovascular benefits has been around for a while. In fact, there’s evidence that when you increase the hemoglobin – reduce the hemoglobin A1C with more intensive insulin therapy, actually, the cardiovascular events when the wrong direction. They got worse. And there’s never been a correlation between hemoglobin A1C reduction and cardiovascular benefits.

So a lot of people suspected that maybe there was something else, and the something else had been alluded to likely to be anti-inflammatory or lipid. And it’s interesting, and I’m sure revision is history, will highlight this, if you go back and look at [inaudible], and [inaudible], and you go back to look at papers, actually some of which are 5, 6, 7 years old and you look at his HDL triglyceride phenomena, it starts to talk to you about maybe there’s going to be a difference in cardiovascular event rates with these two. Whereas, you probably know, rosiglidison doesn’t lower triglycerides. In fact, I think they kind of go up a little bit. And the HL increase is less impressive, more modest. So think – there was some people who way that rosi story evolve and those data stated to put pieces together, which lead to these experiments. So I think that these experiments weren’t just done out of pure randomness, they were driven by this hypothesis, these things were only meant to confirm what I think a lot of us might have believed, which is, it’s the lipid effects that you should focus on. And in fact, why would we chose the dosage we chose, I didn’t how you the hemoglobin A1C curve, but if you believe it was all hemoglobin A1C, and we had plenty advisors who said it’s all about hemoglobin A1C take the highest dose, and if you thought it was about hemoglobin A1C, you would. It was a much more impressive effect on hemoglobin A1C at the higher doses. We took the lower dose because it’s effect on lipids was similar and the – some of the side effects in edema were much less; so a very different approach, but it was based on this sort of belief with maybe less impressive data that we have today, that the lipids changes are actually going to matter more than maybe the hemoglobin A1C.

Nick Turner

Thanks. This is Nick Turner from [inaudible]. And regarding interleukin 13, and blocking antibody, could you clarify for me whether or not there’s a [inaudible] response element on the interleukin 13 gene provided? And if there is, does steroids inhibit interleukin 13? And if so, what’s the rationale for looking at patients who are already on fairly high concentrations of steroids to control their disease?

Hal Barron

Let me look. That’s a good question. I don’t want to misspeak. Let me just look on this slide. My recollection, and I could be wrong, I’ll get back to you on this, and if anyone knows, please jump in, is that this is a complicated pathway and that there may very well be involvements cross talk between steroids. We know that the steroid resistance is going to be simply more than an [inaudible]. There’s genetic data and steps that might be associated with that. There’s certainly other covariants.

But one of the things we’ll be looking at whether steroid resistance, per se, is – increase or possibly decreases the response, the rate of benefit, there’s two competing issues here. One is if this is only, you know, if this is only for people who would benefit from steroids, we’re not really doing a great thing here. That’s why we’re testing in the population, the greatest need. And we think by augmenting with periaustin, we’re going to find that greatest need times where the biology tells us there’s benefit.

It’s a smaller population than if you went to all comers, but it’s one that we believe increases the probability of success. But it’s certainly something we’re looking at. And it’s something that we looked at with Zolar to see what the overlap between steroid resistance or steroid responsiveness is and a new therapy because you don’t want an expensive potentially long development program for something that’s an inhaled steroid. But we don’t really believe that that’s what we’re going to find at all.

Unidentified Analyst

Okay, two questions on aliglitazar. My memory isn’t as good as it might be, but I don’t recall when the analysis was done comparing the cardiovascular risk of rosiglitazar and [inaudible]. There was a great reduction in cardiovascular risk by [inaudible], conversely, of course. There was an increase in [inaudible]. But I think that [inaudible] between [inaudible] was that it didn’t increase risk rather than reduce risk. So therefore, and admittedly, you’re data did suggest that aliglitazar has a more potent effect on [inaudible]. But I'm just wondering how you could relate the effective [inaudible] on [inaudible] but not on cardiovascular risks.

And the other thing that comes to mind from years ago, would be that, remember, and correct me again, that fib rates [inaudible] agonist. And how much of the activity that you see with aliglitzar is [inaudible] driven because the triglicerides are going that direction that you’d expect from a fib rate? And do you actually need the [inaudible] gamma component of ali to get this kind of a thing?

Hal Barron

So great questions. I was trying to remember the name of the trial and somebody in here probably does. But there is a very important trial of palglitison looking at cardiovascular endpoints and the endpoint chose was major adverse cardiac events plus limits for ischemic vascularization, surgical revascularization. And one could argue why they did that, or challenge doing that

Well, I wasn’t around when there was 13, and there’s not 13 today. So if there were 13, I didn’t kow about them. I’m looking over here and everyone’s saying there weren’t 13. So I only know of two, those two. I think the technology is one that will have a lot of proof of concept for when we understand the data from HANNAH and the Rituxan subQ. Once we understand whether this technology is viable, there are plenty of other opportunities for antibodies that can’t be administered subcutaneously on their own.

If the dose of the antibody’s low enough, you can give it subcutaneous without having to get fancy. But when the volume of – the volume is so high that you need to employ this technology, those are areas that we’re actively looking at. And then we have a number of programs where we think, assuming the dose that we think is going to work works, we will leverage that technology to provide a subQ formulation.

Unidentified Analyst

I think [inaudible] just the number of targets that have been defined as we disclose what the [inaudible] balance. [Inaudible].

Unidentified Analyst

Hi. I’m [inaudible]. [Inaudible] asked this question this time last year and hopefully you’ll have more information on this. The infusion rates or reaction rate is very high, 35% on [inaudible] Now, understanding [inaudible] Rituxan, which has a – should have a better IR rate than Rituxan, but it’s the other way around. Can you explain why it’s so high?

Hal Barron

Well, two points. First of all, you’ve got to be a little careful when you’re looking at infusion rate reactions because I don’t know what the number is, but it’s quite substantial in the placebo group too. So infusion reactions of modest severity are possibly due to other things besides the antibody.

Now, the severe ones are unlikely – that’s due to the antibody. And the two things that – or the Grade 3-4s, the ones that are more significant than 1, 2, and what we found was there’s two predictors of, or at least one clear predictor of infusion reactions is simply the speed in which you infuse things. If you – if someone’s having an infusion reaction and you slow down the infusion, you can mitigate the reaction quite substantially. So it’s the rate. And you have to be careful when you compare things to make sure that the rate of the two drugs is given at the same time. And if you’re trying to increase the speed with which the Anti-CD-20 is administered, then you have to control for that in terms of it’s rate.

The second thing is, is that there’s probably different types of infusion reactions. There’s some that are due to the [inaudible] nature of an antibody. But some are due more to the ADCC and complement dependent affects. So I don’t think that you can necessarily always say that if something’s humanized, it’s guaranteed to have less infusion reactions. It’s really more about the severity which we don’t think is actually a problem as well as the infusion rate, which you might be willing to tolerate if they’re very modest in nature for convenience.

And also, it’s important to look at the second infusion. Sometimes you find that if the first infusion is only a phenomena and that the rates are negligible and similar to placebo even after the first injection And when you’re thinking about MS, where people are going to be on this for possibly a long period of time, you’re really focusing on what might be the rate over a five or six year period, it might be very different than just on the first infusion.

Unidentified Analyst

Just to follow up on this one patient who died of [inaudible] system reaction, was that anything to do with the first dose administration?

Hal Barron

No. I mean, we don’t think so. It didn’t seem to be. Yes.

Michael Leacock

Thank you. It’s Michael Leacock from the RBS team. Just on your [inaudible], you presented very nicely [inaudible] as to how these new trials give you nice [inaudible]. But of course, the decision that you made to put aleglitazar into Phase III was made some time ago, or before these studies were published.

So what was it that made you put the drug into Phase III? Was it the same rationale or was it in other factors in this case. What were they?

Hal Barron

Yeah, very good question. Two things. First of all, while I think the data that’s been – I presented in the last nine months, clearly after the decision, we do often have to decide how much more aggressive to be with the development of programs of our molecules. So there’s opportunities to expand further with Ali at some point in the future and this kind of data gives us more or sometimes less confidence in a molecule. But the idea that hemoglobin A1C reduction wasn’t the driver for the cardiovascular benefits has been around for a while. In fact, there’s evidence that when you increase the hemoglobin – reduce the hemoglobin A1C with more intensive insulin therapy, actually, the cardiovascular events when the wrong direction. They got worse. And there’s never been a correlation between hemoglobin A1C reduction and cardiovascular benefits.

So a lot of people suspected that maybe there was something else, and the something else had been alluded to likely to be anti-inflammatory or lipid. And it’s interesting, and I’m sure revision is history, will highlight this, if you go back and look at [inaudible], and [inaudible], and you go back to look at papers, actually some of which are 5, 6, 7 years old and you look at his HDL triglyceride phenomena, it starts to talk to you about maybe there’s going to be a difference in cardiovascular event rates with these two. Whereas, you probably know, rosiglidison doesn’t lower triglycerides. In fact, I think they kind of go up a little bit. And the HL increase is less impressive, more modest. So think – there was some people who way that rosi story evolve and those data stated to put pieces together, which lead to these experiments. So I think that these experiments weren’t just done out of pure randomness, they were driven by this hypothesis, these things were only meant to confirm what I think a lot of us might have believed, which is, it’s the lipid effects that you should focus on. And in fact, why would we chose the dosage we chose, I didn’t how you the hemoglobin A1C curve, but if you believe it was all hemoglobin A1C, and we had plenty advisors who said it’s all about hemoglobin A1C take the highest dose, and if you thought it was about hemoglobin A1C, you would. It was a much more impressive effect on hemoglobin A1C at the higher doses. We took the lower dose because it’s effect on lipids was similar and the – some of the side effects in edema were much less; so a very different approach, but it was based on this sort of belief with maybe less impressive data that we have today, that the lipids changes are actually going to matter more than maybe the hemoglobin A1C.

Nick Turner

Thanks. This is Nick Turner from [inaudible]. And regarding interleukin 13, and blocking antibody, could you clarify for me whether or not there’s a [inaudible] response element on the interleukin 13 gene provided? And if there is, does steroids inhibit interleukin 13? And if so, what’s the rationale for looking at patients who are already on fairly high concentrations of steroids to control their disease?

Hal Barron

Let me look. That’s a good question. I don’t want to misspeak. Let me just look on this slide. My recollection, and I could be wrong, I’ll get back to you on this, and if anyone knows, please jump in, is that this is a complicated pathway and that there may very well be involvements cross talk between steroids. We know that the steroid resistance is going to be simply more than an [inaudible]. There’s genetic data and steps that might be associated with that. There’s certainly other covariants.

But one of the things we’ll be looking at whether steroid resistance, per se, is – increase or possibly decreases the response, the rate of benefit, there’s two competing issues here. One is if this is only, you know, if this is only for people who would benefit from steroids, we’re not really doing a great thing here. That’s why we’re testing in the population, the greatest need. And we think by augmenting with periaustin, we’re going to find that greatest need times where the biology tells us there’s benefit.

It’s a smaller population than if you went to all comers, but it’s one that we believe increases the probability of success. But it’s certainly something we’re looking at. And it’s something that we looked at with Zolar to see what the overlap between steroid resistance or steroid responsiveness is and a new therapy because you don’t want an expensive potentially long development program for something that’s an inhaled steroid. But we don’t really believe that that’s what we’re going to find at all.

Unidentified Analyst

Okay, two questions on aliglitazar. My memory isn’t as good as it might be, but I don’t recall when the analysis was done comparing the cardiovascular risk of rosiglitazar and [inaudible]. There was a great reduction in cardiovascular risk by [inaudible], conversely, of course. There was an increase in [inaudible]. But I think that [inaudible] between [inaudible] was that it didn’t increase risk rather than reduce risk. So therefore, and admittedly, you’re data did suggest that aliglitazar has a more potent effect on [inaudible]. But I'm just wondering how you could relate the effective [inaudible] on [inaudible] but not on cardiovascular risks.

And the other thing that comes to mind from years ago, would be that, remember, and correct me again, that fib rates [inaudible] agonist. And how much of the activity that you see with aliglitzar is [inaudible] driven because the triglicerides are going that direction that you’d expect from a fib rate? And do you actually need the [inaudible] gamma component of ali to get this kind of a thing?

Hal Barron

So great questions. I was trying to remember the name of the trial and somebody in here probably does. But there is a very important trial of palglitison looking at cardiovascular endpoints and the endpoint chose was major adverse cardiac events plus limits for ischemic vascularization, surgical revascularization. And one could argue why they did that, or challenge doing that, but when you use that primary end point, and if anyone here can remember that study - what is it? Proactive, exactly. Thank you. I knew you knew it, so - proactive, and I'll see if I can remember the P value, but it was between like 0.06 and 0.08 for that primary end point, so it failed to achieve statistical significance in reduced cardiovascular events. However, if you were to do a subgroup analysis or post [Inaudible] analysis I'd have argued what terribly post [Inaudible], which was just major adverse cardiovascular events the more solid stuff removing surgical limbry revascularization, which is somewhat subjective. The trial was actually positive the P value of I think 0.03. Secondary subgroup, post [Inaudible], not valid for FDA - blah, blah, blah. But I think if you're starting to understand the biology, you're absolutely right that everything I presented, including the rash, would make sense if there wasn’t at least a trend, if not a benefit, on cardiovascular outcome given all I presented. So I think in my head, and I probably should have put this slide in, I sort of see that pyo almost works in cardiovascular. And what you really need is something a little better to really bring out that treatment effect. Maybe a lot better, but certainly the direction of benefit was there and proactive.

Second question about the alpha gamma, I think it's a great point. I think one of the things we'll look back on all this and say, putting all these drugs in the same class, called P par alpha gammas or gammas or whatever, is just a mistake. These - the dose and the relative contribution of alpha gamma is extremely important to determining whether you get side effects, whether you get treatment effects, whether you get triglycerides versus hemoglobin A1C, etcetera, and you're right, the lipid effects is probably more driven by alpha, but it's not clear you don't need both. It's not clear that only alpha alone would be as good. You just don't know. If you look at some of the thanfibrate data there's some pretty interesting effects with that drug with modest, I would say, impact on HTL triglycerides. So taking to it's extent if you would add another slide on that I think you would say that the alpha effects are more potent than ali than with thanfibrate and that would pretend a very good prognosis, not only for the CV events, but you might start wondering about some of the stuff that thanfibrates do as it relates to proliferate diabetic retinopathy and some of the things - the microvascular complications that diabetics get and whether the alpha effect might help those, or whether that's a gamma effect. So I don't think there's any possibily that reducing hemoglobin A1C is a bad thing. I didn't in any way mean to imply that. So it's incremental value could just be challenged, but it's certainly useful. Yeah?

Jack Scannell - Sanford Berstein

You said something at the start of your talk very deliberately that you described [Inaudible] being science led. If I think about another drug company which might describe itself as being science led, that might be [Inaudible] I think [Inaudible] people may be accused of criticizing science led companies in thinking that it’s best to sort of [Inaudible] from the market, but if you look at R&D productivity you guys and Novartis seem to be looking pretty good. Now [Inaudible] the fact that R is something of a black box for most investors, can you give us some kind of qualitative sense about why a science led approach may end up looking rather good when actually letting scientists get on with this and do stuff seems to generate lots of things coming into the clinic.

Hal Barron

Yeah, that's a great question. Well let me give a little bit of a long answer because I have a lot of passion around this idea. I think you step back and you ask, what's wrong with our pharma model? What's wrong with the business model that we’re in? And many of you have written about this and I've learned from what you've written, but what's extremely intuitive to me is that right now about ten out of every 11 drugs that enter the clinic fail to ever help a patient, i.e. make money, however you want to look at it. And then when you burden the one success with the ten failures you're upwards of $10.6 billion to develop a drug. And that doesn't work out. particularly in the increasingly focused cost payor environment. It's going to work out less well. So when you think about the metric needs to look at, I think, to drive your business model you have to focus on the cost to success. And very, very simplistically there's two levers, reduce cost, increase success rates. If you go from 1:11 to 2:11 that's effectively having the cost of success by 50%. The lever, because we're on such the low side, 9% or whatever it is, you have such an opportunity to increase productivity by increasing probability of success. That doesn't mean cutting costs isn't a bad idea because that also adds further. The combination of cutting costs, but I would argue more importantly increasing probability of success will move you more towards a more innovative and more productive R&D organization. Now, how are you - so fine, who are you going to go from 1:11 to 2:11 or 3:11? Well if you go back and ask, what are the modifiable features of development that prevent you from ever helping a patient? Often times I think there is the unmet need is not great enough for the benefit that you're experiencing. In other words, it works, but with those safety profile - that safety profile, maybe it's cost and side effect profile, it's not useful. So either you've got the wrong reagent, because that pathway's not important or more likely, what I think, and I think a lot of companies - we at Roche think this, but a lot of companies think this, is that there's probably a subset of patients where the treatment effect was robust. If we would understand the biology of the disease and account for the heterogen AD might actually allow two or three reagents to go in the same disease, but in different parts of the disease. And by doing that you might very well dramatically increase the probability of success. Let me give an example. It's pretty striking. If you would take Herceptin and do a randomized clinical trial in all comers, not HER 2 positive, the kappa mar curves if you were to put a computer program to impute them, they're overlapping, there's not even a trend. And what you'd conclude from the experiment is that decades of preclinical scientists epidemiology data and really nice cell models must have all been wrong. But in fact, that would be the wrong conclusion. What would be wrong was, the drug worked terrifically in 25% of the people and 75% of the people didn't, and you can't see that as much as you might. You actually would think there'd be a trend. There's no trend. In fact, what's fascinating, let's say the diagnostic was good but not great, it got 50% of the HER 2s, but it left 50% of the non-HER 2s in, there's no trend. It's negative. And the question is how many diseases are heterogeneous and how many therapies actually would - are succumbing to this problem? Well of the ten, I don't know what number it is, but it's probably not zero. So if one of those is actually going to succeed, just one, you might have a completely transformation in the business model, R&D productivity, and more importantly helping patients because these things are - that's the whole goal. And we're throwing a lot of stuff away potentially that could have worked in the subset. So why did we didn't we do this in the past? I think two things, one, a lot of people thought that five years ago when people were telling us that the industry is flawed because it takes too long and costs too much, what did everyone do? Spent less and went and took less time. Well what does that do? Well if you don't take the time to figure out the biology, took time to figure out the [Inaudible] story, took time to figure the metnab story, and took time to figure out the berab story, if you speed up you're going to prevent yourself from taking the time and money to understand the biology of the disease and then you're just throwing good money after a bet. You're just hoping basically. And that's not really good strategy. So I think that was one. The second one was the tools weren't there. The genetics we're there. The bile markers weren't there. And the willingness of research and clinical to work together both in house, but also external just the - there was the basic science and there was the clinical and there was always - the model goes basic science to clinical. What we know now is that it's basic science to clinical and clinical to basic science and basic science to clinical, and it's one big loop. And you learn from the clinic and you reinvent as you learn. And I think that fundamental appreciation with the tools and for some companies the willingness to actually do the right thing for patients because that's going to result in a very success rate, which reduces the cost of success and makes the model viable. That's overly simplistic, but I think that's why it's so important to do it this way. Otherwise you're just hoping and it's not going to work out.

Alexandra Hauber-Schuele – JPMorgan

Just two questions [Inaudible]. Firstly, you seem to be doing dose finding in phase 3, why is that? And what dose theta do you have up to this point? And the second question is, is this stratification still just [Inaudible] in high low? And what is high low? Is it just a 50% , the top half and the bottom half?

Hal Barron

Yeah, great questions. So I always get confused. It's Molly, was Molly the second one? Yeah, the subsequent phase 2b was intended to give us the right dose and it's pretty obvious we don't know the dose yet. So that's why the three doses. Sometimes when you're doing drug development and you imagine through PK modeling and PD modeling and you've done the right dose selection in phase 2 you wind up finding that it's more flat than you thought. And unfortunately need to do it in phase three, so that's why there's three doses. Want to make sure we're not, although we don't think there's any reason to be concerned about safety whenever you give too much of anything it's possible that you induce a safety signal that you might not have seen had you given a lower dose, so we're pretty confident that doing robust dose ranging is important. And when you don't see the kind of nice minimal effect dose and maximal effect dose, which you hope for, you need to do it in phase three. So that slows down a little bit and it's unfortunate, but a reality of the business. In terms of the periostomy cutoff I think it's a great question. It's not unique to periostonomas, all our diagnostics, whether you look at metnab IC or even HER 2 to be honest, I mean, how do we get to where we are today? It's really complicated to know exactly where the cutoffs of any of these things are. And they relate to a bunch of things. One is how willing or how comfortable are you with missing somebody? And how much toxicity does the drug have? So what we'll do with periostin is we'll have a predefined cutoff which we believe is based on the data of those patients most likely that have the gene signature, which is about the median, but we'll be able to look at the edges and say maybe the people who are in the 50 and 60% do or don't benefit. Or maybe the best cutoff is a little below that. That's somewhat challenging from a regulatory perspective to post [Inaudible] look at these things. But most likely the way we’ll be doing this is you'll have a predefined cutoff, that will be your cutoff. And you refine it as you move forward. It's also probably going to be very age dependent and very population dependent. As you're going to pediatrics the cutoff will be different than when we go into other populations. So the bile marker is very, very useful, but it's not without it's challenges.

Alexandra Hauber-Schuele – JPMorgan

So is that just two buckets into phase 3 or they sample buckets? One for high one for low? Is it just one cut up in that particular study or will it be several?

Hal Barron

For that particular study and all particular studies that we do there has to be a cutoff because you have to predefine it, but you have the opportunity to look at it because it's a continuous variable at areas around the margin to help clinicians or regulators understand what you're worried about. And I would say that's true for virtually all our diagnostics. We have a predefined cutoff that we think is our best guess. Often times we're so confident that that's the only population in phase 3, like Reciptin. Sometimes you're less confident, you know exactly where to cutoff and you end up taking all comers and playing with the cutoff through the data. It's not ideal, but - so yes we do have a cutoff, yes it is diconis because it needs to be, and yes it will be the primary end point for the secondary implements.

Alexandra Hauber-Schuele – JPMorgan

Why do you even need, after that phase, to [Inaudible] low periostomy group in this study?

Hal Barron

I think two reasons. One, you don't know for sure. I mean, these are small sample sets and the data was positive in the low as well. It was less so on FEV1. But also it wasn't screamingly loud in the exacerbation rate. The secondary end point, I think I showed you, the benefit was I think a little greater in the highs, but it wasn't much. So I think it behooves us to understand exactly what happens to the lows given that there was - it certainly wasn't detrimental. It's different when it's detrimental. The second thing is, you do need to know this information often times from a regulatory or from a clinical perspective because people will get the drug used inappropriately. And if it really doesn't work in the lows it's good to know that it really doesn't work and be definitive about it because sometimes people will look at your phase 2 data and say, well it might work, and this person is pretty symptomatic, I might as well try it. And if it doesn't help somebody you should be able to say, look we've looked at this, it doesn't help. There's no need to do that. Or maybe it does help and you need to know that too. And the third and I think the maybe driver for this is, as you said the cutoff is somewhat empiric. And so by having the lows in there you have a database large enough to really ask questions about the margins.

Yeah?

Louisa Hector - Credit Suisse

I've got three questions please. The first one hopefully quite simple. What portion of the pipeline is associated with the bile marker? And is that largely still driven by the oncology pipeline?

Hal Barron

Okay, it's about - it's a little over 50 - about 50 is associated with the bile marker. It depends what you mean by associated with. Sometimes a program can have a very compelling and clear companion diagnostic approach and others it could have a more interesting and possibly viable marker. But it's at least 50. And I don't know - Karl, you know the breakdown between - it's more onc than not onc, but I'm guessing 80-20, but I don't really know. You know, and it somewhat depends on how compelling the bile marker has to be before we volunteer a bucket of bile markers. But I would say part of the reason it's - you know, antibody drug conjugates, which we have 37 by definition, right. So they're going to hit an expressed antigen, they're going to have a somewhat of diagnostic right there alone, so onc has a bit of an antigen that tumors are being so highly scrutinized for all the different pathways. But it's very important outside of onc and we're making headway. I just think we're maybe a few years behind outside of onc.

Louisa Hector - Credit Suisse

And then with the [Inaudible] TDI monfilings sort of imminent have you had a recent dialogue with the FDA to understand the what PFS benefit would be clinically meaningful given that the overall survival data won't be mature? You know, just any updates on that.

Hal Barron

Yeah, I had a very public interaction with the FDA getting feedback on that back in August or whenever that was - during the Avastin breast hearing. I think we heard very loudly about what magnitude benefit medians was exciting to them and we know what numbers are below that and less exciting. And we've used that thinking in our decisions about when to file.

Louisa Hector - Credit Suisse

And then the final question would be on the subcutaneous formulation. And obviously you're running studies and presumably they are what you think will be sufficient for approval, do you think physicians are going to be happy with that amount of data as well given that drugs like Receptin and Rituxan are very well established. I mean, for example, if you look Receptin, was that data in the first line setting, yet you were talking about it being a useful advantage in the Adjuvant setting. Just wondering if there could be any physician concern of just making that switch.

Hal Barron

That's a good question. I think the reason we believe the barrier to switch won't be as high as - it's the same molecule. And once you have similar PK or confidence that the PK is not altered in the way that you think it's going to impact the outcome, and it's the same molecule. I think your barrier to switch is lower. But that's why for Hannah it was important to have pathologic CR not just PK. So you want to see the - you want to see the kinetics, but maybe even more importantly you want to make sure that particularly in the Adjuvant where you're curing women, but you're not compromising, and so everybody's going to have a different threshold. Some people will say, look I don't even - nothing will change me. I'm Receptin and I'm not even going to listen because I don't want to take the 1:1,000,000,000 chance that it's not going to be as good. Most people will probably say, as long as the confidence intervals, and that's how you design the database is to ensure that the confidence intervals - the lower confidence interval will exclude something that's clinically meaningful enough to prevent you from switching. If that makes sense. So the bigger the sample, the smaller confidence intervals, and therefore the less room there is on the other side of the point estimate that would give them the concern. And obviously, the point estimate - if the point estimate is worse or better then it shifts it as well. You want to make sure or do your best job at reducing the amount of uncertainty there is in the lower confidence section. And we believe that this technology will allow us to do that effectively.

Okay, five minute warning. One in the back. How about theirs since I probably ignored you.

Richard Parks – Deutsche Bank

Yes. Richard Parks from Deutsche Bank. I just got a couple of questions. One on T-DM-1 and one on dalecptrapib. So on T-DM-1, I think if you look at the PFS curves, they seem to cross after about six months. I know it’s more than this, but one conclusion could be that if you’ve got a patient that’s very actively progressing, you need the chemotherapy there to get that disease under control. I’m wondering what your thoughts on that, whether that’s a valid conclusion and whether you’ve got any concerns about relevant sort of Phase II data in the first line setting to outcome of a Phase III study in the second line setting?

Hal Barron

Okay. Let me take the first one. So you’re talking about this right here, for those who didn’t catch that. And Phase II is always so hard to interpret anything – anything outside the primary endpoint becomes sketchy. Secondary endpoints are sketchy, they’re under powered. Dissecting curves on capital markers are very risky.

If you ask me what my best is, my bet is that’s noise. Might it not be noise? Might not be. But in my experience, both good and bad, when I’ve seen things that look like this and I was hoping they were real, they don’t turn out to be real. And when I was hoping they weren’t real, they don’t turn out to be real. When I was hoping they weren’t real, they don’t turn out to be real. They almost always are random variations around something – unless the biology in the pre-clinical data or sort of from a [inaudible] perspective, you sort of expected this. And I would say it would be hard to say you expect this. I mean, your hypothesis is not unreasonable, and we see this continually, one might believe it’s real, but I think it’s more likely to be noise. But who knows. I could be wrong about that.

Richard Parks – Deutsche Bank

Okay, great. Thanks. And then on dalceptrapib, I know you – the data on this is a bit contradictory but and it’s gotten a different setting to dell outcomes, but we have been seeing things to suggest that if the patients have reached very low LDL cholesterol levels, then HDL isn’t having as much as an impact. I’m wondering whether in the dell outcomes, if you’re able to look on a blinded basis at patient’s baseline LDL cholesterol levels and their own treatment LDL cholesterol levels?

Hal Barron

Well, are we allow to look at blinded LDL levels, is that your question? I guess in theory we can look at blinded LDL levels, but I think they would be pretty much as we selected and it’s not driven by what we would – first of all, I think the point’s an interesting one, but all of the stuff I just showed you would be vastly different if it was done in patients whose LDL had been taken down to perfect levels. We can all guess but it may be that that alters some of the findings I just showed you and that is one of the most important things to find out, is how does this all work in the real world with patients treated with high-dose statins.

That said, reverse cholesterol transport abnormalities in patients wasn’t dependent on LDL. So if the – no matter how low your LDL goes, that wasn’t a predictor of normalized reverse cholesterol transport. So if you take that phenomena and you would say that reverse cholesterol transport abnormalities, cholesterol reflux capacity abnormalities were just as present in people with high as well as low. And based on the New England paper, statins didn’t affect cholesterol reflux capacity. So with that, you’re left wondering will it be different? Will it might not be different? I think it’s a great question. The outcomes will answer it. The LDL levels in the trial are pretty much what we expected given this kind of population. So they’re not – we haven’t looked, but – and probably could, I guess, but one, we wouldn’t do anything differently and two, they’re most likely right where we expected them to be.

Richard Parks – Deutsche Bank

Just a follow up from that, just, in the real-world setting, do you know what percentage of patients are able to get to those very low LDL cholesterol levels and what aren’t able to achieve that given to intolerability or just…

Hal Barron

I don’t know the number, but it’s not as much as you’d think. I mean, getting – it depend on what you mean really low. You know, there’s a lot of patients who could be optimized further and there’s a difference between real world and clinical trial real world. Because in a clinical trial setting, you’ve got investigators who are much savvier than the typical person out there. One, they tend to get better care. I mean ,there’s plenty of studies looking at your care improves – your outcome improves if you get randomized in clinical trials in cardiovascular rather than randomized to anything. It’s just being in one of those gets your lipids lowers, get your things more in tune. So I don’t really know the number, but it will be substantially less than whatever we see in the clinical trials, which isn’t anywhere near perfect either.

Richard Parks – Deutsche Bank

Thanks.

Hal Barron

And the last question, maybe.

Keyur Parekh - Goldman Sachs

Keyur Parekh from Goldman Sachs. [Inaudible]. First on dalceptrapib, can you remind us the impact on dalceptrapib has on triglycerides and secondly, looking at the data you’ve seen across the four recent papers, is that changing [inaudible] or changing kind of the [inaudible] HDL ratio different – at different base levels of either triglycerides or HDL? And how does that baseline compare across those groups?

Hal Barron

So to the best of my knowledge, dalceptrapib doesn’t affect triglycerides. I’ll look that up, but I’m pretty sure that’s right, at least in aggregate.

The triglyceride HDL story may or may not be right. It’s the one that was in the [inaudible] paper that showed that that’s the most important ratio to focus on. I looked pretty carefully at that paper and didn’t see what the effect was when they just did HDL alone and not triglycerides because in the other two papers, HDL alone, not the ratio, is more important. So I’m not sure.

And they tend, with drug interventions, to go somewhat hand in hand. So they may be very clininier. But I think the takehome for Dal, unlike Ali, the story was simply, does Ali improve cholesterol refluxes. The HDL functional as opposed to what’s the impact on the ratio. I think that ratio maybe a Ppar Alpha Gamma, or may be Ppar Alpha only sort of association. It may not be generalizable outside of that class. But the key thing that I should bring home is that it’s efflux capacity that might be something to look – that is the HDL being generated functional and honing in on that in that study.

Keyur Parekh - Goldman Sachs

In the sense of the [inaudible], it was said previously that you need to see even effects north of 30% for it to be a commercial viable product. [Inaudible] from a commercial perspective you need to see it kind of 30%. How should we think about kind of the 15-percentage point reduction or better OLR in that sense? How does that translate into kind of either progression-free survival or how should we think about it?

Hal Barron

Yeah, let me pull that up. I think – I don’t know what he said or what he meant, but my bet would be – yeah, that he’s talking about the hazard ratio at 0.7 being – 0.7, 0.75, something in that ballpark, not a 30% delta response rate. So the question then becomes what delta response rate would you expect to translate into a 25-30%. And I think we have it on here. But the bottom line is, it’s hard to know, but we’ve done a fair amount of modeling all different data sets that we have as well as data basis in the – or data sets in the public domain and it’s on here. And what we calculated and you know, the only thing about models you know to be true is they’re all wrong, but they’re the best you can get. Is we think somewhere around a 7% - 7 to 8%, maybe a little bit more, but 7 to 8% delta in response rate would translate into a hazard of .75. So you know, it’s always important to know what delta and response rate where you going to call good before you unblind. You have to do that. And we had done this exercise to say anything north of 10 was something that we would find reasonable in terms of likelihood of predicting what’s clinically meaningful on progression free survival.

Now, this is – but earning 15 and knowing our threshold was 8 or 10, at least as it relates to IRF, was reassuring. A five from the investigator was not. So that’s where that discrepancy became a challenge for us and why we’re looking into it. But if you assume, which we are, that the IRF is more accurate, then it exceed what we would have felt need to observe in order to get something clinically meaningful given the air bars.

Keyur Parekh - Goldman Sachs

[Inaudible], but it’s very exciting. I know you are looking at the kind of beyond the cancer indication, so anything you can update us either in kind of breast cancer or any other kind of cancer.

Hal Barron

Yeah. I don’t have it I front of me, but we have – are moving forward on a number of programs tha we think are particularly exciting. I’ll just digress for a second and tell you why I’m pretty excited about a gastric program. We have a program in breast cancer that’s already – I don’t know what percentage is done, but it’s in – it’s certainly already enrolling. We have second line lung, obviously, is the primary indication, but we have front line and different combinations and other aspects of lung. We’re exploring – can I tell – I’m not sure. I always get nervous disclosing things and someone tells me I have to go jail and that’s not – I’ve got two little kids that wouldn’t think that was funny. Glioblastoma, we think there’s a lot of reason to be excited. There’s other GI malignancies where we think [inaudible] expression, like Pancrease might be exciting.

I – you know, in all truthfulness, was – I am very excited about metmab. One piece of data that got me a little frazzled is that when you see benefit in on subgroup and no benefit in the other such that the overall study is not positive, I find that a very different story then when it’s very positive in the subgroup and no effect in the other. That’s typically what you see. We didn’t see that. We saw this – one of the – but we weren’t sure whether the excess was real or not.

One thing that I thought was very helpful for me, and I was very, you know I don’t mean to talk about competitor stuff, but when AmGen just presented their anti-Hgf antibody, I don’t know if you saw that, but to me that was very helpful because although I personally didn’t think it would be as good of an idea, I certainly was interested in that finding and what they found in gastric cancer was that the trial was positive overall and it only appeared to work, at least predominately worked in the c-met high. So this whole concept of maybe only being active didn’t seem that high to me is even more likely to be right given not only the preclinical data that they had observed and now seeing it replicated with a different blockage of the receptor and that the diagnostic wasn’t the ligan level it was the c-met expression. So I’m very excited about lots of communications and I think that would be a good sign for gastric as well. So hopefully we have a number of indications that patients will benefit from the metmab.

Okay. Well, thank you very much for your attention. I appreciate you taking the time.

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