Speaker 1: Yeah, thank you so much, Axel, and UH great to
Speaker 1: be here this morning. As uh, as Axel said, my
Speaker 1: name is Gino Blumenthal, uh uh vice president of Global
Speaker 1: Clinical Development at merk SO. I lead teams that focus
Speaker 1: on development of novel a d c's targeting Trope two
Speaker 1: and HER three and small molecules k s G twelve
Speaker 1: C and previously I was at the UH at the
Speaker 1: f d A for over a decade. Unfortunately, because of circumstances,
Speaker 1: we couldn't have uh someone from the government uh on
Speaker 1: our panel. Uh unfortunately, but UH I'll try to fill
Speaker 1: in as best as I know, how you know, with
Speaker 1: my uh you know, prior prior experience and in current
Speaker 1: interactions with with Project Optimists.
Speaker 2: We also have a great.
Speaker 1: Panel in person and we have a hybrid format as
Speaker 1: well because Andrew Faris Unfortunately there were problems with air
Speaker 1: traffic airlines at Ronald Reagan Airport, so she's but we
Speaker 1: were able to quickly pivot and have her.
Speaker 2: Join us virtually.
Speaker 1: So why don't we start with our panelists and just
Speaker 1: if you could introduce yourself and maybe give kind of
Speaker 1: a teaser, what are your impressions of Project Optimists from
Speaker 1: the last couple of years since its implementation.
Speaker 2: Let's start with Greg.
Speaker 3: I Hello, every ready, thanks you down. So my name
Speaker 3: is Greg Goldmacher. I am a diagnostic radiologist by training.
Speaker 3: I've been at MERK for about ten years, almost ten years,
Speaker 3: and I lead the Clinical Imaging and Pathology function.
Speaker 4: So this is the group that oversees the U use.
Speaker 3: Of imaging and pathology assessments UH to evaluate end points
Speaker 3: in human trials, so from phase one all the way
Speaker 3: all the way through, and in addition to you know,
Speaker 3: so in addition to kind of the routine things that
Speaker 3: we do with imaging for response rates and progression free survival,
Speaker 3: you know, the kind of things that are typically done
Speaker 3: in late phase development, we're doing a lot of active
Speaker 3: exploratory work internally for using more advanced imaging and analysis
Speaker 3: methods to try to get closer to the biology of
Speaker 3: the tumors. And well, so our team functions across therapeutic areas,
Speaker 3: but about eighty five percent of the work we do
Speaker 3: is in oncology, of course, and so we were doing
Speaker 3: a lot of work to try to get closer to
Speaker 3: the biology using various advanced analytic methods, which we'll discuss
Speaker 3: more more here and you know, as a teaser, and
Speaker 3: I guess the you know, the big idea I think
Speaker 3: is that you know, this traditional method of you know,
Speaker 3: you basically dial up the dose until the patient kills over,
Speaker 3: and then you back up a couple of steps and
Speaker 3: use that the maximum tolered does that doesn't really not
Speaker 3: only does that not necessarily that does that not balance
Speaker 3: benefit and risk optimally, but that may not even optimize
Speaker 3: the benefit side of it. And so there's we're going
Speaker 3: through a lot of efforts to try to get closer
Speaker 3: to true analyzes of benefit, with the challenge of course
Speaker 3: being that in early stage development you're working with such
Speaker 3: small numbers and that that's what we have to do better.
Speaker 5: So I'm Ryan Sullivan. I'm a medicaloncologist at mass General Hospital.
Speaker 5: I specialize in skin cancers, predominantly melanoma. And then I'm
Speaker 5: also a faculty member and our Tremier Center for Targeted Therapies,
Speaker 5: which isn't just MO likely targeted therapies but also includes
Speaker 5: immuna therapies. And I've been a Phase one investigator for
Speaker 5: about fifteen plus years in my life. So Project optimists
Speaker 5: fun fact, I kind of like it, and the reason
Speaker 5: I like it is for many of the reasons that
Speaker 5: Greg just said is that it allows us to be
Speaker 5: more thoughtful about choosing doses. But it has also requires
Speaker 5: us to probably spend a little bit more money and
Speaker 5: time as we're designing the trial to end up carrying
Speaker 5: it out.
Speaker 6: So thank you for the invitation to be here. So
Speaker 6: my name is Wim Voss. I'm the CEO Radiomics dot bio,
Speaker 6: a Belgian company that does advanced image analysis on radiology images.
Speaker 6: I'm an aerospace engineer by training, so I have nothing
Speaker 6: with the biology of cancer. But for the past twenty
Speaker 6: years I've been working on extracting more information from radiology
Speaker 6: images to enhance decision making clinical trials, first in the
Speaker 6: respiratory space and now for the past years in the
Speaker 6: oncology space. And I think what we specialize on is
Speaker 6: really finding those early signals of what's going on very detailed,
Speaker 6: not missing an individual tumor using all the information that
Speaker 6: is there to up sample small data sets. And I
Speaker 6: think very much like Ryan, I like Project Optimism. It's
Speaker 6: a step in the right direction. I think it really
Speaker 6: opens the door for personalized combination therapies, which will probably
Speaker 6: be the path way to cure for many patients. I
Speaker 6: think the problem today is that we face this transition period.
Speaker 6: We're actually we're treating patients even in the early clinical
Speaker 6: work to the best way possible for the patient as
Speaker 6: well as these patients are not volunteers, but typically it
Speaker 6: is their therapy the clinical trial, which makes it hard
Speaker 6: to go with lower dosages because you might not be
Speaker 6: optimal for the individual patient. But I think it is
Speaker 6: the way to go. I think we need to really
Speaker 6: understand what's the optimal dose for a given compound to
Speaker 6: get optimal tumor killing capabilities. Once we know that and
Speaker 6: we know in which tumors it is doing it, we
Speaker 6: can also understand the biology of those tumors, true combinations
Speaker 6: of different biomarkers in the end to find the right
Speaker 6: combination therapies. And I think in the coming five to
Speaker 6: ten years we will probably see project optimists evolve, but
Speaker 6: we might also see evolutions in the later stages of development,
Speaker 6: because how do you get a drug to market that
Speaker 6: might have an optimal efficacy in tumor killing characteristics but
Speaker 6: might not have optimal characteristics on a patient survival point
Speaker 6: of view? And I think that that's going to be
Speaker 6: a big challenge for the follow up of Project Optimists
Speaker 6: into the later stages of drug development.
Speaker 2: Thank you, and Andrea, Hi, good morning.
Speaker 7: Sorry I couldn't be there in person, but I'm glad
Speaker 7: I could be here virtually. I'm Andrea Ferris. I'm the
Speaker 7: president and CEO of Longevity Foundation. We are an organization
Speaker 7: that is focusing on transforming lung cancer. So with respect
Speaker 7: to Optimists, I think a lot of the speakers have
Speaker 7: touched on it in lung cancer specifically because there are
Speaker 7: so many treatment options available now, still not enough, but
Speaker 7: there are many more than there were a number of
Speaker 7: years ago. Overtreatment and toxicity is are a real issue,
Speaker 7: and cumulative toxicity is are a real issue. And the
Speaker 7: last speak Girl also touched on it just a little bit,
Speaker 7: but I think under treatment is also a big concern.
Speaker 7: So conceptually, I like the idea of finding.
Speaker 2: The right dose.
Speaker 7: My question is really more the methodology of doing it
Speaker 7: and whether randomized trials are really the best approach to
Speaker 7: finding it and how much additional cost, how many additional patients,
Speaker 7: how much additional time is that going to introduce into
Speaker 7: this new paradigm. So conceptually, I love the fact that
Speaker 7: we're looking at not overtreating, especially as I mentioned, many
Speaker 7: people are going on multiple therapies, so the cumulative toxicity
Speaker 7: is a real issue. But it's just I question the
Speaker 7: how and whether this really is the right approach and
Speaker 7: what's going to happen when we get into combination therapies.
Speaker 1: Great, thanks for the introductions and for the teasers. I
Speaker 1: think this will be a very robust discussion. So we
Speaker 1: have a bunch of different stakeholders true to the IO
Speaker 1: three sixty concepts, who are all you know with the
Speaker 1: vision of you know, developing you know, better, safer, more
Speaker 1: effective treatments for patients. We did hear that, you know,
Speaker 1: cancer therapeutics has evolved so much over the past few decades.
Speaker 1: We were not developing conventional chemotherapy anymore.
Speaker 2: We don't have to take drugs to the maximally tolerated dose.
Speaker 1: We're developing immunotherapies that harness the immune system, the t cells,
Speaker 1: et cetera to attack the cancer. Targeted therapies which you know,
Speaker 1: switch off a certain gene or protein that the tumor
Speaker 1: may be addicted to, and we're treating patients. For many,
Speaker 1: many years, a lot of cancers have been turned into
Speaker 1: chronic diseases, so we have to think about long term
Speaker 1: cumulative toxicities. We're trying to move agents into earlier treatment settings,
Speaker 1: more curative settings. So I think these are things that
Speaker 1: the entire community is is struggling with.
Speaker 2: I'm wondering.
Speaker 1: You know, we heard a little bit from each of
Speaker 1: you about sort of efficiency and in drug development. You know,
Speaker 1: it's intensely competitive. Patients can't wait. You know, we need
Speaker 1: to get good enough therapies, not necessarily perfect therapies, out
Speaker 1: to the clinics as quickly as possible. So how do
Speaker 1: you guys think about sort of trade offs in trying
Speaker 1: to develop you know, early data sets that can be
Speaker 1: acceptable to regulators, to clinicians versus you know, versus speed
Speaker 1: to get things into phase three?
Speaker 5: Maybe right, yeah, maybe I'll start since I do clinical trial,
Speaker 5: so I think it really as the I n D
Speaker 5: submissions are going in and you're actually even before and
Speaker 5: you're designing your strategy, I think keeping in mind project
Speaker 5: optimists is not even ideal, it's a necessity. And you
Speaker 5: have to begin to think about what's the strategy for
Speaker 5: dose escalation and dose finding, and then how can we
Speaker 5: begin to expand patients cohorts at various doses throughout that
Speaker 5: trial that aren't needlessly treating patients at a dose that's
Speaker 5: thought to be ineffective, but exposing patients to doses that
Speaker 5: are thought to have potentially some benefit while we're still
Speaker 5: escalating upwards. And I think the strategy that's been used
Speaker 5: in a lot of phase one clinical trials is the backfill,
Speaker 5: so as you're escalating upwards and you're clearing safety doses,
Speaker 5: that there's some metric of success. And I think we'll
Speaker 5: hear about some of those potential novel metrics of success.
Speaker 5: And once there's either a clear clinical response or a
Speaker 5: reduction in a biomarker that's thought to be associated with
Speaker 5: that disease, then I think you can begin to say
Speaker 5: that level is potentially therapeutic and you can expand enrollment
Speaker 5: at that while you continue to escalate upwards.
Speaker 8: On your dose.
Speaker 5: I think it's hard not to build in dose limiting
Speaker 5: toxicities and some statement about we will identify if it's
Speaker 5: possible to maximum tolerated dose, but ultimately we're going to
Speaker 5: move forward to an optimal dose that's been informed either
Speaker 5: by these expanded cohorts at dose levels that seem to
Speaker 5: be potentially effective, and then also based on the pharmacokinetics,
Speaker 5: the pharmacode dynamics, and really trying to choose a dose
Speaker 5: that hits the sweet spot and that maybe you know
Speaker 5: some I've seen some drugs that have a very linear PK,
Speaker 5: but the PD sort of begins.
Speaker 8: To level off.
Speaker 5: Well, that's that's already telling you something that you probably
Speaker 5: shouldn't keep pushing the dose if your pharmacodynamic effect is plateauing.
Speaker 5: And so I think that's one strategy, and then certainly
Speaker 5: other strategies would be let's find three or four doses
Speaker 5: that we think are possible to move forward with and
Speaker 5: then you start randomizing in your dose expansion or face two.
Speaker 8: But that's just an initial thought.
Speaker 2: Yeah, and we talked about PD.
Speaker 1: So pharmaco dynamic markers, Greg and whim, what are your
Speaker 1: thoughts on, you know, how do you assess what are
Speaker 1: the pharmacodynamic markets from an imaging standpoint you would look
Speaker 1: at to help help select the dose, and then what's
Speaker 1: the future behold start off again.
Speaker 6: So where we see it is really focusing in on
Speaker 6: the concept of using each tumor as its own data point.
Speaker 6: Of course, with the right statistics behind it. What we
Speaker 6: typically see is that when a tumor shows some kind
Speaker 6: of response to therapy, it tends to continued with that response.
Speaker 6: I would say the majority of tumors either grow or shrink.
Speaker 6: If you look at individual tumors, there's very little that
Speaker 6: actually change their course. Of course, if you look at
Speaker 6: a patient level and you look true resist or total
Speaker 6: tumor burden, you see these hockey stick effects. One of
Speaker 6: the questions I have there for myself and for the
Speaker 6: space is if a tumor is decaying at a certain dose,
Speaker 6: does it stop decaying or can we, even with a
Speaker 6: low dose, get that tumor to zero, because it really
Speaker 6: changes the whole paradigm of how we deal with things.
Speaker 6: Another concept for pharmacodynamics, when when we talk about trial design,
Speaker 6: might be to use many more patients as their own
Speaker 6: control using the concept of tumor growth or decay. And
Speaker 6: I think Greg will allude to that later, where you
Speaker 6: actually use the temporal change and not just a volume change,
Speaker 6: but the temporal tendency, which makes it possible to if
Speaker 6: you look at similar time intervals, even when a tumor
Speaker 6: is at a different initial volume, to compare the change
Speaker 6: rate over time, which I would say gives more power
Speaker 6: to the clinical trials because the patient is its own control.
Speaker 6: On the statistical side, it would really uplift that. Plus
Speaker 6: it would also prevent, I think to the point that
Speaker 6: was made before, it would prevent the undertreatment of patients
Speaker 6: because you can step them up to higher dosages.
Speaker 3: All right, So to follow up on this, thanks when
Speaker 3: the distinction that I'm thinking about here is that looking
Speaker 3: at pharmacodynamics and looking at efficacy markers.
Speaker 4: So they're related, but they're not quite the same thing.
Speaker 3: So pharmacodynamics, you know, seeing whether the treatment is having
Speaker 3: some biological effect, and looking at some combination of So
Speaker 3: in the case of radiomics, of course you know this
Speaker 3: idea that that biological changes are reflected in histology, and
Speaker 3: that histology is reflected in pixel level changes that may
Speaker 3: be actually not visible to the human eye.
Speaker 4: So this is something that.
Speaker 3: You know, you need AI models to extract from images,
Speaker 3: but that you can see the biological effects of treatment,
Speaker 3: and so you can establish some kind of a dose
Speaker 3: response curve by looking at you know, just biological change.
Speaker 3: The next level, though, the really challenging bit is translating
Speaker 3: that into long term efficacy and you know, as everybody
Speaker 3: who's done oncology DIRUGT development for a length of time knows,
Speaker 3: what happens is that early on you're looking at response, right,
Speaker 3: radiographic response, What does that mean? That means tumor shrinkage.
Speaker 3: But it's not hard to find things that shrink tumors.
Speaker 3: And unfortunately the history of cancer drug development is littered
Speaker 3: with examples of drugs that looked good based on shrinking
Speaker 3: tumors in small numbers of patients early on, but then
Speaker 3: you take it to phase three and it doesn't improve
Speaker 3: survival and that's, you know, not a great outcome for
Speaker 3: a variety of reasons.
Speaker 4: So what we need are.
Speaker 3: Things that really correlate well to survival ultimately. And if
Speaker 3: you can get to the point where you have dose
Speaker 3: expand you know, so as Ryan said, you know, you.
Speaker 4: Pick a few doses, two or three doses that you.
Speaker 3: Think might be the best dose, the right dose, and
Speaker 3: if you could truly compare those, you know, thirty forty
Speaker 3: patient dose expansion cohorts on os impact, that would sort
Speaker 3: of be the the you know, to really establish an
Speaker 3: optimal dose. And there are approaches around that based on
Speaker 3: tumor kinetic modeling where you incorporate a time component much
Speaker 3: more robustly than just looking at you know, time to
Speaker 3: progression or things like that, where you know, you decompose
Speaker 3: growth into an exponential decay in an exponential growth component,
Speaker 3: and you you know, based on either total tumor burden
Speaker 3: or per lesion back out of the of the curve
Speaker 3: fit the growth kinetic parameter that that you know, in
Speaker 3: a bunch of academic work has been shown to be.
Speaker 4: Robustly correlated with survival.
Speaker 3: And that's seems like a very promising method for once
Speaker 3: you've got your two or three dope potential doses, getting
Speaker 3: to what should you really take forward? Because you know
Speaker 3: what what you know really gives you the best os impact,
Speaker 3: so those so that distinction so you can have the
Speaker 3: kind of the the more radiomics you know, and and
Speaker 3: by the way, it doesn't have to be just radiomex
Speaker 3: it can be other changes.
Speaker 4: That look at that get to biology you.
Speaker 3: Know in aggregate like ct DNA or other you know,
Speaker 3: blood biomarkers or imaging markers on one hand, on the
Speaker 3: on the pharmacodynamics and then the getting as close as
Speaker 3: you can to the biology that makes tumors lethal which
Speaker 3: is growth. Right, what kills you is the growing tumor,
Speaker 3: So that combination could let us optimally estimate benefit.
Speaker 2: Great.
Speaker 1: Yeah, thanks, So Andrea, a question for you, you know,
Speaker 1: you deal?
Speaker 7: Can I comment on the last one first of the.
Speaker 2: Yeah, yeah, please go ahead.
Speaker 7: I'm just wondering, like listening to the conversations, you know,
Speaker 7: I think that it's I like the last speaker in
Speaker 7: terms of the modeling aspect of it. But I worry
Speaker 7: with the first speaker, especially is you know, don't we
Speaker 7: can't let perfect be the enemy of the good, And
Speaker 7: like how many patients do we want to put on
Speaker 7: all these various doses and taking forward and so forth?
Speaker 7: And is it really necessary that the other thing? I
Speaker 7: just would question or caution and I would love to
Speaker 7: hear the perspective of, especially as we are moving into
Speaker 7: earlier stage disease and earlier on is LS really the
Speaker 7: endpoint we want to be looking at there, especially for
Speaker 7: these types of dose escalation studies?
Speaker 2: Yeah, so I think to clarify.
Speaker 1: That, you know, I think Greg and Wim are trying
Speaker 1: to develop imaging endpoints that can help predict.
Speaker 2: Overall survival perfect.
Speaker 1: Yeah, not necessarily obviously, you know, you wouldn't have the
Speaker 1: statistical power, or the randomization, or the numbers to follow,
Speaker 1: or the time to follow patients on phase one dose
Speaker 1: escalation out to survival.
Speaker 3: Thank you to answer the the the how many patients
Speaker 3: do you need?
Speaker 4: I mean, just to refer to.
Speaker 3: There's one interesting publication from I think it was twenty
Speaker 3: twenty the Maitland paper, where this was an effort of
Speaker 3: the foundation of the NH analyzing data from a colorectal
Speaker 3: cancer trial. I think I think it was a I
Speaker 3: think it was a TKI and so interestingly so in
Speaker 3: this trial, the hazard ratio on OS was zero point
Speaker 3: eight two, so, you know, not a huge impact, right,
Speaker 3: you know, a.
Speaker 4: Modest survival effect.
Speaker 3: And what they did is they did kind of a
Speaker 3: resampling method where they would draw you know, n patients whatever,
Speaker 3: five hundred or one thousand times with replacement and see
Speaker 3: on the basis of that sample, if they used these
Speaker 3: various kind of kinetic parameter assessments, how how easily could
Speaker 3: they detect the OS impact. And what they found is
Speaker 3: that they had if they used tumor diameters, they were
Speaker 3: able to detect the OS impact with eighty percent power
Speaker 3: with like forty five patients, and if they used whole
Speaker 3: tumor volume it was something like twenty five patients. So
Speaker 3: we're talking about dose expansion cohorts of you know, thirty
Speaker 3: or forty patients. If this is done appropriately, should be
Speaker 3: able to differences. I mean, again, that's a modest different
Speaker 3: os difference that was predictable with a relatively small sample. So, Andrew,
Speaker 3: to answer your question of how many and how long,
Speaker 3: those are roughly the numbers I think that we're talking
Speaker 3: about here.
Speaker 6: Yeah, there's one thing I want to add to that,
Speaker 6: because it's in the end for the patient's survival is
Speaker 6: the most important thing. However, if we're developing a drug
Speaker 6: that would kill in all patients, like let's say half
Speaker 6: of the tumors very well, very rapid, but it might
Speaker 6: not have a survival benefit because the other tumors will
Speaker 6: continue growing and we kill that drug, we might throw
Speaker 6: away a valuable asset that, in the reality of combination
Speaker 6: therapies could add to the therapies.
Speaker 8: And I think there is a true.
Speaker 6: Danger there that if we don't have the right approach,
Speaker 6: that we're killing a lot of assets, or we're putting
Speaker 6: a lot of assets on the bench that could really
Speaker 6: add a benefit for the patients, and I think Project
Speaker 6: Altmost is a good step in that direction. That we're
Speaker 6: in this phase where we have to put the concepts
Speaker 6: together because on one hand, the FDA wants and the
Speaker 6: patient need additional survival, but on the other hand, we
Speaker 6: also need to focus in and zoom in onto what
Speaker 6: are the assets that have the potential to add to
Speaker 6: combinations that could really dramatically up the level of survival.
Speaker 6: I think, as as Excel showing earlier today, the next
Speaker 6: generation that will bring the survival up another twenty to
Speaker 6: thirty percent.
Speaker 2: Yeah, great point.
Speaker 1: So so back to you, Andrew and bringing us back
Speaker 1: to the here and now. You know, we've had some
Speaker 1: very future oriented thoughts which are really promising and exciting.
Speaker 2: But you deal a lot with the FDA, you deal
Speaker 2: a lot with.
Speaker 1: Investigators, and so what are you hearing about, you know,
Speaker 1: from a drug development standpoint, what are some of the
Speaker 1: current pain points around Project Optimist is it's sort of
Speaker 1: the inconsistency that you're of advice and kind of randomization
Speaker 1: of small cohorts for randomization's sake, just to check a box,
Speaker 1: or lack of clarity. I mean, what are you hearing
Speaker 1: and what are you trying to convey to the regulators.
Speaker 7: Yes to all of the above, especially from the investigators,
Speaker 7: many of them that I've spoken with, you know, they
Speaker 7: they keep coming back to that medicine is more of
Speaker 7: an art than a science, and obviously we need to
Speaker 7: have the science to approve drugs and to demonstrate efficacy
Speaker 7: and so forth. But almost all of them have said,
Speaker 7: you know, the irrespective of what the label says, they're
Speaker 7: going to be playing around with it anyway. And with
Speaker 7: to do a randomized trial with such small number of people,
Speaker 7: what are we really learning and gaining from it? Is
Speaker 7: sort of the perspective that I've heard from, and they're
Speaker 7: mostly academic researchers, you know, at the at the NCI
Speaker 7: aser centers that have been talking about this and complaining
Speaker 7: about it. From the drug developer side again, you know,
Speaker 7: I think, I don't know that anybody is quibbling with
Speaker 7: the concept of it, but it is. It has been
Speaker 7: difficult to figure out what to do and how to
Speaker 7: do it, just because it's still evolving and trying to
Speaker 7: get to those right surrogate points and what can be
Speaker 7: done and what can't be done, and just the concern
Speaker 7: about adding a lot of additional time and cost into
Speaker 7: the whole paradigm that we're talking about with it, So
Speaker 7: at least that's what we're hearing.
Speaker 1: Yep, great, thank you so so Ryan. I'm wondering what
Speaker 1: are your thoughts. We've heard a little bit about combination strategies.
Speaker 1: If you're combining two drugs together, do you need to
Speaker 1: re backfill and re randomize each combination? And then also,
Speaker 1: what are your thoughts if you've established it a mon
Speaker 1: therapy does say in one tumor type, do you need
Speaker 1: to repeat the exercise in a different tumor type?
Speaker 2: Or how do you think about that?
Speaker 5: It's a really good and complicated question with no easy answer.
Speaker 8: I think it really does depend.
Speaker 5: I think if you're giving a monoclonal antibody like pemberleism app,
Speaker 5: the dose optimization happened in a remarkably large phase one trial.
Speaker 5: You know what the toxicity is, you know what the
Speaker 5: benefit is, you know what the benefit is at large
Speaker 5: numbers across a lot of different treatments for a lot
Speaker 5: of different diseases and indications. So I'm not sure you
Speaker 5: need to optimize the dose of pemberleism AP plus drug X.
Speaker 5: I think it becomes very difficult when you're using two
Speaker 5: agents that aren't monoclonal antibodies that are perfectly well characterized
Speaker 5: over fifteen years or whenever it started, And so that's
Speaker 5: really challenging in particularly trying to dose escalate two small
Speaker 5: molecule inhibitors. Now that's usually done in the setting of
Speaker 5: trying to target signaling pathways, but you know, I meane
Speaker 5: cells have signaling pathways too, and more and more of
Speaker 5: our therapies can be small molecules in combination with other things,
Speaker 5: and I think if you're trying to escalate two small
Speaker 5: molecule inhibitors, or certainly anything that can augment the toxicity
Speaker 5: of the other agent, you probably have to be really
Speaker 5: careful in your escalation. You can escalate one with a
Speaker 5: low dose of another and escalate the other. You can
Speaker 5: try these tandem escalation. It looks like a box with
Speaker 5: lots of different weird things happening, but ultimately it's complicated,
Speaker 5: and those often are really they don't.
Speaker 8: End up being all that successful.
Speaker 5: And so in many ways, I think when we're going
Speaker 5: to be combining immunotherapy agents, one probably has to be
Speaker 5: easy and stable and fixed and then you can kind
Speaker 5: of escalate the other one up or you've already done escalation,
Speaker 5: and then you just when you start escalating your combo
Speaker 5: with PD one, which is happening all the time, then
Speaker 5: you're just sort of starting at a level or two
Speaker 5: below where you think you know you're going to be,
Speaker 5: and then you just kind of escalate the other agent.
Speaker 8: That's what's been done.
Speaker 5: And I think on some level you may still need
Speaker 5: to do the opt I mean, you have to do
Speaker 5: the optimist piece, but I guess the question is I
Speaker 5: don't think you need to. You're going into it with
Speaker 5: a much narrower idea of where you're going to end up,
Speaker 5: whereas if you have two drugs you have no idea
Speaker 5: how they're going to mix together, you're probably screwed, and
Speaker 5: it's going to cost a lot of money and a
Speaker 5: lot of patients, and you're probably going to hurt. Like
Speaker 5: it's just going to be hard, because those trials are
Speaker 5: remarkably difficult to do and and very infrequently have they
Speaker 5: led to approve combinations.
Speaker 1: So presumed overlapping toxicity versus not different approaches.
Speaker 8: For sure, and and more importantly, some.
Speaker 5: We may be augmenting toxicities that we have no idea
Speaker 5: that that would happen. I've seen PD one inhibitors augment
Speaker 5: TKA toxics, TKI toxicities. You're like, well, how the heck
Speaker 5: does that happen?
Speaker 2: Right?
Speaker 8: Right?
Speaker 5: Like, it's probably not t cell driven. Why does anti
Speaker 5: PD one x cause drug wise whatever neuropathy to get
Speaker 5: worse or anything, you know, And it's it's just so
Speaker 5: I think I think we have to go in with
Speaker 5: our eyes wide open, because there are surprises from a
Speaker 5: toxicity signal that we sometimes see. But by and large,
Speaker 5: I think it's fair to you know, I think we
Speaker 5: do have to have an idea of where we're going
Speaker 5: with the two drugs, what the doses are individually before
Speaker 5: we start getting too creative, because it's it's complex, it's expensive,
Speaker 5: in it often doesn't go anywhere good.
Speaker 1: Yep, and Greg from operational you know radiology standpoint, I mean,
Speaker 1: what do you think would need to change for these
Speaker 1: more forward thinking futuristic endpoints to be embedded.
Speaker 2: Do we need like more.
Speaker 1: Frequent imaging in early stage development to really capture tumor
Speaker 1: kinetics or how does it feel need to adapt?
Speaker 3: So that's a great question. Let me go back to
Speaker 3: one of the problems that I'm sure many of you
Speaker 3: all recognize just intuitively from RESIST and then put this
Speaker 3: in contrast with these other methods. So a problem with
Speaker 3: RESIST rate is one of the things you do is
Speaker 3: you select quote unquote target lesions, meaning the things you're
Speaker 3: gonna follow quantitatively, and then most of the assessment of efficacy.
Speaker 3: You know, response rate is based on what proportion of
Speaker 3: patients had these sample of tumors shrink. Okay, whenever you
Speaker 3: sample a population, which is the population here is the
Speaker 3: tumor burden overall, and the sample is the lesions you
Speaker 3: pick to measure. Whenever you sample, there is a built
Speaker 3: in assumption that's not voiced, and that is that the
Speaker 3: outcome of interest. And I'm gonna say that that's survival
Speaker 3: is driven by the average. And that's not true.
Speaker 4: Right. It's not the average lesion that kills the patient.
Speaker 3: It's the fast it's the most aggressive, most treatment resistant lesion.
Speaker 3: And if you didn't happen to choose it when you
Speaker 3: were picking out lesions to measure, then whatever methods you apply,
Speaker 3: you're blind to it.
Speaker 2: Right.
Speaker 3: So one of the biggest things is I think you
Speaker 3: know to apply any of these methods including radiomics for pharmacodynamics,
Speaker 3: tumor growth kinetics, for os impact whatever.
Speaker 4: You need to basically be able to measure everything.
Speaker 3: And of course one of the big problems with that
Speaker 3: is that radiologist time is expensive. So when you're going
Speaker 3: through and you say, okay, no, mark every single lesion
Speaker 3: in the scan in a widely descemined you know, we've
Speaker 3: got a melanoma patient with dozens and dozens of lesions.
Speaker 4: That's hard, that's expensive.
Speaker 3: Now, fortunately we've got you know, kind of exponentially improving
Speaker 3: power of AI. AI right now is great at doing
Speaker 3: things like if you can just point to a tumor,
Speaker 3: it'll segment it in three D, it'll propagate it across
Speaker 3: time points. Actually finding the tumors still really requires people
Speaker 3: in most cases.
Speaker 4: That's that's the hard task.
Speaker 3: So there are a lot of efforts in progress now
Speaker 3: to develop that part of it, the identifying and outlining
Speaker 3: all of the tumors so that you can look at
Speaker 3: the biological impact across the entire tumor burden collectively, or
Speaker 3: a zim was saying, you may really need to be
Speaker 3: able to look at, you know, lesion by lesion, but
Speaker 3: you got to find all allegians. So that's the effort
Speaker 3: of a that's the focus of a lot of development
Speaker 3: work that's happening right now by pharma, by tech companies,
Speaker 3: by consortia and across the board.
Speaker 1: And is there a financial model that's viable because this
Speaker 1: is just software that sits on top of a scanner,
Speaker 1: or how do you how do you make sure that's scalable?
Speaker 4: So well?
Speaker 3: In a clinical trial context, a lot of work is
Speaker 3: done by central by core laboratories. So you know, as
Speaker 3: long as you have you know, as long as all
Speaker 3: the scans are going through a central facility, they can
Speaker 3: take care of that. If you want to do this
Speaker 3: at a site, then what you will eventually need is
Speaker 3: software that sits either on the process on the workstation
Speaker 3: next to the scanner, or that is done somewhere in
Speaker 3: the cloud between the scanner and the pack system, which
Speaker 3: allows that sort of more robust detection and segmentation of
Speaker 3: all allegians.
Speaker 2: Okay, I'll open this up to anyone. What else? What
Speaker 2: else are we missing here?
Speaker 1: We have novel cool imaging techniques, what else blood based biomarkers?
Speaker 1: Should we look at patient reported outcomes? Do we need
Speaker 1: you know, predictive markers for toxicity.
Speaker 3: What else I'll mention one and that is you know
Speaker 3: we heard earlier the molecular imaging, right, so molecular imaging
Speaker 3: gets you know where I said is you need to
Speaker 3: get closer to biology. Well, that's definitely a way to
Speaker 3: get closer biology. So you know, for io C D
Speaker 3: eight pet tracers for example, that you look at the
Speaker 3: distribution and movement of T cells, and so that's important.
Speaker 3: The challenges with all of those things is that, of
Speaker 3: course you know novel pet tracers are you know, you
Speaker 3: can easily do that at mg H, but you you
Speaker 3: know you can do that at at Gustav Russ, but
Speaker 3: you can't do that at you know, East Lands and
Speaker 3: Community Hospital or whatever, right, so so you know you
Speaker 3: need to be able to That is a challenge of
Speaker 3: the molecular imaging side of it, but it is very
Speaker 3: powerful and in early development that is.
Speaker 4: A tool that you could also be applied.
Speaker 6: But I think one of the big operational challenges there
Speaker 6: is cost increase because if you want to add all
Speaker 6: these different biomarkers into your phase one study, well you
Speaker 6: will see some kind of cost increase in your phase
Speaker 6: one The only thing you can hope for is that
Speaker 6: a better understanding early on in your drug will lower
Speaker 6: the cost later on. But it changes the risk model,
Speaker 6: and I think that's something that can be seen with
Speaker 6: investors today that Project Optimists might make some investors more
Speaker 6: hesitant to invest into oncology drugs. So that is also
Speaker 6: one of the consequences I think that is coming from
Speaker 6: Project optomists. This changed risk profile where we don't know
Speaker 6: exactly where it's going.
Speaker 5: Right, true, although an argument against that would be that
Speaker 5: if you can optimize the dose in phase one and
Speaker 5: early phase two, that it'll.
Speaker 8: You're more likely to succeed in phase three.
Speaker 5: But until that happens a few times and we have
Speaker 5: those examples where we can point to to our investors,
Speaker 5: then it becomes much more difficult to sort of make
Speaker 5: that point, you know, in the in the ether without
Speaker 5: those tangible examples.
Speaker 2: Yeah. Great, So I'll turn it to Andrea.
Speaker 1: So we have is it a chilling effect that I'll
Speaker 1: dissuade investors and developers to go intown collegey and go
Speaker 1: into obesity and diabetes or is it de risking programs
Speaker 1: and and you know, getting a better return on investment.
Speaker 2: What do you think.
Speaker 7: I think it's somewhere in the middle, quite honestly. Well, firstly,
Speaker 7: your first thought about going abandoning on college and going
Speaker 7: to obesity is horrifying on so many levels. But I
Speaker 7: think de risking the dosing such that the phase three
Speaker 7: does become a little bit more you know, not nothing
Speaker 7: is one hundred percent, but it leads a little more
Speaker 7: predictable is a good thing. In incorporating modeling and some
Speaker 7: of these new modalities into that is good. But I'm
Speaker 7: kind of also just wondering why we don't, you know,
Speaker 7: go old school and this drugs have been approved for many,
Speaker 7: many years without all of this and maybe start looking
Speaker 7: at post marketing studies and using AI with real world data.
Speaker 7: What can we learn from that to land on an
Speaker 7: optimal dose. So I think we've taken it from one
Speaker 7: extreme to the other and there's probably a nice sweet
Speaker 7: spot in the middle.
Speaker 2: Somewhere, right.
Speaker 1: And your thoughts on patient reported outcomes and early is
Speaker 1: that too early to do?
Speaker 7: It's too early to do, And I don't think you
Speaker 7: would have a representative sample, and I don't know that
Speaker 7: really what you would learn from it, quite honestly, Look,
Speaker 7: I think patient report outcomes are always a good thing
Speaker 7: if you ask the right questions. You know, ask the
Speaker 7: wrong question, you get the wrong answers. So really understanding
Speaker 7: what it is that we're looking for. But quite honestly,
Speaker 7: even with the patient reported outcomes, I think you'll get
Speaker 7: better information and better data doing something postmarket on real
Speaker 7: world information.
Speaker 1: Right, we can open it up if anyone has questions.
Speaker 1: I'm going to throw at another question of the panel
Speaker 1: if we see I see some hands going up, but
Speaker 1: before while we get a mic, four mics.
Speaker 2: Okay, all right, let's open it up. Go ahead. Hi,
Speaker 2: thank you.
Speaker 9: That was very interesting and great talk, something which is
Speaker 9: close to my heart as well. So my question to Greg, so,
Speaker 9: if you're doing it in early phase, how do you
Speaker 9: convince your clinical development counterparts to invest in something? It
Speaker 9: has to be done relatively faster, closer, right, So you
Speaker 9: have to invest in getting the imaging, getting a collap
Speaker 9: to do all these assessments and bring it back to
Speaker 9: the clinical development team in time to make the decision.
Speaker 2: So how do you.
Speaker 9: Especially if there is not a lot of data out
Speaker 9: there to give a basis to so, how do you
Speaker 9: convince your clinical counterparts to do that?
Speaker 3: If you have the magic answer to that. I'll I
Speaker 3: want to talk to you. I mean, you look, ultimately,
Speaker 3: it all boils down to data, right, nothing speaks like evidence.
Speaker 3: I think you have to start with potentially retrospective analysis.
Speaker 3: You know, you can take you know, phase three trials
Speaker 3: where there's been a success actually there so there was
Speaker 3: a nice there was a nice paper a couple of
Speaker 3: years ago, I think Renee Bruno is the lead author
Speaker 3: showing that basically, so taking a roche it was a
Speaker 3: TESSO plus a chemo backbone versus the chemo backbone alone
Speaker 3: in non small cell lung and they used a kinetic
Speaker 3: modeling approach and they did a simulation where they took
Speaker 3: you know, a lot of they basically simulated Phase one
Speaker 3: B trials either by sampling from just the just the
Speaker 3: treatment arm at the experimental arm and control arm or
Speaker 3: just control arm versus control arm as sort of like
Speaker 3: a negative control and they were able to show, you know,
Speaker 3: that you made better decisions on go no go with
Speaker 3: the modeling based approach, and you know, just of course,
Speaker 3: I think I'm sure everybody understands this, but that is
Speaker 3: such a high stakes decision. The do you go to
Speaker 3: phase two do you go to phase three. That's a
Speaker 3: huge stakes decision and you have to make it with
Speaker 3: really small patient numbers. So I think if you can
Speaker 3: bring evidence saying that the quality of decision making is
Speaker 3: improved with these methods since starting with retrospectively and then
Speaker 3: implementing them prospectively as an experiment as a as an
Speaker 3: exploratory analysis in a trial, that would be an approach.
Speaker 6: To do it.
Speaker 9: Thank you, sorry, just a fallow up question and a
Speaker 9: whim on that. So I know we are not there
Speaker 9: yet based on Greg's response, So what do you think
Speaker 9: how we can do it real time?
Speaker 2: Not real time?
Speaker 9: Like today we are doing the trial and have results,
Speaker 9: but like within weeks or months to give it to
Speaker 9: the back to the team. Are we there yet to
Speaker 9: do all these analysis radio mixed tumor growth, kinetics and
Speaker 9: total tumor burden in real time?
Speaker 6: I think we are at least we as Radiomics, are
Speaker 6: are ready for that at the moment. It took us
Speaker 6: a while to find the right setup to get through
Speaker 6: this phase of having sufficient capabilities to segment tumors rapidly,
Speaker 6: but today we are ready to have data delivered within
Speaker 6: the typical clinical trial analysis timelines, so have your last
Speaker 6: patient rat within two weeks after the last visit, feeding
Speaker 6: it into the statistical program. So it is something that
Speaker 6: is there, but the big challenge tastes convincing people to
Speaker 6: use it. And as great that data is the key
Speaker 6: to the solution, however, you need some early convinced people
Speaker 6: to help guide and create that data.
Speaker 10: So maybe I can add on another question here. You know,
Speaker 10: this is an interesting topic where you basically are between
Speaker 10: the two extremes of being fast and having to get
Speaker 10: to the next stage of clinical development in a pretty
Speaker 10: competitive environment for many assets, and trying to make the
Speaker 10: best possible decision with that as much data as possible.
Speaker 10: And yes, modeling can help more patients help, randomization helps,
Speaker 10: but it does take time. So the way I have
Speaker 10: understood the Project Optimus design that FDA has asked for
Speaker 10: was somewhat are hybrid here in an attempt to not
Speaker 10: ask too much from sponsors and not add on too
Speaker 10: much extra time, but still get more data than three
Speaker 10: patients per cohored before you make a decision.
Speaker 2: So you know, I.
Speaker 8: Call that a practicality.
Speaker 10: So the question is, you know, how can we keep
Speaker 10: as much practicality here and let sponsors move forward with
Speaker 10: their choices without adding too much extra complexity that has
Speaker 10: merit in some ways. So you know, it's a dance
Speaker 10: between those two components.
Speaker 5: I mean, I think the backfill concept actually fixes a
Speaker 5: lot of that. As a phase one doc, if you
Speaker 5: have three sites, you always have patients each cohort, and
Speaker 5: you get experience with that cohort and you feel like
Speaker 5: you know the drug and you know the dose, and
Speaker 5: you go up. If there's seven or eight or nine
Speaker 5: sites in a phase one trial and it's for some
Speaker 5: reason a three plus three design, then you're not You
Speaker 5: may maybe three months before you see a patient. If
Speaker 5: you have backfail, you can always have patients, and so
Speaker 5: if you have it, it actually works much better for
Speaker 5: a larger number of investigators, whereas a smaller group you
Speaker 5: always have patients and you're always sort of so I
Speaker 5: think actually encourages recruitment. If you say, oh, I have
Speaker 5: a patient with X and I want to get them
Speaker 5: on that trial, do I have a slot? If I
Speaker 5: know I always have a slot, it's way easier to
Speaker 5: do that. And so whereas if my turn comes up
Speaker 5: three months later, it's like, hey, do you have a
Speaker 5: patient now? I's like, oh, I forgot I had that
Speaker 5: trial open. Yeah, let me give me, give me a
Speaker 5: couple of days and I'll have somebody for you.
Speaker 8: I think that that those.
Speaker 5: Types of designs that encourage active participation by the investigators
Speaker 5: and the sites lead to.
Speaker 8: More efficient a cruel and it sort of solves itself
Speaker 8: on some level.
Speaker 5: But you have to get a few dose levels in
Speaker 5: before you feel like there's a biological signal of some activity,
Speaker 5: before you feel comfortable exposing the patient to that to
Speaker 5: that dose level. But I think I think that's probably
Speaker 5: the most efficient way to go through phase one and
Speaker 5: dose escalation.
Speaker 10: I love that answer. Backfields are quite useful tool.
Speaker 1: Yes, yeah, absolutely, I think we have you have quick
Speaker 1: quick question.
Speaker 2: We have twenty seconds, Okay, a quick one.
Speaker 11: I'll take out the first part, just keep the second part.
Speaker 11: A lot of the talk today focused in on you know,
Speaker 11: volume change, looking at growth rates, to k rates, these
Speaker 11: kind of things all focused around the lesion. You know,
Speaker 11: do you think in an early phase trial where you're
Speaker 11: trying to make these go no goes or choose the
Speaker 11: right dose, there's value in looking at things outside of
Speaker 11: the lesion, like, for example, in brain looking at edema
Speaker 11: changes in the lungs, looking at like pleural fusion. Do
Speaker 11: you see a place for quantifying these things in early
Speaker 11: stage or should we be doing that in the later
Speaker 11: stages once we've decided on dose.
Speaker 2: Thank you, good question him.
Speaker 6: Yeah, I think there's definitely space for those kinds of things.
Speaker 6: They add value into your understanding. Of course, they won't
Speaker 6: give you the definitive answer. I think at your early
Speaker 6: stage it's all about hypothesis generation that you do then
Speaker 6: validate later on. But yeah, looking at edema, for example,
Speaker 6: in the brain is very valuable.
Speaker 1: So totality of evidence is key. As with everything in OPTI,
Speaker 1: miss Well, I really want to thank the virtual and
Speaker 1: in person participants. Has been a great discussion.
Speaker 7: M