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00:01:22: Welcome to another episode of The Effective Statistician And Today I'm moving a little bit outside of my comfort zone because i have never actually worked in that area, but I hope then I can ask much better questions.

00:01:48: So Ayon maybe you start by introducing yourself and we can dive into the topic?

00:01:55: Yeah thanks Alexander and before starting I would say thank for having me!

00:02:01: podcast, many episodes of this.

00:02:03: It's a pleasure to be over here speaking with you in the podcast so I am Ion Mukherjee and have been in the clinical domain for past several years.

00:02:12: my initial focus was on adaptive designs especially adaptive rapidization method And then my focus has moved more towards early phase dose escalation and those optimization trials.

00:02:26: at that moment with Eli Lilly working as a TA lead for oncology and CMH, as well as I'm contributing to statistical innovation in terms of designing these clinical trials.

00:02:39: Especially the early phase oncology trials.

00:02:41: Awesome, so we have talked about dose finding and adaptive designs.

00:02:46: And things like this in a couple of episodes should probably be more because that's not my speech but today We want to talk About something that has also lot Of meaning from regulatory perspective and set is project optimus.

00:03:02: Maybe you can introduce us a little bit too what it is?

00:03:08: Right, first of all thanks for asking this question.

00:03:10: It's a very important topic and it has been discussed worldwide in various different ways So I will try to briefly summarize what the overall background is And What The Current Friend Is In Terms Of Thought Process On This Topic.

00:03:28: so Traditionally When Cancers Were Treated With chemotherapies.

00:03:35: The way the chemotherapy works is that it directly calls and burns the tumor cells, as the doses were increased...the toxicity used to increase due to medication as well as efficacy because more tumor cells are getting burnt.

00:03:54: And therefore mathematically assumption of efficacy and toxicity increasing monotonically with an increase in dose was valid for traditional chemotherapy medication.

00:04:06: And we used to use various designs like three plus three designs, and then it went on to Bayesian logistic regression model point design's... ...and there were a lot of such designs which was catered for these types of medications-which is chemotherapy.

00:04:22: but oncology drug development moved from chemotherapy to immunotherapy and targeted therapies.

00:04:29: those types of medication works very differently to how chemotherapy works.

00:04:34: So the way it works is that when a patient takes in the drug, it goes and binds to an immune cell—the drug compound goes and vines into an immune cells—and that target where it vines through releases immune response —and then the immune response kills cancer cells.

00:04:54: And after certain point-of time what happens with an increase in dose, that target binding might get saturated or after that point of time the efficacy of their drug may plateau out or fall off which is quite different to how chemotherapy used work.

00:05:12: So now this assumption of efficacy being as good a toxicity within increasing those remains invalid.

00:05:21: so FDA came up with concept on How To Design Clinical Trials when we are moving away from chemotherapies to targeted therapies and immunotherapies.

00:05:32: And that gave rise to Project Optimus, the initiative by oncology center of excellence.

00:05:38: They formed this initiative with a goal to educate and collaborate with academia industry and regulatory bodies together to have an unified approach towards designing trials for these compounds.

00:05:53: What they said is we need to integrate more data than only the dose-limiting toxicity data, which was main focus for the chemotherapies.

00:06:03: They used to increase their dose levels with observation of number of those limiting toxicities that were happening.

00:06:09: and over here FDA said okay, it's fine but we needed more data to find an optimum dose combining all these datasets efficacy, exposure response models toxicity of course.

00:06:28: As well as not those high-grade toxicities.

00:06:30: but also consider the lower grade toxicities as well because these drugs are taken for a wider period of time compared to chemical therapies.

00:06:39: so even if there is grade one or two toxicities which quite a lot that might be higher grade toxicity patients may drop off due to tolerability issues.

00:06:51: And that might create a problem if an intolerable drug is taken for phase three trial, because there would be a lot of censoring in Phase Three when we're analyzing PFS or OS.

00:07:01: For the immunotherapy?

00:07:03: We take that more in these cycles and then you have pauses between?

00:07:08: Yes!

00:07:08: In immunotherapy... You will take it more continuously.

00:07:13: smaller toxicity grades might actually pile up over time.

00:07:18: And I think probably the earlier studies are not that long term, like the phase three studies becomes much harder to predict what will happen in the phase-three studies?

00:07:27: Absolutely!

00:07:28: This gave rise to this project optimus and we're trying find a dose which goes into Phase II or later stage development save, but also has a good efficacy signal as well as tolerable.

00:07:47: Has got a well-balanced PKPD response or exposure response outcome.

00:07:53: so those are the metrics which we want to optimize to select the dose to go for.

00:07:58: Okay what other data points that we can use to better understand especially Zdose Efficacy Relationship?

00:08:08: FDA has clearly guidance set up four different criteria to design these clinical trials.

00:08:15: And the first and foremost is that we need to have a clear PKA sampling on analysis plan for this types of trials, then there are lot population PK model which has been developed by the pharmacologists within each organization.

00:08:33: what you do is understand what exposure response through those stake or sending mechanistic model, and incorporate those in our design to select the dose to go forward.

00:08:46: So PK&PD are important.

00:08:49: probably PD might take a bit longer than PK to observe depending on all of these operational challenges.

00:08:56: but in terms of data we look at PK-PD information.

00:09:01: We looked at lower grade toxicities some of the tolerability outcomes if available.

00:09:09: We also try to look, once we select a dose for one indication.

00:09:32: progression free

00:09:39: survival at six months, EFS-S which is a binomial endpoint.

00:09:44: So yeah it's difficult to measure those long term endpoints per phase two setting.

00:09:49: Where do we stand with project optimus?

00:09:52: What can you give some key recommendations from that

00:09:55: At the moment?

00:09:56: so since its inception in twenty twenty one till now and twenty twenty five end We have come some way In Project Optimus.

00:10:06: Now people around the globe are speaking about how to design such trials, To find the optimal biological dose.

00:10:14: So we have made some progress.

00:10:17: however We still need to evolve because what I see is that there Is a lack of consensus Of How things Are being designed one and also There's variability of awareness between continents.

00:10:33: i See That it quite actively spoken About in The US side a bit less in the European side, but very less on the Asian and especially Indian side.

00:10:45: The awareness factor is something which we need to really work on and collaborate across continents to raise the bar of awareness about how such designs outsource link to trial set design.

00:10:59: Also We Need To Do Some Cross Functional Collaboration Where We Can go and make the clinicians understand that.

00:11:06: what's the importance of these things.

00:11:09: Yeah, completely see if you don't know about it?

00:11:12: You will not ask for it.

00:11:14: so communicating about What is project of demos?

00:11:17: What this is all about how would we help you as an individual team member?

00:11:22: but also how to really help your study team Is very important.

00:11:27: I see The same thing which they even four estimates estimate.

00:11:30: when he came out in ICG nine R one It was not only meant for only biostatisticians.

00:11:36: Was, or a proper collaboration between the clinical team and statistical teams?

00:11:41: However since twenty-nineteen till now it has been most spoken about by the biostatisticians And clinicians have been like trying to understand what are they importance of?

00:11:52: that is I don't want this be same project often as well.

00:11:56: so i think Communicating with the clinician the importance of it Is very key point.

00:12:03: Yeah, with the estimates someone told me that an addendum to E-Nine is potentially one of the burst effects.

00:12:10: Of giving it so much statistical background when in fact though It's not just a statistical topic and also its very common kind of talked about that source or missing data problem Is also not super helpful.

00:12:26: So there are couple things around.

00:12:29: what I think from communication point always really important is to show people why it matters to them and how they can benefit from that.

00:12:41: And also you can easily get into it, so offer directs helpful steps.

00:12:47: these three things are absolutely key!

00:12:50: That depends on the audience yeah?

00:12:52: So if you talk to statisticians that will be very different than if you talked to pharmacologists or physicians or to people coming out of the regulatory function.

00:13:04: I completely agree, and i think this communication part is very key that amongst a statistician when we discuss about various designs there's one way which we communicate because of the background all these design switch.

00:13:18: you know about those who are working in this area but with me go through the clinician.

00:13:23: we need to step into their mind.

00:13:26: We need to communicate based on how they are used to thinking it.

00:13:31: And that's a very key thing, which every of the statisticians who have been designing this clinical trial is supposed to do and that is something I think needs to come up with irrespective or whichever continent its being worked in.

00:13:48: One of the key challenges for any innovation usually what does regulatory acceptance?

00:13:56: So here you have a big advantage given that there's a lot of documentation, a lot resources around the FDA and Z-project.

00:14:07: You can say yes!

00:14:09: That is not just backed up but even recommended expected... ...and usually set as huge argument for making things happen.

00:14:18: I completely agree.

00:14:19: That makes a lot of life easier because we already know that FDA wants these types of designs in practice, however what i see is that there has been a lot Of this work which have happening In terms publication from academia.

00:14:37: However We See A Lot Less.

00:14:40: Those Designs Are Being Used In Practice And that's where the need of a proper industry academia collaboration needs to come in place.

00:14:50: That okay, FDA is quite open to such designs and be implemented in practice.

00:14:56: but which are those design which have been published?

00:15:01: Is actually implementable?

00:15:02: I see very few subset of such designs are being considered from an application perspective.

00:15:09: So I think a proper industry, academia collaboration needs to come in place or what other challenges in the industry and how the academic people who are publishing these methods can cater for those.

00:15:21: so that is very important thing which need to be evolved.

00:15:25: Completely agree.

00:15:27: working with academia?

00:15:29: In a sense any kind outside expert.

00:15:31: well you're working at Lili.

00:15:33: I guess you have a lot of internal experts, but potentially lots of my listeners work maybe at smaller organizations.

00:15:41: And then they find someone that is either from the academic side or freelancer or CRO where he can learn form?

00:15:51: The other part is potentially any cross-former collaborations and pretty sure there are certain interest groups working group to connect with like-minded statisticians and learn a lot from that.

00:16:06: Yeah, I completely agree!

00:16:07: Like in the US there is working group called Innovative Design Scientific Working Group iADSWG And it's part of Dashu.

00:16:17: They have subgroups specifically dedicated for early phase oncology drug development and designing such trials.

00:16:25: We are a part of this group.

00:16:28: we constantly work together on how to better these designs in practice.

00:16:33: So I think yes, I completely agree.

00:16:35: such cross-industry working group is very much needed.

00:16:38: Let's talk a little bit about more what statisticians can do before we turned on the microphone.

00:16:47: you just mentioned that your traveling at the moment quite a lot to training and various continents of the world.

00:16:53: Can you tell a little bit about the role of training and how it helps with making sure people understand more about project optimus?

00:17:03: I think that's very key part.

00:17:05: Any researcher who is working on these methods or developing methods, developing the method in one park but no methods are useful unless those are used in practice.

00:17:18: so... other important part of it is communicating about these methods and raising the awareness of these methods at various different places.

00:17:29: And I think that's a key part, what the industrial people really wants are some examples of how these trials are being run at the moment?

00:17:41: What Are The Key Challenges That Are Being Faced With The Present Methodology Which Are Already In Place?

00:17:46: Because Some Of The Clinicians Say with the present work, we are getting their approvals.

00:17:51: So why do you need to bother about?

00:17:53: The thing.

00:17:54: so my answer them is that okay, Getting the approval as a very key point for industry but treating the right patients with the right dose Is far more important point or any of the pharmaceutical industry.

00:18:09: If they're missing out on better those by using non optimal methodology to design trial then we are not serving our purpose of being a pharmaceutical industry.

00:18:21: So that is the key point, because nowadays most of the pharma industries have quite driven by the approvals which is very much important for business but also getting those right to the right patients in society it's far more important.

00:18:38: Yes and later on imagine your dose is too high or low.

00:18:46: I'm pretty sure there's a competitor somewhere that will have little bit of closer look and might be able to get much closer to this feed spot.

00:18:57: Yes, And that competitor would have great advantage later on in the market?

00:19:03: Yeah!

00:19:04: To correct it is really difficult.

00:19:09: Oh, yeah.

00:19:10: That is very difficult and Amgen the Lumicross trial which are conducted in Amgen that's a very good example of how things were done after post-marketing research on how doses where modified because it didn't implement proper dose optimization regime.

00:19:30: so thats available for free all day information.

00:19:33: So Thats Very Good Example.

00:19:34: what you just now see.

00:19:36: And then reputation, trust.

00:19:38: all these different things have already gone out of the window and it's really hard to reestablish that.

00:19:46: Now having someone set trains and has experience examples are super helpful!

00:19:55: What tools or documents available make it easier for teams?

00:20:00: I

00:20:03: think that's an excellent question because there are lots of methodologies which are coming in place, but they aren't many tools to implement these.

00:20:12: So once someone develops a proper dose optimization methodology tool needs to be accompanied by their publication or work.

00:20:23: so Andy Anderson is doing quite a lot in the R Shiny AFTools, in their trialdesign.org website which is freely available for anyone and that does all the simulations for design methods which have been developed by Andy Anderson.

00:20:40: I am working very closely with Professor Ying Wang on one of the special issues in performance critical statistics.

00:20:46: we are running on project optimus And i've been saying what they're doing was excellent.

00:20:56: these R packages and R tools to simulate these designs in practice.

00:21:00: because, these are complex design.

00:21:02: These adaptive methods once every cohort of patient comes in the design gets adapted based on whatever background model is complicated And how it performs in various different hypothetical scenario needs to be assessed prior we start a clinical trial.

00:21:19: such kind of R packages & tools needs to be made available for any industry, whether they are working on internally for their own benefit or like MD Anderson.

00:21:29: It is available for public.

00:21:31: these need to be make available for this.

00:21:33: designs together actually implemented in practice.

00:21:35: otherwise clinicians wouldn't know that how theses design's our working impractice.

00:21:40: Yeah, not only clinicians also statisticians.

00:21:43: So if I'm a statistician and i am not super comfortable with that... ...and need to create my own R code for it.. ..I probably will not do it!

00:21:52: yeah?

00:21:53: I would not trust that im doing everything the right way.... And It'll be much harder to convince others that My tool is right.

00:22:01: There's lot of limitations around these things.

00:22:03: so For any researcher who just listening Please make sure that you have good document code available.

00:22:11: That helps the community to pick it up, to further work on it and also make yourself available so that you can train others for it.

00:22:23: And by the way companies are happy to pay researchers For such training yeah?

00:22:29: Also decent amount of money because It can help so much.

00:22:35: I think you need to give that a way for free things.

00:22:37: The tool is usually encoded as for free, but the training around it set us definitely something said.

00:22:44: so we can be reimbursed form.

00:22:46: Yeah absolutely and like many people are quite comfortable using our codes or do their own R programming And some people are not that prone to R and they prefer the point-and-click version.

00:23:01: So the Arshaic app version is better for them who prefer our codes, I think there needs to be well-documented dashboards like Autodoad Dashboards which gives proper art quotes for every different methods that are being brought up in place.

00:23:17: We talked now about a couple of different preferences.

00:23:21: we will add set to the show notes so as if you make it also easy for you too find all these kind of difference resources and tools what they talked about.

00:23:31: So i'm quite excited about this project Optimus And I'm also quite excited about the whole kind of new class of immunotherapy and all benefits that come with it.

00:23:43: Cancer remains one of the biggest enemies, so better we can find a better for all of us!

00:23:50: As you have just explained, statisticians play a huge role in this area to help patients not suffer too much side effects but get enough efficacy.

00:24:03: meets this speed spot of a caret benefit risk creation?

00:24:08: Absolutely.

00:24:08: And for that, the two key factors are collaborating cross-functionally communicating well with people outside their statistical domain about these methods and also other key factor is having good industry academia collaboration where these methods are practically addressed while they've been developed.

00:24:36: This show was created in association with PSI.

00:24:40: Thanks to RAIM and FeeVS, well-positioned background and thank you for listening!

00:24:46: Reach your potential, leak rate science and serve patients – just be an effective statistician.