WEBVTT NOTE This file was generated by Descript 00:00:00.000 --> 00:00:03.210 Judith: Welcome to Berry's In the Interim podcast, where we explore the 00:00:03.210 --> 00:00:07.230 cutting edge of innovative clinical trial design for the pharmaceutical and 00:00:07.230 --> 00:00:09.690 medical industries, and so much more. 00:00:10.600 --> 00:00:11.250 Let's dive in. 00:00:12.375 --> 00:00:14.054 Scott Berry: All right, welcome everybody. 00:00:14.054 --> 00:00:18.585 Back to part B of I Spy two. 00:00:19.304 --> 00:00:24.105 I hope you got the episode, the previous episode where we are visiting here with 00:00:24.105 --> 00:00:32.505 Don Berry and we are revisiting the I SPY two trial and we left off, uh, where 00:00:32.534 --> 00:00:39.974 he's using MRI markers, auxiliary markers to speed up the adaptive algorithm to 00:00:40.034 --> 00:00:43.784 model the rate of PCR six month PCR. 00:00:44.414 --> 00:00:49.429 And he was now talking about one particular application where 00:00:50.144 --> 00:00:53.864 the time machine is so valuable. 00:00:53.864 --> 00:00:59.204 So how, what is a time machine in a clinical trial? 00:01:01.349 --> 00:01:06.629 Don Berry: So let me tell you, in the context of ice I two, um, 00:01:09.059 --> 00:01:13.259 we're, uh, 2014, 00:01:13.275 --> 00:01:13.695 Scott Berry: 14. 00:01:14.339 --> 00:01:18.269 Don Berry: uh, we've got, uh, several arms in the trial. 00:01:19.019 --> 00:01:21.239 Um, one of the arms. 00:01:22.200 --> 00:01:27.120 Uh, used, uh, Pertuzumab, which was an anti her two therapy. 00:01:27.599 --> 00:01:36.840 It was used in addition to Trastuzumab, uh, also called Herceptin, um, in 00:01:36.840 --> 00:01:43.890 her two, uh, positive, uh, women, uh, that is with her two positive tumors. 00:01:44.910 --> 00:01:49.860 And, um, we had other therapies as well. 00:01:49.860 --> 00:01:57.150 The standard arm in her two positive disease included Herceptin and Paclitaxel 00:01:57.150 --> 00:02:03.930 and, uh, doxorubicin, um, in this six month period that we talked about 00:02:03.930 --> 00:02:06.059 it, six months of the, of therapy. 00:02:07.230 --> 00:02:07.829 Um, 00:02:10.170 --> 00:02:18.809 and it's, uh, it, it so happened that, um, gen, uh, Genentech. 00:02:19.875 --> 00:02:27.914 Uh, had been investigating Pertuzumab, uh, in her two positive disease. 00:02:28.664 --> 00:02:34.125 Um, and there was a, another trial, another neoadjuvant trial that was 00:02:34.545 --> 00:02:43.844 comparing it plus Herceptin, uh, to just Herceptin plus the, the chemotherapies 00:02:43.844 --> 00:02:50.385 of, uh, uh, paclitaxel and, uh, doxorubicin and Cyclophosphamide. 00:02:51.224 --> 00:02:53.414 Um, so, 00:02:55.425 --> 00:03:04.184 uh, the, uh, FDA gave accelerated approval to Genentech's Pertuzumab. 00:03:04.245 --> 00:03:04.575 Scott Berry: to ext 00:03:06.704 --> 00:03:10.485 Don Berry: And that meant that, uh, Genentech could sell the drug. 00:03:11.384 --> 00:03:17.684 Um, and they had obligations about coming back with, you know, the 00:03:17.684 --> 00:03:23.054 longer term endpoint, but it was based on an an, an improvement PCR rate. 00:03:24.704 --> 00:03:34.304 Um, and our physicians and our patients, our patient advocates, uh, and our data 00:03:34.304 --> 00:03:40.695 safety monitoring board said You have to drop the standard arm for those patients. 00:03:41.534 --> 00:03:44.954 You can't give the standard arm because it doesn't include pertuzumab. 00:03:47.594 --> 00:03:51.945 Um, and I said, okay, we'll do that. 00:03:51.975 --> 00:03:52.305 Scott Berry: that. 00:03:53.085 --> 00:03:55.244 Don Berry: Um, we did it 00:03:55.844 --> 00:04:00.224 by zeroing out the randomization to the standard arm. 00:04:01.260 --> 00:04:08.970 Uh, we luckily had pertuzumab in the trial already as one of the experimental 00:04:08.970 --> 00:04:16.470 arms, and so we were able to, uh, sort of compare to Pertuzumab, but 00:04:16.470 --> 00:04:20.400 there were only a few patients that were in, and so it wasn't a big deal. 00:04:21.059 --> 00:04:23.820 So I went to Scott and I said, 00:04:24.080 --> 00:04:24.320 Scott Berry: said, 00:04:25.200 --> 00:04:26.430 Don Berry: what am I gonna do? 00:04:27.000 --> 00:04:32.490 I, I'd like to be able to use the controls that we already have in 00:04:32.490 --> 00:04:39.600 that, in the her two positives that did not get pertuzumab, that some 00:04:39.600 --> 00:04:41.520 of which still don't have surgery. 00:04:42.600 --> 00:04:45.630 Um, and I'd like to use them. 00:04:46.950 --> 00:04:48.059 For control. 00:04:49.110 --> 00:04:57.660 Um, but I don't want to keep using them, you know, for, for five years from now 00:04:58.139 --> 00:05:02.820 when, uh, other things will have happened and the disease may have moved on. 00:05:03.329 --> 00:05:12.329 And so maybe I should discount them somehow for, uh, the, um, for the amount 00:05:12.329 --> 00:05:15.119 of time that they've had in follow up. 00:05:15.119 --> 00:05:18.539 And, you know, when they entered the trial, what do I do? 00:05:19.380 --> 00:05:20.849 And Scott said, 00:05:20.974 --> 00:05:21.264 Scott Berry: said, 00:05:22.170 --> 00:05:23.789 Don Berry: you know, I know how to do this. 00:05:24.539 --> 00:05:25.320 I did it. 00:05:25.530 --> 00:05:32.730 I did it in baseball and in golf and in hockey, uh, and Scott, 00:05:32.970 --> 00:05:35.250 uh, to you, the time machine. 00:05:35.849 --> 00:05:41.610 Scott Berry: Yeah, so we in in sports, there's the age old question of how do 00:05:41.610 --> 00:05:44.369 you compare players of different eras? 00:05:45.059 --> 00:05:49.139 How do you compare a Mark McGwire to a Babe Ruth? 00:05:49.380 --> 00:05:53.639 How do you compare Tiger Woods to Jack Nicklaus They never played together. 00:05:54.900 --> 00:06:00.360 The beautiful thing about sport is they didn't play together, but they 00:06:00.360 --> 00:06:03.840 played with players that played with players that played with 00:06:03.840 --> 00:06:05.369 players that did play with them. 00:06:05.369 --> 00:06:11.489 So, babe Ruth May have played with Jimmie Foxx They overlapped in their careers. 00:06:11.799 --> 00:06:14.670 Jimmie Foxx overlapped with Ted Williams. 00:06:14.670 --> 00:06:20.010 Ted Williams overlaps with Mickey Mantle and Reggie Jackson. 00:06:20.280 --> 00:06:22.770 And Hank Aaron overlapped a lot of people. 00:06:22.980 --> 00:06:24.420 There's a bridging. 00:06:24.825 --> 00:06:28.845 Over time of players at different eras. 00:06:29.265 --> 00:06:36.225 And if you can estimate the effect of time on the players, sports is almost 00:06:36.225 --> 00:06:41.715 a little bit more complicated 'cause players age themselves, drugs don't age, 00:06:42.045 --> 00:06:47.325 but that if you could estimate the effect of time, you can isolate the individual 00:06:47.325 --> 00:06:51.945 effects of different players and compare them even if they never played together. 00:06:52.335 --> 00:06:59.625 Well here in a brand new kind of trial, a platform trial, this exact problem 00:06:59.625 --> 00:07:05.475 comes out where arms are in the trial at different times and now you want to 00:07:05.475 --> 00:07:10.425 compare an arm to patients that were enrolled at a control at a different time. 00:07:11.115 --> 00:07:15.135 It's exactly the same scenario where it's only time that's different 00:07:15.405 --> 00:07:17.445 in the scenario to make this. 00:07:19.949 --> 00:07:23.130 Don Berry: So we, we put it in the trial. 00:07:23.880 --> 00:07:28.739 Uh, we're going to use the, uh, all of the controls. 00:07:29.520 --> 00:07:34.619 Uh, and there is this, uh, time machine that's Scott, uh, talked about. 00:07:35.309 --> 00:07:42.420 And in the example I gave in the previous and part A of Pertuzumab, uh. 00:07:43.409 --> 00:07:49.740 Pertuzumab the control arm At the time that we did, uh, the analysis 00:07:49.740 --> 00:07:55.950 and graduated, uh, the control arm had about a 20% in triple negative breast 00:07:55.950 --> 00:07:59.280 cancer had about a 20% path CR rate. 00:08:00.150 --> 00:08:08.429 And the, in, on the basis of the one patient that we had from, uh, Pertuzumab 00:08:08.429 --> 00:08:11.340 who had the results of surgery. 00:08:11.340 --> 00:08:18.240 The result of surgery was A-A-P-C-R and these other controls, uh, these 00:08:18.240 --> 00:08:24.840 other, uh, patients, um, that we use the MRI to predict, predict and to 00:08:24.840 --> 00:08:30.960 conclude that the result was going to be, you know, 60% for, uh, Pertuzumab. 00:08:31.770 --> 00:08:35.099 Um, we very little information. 00:08:35.880 --> 00:08:42.389 About the experimental arm, of course, but that information was so compelling 00:08:42.780 --> 00:08:44.969 that it was to the right of 0.2, 00:08:46.380 --> 00:08:48.509 uh, with high probability. 00:08:49.110 --> 00:08:52.679 And so it met the graduation threshold, which was a predictive 00:08:52.679 --> 00:08:55.110 probability calculation. 00:08:56.639 --> 00:08:58.980 So, and, but we didn't stop there. 00:08:58.980 --> 00:09:01.590 We did it for, for all of I spy two. 00:09:02.040 --> 00:09:06.929 From that point forward, all of I spy two was based on the time machine and 00:09:06.929 --> 00:09:12.000 this, uh, the FDA calls them contemporary controls, controls that were in the 00:09:12.000 --> 00:09:14.610 trial, but not at the same time. 00:09:15.690 --> 00:09:16.199 Um. 00:09:16.619 --> 00:09:21.300 And they've agreed to use that approach. 00:09:21.389 --> 00:09:22.110 It's really 00:09:22.650 --> 00:09:27.929 amazing, uh, the time machine approach in registration trials. 00:09:28.409 --> 00:09:33.119 So the two trials that I mentioned, one in, uh, glioblastoma, the other 00:09:33.119 --> 00:09:39.659 one in pancreatic cancer, uh, they use the, the time machine as a 00:09:39.659 --> 00:09:41.579 fundamental thing from the get go. 00:09:42.179 --> 00:09:47.250 It isn't just when you had to stop the, the experimental, the, uh, 00:09:47.250 --> 00:09:51.360 control arm that this applies, but it applies for everything. 00:09:51.989 --> 00:09:52.590 And 00:09:53.070 --> 00:09:53.279 as 00:09:53.279 --> 00:09:58.230 Scott mentioned, you know, uh, uh, Ted Williams in his list was 00:09:58.230 --> 00:10:00.570 way back in the, in the middle. 00:10:01.500 --> 00:10:04.170 Um, we. 00:10:05.205 --> 00:10:12.675 Uh, we want to use, um, uh, Ted Williams during the that period. 00:10:12.765 --> 00:10:13.695 Uh, and, 00:10:14.265 --> 00:10:16.905 uh, we want to use an experimental arm. 00:10:16.905 --> 00:10:19.605 We want to compare Ted Williams. 00:10:20.115 --> 00:10:26.745 We get, we get a notion of what is the control rate during, uh, Jimmy Fox's 00:10:26.745 --> 00:10:30.495 period during, uh, Hank Aaron's period. 00:10:31.260 --> 00:10:40.800 Uh, and so we in the algorithm explicitly compare Ted Williams to Jimmy Fox. 00:10:41.400 --> 00:10:46.260 We explicitly compare pertuzumab to other therapies. 00:10:46.470 --> 00:10:47.760 It's just we don't tell. 00:10:48.360 --> 00:10:52.800 Merck as the, uh, the drug's, uh, uh, sponsor owner. 00:10:53.700 --> 00:11:00.510 Uh, we don't tell Merck what the Eli Lilly drug was doing. 00:11:00.540 --> 00:11:05.040 We don't tell Eli Lilly what the Merck Drug is doing, but we're comparing 00:11:05.040 --> 00:11:12.900 everything to everything in order to, uh, uh, assess what is the control rate. 00:11:13.375 --> 00:11:13.775 Scott Berry: control. 00:11:15.075 --> 00:11:20.550 So, uh, behind the scenes there's one model that's estimating the individual 00:11:20.550 --> 00:11:25.380 interventions in the trial and provides that estimate to a standardized control. 00:11:25.740 --> 00:11:28.590 And it's using all of the data from all the arms. 00:11:28.590 --> 00:11:33.270 'cause it enables us to estimate the effect of subtypes, the effect 00:11:33.270 --> 00:11:37.860 of time, all of these variables in providing that estimate. 00:11:37.860 --> 00:11:42.270 Then you can hand a particular sponsor the estimate for 00:11:42.270 --> 00:11:44.490 their arm relative to control. 00:11:44.700 --> 00:11:47.160 And the machine is used, all of the data. 00:11:48.360 --> 00:11:52.530 Now you, you mentioned the, um, graduation. 00:11:53.070 --> 00:11:57.240 Uh, which by the way I think is a new word that came out of ICE by 00:11:57.240 --> 00:12:02.940 two as sort of some level of success that wasn't statistical significance 00:12:02.940 --> 00:12:08.400 traditionally, but the success in ICE by Two was innovative in and of itself. 00:12:08.790 --> 00:12:13.410 What was graduation or success from a phase two trial? 00:12:16.335 --> 00:12:21.180 Don Berry: So the way we defined graduation was it's going to do well. 00:12:22.470 --> 00:12:23.070 Um. 00:12:23.790 --> 00:12:27.150 And we did predictive probabilities, which 00:12:27.540 --> 00:12:31.920 is kind of essential in this business. 00:12:32.625 --> 00:12:40.644 That you, you, and integral to the kinds of things that Barry does, um, we look 00:12:40.644 --> 00:12:44.664 at the data, well, we, I say we look at the data, the algorithm looks at the 00:12:44.664 --> 00:12:46.944 data programmed to look at the data. 00:12:47.334 --> 00:12:50.309 It's kinda like, uh, artificial intelligence, um, 00:12:50.334 --> 00:12:54.925 um, and, and learns, uh, what 00:12:55.794 --> 00:12:56.365 data. 00:12:57.084 --> 00:12:59.394 Uh, what, what are the 00:12:59.499 --> 00:12:59.800 Scott Berry: the 00:13:00.474 --> 00:13:04.074 Don Berry: current probabilities of benefit for the various therapies? 00:13:04.854 --> 00:13:06.354 And we do prediction. 00:13:06.354 --> 00:13:14.064 We say, well, is this, does the data at hand suggest that this is a drug? 00:13:14.574 --> 00:13:18.564 That this is a drug that can be marketed, that's gonna be successful 00:13:18.924 --> 00:13:21.084 in the context of ISI two? 00:13:22.404 --> 00:13:24.924 Is it gonna be successful in a phase three trial? 00:13:26.154 --> 00:13:31.044 And in a phase three trial predicting the results of a phase three trial? 00:13:31.764 --> 00:13:35.604 Uh, the Bayesian approach, of course, is critical in this. 00:13:36.114 --> 00:13:37.224 Uh, the, 00:13:37.854 --> 00:13:44.154 uh, PCR rate in the experimental drug has a probability distribution called 00:13:44.154 --> 00:13:48.504 a current probability distribution or posterior probability distribution. 00:13:49.284 --> 00:13:52.344 Uh, but what really matters is the future. 00:13:52.869 --> 00:13:55.510 So there's data, there's two kinds of uncertainty. 00:13:55.510 --> 00:14:01.539 One is in what is the PCR rate, that parameter, uh, and the other 00:14:01.539 --> 00:14:06.909 is what are the data that come forward, the inevitable uncertainty 00:14:07.329 --> 00:14:10.479 associated with the future results. 00:14:10.809 --> 00:14:17.859 So these two uncertainties combine to allow for calculating a prediction. 00:14:19.030 --> 00:14:23.139 What is the probability that a future trial will be successful? 00:14:23.499 --> 00:14:27.939 If it has these characteristics, and that drives everything in. 00:14:27.939 --> 00:14:28.780 I spy too. 00:14:29.050 --> 00:14:36.789 It drives the adaptive randomization, it drives the graduation, it drives the 00:14:36.789 --> 00:14:39.399 possibility of stopping for futility. 00:14:40.180 --> 00:14:42.369 There's a stopping for futility. 00:14:43.119 --> 00:14:47.769 Um, that's across the board that we calculate based on the predictive 00:14:47.769 --> 00:14:50.800 probability, uh, distribution. 00:14:51.339 --> 00:14:58.180 But there's also, as Scott suggested, the, the possibility of stopping in a subtype. 00:14:59.349 --> 00:15:03.909 Um, because the randomization probability is so small that it, the 00:15:03.909 --> 00:15:07.209 patient, you know, no patients are gonna be assigned to this subtype. 00:15:07.720 --> 00:15:09.010 That actually happened. 00:15:09.159 --> 00:15:12.519 Neratinib was one of the early drugs in the trial. 00:15:12.519 --> 00:15:17.319 We published them in New England Journal of Medicine, the results of the Neratinib, 00:15:17.979 --> 00:15:21.639 uh, trial and indicated that there was. 00:15:22.434 --> 00:15:25.615 Some subtypes where it got zeroed out, 00:15:25.734 --> 00:15:25.944 Scott Berry: out, 00:15:26.694 --> 00:15:31.044 Don Berry: but meanwhile, in other subtypes, it was doing very well. 00:15:32.004 --> 00:15:39.175 So it stopped randomizing in the subtypes where it was not doing 00:15:39.175 --> 00:15:42.804 well and increased the probability. 00:15:43.284 --> 00:15:49.824 And the other subtypes graduated since, uh, has been approved by the FDA for 00:15:49.824 --> 00:15:53.454 the, you know, marketing, uh, approval. 00:15:54.175 --> 00:16:01.794 Um, and nobody, as Scott indicated, nobody knew that this happened. 00:16:02.004 --> 00:16:07.254 I mean, if this happened in a two arm trial and you, you said, uh, the DSMB 00:16:07.254 --> 00:16:09.115 tells you, you gotta stop the control, 00:16:09.209 --> 00:16:09.569 Scott Berry: control, 00:16:10.554 --> 00:16:11.964 Don Berry: that would be a big deal. 00:16:12.550 --> 00:16:14.019 How could you stop the control? 00:16:14.019 --> 00:16:20.499 Could you then go in and, uh, get another control or, or wait a while 00:16:20.499 --> 00:16:22.060 until you figure out what to do? 00:16:22.990 --> 00:16:31.060 We never announced that Neratinib was no longer being assigned to those subtypes 00:16:31.089 --> 00:16:35.740 of, of women, um, until after the trial. 00:16:36.100 --> 00:16:42.669 Of course, the, the, uh, DSMB was involved, uh, and the DSMB 00:16:42.669 --> 00:16:44.589 was enthusiastic about doing it. 00:16:44.589 --> 00:16:49.030 In fact, before they realized that it was going, that it wasn't assigning 00:16:49.030 --> 00:16:55.390 probabilities, uh, wasn't assigning, uh, neratinib to any of these subtypes. 00:16:56.019 --> 00:17:02.229 Uh, one of the DSMB members said, we should stop Neratinib in that subtype. 00:17:02.229 --> 00:17:03.550 I said, you don't have to. 00:17:04.720 --> 00:17:06.310 algorithm has already stopped it. 00:17:07.780 --> 00:17:08.140 Scott Berry: Yeah. 00:17:08.709 --> 00:17:09.280 Yep. 00:17:10.749 --> 00:17:11.379 Okay. 00:17:11.379 --> 00:17:14.800 So a little bit of the numbers of I Spy. 00:17:14.800 --> 00:17:16.059 So what are we talking about? 00:17:16.059 --> 00:17:25.870 I SPY two started rolling in 2010 and it ran through 2022, and I 00:17:25.870 --> 00:17:32.169 believe in that time, 23 different investigational therapies went through 00:17:33.979 --> 00:17:34.469 Don Berry: Correct, 00:17:35.320 --> 00:17:39.939 Scott Berry: and nine of those were successfully graduated. 00:17:40.149 --> 00:17:44.590 So you talked about pancreatic cancer, you talked about GBM where. 00:17:45.369 --> 00:17:46.749 Very little works. 00:17:46.749 --> 00:17:49.749 And it's the, the huge challenge of these platform trials is 00:17:49.749 --> 00:17:51.610 to, to try as much as you can. 00:17:51.610 --> 00:17:56.110 And breast cancer therapies worked and some of them in subsets of women, 00:17:56.110 --> 00:18:01.240 the right subsets of women, but nine of 23 successfully graduated. 00:18:01.240 --> 00:18:05.800 And, uh, I, I think you mentioned that four of the nine have 00:18:05.800 --> 00:18:07.599 received marketing approval. 00:18:08.110 --> 00:18:09.280 Don Berry: Yeah, something like that. 00:18:09.280 --> 00:18:12.039 But there's still, the others are still 00:18:12.760 --> 00:18:13.119 Scott Berry: To be 00:18:13.374 --> 00:18:14.024 Don Berry: Yeah, no. 00:18:14.289 --> 00:18:14.740 Scott Berry: Yeah. 00:18:14.829 --> 00:18:15.220 Yeah. 00:18:15.550 --> 00:18:15.939 Yeah. 00:18:16.300 --> 00:18:22.119 So from the perspective of the impact on breast cancer, 00:18:22.629 --> 00:18:25.329 the I-SPY 2 trial was amazing. 00:18:25.329 --> 00:18:31.180 Before I sort of turn to the, the, the, the larger impact of I Spy two and other 00:18:31.180 --> 00:18:35.919 things, I, I think there's a few people that you wanted to make sure that due 00:18:35.919 --> 00:18:39.249 credit, one of them is Janet Woodcock. 00:18:41.439 --> 00:18:41.949 Don Berry: Yes. 00:18:42.249 --> 00:18:47.739 So this is really a story about three dynamic women. 00:18:48.399 --> 00:18:50.709 Uh, one of course is Laura Esserman. 00:18:51.550 --> 00:19:00.489 Um, uh, the other I mentioned is Anna Barker, uh, who was, uh, very helpful in 00:19:00.494 --> 00:19:05.499 the, the funding aspect and then give and giving advice about how, how to do this. 00:19:06.340 --> 00:19:14.050 Uh, and the other is Janet Woodcock, who was at the time the, um, head of 00:19:14.050 --> 00:19:20.590 CDER uh, at the FDA and, um, has been. 00:19:21.514 --> 00:19:27.694 Uh, pushing adaptive designs and, for example, this thing about adaptive 00:19:27.724 --> 00:19:35.194 randomization, she is, um, publicly announced adaptive randomization 00:19:35.524 --> 00:19:37.474 is adequate and well controlled. 00:19:38.494 --> 00:19:39.154 Now, if you know 00:19:39.514 --> 00:19:43.084 regulatory stuff, you know that that's, that's the 00:19:44.074 --> 00:19:47.134 criterion for a phase 3 trial. 00:19:47.639 --> 00:19:52.319 For a pivotal trial, for a trial that, uh, a registrational trial, 00:19:52.949 --> 00:19:54.269 adequate and well controlled. 00:19:55.049 --> 00:20:02.009 So she has been enormously helpful, um, in I-SPY 2 and in setting 00:20:02.009 --> 00:20:09.749 up, uh, GBM Agile and uh, uh, the pancreatic cancer, uh, trial. 00:20:10.066 --> 00:20:13.306 Scott Berry: so, uh, Anna Barker, Laura Esserman. 00:20:13.306 --> 00:20:16.936 Janet Woodcock, a huge role and a couple people, perhaps a 00:20:16.936 --> 00:20:18.406 little bit behind the scenes. 00:20:18.796 --> 00:20:24.766 So Meredith Buxton was there for quite a while, had a huge impact on ice by two. 00:20:26.681 --> 00:20:30.116 Don Berry: And the current circumstance with Meredith Buxton is. 00:20:30.841 --> 00:20:34.171 Scott Berry: Well, she is now, she's graduated, you will. 00:20:34.561 --> 00:20:41.521 Um, uh, she successfully, so she now runs, uh, uh, she's now the CEO of GCAR, 00:20:41.521 --> 00:20:47.311 global Coalition for Adaptive Research, and they run multiple platform trials. 00:20:47.611 --> 00:20:54.961 Uh, so she, she did, uh, incredible work on a lot of the logistics and making, 00:20:55.201 --> 00:20:58.411 making this go and now does this for GCAR. 00:20:58.741 --> 00:21:03.121 And, uh, a little bit on a personal side from Barry Consultant's side is. 00:21:03.496 --> 00:21:10.396 Berry Consultants built the code to run Ice Spy Two, and Ashish Sunil 00:21:10.486 --> 00:21:15.886 at Berry was intricately involved in making sure that code ran, working 00:21:15.886 --> 00:21:19.066 with Meredith running for years. 00:21:19.276 --> 00:21:24.046 Uh, Ashish, Sunil had a huge impact on making that trial run. 00:21:24.346 --> 00:21:30.196 He has sort of, uh, uh, very, very sadly, uh, came down with a LS, 00:21:30.556 --> 00:21:35.416 uh, and, and uh, in that, but he had a huge impact on ice by two. 00:21:36.976 --> 00:21:41.161 Don Berry: Uh, so I should mention, um, Kyle Waltham. 00:21:41.281 --> 00:21:47.551 Kyle was at, uh, MD Anderson with me when we set this up, and he wrote the original 00:21:47.551 --> 00:21:52.651 code, um, and it, it worked great. 00:21:53.371 --> 00:21:59.041 Um, and then when we had to vary from the code for, you know, 00:21:59.041 --> 00:22:07.021 we're adaptive, um, we went back to MD Anderson and, uh, they, we. 00:22:07.191 --> 00:22:09.321 I had lost the code. 00:22:10.281 --> 00:22:12.561 So I went to Scott and I said, 00:22:13.491 --> 00:22:14.271 what can I do? 00:22:15.381 --> 00:22:20.601 Um, and he said, well, I'll, I'll rewrite it now. 00:22:20.601 --> 00:22:27.471 You have to understand the original code was really long and, and 00:22:27.471 --> 00:22:32.361 developing and, and writing and deciding what we wanted to do and all this 00:22:32.361 --> 00:22:35.451 adaptive randomization and et cetera. 00:22:36.381 --> 00:22:43.071 Um, and it took and including with some funding from Anna 00:22:43.071 --> 00:22:46.971 Barker, um, for Kyle's salary. 00:22:47.331 --> 00:22:51.921 I never, uh, had any kind of a of a. 00:22:52.571 --> 00:22:53.981 Uh, salary or anything. 00:22:53.981 --> 00:22:59.051 It was all voluntary and all of Barry consultants' work was, was 00:22:59.051 --> 00:23:01.691 voluntary, uh, in ice py too. 00:23:02.921 --> 00:23:14.951 Um, so Scott took, I think three days instead of, instead of, uh, two years, 00:23:14.951 --> 00:23:18.791 it took Scott three days, but of course he knew what we, what the goal was. 00:23:19.351 --> 00:23:19.571 Scott Berry: Yep, 00:23:20.501 --> 00:23:23.321 Don Berry: Uh, and that's, that's what we've used since and 00:23:23.321 --> 00:23:26.786 model modified it, uh, since I. 00:23:27.361 --> 00:23:27.541 Scott Berry: Yep. 00:23:28.591 --> 00:23:36.041 So I-SPY 2 in many ways was the, uh, a forefather a a a foremother 00:23:36.061 --> 00:23:42.361 for, for platform trials, one of the, the early platform trials. 00:23:42.361 --> 00:23:46.531 Phenomenal success, success in the disease, but is really in 00:23:46.531 --> 00:23:48.691 many ways changed the industry. 00:23:49.051 --> 00:23:51.301 GBM Agile doesn't exist. 00:23:51.301 --> 00:23:55.831 Pancreatic Cancer Action Networks trial PanCan, that, that, that trial 00:23:55.831 --> 00:24:03.841 Precision Promise doesn't exist and COVID hits and, um, operation 00:24:03.871 --> 00:24:06.571 Warp speed, the ACTIV trials. 00:24:07.306 --> 00:24:13.846 Are largely I-SPY 2 for treatment of therapeutic COVID and Janet 00:24:13.846 --> 00:24:21.166 Woodcock's leading the ACTIV network and it's all platform trials. 00:24:21.166 --> 00:24:28.516 So the impact of I-SPY 2 and this idea that you said, you, you bet the farm at 00:24:28.516 --> 00:24:37.486 the time, and you and Laura kind of went on this strange, uh, risky path that 00:24:37.606 --> 00:24:39.766 cooperative groups might not embrace. 00:24:39.766 --> 00:24:45.166 And you get, you know, you get innovative funding, you're getting Safeway 00:24:45.166 --> 00:24:47.566 grocery stores to help fund this. 00:24:47.896 --> 00:24:52.486 Um, and it's, it changed the world literally. 00:24:53.550 --> 00:24:54.580 Don Berry: Thank you, and I agree. 00:24:55.820 --> 00:24:55.940 I 00:24:56.880 --> 00:24:57.270 Scott Berry: Yep. 00:24:58.530 --> 00:24:58.890 Alright. 00:24:58.890 --> 00:25:03.060 So they're sort of b before I spy two and after I spy two. 00:25:03.060 --> 00:25:10.170 So, uh, phenomenal trial, uh, phenomenal effort and, and an amazing story. 00:25:10.710 --> 00:25:14.310 So Don, I appreciate you walking us through that story. 00:25:14.830 --> 00:25:15.860 Don Berry: thank you for 00:25:16.730 --> 00:25:17.820 leading the walk. 00:25:17.995 --> 00:25:18.235 Scott Berry: walk. 00:25:18.795 --> 00:25:19.635 Yeah, right. 00:25:20.065 --> 00:25:25.770 Well, thank you everybody for, for doing two parts of I Spy two and in 00:25:25.770 --> 00:25:28.380 the next time we are in the interim. 00:25:29.130 --> 00:25:29.520 Thank you. 00:25:29.790 --> 00:25:30.220 Don Berry: Thank you. 00:25:30.570 --> 00:25:30.870 Scott Berry: Thank you.