1
00:00:00,880 --> 00:00:04,920
Speaker 1: On this episode of the four late stage coronavirus vaccine

2
00:00:04,960 --> 00:00:08,800
Speaker 1: trials bats by the United States could fail to provide

3
00:00:09,039 --> 00:00:15,240
Speaker 1: positive results. Who will not indoors a vaccine that's not

4
00:00:15,520 --> 00:00:18,880
Speaker 1: effective and safe. These are all guestimates. I mean, if

5
00:00:18,880 --> 00:00:22,159
Speaker 1: you look at the projection of the enrollment and the

6
00:00:22,239 --> 00:00:25,000
Speaker 1: kinds of things you'll need to get a decision about

7
00:00:25,040 --> 00:00:27,560
Speaker 1: whether the vaccine is safe and effective, most of us

8
00:00:27,640 --> 00:00:30,160
Speaker 1: project that that's going to be by November December, by

9
00:00:30,160 --> 00:00:33,360
Speaker 1: the end of the year. Of realistic timelines, we're really

10
00:00:33,760 --> 00:00:37,360
Speaker 1: not expecting to see widespread vaccination until the middle of

11
00:00:37,479 --> 00:00:41,040
Speaker 1: next year. Right now, I will say we're preparing earnestly

12
00:00:41,200 --> 00:00:45,200
Speaker 1: for what I anticipate will be reality, is that there'll

13
00:00:45,240 --> 00:00:49,440
Speaker 1: be one or more vaccines available for US in November December.

14
00:00:49,479 --> 00:00:51,239
Speaker 1: I do not think you're going to see a vaccine

15
00:00:51,280 --> 00:00:54,760
Speaker 1: licensed by the FDA get a biologic slightness application for

16
00:00:54,920 --> 00:00:58,760
Speaker 1: broad distribution in twenty twenty through Operation Warp Speed with

17
00:00:59,520 --> 00:01:03,640
Speaker 1: three vaccines in the final stage of clinical trials. Yesterday,

18
00:01:03,720 --> 00:01:06,240
Speaker 1: Fiser announced that it expects to have the results of

19
00:01:06,280 --> 00:01:10,759
Speaker 1: its trial very very shortly next month. But very shortly.

20
00:01:11,000 --> 00:01:13,920
Speaker 1: We remain attracted deliver a vaccine before the end of

21
00:01:13,920 --> 00:01:18,360
Speaker 1: the year and maybe even before November first. AHI. This

22
00:01:18,480 --> 00:01:20,920
Speaker 1: is new due to the virus. I'm recording from home,

23
00:01:21,319 --> 00:01:27,319
Speaker 1: so you may notice a difference in audio quality on

24
00:01:27,480 --> 00:01:31,039
Speaker 1: this episode of News World. The fastest day vaccine has

25
00:01:31,080 --> 00:01:34,960
Speaker 1: ever been developed is four years, and most take ten

26
00:01:35,000 --> 00:01:39,240
Speaker 1: to fifteen years to develop a test in clinical twelves,

27
00:01:39,280 --> 00:01:42,400
Speaker 1: but scientists are now racing to create a vaccine for

28
00:01:42,520 --> 00:01:46,800
Speaker 1: COVID nineteen in less than one year. Dozens of research

29
00:01:46,840 --> 00:01:50,080
Speaker 1: teams around the world are working to develop a vaccine

30
00:01:50,360 --> 00:01:54,520
Speaker 1: for stars COVID two, the virus that causes COVID nineteen,

31
00:01:55,080 --> 00:01:59,720
Speaker 1: using a mix of established techniques and new technologies. Funding

32
00:01:59,760 --> 00:02:03,160
Speaker 1: for vaccine has never been greater, with billions of dollars

33
00:02:03,160 --> 00:02:05,520
Speaker 1: pouring in for around the world to make a product

34
00:02:05,720 --> 00:02:09,440
Speaker 1: that could help control the pandemic, but the US, China,

35
00:02:09,480 --> 00:02:13,760
Speaker 1: and Europe have invested the most. Several American companies are

36
00:02:13,800 --> 00:02:18,080
Speaker 1: working towards having a safe and effective vaccine distributed by

37
00:02:18,080 --> 00:02:23,360
Speaker 1: the end of twenty twenty. Cambridge, Massachusetts based biotech company Maderna, Inc.

38
00:02:23,680 --> 00:02:28,040
Speaker 1: And the National Institute of Allergy and Infectious Diseases have

39
00:02:28,120 --> 00:02:33,240
Speaker 1: co developed the vaccine known as mRNA twelve seventy three,

40
00:02:33,600 --> 00:02:37,720
Speaker 1: which is now in Phase three clinical trials with thirty

41
00:02:37,720 --> 00:02:42,320
Speaker 1: thousand adult volunteers who do not have COVID nineteen. For

42
00:02:42,360 --> 00:02:45,600
Speaker 1: an update on the Phase three vaccine trials, I am

43
00:02:45,639 --> 00:02:50,400
Speaker 1: pleased to welcome my guest, doctor Talzac's chief medical officer

44
00:02:50,480 --> 00:03:04,720
Speaker 1: of Maderna the overseas clinical development and regulatory affairs across Maderna.

45
00:03:05,960 --> 00:03:10,200
Speaker 1: I'm really delighted to have was my guest today, Doctor

46
00:03:10,200 --> 00:03:14,240
Speaker 1: Tall Sachs, the chief medical officer from Maderna. Before he

47
00:03:14,320 --> 00:03:17,680
Speaker 1: joined Maderna, he was a senior vice president and had

48
00:03:17,720 --> 00:03:22,120
Speaker 1: a global oncology at Sanofi, where he was responsible for

49
00:03:22,160 --> 00:03:27,880
Speaker 1: all aspects of oncology grow, discovery, development and commercialization. Now

50
00:03:27,880 --> 00:03:31,639
Speaker 1: that's really quite a background your especialist in oncology. How

51
00:03:31,639 --> 00:03:36,280
Speaker 1: did you get intrigued with oncology? That takes me back

52
00:03:36,760 --> 00:03:41,120
Speaker 1: growing up studying medicine. It always felt to me like

53
00:03:41,520 --> 00:03:45,520
Speaker 1: one of the true frontiers of unmet medical needs. We've

54
00:03:45,520 --> 00:03:48,960
Speaker 1: all had exposure to this in our families. I had

55
00:03:49,000 --> 00:03:51,960
Speaker 1: an aunt who died when she was in her late thirties,

56
00:03:51,960 --> 00:03:55,000
Speaker 1: and I was a boy. You get the picture of

57
00:03:55,280 --> 00:03:58,120
Speaker 1: sort of two things for me when I was a

58
00:03:58,160 --> 00:04:03,240
Speaker 1: medical student. One is the phenomenal advancement of science. We

59
00:04:03,280 --> 00:04:06,280
Speaker 1: are fortunate to be living in an era that is

60
00:04:06,320 --> 00:04:10,440
Speaker 1: just beautiful in terms of the pace of what we

61
00:04:10,480 --> 00:04:14,160
Speaker 1: are learning and then the implications of it. On the

62
00:04:14,160 --> 00:04:18,440
Speaker 1: other hand, you're walk into a medical ward and it's devastating,

63
00:04:18,880 --> 00:04:22,480
Speaker 1: and especially with oncology, and certainly you know, when I

64
00:04:22,520 --> 00:04:26,279
Speaker 1: trained as a student now roughly thirty years ago in

65
00:04:26,360 --> 00:04:31,080
Speaker 1: metastatic disease, you talk about palliative care and that dichotomic

66
00:04:31,480 --> 00:04:35,600
Speaker 1: advancements of the science and what you see firsthand as

67
00:04:35,640 --> 00:04:39,160
Speaker 1: the endment need, I think is what propelled me into oncology,

68
00:04:39,160 --> 00:04:42,039
Speaker 1: and it's also what propelled me into sort of living

69
00:04:42,080 --> 00:04:46,640
Speaker 1: in this transitional phase how to best translate sides into medicine.

70
00:04:46,839 --> 00:04:48,960
Speaker 1: I spent a lot of time with Andy Vonessian book

71
00:04:49,440 --> 00:04:52,279
Speaker 1: both when he was the head of the National Cancer

72
00:04:52,320 --> 00:04:54,560
Speaker 1: Institute and then was an FDA, and he had this

73
00:04:54,720 --> 00:04:58,440
Speaker 1: sense that we really are in the middle of a

74
00:04:58,520 --> 00:05:03,719
Speaker 1: scientific revolution as it relates to cancer, of such extraordinary

75
00:05:03,760 --> 00:05:06,720
Speaker 1: power that over the next decade or two, we're going

76
00:05:06,760 --> 00:05:08,640
Speaker 1: to really be amazed at how much we get done.

77
00:05:08,800 --> 00:05:11,560
Speaker 1: When you think about your own career, how much has

78
00:05:11,600 --> 00:05:15,000
Speaker 1: the science changed from the time you were in graduate school.

79
00:05:15,960 --> 00:05:19,080
Speaker 1: I think it's changed tremendously. I mean, just to point

80
00:05:19,120 --> 00:05:21,600
Speaker 1: at a few things, the booming of the human genome,

81
00:05:21,920 --> 00:05:25,240
Speaker 1: the technical advances in sequencing since then that allows us

82
00:05:25,240 --> 00:05:27,240
Speaker 1: now to do it in a matter of hours and

83
00:05:27,279 --> 00:05:30,280
Speaker 1: a couple of days for every individual patient. It's just

84
00:05:30,360 --> 00:05:35,600
Speaker 1: been dramatic. When you think about approaching COVID, how does

85
00:05:35,640 --> 00:05:38,839
Speaker 1: that affect? You're thinking, how are we doing it differently?

86
00:05:39,200 --> 00:05:41,919
Speaker 1: I was looking back at the amazing story of the

87
00:05:41,960 --> 00:05:46,240
Speaker 1: polio vaccine. The first epidemic was eighteen ninety four, and

88
00:05:46,320 --> 00:05:50,120
Speaker 1: it really was a single remarkable person who not only

89
00:05:50,160 --> 00:05:53,880
Speaker 1: developed the vaccine but actually tested it initially on himself

90
00:05:53,920 --> 00:05:56,640
Speaker 1: and his family back when we didn't really have a

91
00:05:56,760 --> 00:06:00,560
Speaker 1: rigorous FDA system, and then went out and just got volunteers,

92
00:06:00,560 --> 00:06:03,960
Speaker 1: think one hundred thousand volunteers the first year, and it

93
00:06:04,040 --> 00:06:06,479
Speaker 1: was all just kind of wild open. But you think

94
00:06:06,520 --> 00:06:10,080
Speaker 1: today how much different our systems are, how much more

95
00:06:10,160 --> 00:06:14,359
Speaker 1: rigorous they are. How does this scientific revolution affect the

96
00:06:14,400 --> 00:06:19,320
Speaker 1: way you and Maderna are looking at developing a vaccine

97
00:06:19,360 --> 00:06:23,599
Speaker 1: for COVID nineteen Well, I think profoundly in the sense

98
00:06:23,640 --> 00:06:26,200
Speaker 1: that This wouldn't have been possible ten years ago, maybe

99
00:06:26,200 --> 00:06:30,400
Speaker 1: not even five. And I think it's the advancement of

100
00:06:30,520 --> 00:06:34,720
Speaker 1: the genetic technologies that have enabled a platform like Hours,

101
00:06:34,720 --> 00:06:39,720
Speaker 1: which uses messenger RNA to actually take a page from

102
00:06:39,800 --> 00:06:42,400
Speaker 1: a chapter of life in a way which is understanding

103
00:06:42,440 --> 00:06:46,200
Speaker 1: the sequence of a virus and how to translate that

104
00:06:46,279 --> 00:06:50,440
Speaker 1: sequence into a vaccine based on the same code, but

105
00:06:50,600 --> 00:06:54,440
Speaker 1: now generated synthetically, such that we can educate our own

106
00:06:54,480 --> 00:06:57,000
Speaker 1: body to make the protein as if it had the

107
00:06:57,120 --> 00:07:00,160
Speaker 1: virus in it, without actually having the virus. In fact,

108
00:07:00,160 --> 00:07:02,960
Speaker 1: the company didn't need to have the virus at hand

109
00:07:03,000 --> 00:07:06,039
Speaker 1: to start. We talk about the digital information. This is

110
00:07:06,080 --> 00:07:09,600
Speaker 1: really an application of that digital revolution into the healthcare

111
00:07:09,680 --> 00:07:12,320
Speaker 1: field in the sense that we could go in two

112
00:07:12,400 --> 00:07:16,480
Speaker 1: days from knowing what the sequences to starting production on

113
00:07:16,520 --> 00:07:20,440
Speaker 1: the first vaccine candidate to get tested. And that's because

114
00:07:20,680 --> 00:07:24,320
Speaker 1: we started from information. The Chinese uploaded the sequence to

115
00:07:24,400 --> 00:07:27,440
Speaker 1: the cloud to the benefit of everybody, and we basically

116
00:07:27,440 --> 00:07:30,200
Speaker 1: took a couple of days, looked at that information, translated

117
00:07:30,360 --> 00:07:32,760
Speaker 1: into what should be a vaccine, and off we are.

118
00:07:33,480 --> 00:07:37,640
Speaker 1: About twenty years ago, began to realize that mathematical modeling

119
00:07:37,720 --> 00:07:41,400
Speaker 1: was getting so powerful that actually a great deal of

120
00:07:41,520 --> 00:07:45,400
Speaker 1: aerodynamics was now being studied in what was an effect

121
00:07:46,080 --> 00:07:51,239
Speaker 1: mathematical wind tunnel. They were able to mimic reality so

122
00:07:52,200 --> 00:07:57,000
Speaker 1: brilliantly and really complex levels of how wings respond to

123
00:07:57,080 --> 00:08:01,440
Speaker 1: win that you really didn't have the kind of testing

124
00:08:01,480 --> 00:08:03,720
Speaker 1: you would have had thirty or forty years ago. And

125
00:08:03,840 --> 00:08:06,240
Speaker 1: I'm hearing you say sort of something parallel, which is

126
00:08:06,240 --> 00:08:12,040
Speaker 1: that if you can find the mathematical structure of what

127
00:08:12,240 --> 00:08:16,120
Speaker 1: you're trying to deal with, you actually have an enormous

128
00:08:16,120 --> 00:08:22,440
Speaker 1: advantage over simply dealing directly with the virus itself, because

129
00:08:22,440 --> 00:08:25,800
Speaker 1: you are now analyzing it and working with it, experimenting

130
00:08:25,800 --> 00:08:30,520
Speaker 1: on it, so you can see how it works back

131
00:08:30,560 --> 00:08:32,679
Speaker 1: and forth depending on what you're doing with it. And

132
00:08:32,920 --> 00:08:35,880
Speaker 1: is that a reasonable parallel, I think in a very

133
00:08:35,920 --> 00:08:38,960
Speaker 1: important way, yes, And in a very important way. We

134
00:08:38,960 --> 00:08:41,480
Speaker 1: still have ways to go. So let me explain. Because

135
00:08:41,600 --> 00:08:45,000
Speaker 1: we start from digital information, our ability to test different

136
00:08:45,080 --> 00:08:49,560
Speaker 1: drug and vaccine candidates has written exponentially. So we make

137
00:08:49,760 --> 00:08:53,120
Speaker 1: mRNA molecules at research scale, and this is all done

138
00:08:53,160 --> 00:08:55,960
Speaker 1: with full robotics. We can make up to a thousand

139
00:08:56,040 --> 00:08:59,400
Speaker 1: different constructs a month in our labs for the researchers,

140
00:08:59,440 --> 00:09:01,480
Speaker 1: and so for sure it sure has an idea for one,

141
00:09:01,520 --> 00:09:03,520
Speaker 1: they order one, but if they want to test a

142
00:09:03,640 --> 00:09:06,280
Speaker 1: hundred variants, they just order a hundred. And now you

143
00:09:06,360 --> 00:09:10,560
Speaker 1: become actually models in which apply the material, not the

144
00:09:11,040 --> 00:09:13,679
Speaker 1: chemical matter that you need to synthesize, because you just

145
00:09:13,760 --> 00:09:16,360
Speaker 1: change the code and outcomes the different types of construct

146
00:09:16,640 --> 00:09:19,760
Speaker 1: and that when applied in this way too, because what

147
00:09:19,760 --> 00:09:22,360
Speaker 1: we're doing really our information drugs in a way are

148
00:09:22,440 --> 00:09:25,000
Speaker 1: drugs simply encode the information to make a protein. They

149
00:09:25,040 --> 00:09:28,480
Speaker 1: don't actually encode for a protein. And because they all

150
00:09:28,600 --> 00:09:31,320
Speaker 1: have the same backbone and the same fundamental structure, once

151
00:09:31,320 --> 00:09:35,040
Speaker 1: you solve it once you then replicated almost digitally, and

152
00:09:35,120 --> 00:09:38,959
Speaker 1: so that has been the backbone of why our company

153
00:09:39,000 --> 00:09:41,800
Speaker 1: has been able to be so productive. On the other hand,

154
00:09:42,280 --> 00:09:44,680
Speaker 1: I'm hesitant to go all the way there because I

155
00:09:44,720 --> 00:09:48,360
Speaker 1: don't think the human body and pathophysiology of disease is

156
00:09:48,840 --> 00:09:52,400
Speaker 1: yet fully there where we are with other matters of engineering.

157
00:09:52,960 --> 00:09:56,199
Speaker 1: I think that there's still a lot of unknown. One

158
00:09:56,200 --> 00:09:58,440
Speaker 1: of the things I learned at Maderna, and it's been

159
00:09:58,880 --> 00:10:02,880
Speaker 1: partially because of work so closely with a visionary CEO Staffund,

160
00:10:03,040 --> 00:10:06,000
Speaker 1: who really is an engineer is the difference between the

161
00:10:06,040 --> 00:10:10,080
Speaker 1: engineering mindset and the physician mindset. For an engineer, if

162
00:10:10,080 --> 00:10:13,040
Speaker 1: they know the space and you give them a complicated problem,

163
00:10:13,160 --> 00:10:16,000
Speaker 1: they can very quickly tell you whether a solution exists

164
00:10:16,120 --> 00:10:18,600
Speaker 1: or not. And if it does, what are the resources

165
00:10:18,679 --> 00:10:20,680
Speaker 1: require me? You ask an engineer, can I get to

166
00:10:20,720 --> 00:10:22,280
Speaker 1: the moon? The answer is yes, it'll take you this

167
00:10:22,320 --> 00:10:24,120
Speaker 1: amount of money, this amount of people, et cetera. But

168
00:10:24,160 --> 00:10:26,280
Speaker 1: today we know the answer, and here's what it looks like.

169
00:10:26,440 --> 00:10:29,320
Speaker 1: You ask comedical is a drug going to cure cancer?

170
00:10:29,320 --> 00:10:32,720
Speaker 1: And manser is, I don't know. I think it's worth trying.

171
00:10:32,840 --> 00:10:35,679
Speaker 1: I think it's got a credible scientific hypothesis, but I

172
00:10:35,720 --> 00:10:39,119
Speaker 1: still can't model that to the same level of certainty.

173
00:10:39,520 --> 00:10:42,560
Speaker 1: And that's why drug development, especially for fields like oncology,

174
00:10:42,559 --> 00:10:45,719
Speaker 1: it has been so fraught. Now take it to COVID. Actually,

175
00:10:46,120 --> 00:10:48,640
Speaker 1: we've got the benefit there of knowing a heck of

176
00:10:48,679 --> 00:10:51,360
Speaker 1: a lot more than we do for things like cancer,

177
00:10:51,679 --> 00:10:55,560
Speaker 1: because for infectious disease and especially vaccines, I think a

178
00:10:55,559 --> 00:10:58,120
Speaker 1: lot of it has been worked out. We know that

179
00:10:58,280 --> 00:11:01,280
Speaker 1: neutralizing anybody, for example, are the part of the immune

180
00:11:01,280 --> 00:11:03,640
Speaker 1: response that takes care of these kinds of viruses, and

181
00:11:03,720 --> 00:11:06,280
Speaker 1: that's been proven time and again for other respiratory viruses,

182
00:11:06,440 --> 00:11:09,520
Speaker 1: whether they're close cousins of COVID like stars and mergs,

183
00:11:09,600 --> 00:11:12,360
Speaker 1: or more distant relatives like flu. But we know with

184
00:11:12,440 --> 00:11:15,240
Speaker 1: a long history of vaccine what a vaccine needs to

185
00:11:15,280 --> 00:11:17,760
Speaker 1: do as far as the immune system is concerned to

186
00:11:17,920 --> 00:11:20,240
Speaker 1: generate that kind of immune response that is likely to

187
00:11:20,360 --> 00:11:23,120
Speaker 1: lead to benefit. So I think in infectous of these vaccines,

188
00:11:23,120 --> 00:11:25,400
Speaker 1: we certainly have a leg up in terms of history

189
00:11:25,440 --> 00:11:28,040
Speaker 1: of science that is positioning us well, and it's one

190
00:11:28,080 --> 00:11:47,520
Speaker 1: of the reasons we've been able to move overrapid When

191
00:11:47,520 --> 00:11:50,800
Speaker 1: I look back, and it took about fifty nine years

192
00:11:50,800 --> 00:11:53,880
Speaker 1: to go from the first polio epidemic to the vaccine.

193
00:11:53,920 --> 00:11:57,600
Speaker 1: It took a long time to get the first flu vaccine.

194
00:11:58,320 --> 00:12:00,800
Speaker 1: We sort of understood the virus chicken box in the

195
00:12:00,880 --> 00:12:04,080
Speaker 1: nineteen fifties, but the first vaccine was not developed until

196
00:12:04,160 --> 00:12:07,680
Speaker 1: nineteen seventy and wasn't available in the US till nineteen

197
00:12:07,760 --> 00:12:11,439
Speaker 1: ninety five, thirty four years. Took fifteen years to get

198
00:12:11,440 --> 00:12:14,640
Speaker 1: a vaccine for HPV and four years for the months

199
00:12:15,080 --> 00:12:19,240
Speaker 1: nine years. The neasles is the revolution and information the

200
00:12:19,400 --> 00:12:23,360
Speaker 1: reason that we are able to move so much more

201
00:12:23,400 --> 00:12:28,480
Speaker 1: aggressively and have such a much tighter timeline the historic averages,

202
00:12:28,520 --> 00:12:31,920
Speaker 1: like I think ten point seven years for a vaccine development,

203
00:12:32,240 --> 00:12:35,480
Speaker 1: So we're really cramping it down by almost eighty percent

204
00:12:35,559 --> 00:12:38,199
Speaker 1: or ninety percent. Is that a function of the information

205
00:12:38,280 --> 00:12:42,080
Speaker 1: revolution or are we kidding ourselves? No? No, I think

206
00:12:42,240 --> 00:12:45,160
Speaker 1: r at large that is the number one factor abscribing this.

207
00:12:45,360 --> 00:12:48,280
Speaker 1: I think it's information revolution. I think it's the level

208
00:12:48,320 --> 00:12:51,840
Speaker 1: of sharing, and I think for us in similar platforms,

209
00:12:51,880 --> 00:12:55,000
Speaker 1: it is two other elements that are important. I think

210
00:12:55,120 --> 00:12:58,600
Speaker 1: one is that we didn't come to this denovo in

211
00:12:58,600 --> 00:13:00,920
Speaker 1: the sense that this is the first application of our

212
00:13:01,000 --> 00:13:03,880
Speaker 1: platform to this kind of problem. For the past several

213
00:13:04,000 --> 00:13:07,360
Speaker 1: years we had recognized the utility of this kind of

214
00:13:07,440 --> 00:13:10,880
Speaker 1: platform to apply to a vaccine. In fact, our first

215
00:13:10,960 --> 00:13:14,160
Speaker 1: clinical entry five years ago was against a threat of

216
00:13:14,200 --> 00:13:16,920
Speaker 1: a type of avian influenza that we were afraid would

217
00:13:16,960 --> 00:13:20,800
Speaker 1: come from the East. We were already studying pandemic threats

218
00:13:21,200 --> 00:13:24,720
Speaker 1: as a place to prove the platform. In late last year,

219
00:13:24,720 --> 00:13:27,559
Speaker 1: in September, Stefan and I were down meeting with Tommy

220
00:13:27,559 --> 00:13:30,120
Speaker 1: Fauci and his team and the NAH to give them

221
00:13:30,120 --> 00:13:33,120
Speaker 1: credit had recognized the potential of this platform to move quickly,

222
00:13:33,400 --> 00:13:35,800
Speaker 1: and so we had been talking about whether we should

223
00:13:35,840 --> 00:13:38,360
Speaker 1: do a demonstration project of a viral that is not

224
00:13:38,440 --> 00:13:41,120
Speaker 1: a threat but may become a threat, things like NAPO

225
00:13:41,200 --> 00:13:43,480
Speaker 1: virus that most people haven't heard of, and so we

226
00:13:43,480 --> 00:13:46,040
Speaker 1: were going to do this experiment to say, let's start

227
00:13:46,080 --> 00:13:48,160
Speaker 1: the clock artificially and see how fast we can go,

228
00:13:48,480 --> 00:13:50,640
Speaker 1: when in January it became apparent that this is not

229
00:13:50,720 --> 00:13:53,720
Speaker 1: a drill, this is actually a live fire. And so

230
00:13:53,760 --> 00:13:57,400
Speaker 1: in that regard, I think the preparedness on our hand

231
00:13:57,440 --> 00:14:00,760
Speaker 1: from the platform in terms of collaboration for the world

232
00:14:00,760 --> 00:14:03,640
Speaker 1: at large to share information has enabled us to move

233
00:14:03,679 --> 00:14:06,440
Speaker 1: so quickly. I think just one last point of that

234
00:14:06,520 --> 00:14:09,199
Speaker 1: to give credit where it's do, I think regulatory agencies,

235
00:14:09,240 --> 00:14:13,199
Speaker 1: starting with the FDA here but globally, have really been

236
00:14:13,280 --> 00:14:16,839
Speaker 1: very collaborative and have taken a very forward looking, i

237
00:14:16,960 --> 00:14:19,320
Speaker 1: curative stance in terms of how they work with us,

238
00:14:19,680 --> 00:14:22,880
Speaker 1: and that has also allowed us to move much more

239
00:14:22,960 --> 00:14:25,960
Speaker 1: quickly because we know what regulators expect, we know it

240
00:14:26,000 --> 00:14:29,040
Speaker 1: almost immediately, and we're able to go and have these

241
00:14:29,080 --> 00:14:31,960
Speaker 1: dialogues with them in a timy manner. To what I

242
00:14:32,000 --> 00:14:35,080
Speaker 1: stand does artificial intelligence begin to be helpful or is

243
00:14:35,120 --> 00:14:39,880
Speaker 1: that still several decades away. There are places where it

244
00:14:40,040 --> 00:14:42,880
Speaker 1: is starting to be applied. I think it's still the

245
00:14:42,920 --> 00:14:46,240
Speaker 1: early days. I can give you one example. The biggest

246
00:14:46,360 --> 00:14:49,360
Speaker 1: enablement of speed when it comes to the large phase

247
00:14:49,480 --> 00:14:52,320
Speaker 1: three trials here is going to be our ability to

248
00:14:52,360 --> 00:14:55,160
Speaker 1: predict how do we vaccinate people who are likely to

249
00:14:55,200 --> 00:14:58,560
Speaker 1: get infected. The way you know if a vaccine works

250
00:14:58,680 --> 00:15:00,360
Speaker 1: is you give it a whole bunch of people, and

251
00:15:00,400 --> 00:15:02,640
Speaker 1: then you have a control group, and if you get

252
00:15:02,640 --> 00:15:04,560
Speaker 1: more cases in the control than you did the people

253
00:15:04,600 --> 00:15:07,080
Speaker 1: you've vaccinated, you know the vaccine work. And the fact

254
00:15:07,120 --> 00:15:09,560
Speaker 1: that the proportion of cases between the controls and the

255
00:15:09,640 --> 00:15:12,920
Speaker 1: vaccinated that tells you how well it worked. And so

256
00:15:13,120 --> 00:15:16,240
Speaker 1: the trials are ultimately dependent on seeing enough people come

257
00:15:16,280 --> 00:15:20,200
Speaker 1: down with an infection. If we went to immunize people

258
00:15:20,240 --> 00:15:23,000
Speaker 1: who then never got infected, never got sick, we would

259
00:15:23,080 --> 00:15:26,480
Speaker 1: never know if a vaccine worked. So it's been critical

260
00:15:26,480 --> 00:15:28,960
Speaker 1: when you look at these large phase through trials to

261
00:15:29,080 --> 00:15:32,800
Speaker 1: make sure that you're going into populations where transmission rates

262
00:15:32,840 --> 00:15:35,480
Speaker 1: are high and there I think we and others for

263
00:15:35,560 --> 00:15:38,240
Speaker 1: the past several months have been working very closely with

264
00:15:38,400 --> 00:15:43,400
Speaker 1: mathematical modelers of epidemiology, the science of how things spread

265
00:15:43,400 --> 00:15:47,720
Speaker 1: and where they occur, the population to be ready and

266
00:15:48,000 --> 00:15:52,120
Speaker 1: predict and react to places where transmission is high because

267
00:15:52,200 --> 00:15:54,720
Speaker 1: we want to go vaccinate those people who are most

268
00:15:54,760 --> 00:15:57,840
Speaker 1: likely to get infected. And I think there, I've seen

269
00:15:57,880 --> 00:16:01,760
Speaker 1: a lot of innovation, and we're thinking in terms of

270
00:16:01,800 --> 00:16:06,600
Speaker 1: applying all the way through to artificial intelligence modeling to

271
00:16:06,760 --> 00:16:09,600
Speaker 1: ensure that we are immunizing the right people in the

272
00:16:09,680 --> 00:16:13,720
Speaker 1: right places. If I understand what the one hundred and

273
00:16:13,760 --> 00:16:17,560
Speaker 1: thirty five pre clinical trials, twenty one vaccine trials in

274
00:16:17,600 --> 00:16:21,280
Speaker 1: phase one, thirteen in phase two, eight in phase three,

275
00:16:21,560 --> 00:16:25,480
Speaker 1: and two vaccines approved for earlier limited use, how many

276
00:16:25,480 --> 00:16:29,800
Speaker 1: companies do you think in the end will have vaccines

277
00:16:29,880 --> 00:16:33,960
Speaker 1: that are commercially practical for marketing. I think it's very

278
00:16:33,960 --> 00:16:36,080
Speaker 1: hard to predict. I can tell you one thing for sure.

279
00:16:36,120 --> 00:16:38,760
Speaker 1: I hope many companies succeed. I hope will It's not

280
00:16:38,840 --> 00:16:40,720
Speaker 1: just the pendant on the journal succeeding here. I think

281
00:16:40,720 --> 00:16:43,200
Speaker 1: where the front runner and so far half the best data,

282
00:16:43,480 --> 00:16:46,520
Speaker 1: But certainly I hope others will step up and demonstrate

283
00:16:46,520 --> 00:16:49,760
Speaker 1: that they can also help here. Because ending that rapid

284
00:16:49,760 --> 00:16:53,120
Speaker 1: expansion of manufacturing capacity is going to be critical in

285
00:16:53,160 --> 00:16:56,400
Speaker 1: the coming years. If we really want to vaccinate everybody,

286
00:16:56,480 --> 00:16:58,120
Speaker 1: is a way to get to the other side of COVID.

287
00:16:58,880 --> 00:17:02,560
Speaker 1: We hope this will ultimately established the need for a

288
00:17:02,600 --> 00:17:06,760
Speaker 1: manufacturing footprint for a new technology for mr ANDA that

289
00:17:06,800 --> 00:17:09,959
Speaker 1: will enable us to react even quicker. You know, when

290
00:17:10,080 --> 00:17:13,480
Speaker 1: stars code three hits one day in the future. As

291
00:17:13,480 --> 00:17:20,000
Speaker 1: I understand it, the actual production of vaccines today, let's

292
00:17:20,040 --> 00:17:24,000
Speaker 1: say the flu vaccine is still an egg based model,

293
00:17:24,600 --> 00:17:27,800
Speaker 1: which is very cumbersome and very time consuming. And as

294
00:17:27,840 --> 00:17:31,320
Speaker 1: I think about seventy years old, do you see a

295
00:17:31,400 --> 00:17:36,080
Speaker 1: serious effort to try to radically reshape how we manufacture

296
00:17:36,119 --> 00:17:41,440
Speaker 1: once we get the breakthrough to a reliable vaccine, Absolutely,

297
00:17:41,680 --> 00:17:44,680
Speaker 1: and I think we and others are already looking at it.

298
00:17:45,240 --> 00:17:48,800
Speaker 1: I think the challenge with flu vaccines has been that

299
00:17:49,080 --> 00:17:52,520
Speaker 1: these are very established. They take time to react because

300
00:17:52,600 --> 00:17:55,879
Speaker 1: these are essentially chicken farms, and it takes your time

301
00:17:55,920 --> 00:17:58,720
Speaker 1: every time a use train as announced, which is why

302
00:17:58,800 --> 00:18:02,600
Speaker 1: we sometimes miss that season because the who releases it

303
00:18:02,680 --> 00:18:04,680
Speaker 1: six months ahead of time, and if by the time

304
00:18:04,720 --> 00:18:06,919
Speaker 1: you show up with a vaccine, it turns out that

305
00:18:07,080 --> 00:18:09,879
Speaker 1: it's actually a slightly different strain. Then we don't do

306
00:18:09,880 --> 00:18:12,200
Speaker 1: a good as job as protecting. So I think ours

307
00:18:12,240 --> 00:18:16,640
Speaker 1: and similar technologies will eventually change the paradigm for flu

308
00:18:16,760 --> 00:18:20,400
Speaker 1: vaccines as well, because we should be able to react quickly.

309
00:18:20,760 --> 00:18:23,640
Speaker 1: And that being said, of course, there's been i'd say

310
00:18:23,640 --> 00:18:26,280
Speaker 1: an economic hurdle to that change, given the very low

311
00:18:26,400 --> 00:18:29,320
Speaker 1: commodity price of flu vaccines that make it difficult for

312
00:18:29,359 --> 00:18:31,919
Speaker 1: a new technology to establish there. I think on the

313
00:18:31,960 --> 00:18:35,520
Speaker 1: other side of COVID, the ability of US and others

314
00:18:35,560 --> 00:18:40,280
Speaker 1: to rapidly scale up and improve our cost will ultimately

315
00:18:40,400 --> 00:18:43,600
Speaker 1: enable us to take on those challenges as well. These

316
00:18:43,640 --> 00:18:47,040
Speaker 1: are the relatively simple vaccines in the sense that it's

317
00:18:47,040 --> 00:18:49,439
Speaker 1: a simple antigen. We understand the disease and we know

318
00:18:49,520 --> 00:18:53,520
Speaker 1: what to protect against. There are much more complicated pathogens

319
00:18:53,600 --> 00:18:57,320
Speaker 1: for which we still don't have a vaccine. Cytomegalovirus is

320
00:18:57,359 --> 00:19:00,960
Speaker 1: the number one cause of childhood neurological birth defects. We

321
00:19:00,960 --> 00:19:02,920
Speaker 1: still don't have a vaccine to that, even though we've

322
00:19:02,920 --> 00:19:05,719
Speaker 1: been trying for fifty years, and that's for a various

323
00:19:05,720 --> 00:19:09,160
Speaker 1: complicated reasons of biology. HIV is another one. I think

324
00:19:09,200 --> 00:19:12,760
Speaker 1: that this progress should enable platforms like ours to start

325
00:19:12,840 --> 00:19:15,560
Speaker 1: to tackle them, and in fact, I'm optimistic we're on

326
00:19:15,640 --> 00:19:17,560
Speaker 1: track to start a Phase three trial for sign A

327
00:19:17,600 --> 00:19:21,639
Speaker 1: megalovirus next year, and it's leveraging other digital es of

328
00:19:21,680 --> 00:19:26,199
Speaker 1: our technology, the ability to do multiple sequences in parallel.

329
00:19:26,600 --> 00:19:28,760
Speaker 1: That is what's at the root of I think our

330
00:19:28,880 --> 00:19:32,520
Speaker 1: potential to impact that disease. As I understand that the

331
00:19:32,600 --> 00:19:36,280
Speaker 1: Carrent models you were triving it is literally chicken farms

332
00:19:36,320 --> 00:19:39,680
Speaker 1: and having huge volume of bags this are then used

333
00:19:39,680 --> 00:19:44,000
Speaker 1: to grow the vaccine. Do you have an intellectual model

334
00:19:44,520 --> 00:19:49,159
Speaker 1: of what a replacement system might look like if you

335
00:19:49,200 --> 00:19:53,359
Speaker 1: look at our manufacturing footprints. What we do is we

336
00:19:53,480 --> 00:19:56,919
Speaker 1: basically just take that instruction set and it's encoded in

337
00:19:56,920 --> 00:19:59,600
Speaker 1: a molecule called messenger, your RNA. But the beauty is

338
00:19:59,600 --> 00:20:03,720
Speaker 1: that molecular messenger RNA makes thousands and thousands of proteins

339
00:20:03,760 --> 00:20:06,880
Speaker 1: once it's in your cell. So it's basically the instructions

340
00:20:06,960 --> 00:20:11,560
Speaker 1: that it's a transient copy of the gene required to

341
00:20:11,600 --> 00:20:16,760
Speaker 1: make that protein. And so a large scale manufacturing footprint

342
00:20:16,840 --> 00:20:20,720
Speaker 1: for us is a thirty to sixty liter bag, right,

343
00:20:21,200 --> 00:20:24,480
Speaker 1: That's relatively small when you talk about manufacturing. It fits

344
00:20:24,480 --> 00:20:28,159
Speaker 1: in a small room, and so production facilities for US,

345
00:20:28,200 --> 00:20:31,240
Speaker 1: even at large scale, are going to have much less

346
00:20:31,359 --> 00:20:34,919
Speaker 1: capital intensity and are going to move much more quickly

347
00:20:34,960 --> 00:20:38,000
Speaker 1: because of the underlying nature of the technology. Should that

348
00:20:38,080 --> 00:20:41,240
Speaker 1: bridge in warp feed have as part of it a

349
00:20:41,359 --> 00:20:46,840
Speaker 1: component to help finance the revolutions and manufacturing well, I

350
00:20:46,880 --> 00:20:50,040
Speaker 1: think the US government has always been on the forefront

351
00:20:50,080 --> 00:20:52,639
Speaker 1: of that. I give the government a lot of credit.

352
00:20:52,680 --> 00:20:54,760
Speaker 1: I think for many years they've been trying to push

353
00:20:55,080 --> 00:20:59,119
Speaker 1: not just technologies from a scientific biological perspective, but actually

354
00:20:59,200 --> 00:21:02,360
Speaker 1: also from ufacturing one. So I think it's always been

355
00:21:02,440 --> 00:21:05,560
Speaker 1: part of Barda's remit, and in fact, we had been

356
00:21:05,600 --> 00:21:08,439
Speaker 1: working with them. You may recall Zeka, which was a

357
00:21:08,480 --> 00:21:11,600
Speaker 1: big threat back in twenty sixteen twenty seventeen, and we

358
00:21:11,680 --> 00:21:14,159
Speaker 1: thought was going to come to our southern states. I

359
00:21:14,160 --> 00:21:16,080
Speaker 1: think we got lucky and dodged a bullod and that

360
00:21:16,280 --> 00:21:19,240
Speaker 1: pandemic shifted away. We're all concerned it will come back

361
00:21:19,280 --> 00:21:22,560
Speaker 1: one day, and BARDA had already joined with US back

362
00:21:22,560 --> 00:21:25,640
Speaker 1: in twenty seventeen to develop a Zeca vaccine. I think

363
00:21:25,640 --> 00:21:29,800
Speaker 1: the US government has been looking ahead and trying to

364
00:21:29,840 --> 00:21:33,959
Speaker 1: help sponsor manufacturing innovation. Now that being said, I think

365
00:21:34,000 --> 00:21:37,560
Speaker 1: it's also true that the relative investment of the US

366
00:21:37,680 --> 00:21:41,200
Speaker 1: government to our success has been marginal in the sense

367
00:21:41,280 --> 00:21:46,240
Speaker 1: that we're successful because of years of investment of billions

368
00:21:46,240 --> 00:21:49,399
Speaker 1: of dollars by private entities, and that's what enabled the

369
00:21:49,480 --> 00:21:51,480
Speaker 1: US government then to come in relatively late in the

370
00:21:51,560 --> 00:21:54,280
Speaker 1: day and add in a little bit more investment really

371
00:21:54,320 --> 00:21:57,399
Speaker 1: to ramp up the production and the process to be

372
00:21:57,440 --> 00:22:14,760
Speaker 1: applied to COVID. Something like this seems to show up

373
00:22:14,760 --> 00:22:17,440
Speaker 1: at every ten years, so I think we probably ought

374
00:22:17,440 --> 00:22:20,919
Speaker 1: to quit being surprised from your perspective. Let's say we

375
00:22:20,960 --> 00:22:25,840
Speaker 1: get a workable vaccine, and I know that many people

376
00:22:26,280 --> 00:22:28,800
Speaker 1: really want to try to be able to The whole

377
00:22:28,840 --> 00:22:31,240
Speaker 1: point of Operation Warpspeed is to get twenty three hundred

378
00:22:31,240 --> 00:22:36,080
Speaker 1: million doses of a safe, effective vaccine by January twenty one.

379
00:22:36,760 --> 00:22:40,760
Speaker 1: How likely do you think that is. I'm actually optimistic.

380
00:22:41,119 --> 00:22:44,800
Speaker 1: I think there are enough companies that are stepping up

381
00:22:45,440 --> 00:22:48,680
Speaker 1: that Operation work Speed is likely to achieve its call.

382
00:22:49,080 --> 00:22:52,840
Speaker 1: They've done a pretty good job in terms of aligning

383
00:22:52,920 --> 00:22:55,720
Speaker 1: the interest of the population and insuring the government is

384
00:22:55,760 --> 00:22:59,000
Speaker 1: there to support and organize and harmonize to the degree possible.

385
00:22:59,440 --> 00:23:01,400
Speaker 1: They've got a big mission ahead of them in terms

386
00:23:01,400 --> 00:23:04,240
Speaker 1: of distribution, for sure, and I think that's going to

387
00:23:04,320 --> 00:23:07,200
Speaker 1: be challenging. But I think we and others have already

388
00:23:07,200 --> 00:23:09,800
Speaker 1: been investing at risk to rump up production. So I

389
00:23:09,800 --> 00:23:11,760
Speaker 1: think production is going to come on line to those

390
00:23:11,880 --> 00:23:15,520
Speaker 1: quantities by us and others, and I think by then

391
00:23:15,640 --> 00:23:18,719
Speaker 1: we should also get the first signs of efficacy from

392
00:23:18,760 --> 00:23:21,440
Speaker 1: the first trials reading out, in the sense that we

393
00:23:21,480 --> 00:23:24,960
Speaker 1: should get the confidence that indeed, this vaccine, which has

394
00:23:25,359 --> 00:23:29,040
Speaker 1: prevented viral replication in every species we've put it in

395
00:23:29,119 --> 00:23:33,480
Speaker 1: my non human primates, has generated neutralizing anybody's at levels

396
00:23:33,480 --> 00:23:36,000
Speaker 1: that are even higher than if you get infected. That

397
00:23:36,119 --> 00:23:38,600
Speaker 1: should all translate into efficacy, and we hope to be

398
00:23:38,680 --> 00:23:40,960
Speaker 1: able to show it by them as well. I think

399
00:23:41,040 --> 00:23:43,040
Speaker 1: the likelihood is very high that the day they will

400
00:23:43,119 --> 00:23:46,080
Speaker 1: have been successful in that endeavor. I think the challenge

401
00:23:46,119 --> 00:23:48,760
Speaker 1: that faces us is going to be one of distribution

402
00:23:48,800 --> 00:23:52,159
Speaker 1: in getting into the population. And that's where I have

403
00:23:52,240 --> 00:23:54,760
Speaker 1: to tell you most of my life. If you'd ask

404
00:23:54,800 --> 00:23:56,960
Speaker 1: me six months ago to describe who I am, i'd

405
00:23:56,960 --> 00:23:59,880
Speaker 1: tell you I'm a guy who cares deeply about translating

406
00:24:00,080 --> 00:24:03,879
Speaker 1: science into medicine. I find myself in recent months really

407
00:24:04,240 --> 00:24:07,440
Speaker 1: trying to translate medicine into politics in the sense that

408
00:24:07,800 --> 00:24:12,680
Speaker 1: the public understanding, enthusiasm, and acceptance of this is really

409
00:24:12,840 --> 00:24:17,040
Speaker 1: a medical nature. Politics is what unites us as human

410
00:24:17,080 --> 00:24:19,800
Speaker 1: beings to have shared meaning, and I actually take that

411
00:24:19,880 --> 00:24:22,600
Speaker 1: responsibility very serious, and I think all of us who

412
00:24:22,680 --> 00:24:25,840
Speaker 1: are in the midst of doing that first step of

413
00:24:25,880 --> 00:24:29,680
Speaker 1: translating science into medicines, our job isn't done unless we're

414
00:24:29,720 --> 00:24:33,840
Speaker 1: able to translate those medicines into something that is of real,

415
00:24:33,880 --> 00:24:38,480
Speaker 1: tangible benefit, and that must include ensuring that people understand

416
00:24:38,520 --> 00:24:40,920
Speaker 1: what it is we're doing well. As you know, there's

417
00:24:40,960 --> 00:24:47,200
Speaker 1: a significant minority who are deeply anti vaccine, and including

418
00:24:47,240 --> 00:24:51,960
Speaker 1: some fairly famous people like Robert Kennedy Jinny. What percent

419
00:24:52,000 --> 00:24:56,440
Speaker 1: of the country can avoid taking the vaccine and still

420
00:24:56,480 --> 00:24:59,280
Speaker 1: have it be effective. Let's say we can get sixty

421
00:24:59,400 --> 00:25:03,399
Speaker 1: or seven a country to voluntarily take the vaccine. Is

422
00:25:03,400 --> 00:25:06,919
Speaker 1: that a big enough penetration that it probably minimizes the

423
00:25:06,960 --> 00:25:10,280
Speaker 1: next epidemic or in fact, does it have to be

424
00:25:10,400 --> 00:25:14,400
Speaker 1: much higher than that. It's a good question. It depends

425
00:25:14,400 --> 00:25:18,280
Speaker 1: on two things. It depends on the rate of adoption,

426
00:25:18,480 --> 00:25:21,800
Speaker 1: and it depends on the effectiveness of a vaccine. So

427
00:25:21,840 --> 00:25:25,720
Speaker 1: if a vaccine is fifty percent effective, then you're going

428
00:25:25,760 --> 00:25:28,359
Speaker 1: to want close to everybody to get because you still,

429
00:25:28,520 --> 00:25:31,119
Speaker 1: at the individual level, don't have full protection. If a

430
00:25:31,200 --> 00:25:34,119
Speaker 1: vaccine is eighty to ninety percent effective, then you'll probably

431
00:25:34,119 --> 00:25:37,080
Speaker 1: achieve herd immunity. So I think how good the vaccines

432
00:25:37,160 --> 00:25:40,200
Speaker 1: are is going to matter in this context, and it's

433
00:25:40,240 --> 00:25:42,680
Speaker 1: one of the reasons that we pushed our vaccine to

434
00:25:42,760 --> 00:25:45,520
Speaker 1: a dose that's the highest tolerated, but that we believe

435
00:25:45,600 --> 00:25:48,720
Speaker 1: can actually achieve those high levels of protection. I think

436
00:25:48,760 --> 00:25:51,119
Speaker 1: the second element is a question of sign So what

437
00:25:51,200 --> 00:25:53,520
Speaker 1: do I mean by that? Look, when the vaccine comes out,

438
00:25:53,920 --> 00:25:56,119
Speaker 1: there's not going to be enough for everybody who wants it,

439
00:25:56,520 --> 00:25:58,720
Speaker 1: So to the degree that there is a large proportion

440
00:25:58,760 --> 00:26:01,719
Speaker 1: of the population that does want it, I'm fine with that.

441
00:26:01,960 --> 00:26:04,240
Speaker 1: Let the people who want it in line first. What's

442
00:26:04,240 --> 00:26:06,800
Speaker 1: going to happen is that those people will get protected

443
00:26:07,160 --> 00:26:08,840
Speaker 1: and the people who don't want to get it are

444
00:26:08,840 --> 00:26:10,880
Speaker 1: going to remain at risk. And the more of those

445
00:26:10,920 --> 00:26:13,720
Speaker 1: people there are, the more of the risk is of

446
00:26:13,760 --> 00:26:17,400
Speaker 1: this virus continuing to circulate in the population, and the

447
00:26:17,520 --> 00:26:21,480
Speaker 1: longer it will take for the virus to eventually disappear.

448
00:26:21,640 --> 00:26:24,360
Speaker 1: If it will disappear, if it stays endemic, then they

449
00:26:24,359 --> 00:26:27,199
Speaker 1: will continue to have that risk. This disease is not

450
00:26:27,280 --> 00:26:30,280
Speaker 1: a mild cold, and especially in the elderly right, so

451
00:26:30,320 --> 00:26:33,760
Speaker 1: it is a significant risk. So I worry about those people,

452
00:26:34,160 --> 00:26:35,840
Speaker 1: but at the end of the day, I respect their

453
00:26:35,920 --> 00:26:39,480
Speaker 1: right to say, you know, I don't want it for myself. Unfortunately,

454
00:26:39,560 --> 00:26:43,439
Speaker 1: the way this is circulating in the population, they're going

455
00:26:43,480 --> 00:26:45,600
Speaker 1: to be hurting themselves, and the more of them they

456
00:26:45,600 --> 00:26:49,359
Speaker 1: are on a population basis, the longer that risk will remain.

457
00:26:49,960 --> 00:26:53,840
Speaker 1: If you get a pretty effective scene within one or

458
00:26:53,880 --> 00:26:56,879
Speaker 1: two years, it should be pretty obvious the difference in

459
00:26:57,000 --> 00:27:01,399
Speaker 1: the onness rate between the vaccinated and unvaccinated. So there

460
00:27:01,440 --> 00:27:05,760
Speaker 1: should be a sort of practical fact based stability to

461
00:27:05,800 --> 00:27:08,280
Speaker 1: say to people, look, this is the level of risk

462
00:27:08,320 --> 00:27:12,080
Speaker 1: you're running if you don't get vaccinated. So it's nonecessarily

463
00:27:12,640 --> 00:27:15,679
Speaker 1: the implementation of government FIAT, but there's inability to attract

464
00:27:15,760 --> 00:27:19,200
Speaker 1: both populations and say you're running what is a mathematically

465
00:27:19,240 --> 00:27:24,880
Speaker 1: definable risk which has potentially released serious consequences. Right, And

466
00:27:24,920 --> 00:27:27,120
Speaker 1: I think in this current day of age, I think

467
00:27:27,160 --> 00:27:29,439
Speaker 1: CBC is going to be looking at this very carefully,

468
00:27:29,720 --> 00:27:32,440
Speaker 1: and they're already talking about the type of real world

469
00:27:32,480 --> 00:27:35,840
Speaker 1: observations that they're going to be looking at over time

470
00:27:36,359 --> 00:27:38,680
Speaker 1: to understand the benefit of the vaccine and the risk

471
00:27:38,720 --> 00:27:41,320
Speaker 1: that remains for those who are indecinated. Let me ask

472
00:27:41,320 --> 00:27:43,639
Speaker 1: you two other things. One, to what extent do you

473
00:27:43,680 --> 00:27:47,320
Speaker 1: think COVID is likely to prove to be relatively stable

474
00:27:47,320 --> 00:27:50,679
Speaker 1: as opposed to the flues which change annually. And to

475
00:27:50,680 --> 00:27:53,000
Speaker 1: what extent do you think we may be faced with

476
00:27:53,080 --> 00:27:57,280
Speaker 1: a series of evolutionary COVID threats. The honest truth is,

477
00:27:57,440 --> 00:28:01,000
Speaker 1: I don't know. There's two sort of eating facts that

478
00:28:01,200 --> 00:28:04,040
Speaker 1: play here. One is the fact that the type of

479
00:28:04,119 --> 00:28:06,920
Speaker 1: virus that covid is is not like flu. This virus

480
00:28:06,960 --> 00:28:09,760
Speaker 1: actually has a proofreading ends on this part of its genome,

481
00:28:09,840 --> 00:28:13,760
Speaker 1: which means that it's actually less likely than FLU to mutate.

482
00:28:14,280 --> 00:28:17,320
Speaker 1: And I think that's been true of this type of coronavirus.

483
00:28:17,400 --> 00:28:20,600
Speaker 1: Is now that being said, the level of circulation in humans,

484
00:28:20,600 --> 00:28:25,280
Speaker 1: we've already seen strains emerge that are more likely to infect,

485
00:28:25,720 --> 00:28:29,080
Speaker 1: that have higher infectivity. They're not immunological variants in the

486
00:28:29,119 --> 00:28:31,880
Speaker 1: sense that immunity against one is still immunity against the other,

487
00:28:32,480 --> 00:28:35,240
Speaker 1: but they u tend to infect more. And so this

488
00:28:35,400 --> 00:28:39,040
Speaker 1: virus clearly has shown it has some subtle ability to change,

489
00:28:39,840 --> 00:28:43,600
Speaker 1: And as you said correctly a few minutes ago, we

490
00:28:43,600 --> 00:28:46,040
Speaker 1: shouldn't be surprised and we should be expecting the next

491
00:28:46,040 --> 00:28:47,880
Speaker 1: one in the sense that we had Mars, we had

492
00:28:47,920 --> 00:28:51,520
Speaker 1: Stars or Stars one, and now we've got stars Cove two,

493
00:28:52,000 --> 00:28:54,120
Speaker 1: and I expect in the future there will be a

494
00:28:54,160 --> 00:28:58,520
Speaker 1: Stars Cove three at some point. I'm less concerned about

495
00:28:58,520 --> 00:29:00,920
Speaker 1: it once we get through this, for the simple reason

496
00:29:01,000 --> 00:29:04,600
Speaker 1: that I think if this pandemic allows us to establish

497
00:29:04,680 --> 00:29:09,120
Speaker 1: the utility of platforms like ours to now protect against disease,

498
00:29:09,720 --> 00:29:14,360
Speaker 1: and we establish the manufacturing footprint, and we maintain that

499
00:29:14,480 --> 00:29:18,840
Speaker 1: manufacturing footprint such that we can react quicker the next time,

500
00:29:19,440 --> 00:29:22,400
Speaker 1: then the next time we get source code three, the

501
00:29:22,520 --> 00:29:25,160
Speaker 1: response should be like for flu, which can be in

502
00:29:25,240 --> 00:29:28,160
Speaker 1: a few months, as opposed to like it is now.

503
00:29:28,440 --> 00:29:31,680
Speaker 1: And so between establishing the utility of the platform, we

504
00:29:31,720 --> 00:29:34,000
Speaker 1: don't run a phase three trial every season for every

505
00:29:34,040 --> 00:29:36,720
Speaker 1: new flu. We accept the fact that it works, and

506
00:29:36,880 --> 00:29:39,320
Speaker 1: if we match the strain of the vaccine to the

507
00:29:39,360 --> 00:29:42,520
Speaker 1: strain circulating in the population, it's by and large effective.

508
00:29:42,880 --> 00:29:45,400
Speaker 1: I think the same. You'll see what technology like messenger

509
00:29:45,520 --> 00:29:49,360
Speaker 1: RNA and with a manufacturing footprint that's out there. Internally,

510
00:29:49,360 --> 00:29:51,040
Speaker 1: we have a program in the company. We don't talk

511
00:29:51,040 --> 00:29:52,800
Speaker 1: about it much widely, but we call it the Never

512
00:29:52,840 --> 00:29:55,920
Speaker 1: Again Project. How do we take the learnings from this

513
00:29:56,040 --> 00:29:59,760
Speaker 1: from a manufacturing standpoint and make sure that we retain

514
00:29:59,840 --> 00:30:02,880
Speaker 1: that ability to react in the future. Well, actually, it's

515
00:30:03,040 --> 00:30:07,120
Speaker 1: my last big question, which is totally from a different

516
00:30:07,120 --> 00:30:10,640
Speaker 1: angle that goes back to your original personal passion. What

517
00:30:10,840 --> 00:30:14,120
Speaker 1: is it you've learned out of this experience that gets

518
00:30:14,160 --> 00:30:19,680
Speaker 1: you to rethink approaches to oncology. I didn't care about

519
00:30:19,960 --> 00:30:22,480
Speaker 1: how sexy the drug was. I cared whether it was

520
00:30:22,520 --> 00:30:24,280
Speaker 1: going to have an impact for patient. So as a

521
00:30:24,360 --> 00:30:27,680
Speaker 1: drug develop our phonecology, I looked at every opportunity In

522
00:30:27,720 --> 00:30:32,600
Speaker 1: the last decade, and especially between the revolution of genetics

523
00:30:32,600 --> 00:30:36,080
Speaker 1: that we spoke about earlier here that has enabled us

524
00:30:36,120 --> 00:30:39,840
Speaker 1: to move so effectively against COVID, And I think for

525
00:30:39,960 --> 00:30:42,840
Speaker 1: me personally, closing a circle with my early days at

526
00:30:42,840 --> 00:30:46,240
Speaker 1: the NIAH working with Steve Rosenberg on immunotherapy of cancer,

527
00:30:46,600 --> 00:30:49,480
Speaker 1: I think we continued to look for ways in which

528
00:30:49,560 --> 00:30:51,400
Speaker 1: to marry them. I can tell you one of the

529
00:30:51,440 --> 00:30:53,160
Speaker 1: first thing that I did when I joined Maderna was

530
00:30:53,200 --> 00:30:56,200
Speaker 1: to start an effort for a personalized cancer vaccine. So

531
00:30:56,240 --> 00:30:59,640
Speaker 1: we're actually taking that genomic information the ability to sequence

532
00:30:59,720 --> 00:31:03,160
Speaker 1: every individual now rapidly and cheaply and figure out and

533
00:31:03,240 --> 00:31:05,480
Speaker 1: this is all mathematical modeling, right, So we have a

534
00:31:05,560 --> 00:31:09,240
Speaker 1: personal cancer wrected where we start from the genetic information

535
00:31:09,280 --> 00:31:12,120
Speaker 1: and somebody's cancer, it goes up to the cloud. There's

536
00:31:12,120 --> 00:31:15,880
Speaker 1: a biinformatics mathematical model that within about three hours fits

537
00:31:15,920 --> 00:31:18,440
Speaker 1: out what should be the vaccine that we project is

538
00:31:18,440 --> 00:31:21,320
Speaker 1: going to work to immunize that patient against their own cancer.

539
00:31:21,680 --> 00:31:23,600
Speaker 1: We put it into production and within a couple of

540
00:31:23,640 --> 00:31:25,920
Speaker 1: months it's back conjected into the patient. We've done that

541
00:31:26,000 --> 00:31:28,280
Speaker 1: now and we're actually in the midst of a phase

542
00:31:28,360 --> 00:31:32,120
Speaker 1: two trial for patients with melanoma to see if it works. Now,

543
00:31:32,280 --> 00:31:34,280
Speaker 1: true to what I told you before, I don't know

544
00:31:34,320 --> 00:31:36,240
Speaker 1: if it's going to work, but it's got a very

545
00:31:36,400 --> 00:31:40,880
Speaker 1: solid scientific hypothesis, and this revolution has enabled us to

546
00:31:40,920 --> 00:31:45,520
Speaker 1: go test that hypothesis. That's terrific, that's very exciting. Thank

547
00:31:45,560 --> 00:31:49,160
Speaker 1: you for dedicating so much of your life to improving

548
00:31:49,200 --> 00:31:52,920
Speaker 1: the health of others in this current challenge improving the

549
00:31:52,920 --> 00:31:56,400
Speaker 1: health of the entire country and indirectly the entire world.

550
00:31:56,720 --> 00:31:59,240
Speaker 1: It's very exciting to talk with you, so I thank

551
00:31:59,280 --> 00:32:02,640
Speaker 1: you for the time, and I wish you tremendous success,

552
00:32:03,160 --> 00:32:05,160
Speaker 1: not just in what you're doing right now at Maderna,

553
00:32:05,240 --> 00:32:08,160
Speaker 1: but also in being able to apply it more broadly

554
00:32:08,200 --> 00:32:11,880
Speaker 1: to all the challenges of in colleging. This has been very,

555
00:32:12,080 --> 00:32:14,560
Speaker 1: very help you have team conversation. Thank you for it.

556
00:32:15,920 --> 00:32:17,920
Speaker 1: Thank you so much for taking the time. As I said,

557
00:32:18,040 --> 00:32:20,600
Speaker 1: I relish every opportunity to be true to the mission

558
00:32:20,600 --> 00:32:22,880
Speaker 1: of being able to speak to the public at large,

559
00:32:22,920 --> 00:32:25,160
Speaker 1: to tell the story of what it is we're trying

560
00:32:25,200 --> 00:32:28,320
Speaker 1: to do, because without that understanding, I don't think we'll

561
00:32:28,320 --> 00:32:34,600
Speaker 1: be effective. Thank you to my guest doctor tell Zax.

562
00:32:34,960 --> 00:32:38,120
Speaker 1: You can read more about maderna Sphase three trials of

563
00:32:38,200 --> 00:32:42,520
Speaker 1: the COVID nineteen vaccine on our show page at newtsworld

564
00:32:42,600 --> 00:32:47,400
Speaker 1: dot com. Newtsworld is produced by Gingwish, Sweet sixty and iHeartMedia.

565
00:32:47,800 --> 00:32:52,320
Speaker 1: Our executive producer is Debbie Myers and our producer is

566
00:32:52,360 --> 00:32:55,920
Speaker 1: Garnsey Slump. The artwork for the show was created by

567
00:32:55,960 --> 00:33:00,160
Speaker 1: Steve Penley. Special thanks to team at Gingwish tween sixty.

568
00:33:00,200 --> 00:33:04,200
Speaker 1: Please email me with your questions at Ginwich three sixty

569
00:33:04,640 --> 00:33:08,480
Speaker 1: dot com slash questions. I'll answer a selection of questions

570
00:33:08,760 --> 00:33:12,480
Speaker 1: in future episodes. If you've been enjoying new Tworld, I

571
00:33:12,520 --> 00:33:15,280
Speaker 1: hope you'll go to Apple Podcast and both rate us

572
00:33:15,280 --> 00:33:18,520
Speaker 1: with five stars and give us a review so others

573
00:33:18,520 --> 00:33:23,000
Speaker 1: can learn what it's all about. On the next episode

574
00:33:23,000 --> 00:33:27,840
Speaker 1: of Newsworld, generation of Americans grew up watching TV programs

575
00:33:28,040 --> 00:33:31,440
Speaker 1: and John Wayne movies about the American West, where the

576
00:33:31,520 --> 00:33:34,440
Speaker 1: narratives slanted only to the side of the Union Army

577
00:33:34,440 --> 00:33:38,640
Speaker 1: of the settlers. In his Newest History, Killing Crazyhorse the

578
00:33:38,760 --> 00:33:42,840
Speaker 1: Merciless Indian Wars in America, Bill o'reiley tells both sides

579
00:33:43,160 --> 00:33:46,320
Speaker 1: of this painful chapter in Newest History that helped fourge

580
00:33:46,320 --> 00:33:50,800
Speaker 1: a nation's expansion, but at the automate cost for Native Americans.

581
00:33:50,800 --> 00:33:55,160
Speaker 1: Bill O'Reilly joins me on the next episode, I'm new Gangwish.

582
00:33:55,680 --> 00:33:56,600
Speaker 1: This is new towld